Synoptic and Dynamic Climatology

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Synoptic and Dynamic Climatology

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Synoptic and Dynamic Climatology provides the ﬁrst comprehensive account of the dynamical behavior and mechanisms of the global climate system and its components, together with a modern survey of synoptic-scale weather systems in the tropics and extratropics, and of the methods and applications of synoptic climate classiﬁcation. It is unrivalled in the scope and detail of its contents. The work is thoroughly up to date, with extensive reference sections by chapter. It is illustrated with plates and nearly 300 ﬁgures. • • •

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Part 1 provides an introduction to the global climate system and the space–time scales of weather and climate processes, followed by a chapter on climate data and their analysis. Part 2 describes and explains the characteristics of the general circulation of the global atmosphere, planetary waves and blocking behavior, and the nature and causes of global teleconnection patterns. Part 3 discusses synoptic weather systems in the extratropics and tropics, and satellitebased climatologies of synoptic features. It also describes the methods and applications of synoptic climatology and summarizes current climatic research and its directions.

The book is intended for advanced students in climatology and environmental and atmospheric sciences, as well as for professionals in the ﬁeld of climate dynamics and variability. It presents both established ﬁndings about global climate and unresolved issues. Its comprehensive reference lists provide an invaluable guide to further study. Roger G. Barry is Professor of Geography and Director of the National Snow and Ice Data Center at the University of Colorado and Andrew M. Carleton is Professor of Geography at Pennsylvania State University. 0

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Synoptic and Dynamic Climatology

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Roger G. Barry and Andrew M. Carleton

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First published 2001 by Routledge 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Routledge 29 West 35th Street, New York, NY 10001 Routledge is an imprint of the Taylor & Francis Group

This edition published in the Taylor & Francis e-Library, 2002. © 2001 Roger G. Barry and Andrew M. Carleton The right of Roger G. Barry and Andrew M. Carleton to be identiﬁed as the Authors of this Work has been asserted by them in accordance with the Copyright, Designs and Patents Act 1988

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All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data Barry, Roger Graham. Synoptic and dynamic climatology / Roger G. Barry and Andrew M. Carleton. p. cm. Includes bibliographical references and index. 1. Synoptic climatology. I. Carleton, Andrew M. (Andrew Mark). II. Title. QC981.7.S8 B36 2001 551.6—dc21 ISBN 0–415–03115–x (hbk) ISBN 0–415–03116–8 (pbk) ISBN 0-203-21818-3 Master e-book ISBN ISBN 0-203-21830-2 (Glassbook Format)

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Contents

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List of plates Preface Acknowledgments

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viii ix xi

PART 1

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The climate system and its study

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Introduction 1.1 The global climate system 3 1.2 Time and space scales of weather and climate processes 10 1.3 Dynamic and synoptic climatology 13 1.4 The structure of the book 14

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Climate data and their analysis 2.1 Synoptic meteorological data 16 2.2 Remotely sensed data 17 2.3 Climate variables and their statistical description 31 2.4 Analytical tools for spatial data 40 2.5 Time series 63 2.6 Empirical orthogonal function analysis, clustering, and classiﬁcation 78 Appendix 2.1 Eulerian and Lagrangian methods 84

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Dynamic climatology

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Global climate and the general circulation 3.1 Planetary controls 109 3.2 Basic controls of the atmospheric circulation and its maintenance 113 3.3 Circulation cells 143 3.4 The Earth’s geography 153 3.5 Climate system feedbacks 155

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3.6 General circulation models 161 3.7 The global circulation – description 166 3.8 Centers of action 209 3.9 Global climatic features 223 3.10 Air masses 235 Appendix 3.1 Potential vorticity 240 4

Large-scale circulation and climatic characteristics 4.1 Time-averaged circulation 263 4.2 Jetstreams 270 4.3 Planetary waves 278 4.4 Zonal index 302 4.5 Zonal and blocking ﬂow modes 308 4.6 Blocking mechanisms 313 4.7 Low-frequency circulation variability and persistence 322 4.8 Intraseasonal oscillations 332 Appendix 4.1 Spectral harmonic functions 340 Appendix 4.2 Eliassen–Palm ﬂux 341 Appendix 4.3 Normal modes 342

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Global teleconnections 5.1 Pressure oscillations and teleconnection patterns 358 5.2 The Southern Oscillation and El Niño 361 5.3 ENSO mechanisms 376 5.4 Teleconnections with ENSO 384 5.5 Extratropical teleconnection patterns 396 5.6 North Atlantic Oscillation 397 5.7 North Paciﬁc Oscillation 403 5.8 Zonally symmetric oscillations 404 5.9 The southern hemisphere 408 5.10 Tropical–extratropical teleconnections 410 5.11 Teleconnections and synoptic-scale activity 414 5.12 Time-scale aspects of teleconnections 414 5.13 Interannual to interdecadal oscillations 418 Appendix 5.1 Partitioning between equatorially symmetric and antisymmetric components 424

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Synoptic climatology

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Synoptic systems 6.1 Early studies of extratropical systems 441 6.2 Climatology of cyclones and anticyclone 442 6.3 Development of cyclones 450

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6.4 Storm tracks 465 6.5 Satellite-based climatologies of synoptic features 476 6.6 Synoptic-scale systems in the tropics 506 Appendix 6.1 The Q-vector formulation 524

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Synoptic climatology and its applications

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ROGER G. BARRY AND ALLEN H. PERRY

7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9

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Synoptic pattern classiﬁcation 547 Subjective typing procedures 549 Objective typing procedures 551 Principal catalogs and their uses 561 Regional applications 574 Analogs 578 Seasonal structure 578 Climatic trends 587 Environmental applications 589

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Retrospect and prospect

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Further reading Index

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Plates

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The following plates appear between pages 304–5 1 Relief of the surface of the Earth, showing land elevation and ocean bathmetry, based on the ETOPO 5 database 2 Average extent of snow cover, 1971–95, and sea ice, 1978–95, in the northern hemisphere for February and August 3 Global images of the normalized difference vegetation index for January, April, July, and the standard deviation values for July 7 (a) Thermal IR image from the Japanese GMS, rectiﬁed to a polar stereographic format, for 23.30 UTC, November 7 1992. (b)–(f) Color maps of the DMSP SSM/I retrievals of marine atmospheric variables, for around 22.00 UTC, November 7 1992 Black-and-white

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4 Sequence of infrared images illustrating the breakdown and redevelopment of the ITCZ cloud band over the eastern tropical Paciﬁc Ocean between July 26 and August 12 1988 5 GOES-E enhanced thermal infrared images of the North America and Central America regions on June 25 1988 at 06.00 and 18.00 UTC, showing convective and frontal high cold cloud tops 6 DMSP visible channel mosaic of the Europe/western Russia sector, February 22 1978, showing a jetstream “shadow band” over the snow cover 8 DMSP infrared image of an instant occlusion about to be initiated on the frontal cloud band of a North Paciﬁc extratropical cyclone through the merging of a cold-air mesocyclone at about 36°N, 164°E 9 DMSP infrared images of (a) a comma cloud mesocyclone and (b) a spiraliform mesocyclone in the Labrador Sea during January 1979 10 GOES-E enhanced infrared image showing a large MCS over the central United States on August 13 1982 11 GOES-W enhanced infrared image showing an MCC in central Arizona during the summer “monsoon” season on August 12 1982 12 DMSP infrared mosaic showing a tropical–extratropical cloud band connection in the western Paciﬁc during October 1977 13 Infrared mosaic of the southern hemisphere from the NOAA SR (Scanning Radiometer) for June 17 1975 14 GOES-W infrared images showing a moisture plume extending from the eastern North Paciﬁc into the central United States 15 GOES-E image of Hurricane Gilbert on September 12 and 15 1988

187 479 481 489 491 496 497 502 504 505 518

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In his book The Earth’s Problem Climates (1961) Professor Glenn Trewartha attempted to account for apparent regional departures from the “expected” climatic pattern in various parts of the world. In contrast, this book is principally concerned with understanding the large-scale regularities of the earth’s climatic patterns. The empirical reconstructions of climatic patterns on an ideal continent have in fact been shown by numerical model studies to be unrealistic in several respects. The actual distributions of climatic regions, on which most classiﬁcation schemes are based, are signiﬁcantly modiﬁed by geographical features – mountain ranges and ocean currents. Our approach is to demonstrate, ﬁrst, the relationships between the dynamical controls of the global circulation and the major climatic zones, and then to examine the longitudinal variations introduced by the planetary and synoptic wave systems, interacting with the earth’s surface. The principal time scales of interest are days to years, and we are not considering the “slow physics” that gives rise to decadal and longer climatic ﬂuctuations, except in so far as the variability of circulation systems themselves may be involved. The second part of the book focuses on synoptic climatology. Twenty-seven years have elapsed since the publication of Synoptic Climatology: Methods and Applications (Barry and Perry, 1973) and the ﬁeld has advanced considerably during that time (Carleton, 1999). Computer-based classiﬁcations have become the rule, and the ready availability of extensive climatic data bases has permitted diverse analytical studies to be carried out and evaluated. Results from these studies now allow us to state more conﬁdently the most useful procedures for the synoptic classiﬁcation and analysis of regional or local climatic conditions. The widespread practical application of these studies is illustrated, with particular emphasis on the linkages between regional and global-scale climate processes. This is an essential aspect of the problem of interpreting global climate model simulations in a regional context. All areas of science have experienced phenomenal growth since the Second World War. The number of scientiﬁc papers published between 1960 and 1980 far exceeded the sum total of previous works, according to Geerts (1960). Six major atmospheric science journals started in the 1970s, there are now at least ﬁve climatology journals, and established journals have added extra issues and more pages. This information explosion makes a synthesis of the literature a daunting task and yet such attempts are increasingly necessary, both for educational purposes and for scientists in cognate disciplines. We hope that this book will provide an up-to-date and comprehensive treatment of dynamic and synoptic climatology for these audiences. We begin with an introduction to the global climate system and related time and space scales. Chapter 2 on climatic data and their analysis may be read in sequence, or used for reference when particular methods are noted in subsequent chapters.

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References Barry, R.G. and Perry, A.H. 1973. Synoptic Climatology: Methods and Applications, Methuen, London, 555 pp. Carleton, A.M. 1999. Methodology in climatology. Prof. Geogr., 51: 713–35. Greets, B. 1998. Trends in atmospheric science journals: a reader’s perspective. Bull. Amer. Met. Soc., 80: 639–50. Trewartha, G.T. 1961. The Earth’s Problem Climates, McGraw-Hill, New York, 334 pp.

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The preparation of this book has involved many individuals. First and foremost I wish to thank the following secretarial and clerical staff for their word-processing skills in preparing the extensive text and many updates: Margaret Strauch, Cindy Brekke-Bauer, Lyn Ryder, and students Megan Phelan, Shari Fox and Mathew Stones. Pat Hofman and student Devon Lehman assisted with library materials and Mathew Stones ably scanned most of the diagrams. A number of colleagues provided valuable comments on chapter drafts: Dr George Kiladis, NOAA, Dr Rol Madden and Dr Gerry Meehl, NCAR, Dr Jeff Key, Boston University, Professor Atsumu Ohmura, ETH, Zürich, and Anton Seimon, University of Colorado, but remaining errors and omissions are my own responsibility. Finally, the editorial staff of Routledge are thanked for their assistance in getting the book to publication. For my early training in this ﬁeld I am indebted to F. Kenneth Hare, then Professor of Geography and head of the Arctic Meteorology Research Group at McGill University, Montreal. Sabbatical leave from the University of Colorado Graduate School in spring 1997, when I was hosted by the Geographical Institute of ETH, Zürich, enabled me to accelerate the completion of the text. The library facilities of NOAA in Boulder and of the Swiss Meteorological Institute in Zürich provided indispensable literature resources. The sources of all ﬁgures and plates, and associated copyrights, are listed by chapter. The cooperation of all publishers, professional societies, and individuals is gratefully acknowledged. Roger G. Barry The authors and publishers would like to thank the following learned societies, editors, publishers, organizations, and individuals for granting permission to reproduce Plates, Figures, and Tables in this work. (Please note that the ﬁgure number refers to the ﬁgure number in this work, but that any page numbers refer to the page of the original publication that the work was taken from.)

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American Geographical Society for Figure 4.6 on p. 535 of “The Stratosphere,” by F.K. Hare, 1962, Geographical Review 52: 525–47. American Geophysical Union, Washington DC, for Figures 3.30, 4.20, from the Review of Geophysics and Space Physics; for Figure 3.29 from Climate Processes and Climate Sensitivity, by J. Hansen et al.; for Figures 3.17, 3.19, 3.33, 4.27, from the Journal of Geophysical Research. American Meteorological Society for Plate 3 and Figures 2.22, 2.23, 2.24, 3.6, 3.78, 4.36, 5.5, 6.8, from the Bulletin; for Figures 2.15, 2.17, 3.40, 3.65 from the Journal of Applied Meteorology; for Figures 3.66, 3.67, 4.5, 4.7, 4.11, 6.14 from the Meteorological

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Monographs; for Plate 4 and Figures 3.5, 3.22, 3.26, 3.39, 3.43, 3.69, 4.15, 4.21, 4.23, 4.25, 4.26, 4.47, A4.2.1, A4.2.2, A4.2.3, 5.17, 5.35, 5.36, 6.21, 6.22, 6.23, 6.24, 6.25, 6.26, 6.40, 6.42, from the Journal of Atmospheric Sciences; for Figures 3.62, 3.64, 3.81, 4.50, 4.53, A4.1, 5.1, 5.3, 5.8, 5.9, 5.10, 5.18, 5.23, 5.25, 5.26 a and b, 5.29, 5.31, 5.32, 5.33, 5.34, 6.11, 6.13, 6.16, 6.17, 6.36, 7.14, from the Monthly Weather review; for Figures 2.3, 6.20 from Weather and Forecasting; for Figure 2.2 from p. 19 of Weather Satellites, by P.K. Rao; for Figures 3.9, 3.32, 3.44, 3.70, 3.72, 3.77, 3.82, 3.83, 4.1, 4.2, 4.32, 4.33, 5.12, 5.14, 5.15, 5.30, 5.37, 5.38, 5.39, 6.4 from the Journal of Climate; Figure 6.10 from the Eric Palmen Memorial Volume; Figure 7.4 from the Sixth Conference on Probability and Statistics in Atmospheric Science. Association of American Geographers for Figure 4.12 from Synoptic Climatology of the Westerlies, by J.R. Harman. Geographical Association, UK, for Figure 7.6 from Geography. Geological Society, London, for Figure 3.27 from the Journal of the Geological Society. Meteorological Society of Japan, Tokyo, for Figures 3.63, 4.51, 6.32, and 6.38 from the Journal of the Meteorological Society of Japan; for Figure 4.30 from the Geophysical Magazine. Royal Geographical Society/Institute of British Geographers for Figure 7.8 from the Transactions; for Figure 5.10 from the Geographical Journal. Royal Meteorological Society for Figures 1.8, 2.4, 2.13, 3.36, 3.42, 3.45, 3.48, 3.49, 4.18, 4.39, 4.40, 4.49, 4.51, A4.1.1, 5.16, 5.20, 5.22, 6.18, 6.31, 6.37, 6.41, A6.1 from the Quarterly Journal; for Figure 3.14 from p. 63 of The Global Circulation of the Atmosphere, edited by G.A. Corby, 1970; for Figure 3.80 by E. Augstein from p. 75 of Meteorology over the Tropical Oceans, edited by D.B. Shaw, 1978; for Figure 6.12 from Meteorology Applied; for Figure 4.37 from Weather. Publishers

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Academic Press, New York for Figures 1.2, 3.12, 3.16, 4.44, 4.45, and Table 4.1 from Advances in Geophysics; for Figures 3.58 (p. 26), 3.60 (p. 48), 3.61 (p. 54) from Monsoon Meteorology by C.S. Ramage, 1971. Academic Press, San Diego, California, for Figure 2.8 from Statistical Methods in the Atmospheric Sciences, by D.S. Wilks, 1995. Academic Press, Orlando, Florida, for Figures 4.13 and 4.14 from J.M. Wallace (pp. 27–53), Figure 4.24 from I.M. Held (p. 132), Figures 4.43 and 5.2 from J.M. Wallace and M.L. Blackmon (pp. 55–94), all in Large Scale Dynamical Processes in the Atmosphere, edited by B.J. Hoskins and R.P. Pearce, 1983. Annual Reviews (www.annualreviews.org) for Figure 3.8 from Annual Review of Fluid Mechanics. Birkhaeuser Publishing, Basel, Switzerland, for Figure 3.55 (in “Mechanics effecting the state, evolution and transition of the planetry scale monsoon,” 1977, by P.J. Webster et al., 15: 1465) and 3.79 from Pure Applied Geophysics. British Crown Copyright for Figures 4.41 and 4.42 from the Meteorological Ofﬁce of the UK. Cambridge University Press, Cambridge, for Figure 1.3 from p. 144 of The Earth as Transformed by Human Actions, eds B.L. Turner et al.; for Figure 1.4 from p. 142 of The Global Climate, ed. J.T. Houghton; for Figure 3.54 (p. 63) in Monsoon Dynamics, edited by Sir J. Lighthill and R.P. Pearce; for Figure 5.6 by K.E. Trenberth, from p. 14 and Figure 5.18 by E.M. Rasmusson from p. 323 of Teleconnections linking Worldwide Climate Anomalies, edited by M.H. Glantz et al., 1991; for Figure 5.7 by H.F. Diaz and G. Kiladis, from p. 21 of El Nino, edited by H.F. Diaz and V. Markgraf, 1992. The Controller, Her Majesty’s Stationery Ofﬁce, for Figure 3.51 from Geophysical Memoir 115 by J. Findlater.

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Elsevier Science for Figure 3.74 from Advances in Space Research. Kluwer Academic Publishers, for Figure 3.61 (p. 246) of Climate Dynamics of the Tropics, by S. Hastenrath; for Figure 3.35 from The Climate of Europe, Past, Present and Future, by H. Flohn, 1984; for Figure 3.5 from Milankovitch and Climate. Pearson Education Ltd, London, for Figure 2.25 from p. 143 of Multivariate Statistical Analysis in Geography, by R.J. Johnston, Longman; for Figure 3.38 from Concepts in Climatology, by P.R. Crowe, Longman. MeteoSchweiz, Zuerich, Switzerland, for Figure 7.12 by M. Schuepp, from Klimatologie der Schweiz, Vol.3, 1979. Munksgaard International Publishers Ltd, Copenhagen, Denmark for Plates 8 and 9, and Figures 1.6, 2.14, 3.37, 3.72, 3.85, 4.9, 5.24, 6.30, and 7.10 from Tellus. National Academy Press, Washington DC, for Figure 4.46 from Understanding Climate Change, by the National Academy of Sciences, 1975. Oxford University Press Inc., New York, for Figures 3.3 and 3.5 from Paleoclimatology, by T.J. Crowley and G.R. North, 1991; for Figures 3.20, 3.75 and 4.22 from Global Atmospheric Circulations, by R. Grotjahn, 1993; for Figure 4.34, by R.M. Dole, pp. 93–9 of Encyclopedia of Climate and Weather, edited by S.H. Schneider, 1996. Springer, Berlin & Heidelberg, for Figures 2.21 from p. 455 of Decadal Climate Variability, edited by D. Anderson and J. Willebrand; for Figures 3.15, 3.23, 3.24, 3.25, 6.1 (D.A. Jones and I. Simmonds, “A Climatology of Southern Hemisphere Anticyclones,” 1994, 10:333–48) and 6.2 from Climate Dynamics. Springer, Vienna and New York, for Figure 4.31 from Archiv fur Meteorologie, Geophysik and Bioklimatologie; for Figures 3.57, 4.52, 5.19, and 5.21 from Meteorology & Atmosphere Physics; for Figure 4.3 from Theoretical and Applied Climatology; for Figures 3.10 (p. 243), 3.11 (p. 253), 3.13 (p. 159), 3.21 (p. 383), 3.31 (p. 454) from the Physics of Climate, by J.P. Peixoto and A.H. Oort, American Institute of Physics, New York. Taylor & Francis, London, for Plate 12 from the International Journal of Remote Sensing (www.tandf.co.uk/journals) and for Figures 1.8 (p. 90), 2.5 (p. 216), 2.9 (p. 23), 2.10 (p. 26), 2.11 (p. 29), 2.12 (p. 32), 2.13 (p. 33), 2.16 (p. 387), 2.18 (p. 230), 2.19 (p. 232–3), 2.20 (p. 237), 3.50 (p. 447), 3.84 (p. 185), 3.87 (p. 185), 4.31, 6.11 (p. 60), 7.11 (p. 166–7) from Synoptic Climatology, by R.G. Barry and A.H. Perry, 1973, Methuen; for Figure 2.7 (p. 484) from Maps and Diagrams, 3rd edition, edited by F.J. Monkhouse and H.R. Wilkinson, 1971, Methuen; for Figure 3.7, from Climate Past, Present and Future, by H.H. Lamb, 1977, Methuen; for Figures 3.34 (p. 169), 3.41 (p. 127), 3.59 (p. 259), 3.76 (p. 68), 3.86 (p. 156), 4.4 (p. 121), 5.4 (p. 278), 6.39 (p. 244), 7.15 (p. 195) from Atmosphere, Weather & Climate, 7th edition, by R.G. Barry and R.J. Chorley, 1998, Routledge; for Figures 4.10 and 6.7 from Models in Geography, edited by R.J. Chorley and P. Haggett, 1967, Methuen; for Figures 4.17 (p. 108), 4.19 (p. 114) from Dynamical Meteorology, by B.W. Atkinson, 1981, Methuen; for Figure 1.5 (p. 70) from The Atmosphere and Ocean, by N. Wells, Taylor & Francis. University of Chicago Press, for Figure 3.28 from the Journal of Geology. John Wiley and Sons, Chichester, for Figures 2.26, 4.38, 5.28, 5.29, 6.3, 6.19, 7.2, 7.9, 3.76, 5.27, 6.5, 7.1 and 7.3 from the International Journal of Climatology; for Figure 2.27 from Computer Applications in Stratigraphic Analysis, 1968, by J.W. Harbaugh and D.P. Merriam; for Figures 3.52 (p. 5), 3.53 (p. 214), 3.56 (p. 293), 6.33 (p. 240) from Monsoons, edited by J.S. Fein and P.L. Stephens, 1987; for Plate 15 from Satellite Remote Sensing in Climatology, by A.M. Carleton, 1991, Belhaven Press. Organizations

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Arctic Institute of North America, for Figure 6.6 by E.F. LeDrew and D.G. Barber, 1994, “The SIMMS Program,” Arctic 47: 256–64. Australian Bureau of Meteorology, Melbourne, for Plate 7a and Figures 6.27 and 6.28.

xiv Acknowledgments

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Climate Research Unit, University of East Anglia, UK, for Figure 7.7 by H.H. Lamb, 1994, “British Isles daily wind and weather patterns,” Climate Monitor 20: 47–71. Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, for Figure 4.16. The Commonwealth of Australia for Figure 3.71 from the Australian Meteorological Magazine. ETH, Institute for Climate Research, for Figures 1.1, 3.1, and 3.2 from Zurcher Geographische Schriften (now Zurcher Klimaschriften). European Centre for Medium Range Weather Forecasts, for Figure 7.5 from the Workshop in Meteorology, Reading, UK, 1977. National Center for Atmospheric Research (NCAR), Boulder, Colorado, for Figures 2.1, 3.68, 4.7, 4.28, and 4.29. National Geophysical Data Center (NGDC), Boulder, Colorado, for Plate 1. National Research Council Research Press, Canada, for Figure 5.13 from the Canadian Journal of Aquatic Sciences. National Oceanic and Atmospheric Administration (NOAA), Washington DC, for Plates 1, 5, 10, 11, 13, 14. National Snow and Ice Data Center (NSIDC), Boulder, Colorado, for Plates 1, 2, 6, 8, 9, and 12. National Weather Service, USA, for Plates 11 and 14. Woods Hole Oceanographic Institute, for Figure 3.4 from “Orbital Geometry,” 1986, by N.G. Pisias and J. Imbrie, Oceanus 29: 46. World Meteorological Organisation, Geneva for Figures 3.18, 4.19 and 6.34. Editor Geocarto International for Figure 6.29. Individuals

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Professor B.W. Atkinson, Queen Mary College London, for Figures 1.7 and 1.9. Professor R.A. Bryson, University of Wisconsin, Madison, for Figures 2.6, 7.16. Professor A.M. Carleton, Pennsylvania State University, for Plate 15. Dr H. Diaz, NOAA, for Figure 5.7. Professor F. Fliri for Figure 7.13. Professor M. Ghil, University of California, Los Angeles, for Figure 4.48. Professor S. Grønäs, University of Bergen, Norway for Figures 6.9 and 6.15. Dr G. Gutman, NOAA, for Plate 3. Dr A.R. Hansen for Figure 4.45. Dr E. Harrison for Table 3.4. Dr I. Held, NOAA, for Figure 4.24. Professor B.J. Hoskins for Figures 4.13 and 4.14. Dr L.T. Julian, NOAA, for Figure 4.35. Dr E.F. LeDrew, University of Waterloo, for Figure 6.6. Dr L. McMudie and Dr A.M. Carleton for Plates 7b–7f. Dr P. Niiler for Figure 5.11. Professor A. Ohmura, Swiss Federal Institute of Technology, Zurich, for Figures 1.1, 3.1, and 3.2. Professor A. Oort for Figures 3.12 and 3.16. Professor C.S. Ramage for Figures 3.58, 3.60, 3.61. Professor B. Saltzman, Yale University, for Figure 1.2. Dr A. Sutera, Universita Di Camerino, Italy, for Figure 4.44. Dr K.E. Trenberth, NCAR, for Figure 5.6. Professor J.M. Wallace, University of Washington, for Figures 4.43 and 5.2.

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Professor T. Webb III, Brown University, for Figure 1.3. Professor D.S. Wilks, Cornell University, for Figure 2.8. Dr J. Woods for Figure 1.4. Every effort has been made to contact copyright holders for their permission to reprint material in this book. The publishers would be grateful to hear from any copyright holder who is not here acknowledged and will undertake to rectify any errors or omissions in future editions of this book.

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Climate can be deﬁned as “the synthesis of weather” considered over a time interval long enough to determine its essential statistical properties. More broadly, it is the time-averaged state of the physical system that involves the atmosphere, hydrosphere, cryosphere, lithosphere and biosphere (Bolle, 1985) and their interactions on many different time and space scales (Figure 1.1). The main domains that comprise the climate system have a wide range of equilibration times, as shown schematically in Figure 1.2, illustrating the many time scales involved. Internally, the climate system as a whole is a closed system for exchanges of matter but is subject to external forcing by solar radiation, gravitational forces, geological processes, and human activity. The whole complex constitutes a “cascading system,” or chain of subsystems, interconnected by ﬂows of energy, matter, and momentum. The spatial dimensions of the components of the climate system are indicated in Figure 1.3. The atmosphere, with a representative thickness of 10 km, is a thin skin of gases rotating with the earth. Ninety-nine percent of the atmosphere’s mass is in the lowest 30 km. Air is compressible and so its density () decreases nearly exponentially with

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Figure 1.1 The climate system. (From Hutter et al., 1990)

4 Synoptic and dynamic climatology

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1 Figure 1.2 Schematic summary of the domains of the climate system, showing approximate times required for equilibrium to be re-established after a perturbation is imposed at the boundary of each subsystem. (From Saltzman, 1983)

altitude, owing to gravity (g). The essential balance between them is expressed in the hydrostatic relationship1 describing the decrease of pressure (p) with altitude (z). The processing of solar radiation by the atmosphere and the earth’s surface as the fundamental driver of the climate system is treated brieﬂy in Chapter 3. More important for understanding the variability of the climate system are the internal variables that undergo seasonal changes at the earth’s surface. Accordingly, it is useful to outline the spatiotemporal patterns of these basic components of the climate system. 1.1.1 Land surface Land areas represent about 30 percent of the earth’s surface, of which about one-quarter is largely unvegetated. The soil layer plays an important role in the surface energy balance through its albedo, thermal properties, and moisture content. The lithosphere is essentially constant other than on geological time scales, with the exception of inputs of dust, volcanic particles, and gases into the atmosphere, and the weathering of carbonate rocks, which removes carbon from them. However, large-scale controls on surface–atmosphere interaction are exerted by the elevation of the land surface, and the orientation and extent of

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Figure 1.3 Spatial and temporal scales of variations in weather and climate. Short-term variations are limited primarily to the atmosphere, but longer-term variations involve progressively more earth systems. Each bubble encloses a group of related types of variations, in which the shorter-term, smaller-area features are part of the longer-term, larger-area features. (From McDowell et al., 1990)

mountain ranges and plateaus. Most of the land masses are in the northern hemisphere; Table 1.1 underlines the dominance of oceans in middle latitudes of the southern hemisphere. Plate 1 depicts the major features of the land surface which have signiﬁcant effects on the atmospheric circulation and, in turn, on climatic variables such as cloudiness, precipitation, and wind. Note that in the Americas, the Cordilleran ranges are essentially perpendicular to the global wind systems of low and middle latitudes. 1.1.2 The hydrosphere

0 11

The bulk of the hydrosphere is in the oceans, although the small fraction in the atmosphere (as vapor, liquid water droplets, or ice crystals) is a prime determinant of weather processes through clouds and precipitation. The phase changes involved in evaporation/condensation play an important role in energy transfers. It is also important to note that terrestrial temperatures are generally close to the triple point of water where the three phases (vapor, liquid, and solid) may coexist in equilibrium. The six transitions – melting/ freezing, evaporation/condensation, and sublimation/deposition (or crystallization) – each involve substantial cooling (heating) in passing from a lower to higher (higher to lower) state, respectively. The latent heat parameters involved are, respectively, those of fusion (0.335 J kg1), vaporization (2.5 J kg1) and sublimation (2.835 J kg1 at 0° C). Cloud

6 Synoptic and dynamic climatology Table 1.1 Dimensions of the Earth

1

Latitude

% of total area of hemisphere located poleward

90 80 70 60 50 40 30 20 10 0

0 1.5 6.1 13.5 23.5 35.9 50.1 65.9 82.7 100

Area of zone between two 10° circles of latitude (106 km2) 3.9 11.6 18.9 25.6 31.5 36.4 40.2 42.8 44.1

Mean (%) Total (106 km2)

255

Length of 10° longitude (km)

0 193.9 381.9 558.0 717.0 854.0 964.9 1,046.5 1,096.4 1,113.2

% of zone ocean, northern hemisphere

% of zone ocean, southern hemisphere

90 70 30 43 48 57 63 74 77

0 27 91 99 97 89 77 78 76

60.7

80.9

154.8a

206.3a

Source: after List (1958). Note a The ocean area totals 361 106 km2 out of a global surface area of 510 106 km2.

1

reﬂectance contributes the major part of the planetary albedo and its net effect on the global energy balance is one of cooling. The terrestrial components rivers, lakes, and groundwater are key elements in the hydrological cycle, and affect local and regional climates also. The hydrological reservoirs and the water transfers between them are still poorly known. Table 1.2 summarizing recent estimates illustrates the predominance of the ocean surfaces in water exchanges and storage (97 percent of total water). However, land ice represents the largest reservoir of freshwater, 76 percent of the total. There is a net imbalance over land (Table 1.2B) as a result of uncertainties in the estimates and possible trends in water storage in glaciers and groundwater. The oceans cover 71 percent of the earth, but this extent differs greatly between hemispheres and according to latitude (Table 1.1). As a consequence, the majority of the solar radiation absorbed by the earth’s surface enters the oceans (Figure 1.4). The direct solar heating of the oceans averages almost four times that of the land areas. The temperatures at the ocean surface vary mainly with latitude in the southern hemisphere, but in the northern hemisphere the major wind-driven current gyres in the North Atlantic and North Paciﬁc oceans (Figure 1.5) create signiﬁcant longitudinal contrasts in climate through their effects on heat and moisture ﬂuxes, on weather systems, and on the large-scale mean atmospheric circulation. The oceans are more strongly stratiﬁed than the atmosphere, owing to their high density. Their large heat capacity also creates great thermal inertia so that they serve as a buffer for changes of temperature, gaseous transfers (such as CO2) etc. The upper ocean, of the order of 100 m thick, is the most active part, closely interconnected with the atmosphere, but with a relaxation time of weeks to months. The deep ocean, below the thermocline (the zone of sharp temperature gradient marking the penetration of annual heating/cooling) is largely isolated from the surface layers, except on time scales of 102–103 year. Links between the ocean and atmosphere involve complex feedbacks on a wide range of time and space scales which are discussed in subsequent chapters. However, their

Introduction 11

7

Table 1.2 Reservoirs and transports involved in the hydrological cycle A Principal reservoirs

Water volume (km3)

Oceans Ice sheets and glaciers Groundwater (to 4 km depth) Inland seas Lakes and reservoirs Atmosphere

1,350.0 106 32.4 106 8.2 106 105,000 140,000 130,000

0

111

B Annual transports (km3)

Over oceans

Over land

Precipitation Evaporation Runoff Rivers Groundwater Glaciers

385 425

111 71 27 12 2.5

Sources: after Speidel and Agnew (1982), Baumgartner and Reichel (1975), van der Leeden et al. (1990).

0

0111

0

Figure 1.4 The zonally averaged distribution of energy incident on the top of the atmosphere in one year, showing the partition between the continents (black) and the oceans (shaded) in each 5° band. More than half of the energy entering the earth’s climate system is ﬁrst absorbed inside the oceans. (From Woods, 1984)

0 11

1

Figure 1.5 Major global ocean current systems in February–March. (From Wells, 1986)

8 Verso running head

1

Introduction 11

9

fundamental importance can be illustrated by noting that the cooling by 0.1°C of a meterthick layer of water is sufﬁcient to raise the temperature of an overlying air layer 30 m deep by 10°C, through turbulent heat transfer. 1.1.3 The cryosphere

0

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0

Snow and ice features are collectively referred to as the cryosphere. Ice sheets grow and decay over 104–105 year intervals, causing lowering/raising of the global sea level by tens of meters to over 100 m. However, snow cover and sea ice are annually cyclical components which markedly affect the surface absorption of solar radiation in high and middle latitudes, owing to their high reﬂectivity (or albedo). Sea ice also insulates the ocean surface from the atmosphere, eliminating the ﬂux of moisture by evaporation and greatly reducing direct heat transfer. Ninety percent of the ice volume is contained in the Antarctic ice sheet but, in terms of areal extent, snow and sea ice cover almost a quarter of the northern hemisphere in winter, with snow covering half of the land surface in February (Plate 2). The principal climatic roles of snow and ice relate to their high reﬂectivity, low thermal conductivity, which insulates the underlying ground or ocean, and their thermal inertia effects. Typical integrated albedo values for the spectral range of solar radiation are 0.8–0.9 for fresh snow, 0.6 for bare ice and 0.3–0.4 for melting sea ice with puddles. However, snow-covered forest areas may have albedos of only 0.25–0.40. Snow cover develops a cold reserve in winter that stabilizes the lower atmosphere overlying it and depresses temperatures by 5°–10°C until sufﬁcient energy is absorbed in spring to raise the snowpack temperature to 0°C and then to melt it (1.8 106 J kg1). 1.1.4 The biosphere

0111

0

The biosphere (both terrestrial and oceanic) is important through its effects on exchanges of energy, moisture, and matter. Land vegetation affects surface albedo, roughness, rainfall interception, soil moisture, evaporation, and runoff, for example (Dickinson, 1983). Photosynthesis and respiration play a major role in the carbon cycle, and the biosphere is also involved in other signiﬁcant biogeochemical cycles that affect atmospheric composition and photochemical processes. From a meteorological perspective, surface properties (vegetation, agricultural use, soils) are poorly deﬁned. In particular, the open or closed character of the canopy and its height are of prime importance (Graetz, 1991). Globally, about 70 percent of canopies are open and 30 percent closed. Maps of global vegetation types fail to show agricultural land use or the seasonal variation in the biosphere but specialized archives have been devised for use in modeling (Mathews, 1985; Wilson and Henderson-Sellers, 1985). Satellite remote sensing using visible and infrared wavelength sensors can now be used to map a “normalized difference vegetation index” (NDVI) on a regular basis (Justice et al., 1985). NDVI =

0 11

(RNIR Rvis) (RNIR Rvis)

where R radiance in the visible (vis) channel, 0.55–0.68 m, and near-infrared (NIR) channel, 0.73–1.1 m. This is a measure of photosynthetic capacity, although the precise interpretation remains to be speciﬁed. NDVI ranges from <0.2 for deserts to >0.5 for green canopies. The seasonal characteristics of NDVI are illustrated in Plate 3 from ﬁve-year averaged data (April 1985–87 and March 1989–91). For July the standard deviation is included and this identiﬁes areas of high interannual variability in northeast Brazil, central Siberia and southeast Australia. Other attempts have been made to assemble land use and soil

10 Synoptic and dynamic climatology information from atlases and other maps for use in climate models, but such global data sets are recognized to be heterogeneous in their information content.

1.2 Time and space scales of weather and climate processes

1

The time and space scales of atmospheric motion range from seconds to weeks (and longer), and from essentially random, small-scale turbulence to large-scale horizontal eddies (weather systems) and the slowly varying global circulation features. Kinetic energy spectra based on station wind data (e.g. Vinnichenko, 1970) indicate that, apart from diurnal and annual cycles, the major peak is in the range ﬁve to thirty days, associated with synoptic-scale atmospheric systems. There is a further peak around one minute, caused by small-scale turbulence in the boundary layer. A clear spectral gap exists between these peaks (Figure 1.6). Space and time scales of atmospheric motion are closely related. Figure 1.7 illustrates the full spectrum of motion systems and delimits the scales with which we are concerned. The principal ones are: 1 2 3 4 5 6

Irregular large-scale ﬂuctuations of longer duration and recurrence interval than the annual cycle (e.g. ENSO events). Seasonal ﬂuctuations of major wind systems (e.g. monsoon systems). Persistent large-scale circulation regimes (e.g. blocking). Planetary waves. Synoptic systems. Subsynoptic cloud clusters and mesoscale features (as they relate to the synoptic and larger-scale systems).

1

Figure 1.6 Spectrum of atmospheric kinetic energy. The abscissa shows frequency (log f in day1) and the ordinate is f s2( f ) in m2 s2, where s2(f) is the explained variance; the area under the curve is equal to the total variance. (After Vinnichenko, 1970, from Peixoto and Oort, 1992)

Introduction

11

11

Figure 1.7 The characteristic time and length scales of atmospheric processes. See text. (Courtesy B.W. Atkinson, 1984)

Descriptive scaling of atmospheric motion is usually made in terms of characteristic horizontal dimensions, or wave number relative to planetary circumference (~40,000 km), and time periods as illustrated in Figure 1.8. A dynamic basis using three fundamental frequencies is also possible (Atkinson, 1984). These are: 0

111

1

The inertial frequency due to the earth’s rotation: f 2 sin ~104 s1; latitude, angular velocity of the earth (see Figure 1.9).

2

The planetary frequency associated with the latitudinal variation of the Coriolis parameter (the “beta effect”): P (U )1/2 ~ 106 s1 where f/ y, f the Coriolis parameter, U horizontal air velocity.

0

3

The Brunt–Väisälä frequency for vertical oscillations: N=

冢g ∂∂z 冣 ~ 10

2 1

s

where potential temperature, g gravitational acceleration, and z altitude. N is a measure of the static stability of the atmosphere.

0111

12 Synoptic and dynamic climatology

1

Figure 1.8 Time and space dimensions of atmospheric weather systems and climatic regimes. (After Mason, 1970, and Barry and Perry, 1973)

1

Figure 1.9 Important parameters and variables relating to the earth’s atmosphere and its motion. (Courtesy B.W. Atkinson, 1984)

Introduction 11

These frequencies can be used to distinguish four scales of motion: 1 2 3 4

0

111

13

Small scale, where F (the atmospheric frequency) > N. Mesoscale, f < F < N. Synoptic scale, P < F < f. Planetary scale, F < P.

According to classical views of atmospheric motion, kinetic energy cascades from the largest scale to the smallest in a dissipative process. However, research on the general circulation and its transports of momentum and energy in the 1950s showed this view to be incorrect. Instead, the eddies in the circulation, especially those in a horizontal plane (i.e. upper-level waves, surface cyclones and anticyclones), were found to be essential to the maintenance of the hemispheric zonal ﬂows. Indeed, the eddies transfer momentum and energy to the larger-scale motion, rather than feeding off it. This phenomenon, termed “negative viscosity” by Starr (1968), is a general characteristic of planetary circulation. Further details are discussed in Chapter 3. Here it is important to note that this result underlines the fundamental role played by synoptic systems and planetary waves in the global circulation and climate. Indeed, it provides the rationale for the focus of this book on circulation regimes, planetary waves, and synoptic systems.

0

1.3 Dynamic and synoptic climatology

0111

0

0 11

The term “climate” is usually understood to imply the ensemble of events at a location, or over a homogeneous area, as represented by the mean (or modal) value and appropriate statistics of the frequency distributions of weather elements. Bryson (1997) proposes the axiom: “Climate is the thermodynamic/hydrodynamic status of the global boundary conditions that determine the concurrent array of weather patterns.” Thus climate theory should be concerned with the boundary conditions, especially ﬂuxes at the surface, in Bryson’s view. Surface ﬂuxes are treated in micrometeorology, boundary-layer meteorology, and on a regional to global scale, in physical climatology, as exempliﬁed by Budyko’s (1956) Atlas of the Heat Budget. However, the direct linkages between surface ﬂuxes and weather patterns are only part of the story. Climates are also determined by the combined effects of the global atmospheric and oceanic circulations, planetary waves, synoptic and smaller-scale weather systems and their interactions. This book addresses these dynamical elements of climate by treating two major components of climatology. Dynamic climatology (now frequently termed “climate dynamics”) is the study of the global climate system in terms of its origin and maintenance. Hare (1957) deﬁned dynamic climatology as “the explanatory description of world climates in terms of the circulation or disturbances of the atmosphere.” In the 1930s, however, Hesselberg (1932) stated, “dynamic climatology must be concerned with the quantitative application of the laws of hydrodynamics and thermodynamics . . . to investigate the general circulation and state of the atmosphere, as well as the average state and motion for shorter time intervals.” Thus it involves the understanding of the dynamic and thermodynamic controls of the mean features of the large-scale circulation (Marotz, 1987) and their variability on monthly to interannual time scales. The approaches to these questions are empirical, theoretical, and model-based (Smagorinsky, 1981; Peixoto and Oort, 1992; Grotjahn, 1993). A history of the origin of the term “dynamic climatology” proposed by Tor Bergeron in 1930 is provided by Raynor et al. (1991). However, there has been an explosion of ideas and information since the 1960s and most of this remains unassimilated in climate textbooks. Synoptic climatology examines the relationship of local and regional climatic conditions to the atmospheric circulation. Synoptic meteorological data are used to categorize

14 Synoptic and dynamic climatology

1

selected characteristics of the atmospheric circulation and associated weather phenomena. The historical evolution of synoptic climatology is treated in detail elsewhere (Barry and Perry, 1973; see also Harman and Winkler, 1991). These two aspects of climatology have developed rapidly since the Second World War through extraordinary developments in computing and modeling, as well as through the increased availability of upper air data and, since the 1960s and 1970s, of satellite data. In the last decade or so, many aspects of synoptic and mesoscale meteorology have been transformed by the use of new measurement technologies, including radar and lidar proﬁling of the lower troposphere, mesoscale networks of automatic stations and improved instrumentation generally. Combined with high-resolution multichannel satellite data, through new image display and visualization techniques, this wealth of information has revealed the detailed structure of frontal cyclones, mesoscale convective systems, tropical waves and cyclones, and boundary-layer structure for research purposes, in addition to providing input to “nowcasting” services (Browning and Szejwach, 1994; Conway et al., 1996). While it is perhaps too soon and, in any case, impracticable to assimilate fully all of these ideas here, an effort is made to illustrate the additional levels of space–time data and models now available and to incorporate these ﬁndings into the discussion where appropriate.

1.4 The structure of the book

1

The book opens with a discussion of data sources for large-scale climatological studies from conventional weather map and geopotential height data and remote sensing products and data analysis techniques. The global atmospheric circulation controls and the characteristics of global wind belts and pressure contours are treated in Chapter 3. Then the planetary waves and circulation modes are examined in depth, followed by a discussion of global teleconnections and their forcings. Chapter 6 treats synoptic-scale systems in the extratropics and the tropics, and this is followed by a discussion of modern synoptic climatology and its applications.

Note 1

The hydrostatic equation ∂p/∂z g where p pressure, z altitude, g acceleration

due to gravity and air density, expresses the approximate balance in the atmosphere between gravitational acceleration (downward) and the vertical pressure gradient (upward).

References Atkinson, B.W. 1984. The Mesoscale Atmosphere. Inaugural lecture, Queen Mary College, University of London, 30 pp. Barry, R.G. 1970. A framework for climatological research with particular reference to scale concepts. Trans. Inst. Brit. Geog., 49: 61–70. Barry, R.G. and Perry, A.H. 1973. Synoptic Climatology: Methods and Applications. Methuen, London, 555 pp. Baumgartner, A. and Reichel, E. 1975. The World Water Balance. Elsevier, Amsterdam, 179 pp. Bolle, H.J. 1985. What is climate? In: G. Ohring and H.J. Bolle, eds, Space Observations for Climate Studies, Adv. Space Research, 5 (6): 5–14. Browning, K.A. and Szejwach, G. 1994. Developments in operational systems for weather forecasting. Met. Applications, 1: 3–22. Bryson, R.A. 1997. The paradigm of climatology: an essay. Bull. Amer. Met. Soc., 78 (3): 449–56. Budyko, M.I. 1956. The Heat Balance of the Earth’s Surface (trans. N.I. Stepanova), US Weather Bureau, Washington DC, 255 pp.

Introduction 11

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Conway, J., Gerard, L., Labrousse, J., Liljas, E., Senesi, S., Sunde, J., and Zwatz-Meise, V. (eds). 1996. COST 78. Meteorology Nowcasting: a Survey of Current Knowledge, Techniques and Practice. European Commission, Directorate General XII (EUR 16861 EN), Brussels, 499 pp. Dickinson, R.E. 1983. Land surface processes and climate–surface albedos and energy balance. Adv. Geophys., 25: 305–53. Graetz, R.D. 1991. The nature and signiﬁcance of the feedback of changes in terrestrial vegetation on global atmospheric and climatic change. Climatic Change, 18: 147–73. Grotjahn, R. 1993. Global Atmospheric Circulations: Observations and Theories, Oxford University Press, Oxford, 430 pp. Gutman, G., Tarpley, D., Ignatov, A., and Olson, S. 1995. The enhanced NOAA global land data set from the Advanced Very High Resolution Radiometer. Bull. Amer. Met. Soc., 76 (7): 1141–56. Hare, F.K. 1957. The dynamic aspects of climatology. Geogr. Annal., 39: 87–104. Harman, J.R. and Winkler, J.A. 1991. Synoptic climatology: themes, applications and prospects. Phys. Geogr., 12: 220–30. Hesselberg, T. 1932. Arbeitsmethoden einer dynamischen Klimatologie. Beitr. Phy. f. Atmos., 19: 291–305. Hutter, K., Blatter, H., and Ohmura, A. 1990. Climatic Changes, Ice Sheet Dynamics and Sea Level Variations. Zürcher Geogr. Schriften, 37, ETH, Zürich, 82 pp. Justice, C.O., Townshend, J.R.G., Holben, B.N., and Tucker, C.J. 1985. Analysis of the phenology of global vegetation using meteorological satellite data. Intl. J. Rem. Sensing, 8: 1271–318. List, R.J. (ed.). 1958. Smithsonian Meteorological Tables. Smithsonian Misc. Collections 114, 6th edition, Smithsonian Institution Press, Washington DC, pp. 483–4. Marotz, G.A. 1987. Dynamic climatology. In: J.E. Olivier and R.E. Fairbridge, eds, The Encyclopedia of Climatology, Van Nostrand Reinhold, New York, pp. 395–403. Mason, B.J. 1970. Future developments in meteorology: an outlook to the year 2000. Quart. J. Roy. Met. Soc., 96: 349–68. Mathews, E. 1985. Atlas of Archived Vegetation, Land-use and Seasonal Albedo Data Sets. NASA Tech. Mem. 86199. McDowell, P.F., Webb, T. III, and Bartlein, P.J. 1990. Long-term environmental change. In: B.L. Turner II, W.C. Clark, R.W. Kates, J.F. Richards, J. Mathews and W.B. Meyer, eds, The Earth as Transformed by Human Actions, Cambridge University Press, Cambridge, pp. 143–62. Peixoto, J.P. and Oort, A.H. 1992. Physics of Climate, American Institute of Physics, New York, 520 pp. Raynor, J.N., Hobgood, J.S., and Howarth, D.A. 1991. Dynamic climatology: its history and future. Phys. Geog., 12: 207–19. Saltzman, B. 1983. Climatic systems analysis. In: B. Saltzman, ed., Theory of Climate. Adv. Geophys., 25: 173–233. Smagorinsky, J. 1981. Epilogue: a perspective on dynamical meteorology. In: B.W. Atkinson, ed., Dynamical Meteorology: An Introductory Selection, Methuen, London, pp. 205–19. Speidel, D.H. and Agnew, A.F. 1982. Water: The Natural Geochemistry of our Environment. Westview Press, Boulder CO. Starr, V.P. 1968. Physics of Negative Viscosity Phenomena, McGraw-Hill, New York, 256 pp. van der Leeden, F., Troise, F.L., and Todd, D.K. 1990. The Water Encyclopedia, 2nd edition. Lewis, Chelsea MI, 808 pp. Vinnichenko, N.K. 1970. The kinetic energy spectrum in the free atmosphere – 1 second to 5 years. Tellus, 22: 158–66. Wells, N. 1986. The Atmosphere and Ocean: A Physical Introduction. Taylor & Francis, London and Philadelphia, 347 pp. Wilson, M.F. and Henderson-Sellers, A. 1985. A global archive of land cover and soil data sets for use in general circulation climate models. J. Climatol., 7: 319–43. Woods, J.D. 1984. The upper ocean and air–sea interaction in global climate. In: J.T. Houghton, ed., The Global Climate, Cambridge University Press, Cambridge, pp. 141–87.

2

1

Climate data and their analysis

2.1 Synoptic meteorological data 2.1.1 Surface reports

1

Standard weather observations include instrumental measurements of air temperature, dewpoint temperature, station pressure (adjusted to mean sea level), wind speed and direction, pressure change over the last three hours, and pressure tendency; also, visual observations are made of cloud amount, type, and cloud base height (for low, middle, and high cloud layers), visibility, and present and past weather. In addition, precipitation amounts are recorded six-hourly and snow depth once a day. Each element is reported in the international synoptic code (Stubbs, 1981; World Meteorological Organization, 1995). Observations are made at synoptic weather stations at 00.00, 00.06, 12.00 and 18.00 hours UTC (Universal Coordinated Time based on Greenwich 0° meridian) and collected at international centers. Under the World Weather Watch program, synoptic reports are made worldwide at about 4,000 land stations and by 7,000 ships (Figure 2.1). Ships also report “sea surface” temperature (nowadays usually engine room intake temperature), sea state, and, if present, sea ice conditions. Observations of surface weather with primitive instruments began in various European countries in the mid-seventeenth century. However, the establishment of networks of weather stations using standard instruments and procedures largely followed on the heels of the expansion of telegraphy in the 1850s and 1860s and the organization of national weather services between the 1840s and the 1880s (Khrgian, 1970, p. 137; Fleming, 1990, p. 141). Following the ﬁrst International Meteorological Congress in Vienna in 1873, an international (“Utrecht”) weather code was adopted in European countries (excluding Holland, Portugal, Spain, and Turkey) and in Russia, but not the United States, in 1875. Cloud types, weather, and visibility were not included in international codes, however, until 1921. 2.1.2 Upper-air reports Upper-air soundings by instrumental kites and balloons began in the 1890s and high altitude temperature measurements led to the discovery of the isothermal zone or inversion around 12 km by Teisserenc de Bort, and its conﬁrmation by R. Assmann, in 1902 (Hoinka, 1997). Pilot balloon measurements of winds aloft began in the early 1900s, followed in the 1920s by instrumented aircraft soundings. Radiosondes were ﬁrst developed in the 1930s in Russia and the United States, but standardized, calibrated measurements and radar tracking became widespread only in the late 1940s. Networks in China and India were established only after 1945. About 900 stations make upper-air soundings of temperature, pressure, humidity, and wind (at 00.00 and 12.00 UTC, or in some countries only once daily). Errors in temperature measurements have been reduced from 0.7–1.0°C to 0.2–0.5°C in recent years. Radar wind measurements are within 1–2 m s1 and 5–10° for

Climate data and their analysis 17 11

0

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0111

Figure 2.1 The distribution of synoptic reports from land stations and ships (above) and upper-air soundings (below) available over the Global Telecommunications System at the National Meteorological Center, Washington, DC, for January 1996. There are 7,829 surface and 1,002 upper-air stations shown (Courtesy National Center for Atmospheric Research, Boulder CO)

0

direction, and height determinations are within 100–50 m up to 50 mb. However, relative humidity data still have large departures, especially at the low temperature and humidity levels characteristic of the upper troposphere (Elliot and Gaffen, 1991; Zaitseva and Ivanov, 1998). The effects of changes in sensor and corrections applied to the data cause signiﬁcant temporal inhomogeneities in station records, especially in the stratosphere (Gaffen, 1994). 0

2.2 Remotely sensed data 11

The inhomogeneities of the global surface and upper-air reporting network (section 2.1) are alleviated through the acquisition of satellite remotely sensed data on surface and

18 Synoptic and dynamic climatology

1

1

atmospheric temperatures, cloud parameters, precipitation occurrence, and estimated rain rates, surface conditions (e.g. snow cover, sea ice, vegetation, soil moisture, ocean phytoplankton abundance), and atmospheric gaseous abundances and particulate concentrations (especially O3, CO2, water vapor, dust, soot) (Carleton, 1991). At the synoptic level, the satellite cloud image is the primary data source (Barrett, 1970; Salby et al., 1991), with supplementary information on temperatures, moisture, and radiation provided by vertical sounding of the atmosphere at mesoscales (Claud et al., 1991, 1992, 1995; Carleton et al., 1995). Only certain portions of the electromagnetic spectrum (EMS) are used routinely in satellite remote sensing of the Earth’s atmosphere and surface. The wavelengths selected for the retrieval of target information are determined, primarily, by the spectral, spatial, and temporal attributes of the climatic phenomenon under consideration (cf. clouds, land cover, SSTs) (Smith et al., 1986; Yates et al., 1986). Accordingly, multispectral remote sensing, involving the use of several narrow wavelength bands, is usually superior to bi-spectral and particularly broad-band uni-spectral methods for discrimination of targets at the Earth’s surface and in the atmosphere (e.g. clouds). This is also the basis of the satellite retrieval of vertical temperature and moisture proﬁles, such as those from the NOAA/TOVS (TIROS Operational Vertical Sounder) and GOES VAS (VISSR: Visible-Infrared Spin Scan Radiometer, Atmospheric Sounder). Moreover, narrow-band remote sensing better permits the removal of “contamination” from sources at the Earth’s surface or in the atmosphere that is necessary to retrieve accurate information (atmospheric water vapor patterns, snow cover discriminated from clouds) (Kidder and Wu, 1984; Hutchison and Locke, 1997). Satellite remote sensing for synoptic and dynamic climatology has traditionally involved the use of bands in the visible: VIS (0.4–0.7 m) and thermal infrared “window”: IR (8–14 m) regions of the EMS, available from meteorological polar orbiting platforms since the early 1960s (Bugaev, 1973; Barrett, 1974, 1987). This was supplemented by sensors in the infrared absorption band at around 6.5 m, used to retrieve the mid-tropospheric water vapor; initially from geosynchronous satellites (Eyre, 1981; Schmetz and Turpeinen, 1988). More recently, information on precipitation rates, the occurrence of solid versus liquid precipitation in convective situations, the near-surface wind speed and the column-integrated water vapor and cloud liquid water over ocean areas, has become available operationally from passive microwave sensors such as the DMSP (Defense Meteorological Satellite Program) SSM/I (Special Sensor Microwave/Imager) and the European Remote Sensing (ERS-1) satellite (Goodberlet et al., 1989; Rabin et al., 1991; Tjemkes et al., 1991; Claud et al., 1992; Bauer and Schluessel, 1993; Liu and Curry, 1993; Rao and MacArthur, 1994; Weng and Grody, 1994; Siefridt et al., 1998). These operational products build upon the advances made in oceanic remote sensing using active and passive microwave sensors, by NASA’s Seasat and Nimbus-7 platforms; speciﬁcally the SASS (Seasat-A Satellite Scatterometer) and SMMR (Special Sensor Microwave Imager) (Alishouse, 1983; Gloersen et al., 1984; Prabhakara et al., 1983; McMurdie et al., 1987; Katsaros et al., 1989). 2.2.1 History The era of routine satellite remote sensing of clouds and weather systems began on April 1 1960 when the Television and Infrared Observing Satellite (TIROS-1) was successfully launched, providing visible and infrared band images over much of the globe. These essentially experimental systems continued through 1965, when they were succeeded by the TIROS Operational Satellites (TOS), known as ESSA 1–9 (for the Environmental Sciences Services Administration – a forerunner of NOAA), during 1966–69 (Figure 2.2). These satellites were all in near-polar orbit, initially at about 750 km altitude and later around 1400 km, making approximately twelve orbits per twenty-four hours. Since 1978 the TIROS N series satellites have been at about 850 km (Smith, 1985). The later series were

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11

Figure 2.2 The history of meteorological satellites since 1960. (updated from Rao et al., 1990)

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111

20 Synoptic and dynamic climatology

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sun-synchronous, with one satellite having an equatorial crossing time about 09.00 local solar time and another about 15.00. The data permitted valuable analyses to be made of snow and ice extent in addition to information used to identify weather systems and map cloud characteristics – so-called nephanalyses (Barrett, 1974; Matson et al., 1986). A further major advance was marked in 1966 with the launch of the Geostationary Operational Environmental Satellites (GOES). Positioned at 60° intervals over the equator at 41,000 km altitude for the First GARP Global Experiment, December 1978–November 1979 (Mason, 1971), they provided views of the globe effectively between about 55°N and 55°S at twenty-minute intervals. Daily visible and infrared image mosaics in polar stereographic projections from the polar orbiting satellite were published in an extensive series of monthly reports between November 1972 and May 1984 (Environmental Data Service NOAA, 1972) and these were at a scale suitable for use in many synoptic-climatological analyses. The Meteorological Satellite Center, Japan (1978–96) has published full disk visible and infrared images centered at 135°E for 13.00 UTC daily since April 1978 as well as regional cloud analysis charts for East Asia and tabulated cloud vectors. For Europe there is a NOAA-derived daily image available from 1974 to the present (Institut für Meteorologie, Berlin). NOAA initiated low-cost Automatic Picture Transmission (APT) to ground stations in 1963 and High Resolution Picture Transmission (HRPT) in 1972 (Rao et al., 1990). Technical advances, with improved spatial resolution of terrestrial and atmospheric features, as well as channels in additional spectral wavelengths, evolved rapidly in the 1970s (Rao et al., 1990). The Polar Orbiting Environmental Satellites (POES) featured the Improved TIROS Operational System (ITOS) satellites of NOAA during 1970–78, and the complementary POES of the Defense Meteorological Satellite Program (DMSP) which supplied broad-band visible and infrared imagery with global coverage at 2.7 km resolution and local 0.6 km resolution read-out products archived from 1973 to 1990/91 (Scharfen et al., 1995); a digital archive was established by NOAA from 1993 (Kroehl et al., 1994). On the NOAA satellites, the Very High Resolution Radiometer (VHRR) replaced the earlier scanning radiometer sensors in 1972 and the data were made available as Global Area Coverage 4 km resolution products, with limited 1.1 km resolution Local Area Coverage. Broadly comparable systems were operated on the Meteor series of the Cosmos satellites of the former Soviet Union from 1969 although the images were not generally available beyond the State Hydrometeorological Service (Massom, 1991). The GOES system with the Synchronous Meteorological Satellites (SMS) provided extensive coverage of the tropics during 1974–89 with satellites of NOAA at longitudes 75°W and 135°W, complemented by the European Meteosat at 0°, the Japanese Geostationary Meteorological Satellite (GMS) at 135°E; the Indian INSAT was also geostationary although not formally part of the international system. The TIROS N series from 1978 to 1989 supplied not only visible and infrared channel data with the Advanced Very High Resolution Radiometer (AVHRR), but also the TIROS Operational Vertical Sounders (TOVS) yielding global information on the large-scale vertical temperature and moisture structure (Susskind, 1993). From 1979 sea surface temperatures were derived for cloud-free areas from AVHRR with an improved multichannel algorithm employed from 1981 (see Njoku and Brown, 1993). Following the success of the single-channel Electrically Scanning Microwave Radiometer (ESMR) on a NASA Research and Development satellite (Nimbus 5), in mapping sea ice year-round during 1973–76, the Scanning Multichannel Microwave Radiometer (SMMR) provided invaluable new information for research on the annual cycle of sea ice and its regional variations (Gloersen et al., 1992) as well as unique information on precipitation over the oceans (Arkin and Ardanuy, 1989) and surface wind velocity over the oceans (Atlas et al., 1993). SMMR operated during 1978–87 and its

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data have been continued up to the present via the closely similar Special Sensor Microwave Imager (SSM/I) on DMSP satellites (Barry, 1991). The current series of NOAA polar orbiting satellites have continued the tried and tested systems and added enhancements in spectral coverage with a near-infrared water vapor channel (3.7 m and/or 1.6 m). Apart from the many operational uses of satellite data, concerted efforts are ﬁnally being made to generate consistent high-quality research data sets. An example of this is an archive of ﬁve years of International Satellite Cloud Climatology (ISCCP) (Rossow, 1993) and Earth Radiation Budget Experiment (ERBE) data at the National Center for Atmospheric Research, Boulder, Colorado (Hurrell and Campbell, 1992). A nationally coordinated effort is under way in the United States through the NOAA–NASA Pathﬁnder Program; this aims to assemble data sets from operational programs. The products include an archive of AVHRR, Global Area Coverage (GAC) 5 km data products for 1982–97 and TOVS products for 1979–97. Part of this endeavor is focused on a suite of cryospheric products for the polar regions (Barry, 1997). The polar products are all being generated on a common Equal Area Scalable Earth (EASE) grid so that data from different sensors and satellites can be directly overlaid. The grid accepts data of differing resolutions in multiples/fractions of the basic 5 km format. 2.2.2 Signiﬁcance Satellite data are important to synoptic and dynamic climatology in three main areas: 1

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They ﬁll in the data-void areas between conventional reporting stations, especially over the oceans. Figure 2.3 shows the typical coverage of a NOAA polar orbiting satellite for the 12.00 UTC analysis. It may be compared with Figure 2.1 showing the heterogeneity of the conventional synoptic network. In this way, pattern recognition of the vortical cloud patterns associated with synoptic and subsynoptic cyclone systems, as they appear on VIS and infrared data, offers clues to the associated thermodynamic and dynamic processes (Evans et al., 1994; Carleton, 1995; Pankiewicz, 1995; Smigielski and Mogil, 1995; Forsythe and Vonder Haar, 1996). This is how our conceptual models of organized cloud systems, especially frontal and tropical cyclones, have evolved (Zillman and Price, 1972; Troup and Streten, 1972; Streten and Kellas, 1973; Dvorak, 1975; Burtt and Junker, 1976; Junker and Haller, 1980; Jaeger, 1984; Reed and Albright, 1997). Moreover, previously unidentiﬁed features, such as Tropical-Extratropical Cloud Bands (TECBs) linking low and high latitudes (Kuhnel, 1989, 1990), so-called “instant occlusions,” Mesoscale Convective Systems (MCSs), cold-air mesocyclones and “polar lows,” and actiniform cloud patterns in areas of subsidence were also discovered through satellite image analysis (Anderson et al., 1969; Reed, 1979; Rasmussen, 1979, 1981; Maddox, 1980; Carleton, 1985; Forbes and Lottes, 1985; McGinnigle, 1988, 1990; Augustine and Howard, 1991; Laing and Fritsch, 1993a, b; Pearson and Stewart, 1994; Carleton, 1996). In most situations, satellite data give signiﬁcant improvements to the synoptic analysis and prediction ﬁelds, as well as time-averaged ﬁelds, especially for the oceans of the southern hemisphere (Salstein et al., 1987; Heckley et al., 1990; Anderson et al., 1991; Keller and Johnson, 1992; Lamberty and Smith, 1993). Satellites provide information to supplement that obtained from conventional meteorological sources in data-rich areas, and permit the associations with synoptic features to be determined (Zillman et al., 1990). Thus the outgoing long-wave (thermal) radiation (OLR) ﬂuxes show strong spatial variations on synoptic time and space scales, and these are dominated by clouds (Cahalan et al., 1982). By temporal ﬁltering of the radiant ﬂux variations, or setting threshold values of satellite-retrieved cloud-top

22 Synoptic and dynamic climatology

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temperature (TCT), important information about the radiative and dynamic effects of clouds, and the likeliest regions of convective precipitation can be obtained (Arkin and Meisner, 1987; Rossow and Lacis, 1990; Ardanuy et al., 1991; Sohn and Smith, 1992; Thiao and Turpeinen, 1992; Rieland and Stuhlmann, 1993; Gupta et al., 1993). Moreover, satellite sensors may be used to determine upper-tropospheric thermal anomalies or to retrieve trace-gas abundances, particularly stratospheric ozone. These show associations with synoptic features, notably jetstreams, and frontal and tropical cyclones (Shapiro et al., 1982; Velden 1989, 1992; Bailey et al., 1993). Satellite data can be used both to initialize the GCMs and LFMs used in numerical weather prediction (NWP), to provide boundary conditions for climate modeling using GCMs, and also to test the model output against observed data (Stoffelen and Cats, 1991; Puri and Davidson, 1992). Examples of the satellite-retrieved data used to provide boundary conditions are SSTs, soil moisture, snow cover and sea ice extent (Ose et al., 1994; van den Hurk et al., 1997; Yamanouchi and Charlock, 1997). Satellite data comparisons with model-generated ﬁelds include those of clouds, precipitation rates and OLR (Morcrette, 1991; Raustein et al., 1991; Janowiak, 1992; Mo and Rasmussen, 1993).

Temporally, satellites are asynoptic (Salby, 1989) because of their nominal capability of retrieving information over the entire orbit (for polar orbiting satellite) or scan period (geosynchronous platform) (Hayden et al., 1996). In combination with surface-based radar data, this makes them suitable for “nowcasting” the weather out to several hours (Menzel et al., 1998). Satellite data are also a critical component of 4-D data assimilation in NWP. When used to develop statistical “models” of cloudy circulation systems, only the satellite data within a certain time window either side of the synoptic hour (e.g. ± three hours) are typically used (Streten and Troup, 1973; Carleton, 1987; Song and Carleton, 1997). The usable window is dictated by the space and time scales of the variable being considered. For example, moisture in the mid- and upper troposphere (600–300 mb), acquired using the infrared absorption band at around 6.7 m, changes less quickly than that at lower levels, which is dominated by the diurnal variations in energy budget and temperature at the Earth’s surface and in the adjacent boundary layer (Chesters et al., 1983, 1987). The sensitivity of climate-scale studies to the satellite asynoptic data is greatest when using polar orbiting platforms, and for phenomena having a strong diurnal cycle,

Figure 2.3 The typical coverage of a NOAA polar orbiting satellite for 12.00 UTC. (Dey, 1989)

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such as clouds, surface temperature, and OLR (McGregor and Gorman, 1994; Salby and Callaghan, 1997). The undersampling that occurs leads to aliasing (see section 2.5) in time-averaged properties (Zeng and Levy, 1995). This can be reduced either by increasing the frequency of the satellite observations (e.g. using data from geosynchronous platforms rather than from one polar orbiter) or by ensuring that the satellite overpasses occur near times when the diurnal cycle of the phenomenon can be better sampled. For example, the Heat Capacity Mapping Mission (HCMM) sampled surface temperatures for thermal inertia studies in the early to mid-afternoon, and again in the early morning. Also, the Tropical Rainfall Measuring Mission (TRMM) satellite represents an attempt to accommodate the strong diurnal cycle of convective precipitation in the tropics (Simpson et al. 1988, 1998; Bell and Reid, 1993). For monitoring rapidly changing phenomena at higher latitudes, particularly the development of “polar lows,” Kidder and Vonder Haar (1990) advocate the wider application of the Molniya-type orbit of satellites launched by the former Soviet Union. The Molniya orbit is highly eccentric and inclined steeply to the Earth’s equator, thereby providing the equivalent of a “polar geosynchronous” perspective for one hemisphere during the approximately eight-hour time period of orbital apogee. A further contrast between conventional synoptic data and satellite observations lies in the spatially aggregated nature of the satellite retrievals. Conventional synoptic data are for point observations, whereas those from satellites are areal averages determined by the sensor spatial resolution, and where the pixel (“picture element”) is the fundamental unit of the remotely sensed information. The nominal scale represented by the pixel (or instantaneous ﬁeld of view) varies according to the intensity of the radiation that is being sensed (see below); thus higher spatial resolutions are possible when sensing reﬂected solar radiation than for emitted thermal infrared, and both are greater than the passive microwave wavelengths, which have nominal “footprints” on the order of 750 km2. Studies calibrating satellite and conventional data on cloud amount, precipitation, surface insolation receipt (the surface solar irradiance), and SSTs suggest that the two types of observing technique show the highest correspondence when the conventional observations are averaged over scales of approximately 50 km2 (Barrett and Grant, 1979; Henderson-Sellers et al., 1981; Njoku, 1985; Grifﬁth, 1987). Thus for example, surface and satellite-retrieved total cloud amounts show closest correspondence for clear and cloudy skies, but differ by only about 1 okta when averaged over all sky conditions (Henderson-Sellers et al., 1987). There is also a “trade-off” between the maximum resolution that can be achieved and the areal coverage, given by the swath width of a polar orbiter. The swath width inﬂuences how frequently the same location on the Earth’s surface is revisited and, thus is a component of the temporal undersampling problem (Zeng and Levy, 1995; Salby and Callaghan, 1997). One may contrast the imaged area acquired by VIS/IR sensors on board meteorological polar orbiting satellites (approximately 1,000 km altitude) with that acquired by the higher-resolution sensors on board Earth resources satellites such as Landsat and SPOT (Système probatoire pour l’observation de terre), which are at 185 km and 60 km altitude, respectively. This means that meteorological polar orbiters, particularly the NOAA AVHRR with its nominal pixel resolution of 1.1 km, revisit a given location twice per day (twelve hours apart); more frequently at higher latitudes owing to the reduced Earth surface area. The latter permits the derivation of the cloud-drift winds (for wind speed and direction estimates) associated with synoptic-scale and subsynoptic systems in polar regions, over time scales of several hours (Turner and Warren, 1989; King and Turner, 1997). 2.2.3 Principles of satellite remote sensing and applications The fundamental information obtained by a satellite remote sensing system, in common with the physical basis for climate, is the radiation interaction in a given wavelength, or group of wavelengths, with targets at the Earth’s surface and in the atmosphere. However,

24 Synoptic and dynamic climatology unlike the “conventional” retrieval of the radiation and energy balance from known information on temperature and absorption (due to atmospheric gases), the problem is the reverse in remote sensing; that is, temperature and moisture characteristics have to be retrieved from the radiation ﬁelds by so-called inversion techniques. Inverting the satellite radiances to yield climatic properties such as surface temperature, precipitation rate, snow depth, layer-averaged water vapor, or near-surface wind speed, can be handled using either physically based modeling, or empirical methods derived from regressing the satellite retrievals with conventional data, or a mixture of the two (Petty, 1994a, b). The utility of satellite remotely sensed data for synoptic and dynamic climatological studies is predicated on the underlying physical principles, particularly the radiation laws. There is an inverse relationship between the radiation wavelength ( ), expressed in micrometers (m: 1 106 m) and nanometers (1 nm: 1 109 m), and the frequency (v) which represents the number of wave peaks passing a ﬁxed point per unit time, or: 1

c v

(1)

where c the speed of light (2.99792 10 m s ). One commonly used measure of v is the gigaHertz (GHz), or 1 109 s1. Whereas the wavelength (in m) is used typically to denote radiation in the visible and near-infrared (i.e. solar), and IR regions, the frequency is more commonly used to denote radiation intensity in the microwave region. There, wavelengths are of the order of centimeters to meters. Thus from equation 1, wavelength increases as the frequency decreases so that, for example, 1 cm ⬵ 30 GHz and 0.3 cm ⬵ 100 GHz. The 19.35 GHz channel of the Nimbus-5 ESMR is at 1.55 cm. The radiation emission from all bodies having an absolute temperature (Tabs) exceeding 0 K (273.15°C) is expressed by Stefan–Boltzmann’s law: 8

1

E T 4

(2)

where E total radiant exitance (W m ), Stefan–Boltzmann constant, or 5.6677 108 W m2 K4, and T Tabs (K). In the case of the Sun, this gives a black body curve for an assumed temperature of 6,000 K, as shown in Figure 2.4; the corresponding curve for an assumed temperature of 255 K (the Earth’s effective temperature) is also shown. The intensity of the radiation emission, or ﬂux density, differs according to the wavelengths considered. This can be seen best by considering the relationship of wavelength to the energy content of photons (quanta), or “packets” of energy, which is given as: 2

1

Q hv

(3)

where Q energy of a quantum (J), and h Planck’s constant (6.626 10 J s ). Thus the energy of a quantum increases with increasing frequency, and is inversely related to wavelength (from solving equation 1 for v and substituting into equation 3): longer wavelengths (or lower frequencies) are less intense than shorter wavelengths (or higher frequencies). The spectral distribution of radiation intensity across all wavelengths for bodies of different temperature is given by Planck’s law. Moreover the wavelength of maximum radiation emission ( max), which is based on Planck’s law, is expressed empirically by Wien’s displacement law: 34

max 2,897/T

1

(4)

where 2,897 a constant (m K). Thus there is an inverse relationship between the temperature of a body and its wavelength of maximum emission. Substituting the temperature values for the Sun (6,000 K) and the Earth’s surface (288 K) into the denominator in equation 4 gives values of max close to 0.5 m and 10 m, respectively. Thus the Sun has a radiation peak in the wavelengths of visible light, and the Earth emits radiation in the longer IR wavelengths. Moreover, Earth targets of different temperatures have

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Figure 2.4 (a) Black-body radiation (Planck) curves for temperatures of 6,000 K (the Sun) and 255 K (the Earth’s surface) normalized to give equal energies (areas). (b) The percentage absorption in the total atmospheric column. (c) The percentage absorption from 11 km to the top of the atmosphere. (d) The absorption spectra of individual gases contributing to (b). (From Harries, 1996)

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26 Synoptic and dynamic climatology different emission rates and max; compare, for example, high cold clouds, Antarctica, and the subtropical deserts in summer. This is important for satellite remote sensing because targets at the Earth’s surface or in the atmosphere (clouds, aerosols, water vapor, O3 and other gases) can be determined by their effects on the incoming solar radiation, and by their inﬂuence on the long-wave radiation emitted from the Earth’s surface and its atmosphere. A fundamental assumption in equations 2 and 4 is that the radiation source (Sun, Earth) emits at the maximum rate for its temperature (the idealized Planck curves in Figure 2.4). However, this will occur only when the incident radiation in a given wavelength or group of wavelengths absorbed by the body is reradiated at maximum efﬁciency (i.e. there is no attenuation, or depletion of power due to reﬂection of radiation away from the body or by transmission through the target). This is expressed by the principle of energy conservation, thus: 1

1

EI ( ) ER( ) EA( ) ET( )

(5)

where EI the incident radiation, ER the reﬂected radiation, EA the absorbed radiation, and ET the transmitted radiation. From equation 5 it should be evident that EI ( ) EA( ), and therefore ER ET 0, only in the case of the Sun, which is a black body. For the Earth–atmosphere system EA < EI and, thus there is some transmission and also reﬂection of the incident radiation. The radiation spectrum of the Earth–atmosphere system, therefore, features various absorption bands due to the selective effects of atmospheric gases on the outgoing long-wave radiation, or OLR (see Figure 2.4d). The major gases involved are water vapor, CO2 , methane, and, to a lesser extent, O3. For most of the 8–14 m region the gases do not hinder appreciably the escape of OLR to space (or to a satellite sensor). Thus the IR wavelength band is mostly an atmospheric window region having maximum transmission and minimum absorption of OLR. This is appropriate for determining the temperatures of targets at the Earth’s surface (land, ocean) and also of clouds, for example using the so-called split window method, which involves comparing the radiances in two non-adjacent bands, such as AVHRR bands 4 (11 m) and 5 (12 m), for which the absorption characteristics are known (Coll and Caselles, 1997; Czajkowski et al., 1998). There are certain wavelength bands of IR radiation in which the absorption and re-emission by the atmospheric gases (primarily water vapor, CO2) increases and, correspondingly, the transmission to space decreases. Satellite remote sensing of the upwelling IR radiation by a particular gas in an absorption band can be compared with radiances for the wavelengths on either side, yielding information about the abundance of that gas; for example, using the split window method, whereby there is greater absorption by water vapor in AVHRR channel 5 compared with channel 4 (Eck and Holben, 1994; Suggs et al., 1998). This principle is also the basis of satellite sounding of the atmosphere in many narrow bands to determine vertical proﬁles of the temperature from gases that are well mixed throughout the atmosphere (e.g. oxygen, CO2), and the relative humidity (from water vapor absorption/re-emission). In the synoptic context, sensing from geosynchronous orbit in the absorption band near 6.5 m has proven useful in a variety of ways: for identifying and tracking water vapor “features” (Allison et al., 1972); showing regions of strong ascent (moistening) and descent (drying) associated with the equatorward and poleward sides, respectively, of jetstreams and tropopause breaks (Martin and Salomonson, 1970; Ramond et al., 1981; Mueller and Fuelberg, 1990); for determining the layer–mean wind vectors in the mid-troposphere (Eigenwillig and Fischer, 1982); and for depicting time-averaged features of the circulation, particularly in lower latitudes (Picon and Desbois, 1990; Wu et al., 1993; Soden and Bretherton, 1993, 1996). The efﬁciency of absorption and re-emission of the incident radiation, or emissivity (), as a function of the wavelength and temperature is expressed by Kirchhoff’s law, or:

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=

Eemit = f ( , T ) Eabs

(6)

where Eemit the intensity of the energy re-emission (W m2) and Eabs the intensity of the energy absorbed (W m2). Thus the emissivity is a ratio varying between 0 (white body) and 1.0 (black body). Because of the change of emissivity according to wavelength and temperature, it is possible for targets in the Earth–atmosphere system to have very different emissivity values according to the wavelength bands that are used in remote sensing. For example, most clouds (with the notable exception of cirrus) approach black body status ( ⬵1) in the IR window (8–14 m) wavelengths. Clouds that have high emissivity are optically thick, which means that they also tend to be highly reﬂective in visible wavelengths; hence cumulonimbus cloud masses are known as highly reﬂective clouds (HRCs). Their distribution in the tropics has been mapped by Garcia (1985). The occurrence of HRCs is a good proxy for deep convection in the tropics (Grossman and Garcia, 1990; Morrissey and Greene, 1993). Puri and Davidson (1992) show that HRCs are important sources of diabatic heating for the troposphere. The emissivity is also a function of Earth surface properties, such as surface roughness, particle size, water-holding capacity, and thermal inertia. These inﬂuence target identiﬁcation in the microwave region. Most land surfaces have emissivities around 0.9 in the IR, and they remain relatively high in the microwave region. Water targets, such as the ocean, possess an emissivity very close to unity in the IR (i.e. are radiometrically warm), but only around 0.4 in the microwave region around 19 GHz (i.e. are radiometrically cold). The latter attribute facilitates the detection of the rather weak atmospheric signal (humidity, rain) over ocean areas in the passive microwave, which is not possible over the higher emissivity and spatially more variable land surfaces at these frequencies. The exception is the detection of microwave radiation at higher frequencies (85–90 GHz) that is scattered by large ice aggregates (hail, graupel) in the upper parts of convective cloud systems, and which enables thunderstorms to be detected over land as well as sea (Negri et al., 1989; Mugnai et al., 1990). Radiation transfer within the Earth’s atmosphere by absorption or scattering is described by Schwarzschild’s law (equation 7). This states that the net loss of radiation passing through the atmosphere (dEI ( )) results from its partial absorption at one wavelength (e.g. solar radiation) and re-emission by the atmosphere at another longer wavelength, or: dEI ( ) a( ) EI ( ) a ( ) f ( , T)

(7)

where a is a coefﬁcient of absorption (or scattering) that is dependent upon the density of the gas and the thickness of the atmosphere. The attenuation of short-wave radiation with increasing path length is a function of the zenith angle (from Beer’s law). In equation 7 the absorption is negative, or “warming,” and the emitted intensity is given by Kirchhoff’s law (equation 6). The emissivity differences between targets in the Earth–atmosphere system at a given wavelength, and for the same target at different wavelengths, comprise a fundamental principle of target differentiation using satellite IR remote sensing by their effects on the retrieved temperature. In the IR portion of the EMS the temperature sensed by a satellite radiometer (Trad) or sounder is related to the actual, or kinetic, temperature (Tkin), as follows:

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(8)

Thus for black bodies, where 1.0, Trad Tkin, and the retrieved target temperature is the actual measured temperature. For actual emitters, Trad and Tkin differ. Detailed information on the emissivity of most land surfaces is lacking and, moreover, these surfaces are typically heterogeneous at satellite pixel resolutions. Hence the retrieved surface

28 Synoptic and dynamic climatology

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temperature in the infrared window region (or TBB – equivalent black-body temperature) is only approximately related to the actual surface temperature, and is, in effect, a “skin” (rather than bulk) temperature that is inﬂuenced by solar radiation absorption and evaporation effects. An emissivity 1.0 is often assumed in automated cloud retrieval algorithms, such as that of the US Air Force DMSP 3-D Neph program (McGufﬁe et al., 1989; McGufﬁe, 1993), since most clouds approach black-body status in the infrared. Thus their TBB is close to the actual TCT measured by radiosondes. Moreover, the detection of optically thin clouds (i.e. clouds for which there is signiﬁcant transmission to the satellite sensor of infrared radiation from lower levels in the atmosphere and from the ground), and assignment of their altitude, is problematic (Brogniez et al., 1995). When detected, these clouds appear to be lower and warmer than, in fact, they are. However, the failure to detect some thin clouds complicates the retrieval of accurate SSTs (Wu, 1984). Similar problems arise when a dust or ash layer is located between the target and the satellite, as occurred with the eruptions of El Chichon in 1982 and Mount Pinatubo in 1991, which disrupted the satellite mapping of SSTs over large areas of the tropics during a major short-term shift in Paciﬁc sea surface temperature (see section 5.3). Inferring rain rates using the so-called “indirect” (or infrared) method (Arkin and Ardanuy, 1989) relies upon the general negative relationship of precipitation with TCT, and its positive association with the visible reﬂectance. This is maximized in the case of HRCs in the tropics, which are also sites of minima in the OLR ﬁeld (Hendon and Woodberry, 1993). While the assumptions underlying the infrared rain-rate estimation method are generally valid, they are most applicable to tropical and subtropical regions (Martin et al., 1990; Ebert and LeMarshall, 1995; Todd et al., 1995; Ba and Nicholson, 1998), and can yield reliable approximations to the precipitation rate on climatic, rather than daily, time scales (Rasmusson and Arkin, 1993). Monthly and seasonal maps of OLR are used to monitor tropical convection related to large-scale atmospheric circulation changes, particularly the El Niño Southern Oscillation (ENSO) (see Chapter 5). However, the accuracy of the indirect method is inﬂuenced by factors such as the location (continental versus oceanic) of the precipitating clouds; the humidity in the sub-cloud layer (arid or semi-arid versus humid climates); season; and the confusion between nonprecipitating thick cirrus clouds and the upper parts of cumulonimbus clouds (Carleton, 1991, chapter 5). The infrared method forms the basis of the operational satellite GPI (GOES Precipitation Index) (Arkin and Meisner, 1987; Herman et al., 1997), which is a mapped time-integrated measure of rainfall amounts from geosynchronous VIS and infrared images. In the microwave portion of the EMS, where the atmospheric transmittance is at a maximum, the radiance is proportional to the temperature (or Rayleigh–Jeans approximation). This brightness temperature (TB) is essentially the corollary of the TBB in the IR. Thus target differences in TB are directly related to changes in emissivity. In the frequency range 9–90 GHz there are three atmospheric windows that permit detection of the surface black-body radiation. These are bounded by absorption lines due to water vapor near 22.2 GHz and oxygen near 60 GHz and 118.8 GHz. Over oceanic regions, and in low frequencies (e.g. 19 GHz, 22 GHz), the infrared method of rain estimation can be compared with passive microwave estimates (the so-called “direct” method: Arkin and Ardanuy, 1989). Microwave sensing yields “instantaneous” (approximately twenty-minute average according to Barrett et al., 1990) rain rates that are derived from the absorption and reemission characteristics of raindrops (increasing TB ) occurring over a low-emissivity ocean background (Alishouse et al., 1990; Petty and Katsaros, 1992). As the frequency of the microwave radiation considered increases to around 37 GHz (i.e. the wavelength decreases: equation 1), the signal becomes progressively less emission-based and more due to scattering, with a greater proportion due to hydrometeors occurring higher in the cloud system (Spencer et al., 1989). Thus at around 19 GHz, mostly liquid precipitation in the lower parts of the clouds is sensed; at around 37 GHz, rain and ice can potentially be detected,

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and at 85 GHz and higher frequencies, scattering predominates owing to ice (hail, graupel) and snow aggregates in the upper parts of clouds. The last property makes it possible to sense deep convective situations occurring over land as well as sea (e.g. Mohr and Zipser, 1996a, b), and to estimate rainfall over land using algorithms that combine the 37 GHz and 85 GHz TB (Adler et al., 1993; Ferraro, 1997). Moreover, because the resolution of the microwave radiometer increases with increasing microwave frequency; from about 30 30 km2 at 19 GHz to around 15 15 km2 at 85 GHz, the problem of beam ﬁlling that results from precipitating convection below sensor resolution (Kummerow, 1998) is greatest at the lower frequencies. Accordingly, corrections for this effect need to be applied where it cannot be assumed that individual pixels are ﬁlled with rain (Shin et al., 1990; Liu and Curry, 1992; Chiu et al., 1993). Thus oceanic rain rates based on the lowerfrequency microwave data from the Nimbus-5 ESMR underestimated the rain rates measured using rain gauges. Comparisons of the satellite GPI and passive microwave (SSM/I) estimates of monthly rain rates over the global oceans for an approximately three-year period (Chiu et al., 1993) show areas of consistently higher and also lower SSM/I estimates that are differentiated regionally (Ferraro, 1997). These are interpreted as resulting from the merger of different satellite data sets to develop the GPI, and the interpretation of high and cold cirrus clouds as raining in the infrared data. Generally, GPI (passive microwave) methods work better in the tropics (extratropics), and in areas where the SSM/I works well a combination of GPI-passive microwave sensing gives good estimates of monthly precipitation over both land and adjacent water (Adler et al., 1993; Negri et al., 1993). When merged with surface rain gauge observations the satellite remotely sensed data form the basis of the Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset, initially developed for the period July 1987 through December 1995 (Huffman et al., 1997). Given the lighter rain rates typical of higher latitude weather systems and the generally poorer performance of passive microwave methods there, a simpler method of demarcating the likelihood of rain occurrence (the P37 index) in given pixels has been suggested by Petty and Katsaros (1992) and Carleton et al. (1995). This is similar to the polarization-corrected brightness temperature (PCT) developed by Spencer et al. (1989) for the SSM/I 85 GHz channels. This method has been used to help retrieve precipitation over ocean in higher latitudes (Lachlan-Cope and Turner, 1997), and over heterogeneous (i.e. variable emissivity) surfaces in a mixture of synoptic conditions (Kidd, 1998). A recent advance involves comparing GOES infrared radiation data with surfacebased radar (i.e. active microwave) for real-time estimates of precipitation rate in the United States (Vincente et al., 1998). The column-integrated atmospheric water vapor content and the cloud liquid water content (in kg m2) can also be detected over low-emissivity ocean surfaces in the microwave region. Retrieval algorithms of these quantities utilize the dual-polarization measurements of TB for combinations of the SSM/I channels (Petty, 1994a, b). While these passive microwave measurements have been used to develop large-scale climatologies (e.g. Prabhakara et al., 1992; Martin et al., 1993; Ferraro et al., 1996), they also have proven invaluable for gaining insights into the mesoscale structure of synoptic and subsynoptic storms over the oceans (Katsaros and Lewis, 1986; Katsaros et al., 1989; McMurdie and Katsaros, 1985, 1991; Chang et al., 1993; Carleton et al., 1995; Claud et al., 1995; also Chapter 6). Moreover the emission of passive microwave radiation by the ocean surface changes in response to the spray and foam generated by near-surface winds (Goodberlet et al., 1989). Thus the change in TB measured using dual polarization at lower microwave frequencies that occurs as wind speed increases can be inverted to yield a near-surface (approximately 20 m height) wind speed (in m s1). The spatial variations of the wind speed associated with synoptic cyclones, as well as with cold-air mesocyclones, have been identiﬁed using the SMMR and SSM/I sensors (Claud et al.,

30 Synoptic and dynamic climatology

1

1

1993; McMurdie et al., 1997; Song and Carleton, 1997). However, the retrievals are erroneous in regions of heavy rainfall; typically near cold fronts and in tropical cyclones, since raindrops falling on the sea surface inﬂuence the microwave emission. In these areas, transects (swaths of approximately 10 km width) of wind speed data provided by non-imaging active microwave sensors, particularly the Geosat and TOPEX-Poseidon radar altimeters (Mognard and Katsaros, 1995a, b), supplement the information obtained from microwave radiometry. Moreover the near-surface wind speed and direction vectors acquired over wider swaths by the active microwave scatterometer of the ERS-1 satellite are becoming indispensable to weather analysis and prediction, as well as for depicting details of the near-surface climatology over the oceans; especially in the middle and higher latitudes dominated by traveling synoptic and mesoscale storms (Marshall and Turner, 1997; Siefridt et al., 1998). Comparisons between passive and active microwave measurements of ocean surface wind speeds indicate good agreement globally (Boutin and Etcheto, 1990), but with regional-scale differences that appear to result mostly from the effects of atmospheric attenuation (Mognard and Katsaros, 1995b). Given the negative association between wind speed and isobaric spacing, and the modifying effects of atmospheric stability on the wind speed in the boundary layer, pressure patterns over data-void ocean areas can be reﬁned greatly when the scatterometer data are input to a PBL model (Brown, 1986; Levy and Brown, 1991; Brown and Zheng, 1994; Hsu et al., 1997). The ﬁrst all-year weather mapping of polar surfaces was made possible by the launch of the 19.35 GHz passive microwave sensor on Nimbus-5 in December 1972. ESMR demonstrated the ability of microwave radiometry to detect sea ice in polar regions, from the strong increase in emissivity and TB that occurs between open ocean and ice-covered areas, and the relative transparency of most clouds at these latitudes to microwave radiation. Three-day time-lapse movies, and case-study periods, of the ESMR data for the period 1973–76 clearly revealed the movements of the sea ice edge in response to the dynamic (wind-ﬁeld) and thermodynamic (temperature advection) forcings of synopticscale cyclones (Campbell et al., 1980; Crane et al., 1982; Zwally et al., 1983; Parkinson et al., 1987; Carleton 1984). The unambiguous retrieval of the ice-water concentration and ice type (ﬁrst year versus multi-year) parameters for the entire seasonal cycle requires polarization measurements of the microwave emission which the nadir-pointing, singlechannel, horizontally polarized (the plane of polarization of the antenna is parallel to the earth’s surface) ESMR did not possess. However, the separation of ice concentration effects on TB from those related to different ice types (age) was possible for times lacking signiﬁcant surface melting (i.e. in winter). The SMMR instrument which operated on Nimbus-7 from October 1978 through August 1987 was dual-polarized at ﬁve frequencies: 6.6 GHz, 10.7 GHz, 18 GHz, 21 GHz and 37 GHz. The SSM/I sensors on DMSP satellites, which were launched in July 1987 and continue through the present, provide seven channels of data: dual polarized radiances at 19.3 GHz, 37 GHz and 85.5 GHz and vertically polarized radiances at 22 GHz . The 18 GHz (19.3 GHz) and 37 GHz channels are the primary ones used in algorithms for the mapping of sea ice extent, type and concentration (see Carsey, 1992, for detailed descriptions of the techniques and results). The dual-polarization measurements from SMMR (Gloersen et al., 1984) and SSM/I (Barry, 1991) made possible the continuous mapping of ice type (age) and concentration changes in both polar regions (Gloersen et al., 1992; Steffen et al., 1992) and have provided critical information on recent trends in sea ice cover. There was a decadal decrease of 2.9 percent in Arctic summer ice extent during 1978–96, for example, with most of it occurring since the mid-1980s (Bjørgo et al., 1997; Cavalieri et al., 1997). Low-pressure systems in the Arctic basin appear to have played a signiﬁcant role in reducing ice concentrations, especially in the Eurasian shelf seas, in summers 1990, 1993 and 1995 (Maslanik et al., 1995; 1996). The determination of summer ice concentration from passive microwave data is still problematic owing to the effect of surface snow

Climate data and their analysis 31 11

0

111

0

0111

0

melt and melt-pond formation (Comiso, 1990). Moreover, atmospheric inﬂuences in the presence of high totals of column water vapor and cloud liquid water are known to cause overestimates in total ice cover, and to increase (decrease) the apparent concentrations of ﬁrst-year (multi-year) ice (Maslanik, 1992; Oelke, 1997). Procedures to reduce weather effects on the calculation of ice concentrations have been developed (Cavalieri et al., 1995). SMMR and SSM/I data have also enabled the identiﬁcation of surface melt onset in the Arctic (Anderson, 1987; Crane and Anderson, 1994) and recently maps of Arctic sea ice melt and freeze-up dates have been constructed (Smith, 1998). However, mapping of the progression of summer albedo values for the Arctic Ocean ice has been based on DMSP visible band imagery (Robinson et al., 1992) and AVHRR-derived radiances (Lindsay and Rothrock, 1994; Schweiger et al., 1993). Information on lead (linear ice fracture) statistics has also depended on these last two sources (Lindsay and Rothrock, 1995; Miles and Barry, 1998). Sea ice motion products have been developed for limited regions with visible band AVHRR data and synthetic aperture radar (SAR) data, individually or as blended products including drifting buoy records, and for the polar oceans using 85 GHz passive microwave data (Emery et al., 1997; Agnew et al., 1997). High resolution (10–100 m) active microwave (SAR) remotely sensed data can often complement other sensors in assessing such features as leads and ice type (Onstott, 1992; Kwok et al., 1992), as well as deformation, ridging, and motion. Barber et al. (1991) also discuss the possibilities of estimating climatic state variables (albedo, latent heat, and atmospheric drag coefﬁcients) from ice type and cover information. At present, SAR data (ERS-1 and -2 and Radarsat) are used primarily for operational purposes, although as a result they now enter into the ice charts prepared by the national ice services. In the case of snow-covered ground the microwave energy emitted is determined by the amount of that energy scattered by the snow pack. For dry snow there is a sharp emissivity decrease in the presence of a snow cover relative to snow-free ground (Maetzler, 1994). Scattering is a function of snow depth and grain size (Armstrong et al., 1993). Of greater importance on a global scale is the emission from the vegetation canopy or land cover, especially in the boreal forest zone (Foster et al., 1991; Tait, 1998). Weekly snow cover extent is mapped by NOAA NESDIS for the northern hemisphere from AVHRR and the NOAA data product is widely used in climate studies (Robinson et al., 1993). Operational SSM/I snow products are also now available (Grody 1991; Goodison and Walker, 1995). Research products based on 18 GHz and 37 GHz passive microwave data for snow extent and estimated snow water equivalent (SWE) have a long history (Chang et al., 1987; Rott, 1987). The SMMR/SSM/I data for 1978–97 are used by Armstrong and Brodzik (1999) to map snow extent on a daily basis with 25 km resolution using the algorithm of Chang et al. (1987). The trend in the twenty-year record shows an annual decrease in snow cover of 46,000 km2 compared with a corresponding trend of 64,000 km2 in the NOAA visible band data. Shallow and/or wet snow is not mapped consistently by the passive microwave (Basist et al., 1996). Thin snow may not be detectable and snow melt causes a loss of signature but the 37 GHz polarization difference can serve as a wet-snow indicator (Walker and Goodison, 1993), enabling corrections to be applied. Algorithms for the determination of snow depth or SWE, however, are still limited geographically and/or temporally in their applicability (Goodison, 1989; Tait, 1998).

2.3 Climate variables and their statistical description 0

In the words of Durst (1951), “climate is but the synthesis of weather.” There are various levels of climatological synthesis. Table 2.1 summarizes a conceptual view of these levels that takes into account the variation in the number of weather elements (e), in time (t) 11

32 Synoptic and dynamic climatology Table 2.1 Levels of climatic synthesis

1

Order

Variables

Description

Representation

1

ƒ (e0 s0t0)

Instantaneous value of one element at one instant

Synoptic observation of one element

1

ƒ (e0 t0s)

Spatial distribution of one element at one instant

“Synoptic map” of one element; satellite cloud photo

1

ƒ (e0 s0t)

Time variation of one element at a point

Time series (autographic record)

1

ƒ (s0 t0e)

Instantaneous, point value of several elements

Station weather report

2

ƒ (e0 st)

Changes in the distribution of one element with time

Series of pressure maps

2

ƒ (t0es)

Spatial covariance of weather elements

Synoptic weather map

2

ƒ (s0et)

Time covariance of weather elements

Climogram

3

ƒ (est)

Covariance of weather elements in space and time

Series of synoptic weather maps

Source: modiﬁed from Godske (1966).

and in space (s). The zeroth level is the instantaneous value of a single weather element, such as air temperature, at a given location. It should be noted that the time variation may refer to any range of time scale (diurnal, intraseasonal, annual, or interannual variation). In the table, space is regarded as having two dimensions. 1

1

2.3.1 Frequency distributions Climatological synthesis is not made completely explicit in Table 2.1. For many purposes, the description of a climatic variable may involve determination of the frequency of occurrence of the complete range of values of the variable, as well as statistics such as the variance, or standard deviation, and the higher moments of the frequency distribution (skewness and kurtosis). The appropriate statistics depend heavily on the characteristics of the frequency distribution for each element and averaging interval. Each weather element tends to have a distinctive frequency distribution according to the climatic regime and averaging period. Many distributions are not Gaussian (or normal). Examples of the frequency distributions for selected weather elements at Bergen, Norway, are illustrated in Figure 2.5. The distribution of pressure values is close to Gaussian whereas the temperature plots are positively skewed, with a tail towards higher values. The distribution of summer visibility values is J-shaped while daily precipitation amounts show a characteristic reversed J shape. The bimodal distribution of cloud amounts is also typical of station observations of cloud cover, reﬂecting the fact that the sky conditions overhead approximate a binary state. A multimodal distribution, such as that for Bergen evening temperatures in September (Figure 2.5), commonly represents a mixed population. The peaks here probably indicate the occurrence of different air masses or airﬂow directions (Fiedler, 1965). A numerical method to decompose such distributions into several partial frequency distributions or collectives was developed by Essenwanger (1955, 1960a), although Bryson (1966) shows that a close approximation can be obtained by direct graphical analysis (Figure 2.6).

Climate data and their analysis 33 11

0

111

0

0111

Figure 2.5 Frequency distributions for selected climatic parameters at Bergen, Norway. P pressure, T temperature (°C); subscripts or numerals refer to the hour of observation. (After Godske, 1966, from Barry and Perry, 1973)

0

2.3.2 Exploratory data analysis

0 11

Climatic data are often analyzed using standard parametric techniques which assume that the data distribution is Gaussian. For many practical purposes, non-parametric techniques of exploratory data analysis (EDA) are actually preferable (Hoaglin et al., 1985). Examples of climatological applications are provided by Kleiner and Graedel (1980) and Lanzante (1996). The central tendency of a distribution can usefully be expressed by the median, or middle value of a distribution. This measure is resistant to outliers (extreme events or erroneous data). However, its efﬁciency, representing the effect of sampling variability on the median, is less than that of the arithmetic mean. The variability can be described by the interquartile range (IQR), which is the difference of the upper quartile minus the lower quartile (the 75 percent value minus the 25 percent value). For a normal distribution the IQR 1.349 . Thus a pseudo-standard deviation can be deﬁned as IQR/1.349. Examples of upper-air data are given by Lanzante (1996).

34 Synoptic and dynamic climatology

1

Figure 2.6 Schematic multimodal distribution (heavy line) illustrating two methods of estimating partial collectives (light lines). Method A: identify ordinate of the median; express the ordinates one unit above and below the median as a fraction of the median; calculate the standard deviation and reconstruct the partial collective. Method B: obtain end collectives by folding the distribution along the median ordinate and subtracting from the total distribution. (From Bryson, 1966)

2.3.3 Contingency analysis 1

A question that often arises in climatic analysis is the co-frequency of two (or more) variables. The co-frequencies can be arranged in a two-way contingency table in which each cell refers to a speciﬁed subset of the variables. Examples of synoptic climatological applications are given by Murray and Lewis (1966), for rainfall categories over England and Wales against a cyclonicity index, and by Namias (1991), who examines summer temperatures over the Great Plains as a function of antecedent spring temperature and precipitation, using tercile categories in each case. The contingency table can be readily used for signiﬁcance tests of association such as the non-parametric 2 statistic (see Wilks, 1995, for example). Carleton (1995) adopts this approach in examining the relationship between polar low occurrences in relation to surface type over the Southern Ocean and their cloud cover attributes. 2.2.4 Probability Probability theory concerns the likelihood of speciﬁc chance events happening. Probabilities (p) are expressed on the scale p 0 to 1.0 (or 0–100 percent). The total probability in a situation where there are several possible outcomes is always equal to 1.0. Thus if the average frequency of rainy days in April at a station is equal to six, then the probability of any day in the month having rain is p 0.2 (assuming that rain days occur at random) and the probability of it being dry is 1 p 0.8. For statistically independent variables, joint probabilities are given simply by the product of the individual probabilities. Suppose, for example, that the wind speed at the same station is unrelated to precipitation and that the probability of a day with winds below 5 m s1 is 0.05 in April, then the joint probability of a rainy day with light winds is

Climate data and their analysis 35 11

p 0.2 0.05 0.01 If two variables are not independent, then their conditional probability – the probability of one occurring given that the other occurred – must be evaluated. The properties of four statistical distributions that are of particular importance in determining climatological probabilities are now brieﬂy discussed. They are the normal, binomial, Poisson and gamma distributions. A useful summary of the properties of probability distributions and their relationships to one another is given by Rothschild and Logothetis (1986).

0 Normal distribution 111

The well known normal or Gaussian distribution, which is symmetrical and bell-shaped (Figure 2.7), is the basis of many parametric statistical methods. Its statistics are the arithmetic mean value: x=

1 n

n

兺x

i

i=1

and the second moment, or variance:

0

s2 =

1 n

n

兺 (x x)

2

i

i=1

The standard deviation is given by s. The rth moment is: r = 0111

1 n

n

兺 (x x )

r

i

i=1

Skewness is usually determined from 3/s3 and the kurtosis from 4/s4. Skewness is a measure of the symmetry of the distribution; the skewness is positive (negative) when the distribution has a long tail towards high (low) values of the variate. Kurtosis describes

0

0 11

Figure 2.7 The normal (Gaussian) frequency distribution. (From Barry, 1971)

36 Synoptic and dynamic climatology the amplitude of the peak relative to the normal curve. Kaplansky (1945) shows that there is no reliable relationship between skewness and kurtosis. The probability density function (pdf) for a normal distribution is written: f (x) =

1

1 [(x)2/ 2 2] 1/2 e

(2)

where e the constant 2.71828, is the population mean estimated by the sample mean x–; is the population standard deviation estimated from s, except that the sample size is replaced by n1 to represent the number of degrees of freedom (i.e. the number of independent observations in a sample minus the number of population parameters that must be estimated from the sample observations). A sample mean, as an estimate of the population mean, has a standard error of s/(n1)0.5. This allows the “true” mean to be estimated within a given range for a speciﬁed probability level. The pdf of the normal distribution expresses the proportion of the distribution under a speciﬁed portion of the normal curve when the total area beneath the curve is equal to 1. The proportion of the distribution is speciﬁed in terms of . For example, 68.26 percent of the distribution is within ±1 of the mean; 95.46 percent of the distribution is within ±2 of the mean; 99.73 percent of the distribution is within ±3 of the mean. Tables of areas under the standard normal curve are available in statistical texts and reference tables. The 3 limit is commonly used to test for likely outliers in observational time series. For two-dimensional orientation data, such as wind velocity, a circular normal distribution can be used (Gumbel, 1954; Curray, 1956; Fisher, 1993; Klink, 1998). The resultant vector provides a measure of central tendency and magnitude, independent of origin. Its azimuth is obtained from: = arctan

1

V sin V cos

– where is the azimuth of each wind direction (0–360º), V is the magnitude of the wind velocity (or the number of observations in the case of grouped data); the numerator (denominator) represents the u(v) component of the wind. The mean resultant velocity is: V=

r V

where r [(V sin )2 (V cos )2]0.5. The standard vector deviation is:

冢VN V 冣 2

=

0.5

2

– where V2/N is the mean square velocity and V is the mean resultant velocity. The mean resultant (geostrophic) wind can be inferred from mean pressure maps, but these cannot discriminate between strong, variable pressure gradients and predominantly weak ones. Tucker (1960), for example, provides resultant and standard vector deviation statistics for upper-level winds. It is worth pointing out that the bivariate normal distribution can be used for cases where there is a pair of related variables, such as wind direction and pollutant concentration (Essenwanger, 1976). Related discussions on angular correlation for circular data are provided by Johnson and Wehrly (1977) and Fisher and Lee (1983).

Climate data and their analysis 37 11

Binomial distribution This is an approximation to the normal curve for data in discrete classes. It is determined from the expansion (p q)n where q 1 p, when p and q are mutually exclusive events. The binomial probability law (probability mass function, because discrete values are involved) is:

冢nx冣 p q , for x 0, 1, 2, . . . n 冢nx冣 is the number of combinations of x items out of a total of n,

ƒ (x) 0

where

111

x nx

冢nx冣 = x!(nn! x)!

x! (x factorial) denotes the expression x(x 1)(x 2) . . . 3, 2, 1. The mean of a binomial function is np and the variance is npq. In a simple case we might be interested in the probability of days being wet or dry. If p 0.5 for a wet/dry day, and assuming no interdependence, the probabilities for two days picked at random are: 0 State: Probability:

Two wet p2 0.25

One wet 0.25

One dry 2pq 0.5

Two dry q2 0.25

Total (p q)2 1

For probabilities over three days we use the expansion (p q)3 p3 3p2q 3pq2 q3 and so on. It will be noted that the distribution assumes constant probability, a situation referred to as Bernoulli trials. An illustration of the use of the binomial distribution to test for a change in the frequency of large rainfall events in Wales is provided by Joliffe (1983). 0111

Poisson distribution For many types of meteorological event the frequency of non-occurrence cannot be speciﬁed. This is true of storms, ﬂoods, and droughts, all of which are rather rare events occurring “at random.” The Poisson distribution, a limiting form of the binomial, is applicable to many of these situations. However, it does require large data sets in order to assess rare events. The frequency distribution follows an exponential form. If z is the average number of events during the total time interval, it is assumed that the average remains constant from trial to trial, so that there is no time trend, and that the probability of an event is unaffected by the time elapsed since the preceding one.

0

ez = 1 z

zr z2 z3 ... ...= 2! 3! r!

∞

兺 x=0

zx x!

This inﬁnite series converges to ez for all values of z. Now, for positive integers of a random variable x, f (x) = 0

zx ez x!

This function satisﬁes the conditions of a probability density function since 11

ƒ (x) > 0

38 Synoptic and dynamic climatology and ∞

兺

∞

f (x) =

x=0

兺 x=0

z x ez = ezez = 1 x!

(i.e. the total area beneath the frequency curve is 1). A variable satisfying a function of this form is said to be Poisson-distributed. A characteristic of the Poisson distribution is that the mean and variance are both equal to z. The more extreme an event (e.g. four hurricanes in one season) the much less likely it is to occur in a given interval. The model is useful for computing the probability that exactly k events occur in a speciﬁc interval, given that the average occurrence in that interval is z. 1

Gamma distribution The gamma distribution, like the Poisson, is positively skewed, but it is a continuous distribution of x between 0 and (Thom, 1958). Here the assumption is that the events constitute a “renewal process” where the time intervals between these events are distributed independently and identically (Cox, 1962). The gamma function is: () (1)!

where is any positive integer. It can be shown that: () =

冕

∞

xa1 ex dz

0

is a shape parameter estimated from:

1 =

冦冢

1 4A 1 1 4A 3

冣冧 1/2

where:

冢兺ln x冣 n

A = ln x

n

and x–

2 2

where is a scale parameter. The probability density function is f (x) =

x1 ex/ ()

To determine cumulative probabilities, for example, of precipitation xi , we use: t (F) xi / . Tables of this incomplete gamma function are available (Pearson 1951) for F against

and t(F).

Climate data and their analysis 39 11

The gamma distribution is particularly useful for zero-bounded variables such as short-period precipitation totals (Suzuki, 1967) and for cloud amounts (Henderson-Sellers, 1978). Weibull distribution The Weibull distribution has certain characteristics in common with both the gamma and the exponential distributions (Olkin et al., 1980; Devore, 1995). The pdf is:

冦ab (x bc)

冤

a1

0

f (x) =

exp

(x c)a b

0

if x c if x c

0 111

冥冧

As with the gamma distribution, the parameters a and b determine, respectively, the shape and the scale of the density distribution; c serves as a measure of its location. For a 1, both the gamma and Weibull distributions correspond to an exponential distribution. The Weibull distribution is commonly applied in analyses of wind speed data. Beta distribution The beta distribution is appropriate for variables that vary from 0.0 percent to 1.0 percent or 0 percent to 100 percent such as cloud amount or relative humidity (Wilks, 1995). The pdf of the beta distribution is: f (x) =

0111

(a b) x 冤(a) (b)冥

a1

(1 x)b1

for 0 x 1, a, b > 0. The ranges of a and b determine the typical shape of the frequency curve (Figure 2.8). Henderson-Sellers (1978) summarizes this as follows: Shape

a

Single peak J-shape Reverse J U-shape

> < > <

b 1 1 1 1

> > < <

1 1 1 1

Although Henderson-Sellers (1978) used the beta distribution for global cloud frequencies, subsequent analysis indicates that the Burger distribution is a more robust model for this variable (Henderson-Sellers and McGufﬁe, 1991). Easterling (1989) examines thunderstorm rainfall over the United States using both the gamma and beta distributions.

0

Transformations Not all meteorological data series can be ﬁtted by the common distribution functions. In some cases a normal distribution can be approximated by an appropriate transformation of the original data series. Precipitation data are commonly truncated on the “dry side,” for example, and a log-normal or cube root transformation is often used to transform the data (Essenwanger, 1960b; Stidd, 1953). The goodness of ﬁt of a raw or transformed data series to any distribution function can readily be tested by using the 2 function to compare the observed frequencies with those expected from the assumed model.

0 11

40 Synoptic and dynamic climatology

1

Figure 2.8 Examples of the beta distribution pdf for different ranges of the parameters (here labeled p and q). Mirror images of the distributions are obtained by reversing these parameters. (From Wilks, 1995)

2.4 Analytical tools for spatial data 2.4.1 Synoptic maps 1

The mean sea level (MSL) pressure ﬁeld is the most common type of synoptic map. Station pressure observations are adjusted (“reduced”) to the theoretical value at mean sea level and standard gravity. Since the necessary correction involves the observed temperature at the station and the assumed lapse rate, “ﬁctitious” MSL pressures may be reported over montane regions, Greenland and Antarctica (Streten, 1980). The apparent intensity of the Siberian winter anticyclone is due, in part, to this correlation (Walker, 1967). In synoptic analyses for the hemisphere, MSL isobars are usually drawn at 4 mb or 5 mb (hPa) intervals and mesoscale features are deliberately smoothed out of the analysis. Hemispheric or global analyses are made for the main synoptic hours (00.00, 06.00, 12.00, 18.00 UTC), and more frequent analyses may be made for a restricted area, by national meteorological centers. Daily MSL pressure maps are available for Europe and most of North America since the 1870s, but the temporal coverage on a hemispheric or global basis is more limited (see below). Synoptic pressure maps also provide plotted station weather reports in coded symbols. Contour charts for constant pressure surfaces have been the international standard for upper-air analyses since 1945. Earlier, beginning in 1933, pressure maps were drawn at constant high levels in Great Britain and North America. Standard contour charts are widely prepared for 1,000 mb, 850 mb, 700 mb, 500 mb, 300 mb, 200 mb, 100 mb levels; specialized centers may also analyze 50 mb, 30 mb (or 25 mb), 10 mb and 5 mb levels. Contour heights are expressed in geopotential meters (gpm), where 1 gpm =

g geometric (meter) 9.81

g acceleration due to gravity (m s2).

Climate data and their analysis 41 11

0

111

0

0111

0

0 11

2.4.2 Quality of hemispheric and global analyses In discussing circulation features it is important to consider the quality and spatial coverage of the data available for the analyses. Jenne and McKee (1985) and Shea et al. (1996) provide an overview of sources of atmospheric and oceanographic data. Also, for more recent products it needs to be recognized that analyses of surface pressure and geopotential height ﬁelds commonly incorporate a prognostic map as the ﬁrst guess ﬁeld for a subsequent analysis (Trenberth and Olson, 1988). This can lead to good data being rejected in the analysis! Further inhomogeneities arise as parameterizations of physical processes are improved, and methods of assimilating satellite or other observations, or of smoothing and interpolating data, are modiﬁed in the analysis procedures (Simmons et al., 1989; Arpe, 1991). The effect of such changes on the data products are now well recognized and reanalyses are under way at the National Center for Environmental Prediction (NCEP), Washington, DC (Kalnay et al., 1996, 1998) and the European Centre for Medium Range Forecasts (ECMWF) (Bengtsson and Shukla, 1988; Gibson, 1998), to reanalyze the complete input data streams for the last forty years, or more, to obtain consistent products. Intercomparison of global data sets prepared by NCEP and ECMWF shows less reliable analysis in the tropics and southern hemisphere (Trenberth and Olson, 1988). The main problems so far identiﬁed in widely used historical series of hemispheric and global data sets are summarized in Table 2.2. Problems are particularly severe in the southern hemisphere over the ocean areas with few stations (Barnett and Jones, 1992). Jones (1991) notes that reconstructions of mean sea-level pressure for 15°–60°S back to 1951 are possible in areas with numerous ocean islands but excluding the eastern equatorial Paciﬁc, the southeastern and far southern Paciﬁc Ocean and far southern Indian Ocean areas. Even mean pressure ﬁelds for 45°–65°S need to be treated with great caution. Reconstructions of monthly grid-point sea-level pressures have been obtained for Europe back to 1780 and for North America back to 1858 (Jones et al., 1987). These are based on regression equations relating principal components of surface pressure to station data on monthly mean pressure, temperature, and precipitation. The analyses use a calibration Table 2.2 Major inconsistencies and problems of data quality in hemispheric and global map analyses Source

Problems/Changes

References

US Weather Bureau: historical weather maps, 1899–1945

Positive biases in Arctic pressures, especially pre-1930s to 1940s

Jones (1987)

US National Meteorological Center: northern hemisphere 500 mb charts 1946–present.

Changes in analysis techniques caused major shifts in the data in 1953, 1955, 1962 (especially), and 1978

Lambert, 1990

Northern hemisphere sea-level pressure grids, 20°N to the pole. NMC global analyses, July 1976–present

The archived analyses were from US Navy charts up to 1963. In 1979 Arctic analyses improved through the inclusion of drifting buoy data. A reanalysis is being prepared

Trenberth and Paolino (1980)

European Center for Medium Range Weather Forecasts (ECMWF) global analyses, 1980–present

May 1985, New T106 model; May 1986, nineteen model levels; July 1986, gravity wave drag incorporated; September 1986, modiﬁed analysis scheme; May 1989, new radiation scheme

Shaw, et al. (1987), Simmons et al. (1989), and Arpe (1991)

42 Synoptic and dynamic climatology

1

period of 1900–74 for Europe and 1921–80 for North America and have been tested with independent data for a different period of years.The European reconstuctions for 35°–70°N, 30°W–40°E are updated to 1995 by Jones et al. (1999), using 1936–95 for the calibration period and 1881–1935 for veriﬁcation. The application of synoptic weather mapping to the historical reconstruction of the circulation over Europe for AD 1780–1820 is presented by Kington (1991). This was subsequently extended to the period AD 1675–1704; mean surface pressure maps over Europe were prepared for the winter and spring months by J. Kington and H.H. Lamb using the Lamb types for the British Isles. The maps are presented by Wanner et al. (1994). The pressure ﬁelds were interpreted on the basis of contemporary documentary records of anomalous weather events. However, Jones et al. (1999) report systematic biases in the reconstructions of Kington and Lamb. Subsequently, monthly mean grid-point sea-level pressure charts for the eastern North Atlantic–European region (35°–70°N, 25°W–30°E) have been reconstructed for the entire Late Maunder Minimum period (AD 1675–1715), using canonical correlation analysis (Pﬁster et al., 1998). Statistical relationships were established between atmospheric circulation patterns and station measurements of pressure (Paris), air temperature (Kew and Paris), air temperature indices (Budapest, Lisbon and Zürich), precipitation indices (Barcelona, Budapest, Kew, Lisbon, Madrid and Zürich), and the western Baltic sea ice index. A basis for the statistical relationships was ﬁrst established using station observations and pressure data for 1901–90 and used for linear prediction of conditions during AD 1675–1715 assuming climatic stationarity. 2.4.3 Kinematic properties of the wind ﬁeld

1

Wind velocity, like any vector quality, may be represented by a directional arrow (showing the direction from which the wind is blowing) proportionate in length to the magnitude. For many purposes this type of presentation is cumbersome although it is commonly used to show the ﬂux of properties such as water vapor, for example. It is sometimes useful to consider the westerly (u) and southerly (v) components of the horizontal wind velocity (VH). In cartesian coordinates the u component (positive for west wind) is given by the projection of VH on the west–east (x) axis and the v component (positive for south wind) by the projection of VH on the south–north (y) axis. Where the wind direction is the azimuth determined from north (360°) corresponding to the y axis of a cartesian graph: u VH sin v VH cos In evaluating the sign it is helpful to remember that in the sector 0°–90° 90°–180° 180°–270° 270°–360°

sin sin sin sin

cos cos cos cos

Component ﬁelds are rarely plotted for synoptic analyses, but we shall return to them again in connection with the global circulation (see section 3.2). In general the most convenient means of analysis is to examine the scalar quantities of direction and speed individually. These can be depicted in the form of isogon maps of constant direction and isotach maps of constant speed (see Figure 2.9). It is more usual, however, to determine streamlines, or line tangent to the instantaneous motion at each point. These can be constructed by sketching directly from the velocity vectors. A more precise method, using an isogon map, is to draw line segments by tangent curves. This approach is particularly valuable in that wave motions in the ﬂow are readily detected by

Climate data and their analysis 43 11

0

111

0

0111

0

Figure 2.9 An example of isogon and streamline analysis at 850 mb and the corresponding MSL pressure map. (After Schüepp, 1963, from Barry and Perry, 1973)

0 11

isogons. The wind speed may be shown by superimposed isotachs with the spacing of the streamlines independent of the wind speed (Palmer, 1952) or, alternatively, the spacing of the streamlines is made inversely proportional to the speed (Watts, 1955). Streamlines may respectively converge into, or diverge from, centers of inﬂow and outﬂow known as “singular points.” Inﬂow may also occur along a singular line or asymptote of conver-

44 Synoptic and dynamic climatology

1

Figure 2.10 The basic patterns of streamline curvature and difﬂuence (conﬂuence) for the anticyclonic case. (After Jarvis, 1967; from Barry and Perry, 1973)

1

gence. The rate at which ﬂow is converging or diverging with respect to an axis perpendicular to the ﬂow is referred to as conﬂuence or difﬂuence, respectively. The basic patterns of streamline curvature and difﬂuence or conﬂuence are illustrated for the anticyclonic cases in Figure 2.10. The corresponding cases of cyclonic curvature give rise to patterns which are the exact reverse of the anticyclonic ones. Streamline analysis is used most frequently in low-latitude analysis, since pressure gradients are generally small and consequently the geostrophic wind ﬁeld is not readily determined. Illustrations of such streamline maps may be found in Sadler (1965). Because small variations in the wind vector may be signiﬁcant in the tropics, and the reliability and representativeness of observations at a single level is sometimes in doubt, it has become common to use mean layer winds for 1,000–3,000 m. These averages are routinely reported in tropical RAWIN-sonde ascents (Zipser and Colon, 1962). 2.4.4 Derived data Horizontal derivatives Four primary characteristics, which involve combinations of the horizontal derivatives of the velocity components, are derived from the wind ﬁeld (Figure 2.11). They are: ∂u ∂v = horizontal divergence (H·V in vector notation) ∂x ∂y

Climate data and their analysis 45 11

∂u ∂v = “stretching” deformation ∂x ∂y ∂v ∂u = “shearing” deformation ∂x ∂y ∂v ∂u = relative vorticity about the vertical axis, HV. ∂x ∂y

0

A word must be said about vector notation (see Appendix 2.1). The gradient operator (del) is deﬁned as: 111

≡i

∂ ∂ ∂ j k ∂x ∂y ∂z

where i, j and k are unit vectors in the x, y and z directions respectively. In the horizontal case (H) the vertical term / z is of course omitted. By deﬁnition, . ( ) is the divergence of, ( ) is the curl (or vorticity) of the term following the operator. The horizontal divergence may be estimated by using a ﬁnite difference grid. That is to say, u/ x and v/ y are approximated by ﬁnite values u/x and v/y of the velocity components and lengths. Thus with reference to the coordinates of Figure 2.28:

0

H · V =

ux ux L

vy vy L

where L a unit length in a rectangular grid (Miller, 1948; Panofsky, 1951), L/2 x x y y (see Figure 2.28). By deﬁnition divergence is positive. Divergence 0111

(convergence) which measures the overall expansion (contraction) of the wind velocity ﬁeld must not be confused with conﬂuence (difﬂuence). Difﬂuent streamlines may, for example, be associated with decreased wind speed so that the two effects tend to cancel out (Figure 2.11a). In practice, wind data are usually inadequate for obtaining very reliable estimates of the divergence because of inaccuracy in the basic measurements and because the wind is nearly geostrophic above the friction layers. The horizontal divergence is zero for geostrophic ﬂow (when the horizontal pressure force is exactly balanced by the Coriolis acceleration).

0

0 11

Figure 2.11 Stream function and wind velocity for two-dimensional non-divergent ﬂow. (From Barry and Perry, 1973)

46 Synoptic and dynamic climatology

1

1

Figure 2.12 Schematic models illustrating (a) divergence/convergence and (b) relative vorticity due to streamline curvature and lateral shear. The dashed lines are isotachs (nominally m s1). (From Barry and Perry, 1973)

Climate data and their analysis 47 11

0

The special case of divergence induced by differential stress at a coastline has been examined by Bryson and Kuhn (1961). Onshore winds produce convergence through the slowing down of the ﬂow due to increased friction over land. Winds parallel to the coast, with high pressure over the land (in the northern hemisphere), also produce convergence, as a result of the shear set up by the reduced speed over land. The air is thus forced up the pressure gradient. Bryson and Kuhn determine the divergence by examining the frictional drag across a coastal strip. The vertical component of relative vorticity, or H V, which is that due to the local rotation about an axis vertical to the Earth’s surface, is determined by ﬁnite differences (see Figure 2.28) from: H· V =

vx vx L

uy uy L

111 The vertical relative vorticity in plane polar coordinates is made up of two elements – lateral shear and streamline curvature (Scorer, 1957, 1958). We can write: = 0

0111

∂VS ∂VS ∂r r

where VS horizontal velocity along a streamline and r radius of curvature of the streamline. These elements may reinforce one another or tend to cancel out, as illustrated in Figure 2.12b. By deﬁnition, vorticity in the same sense as the Earth’s rotation, cyclonic in the northern hemisphere, is positive (Figure 2.12b). Relative vorticity is a most important synoptic parameter, but its climatological use has been minimal up to the present. For some purposes it is necessary to consider absolute vorticity. The vertical component of absolute vorticity is made up of the sum of the local value of the Coriolis parameter, f, and the relative vorticity determined by the circulation pattern. The Coriolis parameter which is due to the Earth’s rotation has a value of 2 sin , where latitude angle and the earth’s angular velocity. It increases from zero at the equator to a maximum of 1.458 104 s1 at the poles. A detailed review of the Coriolis force is presented by Persson (1998). The rate of change of absolute vorticity (following the motion) and divergence are related through the “vorticity equation”: d( f ) ⬵ ( f ) H· V dt if we neglect the effects of baroclinicity, tilting of the vortex axis, and friction. This relationship, which shows that horizontal convergence is associated with increased absolute vorticity, is important in meteorological analysis. Finite difference estimates of are rather more reliable than those of divergence since they are less commonly close to zero. However, the neglected terms and the effects of time changes in intensity, and in the relative motion of a circulation system, all lead to inaccuracy in the estimation of d( f )/dt. The deformation terms are indicators of zones where frontogenesis is likely to occur. The two elements of deformation are often considered together, although in modern synoptic practice new parameters relating to frontal zones are being used (see p. 456). Finally, we may note that if there is no net divergence, vorticity and deformation of the motion, then the streamlines are straight and the velocity is unchanged. This distribution is referred to as pure translation.

0

0 11

48 Synoptic and dynamic climatology

1

(a)

1

(b) Figure 2.13 (a) A stream function ﬁeld (105 m2 s1) at 200 mb over the western Paciﬁc Ocean, 12.00 GMT, 1 March 1965. (b) The corresponding streamline (solid) and isotach (dashed, kt) analysis. The streamlines show two anticyclonic centers in each hemisphere, the streamfunction analysis only one. (After Krishnamurti, 1969, from Barry and Perry, 1973)

Climate data and their analysis 49 11

Stream function A horizontal wind vector, VH, can be separated into a non-divergent part and an irrotational part, thus: VH (k ) where a horizontal stream function, a horizontal velocity parameter, k a unit vertical vector, and a gradient operator. For horizontal, non-divergent ﬂow and VH parallel to lines of constant as shown in Figure 2.12:

0

111

∂ ∂ dx dy = 0 ∂x ∂y so that is deﬁned by: u=

0

∂ ∂ , v= ∂y ∂x

If variations in the Coriolis parameter (f ) are ignored, in an isobaric surface is proportional to gz, i.e. g z /f. Figure 2.13 illustrates a streamline ﬁeld for the 200 mb level and the equivalent stream functions of the rotational non-divergent wind. In the tropics at 200 mb this comprises the major part of the total wind (Krishnamurti, 1971). The velocity potential, , is deﬁned by: u=

∂ ∂ , v= ∂x ∂y

It follows from the above that: 0111

Horizontal divergence =

∂2 ∂2 ≡ 2 ∂x2 ∂y2

Relative vertical vorticity =

0

∂2 ∂2 2 ≡ 2 ∂x2 ∂y

where 2 the two-dimensional Laplacian operator (see Appendix 2.1). The point of such procedures is often the elimination of some undesirable characteristic of the kinematic properties of the wind ﬁeld. For instance, for purposes such as smoothing, or in order to obtain conformity with a particular synoptic model in the estimation of wind ﬂow from satellite photographs. Unfortunately the solution of the appropriate equations in and raises serious difﬁculties, and for this reason Endlich (1967) proposes an alternative procedure. By iterative methods he produces, for example, a non-divergent wind ﬁeld which still retains the original vorticity. Hence the irrotational wind is the difference between the original and the non-divergent patterns. 2.4.5 Vertical velocity

0 11

In terms of its direct signiﬁcance in the production of weather phenomena, vertical air motion is undoubtedly the most important single parameter. Unfortunately, it cannot in general be directly measured to provide routine information. Instead it has to be estimated by one of a variety of techniques, depending on the scale of motion which is of interest and the applicability of their various assumptions in speciﬁc instances. Large-scale vertical motion is normally almost imperceptible (see Table 2.3) whereas the vertical velocities

50 Synoptic and dynamic climatology Table 2.3 Relative magnitudes of vertical motion in different scales of system

1

System

Velocity (cm s1)

Time scale

Thunderstorm Tropical storm; subsynoptic (frontal zone) Intense depression Average depression Planetary wave

103 1025 102

One hour Six hours

10 5 1

Six to twelve hours One to two days One week

associated with local storm systems may be dramatically evidenced by the build-up of cumulonimbus heads. The simplest means of determining synoptic or larger-scale vertical velocity is based on the continuity equation relating the local rate of density change to the mass divergence per unit volume:

冤

∂p ∂(u) ∂(v) ∂(w) = ·(pV) = ∂t ∂x ∂y ∂z

冥

where density, V wind velocity, and w vertical velocity. From this equation it can be shown that: wz =

(zH·V) z

where z an arbitrary height and the bar denotes a vertical average between the surface and z. For a steady state: 1

wz = zH·V Also, where density changes are negligible, the continuity equation becomes: ∂w ∂u ∂v = ∂x ∂y ∂z In practice, pressure is used as vertical coordinate and the equivalent of vertical velocity () at an arbitrary pressure level, p, is determined by integration. Thus: p = p1

冕

p1

H ·V dp

p

where dp/dt. Accordingly, the continuity equation here becomes: ∂ ∂u ∂v = ∂x ∂y ∂p It is generally assumed that 0 at the surface pressure value. This is satisfactory as long as the topography has little slope. The integral is approximated by summation over a number of isobaric layers (Rex, 1958; Vaisanen, 1961). The horizontal divergence is calculated by the cartesian grid or streamline methods outlined on p. 46, or by the objective triangle method of Bellamy (1949). The triangle method is somewhat unsatisfactory in that the value does not refer to any particular point.

Climate data and their analysis 51 11

0

111

These kinematic methods are subject to considerable inaccuracies even when the available winds are numerous (Landers, 1955), and smoothing of any computations is essential (Palmén and Holopainen, 1962, for example). An advanced method of smoothing based on spatial autocorrelation has been described by Eddy (1964). A second method of computing vertical velocity relates to temperature changes in the free atmosphere. The temperature ﬁeld is assumed to be altered by horizontal advection and vertical advection only. Observations of the former and of the local temperature change allow the vertical motion necessary for balance to be computed: w

∂T dT ∂T = V·H T ∂z dt ∂t

where T/ t denotes the local rate of temperature change and dT/dt that following the motion of a particle. V·H T is the advection of the horizontal temperature ﬁeld. For adiabatic changes: dT = w dt

0

where the dry adiabatic lapse rate. Thus: w=

冢(∂T/ ∂t) V· T 冣 = 冢T/t冣 H

where the environmental lapse rate and T/ t temperature change along a horizontal trajectory. Alternatively, using potential temperature on isobaric surfaces:

0111

0

0 11

w=

[(∂/ ∂t) V (∂/ ∂s)] ∂/ ∂p

where V horizontal wind speed, / t local change of potential temperature on an isobaric surface, and / s variation of potential temperature along a streamline. This approach is particularly suited to analysis based on records from constant-level balloons, provided diabatic effects (particularly radiative ones) can be ignored. Alternatively, trajectories can be estimated from geostrophic winds. It should be noted also that in slant ascent in a changing pressure ﬁeld the adiabatic lapse rate itself may be overestimated by up to 1°C km1 (Staley, 1966). Such errors could seriously affect vertical velocities estimated by this method. Computations of vertical velocity in conjunction with numerical models have been based on the vorticity equation (Collins and Kuhn, 1954), the vorticity and thickness tendency equation (Sawyer, 1949; Bushby, 1952; Knighting, 1960), the “omega equation” (Pettersen et al., 1962) and the “primitive equations” of motion. It is beyond the scope of this book to do more than outline the basis of the ﬁrst two of these methods. They all involve heavy computational demands and the details are, in any case, subject to continual improvement in terms of the degree of resolution possible and of the mathematical procedures. The approximate vorticity equation (see p. 47) is: ∂a ∂ da ⬵ V·a a dt ∂p ∂p

52 Synoptic and dynamic climatology where a the vertical component of absolute vorticity if the effects of friction and the turning of vortex lines are disregarded. The latter is important in frontal zones, however. Approximate integration of the above equation leads to the following expression:

冢冣 = 冢冣 a p

1

a p1000

1 2

冤冢(∂ /∂t) V· 冣 a

a

2

a

p1000

冢(∂ /∂t) V· 冣冥 a

a

2

a

p

where p1000 1,000 mb level and p an arbitrary pressure level. Collins and Kuhn (1954) computed a from f. Charts at six or twelve-hourly intervals are used to give

a / t. The vorticity advection V·a can also be calculated by graphical methods. Knowing these terms, and assuming 0 at the 1,000 mb level, we can determine (/a) and therefore w at any level p from the hydrostatic assumption, w /pg. The method provides good estimates of vertical vorticity on a synoptic scale so long as the absolute vorticity is not too small. Penner (1963) has developed this type of approach in terms of thickness advection and vorticity advection. The original method related to charts of space–mean vorticity at 500 mb and was modiﬁed for use with the 500 mb absolute vorticity analyses subsequently adopted by the Canadian Weather Service (Harley et al., 1964). At the level of non-divergence (typically near 600 mb but in practice the 500 mb is assumed), vertical velocity (6) can be determined from an expression: 6 k1 (Aa )5 k2 (Az)

1

where (Aa )5 horizontal advection of absolute vorticity at 500 mb and Az horizontal advection of 1,000–500 mb thickness. k1 and k2 incorporate scale factors including the Coriolis parameter. 6 is expressed in units of 103 mb s1 (⬵ cm s1). The two terms of the equation can be determined graphically using a special geostrophic advection scale (Ferguson, 1961, 1963) and their individual contributions are evaluated in vertical velocity units before adding. The basis of the “omega equation” approach used by Pettersen et al. (1962) is that the advection of temperature and vorticity will disturb geostrophic balance unless compensated for by horizontal divergence and therefore the vertical motion ﬁeld. The relationship is formulated in terms of divergence and the advection of vorticity by the thermal wind. Petterssen et al. show that vertical velocity can be separated into a component due to dry adiabatic motion and one due to diabatic heating by the input of sensible and latent heat. Patterns of thickness tendency associated with these components were analyzed for typical stages of development of mid-latitude cyclones. This question has been examined further by Danard (1964), who showed that the heating term, primarily released latent heat, is very important with respect to the computed vertical velocity in precipitation areas. Indeed, vertical velocity is only 25 percent of kinematic estimates if this effect is not incorporated in the computations. The quasi-geostrophic omega equation is described by Holton (1992, p. 167); Gordon et al. (1998) provide a brief summary (see Appendix 6.1). The generalized omega equation which assumes only hydrostatic balance is used by Räisänen (1995) to examine ageostophic and diabatic effects. He ﬁnds that the correlation between the vertical motion determined by the adiabatic quasi-geostrophic omega equation and the generalized formulation for the middle and upper troposphere is 0.85 between 60°N and 90°N, 0.7 for 30°–60°N and 0.6 for 15°–30°N. The most recent generation of numerical prediction models uses the “primitive equations” of motion (Lorenz, 1967). These are essentially one step removed from the exact hydrodynamic equations incorporating, for example, hydrostatic equilibrium. Some general circulation models compute vertical motion on this basis from an equation ﬁrst formulated by Richardson (1922) involving horizontal divergence and pressure change. The determination of vertical motion on various scales remains a major problem in

Climate data and their analysis 53 11

synoptic meteorology. However, vertical motion ﬁelds are now routinely available for the NCEP and ECMWF reanalysis products. Vertical velocity at the lower boundary (in practice the surface) is usually assumed to be zero in order to simplify the computations. However, the inﬂuence of terrain (and also of friction) on airﬂow has been stressed by Graystone (1962), Haltiner et al. (1963), and Jarvis and Agnew (1970). Quantitative assessment of terrain-induced vertical velocity, on a synoptic scale over a time period of about one day, can be performed as follows: w H V g · H

0

111

where Vg horizontal geostrophic wind and H vertical relief, smoothed over a grid length of the order of 200–300 km. Using this approach, Jarvis and Leonard (1969) have prepared maps of wH over central and eastern North America for 10 knot winds from each cardinal point. It should of course be noted that in the case of major topographic barriers the ﬂow may be partially diverted or more or less wholly blocked. Friction effects are now incorporated in numerical models but, where necessary, a simple approach could be made along the lines of Bryson and Kuhn’s (1961) estimates of frictional divergence. 2.4.6 Isentropic charts An isentropic chart shows meteorological elements on a surface of constant potential temperature (an isentropic surface). Potential temperature () is the temperature an air parcel attains if brought dry adiabatically to a reference pressure, usually 1,000 mb:

0

=T

0111

0

冢

1,000 p

冣

where (Poisson constant) (cp cv)/ cp 0.288, cp the speciﬁc heat at constant pressure, and cv the speciﬁc heat at constant volume (for dry air). Potential temperature is a valuable diagnostic measure where temperatures are to be compared at stations with different elevations. This is illustrated in a study of katabatic winds in Antarctica by Breckenridge et al. (1993). Because air motion tends to be dry adiabatic, the potential temperature of an air parcel is conserved. It may be noted that the thermodynamic diagram known as the pseudo-adiabatic chart has p0.288 as ordinate so that dry adiabats (isentropic surfaces) are straight lines. The tephigram chart is similar in this respect. In the atmosphere isentropic surfaces slope upward towards the poles, i.e. towards cold air, as long as the air is stable. The slope is about 1/500 in air masses and 1/100 in frontal zones. The technique of isentropic analysis was ﬁrst proposed by Sir Napier Shaw (1930, p. 259) and was used extensively in the United States during the 1930s and 1940s by C.G. Rossby and his associates (1937), although it was abandoned for operational purposes at the end of the Second World War owing to the labor involved. The isentropic surfaces suggested for analysis over the United States are 290–5 K in winter and 310–20 K in summer (Namias, 1940). Trajectories of air motion computed on such surfaces take account of dry adiabatic vertical motion, as described below. 2.4.7 Trajectories

0 11

A trajectory describes the actual path of an individual air parcel moving in space and time. This represents a Lagrangian view, where the coordinate system moves with the parcels. It can be contrasted with the more common Eulerian analysis of motion ﬁelds, where there is a ﬁxed reference frame in space through which the air is ﬂowing. Here the instantaneous motion at speciﬁed times is depicted by streamlines of ﬂow direction

54 Synoptic and dynamic climatology that are tangent to the velocity vectors everywhere. Streamlines and trajectories coincide only under stationary conditions. Some of the earliest work on air trajectories in mid-latitude depressions by Shaw and Lempfert (1906) was a major stimulus to synoptic meteorology. Modern studies of the transport of pollutants and aerosols require accurate reconstruction of trajectories and transport, and dispersion modeling has undergone progressive reﬁnements since the 1950s. To clarify the issues involved, we will ﬁrst illustrate the simplest approaches. Suppose we wish to trace the horizontal movement of an air parcel now at point P at some level above the friction layer during the preceding twenty-four hours, using sixhourly charts. The simplest, but least accurate, method is as follows: 1 1

2 3

Determine the geostrophic velocity, V0 (km hr1), from the contour spacing at a selected point, P0 , at the terminal time, t. Six hours earlier (t 6) the air parcel was 6 V0 km in the upwind direction, based on the streamline at P0. Call this point P1. Determine the geostrophic velocity, V1, at t 6 at point P1. Based on the streamline at the point the air parcel was 6 V1 miles upwind at t 12. Call this point P2, and so on.

Errors arise in the approximation of the trajectory over some time interval by a streamline tangent to isobars, or height contours, owing to the displacement of synoptic systems over time (Hogben, 1946). The accuracy may be improved if we use the mean speed at the beginning and end of each time interval, i.e.: (V0 V1)/2 for the period t to t 6 (V1 V2)/2 for the period t 6 to t 12, etc.

1

A reﬁnement introduced by Petterssen (1956, p. 27) uses successive approximations based on the vector mean wind for each time interval. In the boundary layer, allowance has to be made for the effects of friction. On average, the surface (10 m) wind is backed, or turned counterclockwise, from the geostrophic wind by about 10°–15° over the ocean and 20°–25° over land, owing to the frictional effect on the speed and therefore on the Coriolis deﬂection. The sources of error involved in trajectory calculation are both intrinsic and extrinsic. The basic trajectory equation involves a position vector, x, and a wind velocity vector, V: dx V[x(t)] dt This is normally expanded in a truncated Taylor series: x (t1) ~ x (t0) t V(t0) which is known as a zero acceleration solution (Stohl, 1998). The truncation error involved in the omission of the higher order terms can be constrained by selecting sufﬁciently short time steps (Walmsley and Mailhot, 1983; Seibert, 1993). To avoid aliasing, the time step should be ≥0.5L/U where L is the length of the smallest pattern resolved in the gridded wind ﬁeld and U is the typical velocity. A ﬁxed time step of 0.5 hr is recommended. A more accurate approximation is the constant acceleration solution originally formulated by Petterssen: 1 x (t1) ~ x(t0) (t)[V(t0) V(t1)] 2 It needs to be solved by iteration, since V(t1) is not known a priori.

Climate data and their analysis 55 11

0

111

0

0111

The two-dimensional iterative kinematic method developed by Petterssen provides the simplest possible solution with ﬁrst-order accuracy. It forms the basis of the NOAA Air Resources Laboratory Atmospheric Transport and Diffusion (ARL-ATAD) model (Artz et al., 1985). In essence, three approaches to trajectory calculation are currently possible: threedimensional kinematic analysis, isobaric analysis or isentropic analysis. In complex topography isoeta, or terrain-following, trajectories may be used. For a three-dimensional kinematic calculation (Draxler, 1996, for example), the wind at V1 x0 V0 t and the new vertical position is p2 p0 0.5(0 1). Draxler evaluates three-dimensional kinematic and isentropic trajectories to Nova Scotia in August 1993. The motion is calculated from two-hourly NMC nested grid (90 km resolution) output of u, v, and (dp/dt) at ten sigma levels. Isobaric trajectories often show systematic errors as a result of the warm-air advection associated with rising motion and the clockwise (in the northern hemisphere) turning of the wind with height. Two-dimensional kinematic trajectory analysis can also be performed on isentropic surfaces. However, dynamic methods which link information on the mass and velocity ﬁelds are also available (Danielsen, 1961, 1974; Merrill et al., 1986). In so far as the threedimensional motion is adiabatic, an isentropic surface can be used to calculate trajectories. Figure 2.14 illustrates the contrasts between 500 mb isobaric and isentropic trajectories over a ﬁve-day period in the Antarctic. The curvatures may be of opposite sign, and the isentropic trajectory is generally shorter. The ascent of the air in the cases illustrated is attributable to the air parcels crossing a steep temperature gradient and the fact that isentropes slope upwards towards cold air. The method of Peterson and Uccellini (1979) is based on the equation of motion for adiabatic ﬂow in isentropic coordinates: dVh M f k Vh 0 dt where Vh is the horizontal wind vector, is the gradient on an isentropic surface, f is the Coriolis parameter, k is a unit vector in the z direction (see Appendix 2.1) and M is the Montgomery potential (Montgomery, 1937) or isentropic stream function () M cpT gz where cpT is referred to as the speciﬁc enthalpy and gz is the geopotential, with reference to an isentropic surface. The magnitude of M is given by: M (1.0046T 9.806z) 103 m2 s2

0

Trajectory wind vectors are calculated by integrating the equation of motion in isentropic coordinates using a wind estimate at a starting position. A simple isentropic model used by Pickering et al. (1994) interpolates gridded u and v components to selected potential temperature surfaces. Parcel velocities at locations on the isentropic surfaces are obtained by bilinear interpolation of the gridded data. Parcels are advected (after the ﬁrst time step) via a leapfrog scheme: x(t) x(t0 t) 2t{V[x(t0)]}

0 11

where x(t) is the new parcel position at time t0 t; x(t0) is the old position and V is the parcel velocity. Temperature is interpolated on to the parcel path and is used with the assumption of potential temperature conservation to compute the pressure of a parcel at its successive positions along the trajectory. Draxler (1996) concludes that 90 percent of all kinematic and isentropic trajectory source pairs are located within ±75 mb of one another and with a horizontal separation of <10 percent of the travel distance. Wind data are available either from irregularly spaced stations and soundings or from gridded analyses of observations or model output. Consequently errors arise owing to

56 Synoptic and dynamic climatology

1

1

Figure 2.14 (a) 500 mb isobaric trajectories and (b) 280 K isentropic trajectories arriving at the south pole (SPO) on July 16 1989 (upper) and July 13 1989 (lower). The trajectories are marked at one-day intervals up wind by a numeral. The solid line indicates 00.00 UT and the dashed line 12.00 UT. The plots in the lower right show the heights of the trajectories (km) on each day. SPO is 2.8 km altitude. (From Harris, 1992)

spatial interpolation (see section below). In a number of related studies Kahl and Samson (1988) found errors in mean trajectory position of between 400 km and 500 km after seventy-two hours. The effect of interpolation from gridded ﬁelds can be examined by artiﬁcially degrading high-resolution gridded values and by varying the temporal sampling. While linear interpolation is most accurate in time, errors in spatial interpolation can be reduced by using higher-order interpolations (Walmsley and Mailhot, 1983; Stohl et al., 1995). The largest errors arise through interpolation of the vertical motion due to its great

Climate data and their analysis 57 11

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local variability. Trajectory calculations are also sensitive to the temporal frequency of wind data (Doty and Perkey, 1993). If diurnal variations in ﬂow are important, a minimum time step of six hours is required to capture these variations. At high spatial resolutions of 90 km or 180 km, Rolph and Draxler (1990) ﬁnd that trajectory errors can be reduced most by shorter time steps. However, at 360 km resolution this is no longer true below about six hours. Where three-dimensional motion has to be considered, the largest errors generally arise through interpolation of the vertical component owing to its high variability. From an analysis of long-range trajectories into the Arctic, Kahl et al. (1989) demonstrate that the greatest uncertainty in the calculations arises from model sensitivity to input data rather than through the parameterization of vertical motion. Kinematic methods can give results that are in most cases accurate, if the three-dimensional wind data used in the calculations are from dynamically consistent analyses (Fuelberg et al. 1996). Stohl and Seibert (1998) also ﬁnd that most inaccuracies result from dynamic inconsistencies between meteorological ﬁelds. They use potential temperature, potential vorticity and speciﬁc humidity as three-dimensional tracers. They show that three-dimensional isentropic trajectories are optimal but, in the troposphere, kinematic isentropic ones are the next best option. Trajectory models developed by Heffter (1980, 1983) have been widely applied in research on precipitation chemistry (Raynor and Hayes, 1982), pollen transport in eastern Canada (Barry et al., 1981), and ﬂow patterns for South Pole (Harris, 1992). Usually, ﬁve-day or ten-day back trajectories are determined, but forward trajectories may also be obtained (Small and Sansom, 1983). Harris and Kahl (1990, 1994) provide climatologies of back trajectories for Mauna Loa, Hawaii, and Barrow, Alaska. For Mauna Loa, Hawaii, at 700 mb and 500 mb an East Asian source is shown to be frequently present, especially in winter when the trajectories are longest. Kahl et al. (1997) map ten-day isobaric back trajectories at 500 mb to Summit, Greenland, for each day of 1946–89. Cluster analysis was then used to identify seasonal source regions and transport routes. In winter 58 percent of trajectories extend westward into East Asia, while 27 percent follow a slower zonal path from Canada. In summer East Asia accounts for only 34 percent, the North Paciﬁc 31 percent, and Canada 26 percent. A climatology of seasonal air mass trajectories for North America has also been developed by Bryson (1966). Trajectories have been studied in relation to synoptic types for the North Atlantic (Haagenson and Sperry, 1989) and northern England (Dorling et al., 1992). 2.4.8 Spatial interpolation A common problem in climatological analysis is the contouring of irregularly spaced point data. Various methods are available for such spatial interpolation (Watson, 1992) and for smoothing and ﬁltering (Essenwanger, 1986). In general, spatial interpolation uses linear combinations of known values at control points to estimate the values at intermediate grid points. A well established procedure in the meteorological literature is the Cressman (1959) interpolation scheme. This involves a distance-dependent weighting of data at the control points within a speciﬁed search radius. For example, an estimate of a climatic variable (E) at a grid point i, j is calculated from:

0

兺E W 兺W 1

E=

1

1

0

where W1 is the weight given to observation E1. 11

W=

N2 d2 N 2 d2

58 Synoptic and dynamic climatology where d is the distance between the control point and the i, j grid point, and N is the search radius at which W → 0. Nuss and Titley (1994) propose an improvement over the Cressman interpolation scheme, in terms of RMS errors, using a multiquadratic radial basis function. This has the form: Q [X Xi] where the argument represents the vector between an observation point Xi and any other point in the domain. The spatial ﬁeld S(X) is described by: N

S (X) =

兺 Q (X X ) i

i

i1

1

where the coefﬁcients i are weighting factors. For a two-dimensional case the basis function becomes Qi (x, y) =

1

{| x xi |2 | y yi |2 1.0}1/2 c2

where c is an arbitrary, typically small, constant. For mapping on a regional scale, and where there is a dense network of observations, spatial interpolation can generally ignore the effects of the earth’s curvature. However, errors grow in data-sparse regions as the discrepancy between true spherical distances and approximate planar distances (and angles) increases with wider spacing of data points. For continental- to global-scale analyses, simple interpolation methods within a cartographic projection can introduce signiﬁcant errors (Willmott et al., 1985). Such errors may be apparent on global maps where the latitudes of isolines differ at the longitudinal margins, 180°E and 180°W, for example, or where there are multiple isolines passing through the poles, as depicted on global-scale rectangular projections (Robeson, 1997). Techniques are now available enabling a number of spatial interpolation procedures to be applied on a sphere. The main categories are: inverse-distance weighting, kriging, optimal statistical objective analysis (OSOA), spline methods, tesselation, and spherical harmonics (Robeson, 1997). An overall consideration is whether the method adopted uses a subset of the data at each step (a “local” method) or whether all the data are used together (a “global” method). An inverse-distance weighting scheme is used in the analysis of both global surface air temperature and continental precipitation by Legates and Willmott (1990) and Willmott et al. (1994). In addition to weighting control points according to great circle distances, the angular distribution of control points relative to a grid point and the spatial gradients within the data are considered. An optimally modiﬁed variant of distance weighting is the OSOA approach pioneered by Gandin (1965). Here the appropriate form and parameters of the distance-weighting function are determined from the spatial covariance in the data. For example, the decay of spatial correlation in contour height ﬁelds has been investigated for the standard isobaric levels at stations in Europe and the North Atlantic by Bertoni and Lund (1963), as illustrated in Figure 2.15. For tropospheric height, wind and temperature ﬁelds the spatial covariance can often be modeled by a low-order trend surface. Exponential functions are often appropriate, although sometimes they are directionally dependent, or anisotropic (Thiebaux and Pedder, 1987). A method that is similar conceptually to OSOA, known as kriging, may also be used. In meteorology a temporal series of ﬁelds (or realizations), is usually available, whereas for terrestrial science applications there may be only one data set. Kriging, which also accounts for the distance–decay relationship in the observed variable, uses a measure of the variation of the observations with increasing separation distance known as the semivariogram. Kriging involves a two-step procedure. First, a spatial structure function (the

0

111

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0111

0

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11 Figure 2.15 Illustration of the spatial decay of correlation in sealevel pressure and 500 mb height ﬁelds over Europe (Hanover, Germany) and the North Atlantic (Ocean Weather Ship C). (From Bertoni and Lund, 1963)

11

60 Synoptic and dynamic climatology semi-variogram) is calculated from the data and ﬁtted by a model that describes the spatial continuity of the data. Observations are split into a random part and a deterministic part that is described by the spatial structure. Then grid values are estimated (kriging) at each grid node based on data points in their neighborhood and the variogram model. The semivariogram describes the transition from good covariation for nearest-neighbor samples to weaker relationships with increasing sample distance; the form of the variogram may be linear, Gaussian, or spectral. In kriging the unknown covariances are replaced by the variogram model values so as to minimize the error variance. An example is provided for the distribution of January temperature in Scotland by Hudson and Wackernagel (1994). The spatial variability of some climatic element (x) at different spacings is assessed by a dissimilarity measure: 1

* =

(x2 x1)2 2

The dissimilarity * depends on the spacing and orientation of a pair of points which is described by a vector, h ( x2 x1): *(h) = 0.5[z(x1 h) z(x1)]2 The experimental variogram is obtained by forming the average of the *(h) for all Nh point pairs that can be linked by a vector h: *(h) =

1

0.5 Nh

Nh

兺 [z(x h) z(x )]

2

k

k

k=1

In general, topography modiﬁes the spatial pattern of mean precipitation and temperature. Information on altitude variations can be included in the estimation procedure through the method of cokriging, which treats two interrelated variables. Digital elevation data can be incorporated to improve the spatial representation of the rainfall amounts by determining variogram functions for both precipitation and altitude, and a crossvariogram of the two variables. Further improvement may be achieved through kriging with external drift. This model uses a functional relationship between precipitation and altitude. It is mathematically simpler than cokriging because it only requires a variogram be calculated for mean precipitation. Examples of Thiessen polygon, kriging, cokriging, and kriging with external drift (KED) estimates of rainfall in a region of southern Spain are presented by Pardo-Iguzquiza (1998). He ﬁnds that the KED approach gives the most coherent results, based on a cross-validation assessment. This consists of (1) deleting the data points from the data set one at a time (2) recalculating the estimates using n1 points, and (3) averaging the estimates over the n deletions of a data point (see Efron and Gong, 1983). Spline methods involve local interpolation and are most commonly used for one-dimensional data such as time series. A common interpolation tool is the cubic spline which locally ﬁts a parabola piecewise to a curve. Constraints can be imposed by ensuring the existence and continuity of both ﬁrst and second derivatives of the data series (i.e. smoothness of ﬁt). A thin-plate spline ﬁtting of annually averaged air temperature anomalies is illustrated by Robeson (1997). Such splines solve a differential equation that describes the ﬂexing of an inﬁnitely thin plate constrained by point loads at the control points (Wahba, 1990). They are useful in eliminating small-scale variability or errors in a data set. Tesselation methods allow a network of control points to be decomposed into a number of polygons or surface “patches.” This approach was ﬁrst proposed by Thiessen (1911), using triangulation between stations to calculate areal average precipitation. The sides of the triangles are bisected by perpendicular lines which form the sides of poly-

Climate data and their analysis 61 11

0

gons surrounding each sample point. It is essentially a nearest-neighbor method; all points inside the polygon are estimated by the nearest sample point. Weights for each station, or sample point, are proportional to the area ascribed to its polygon. In tesselation an interpolation surface is ﬁtted to data values at the vertices of the polygon as local control points. The Thiessen method has been developed subsequently by Diskin (1970) and others for computer application. A practical problem needs to be noted, that data for all stations in a network may not always be available and networks change over time, requiring new weights to be determined. Theoretical questions of the averaging of meteorological ﬁelds, and related practical considerations, are discussed in depth by Kagan (1997). Spherical harmonic functions (see Appendix 4.1) are widely used in global meteorological analyses and general circulation models, but they have been little used to date in the spatial interpolation of global observations.

111 2.4.9 Spatial coherence

0

0111

0

0 11

Meteorological ﬁelds exhibit varying degrees of spatial coherence according to the variable under consideration. The synoptic-scale decay of spatial autocorrelation in geopotential height ﬁelds has been demonstrated by Bertoni and Lund (1963). Figure 2.15 shows that there is little change in the scale of the autocorrelation from the surface to 100 mb. Gandin (1965) also examines the correlation structure of MSL pressure, 500 mb heights and 850 mb dewpoint temperature. Correlation ﬁelds for six-day and monthly precipitation totals are usually elliptical in shape (Cornish et al., 1961). The complex interaction of moisture sources, precipitation type and topography help shape this anisotropic structure (Caffey, 1965). For storm rainfall in Illinois the spatial variability and therefore the correlation decay are largest with air mass showers and least near lowpressure centers (Huff and Shipp, 1968). The variability also increases as the area considered is enlarged and decreases exponentially as the network mean precipitation increases. Based on observations of simultaneous precipitation occurrence in winter, using the synoptic “present weather” code for stations around Frankfurt, Germany, Wachter (1968) ﬁnds that the frequency distribution is bimodal at around 500 km radial distance because the area covered by the stations is smaller than the average precipitation area. Isocorrelate maps of annual precipitation in Great Britain, based on observations at Oxford and Glenquoich in western Scotland, prepared by Glasspoole (1925), show that at least two precipitation regions can be distinguished. Similar analyses of monthly and seasonal precipitation have been carried out for stations north and south of the Alps (Fliri, 1967) and over Norway (Nordø and Hjortnaes, 1967). The spatial relationship between climatic elements and atmospheric circulation features has also been extensively investigated (Stidd, 1954; Klein, 1963 1965; Klein and Kline, 1984). Commonly, station values of temperature or precipitation are correlated with concurrent MSL pressure or geopotential height anomalies. The isocorrelate lines indicate the direction, curvature, and origin of anomalous ﬂow components. Figure 2.16, for example, illustrates the patterns of correlation between surface temperature and 700 mb height in winter in the contiguous United States. Dickson (1971) correlates the variance of daily mean temperatures at eight stations in the United States, separated into three temporal spectral bands, with seasonal mean 700 mb heights over the North Paciﬁc and North America. He demonstrates increasing wave amplitude in winter for temperature oscillations with periods longer than twenty-two days compared with three to ten days. 2.4.10 Field intercomparison The intercomparison of meteorological ﬁelds is greatly complicated by the presence of spatial autocorrelation. For static or mean climatological distributions a procedure based

62 Synoptic and dynamic climatology

1

1

Figure 2.16 Patterns of correlation (negative maximum shaded) between winter temperature and 700 mb height over the contiguous United States. (Klein and Kline, 1984)

on nominal data may be appropriate. Monserud and Leemans (1992) use the kappa () statistic (Cohen, 1960) to intercompare global vegetation patterns. A subjective scale of comparability is used where < 0.4 is a poor match and > 0.85 is an excellent match. The comparison of a pair of, or more, meteorological ﬁelds raises the question of patterns of statistical signiﬁcance. Correlation ﬁelds can be evaluated ﬁrst by calculating the Student’s t statistic at all available grid points and evaluating the statistical signiﬁcance at the 5 percent level. However, some areas are likely to be correlated by chance and therefore it is necessary to assess the ﬁeld signiﬁcance rather than just the local signiﬁcance

Climate data and their analysis 63 11

0

111

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(Livezey and Chen, 1983). Moreover, because the spatial correlation diminishes the degrees of freedom the criteria for ﬁeld signiﬁcance are more stringent. The spatial degree of freedom in a climatic ﬁeld is considered by Wang and Shen (1999), using four different methods. They ﬁnd that approaches based on 2 for the distribution of squared differences between realizations of a ﬁeld, the transformed Z score of pattern correlation coefﬁcients between two realizations, and the ratio of the variance of the mean to the mean variance, all tend to give underestimates. This occurs when there is an insufﬁcient number of realizations of the ﬁeld, or when the mean and variance vary spatially. Estimation based on the binomial distribution with Monte Carlo trials (Livezey and Chen, 1983) is preferable. The spatial degrees of freedom estimated for 5° × 5° gridded air temperature data by Wang and Shen are as follows: for 1,002 available grid points in the northern hemisphere there are about sixty (ninety) degrees of freedom in winter (summer). In the southern hemisphere for 658 grid points there are between thirtyﬁve and ﬁfty degrees of freedom, with no clear seasonal cycle. In the case where the correlation between a temporal index, such as that for the Southern Oscillation, and global climatic anomalies is examined, two approaches are possible in assessing the statistical evidence for pattern correlation (Livezey, 1995). A random series can be generated for the temporal index by random reordering of the data; the local correlations are then recalculated and the number of chance correlations is determined. The resampled series are formed either without replacement (permutation) or with replacement (bootstrap) (Efron and Gong, 1983). The percent of area where the correlations are signiﬁcant at some prescribed probability level can then be evaluated. In the case of a pair of maps, best expressed as anomaly ﬁelds with zero mean, the grid-point values on one of the maps are resampled, giving a distribution from which the probabilities of statistical signiﬁcance can be calculated. Graham et al. (1994) discuss the comparison of time-dependent model simulated ﬁelds with observed ﬁelds based on some long-term record (say 1951–90). For example, resampling could be based on twenty-six sets of ﬁfteen-year contiguous records: say, 1951–65, 1952–66 . . . 1976–90. The pointwise correlation ﬁeld for the matching simulated and observed data is compared with the non-matching cases for the different sets of years. Probabilities of obtaining the matching case value, at each point, are compared with those from the non-matching sets. This method retains any temporal autocorrelation between the years or seasons, which is lost when random sets are drawn by permutation.

2.5 Time series 2.5.1 Serial correlation 0

0 11

Almost any set of climatological data is ordered chronologically (see Table 2.1) and so constitutes a time series. Time series of meteorological variables are usually based on equally spaced observations, although this is not always the case. They exhibit a wide range of behavior. Typically there are quasi-periodic or irregular ﬂuctuations about some general mean level. However, for daily and annual series there are more regular periodic variations. Over several decades a series may also contain some apparently abrupt changes, marked by shifts in the mean, as well as trends in the mean level. Alternatively the mean may remain unchanged but the variability, or frequency of extremes, increases or decreases (Hare, 1979). These changes may be the result of an artifact, such as a change in instrumentation, observational procedures, station location or its surroundings, or due to errors in data recording and transcribing. There is a large literature on appropriate statistical techniques for the analysis of time series in general and meteorological data in particular. Reference works include Brooks and Carruthers (1953), Chatﬁeld (1989), Box and Jenkins (1976), Kendall (1976), Essenwanger (1986), and Polyak (1996). Here only a few selected issues will be addressed.

64 Synoptic and dynamic climatology A time series usually consists of a deterministic element and a random or stochastic element, often referred to as white noise. The deterministic element may be strictly periodic (annual and diurnal cycles, for example) or it may be transient, such as a trend of indeﬁnite period. White noise can be simulated by a random number generator available in statistical program packages. Determination of the characteristics of a time series is commonly hampered by the availability of only a limited record length, i.e. the series is truncated. The observations represent a sample of a population that cannot in general be deﬁned because meteorological phenomena tend not to occur randomly. It is well recognized that most meteorological time series possess a high degree of persistence or serial correlation (autocorrelation) (Schumann and Hofmeyr, 1942). Such persistence is also termed red noise. The time series of a simple ﬁrst-order autoregressive (AR(1) or Markov) process, consisting of a trend and stochastic ﬂuctuations, can be written: 1

1

xt = r1[xt1 ] t where is the population mean, r1 is the lag-1 serial correlation of xt , t is time, and t is Gaussian white noise (with zero mean and constant variance). Autoregressive moving average (ARMA) processes are commonly used to model time series. Katz and Skaggs (1981) recommend that the data be transformed if they are non-Gaussian (see above) before ﬁtting the ARMA process. Non-stationarity effects can be suppressed by forming differenced series (Yt Yt1) and ﬁtting the ARMA process to them. They show that an AR(1) Markov (or red noise) process ﬁts 90 percent of Palmer drought index data for climate divisions in the United States. There are similar examples for dry and wet spells in many other locations. At the daily time scale, persistence is readily apparent for variables like pressure and temperature. For surface air temperature in winter, the one-day lag correlation varies between 0.7 and 0.8 over the western half of the continental United States and is about 0.6 on the east coast (Madden, 1979). For sea-level pressure over the northern hemisphere in winter, the one-day lag correlation varies from 0.8 over western Europe and the subtropical western North Paciﬁc to <0.5 over most of eastern North America and off Japan (Schumann and Van Rooy, 1952). For a lag-1 correlation of 0.8 (0.5), the characteristic time between independent estimates is approximately eight (three) days, respectively, assuming a ﬁrst-order autoregressive model (Madden, 1979). These estimates imply that, for daily data, a stable estimate of the monthly mean can be acquired from pressure observations on only four days, each eight days apart (or ten days, each three days apart), according to location, instead of the entire thirty or thirty-one days in the month (Figure 2.17). The time between independent samples of surface pressure also varies seasonally. At Zurich, Switzerland, it is 4.8 days in summer and autumn, 6.6 days in spring, and 12.3 days in winter (Madden and Sadeh, 1975). For twice-daily winter isobaric height ﬁelds (850 mb, 500 mb and 200 mb) covering most of the northern hemisphere for 1963–67, Lorenz (1973) reports detectable and signiﬁcant persistence of the patterns out to twelve days, although at lag-4 the autocorrelation drops below 0.3. The characteristic time between independent estimates (T0) for different averaging times (T) in an AR(1) model is depicted in Figure 2.17. Here T0 is determined from the expression T0 [1 2(1 1/T)r1 2(1 2/T )r12 . . . (2/T)r1T1] where r1 is the lag-1 autocorrelation. The corresponding variance of the time average of length T is given by

T 2 =

T0 2

T

where 2 is the variance of daily data (Madden, 1979).

Climate data and their analysis 65 11

0

111

Figure 2.17 The characteristic time between independent estimates (T0) for different averaging times (T ) of a ﬁrst-order autoregressive (Markov) process. (From Madden, 1979)

0

0111

Persistence on long time scales is typical of many geophysical time series. This tendency in hydrometeorological records was labeled the “Joseph effect” (seven lean years and seven fat years) by Mandelbrot and Wallis (1968, 1969c). Another characteristic of long series, their tendency to contain very extreme high and low values, was referred to as the “Noah effect.” Statistical models to treat such records were developed by Mandelbrot and Wallis (1969a, b, d). In particular, a statistic proposed by Hurst (1951) for data rescaling was found to provide a useful analysis tool. The Hurst approach is to remove the mean from each of n observations and to accumulate the deviations. The Adjusted Range R(n) (Rmax Rmin) and the Rescaled Range R(n)/s, where s the standard deviation, varies as nH. The Hurst exponent: H=

0

0 11

log [R(n)/s] log n

has an expected value of about 0.5, but is commonly in the range 0.6–0.9. This tendency, referred to as the Hurst phenomenon, is interpreted to indicate persistence (Mandelbrot and Wallis, 1969d). However, it may show non-stationarity of the mean (Klemes, 1974), including that caused by artifacts such as a move in station location or a change in instrumentation. Outcalt et al. (1997) used the Hurst rescaling to identify and characterize periods within a time series having different regimes. The method is illustrated for the sunspot time series, as well as for precipitation, discharge, and soil temperature data. There are various techniques available for ﬁltering a time series in order to exclude certain speciﬁed frequencies. The simple moving average, or running mean, has equally weighted ﬁlters. These have the effect of reducing to zero the amplitude of oscillations with a period that is an integer multiple of the length of the moving average, i.e. if the length of the moving average is t, the response function is 0 for periods of length t, 2t, 3t, etc. Moreover, the response function is negative for periods between t and 2t, 3t and 4t, . . . which means that the phase of such periodicities in the smoothed series is inverted by comparison with the original data. This tendency can be lessened by the use of weighted moving averages, especially with weights such as 0.25 Xt1, 0.5Xt , 0.25Xt1 (the Hanning ﬁlter). It will also be noted that the smoothed series is shorter than the original one, losing

66 Synoptic and dynamic climatology

1

one value at each end for a three-term ﬁlter and two values at each end for a ﬁve-term ﬁlter. If the number of terms in the ﬁlter is odd the new series can be centered, beginning on the second (third) term in the original series for a three (ﬁve)-term ﬁlter, respectively. If the ﬁlter has an even number of terms, centering requires a second step in the analysis. Thus for a twelve-month running mean, centering is achieved by determining a two-month moving average of the values from the ﬁrst ﬁltering step. The initial value of the twelve-month centered moving average then refers to July if the original series began in January, so that six values are lost at each end. Special ﬁlters can be designed to eliminate or suppress unwanted frequencies. Lowpass ﬁlters are used to examine long-term ﬂuctuations (Craddock, 1957) while band-pass ﬁlters select only variations close to a speciﬁed period (Landsberg et al., 1963). A detailed discussion of the design of ﬁlters with different polynomial degree and ﬁlter width is given by Polyak (1996). Serial correlation is a serious concern for many standard tests of statistical signiﬁcance which require statistically independent data samples (von Storch, 1995; Zwiers and von Storch, 1995). If the latter condition is not fulﬁlled, the number of degrees of freedom has to be adjusted. For example, for an AR(1) process with thirty cases, the effective sample size is reduced to seventeen for r1 0.3, and to only eight for r1 0.6 (Thiebaux and Zwiers, 1984). For this reason the standard error of a sample mean has to be expressed as s/(n1)0.5 where n1 denotes the number of independent samples. The effects of autocorrelation for a ﬁrst-order autoregressive series can be ﬁltered to remove the red noise by “pre-whitening” the series (von Storch, 1995). Here the original time series xt is replaced by: yt xt rˆ1 xt1 where rˆ1 is the estimated lag-1 autocorrelation and t is time.The serial correlation coefﬁcient rk , where k is the number of lags, is given approximately by:

1

兺(x x )(x x ) 兺(x x ) i

rk =

ik

2

i

Most techniques of time series analysis assume that the series is stationary, meaning that a shift in the time origin has no effect on the distribution function. In other words, the mean, the variance, and the higher moments are essentially independent of time. In reality, climatic time series are commonly non-stationary. A common problem is identifying shifts or breaks in a time series. A traditional solution in hydrometeorology is the double mass curve analysis (Kohler, 1949). Cumulative values of the variable at a station being tested are plotted on the ordinate against the cumulative mean values for six to ten neighboring stations on the abscissa. If the series is not homogeneous through time the plot will show a break in the slope of the line. The amount of change can be estimated and the date when it occurs. However, there is no objective basis for assessing whether a change has actually occurred. Also, suitable neighboring stations are not always available. There are various other methods in use. Statistical evaluation of homogeneity can be made using a non-parametric “runs test” with respect to values above and below the median (Thom, 1966). A procedure for detecting a break and assessing its “quality” is developed and illustrated by Oerlemans (1978). A break is deﬁned as the ratio of the amplitude of a change to the corresponding root-mean square (RMS) difference between the observed change and a “typical” one that is deﬁned a priori. Oerlemans provides a sample analysis of daily temperature, precipitation and sunshine duration time series at de Bilt, Netherlands for 1949–74 and ﬁnds little correlation between identiﬁed breaks. He also presents a spatial application for geopotential

Climate data and their analysis 67 11

height ﬁelds and shows a preferred blocking sector over the northeast Atlantic. Howell (1995) describes a procedure based on adaptive ﬁltering where the record is divided into blocks and the mean shifts at block boundaries are determined. A bivariate test to detect a shift in the mean of a given series against a second correlated series was proposed by Maronna and Yohai (1978). Its application to a precipitation series is described by Potter (1981), who also demonstrates that it is closely related to double mass analysis. The test indicates whether a change has occurred and provides maximum likelihood estimates of both the time (i) and the amount (d) of the change in the mean. Potter shows that the original formulation can be written such that the linear estimator of a sequence:

0 yˆi = Y

冢 S 冣 (x X ) Sxy

i

x

111

where n

Sxy =

兺 (x X) (y Y ) j

j

j =1

– – is the covariance of the two series. Sx the variance of Xi , X and Y are the series means. A corresponding estimator for Yi where:

0

Yi =

1 i

i

兺y

j

j =1

is: Yˆi = Y

冢 S 冣 (X X ) . Sxy

i

x

0111

The difference between the regression estimate of Yi and the actual value, with a correction term (in brackets [ ]) to account for the effects of non-zero d on Sxy , is: Di =

(Yˆi Yi ) [(n i) Fi /nSx]

where Fi = Sx (Xi X )2 0

冢n ni i冣 ,

i
The test statistic is: T0 = max {Ti} i
where Ti = 0 11

[i (n i) Di2 Fi] Sx Sy Sxy2

When T0 exceeds a critical value depending on n, the likelihood ratio test shows rejection of the null hypothesis of no shift in the mean. Potter (1981) gives critical values of T0 as follows: for n 30, T0 8.2 for the 0.05 signiﬁcance level and 10.7 for the 0.01 signiﬁcance level; for n 100, T0 9.3 and 12.5, respectively. The value of i for which

68 Synoptic and dynamic climatology

1

T0 is a maximum is the maximum likelihood estimator (i0) of the year before the change; the corresponding estimator of d is Di 0 . The test assumes a normal distribution and independent values in the series, which is generally the case for annual precipitation totals. Another method, developed by Easterling and Peterson (1995), combines regression and non-parametric statistics. A simple linear regression is ﬁrst ﬁtted to a difference series of the candidate station from a homogeneous reference series based on several correlated stations. The residual sum of squares (RSS) is calculated and then a further two-phase regression is performed. Segments are tested by adding one year at a time. The point with the minimum RSS sum from each of the two regressions is noted as a possible discontinuity. The signiﬁcance of the two-phase ﬁt is tested by a likelihood ratio statistic (Solow, 1987). The detection of trends is more problematic. A frequently used method is the Kendall statistic, illustrated for monotonic trends by Dettinger and Cayan (1995). A modiﬁed form is presented by Hamed and Rao (1998). The number of years of record needed to detect a trend depends on the magnitude of both the variance and the autocorrelation of the noise in the series (Weatherhead et al., 1998). 2.5.2 Periodic components Periodic components of a time series can often be represented by a series of sine and cosine functions. Figure 2.18 illustrates these basic trigonometric functions for the range 0 to 2 , which can be equated with a diurnal or annual time interval, and the harmonic curves for n 1, 2, and 4. The series for a ﬁnite function f (x) is:

1

Figure 2.18 (a) Sine and cosine functions between 0 and 2 (0–360°). (b) Simple harmonic curves for n 1, 2, and 4; the amplitude is shown as being reduced by 50 percent from n 1 to n 2 to n 4. (From Barry and Perry, 1973)

Climate data and their analysis 69 11

f (x) a0 a1 cos x a2 cos 2x . . . an cos nx b1 sin x b2 sin 2x . . . bn sin nx . . . 2 n

= a0

冤兺 a cos kx 兺 b sin kx冥 n

n

k

k=1

k

k=1

where a0 the mean, n the total length of the time period. Note that in total there are n/2 harmonics. The expression can be rewritten: n/2

0

f (x) = a0

兺 [A cos (kx )] k

k

k=1

where Ak (ak2 bk2)1/2, the amplitude of the harmonic, and: 111

arctan (b/a) arcsin (b/A)

which avoids the ambiguity of two solutions for the tangent between 0 and 2. represents the phase difference (time interval) between each harmonic or wave. It follows that: a = A cos and b = [A cos (kx )]

0

Computer programs for determining the amplitude and phase are widely available. They incorporate an algorithm known as the fast Fourier transform which signiﬁcantly shortens the computation time. Many studies of annual precipitation have used harmonic analysis to describe the characteristics of the regime and to provide a basis for the delimitation of regions having a similar regime (Horn and Bryson, 1960; McGee and Hastenrath, 1966). The ﬁrst harmonic denotes an annual cycle with a maximum and minimum and the second a semi-annual cycle as illustrated in Figure 2.18. The contribution of each harmonic to the total variance is calculated from: 0111

Ak2 2s2 (except for k n/2, where the contribution is twice the value) where n/2

2s2 =

兺A

2

k=1

0

0 11

In cases where the ﬁrst harmonic accounts for a large proportion of the variance it is worth while mapping this value (the amplitude) and the phase angle. Figure 2.19 shows the phase angle of the ﬁrst harmonic over the United States and the ratio of the second to the ﬁrst harmonic. The latter indicates the relative strength of the semi-annual component over the intermontane west. Figure 2.19a indicates that the midwinter maximum (90°) is earlier going southward in Baja California although the amplitude (not shown) also decreased sharply, while east of the Rockies the spring maximum (300°–315°) in Wyoming is delayed until mid-July (270°) in Wisconsin. In Tennessee the 60° line demarcates a February maximum. The map indicates some marked changes across New England but, in spite of the importance of the ﬁrst harmonic evidenced by the ratio map, it should be noted that the amplitude of the ﬁrst harmonic is small. Seasonal contrasts in this region are weak. The results of harmonic analysis need to be interpreted with care. Periodic processes shorter than semi-annual are unrealistic (Rayner, 1971). Moreover, harmonics of the annual cycle with frequencies of two, three, four, ﬁve, and six cycles a year are superimposed on the computations when monthly data are used.

70 Synoptic and dynamic climatology

1

1

Figure 2.19 (a) Phase angle of the ﬁrst harmonic for precipitation over the United States. (b) Ratio of the second to the ﬁrst harmonic. (After Horn and Bryson, 1960)

Climate data and their analysis 71 11

Early literature on time series made extensive use of correlograms showing the serial correlation plotted against the time lag in days or years (Alter, 1933; Wallis and Matalas, 1971). Pronounced peaks in a correlogram represent periodicities at the given lag. However, the statistical signiﬁcance of the autocorrelations is not easily determined and the correlogram cannot be interpreted for long lags because of limited record lengths. 2.5.3 The frequency domain

0

111

0

0111

Because of the severe limitations of analyses in the time domain, time series are now usually investigated through the frequency domain. The variance spectrum (or power spectrum) allows readier physical interpretation of a time series and is considerably more tractable in the evaluation of statistical signiﬁcance. Essentially, the variance of the quantity examined (Ak2 /2) is plotted against the frequency (cycles per unit time interval). Frequency is the reciprocal of period; thus 0.14 cycle per day corresponds to a period of approximately one week and 0.33 cycle per day to a period of one month. In practice, this direct approach is unworkable owing to sampling variations. Instead, the serial correlations are computed and subjected to Fourier analysis. The coefﬁcients, ak , are then smoothed by a weighted moving average and plotted against frequency. The spectrum is normalized by the use of the serial correlation coefﬁcients (which have sx2 in the denominator), so that there is unit area under the curve. The basic procedures of spectral analysis are detailed in numerous sources (Blackman and Tukey, 1958; Julian, 1967; Rayner, 1971). Several basic types of variance spectrum can be identiﬁed. Figure 2.20a shows, schematically, spectra for white noise (random uncorrelated data), quasi-periodicities, and red noise, where low frequencies (long periods) contribute most of the variance. A pure periodicity would appear as a vertical line in the frequency domain. In this diagram frequency is plotted on the abscissa, although it is often preferable to plot the log of frequency. Mandelbrot and Wallis (1969a) suggest that simple frequency plots may indicate randomness where none exists. An important concern in spectral analysis is the occurrence of aliasing between different frequencies. The shortest period (highest frequency) that can be identiﬁed in a data series is 1/2 t where t is the time interval between observations. This is known as the Nyquist frequency, F0. Shorter ﬂuctuations cannot be resolved by the available data. For example, at least two observations per day are necessary to estimate the diurnal variation. For any frequency, f : f, 2F0 f, 2F0 f, 4F0 f, 4F0 f … are aliases such that variance is added to them by unresolved frequencies which exceed F0. Figure 2.20b illustrates aliasing between frequencies. The low-frequency limit in spectral analysis is between one cycle per n/5 to n/10 , where n is the number of observations. Modern methods of time series analysis can be divided into four categories: Fourier, maximum entropy, singular spectrum, and wavelet techniques (Yiou et al., 1996). Only a brief outline of these approaches and their differences can be given here. The basic technique of Blackman and Tukey (1958) depends on the fact that the lag autocorrelation function (x) of a time series corresponds to a Fourier transform of its variance spectrum (Vx) and vice versa (Jenkins and Watts, 1968). The variance spectrum is estimated via this relationship using discrete functions:

0

M

Vx( f ) ≈

0

兺 f (k) k e

if k

x

k=0

11

where the ﬁrst M1 autocorrelation coefﬁcients are {x (k), k 0 . . . M}, and f denotes frequency. A plot of the autocorrelation function x(k) against lag k is called a correlogram.

72 Synoptic and dynamic climatology

1

1

Figure 2.20 (a) Schematic variance spectra for (i) random data (white noise); (ii) quasi-periodic oscillations at three frequencies; (iii) an autoregressive series with low frequencies (persistence or red noise) predominating; (iv) an autoregressive series with high frequencies predominating. (b) Aliasing of waves (after Blackman and Tukey, 1958). Sampling causes the sinusoidal wave with a frequency of 4 to appear as a wave of frequency 1. For time interval 1 the Nyquist frequency is 0.5. For a time interval of 0.2 the Nyquist frequency is 2.5. (From Barry and Perry, 1973)

Climate data and their analysis 73 11

0

111

0

0111

The above approximation smoothes the spectrum, reducing the variance of the spectral estimates. M is usually chosen to be less than n/5 to avoid spurious results, as noted above. Estimation of a continuous spectrum by a discrete Fourier transform causes spectral leakage of variance due to the side lobes associated with the discrete step (or boxcar) function. A window or taper (f(k)) is selected which decreases this leakage. It may be a modiﬁed cosine, cubic or tent function. The calculations have been transformed by the use of the fast Fourier transform or FFT (Rayment, 1970). This provides a computationally efﬁcient means of transforming a function into its Fourier components. The spectral resolution is generally poor when the number of data points, n, and the truncation of M are low. The multi-taper method is a non-parametric spectral method. It provides an optimal set of tapers, designed to minimize spectral leakage, that are based on eigen vectors. The total variance spectrum is obtained by averaging the individual spectra from the independent estimates of each tapered signal (Yiou et al., 1996). There are generally no more than seven tapers. However, the number of tapers and their bandwidth need to be varied to ensure that the frequency estimates are stable. The method appears suitable for identifying low-amplitude oscillations in relatively short time series (Mann et al., 1995). The maximum entropy method (MEM) (Ulrych and Bishop, 1975) is suitable when the time series is stationary and is the result of a ﬁrst-order autoregressive process (Ghil and Yiou, 1996). However, the existence of an AR(1) process is sometimes a contentious issue, as shown for sunspot numbers by Sneyers (1976). The basis of the method is the determination of the variance spectrum which corresponds to the most random process having the same autocorrelation coefﬁcients . In terms of Shannon’s information theory this random state represents maximal entropy, hence the name. Spectral estimates by MEM depend heavily on the choice of M. Various heuristic criteria have been suggested but these do not necessarily prevent errors in the method, according to Penland et al. (1991). Singular spectrum analysis (SSA) is speciﬁcally designed to treat short, noisy time series resulting from non-linear dynamical processes (Penland et al., 1991; Vautard et al., 1992). The method involves embedding a time series of observations, {xt}, in a vector space with dimension D. This dimension D is required to be larger than 2d1, where d is the effective dimension of the underlying system. Generally, it is appropriate to choose D < n/5. In decomposing a monthly index of the Southern Oscillation for 1942–90, for example, Ghil and Yiou (1996) use a vector space with D 60, corresponding to a ﬁveyear window. To embed the data, lagged copies of the series {xt}, with t 1 . . . n, are used to construct a sequence {xt*} of D-dimensional vectors: {xt*} {xt , xt 1 . . . xtD1}

0

0 11

with t 1, . . . nD1. Elementary oscillation patterns are extracted from the phase space of the lagged sequence by decomposing the augmented vectors. The SSA is performed by calculating the D D covariance matrix and obtaining its eigen elements. These comprise the orthogonal eigen vectors (empirical orthogonal functions, or EOFs) representing patterns in the time series, and the corresponding eigen values give the associated variance. By projecting the original time series onto each EOF we can obtain the principal components (PCs). Figure 2.21 illustrates PCs 1–4 of the Southern Oscillation index time series. If these four PCs are combined, a smoothed record of the index is obtained, conﬁrming that the main oscillation features have been captured. The phases of pairs 1 and 2, and 3 and 4 of these PCs and the original EOFs are evidently in quadrature. They are interpreted as the nonlinear analogue of the sine–cosine pairs in standard Fourier decomposition shown in Figure 2.22 although red noise can cause spurious pairs. Statistical tests of SSA signals are described by Yiou et al. (1996). The most recent technique developed to study the occurrence of frequency variations over time is wavelet analysis (Lau and Weng, 1995). The unique feature of the wavelet

74 Synoptic and dynamic climatology

1

Figure 2.21 The ﬁrst four principal components of a Southern Oscillation index time series for 1942–90, derived from singular spectrum analysis. (From Ghil and Yiou, 1996)

1

function is its ability to detect intermittent or transient frequency components (i.e. local waves), whereas Fourier analysis gives oscillations that continue indeﬁnitely. Figure 2.22 shows the different time–frequency windows used in Fourier and wavelet transforms and the corresponding time series plotted in the time and frequency domains. In contrast to Fourier analysis, which gives a projection of the signal on to harmonic modes, and to SSA, which gives a projection on to EOFs, wavelet analysis projects on to either an orthogonal or a non-orthogonal family of functions (Yiou et al., 1996; Torrence and Compo, 1998). The wavelet transform uses functions that are ﬂexible in their time and frequency resolution. The wavelet domain widens to identify low-frequency events and narrows to a high-pass ﬁlter for edge detection. There are a variety of algorithms in the literature. The Haar wavelet is a boxcar function which is useful to examine step changes in a series. Among the many others in use are the Morlet, Paul, Derivative of a Gaussian (DoG), and Mexican Hat wavelets; Figure 2.23 illustrates these last four wavelets in the time and frequency domains. It may be necessary to use several algorithms in order to determine whether the results are robust. Brieﬂy, the wavelet transform decomposes a signal X(t) into elementary functions b,a (t) that are derived from a “mother wavelet” (t) through translation or position change, identiﬁed by b, and scaling or dilation, a, of the wavelet (Lau and Weng, 1995): b,a(t) =

冢冣

1 tb 0.5 a a

The factor a0.5 normalizes the energy of the analyzing wavelets with that of the mother wavelet. The wavelet transform W(b, a) of the sequence X(t) can be deﬁned as:

0

111

0

0111

0

0

11

Figure 2.22 Above The time (t) and frequency () windows used in (a) the Fourier transform, (b) the windowed Fourier transform, and (c) the wavelet transform. Below Their corresponding time series plotted in time space (left) and frequency space (right). (From Lau and Weng, 1995)

11

76 Synoptic and dynamic climatology

1

1

Figure 2.23 Plots of the (a) Morlet, (b) Paul, (c) Mexican Hat, and (d) Derivative of Gaussian (DoG) wavelets in the time domain (left) and frequency domain (right). In (a) and (b), left column, the solid line shows the real part and the dotted line the imaginary part of the function. The wavelet scale s is taken to be ten times the time spacing, dt. m is the derivative in the wavelet basis function, m 2 in (c) and 6 in (d). (From Torrence and Compo, 1998)

W (b, a) =

冕冢冣

1 tb * X (t) dt a0.5 a

where * is the complex conjugate1 of deﬁned on the real half-plane (b, a). For the Morlet wavelet, which comprises a plane wave modulated by a Gaussian (see Figure 2.23a), the Fourier transform is known analytically:

冤

( f ) = (2)0.5 exp

冥

( f f0) , f >0 2

where f0 is the non-dimensional frequency.

Climate data and their analysis 77 11

0

111

0

0111

Figure 2.24 Wavelet analysis of (a) sea surface temperatures, 1875–1992, in the Niño 3 region of the eastern tropical Paciﬁc Ocean and (b) the corresponding all-India rainfall index. The contours show the normalized variances of 2, 5 and 10 explained at a particular frequency (1/period) with respect to time. The Paul wavelet gives better time than frequency localization. The thick black contours denote 95 percent signiﬁcance; the hatching in the lower corners identiﬁes the “cone of inﬂuence” areas where the variance in those periods cannot be reliably determined from the data. The lower plot shows the observations (ﬁne line) and the percent of total variance explained by all frequencies over time (solid curve) for the all-India rainfall index. (From Torrence and Compo, 1998; Webster et al., 1998)

0

11

0 11

The results of a wavelet decomposition are presented on a frequency–time plot. Figure 2.24 is an example of a wavelet analysis of sea surface temperature in the eastern tropical Paciﬁc and Indian rainfall (Webster et al., 1998). There is little inter-annual variability between 1920 and 1950 whereas during 1880–1920 and since the 1950s there are strong signals in the two-six-year and ten to twenty-ﬁve-year ranges. In general, wavelet analysis is applicable for identifying the modulation of a signal amplitude or its frequency, as well as for recognizing abrupt changes in a time series or its frequency characteristics.

78 Synoptic and dynamic climatology

2.6 Empirical orthogonal function analysis, clustering, and classiﬁcation 2.6.1 Empirical orthogonal functions and clustering

1

The purpose of determining empirical orthogonal functions (EOFs) or principal component analysis (PCA) is to obtain structures, via a new set of variables, that most closely and efﬁciently represent an original data set of N observations on M interconnected variables; in other words, to simplify the data set for purposes of interpretation and understanding. The new variables are orthogonal to one another and therefore uncorrelated. A summary of the basic concepts is given here. Details and examples may be found in Gould (1967), Johnston (1978), Joliffe (1986, 1990, 1993), and Jackson (1991). A matrix of covariances (or correlations) is set up among the M original variables (M M ). The ﬁrst new variable (ﬁrst principal component, or EOF 1) which contains the largest proportion of the total variance in the data set is extracted (by computer routine). It represents a linear function of the M variables of the form: zi = a11x1 a12x2 … a1MxM where a11, a12 … a1M are constants. This set of coefﬁcients (also called loadings or weights) a11, a12 … a1M in the ﬁrst principal component comprises the ﬁrst eigen vector. EOF 1 represents an “average” variable for the (M M ) matrix. The second PC (EOF 2) is orthogonal to the ﬁrst and extracts the largest proportion of the remaining variance, and so on. The ith principal component (or EOF i) can be expressed as: zi = ai1x1 ai2x2 … aiMxM

1

where i 1, 2 … M … . The correlations between EOF 1 and the M variables are termed the component loadings, and the square of the loadings is the proportion of associated variance. The sum of these squared loadings is the “explained” variance by EOF1, known as the eigen value 1. Figure 2.25 illustrates the elliptical hyperspace of a simple bivariate distribution and the lines representing the paired correlations. Note that the cosine of the angle, , between these lines is equal to the correlation coefﬁcient between the variables (e.g. 50°; cos r12 0.64). EOF 1 is the principal axis of the ellipse and bisects the angle between the two vectors that bracket the correlation. EOF 2 correspondingly bisects the complements of the angle and is represented by the shortest axis of the ellipse. The length of the two axes corresponds to the eigen values; 0A 1 , and 0B 2. It should be noted that EOF analysis requires the ratio of the degrees of freedom to the number of samples to be not 1/2 in order to obtain a non-biased determination of eigenvalue estimates in studies with small samples (von Storch and Hannoschock, 1985). For most real problems there is a large (M M) matrix and a multi-dimensional hyperspace. The number of EOFs that are usefully interpreted is approximately deﬁned by those EOFs for which > 1.0 (the variance of the original M variables). If the M variables are used in their original units of measurement, the analysis is based on a covariance matrix between the variables. However, this allows the variables with a large variance to dominate the ﬁrst few PCs. This arbitrary inﬂuence can be avoided by standardizing the terms x1, x2 , … xM by dividing each by its standard deviation(s). In this case the matrix between the variables uses correlations between x1, x2, … xM . To normalize the variance of zi, a constraint can be imposed on the coefﬁcients ai1, … aiM : M

兺a

2 in

n=1

= 1.

Climate data and their analysis 79 11

0

111

0

0111

0

0 11

Figure 2.25 Upper The elliptical hyperspace of a bivariate distribution; the lines show the paired correlations. Lower Principal components 1 and 2 as axes of the bivariate scatter plot. (From Johnston, 1978)

80 Synoptic and dynamic climatology In the terminology of Richman (1986) this procedure gives EOFs. Alternatively, some standard statistical packages (SPSS, BMDP) use the normalization: M

兺a

2 in

= i

n=1

1

1

where i is the variance of zi . Richman (1986) uses “principal components” to refer to zi with this normalization. It has the effect of increasing the loadings in the ﬁrst few PCs relative to the subsequent ones. A convenient feature of this normalization is that, where a correlation matrix is used, aij is the correlation between xj and the ith principal component. Note that the basic interpretation of a principal component is unaffected by the normalization chosen. Multiplying the eigen vector (ai1 … aiM ) of principal component i by the original (standardized) data (x1/s1 … xM /sM ), and summing over the M variables, gives the projection of the data on to the component axes; this is called the component score. In the case where a covariance matrix is used, the component score is the product of the loading and the original data, summed over the M variables. To obtain classes from a PCA, the component scores can be grouped via cluster analysis. A common procedure involves hierarchical clustering. One approach is average linkage clustering, where a set of groups is obtained by agglomeration; similarity is determined by the mean distance between all objects in two clusters, weighted by the number of members (Hawkins et al., 1982; Johnson and Wichern, 1982, p. 543; Kalkstein et al., 1987). Average linkage tends to generate many clusters with few members that commonly represent outliers. To overcome this problem, the initial clusters may be rearranged by iteration of a non-hierarchical procedure such as convergent k-means clustering (MacQueen, 1967) applied to the mean component loadings of each average linkage cluster. This two-stage procedure is illustrated by Davis and Kalkstein (1990). Wilson et al. (1992) examine a daily weather classiﬁcation in the US Paciﬁc Northwest using k-means cluster analysis, fuzzy cluster analysis, and principal components. A comprehensive evaluation via Monte Carlo simulations of the reliability of clusters derived by a suite of procedures (Gong and Richman, 1995) provides a valuable guide to the options available. The various options include: hierarchical/non-hierarchical methods; various measures of distance between samples within a cluster, or between clusters; and the use of “hard” (discrete) clusters versus “fuzzy” (overlapping) groups. In general, they conclude that non-hierarchical methods are better than hierarchical ones, with rotated PCs giving greatest accuracy. Nuclear agglomeration is the best of the hard cluster methods; simple linkage in contrast leads to “chaining.” The Euclidean measure of distance is shown to be slightly better than inverse correlation. For meteorological patterns, in particular, EOF 1 is an average variable and the subsequent components are orthogonal to it. Buell (1975, 1979) showed that the leading EOFs of meteorological ﬁelds over a region display a characteristic set of patterns that is descriptively referred to as “one fried egg,” “two fried eggs,” and so on. These were attributed to the commonly rectangular boundaries of a limited domain, but Legates (1991) argues that they are fundamentally related to the underlying spatial correlation structure. Further discussion is provided by Richman (1993). For this reason these EOFs may not identify realistic groups of variables. A procedure to achieve this involves rotation of the axes around the origin, while retaining orthogonality, to obtain an ideal “simple structure.” The problem of deciding how many modes to rotate is discussed for geopotential ﬁelds by O’Lenic and Livezey (1988). An alternative procedure is an oblique rotation which identiﬁes factors representing groups of interrelated variables; the axes are no longer orthogonal and consequently the interpretation requires additional care (Richman, 1986). An application of oblique rotation for precipitation regions is provided by Comrie and Glenn (1998).

Climate data and their analysis 81 11

0

111

0

0111

0

The variance accounted for by PCs is additive. However, it is necessary to determine how many components are worth retaining by using one of several tests (Cattell, 1966; North et al., 1982; Overland and Preisendorfer, 1982). Cattell developed the simple scree plot in which the eigen value (explained variance) is plotted against the PC number (1, 2 … n). This is inspected visually to select the point where the variance declines to a tail. Davis and Kalkstein (1990) suggest some further modiﬁcations. In the case of rotation, the Guttman (1954) criterion suggests rotating PCs which contribute more total variance than the normalized time series, i.e. one unit of total variance. Joliffe (1986, p. 95) suggests that, when using the correlation matrix, PCs with variance 0.7 are worth retaining. A pragmatic approach is to retain only PCs that can be physically interpreted. The data matrix discussed above comprised M variables (columns) and the N point observations (rows). This common analysis in meteorological studies is designated as R mode; its transpose in which the N observations form the columns and the M variables are the rows is a Q-mode analysis. Figure 2.26 illustrates the possible modes of decomposition of data matrices (Richman, 1986). The R mode examines correlations between variables and the Q mode focuses on correlations between spatial observations. In cases where time is one dimension, the modes are termed O mode, where time points (t) are the columns, and the M variables are the rows; this examines time-dependent correlations. Its transpose, the P mode, considers correlations between variables over time. Finally, for a single variable, there is the S mode (N point observations form the columns and t time intervals the rows), which considers spatial correlations of the variable over time and its transpose the T mode examines correlations between time periods. The S and T modes are used to study trends of a single variable (e.g. Perry, 1970). S-mode analysis, where observations are made over time at a network of stations (as variables), provides time series of component scores. Dyer (1975) delimits climatic regions by identifying geographical areas with similar component loadings. The component scores of a corresponding T-mode analysis, where time is the variable, yields spatial variations in temporal eigen values. Green et al. (1993), for example, obtain seasonal divisions from monthly temperature, precipitation, and wind data based on similarities among the component loadings. An analogous tool is singular value decomposition (SVD); this can be used to identify a hierarchy of paired spatial patterns in two ﬁelds (Wallace et al., 1992). By performing an SVD on a temporal covariance matrix between the ith grid point of one ﬁeld and the corresponding jth grid point of the second ﬁeld, the principal mode accounts for the maximum possible fraction of the squared covariance between the ﬁelds. The singular vectors for each ﬁeld are mutually orthogonal. As with EOF analysis, the ﬁelds at a speciﬁed observation time can be projected on to the singular vectors to derive expansion coefﬁcients. This approach was ﬁrst used in meteorology by Prohaska (1976). Shapiro and Goldenberg (1998) examine the dominant covarying modes of sea surface temperature gradients and tropospheric vertical wind shear in relation to Atlantic hurricanes, using the SVD approach. A comparison between methods of identifying coupled patterns in meteorological ﬁelds is provided by Bretherton et al. (1992). Recently, EOF techniques have been greatly elaborated to treat different frequencies (SSA, discussed in section 2.5.3), as well as patterns that deform or migrate with time. These methods are beyond the scope of this discussion, but a useful comparison of eight such techniques is given by Kim and Wu (1999). 2.6.2 Classiﬁcatory methods

0 11

Classiﬁcation serves the purpose of naming sets of things, and also of grouping things by resemblance, by relationship, or both. The fundamental objective of all systems of classiﬁcation is to obtain the least variability within groups and the maximum differences between groups. Groups (or classes) can be derived by two basic methods – by division

82 Synoptic and dynamic climatology

1

1

Figure 2.26 The six modes of decomposition (O–T) in principal component analysis (rows) and the corresponding matrix conﬁgurations for the data matrix (variable in the columns and individual index in the rows), correlation or covariance matrix, component loading matrix and component score matrix. (From Richman, 1986)

of a data set into subsets, or by agglomeration of similar individuals into groups. Division generally follows a hierarchical approach where subdivisions are based on the occurrence or non-occurrence of a single speciﬁc attribute. Agglomerative, or clustering, procedures, in contrast, take all measured attributes into consideration. The groups can be arranged hierarchically or in a reticulate system. Divisions are quicker to calculate and anomalous

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data have little effect on the groups, whereas, in the agglomerative process, anomalies may cause biased groups at the lower levels and these affect subsequent higher order groups. Classiﬁcation involves three separate considerations; the delimitation of groups; the assignment of new cases to established groups with minimum likelihood of error; and tests of signiﬁcance for established groups. The use of correlation to assess the homogeneity of a region for a single climatic parameter has been discussed above (section 2.4). The application of this approach to teleconnections and to typing synoptic pressure ﬁelds is examined in Chapters 5 and 7 respectively. Statistical issues in such analyses concern the effects of spatial autocorrelation and of non-linearity on the calculated product–moment correlation coefﬁcients. In cases where the correlation between two sets of variables (or two eigen vector patterns determined on two data sets) need to be evaluated, it can be performed through canonical correlation analysis, or CCA (Glahn, 1968; Johnston, 1980; von Storch, 1995). Examples include its use by Crane (1983) in comparing Arctic sea ice conditions and atmospheric circulation and by Nicholls (1987) in a study of the temporal structure of teleconnections. Werner and von Storch (1993) analyze anomalies of winter mean sealevel pressure over the eastern North Atlantic and Europe and air temperatures in central Europe using a CCA of the data projected on to EOFs. The division of data consisting of a combination of variables into classes can be readily performed by discriminant analysis. The procedures, ﬁrst developed by R.A. Fisher, have been adapted for meteorology by Panofsky and Brier (1958, p. 118) and Miller (1962). The method is based on multiple regression and so is applicable to normally distributed and moderately skewed data if the group variances are similar. Suzuki (1969) has extended it for heterogeneous (discrete and continuous) variables. To illustrate discrimination for two variables (predictors), x1 and x2, we deﬁne a linear discriminant function, D, with D1 greater than a critical value for occurrence, and D2 less than a critical value for nonoccurrence of some speciﬁed condition of interest: D a0 a1x1 a2x2

– – a1 and a2 are chosen so as to maximize (D1 D2)/sD where the overbar denotes the means of the respective classes and sD is the standard deviation of the pooled classes. Figure 2.27 shows a simple discriminant function between two groups.

0

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Figure 2.27 A single discriminant function separating two bivariate groups. D2 measures the distance between the group means. (From Harbaugh and Merriam, 1968)

84 Synoptic and dynamic climatology The method has been used to distinguish chinook and non-chinook winds at Calgary (Brinkmann, 1970) and to identify weather patterns in southern California (McCutchan and Schroeder, 1973). Discriminant analysis can also be used to assign new observations to established classes with a minimum of error. This is a valuable feature, since EOFs have to be recalculated when an observational record is extended. When a classiﬁcation has been established, it is important to ensure that the categories – are – distinct from one another. If only two groups are involved, the sample means a and b may be compared using Student’s t statistic: t=

1

|a b| {(s2a /na ) (sb2 /nb )}1/2

with (nanb2) degrees of freedom, for sample sizes of thirty or more (Thiebeaux and Zwiers, 1984). For multiple groups, an analysis of variance within and between groups may be appropriate. The procedures are discussed in most introductory statistical texts. Nosek (1967) applies this method to examine the temperature characteristics of weather types identiﬁed at Brno, Czech Republic.

Appendix 2.1 Eulerian and Lagrangian methods There are two basic analytical approaches to studies of ﬂuid motion. One refers to the ﬂuid motion past ﬁxed points (Eulerian methods) and the other to the movement of individual ﬂuid elements (Lagrangian methods). In the latter system total differentials are involved, since our reference frame is the trajectory of an individual particle. For example, the speed components are: u= 1

dx dy dz , v= , w= dt dt dt

The various approaches to trajectory analysis and their limitations are presented in Section 2.4.7 In the Eulerian system we consider the instantaneous change at individual points as the ﬂuid passes. The acceleration components are now partial derivatives (i.e. functions of more than one variable): ax =

∂u ∂u ∂u Du ∂u u v w ≡ ∂x ∂y ∂z ∂t Dt

ay =

∂v ∂v ∂v ∂u Dv u v w ≡ ∂x ∂y ∂z ∂t Dt

az =

∂w ∂w ∂w Dw ∂w u v w ≡ ∂x ∂y ∂z ∂t Dt

where ∂ ∂ ∂ D ∂ = u v w Dt ∂y ∂y ∂z ∂t (Stokes’s operator).

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Figure 2.28 Grid framework for ﬁnite difference evaluation of partial derivatives

0

Finite differences of partial derivatives ∂S (Sx Sx) ≈ ∂x L where S is some ﬁeld variable (see Figure 2.28).

冢冣

(S 2Sx Sx) ∂2S ∂ ∂S = = 2x ∂x2 ∂x ∂x L2 0111 2S =

∂2S ∂2S (Sx Sx Sy Sy 4S0) ≈ ∂x2 ∂y2 L2

Scalars and vectors

0

A scalar quantity has only magnitude; for example, temperature or pressure. A vector quantity has magnitude and direction; for example, wind velocity, the gradient of a temperature ﬁeld. A wind vector (V) can be resolved into components in a cartesian coordinate system (x, y). The scalars u and v are the lengths of the projections of V on to the x and y axes, respectively. In three dimensions a similar projection for the w component may be made on the vertical z axis: V iu jv kw

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where u and v horizontal wind components (in x and y directions, respectively), w vertical wind component (in z direction), i, j and k unit vectors denoting the directions of u, v, w respectively. The magnitude of V (u2 v2 w2)1/2. The direction of the vector is measured by cosines of the angles between the vector V and the three axes x, y, and z. The sum of the two vectors is illustrated in Figure 2.29 (b) and (c) for positive and negative cases. From the deﬁnition of V above it follows that: V1 ± V2 = i (u1 ± u2) j(v1 ± v2) k(w1 ± w2)

86 Synoptic and dynamic climatology

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Figure 2.29 (a) Vector and components in cartesian coordinates. (b) Vector addition. (c) Vector subtraction. (d) Vector cross-product

1 The two principal multiplication operations are the dot product and the cross-product of the two vectors. The deﬁnitions are: V1 ·V2 V1V2 cos where is the angle 180° between the vectors V1 and V2 . Also V1 · V2 V2 ·V1. The dot product is a scalar. For unit vectors: i ·i j· j k· k 1 and i ·j j·k k· i 0 The cross-product gives a vector of magnitude: V1 V2 V1V2 sin and direction normal to both vectors, positive in the sense of a right-hand screw when turned from direction V1 to V2 through an angle 180° (Figure 2.29d). For unit vectors: ijk jki k i j

Climate data and their analysis 87 j i k

11

iijjkk0 grad S = S = i

0

∂S ∂S ∂S j k ∂x ∂y ∂z

Vector operators 1

Gradient: where i, j, and k are unit vectors in the directions of x, y, and z respectively. S any scalar. The gradient is deﬁned as positive towards lower values of S.

2

Divergence:

111 div S = ·S = 0

∂Sx ∂Sy ∂Sz ∂x ∂y ∂z

Divergence is the net outﬂow of ﬂuid from unit volume in unit time. 3

Laplacian operator: 2 = · =

4

∂2 ∂2 ∂2 2 2 ∂x ∂y ∂z2

Curl: curl S = S = i

0111

冢∂S∂y ∂S∂z 冣 j 冢∂S∂z ∂S∂x 冣 k 冢∂S∂x ∂S∂y 冣 z

y

x

z

y

x

Note that the vertical relative vorticity, , is: =

冢∂v∂x ∂u∂y冣

corresponding to the last term of the complete three-dimensional curl.

0

Note 1 The complex conjugate of any complex number xiy is xiy.

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Steffen, K., Key, J., Cavalieri, D.J., Comiso, J., Gloersen, P., St Germain, K., and Rubinstein, I. 1992. The estimation of geophysical parameters using passive microwave algorithms. In: F.D. Carsey, ed., Microwave Remote Sensing of Sea Ice. Geophys. Monogr. 68, American Geophysical Union, Washington DC, pp. 201–301. Stidd, C.K. 1953. The cube-root normal precipitation distribution. Trans. Amer. Geophys. Union, 15: 31–4. Stidd, C.K. 1954. The use of correlation ﬁelds in relating precipitation to circulation. J. Met., 11: 202–13. Stoffelen, A.C.M. and Cats, G.J. 1991. The impact of Seasat-A scatterometer data on high-resolution analyses and forecasts: the development of the QE II storm. Mon. Wea. Rev., 119: 2794–802. Stohl, A. 1998. Computation, accuracy and applications of trajectories: a review and bibliography. Atmos. Environ., 32 (6): 947–66. Stohl, A. and Seibert, P. 1998. Accuracy of trajectories as determined from the conservation of meteorological tracers. Quart. J. Roy. Met. Soc., 124: 1465–84. Stohl, A., Wotawa, G., and Kromp-Kolb, H. 1995. Interpolation errors in wind ﬁelds as a function of spatial and temporal resolution and their impact on different kinds of kinematic trajectories. J. Appl. Met., 34 (10): 2149–65. Streten, N.A. 1980. Some synoptic indices of the southern hemisphere mean sea level circulation, 1972–77. Mon. Wea. Rev., 108: 18–36. Streten, N.A. and Kellas, W.R. 1973. Aspects of cloud pattern signatures of depressions in maturity and decay. J. Appl. Met., 12: 23–7. Streten, N.A. and Troup, A.J. 1973. A synoptic climatology of satellite observed cloud vortices over the southern hemisphere I. Quart. J. Roy. Met. Soc., 99: 56–72. Stubbs, M.W. 1981. New code for reporting surface observations: an introduction. Weather, 36 (12): 357–66. Suggs, R.J., Jedlovec, G.J., and Guillory, A.R. 1998. Retrieval of geophysical parameters from GOES: evaluation of a split window technique. J. Appl. Met., 37: 1205–27. Susskind, J. 1993. Water vapor and temperature. In: R.J. Gurney, J.L. Foster and C.L. Parkinson, eds, Atlas of Satellite Observations related to Global Change, Cambridge University Press, Cambridge, pp. 89–128. Suzuki, E. 1967. A statistical and climatological study on the rainfall in Japan. Pap. Met. Geophys. (Tokyo), 18: 103–82. Suzuki, E. 1969. A discrimination theory based on categorical variables and its application to meteorological variables. J. Met. Soc. Japan, ser. 2, 47: 145–58. Tait, A.B. 1998. Estimation of snow water equivalent using passive microwave radiation data. Rem. Sensing. Environ., 64: 286–9. Thiao, W. and Turpeinen, O.M. 1992. Large-scale diurnal variations of tropical cold cloudiness based on a simple cloud indexing method. J. Climate, 5: 173–80. Thiebaux, H.J. and Pedder, M.A. 1987. Spatial Objective Analysis, Academic Press, New York. Thiebeaux, H.J. and Zwiers, F.W. 1984. The interpretation and estimation of effective sample size. J. Clim. Appl. Met., 23: 800–11. Thiessen, A.H. 1911. Precipitation averages for large areas. Mon. Wea. Rev., 39: 1082–4. Thom, H.C.S. 1958. A note on the gamma distribution. Mon. Wea. Rev., 86: 117–22. Thom, H.C.S. 1966. Some Methods of Climatological Analysis. Tech. Note 66, World Meteorological Organization, Geneva, pp. 31–45. Tjemkes, S.A., Stephens, G.L., and Jackson, D.L. 1991. Spaceborne observations of columnar water vapor: SSMI observations and algorithm. J. Geophys. Res., 96: 10941–54. Todd, M.C., Barrett, E.C., Beaumont, M.J., and Green, J.L. 1995. Satellite identiﬁcation of rain days over the upper Nile river basin using an optimum infrared rain/no-rain threshold temperature model. J. Appl. Met., 34: 2600–11. Torrence, C. and Compo, G. 1998. A practical guide to wavelet analysis. Bull. Amer. Met. Soc., 79 (1): 61–78. Trenberth, K.E. and Olson, J.G. 1988. An evaluation and intercomparison of global analyses from the National Meteorological Center and the European Center for Medium Range Weather Forecasts. Bull. Amer. Met. Soc., 69: 1047–57. Trenberth, K.E. and Paolino, D.A., Jr. 1980. The northern hemisphere sea-level pressure data set: trends, errors and discontinuities. Mon. Wea. Rev., 108: 855–72.

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Part 2

Dynamic climatology 0

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Global climate and the general circulation

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Early treatments of global climate and its regional anomalies were limited to attempts to generalize the observed climatic features. The studies of W. Köppen (1931) based on temperature and precipitation classes sought to identify the hypothetical climate of an ideal continent, for example, but this approach was limited by the unavoidable inclusion of the large-scale effects of land–sea distribution and of major mountain barriers on the atmospheric circulation. In a review of G.T. Trewartha’s (1961) book The Earth’s Problem Climates, Tucker (1962) commented that adequate explanations of “normal” climatic patterns on the continents are for the most part lacking. This underlying framework is perhaps more problematic than the so-called anomalies. Climate modeling studies have clariﬁed considerably the determinants of the background planetary climate, the contributory role of the Earth’s geography, and the feedbacks internal to the climate system. These are discussed in turn. There are a number of ways in which the global circulation of the atmosphere can be studied. The most obvious approach is empirical, using measurements of upper atmospheric winds, temperature and vapor pressure collected at rawinsonde (radar wind sounding) stations worldwide. However, as we have seen, the spatial coverage is heterogeneous and incomplete while the length of the record is barely ﬁfty years. A second approach is to use analog models, such as the rotating dishpan, to study highly simpliﬁed rotating ﬂuid systems in a controlled situation. A third is the development of increasingly complex numerical general circulation models (GCMs) to simulate the three-dimensional behavior of the atmosphere and its time evolution. Modern climates are simulated, as well as the response of the model either to various imposed forcings (changes of solar input or atmospheric carbon dioxide concentration, for example), or to changes in surface boundary conditions (such as sea surface temperature anomalies, and Pleistocene ice sheets). A fourth possibility is the comparative study of the contrasting circulation features of planetary atmospheres using theory and limited observations. Elements of each of these approaches are incorporated in the following survey. We begin by considering the planetary setting.

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3.1 Planetary controls

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The phenomena of planetary atmospheres and their differing characteristics (Lewis and Prinn, 1983; Chamberlin and Hunter, 1987) are the concern of dynamic meteorology. A brief review of these questions serves as an introduction to the Earth’s atmosphere. The basic factors determining planetary climate are its mean solar distance, orbit, mass, and rotational characteristics, acting through the several laws of conservation. To these must be added its albedo and atmospheric constituents, although their evolution is partly a response to the astronomical situation (Figure 3.1). Table 3.1 summarizes the differences in the physical constants (parameters) of the three terrestrial planets and Jupiter. Figure 3.2 illustrates the thermal conditions at the surface of each terrestrial planet and

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Figure 3.1 The effective temperatures of the four inner planets in relation to distance from the Sun. Separate lines are given for the black-body radiation temperature (b.b.r.) and for different albedos (p). For Venus, Earth, and Mars the radiation temperature is the lower cross and the mean surface temperature the upper dot. (After Hutter et al., 1990) Table 3.1 Characteristics of the three terrestrial planets and Jupiter Planet

Distance from sun (A.U.)a

Mass (relative to earth = 1)

Gravitational acceleration (m s2)

Sidereal year (days)

Rotational period (hr)

Venus Earth Mars Jupiter

0.72 1.00 1.52 5.20

0.82 1.00 0.11 318.00

8.88 9.78 3.73 23.20

244.70 365.26 687.00 11.86 (yr)

5,820.0b 24.0 24.7 10.0

Sources: after Pollack and Yung (1980) and Wells (1986). Notes: a Half the major axis of the ellipse, relative to the Earth = 1 Astronomical Unit (A.U.). b Clockwise rotation.

the respective state diagrams for water and carbon dioxide. Intercomparisons of planetary meteorology are now becoming feasible through projects such as the Mars Pathﬁnder (Scholeﬁeld et al., 1998). The rotation rate determines both day length and the Coriolis parameter. The orbital parameters determine a planet’s annual cycle; the orbit itself controls the length of the year, while the inclination of the planet’s axis to the plane of the ecliptic and the longitude of the vernal equinox determine the length and character of the seasons. The major gaseous constituents determine the speciﬁc heats (cp and cv ) of the atmosphere, whereas its absorbtivity is a result of the proportions of radiatively active gases, especially H2O, CO2, and O3 (see Table 3.2). Other atmospheric components, aerosols and clouds, together with surface properties, determine the radiation budget, whereas differences in vegetation and hydrological properties affect the exchanges of heat, moisture, and momentum, as well as of gases and aerosols. The mean temperature of the Earth is determined by the balance of incoming solar radiation and outgoing terrestrial radiation. Thus: S (1 p) r2 = Te4 4r 2

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Figure 3.2 Conditions at the surfaces of the terrestrial planets and state diagrams for water and carbon dioxide. T = the triple point. The range of pressures and temperatures reﬂects the latitudinal and topographic ranges on the surface. (After Hutter et al., 1990)

0

Table 3.2 Atmospheric properties of planets Planet Solar Planetary Surface Surface Dry Major irradiancea albedo pressure temperatureb adiabatic gasesc (W m2) (105 Pa) (K) lapse (K km1) Venus 2,630

0.77

90

730 (~230)

10.7

Earth

1,368

0.30

1

287 (254)

9.8

Mars

590

0.15

0.007

222 (~215)

4.5

–

100

~129

0

Jupiter 51

Sources: modiﬁed after Pollack and Yung (1980) and Wells (1986). Notes a Solar radiation ﬂux per unit area at the top of the atmosphere. b Effective temperature in parentheses. c Numbers in parentheses are volume mixing ratios.

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CO2 (0.96), N2 N2 (0.77), O2 (0.21)

Trace gases

Aerosols

H2O, SO2, H2SO4 CO, HCl H2O, CO2, Water, O3, N2O H2SO4, sea salts, organics, dust CO2 (0.95), O2, CO, Water ice, N2 H2O dust, CO2 ice H2 (0.89), HD, CH4, Ammonia He NH3, C2H6 ice, water

112 Synoptic and dynamic climatology and Te =

冤

S (1 p) 4

冥

1/4

For the appropriate values of solar irradiance (S 1368 W m2) received at the top of the atmosphere at normal incidence on the Earth’s disc ( r 2) and planetary albedo (p – 0.30), the equivalent mean Earth temperature (T e) for the planetary surface (4r 2) is about 255 K; this corresponds to a level in the real atmosphere of about 6 km or 500 – mb. A one percent anomaly in the solar constant will give a 0.6 K anomaly in (T e). A one – percent planetary albedo anomaly results in a 1.2 K anomaly in (T e). The variation in solar distance due to the elliptical orbit of the Earth causes an annual variation of ±34 percent in the solar irradiance from 1.034 S at perihelion (currently January 4) to 0.965 S at aphelion (July 3). It is worth pointing out that the above calculation of the Earth’s “effective” temperature includes the short-wave effect of cloud reﬂection in the planetary albedo but neglects the trapping effect of clouds on infrared radiation, which decreases the net outgoing total (Lindzen, 1994). For a waterless, cloud-free earth with a planetary – albedo of about 0.08, T e would be 273 K. The earth’s orbit round the sun is perturbed by the gravitational effects of the sun and other planets. There are three principal effects, as illustrated in Figure 3.3. The orbital ellipticity, e, varies on long time scales (95,000 to 125,000 years and about 400,000 years), currently being near its minimum (0.0167). Variations of e affect the annual radiation income for the Earth by about ±0.2 percent. The tilt of the earth’s axis (or obliquity, ) oscillates with a period of about 41,000 years between 21.8° and 24.4°; currently it is 23°27′. A large tilt ampliﬁes simultaneously the seasonal cycle in both polar regions, but the effect is small in low latitudes. Due to a wobble in the earth’s axis of rotation, with a 26,000 year period, combined with a precession of the orbital ellipse, the vernal equinox precesses over a 22,000 year interval (Figure 3.4). However, this is modulated by eccenTable 3.3 Orbital forcings and characteristics Element

Index range

Present value

Average periodicity (ka)

Obliquity of ecliptic () (tilt of axis of rotation) Effects equal in both hemispheres, effect intensiﬁes polewards (for caloric seasons)

22° to

23.4° 24.5°

41

0.05 to 0.05

0.0164

19, 23

0.0050 to 0.0607

0.0167

410, 95

Low High Weak seasonality, Strong seasonality, more steep poleward summer radiation at poles, radiation gradient weaker radiation gradient Precession of equinox () (wobble of axis of rotation) Changing Earth–Sun distance alters seasonal cycle structure; complex effect, modulated by eccentricity of orbit Eccentricity of orbit (e) Gives 0.02% variation in incoming radiation; modiﬁes amplitude of precession cycle changing seasonal duration and intensity; effects opposite in each hemisphere; greatest in low latitudes

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Figure 3.3 The planetary forces on the Earth’s axis and orbit that cause changes in the eccentricity (ellipticity) of the orbit (a), the tilt (obliquity) of the pole of rotation, and the gyroscopic spin of the planet (or precession). (From Crowley and North, 1991)

0 tricity, splitting the precession frequency into periods of 19,000 and 23,000 years (Figure 3.5). About 10,000 years ago the perihelion occurred during the northern summer; this enhanced seasonality in the northern hemisphere (Figure 3.4c). The precession effect is greatest in low latitudes and is opposite between hemispheres. Its effect on the annual temperature range and on the strength of the Asian monsoon circulation has been demonstrated by Kutzbach and Otto-Bliesner (1982) and Kutzbach and Gallimore (1988). They show that solar radiation increases of about 7–8 percent in July 11,000 years ago caused a rise in summer temperature over the northern continents of 2–4°C; changes over the oceans were small and thus the summer monsoon of South Asia and West Africa was strengthened. Figure 3.5 illustrates these astronomical periodicities over the past 0.8 million years and their combined effect expressed as a climate index (Berger, 1978, 1979; Imbrie et al., 1984). Table 3.3 summarizes the characteristics of the orbital forcings.

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3.2 Basic controls of the atmospheric circulation and its maintenance

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The general characteristics of the atmospheric circulation are determined ﬁrst by the Earth–sun distance and orbital geometry, which account for the terrestrial receipt of solar radiation. The observed latitudinal and vertical gradients of heating rate and temperature are established by the distributions of solar radiation forcing and planetary factors, particularly atmospheric composition, cloud cover, and surface properties (albedo, vegetation). The second most important determinants of the atmospheric motion are the planetary

114 Synoptic and dynamic climatology

Figure 3.4 The components of precession of the earth’s (a) axial precession (wobble), (b) effect due to changes in eccentricity of the orbit (c) combined effect on the equinoxes in relation to the elliptical orbit. (From Pisias and Imbrie, 1986)

rotation rate and the Earth’s dimensions, which together determine the angular momentum of the Earth–atmosphere system. Table 3.2 summarizes key properties of the Earth and other terrestrial planets; the signiﬁcance of some of these differences is discussed below. 3.2.1 Energy balance The energy balance of the global Earth–atmosphere system involves the balance of incoming and outgoing solar and terrestrial radiation at the top of the atmosphere and at the Earth’s surface, as well as the horizontal and vertical transfers of sensible and latent heat. Details of the radiation laws and radiative transfer are not discussed here, but may be found in various standard sources, including Houghton (1985) and Peixoto and Oort (1992, chapter 6), for example. A summary of the global radiation budget is given in Figure 3.6. The incoming solar radiation is almost all in the wavelength range 0.15–5.0 m, comprising 9 percent

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Figure 3.5 Variations in eccentricity, obliquity (tilt) in units of degrees angle, and precession index (e sin) over the last 800,000 years (after Berger, 1978). The normalized sum of these terms is the curve labeled ETP (units of standard deviation). The calculated variance spectra and dominant periods (k years) are shown at the right. (After Imbrie et al., 1984)

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Figure 3.6 The disposition of solar radiation entering the atmosphere and infrared radiation emitted by the atmosphere and surface(W m–2). (From Kiehl and Trenberth, 1997)

116 Synoptic and dynamic climatology ultraviolet ( < 0.4 m), 49 percent visible (0.4–0.8 m) and 42 percent infrared radiation (> 0.8 m). The peak is around 0.475 m, corresponding to a solar black body temperature of 6,100 K according to Wien’s law.1 The solar “constant” calculated for mean solar distance (150 106 km) is approximately 1368 W m2. It refers to the radiation received on a surface normal to the solar beam at the top of the atmosphere (TOA). Averaged annually over the earth, the TOA amount is 1368 (r2/4r2), or 342 W m2. The solar radiation entering the atmosphere is absorbed by radiatively active gases (stratospheric ozone <0.3 m and water vapor and aerosols in bands between 1.0 m and 3.0 m), as well as by dust and smoke particle aerosols (sixteen units). There is also some absorption by cloud droplets and ice crystals; recent studies indicate that this may be larger than previously thought. Several authors propose that the modeled absorption of solar radiation by clouds may be underestimated (Cess et al., 1995; Collins, 1998), while others present evidence that the calculated absorption in a cloud-free atmosphere may also be too low (Wild et al., 1995; Arking, 1996; Li et al., 1997). For sites in Germany, the surface solar radiation calculated by the ECMWF – University of Hamburg (ECHAM)4 general circulation model for cloudfree conditions agrees closely with high-quality measurements at ground stations (Wild and Ohmura, 1999). Relative to other radiation schemes, the ECHAM4 shows greater absorption of solar radiation in the near infrared. Wild and Ohmura suggest that about 21 percent (72 W m2) of the solar radiation incident at the top of the atmosphere is absorbed in a cloud-free atmosphere. They also estimate an all-sky absorption of 90 W m2. Solar radiation is also scattered forward and backward by air molecules of small diameter compared with the wavelengths of the radiation (Rayleigh scattering is proportional to 4), by larger diameter particles, cloud drops, and ice crystals (Mie scattering approximates 1 dependence). Total backscatter by the atmosphere contributes about four units to the reﬂected solar radiation, but reﬂection of the incident radiation by clouds and the Earth’s surface is more signiﬁcant (Figure 3.6). The wavelength-integrated reﬂectivity, or albedo, of clouds depends on their fractional coverage, thickness, liquid water content, and droplet radius, as well as on the solar zenith angle. Thick stratiform or cumuliform cloud layers have albedos of 0.60–0.70, whereas cirrus or thin stratus clouds have albedos of 0.20–0.30. Average global cloudiness is approximately 62 percent. Hence cloud reﬂection accounts for about twenty units or two-thirds of the mean planetary albedo (0.30). The earth’s surface has an average albedo of 0.16 (Ohmura and Gilgen, 1993), relative to the radiation incident at its surface. The ocean surface and terrestrial vegetation effects are dominant. Consequently, the contribution to the planetary albedo is modest, only six units. The net solar radiation absorbed by the Earth’s surface is between about 142 W m2 and 170 W m2 (Ohmura and Gilgen, 1993; Li and Leighton, 1993; Kiehl and Trenberth, 1997) or forty-two to ﬁfty units. The terrestrial surface has a mean global temperature of about 288 K, giving rise to a peak emission of infrared radiation near 10 m.1 The emissivity of the surface in these wavelengths ranges from about 0.92 for soil and rock, to 0.97 for water and 0.98 for vegetation. The radiation emitted by the Earth’s surface is predominantly absorbed by the atmospheric greenhouse gases: water vapor (4.5–8.0 m and around 20 m), carbon dioxide (4 m and 13–17 m) and ozone (9.6 m). This energy is re-radiated back to the surface and upward to the overlying layers of the atmosphere (Figure 3.6). Hence the net surface emission is small; Ohmura and Gilgen (1993) estimate a mean annual value of 40 W m2. However, in the “window regions” of the spectrum, between about 8 m and 12 m, most of the radiation escapes directly to space (twelve units). A majority of the outgoing long-wave radiation is emitted by the greenhouse gases, with an additional contribution from cloud emission. The greenhouse analogy refers to the atmosphere’s transmittance of most incoming short-wave radiation and absorption of most outgoing long-wave radiation, although in an actual greenhouse heat is also trapped by the glass barrier and air movement is restricted. In the Earth–atmosphere system, the Earth’s surface

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0

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heats the atmosphere by turbulent transfer of sensible heat (enthalpy) (six units) and also transfers moisture to the atmosphere through evaporation. This provides latent heat of vaporization when condensation takes place (twenty-four units), and this heat is eventually lost to space by radiation. The mean surface net radiation for the globe is approximately 100 W m2, or 30 percent of the extraterrestrial radiation. It is important to note that the atmospheric absorption of solar radiation contributes to heating of the troposphere by about 0.5 K/day, whereas it is cooled about 1.5–2.0 K/day by long-wave radiation. Turbulent heat transfer offsets this radiative imbalance. The relative contributions of sensible and latent heat clearly vary widely from dry desert surfaces to irrigated areas or swamps and the oceans. The ratio of sensible heat to latent heat ﬂux from a surface is termed the Bowen ratio ( ), which averages about 0.3 globally; it is less than unity for wet surfaces and ! 1 for dry surfaces. The Bowen ratio is an important index of the partitioning of the turbulent ﬂuxes of energy from the surface to the atmosphere and it varies on a range of time scales from subdaily to annual. Details of these transfers of radiation and energy for different surfaces may be found in Oke (1987) and other sources. The atmosphere is often likened to a heat engine, but it appears to be a remarkably inefﬁcient one. As we have seen, averaged over the Earth, about 342 W m2 of solar radiation in the 0.3–3.5 m wavelength range enters the atmosphere. Thirty percent of this incident radiation is reﬂected to space, mainly by clouds (Figure 3.6) and does not take further part in atmospheric processes. Twenty percent is absorbed in the atmosphere, primarily by water vapor, and 50 percent is absorbed by the surface. This absorbed energy heats the surface and the atmosphere, which then re-emit infrared radiation (3–50 m wavelength) corresponding to a much lower temperature (255 K for the effective planetary temperature and 288 K for the mean surface temperature as a result of the greenhouse effect) compared with the sun (6,000 K). Although a large amount of radiation is emitted by the Earth’s surface, most of it is absorbed by the atmosphere and reradiated, thereby returning much of it to the surface. The heating of the atmosphere by absorbed solar radiation, reabsorption of infrared radiation emitted by the surface, and by turbulent heat ﬂuxes from the surface, determine its internal and potential energy. This is the order of 1024 J. Only a minute fraction of this (~1020 J) is converted into kinetic energy, which maintains the circulation of the atmosphere and oceans against friction. Frictional dissipation is of the order of 2 W m2, or 0.6 percent of the absorbed solar radiation. However, this determination of atmospheric energy efﬁciency is misleading, because a large fraction of the total potential energy is not in fact available for conversion. This question is examined further below (p.140). It might be anticipated that the Earth’s energy balance would undergo seasonal variations as a result of changes in orbital geometry and solar declination angle. In actuality its variations are quite small when integrated over the globe. Table 3.4 shows the average monthly TOA values for February 1985-March 1989 based on the Earth Radiation Budget Sensor (ERBS) on NOAA-9 and 10. The annual net value of 5 W m2 indicates the residual uncertainty in the budget components. The annual cycle in solar irradiance is a result of the timing of perihelion, while that in the long-wave emission suggests the role of northern summer heating of the continents. The planetary albedo is highest in the boreal winter associated with the combined inﬂuence of ocean cloudiness along the storm tracks and snow cover on the northern continents. The reason for the minima in the transition seasons is less obvious, although radiation receipts are then large over low latitudes, while snow and ice cover in the northern hemisphere is least extensive in September and Antarctic ice is at its minimum in March. The relative seasonal contributions of cloudiness and surface albedo still need to be determined more accurately. The latitudinal distribution of the net radiation (solar minus infrared radiation) in the Earth–atmosphere system and its seasonal variations are vital elements in maintaining the atmospheric and oceanic circulations. There is an energy surplus in lower latitudes and

118 Synoptic and dynamic climatology Table 3.4 Radiation balance (W m2) at the top of the atmosphere based on ERBS data for February 1985–May 1989

Solar irradiance Reﬂected solar radiation Long-wave emission Net radiation Planetary albedo

J

F

M

A

M

J

J

A

S

O

N

D

Annual

353

351

346

340

335

332

331

333

338

344

350

353

342

108

104

100

100

102

99

99

96

96

101

108

110

102

233

232

233

234

236

238

239

240

238

235

233

232

235

12 14 13 6 3 6 7 3 4 8 8 11 5 0.307 0.296 0.290 0.294 0.305 0.299 0.300 0.289 0.285 0.294 0.310 0.311 0.298

Source: courtesy E.F. Harrison.

Table 3.5 Mean temperatures for each latitude; reduced to sea level, except for south pole (2,835 m) (°C) Latitude January 90°N 80° 70° 60° 50° 40° 30° 20° 10° Equator 10° 20° 30° 40° 50° 60° 70° 90°S

32 27.9 22.6 16.1 7.1

5.0 14.5 21.8 25.8 26.4 26.3 25.4 21.9 15.6 8.0 2.5 2 [27]

Air temperature July 1

1.5 7.3 14.1 18.1 24.0 27.3 28.0 26.9 25.6 23.9 20.0 14.7 9.0 4.0 5 20 [59]

Year 20 17 9.8 1.1

5.8 14.1 20.4 25.3 26.7 26.2 25.3 22.9 18.4 11.9 5.0 2 13 [49]

Sea surface temperature 1.7 1.7

0.7 4.8 7.9 14.1 21.3 25.4 27.2 27.1 25.8 24.0 19.5 13.3 6.5 0.5 1.3

Source: modiﬁed after Lamb (1977).

a deﬁcit in higher latitudes with, for the annual average, a balance at about 35° latitude, necessitating poleward heat ﬂows in order to maintain the observed latitudinal gradient of temperature (Table 3.5 and Figure 3.7). General Circulation Model (GCM) experiments by Hunt (1979) indicate that the equator–pole temperature gradient decreases as the rotation rate increases. Similarly, Kuhn et al. (1988) calculate a 15 K reduction in the gradient with a rotation rate 1.6 present around 3.5 billion years ago. The reduction is mostly determined by the higher polar temperatures associated with enhanced sensible heat ﬂux. Stone (1972) shows that this ﬂux is related to rotation rate as a function of ( / y)2/f 2, where / y is the local meridional gradient of potential temperature and ƒ is the Coriolis parameter (2 " sin , where " the Earth’s angular velocity and latitude).

Global climate and the general circulation 119 11

0

0

Figure 3.7 Mean zonal temperatures adjusted to sea level for January and July. (Based on Lamb, 1977, II, p. 560; Rigor et al., 2000) The inset shows the land fraction of the global surface. (Based on Lamb, 1972, I, p. 479)

0 Latitudinal temperature gradient

0

0 11

The observed latitudinal gradient of annual mean surface air temperature is 46.7°C for 10°N to 90°N, while the corresponding value for sea surface temperature (SST) is only 29°C (Table 3.5). In January there is an equator–north pole gradient of 58.4°C that decreases by half in July between 20°N and 90°N. The effects of latitudinal temperature gradients on global climate have been analysed by GCM experiments. Rind (1998) examines cases where the latitudinal gradient of zonal mean surface air temperature between the equator and north pole is varied by ±3.6°C from a control case value of 45.9°C by variously raising and lowering low and high-latitude SSTs. For an increased latitudinal gradient there is an increase in global average surface winds, as expected, as well as intensiﬁed subtropical jetstreams. The mean meridional Hadley cell in low latitudes is also strengthened, giving drier conditions in the subtropics. However, increased polar energy transport by the atmosphere appears to be compensated for by weaker ocean transports. The tropospheric lapse rate is reduced by about 10 percent for a 7.2°C reduction in gradient. Global average integrated water vapor is 25 mm for increased gradient, versus 22.9 mm for the control and 21.3 mm for decreased gradient, illustrating a non-linear asymmetric response to the forcing. Paleoclimatic reconstructions indicate that the latitudinal distribution of zonally averaged surface temperature varies considerably over geological time. Budyko and Izrael (1991, tables 8.3–4) estimate that the global mean surface temperature differed from present (TG) by 3.6ºC for the Pliocene maximum (4–5 Ma) and by 2.0°C for the Eemian Last Interglacial maximum (125 ka). They also suggest that the latitudinal temperature

120 Synoptic and dynamic climatology

Figure 3.8 Proposed universal relationship for the variation of global and latitudinal surface temperature change. (Based on Budyko and Izrael, 1991; Lindzen, 1994)

differences (T) expressed as a function of (TG), (i.e. T/TG) display a consistent relationship with latitude (sin ) (Figure 3.8). The scaling of changes in the latitudinal temperature gradient by the global mean change, implied by Figure 3.8, raises important questions (Lindzen, 1994): 1 2

What keeps changes in equatorial temperatures small? A large (small) latitudinal gradient should increase (reduce) poleward heat transport, implying strong (weak) thermal forcing in low latitudes. Changes in the zonal temperature gradient and associated changes in poleward heat ﬂux should produce temperature variations in low/high latitudes that are out of phase. Observed twentieth-century variations suggest such contrasts, although they are not apparent on long time scales.

At the present, the required poleward horizontal transport of energy in middle latitude is approximately 6 1015 W (Trenberth and Solomon, 1994). This takes place in both the atmosphere (about two-thirds) and the oceans (about one-third) (Figure 3.9). Model calculations for glacial maximum conditions suggest that there may have been a 70 percent decrease in poleward transport in the North Atlantic (Miller and Russell, 1989). Energy balance considerations help provide constraints on model calculations of oceanic and atmospheric heat transports implied by paleotemperature reconstructions (Horrell, 1990). Vertical temperature structure During the eighteenth century there was considerable controversy over the cause of the observed decrease of temperature with height on mountains (Barry, 1978). More surprising was the discovery from balloon measurements in the 1890s (Teisserenc de Bort, 1902), independently conﬁrmed by R. Assmann, that an isothermal layer or inversion around 10–12 km capped the troposphere. Hoinka (1997) recounts the history of this unexpected ﬁnding and the surrounding debate. The vertical structure of the atmosphere is most clearly demarcated by the vertical temperature distribution. Four distinct layers are separated by levels where the rate of

Global climate and the general circulation 121 11

0

0 Figure 3.9 Zonal mean distribution of northward energy transport by the oceans and atmosphere (1015 W). (From Sohn and Smith, 1993)

0

0

0 11

temperature change with height, or lapse rate, undergoes a rapid shift overlain by a reversal. The layers are from the Earth’s surface upward, and their altitudes, in midlatitudes, are: the troposphere, separated from the stratosphere by the tropopause around 12 km (200 mb); the stratosphere, separated from the mesosphere by the stratopause around 50 km (1 mb); and the mesosphere, separated from the thermosphere by the mesopause around 80 km (0.01 mb). The terms troposphere and stratosphere were coined by Teisserenc de Bort in 1908. The word tropopause was introduced in Great Britain during World War I and its usage was established by Sir Napier Shaw. The stratopause and mesopause were so named only in 1962 (Sawyer, 1963). It is interesting to note that neither Mars nor Venus has a warm stratopause due to their gaseous composition (see Table 3.2). The troposphere represents 80 percent of the atmospheric mass and contains almost all of the water vapor and clouds. Weather processes are primarily active in the troposphere. The mean environmental lapse rate in the troposphere is about 6.0 K km 1. This rate is determined partly by the radiative equilibrium. This equilibrium involves the absorption of incoming solar radiation and of upwelling infrared radiation from the surface and underlying atmosphere, on the one hand, and the emission of infrared radiation by successive overlying atmospheric layers, on the other. The details of this absorption and emission are controlled by the vertical distribution of the principal radiatively active gases – water vapor, carbon dioxide and ozone. Water vapor is concentrated below 300 mb, and ozone is in the stratosphere. Radiative equilibrium is modiﬁed, however, by convective overturning towards a statically neutral state. A radiative–convective model calculation by Manabe and Strickler (1964) of the temperature proﬁle produced by the absorption and emission of these gases in combination, in the absence of clouds, and with a convective adjustment, reproduces the main features of the temperature structure up to about 40 km. As noted earlier, the mean global surface temperature is 288 K whereas the mean effective planetary temperature is 255 K. The latter corresponds to the temperature at the level, where there is approximately one half of the atmospheric mass, or 5.5 km. From these values, the mean lapse rate is 6.0 K km1. The simple radiative–convective model of vertical temperature structure is deﬁcient in the extratropics, where the lapse rate is

122 Synoptic and dynamic climatology typically 5 K km1. A further possible explanation invokes “baroclinic adjustment,” which implies that the atmosphere is made baroclinically unstable by large-scale forcing and that the baroclinic eddies maintain a state close to neutral stability. Like convective adjustment, this represents a dynamical as opposed to a radiative constraint on the tropospheric lapse rate. The standard World Meteorological Organisation deﬁnition of the tropopause is based on a lapse rate decreasing to ≤ 2 K km1, and not exceeding 2 K km1 through a 2 km deep layer. Based on such a deﬁnition, the tropopause is located at 17–18 km in low latitudes, 12 km in mid-latitudes, and 9 km in high latitudes. The change in altitude occurs in steps in association with the upper-level polar and subtropical frontal zones, or there may exist tropopause folds in these zones (Defant and Taba, 1957; Reed and Danielsen, 1959). There are also seasonal changes in the tropopause height. Over the central Arctic Ocean the tropopause is lowest in April (between 8.5 km and 11 km) and highest in August (9.5–11.5 km), with a secondary minimum in November and a secondary maximum in December–January, based on north pole drifting station soundings for 1954–91 (Nagurny, 1998). In addition to the discontinuity in lapse rate at the tropopause, it acts as a lid on vertical motion in the troposphere. This can be understood by noting that the Brunt–Väisälä frequency, N S1/2/2, where S is the static stability ( ), increases in magnitude across the tropopause from 102 rad s1 to ≥ 2 102 rad s1. In addition, only long Rossby waves, with typical zonal wave numbers ≤ 2, are able to propagate vertically into the stratosphere. Consequently, vertical motions are small above the tropopause. In the tropics, the conventional lapse rate tropopause deﬁnition is often not relevant (Highwood and Hopkins, 1998). Other deﬁnitions include the top of the layer with convective heating, but this is generally below the conventional tropopause. Another deﬁnition is the altitude of temperature minimum which reaches 80°C near the equator; this is reliable only in the deep tropics, where the lower stratosphere is not isothermal. The temperature minimum may otherwise occur above the conventional lapse rate tropopause. Using European Centre for Medium Range Weather Forecasts (ECMWF) data for nearly four years, Highwood and Hopkins show that the main level of convective outﬂow is around 12 km (200 mb), with the top of such outﬂow at 14 km (145 mb), where 360 K; the standard tropopause is at 16 km (100 mb) and 375 K; the temperature minimum is at 18 km (80 mb) and 400 K. A stabilization level is identiﬁed around 130–40 mb. The lapse rate deﬁnition has disadvantages because it is non-physical and the analyzed surface often shows erratic displacements associated with synoptic disturbances. A different approach makes use of a potential vorticity (PV) surface for global analysis. PV is a conservative property, and a PV surface represents a material surface (a closed surface that moves with the ﬂow) on a time scale of days. In a frictionless, adiabatic atmosphere, PV is conserved for 2-D motion on an isentropic surface (Hoskins, 1991). In the tropics, PV surfaces are almost vertical as a result of the marked equatorward increase of absolute vorticity. Poleward of about 25° latitude, however, the tropopause can be delineated by a PV surface corresponding to about two PV units. Hoerling et al. (1991) suggest that outside the tropics the tropopause may be delineated by potential vorticity values within the range 1 to 3.5 106 K m2 kg1 s1 (1–3.5 PV units). Above the extratropical tropopause, PV magnitudes in the lower stratosphere are around four PV units and they increase rapidly upward and poleward. For this reason, the actual PV value selected to deﬁne the tropopause is arbitrary. The Ertel potential vorticity on an isentropic () level (see Appendix 3.1) may be calculated from: (PV) g ( f ) ( p/ ) where is the vertical component of the relative vorticity on an isentropic surface. The term:

Global climate and the general circulation 123 11

1 g p

corresponds to the density in coordinates. The absolute vorticity term is: ( f ) =

0

0

0

0

0 11

冢冣

1

M r 2 cos

where the angular momentum M ua cos "r 2 cos2 , r the earth’s radius, latitude. Differences in tropopause height between the lapse rate deﬁnition and a PV deﬁnition can occur, particularly in association with frontal zones, but the large-scale features of the time-mean tropopause determined by the two procedures show reasonable overall agreement. The existence of the tropopause is not well understood. Radiative heating of the Earth’s surface and the atmosphere produces low static stability near the ground and high values in the stratosphere, where ozone absorption gives large heating rates. However, the transition between these is sharp, not gradual. In low latitudes the primary factor appears to be deep convection, which heats the troposphere by releasing latent heat. The tropospheric lapse rate approximates neutral stability for a saturated atmosphere. The tropopause is close to the level of the high cloud tops, and, because of its height, temperatures are as low as 215 K. A composite mapping of the global tropopause, in terms of pressure level, is provided by Hoinka (1998), using lapse rate and PV criteria. He ﬁnds that a consistent spatial pattern is obtained using a lapse rate criterion equatorward of 19°N, combined lapse rate and PV criteria for 19°–36°N, and a 3.5 PV units deﬁnition poleward of 36°N. The southern hemisphere tropopause is pre-eminently zonal, whereas in the northern hemisphere there is a planetary two to four wave pattern. In the Arctic the tropopause heights are circumpolar in summer, but in winter the maximum pressure (around 300 mb) is displaced to 70°N along 90°W. In the Antarctic the maximum pressures are near 75°S, 180° longitude in both summer and winter. Potential temperature surfaces where is between about 300 K and 340 K are in the troposphere in low latitudes, but in the lower stratosphere at high latitudes. Potential vorticity analyses on such isentropic surfaces show that the extratropical tropopause can develop troughs and ridges and cut-off features through the action of deep baroclinic disturbances. These convolutions transport high (low) PV air horizontally equatorward (poleward), respectively. This process strips high PV air from the edge of the stratospheric vortex, sharpening the transition to the lower PV air in the subtropical upper troposphere (James, 1994). Such “vortex stripping” thereby sharpens the tropopause. Ambaum (1997) considers that the extratropical tropopause is established by a dynamic equilibrium between diabatic heating and vortex stripping. He also suggests that, in midlatitudes, orography plays a role in enhancing meridional PV gradients. In high latitudes, vertical motion is generally weak in the troposphere and in winter subsidence tends to balance radiative heat loss. Thus the polar tropopause is usually not well developed. The sensitivity of the height of the tropopause to external parameters can be examined using a general circulation model that incorporates the various radiative and dynamical processes that affect the tropospheric lapse rate. Thuburn and Craig (1997) use the UK Universities’ Global Atmospheric Modelling Programme (UGAMP) GCM with thirtythree vertical levels in a January mode for this purpose. They modify surface temperature, stratospheric ozone and planetary rotation rate. The results show that tropopause height depends strongly on surface temperature acting through changes in the moisture distribution and its radiative effects. It is less sensitive to ozone distribution and is little affected by 40 percent changes in the earth’s rotation. Further tests of two types of baroclinic adjustment suggest no apparent relationship with tropopause height.

124 Synoptic and dynamic climatology 3.2.2 Angular momentum balance The total angular momentum of the rotating Earth–atmosphere–ocean system is virtually constant in time, apart from minor effects due to tidal friction and a gradual secular decrease caused by the gravitational effects of the moon and planets. For the Earth, the angular momentum is 5.86 1033 kg m2 s1, compared with about 1 1028 kg m2 s1 for the atmosphere, assumed to be in solid rotation with the Earth (Peixoto and Oort, 1992, chapter 11). The absolute angular momentum of a unit of atmospheric mass about the Earth’s axis of rotation (M) comprises a component representing the atmosphere in solid rotation (M") and the angular momentum relative to the rotating earth (MR ) (see Figure 3.10). For an atmosphere in rotation the absolute angular momentum per unit mass about the Earth’s axis of rotation is: M = M" MR where M" = " r2 cos2 and MR = u r cos2 where " the Earth’s angular velocity (7.292 105 rad s1), r the Earth’s radius (6,371 106 m), = latitude angle, and u zonal wind speed. Table 3.6 illustrates the theoretical effect of a poleward displacement on the zonal velocity of an air particle initially at rest for each 10° latitude displacement poleward. In the upper troposphere, at jetstream levels, a rapid increase of zonal velocity is observed, on average, poleward to about 30° latitude, implying that absolute angular momentum is

Figure 3.10 Schematic illustration of absolute (M ) and relative angular momentum (Mr ). (Peixoto and Oort, 1992)

Global climate and the general circulation 125 11

0

Table 3.6 Earth’s rotational velocity and its consequences Latitude

Circumference (km)

Rotational velocity (m s1)

Increment of westerly motion for stationary air transferred 10° poleward (m s1)a

Coriolis parameterb (f) (104 s1)

90° 80° 70° 60° 50° 40° 30° 20° 10° 0°

0 6,950 13,680 20,000 25,700 30,600 34,600 37,600 39,400 40,000

0 81 158 232 300 357 403 437 458 465

81 77 74 68 57 46 34 21 7

1.458 1.436 1.370 1.263 1.117 0.937 0.0729 0.499 0.253 0

Source: Lamb (1972, p. 486). Notes a Values refer to intermediate 10° latitude zones. b Positive in the northern hemisphere, negative in the southern hemisphere, where f = 2" sin .

0

conserved. Further poleward and at lower levels, however, this is clearly not the case. Consequently, a poleward gradient of absolute angular momentum exists. The mean relative angular momentum is of the order of 14 1025 kg m2 s1 on an annual basis, or 1 percent of M". There is a large annual cycle in M associated with seasonal variations in wind velocity. In the southern hemisphere there is about a 40 percent decrease in M from winter to summer, while in the northern hemisphere the summer value is only 15 percent of that in winter as a result of the weak summer westerlies (Rosen and Salstein, 1983). The seasonal variations in atmospheric angular momentum, of the order of 5 1025 kg m2 s1, cause the Earth’s rotation rate and angular momentum to vary also. The rotation rate is greater in July than in January, with a corresponding change in length of day (LOD). Rosen et al. (1987) indicate that:

0

LOD (milliseconds) 0.168 Mr (1025 kg m2 s1)

so that the LOD is about 0.8 milliseconds longer in January than in July. The time rate of change of atmospheric total angular momentum (M) is essentially determined by pressure and friction torques in the zonal (west–east) direction. For a unit volume, dM 1 p = F r cos dt

0

0 11

where F frictional stress in the zonal direction; = longitude. The pressure and friction torques are considered positive where they increase the westerly (eastward) angular momentum. Mountain barriers generate a pressure torque by east–west pressure differences across them. Calculations suggest that the mean annual net effect for the major mountain ranges at 40°–50°N is of the order of 2 1018 kg m2 s2, comprising about 40 percent of the total surface torque due to friction and mountains of 5 1018 kg m2 s2.2 Frictional stress at the Earth’s surface transfers westerly angular momentum from the atmosphere to the Earth in the zones of surface westerlies and, conversely, the atmosphere gains relative momentum in the regions of easterlies, where the air rotates more slowly about the axis of rotation than the Earth. Overall the net torques of westerlies and easterlies must balance in area in order for angular momentum to be conserved (Lorenz,

126 Synoptic and dynamic climatology 1967). The observed distribution of wind belts qualitatively supports this concept. Moreover the tropics represent a source of absolute angular momentum in the atmosphere and the mid-latitudes a sink, with poleward transport across the subtropics. For the total Earth–atmosphere system, the angular momentum remains constant with time (the principle of the conservation of absolute angular momentum). Zonal westerly (easterly) surface winds will add (extract) angular momentum to (from) the earth through friction. Overall, therefore, the net torques of westerly and easterly winds must balance as a consequence of the atmospheric general circulation. The low-level easterlies in low latitudes acquire westerly relative angular momentum, whereas the mid-latitude westerlies transfer their momentum to the surface. Calculations suggest that the westerlies would cease in about ten days if their supply of angular momentum were cut off. How is this supply maintained? The transport of angular momentum from the tropics to mid-latitudes is accomplished by the meridional transport of relative angular momentum (Mr). The contribution to the total angular momentum due to solid rotation with the Earth is very large because the angular velocity ("r cos ) is 465 m s1 at the equator, 328 m s1 at 45° latitude and 232 m s1 at 60° latitude, whereas typical wind speeds are an order of magnitude smaller. However, mass conservation dictates that, in the mean, there is no meridional transport of the " component of angular momentum. The speciﬁc mechanisms of horizontal transport can be calculated by analyzing the motion using the concept developed for ﬂuid ﬂow by Osborne Reynolds (1884). The total zonal ﬂow past a point, u, can be regarded as comprising the time-mean ﬂow, u– , and a deviation, u′, from the time average: u u– u′ We can also consider departures with respect to space averages: u [u] u* where [ ] denotes an average along a latitude circle and * a spatial deviation from this average. Thus considering joint time and space averages: u [u–] u– * [u]′ u*′ where [u–] zonal winds averaged along a latitude circle and over a time interval, u– * local departure from the longitudinal average, averaged in time, [u]′ time departures from the longitudinal average, and u*′ instantaneous local departures from the time and longitudinal averages. It follows that: u– [u–] u– * and v– [v–] v–* where v– is the time-mean meridional ﬂow component. By convention, westerly zonal winds are southerly, meridional winds are positive. By expansion, the mean poleward transport of relative angular momentum per unit mass as one level across a latitude circle is: [uv] = [u ][v ] [u]′[v]′ [u*v*] [u*′v*′]

(1)

where the terms on the right refer, respectively, to transports by standing (meridional) cells, transient cells, standing (zonal) eddies, and transient eddies. When the analysis is extended to the vertical dimension the terms corresponding to those in equation 1 represent, respectively, mean and instantaneous cell circulation in the vertical plane along a meridian, and mean and transient circulations in the horizontal plane across a latitude circle (see Grotjahn, 1993, pp. 386–90; Peixoto and Oort, 1992, pp. 61–5). In the literature, two different schemes may be encountered. These are compared by Starr and White (1952):

Global climate and the general circulation 127 11

关uv兴 = 关u兴关v兴 [u]′[v]′ 关u*v*兴

(2)

关uv兴 = 关u兴关v兴关u*v*兴关 u′v′ 兴

(3)

and

Note the difference in the order of averaging indicated by the bar over, or within, the brackets. The time and space-averaged products are identical: [uv] = 关 uv 兴

0

since space averaging and time averaging are commutative. Similarly: [u] [v] = 关u兴关v兴

0

0

0

0 11

The other equivalent terms in the two equations are not equal, however. [u]′[v]′ is due to time variations in the meridional ﬂow, while [u– *v– *] is due to the asymmetry of the mean –—– streamlines, i.e. a stationary wave; both terms appear in equation 1 above. [u*v*] is asso— ciated with the asymmetry of instantaneous streamlines and [u′v ′] corresponds to time variations of wind at a given point. These two terms are combined in the transient eddy term in equation 1. The horizontal poleward transport of relative (westerly) angular momentum is accomplished primarily through the asymmetry of the upper-air streamline patterns. This mechanism, ﬁrst proposed by Starr (1948), is illustrated schematically for both hemispheres in Figure 3.11. The annual vertically and zonally averaged poleward momentum —– transport [uv] is of the order of 15 m2 s2 near 35°S and 12 m2 s2 at 30°N (corresponding to angular momentum transports of 26 1018 and 23 1018 kg m2 s2, respectively2 and there is a larger seasonal contrast in the northern than in the southern hemisphere (Oort and Peixoto, 1983). Antarctica is also a signiﬁcant source of equatorward momentum ﬂux. Nearly all of the poleward transport is accomplished by transient eddies [u— ′v ′].. However, in the northern winter there is a signiﬁcant ﬂux contribution also from stationary eddies [u– *v– *] directed poleward at 30°N and equatorward at 60°–70°N (Figure 3.12) (Peixoto and Oort, 1992). There are also modest contributions from the mean meridional cells of —– the Hadley circulations [uv], primarily in the respective winter hemisphere. Vertical crosssections of zonally averaged momentum ﬂux show that the transports take place predominantly in the upper troposphere, around 200 mb at 20°–30°N and about 300 mb at 30°–35°S, although the mean meridional cells are most signiﬁcant below 850 mb. To complete the picture, the vertical transports of momentum (involving frictional stress and mountain pressure differences) need to be considered. In the annual mean, there are sources in the easterlies of low latitudes, symmetric about the equator, with sinks in the mid-latitude westerlies. This reﬂects the upward and downward ﬂows of mass in the thermally direct-meridional Hadley circulation, where warm air rises and cooler air sinks, and a much weaker (indirect) Ferrel cell in mid-latitudes (discussed further in section 3.3). The Hadley cells are much stronger in the respective winter hemispheres, with some crossequatorward momentum transport from the source regions, southward (northward) in the boreal (austral) winter (Figure 3.13). In summary, westerly momentum acquired in the tropical source regions is transferred upward within the Hadley cells via the w"r2 cos2 term, poleward mainly by large-scale horizontal eddies in the upper troposphere, and then downward by the sinking arm of the Hadley cells in the subtropics. The high-latitude direct cells associated with the subpolar easterlies are an additional minor source of westerly momentum for mid-latitudes. Completion of the cycle of angular momentum requires that a return of momentum must take place from middle to low latitudes either within the oceans or within the litho-

128 Synoptic and dynamic climatology

Figure 3.11 Schematic model of the net poleward transport of westerly angular momentum by horizontal eddies in mid-latitudes. The streamlines have a predominant southwest–northeast (southeast–northwest) tilt in the northern (southern) hemisphere. (After V.P. Starr, from Peixoto and Oort, 1992)

sphere. Oort (1985, 1989) shows that the oceans largely transport angular momentum zonally, not meridionally. He suggests that west–east gradients of sea level, associated with the tropical easterly winds piling up water on the east coasts of Asia and the Americas, may generate westward torques on the continents analogous to atmospheric pressure torques across mountains. Correspondingly, the mid-latitude westerlies will create an eastward continental torque. The return ﬂow of momentum may involve stress release in the crust via a preferred tilting in fault displacements. The total atmospheric mass is 5.1361 1018 kg with a seasonal variation of ±0.0010 (maximum: July, minimum: January) due to the annual cycle of atmospheric moisture content (speciﬁc humidity q ~ 1.0 1015 kg) (Trenberth et al., 1987). Mean surface pressure (ps ) based on ECMWF data for December 1978–December 1985 is 981.9 mb in the northern hemisphere (where the mean elevation is 284 m). The effect of orography alone is estimated to cause surface pressure values 1.1 mb lower than otherwise in the northern hemisphere and 0.5 mb higher in the southern hemisphere. Water vapor contributes 2.7 mb of total surface pressure. Seasonal heating and cooling cause interhemispheric mass exchanges. The maximum interhemispheric transfer is from the northern to the southern hemisphere in boreal spring, with the reverse in austral spring. Water vapor is transported from the winter to the summer hemisphere, mainly in the monsoon regions (Chen et al., 1996). The mass deﬁcit in a particular hemisphere is accompanied by a 5 percent increase in that hemisphere’s angular momentum, implying that the mass change is a dynamic response rather than a mechanical tendency to mass conservation (Christy et al., 1989). This may reﬂect the jetstream strength and the locations of storm tracks. The primary interhemispheric mode for anomalies of (P cos ) describes exchanges between northern mid-latitudes and the entire tropics plus southern subtropics with south-

Global climate and the general circulation 129 11

0

0

0

Figure 3.12 Zonal-mean annually averaged northward ﬂux of momentum in the atmosphere: (a) total, (b) transient eddies, (c) stationary eddies, (d) mean meridional cell (m2 s2) (for units see note2) (From Oort and Peixoto, 1983)

0

ward propagation of anomalies. During the seven-year record (1978–85) Christy et al. identify eighteen long-lasting, extreme surface pressure anomalies, particularly when the hemispheric mean is above or below normal. This suggests that regionally persistent anomalies are related to global, rather than local, redistributions of mass. 3.2.3 Energy transports

0 11

To understand the processes involved in the atmospheric energy cycle we begin by considering the potential energy generated by diabatic heating (i.e. radiational heating/cooling, sensible heat transfer from the surface, and latent heat released by condensation during cloud formation). An evaluation has been made of atmospheric heat sources and sinks between 50°N and 50°S by Yanai and Tomita (1998), using the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalyses for 1980–94. In the northern winter they identify three major heat sources:

130 Synoptic and dynamic climatology

Figure 3.13 Mean meridional circulation cells in the atmosphere illustrated by the mass stream functions (1010 kg s1) for annual, DJF and JJA conditions. (Peixoto and Oort, 1992)

1 2 3

The tropical Indian Ocean – Indonesia and the Southwest Paciﬁc Convergence Zone. The Congo and Amazon basins. The ocean areas off East Asia and eastern North America.

In the northern summer these are replaced by three other centers: 4 5 6

The Bay of Bengal. The western tropical Paciﬁc. Central America.

Year-round heat sinks are located over the South Indian Ocean, the eastern North and South Paciﬁc, and the eastern North and South Atlantic. In addition, the subtropical deserts are a large source of sensible heat at the surface, but have strong radiational cooling aloft. On a global scale the major heat sinks are in high latitudes, as implied by Figure 3.9. Nakamura and Oort (1988) show annual ﬂuxes into the polar caps across 70°N and S of about 95 W m2. Figure 3.14 shows that, in the mean, the troposphere is radiatively cooled by about 1°–2°C day1. The troposphere warms through sensible heat transfer approximately 0.5°–1°C day1 in the lowest 1–2 km in the tropics and northern middle latitudes. Latent heat release exceeds 2°C day1 in the tropical middle troposphere and is about 1.5°C day1 around 2–3 km altitude in mid-latitudes. Net diabatic heating rates for the troposphere in winter and summer are of the order of ± 0.5°–1°C day1. The annual-mean net planetary radiation budget (at the top of the atmosphere) is about 70 W m2 at the equator and 100 W m2 at the poles (Kyle et al., 1993). To restore balance, energy is transferred poleward by the atmosphere and ocean (Figure 3.9). For the oceans, the total energy transport across a latitude circle is, for practical purposes, that due to heat transport:

冮[ cov T ]dz

Global climate and the general circulation 131 11

0

0

0

0

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Figure 3.14 The vertical and latitudinal distribution of (a) total diabatic heating in the atmosphere for December–February by (b) net radiation, (c) latent heat of condensation and (d) sensible heat in the boundary layer. (From Newell et al., 1970)

132 Synoptic and dynamic climatology where co speciﬁc heat of ocean water (4,187 J kg1 K1). The calculation of a zonal mean involves summing the contributions of each ocean basin, taking account of bathymetry. Since direct measurements of ocean currents and temperatures are sparse, the ocean transports are estimated either as a residual between the required global energy transport from net radiation data and the calculated atmospheric contribution (Carissimo et al., 1985), or from regional surface heat balance calculations (Hastenrath, 1982). The ocean heat ﬂux in Figure 3.9 is based on the former procedure, with the atmospheric component constrained by a maximum entropy production principle. It indicates maximum poleward transports in the ocean of about 2.4 1015 W m2 at 18°N and 1.3 1015 W m2 at 18ºS, compared with atmospheric maxima of 4.5 1015 W m2 at 37°N and 4.7 1015 W m2 at 37°S. The North Paciﬁc Ocean dominates the northward transport, whereas in the southern hemisphere the Indian Ocean dominates in low latitudes and the South Paciﬁc Ocean near 55°S. The Atlantic Ocean heat transport is northward in both hemispheres. The meridional energy transfer across a latitude circle in an atmospheric column is given by: 1 g

冕关

v (cp T gz Lq) 兴 dp

where cp T is the sensible heat (enthalpy), gz is the gravitational potential energy, and Lq is the latent heat. The overbar denotes a time mean and the brackets a zonal mean. Kinetic energy is negligible in this regard. Figure 3.15 illustrates the total northward energy transports and the transient component, showing the importance of sensible heat and the transient terms. The poleward transport of sensible heat is a function of the meridional gradient of temperature and the v component of wind (van Loon, 1979; Stone and Miller, 1980). It comprises three contributions, as discussed above, for angular momentum: cp[v][T] Vertically Mean integrated meridional zonal mean cell [(cpvT )]

cp [vT ] Mean stationary eddies

cp [v′T ′] Mean transient eddies

For sensible heat, the meridional cell term dominates between ± 20º latitude, whereas the transient eddy term provides the major contribution to poleward transfer in mid-latitudes, with maxima around 850 mb and 250 mb. However, the meridional cell transports of potential energy have patterns similar to those of sensible heat but with opposite sign (because, for adiabatic conditions, g dz cp dT). The net effect of the two terms in low latitudes is an energy transfer across the equator into the winter hemisphere (Peixoto and Oort, 1992). Figure 3.16 illustrates the annually averaged northward transport of total energy due to the three components of circulation. The latitudinal patterns are similar to those described for sensible heat; the standing eddy transport is prominent only in winter in the northern higher mid-latitudes, while in the southern hemisphere the ocean transports fulﬁll at least part of this role. The transient eddy transports are nearly equivalent in both seasons, especially in the southern hemisphere. The poleward transfers of sensible and latent heat are generally positively correlated in mid and higher latitudes (Figure 3.17). This correlation is strongest along the midlatitude storm tracks (see section 6.4). The primary climatic factor is the meridional temperature gradient (see Table 3.5) and the associated advection of heat and moisture by the meridional wind component. Raphael (1997) shows that, when the temperature gradient weakens, the heat and moisture ﬂuxes (at 850 mb) are more dependent on moisture availability and land–sea distribution and the correlation between sensible and latent heat ﬂuxes is weaker.

0

0

0

0

0

Figure 3.15 Zonal-mean northward energy transport (PW) for January (upper) and July (lower). The left panels show the total transport, the right-hand panels show the transient component. The total (—) comprises the dry static energy (DSE), latent energy (LE), and kinetic energy (not shown). (From Trenberth and Solomon, 1994)

11

11

134 Synoptic and dynamic climatology

Figure 3.16 Zonal-mean annually averaged northward transport of (a) total energy in the atmosphere, and the contributions of: (b) transient eddies, (c) stationary eddies and (d) mean meridional cell (°C m s1). See note 2. (From Oort and Peixoto, 1983)

The contrasts in land–sea distribution between the northern and southern hemisphere mid-latitudes result in differences in the amplitude and intensity of the tropospheric stationary waves. Accordingly, the meridional transports of sensible heat and momentum carried out by the atmospheric eddies (stationary waves, transients – cyclones and anticyclones), and the role of the upper ocean circulations, vary substantially between the two hemispheres (Adler, 1975; Kalnay et al., 1986). In the southern hemisphere, much as in the northern hemisphere, the eddy transports of sensible heat tend to increase over middle latitudes when the tropospheric temperature gradient is strong in the subtropics, and vice versa (van Loon, 1979; van Loon and Williams, 1980). Additionally, in the southern hemisphere the temperature gradients and thermal winds are inversely related between the subtropical and high latitudes (van Loon and Kidson, 1993). The atmospheric levels of maximum in the eddy heat ﬂuxes are, primarily, at around 850 mb (Kirk, 1987)

Global climate and the general circulation 135 11

0

0

0

Figure 3.17 Correlations (statistically signiﬁcant) between anomalies of transient eddy ﬂuxes of sensible and latent heat at 850 mb for (a) January and (b) July. (Raphael, 1997)

0

0 11

and, secondarily, at 200 mb; the latter connected with the maintenance of the upper tropospheric temperature structure and the strength of the zonal mean jet just below the tropopause (Solomon, 1997). This secondary heat ﬂux maximum is dominated by wave Nos 2–3 in the winter and spring of both northern and southern hemispheres. The ﬂux of the eddy momentum peaks in the upper troposphere between about 300 mb and 250 mb (Tucker, 1979; Kirk, 1987; Trenberth, 1987; Karoly et al., 1998). Van Loon (1979) used the Australian and US National Meteorological Center (NMC) grid-point analyses to compare the tropospheric waves in the southern and northern hemispheres for the extreme seasons. The derivation of accurate spatial patterns of sensitive quantities such as heat ﬂuxes from the Australian analyses is complicated by the undue inﬂuence exerted by the Southern Ocean island stations in the analyses but these effects can be greatly reduced by zonal averaging (van Loon, 1980). The southern hemisphere quasi-stationary waves are, essentially, barotropic up to at least 70°S, even in winter, in

136 Synoptic and dynamic climatology contrast with the waves in the northern hemisphere. At the latitude of largest amplitude in the troposphere (55°S, 50°N), the temperature and height waves comprising standing wave No.1 are separated by between about 34°–55° of longitude in the 700–500 mb layer in the northern hemisphere, but only by about 15° of longitude in the southern hemisphere. These contrasts in the vertical structure of the stationary waves between the two hemispheres mean that the stationary eddies (transient eddies) assume relatively greater importance for transporting heat over most extratropical latitudes of the northern (southern) hemisphere (van Loon, 1979; Solomon, 1997). Accordingly, the meridional transports of heat and momentum in the southern hemisphere are effected mostly by the transient eddies (Kirk, 1987; van Loon and Kidson, 1993), and also by the patterns of upwelling and downwelling in the upper ocean (Trenberth, 1979). The latter are induced by the windstress patterns in the standing waves (Kalnay et al., 1986): surface divergence and upwelling to the west (surface convergence and downwelling east) of a trough. Given the dominant role of the extratropical frontal cyclones in the atmospheric total eddy heat transport, Tucker (1979) and Carleton and Whalley (1988) used satellite-observed cloud vortex summaries (by latitude zone), combined with estimates of the heat ﬂuxes, to arrive at the “ﬂux rate efﬁciencies” of successive cyclone stages. The stages show a strong latitude dependence in the southern hemisphere. These studies show that the “early development” vortex (Streten and Troup, 1973, “W” and “B” stages: see section 6.3) transport heat strongly poleward. In winter the mature (“C” type) and dissipation (“D” type) stages transports heat equatorward, leading to a ﬂux convergence of the eddy sensible heat over middle latitudes. This ﬂux convergence is less evident in the intermediate seasons studied by Tucker (1979), but is much more characteristic of the eddy ﬂuxes of momentum in those seasons. Regional differences in the eddy ﬂuxes of heat and momentum are also evident. Kirk (1987) shows, for the Weddell Sea region, that the transient eddy heat ﬂux decreases south of about 60°S, with the stationary waves increasing in importance close to Antarctica. Carleton and Whalley (1988) also show an El Niño Southern Oscillation (ENSO) inﬂuence in the transient eddy heat ﬂux efﬁciencies by cyclone type at least for the ﬁve winters they studied (1973–77). About 36 percent of the total variance in the meridional eddy heat transport by the cloud vortices is explained by the Southern Oscillation Index (SOI) for those winters, with the ﬂux efﬁciency greatest (lowest) when the SOI of the following summer is low: El Niño (high: La Niña) (see section 5.2). Similarly, the zonally asymmetric teleconnection pattern described above exhibits anomalies with the concomitant variations in cyclonic activity (Karoly, 1990). Moisture transport It is appropriate here to examine the meridional transfer of water vapor by the atmosphere. This represents the latent heat component of the total energy transport and it also provides the linkage between the water and energy budgets (see section 1.1). The atmospheric vapor content, averaging about 25 mm of precipitable water, accounts for only 105 of global water (Table 1.2), but its redistribution by the atmosphere is the primary driver of the hydrological cycle. Thus net precipitation minus evaporation can be determined from: PE=·Q

W

t

where # Q is the mean horizontal divergence of moisture in the air column, W/ t the change in precipitable water stored in the column. For annual averages this storage term is near zero. The meridional transfer of moisture across a latitude circle is given by:

Global climate and the general circulation 137 11

0

1 g

冕关

qv兴 dp

which can be expanded in a form corresponding to that for sensible heat transport. Figure 3.18 illustrates a cross-section of the northward transport of zonal-mean water vapor by the total circulation and its three components. The Hadley cell transports by the trade winds, mainly below 850 mb, are directed equatorward. The stationary eddy term is a major contributor in the northern subtropics in summer, but the transient component is dominant year-round in mid-latitudes. A different approach identiﬁes most of the vapor ﬂux as occurring in narrow atmospheric ﬁlaments, termed “tropospheric rivers” by Zhu and Newell (1998). The separation of these ﬁlaments (QR), from the overall vector ﬁeld is made by a threshold criterion based on the latitudinal zonal mean ﬂux [Q], plus an anomaly related to the relative maximum departure from the mean: QR ≥ [Q] 0.3 (Qmax – [Q])

0

Zhu and Newell show that four or ﬁve tropospheric rivers account for most of the meridional moisture ﬂux. In middle latitudes northeastward streams are prominent in January in the central eastern North Paciﬁc and the central North Atlantic, as well as southeastward in the central South Atlantic. Southward cross-equatorial ﬂows occur over Indonesia,

0

0

0 11

Figure 3.18 Annual mean latitudinal proﬁle of precipitation and evaporation (103 m3 s1) averaged over 5° latitude bands (103 m3 s1 = 31.5 109 m3 yr1). Three different estimates of precipitation are presented showing the wide range of uncertainty in middle latitudes. (After WCRP, 1988)

138 Synoptic and dynamic climatology along the coast of East Africa, and, less continuously, over the Amazon. In July the Indian Ocean–South Asian monsoon ﬂow is dominant, with strong northward ﬂow also over East Africa and the northwest Paciﬁc. These “rivers” are analogous to the conveyor belts in extratropical cyclones (see section 6.3). They also resemble the tropical–extratropical cloud bands identiﬁed in satellite imagery. Figure 3.19 illustrates the mean latitudinal proﬁle of precipitation and evaporation and shows the considerable uncertainty remaining in estimates of these basic quantities – due – to limited network coverage and measurement errors. The indirect estimation of (P E ) from atmospheric wind and speciﬁc humidity measurements is similarly constrained by inadequate upper-air sounding data. Table – 3.7– summarizes mean annual values of the precipitation, evaporation and estimated (P E ) from atmospheric data for 10° latitude zones, showing the overall convergence of moisture except in the tropics and subtropics. It was pointed out above that the seasonal variation of global-mean atmospheric mass, and therefore of surface pressure, results from the variation of global-mean vapor pressure. Reanalysis data for 1985–93 show that the seasonal variation of global, column-integrated water vapor content is maintained by three pairs of centers of vapor ﬂux divergence– convergence (Chen et al., 1996). The centers are located on each side of the equator over

Figure 3.19 As Figure 3.16 for water vapor (ms1 g kg1). (From Peixoto and Oort, 1992)

Global climate and the general circulation 139 11

Table 3.7 Mean annual estimates of precipitation and evaporation (mm) for 10° latitude belts (Baumgartner and Reichel, 1975) and (P E) calculated from the zonal mean ﬂux convergence of water vapor. (From Peixoto and Oort, 1992) Northern hemisphere

0

Southern hemisphere

Zone

P

E

(P E)

80°–90° 70°–80° 80°–70° 50°–60° 40°–50° 30°–40° 20°–30° 10°–20° 0°–10°

46 200 507 840 874 761 675 1,117 1,885

36 126 276 447 640 971 1,110 1,284 1,250

93 124 224 250 156 23 435 322 478

a

冧 (160)

b

P

E

(P E)

73 230 549 1,003 1,128 875 777 1,009 1,435

12 54 229 553 862 1,181 1,305 1,507 1,371

32 98 245 278 150 128 312 342 144

Notes: a The surface area of each 10° latitude zone is shown in Table 1.1. b 70°–90°N as calculated by Serreze et al. (1995).

0

0

0

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the tropical continents, with the ones on the same side of the equator having the same sign. Contrary to classical views, Van den Dool and Saha (1993) propose a “water mass forcing mechanism” whereby the atmospheric circulation is driven by sources and sinks of water vapor. Some support for this argument is provided by Chen et al. (1997). They show that the seasonal variations of regional surface pressure (ps) and vapor pressure (e) tend to be spatially out of phase, especially in the subtropics. Surface pressure departures are maintained by mass divergence from centers of positive ps departure to negative centers. Conversely, water vapor converges from centers of negative to positive departures of e, both regionally and hemispherically, through the global divergent circulation3. Divergent circulations are driven by latent heat released by tropical convection in areas where (E P) < 0 (or water vapor sinks). Note that, without a phase change, water vapor is a passive constituent transported by low-level divergent ﬂow. The global divergent circulations transport air mass in response to the large land–ocean pressure differences in the northern hemisphere, and also transport water vapor to maintain the anomalies in the sources and sinks. In the southern hemisphere, in contrast, the pressure gradients are mainly north–south between Antarctica and the subtropical continents. Atmospheric moisture is depleted by precipitation and restored by evaporation and moisture ﬂux convergence. Annual mean precipitation for 1979–95 averages 2–4 mm day1 over large areas of the mid-latitude oceans and is up to 5–10 mm day1 in the intertropical convergence zone, whereas the corresponding evaporation values reach 3–6 mm day1 over the tropical oceans and equatorial land areas, and 2–3 mm day or less over most middle and higher latitudes (Trenberth, 1998). Temporarily ignoring horizontal moisture transport, Trenberth shows that the e-folding time constant for the depletion of atmospheric precipitable water ranges from generally about seven to ﬁfteen days; it is only ﬁve days in the tropical convergence zones, but exceeds one month in desert areas. The time constants for the globally averaged precipitation and evaporation ﬁeld are 8.1 days and 8.5 days, respectively. Nevertheless, rainfall is very concentrated in time. It has long been recognized that in tropical and other rainstorms about 50 percent of the rainfall total occurs in 10 percent of the time (Riehl, 1954). At an individual location, precipitation is all derived from atmospheric moisture transport, while for the surface of the earth the source is evaporation. The contributions of atmospheric moisture transport and local evaporation to precipitation in the same area thus depend on the size of the area being considered. Estimation of the “intensity of the

140 Synoptic and dynamic climatology hydrological cycle” (Drozdov and Grigor’eva, 1965), or the fraction of the moisture transport that is precipitated in a region, can be expressed as: I PL/F where P precipitation, F moisture transported through an atmospheric column, and L a length scale for the region ( area0.5) according to Budyko and Drozdov (1953). It is assumed that the ratio of precipitation falling from advected moisture (Pa ) versus that from local evaporation (Pe ) is equal to the ratio the average atmospheric moisture advected versus that evaporated. The recycling ratio Pe /P can be expressed after Brubaker et al. (1993) as: EL PL 2F Global mean values, for L 500 (1,000) km, are close to 10 (20) percent, respectively, according to Trenberth (1998, 1999). Estimates of local precipitation recycling suggest values of 10–24 percent over a 1,500 km scale for the Mississippi basin and 25–35 percent in the Amazon basin over the larger 2,500 km scale (Eltahir and Bras, 1996). They suggest that, where local evaporation dominates, the recycling ratio is independent of L. However, Burde et al. (1996) consider that the above model, which applies the length scale to an area, makes an inappropriate substitution. They develop a correction factor which takes account of the structure of the advective ﬂow (zonal or meandering), although no comparisons are made for speciﬁc regions. 3.2.4 Energy conversion The classical idea of momentum and energy conversions in the atmosphere is embodied in L.F. Richardson’s dictum that: Big whirls have little whirls that feed on their velocity, little whirls have lesser whirls, and so on to viscosity. In fact, this cascade of momentum from larger to smaller scales is often reversed in the atmosphere – a process termed “negative viscosity” by Starr (1968). Lorenz (1955) established that the relevant potential energy for the atmosphere is the available potential energy (APE). This refers to the potential energy above a reference state in which the atmosphere is rearranged through a dry adiabatic process such that it is statically stable (lowest potential temperature at the surface), with horizontal isentropic and isobaric surfaces. It is deﬁned by Haltiner and Williams (1980) as:

APE = 1/2

冕

Ps

o

T

––– 2 T′

冢 T 冣 dp

where the dry adiabatic lapse rate (DALR), actual lapse rate, the overbar denotes a mean value and the prime is the local departure and

冕

Ps

integration over the entire depth of the atmosphere.

o

The APE is of the order of 0.5 percent of the total potential energy. APE is created by diabatic and frictional processes. It can be separated into a zonal mean (AZ) and an eddy component (AE); the former is a result of meridional gradients of temperature/density,

Global climate and the general circulation 141 11

i.e. differences between latitude circles, while the latter involves departures from the zonal mean along a latitude circle. Correspondingly, the kinetic energy KE =

0

冕

Ps

V 2 dp

o

For the atmosphere, KE/APE ~ 0.1. KE can also be separated into zonal (KZ) and eddy (KE) components. Potential and kinetic energies undergo a variety of conversion processes in the atmosphere. These include the initial generation of zonal (GZ) and eddy (GE) available potential energy and the eventual dissipation components of kinetic energy (DZ and DE). Table 3.8 summarizes the atmosphere mechanisms involved in each conversion process. Intermediate transformations involve: CA CE CK CZ

0

1 2g

is is is is

the the the the

conversion conversion conversion conversion

of of of of

AZ → AE AE → KE KE → KZ AZ → KZ

In evaluating the terms in the atmospheric energy conversion cycle (Figure 3.20), the kinetic energy (KE) and available potential energy (APE) are partitioned into zonal and eddy Table 3.8 Energy conversions in the atmosphere Term

Process

Mechanism

GZ

Generation of AZ

GE CA

Generation of AE Conversion of AZ to AE

CE

Conversion of AE to KE

CZ

Conversion of AZ to KZ

CK

Conversion of KE to KZ

DE DZ

Dissipation of KE Dissipation of KZ

Meridionally differentiated diabatic heating; warming of tropics, cooling of higher latitudes Diabatic heating augments east–west temperature differences Horizontal meridional transport by waves and eddies of warm (cold) air poleward (equatorward) reducing the meridional temperature gradient and therefore AZ Warm (cold) air rises (sinks) in zonal circulation cells (Walker circulation) Warm (cold) air rises (sinks) in low/high latitudes as in the Hadley cell. The indirect Ferrel cell converts in the opposite sense by cold (warm) air rising (sinking) Eddies transfer angular momentum from regions of high to low momentum and thus kinetic energy is lost (gained) by zonally averaged symmetric (asymmetric) motion Frictional decay of eddy kinetic energy Frictional decay of zonal kinetic energy

0

0

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Figure 3.20 Schematic illustration of the energy cycle components in the atmosphere; see text. (From Grotjahn, 1993)

142 Synoptic and dynamic climatology contributions, KZ, KE and AZ, AE respectively. KZ and KE are determined by resolving the velocity ﬁeld into zonally averaged motion [u] and eddy motion u* (Lorenz, 1967, p. 108): 1 KZ = { [u] · [u]} 2 1 KE = {u* · u*} 2 Lorenz emphasizes that KZ is not equivalent to the KE of the zonal motion ( u 2/2), or to zonally averaged KE ( [u # u]/2). The derivation of the equations for KE is presented in detail by Grotjahn (1993, pp. 96–103). The approximate equations for the APE components (after Lorenz, 1967) are:

冦

冧

1 ⬃ ⬃ 2 AZ = cp ( ⬃ )1 T 1[T ]* 2

冦

2 1 ⬃ AE = cp ( ⬃ )1 T 1(T [T]) 2

冧

where the DALR, the lapse rate, ~ average over an isobaric surface, and * deviation from the average. [T] zonal mean temperature along the isobaric surface and ( ˜) represents the static stability. Thus APE is expressed as a weighted average of the horizontal (isobaric) variance of temperature, T. APE is produced either by heating (cooling) of warm (cold) regions, or by heating (cooling) of the lower (upper) troposphere and so reducing the stability. In corresponding manner, the energy generation (G), conversion (C) and dissipation (D) terms are also expressed as covariances of zonal-average or eddy departure quantities. Lorenz (1967) deﬁnes the generation terms: ⬃ GZ = {( ⬃ )1 T 1[Q*][T*]} ⬃ )1 T 1(Q [Q])(T [T])} GE = {( ⬃ where Q net heat per unit mass. The conversion of total PE into KE, which involves reversible adiabatic processes, can be represented by: C g(w z/ p) g(U ⴢ z). There are corresponding equations for the conversions CZ, CE, CK and CA. The dissipation term is expressed by: D {U ⴢ F} where F frictional force per unit mass. Figure 3.20 illustrates these conversions and the associated reservoirs schematically; the magnitudes of the various components in the global energy cycle are discussed below. Typical atmospheric processes which act to effect these conversions are summarized in Table 3.8. The conversion ± CK involves barotropic exchanges whereas ± CZ and ± CE involve baroclinic processes. The major pathway for the global atmospheric circulation illustrated in Figure 3.21 is from: Gz → Az → AE → KE → DE ↓→ KZ → ↑

Global climate and the general circulation 143 11

0

0

Figure 3.21 The observed annual mean cycle of energy generation, conversion, and dissipation (W m2) in the atmosphere. The energy reservoirs shown in the boxes are almost constant (105 J m2). Parentheses indicate terms derived as residual values. P denotes APE and M a zonal mean. (From Peixoto and Oort, 1992)

In middle latitudes, baroclinic processes dominate in the generation of extratropical cyclones and, from an initial zonal circulation regime, the sequence AZ → AE →KE leads to an enhancement of meridional circulation resulting in a low index or blocking pattern. The conversion CK in Figure 3.21 contradicts the classical view of turbulent decay from larger to smaller scales, as noted earlier. The spectral distribution of energy across wave numbers has received much attention in studies of turbulent decay. For three-dimensional quasi-geostrophic ﬂow, an m3 dependence has been determined for the horizontal and vertical wave numbers (m) of the spectral variance of kinetic energy and temperature (Charney, 1971). This contrasts with the well known 5/3 law of Kolmogorov (1942) for three-dimensional isotropic turbulence in the inertial subrange. Kraichnan (1967) ﬁrst derived the 3 relationship for large-scale ﬂow that was attributed to its essentially two-dimensional characteristics. However, Charney considered this to be a fortuitous result of the geostrophic constraint on the motion. He showed that 2-D inviscid ﬂow is governed by vorticity conservation whereas 3-D quasigeostrophic ﬂow is governed by the conservation of both potential vorticity and potential temperature. By combining these latter two adiabatic constants of motion into a pseudopotential vorticity parameter Charney demonstrated that the conservation of this property can forbid an energy cascade. Following from this, the 3 relationship is obtained for the variance spectra of horizontal kinetic energy and temperature where the wave number exceeds the excitation wave number.

0

0

0

3.3 Circulation cells 11

The preceding discussion indicates that the atmospheric circulation must satisfy two conditions: energy must be transported from the zone of surplus in lower latitudes to the

144 Synoptic and dynamic climatology higher-latitude zone of deﬁcit, and absolute angular momentum must similarly be transported poleward. The form of circulation that can satisfy these requirements is not immediately obvious. Early theoreticians postulated circulations in a meridional plane. In 1735 Geoffrey Hadley proposed that rising warm air in low latitudes ﬂowed poleward at high levels, cooling and sinking in higher latitudes, with a low-level return ﬂow towards the equator. The upper poleward and lower equatorward motions would be affected by the Earth’s rotation (although the effect of Coriolis acceleration was not speciﬁcally recognized), giving in the northern hemisphere southwesterly components aloft and northeasterly components (the Trades) at low levels. The mean thermally direct circulation in low latitudes is still described as a Hadley cell (Figure 3.12) to honor this early work. Classical vertical plane models of the atmospheric circulation depict an intertropical convergence zone (ITCZ) as the upward limb of the adjacent Hadley cells in the two hemispheres (see Figure 3.12). However, when the ITCZ moves away from the equator a reversed equatorial cell may separate the two Hadley cells, as proposed by Asnani (1968). By the mid-nineteenth century, wind observations showed that the mid-latitude westerlies moved poleward, not equatorward as suggested by Hadley’s direct cell model. Wind charts compiled by James Cofﬁn (1853) for the northern hemisphere, and later for the globe (Cofﬁn, 1876), identiﬁed three surface wind belts – easterly trades, mid-latitude westerlies and polar easterlies. William Ferrel (1856, 1859–60) used these maps to illustrate the effects of the Earth’s rotation on the atmospheric motion. He applied the principles of ﬂuid motion and the conservation of absolute angular momentum developed by the Marquis de Laplace, whose work had been translated into English thirty years earlier, and the idea of a deﬂective force (G. Coriolis’s work), to the circulation of the atmosphere and ocean currents (see Fleming, 1989, p. 138). Ferrel proposed a model with three meridional circulation cells, the subtropical high-pressure belt, a zone of trade wind convergence and another zone of convergence near the polar circles. The scheme included the indirect circulation (where cold air rises and warm air sinks) in middle latitudes, which is now called the Ferrel cell in recognition of his contribution. Key characteristics of the Hadley circulations, based on global tropospheric wind data for 1964–89, are summarized in Table 3.9. The seasonal cycle is analogous in both hemispheres, with maximum values (~20 1010 kg s1) of the mass stream function $ occurring near 8° latitude in the respective winter seasons.4 The ill-deﬁned summer maxima located near 26º latitude are an order of magnitude smaller. In the annual mean the Hadley cells are fairly symmetrical about the equator, with the ascent of warm, moist equatorial air, associated with the convergence of air in the mean intertropical convergence, and descending motion in the subtropics. This symmetry is observed in the transition seasons, particularly October (Oort and Yienger, 1996). In the other seasons, in contrast, there is an intensiﬁed Hadley circulation in the winter hemisphere, strong cross-equatorial ﬂow into Table 3.9 Measures of the Hadley circulation, 1964–89 Measure

January

April

July

Max NH $ (1010 kg s1) Latitude $ max (°N) Max SH $ (1010 kg s1) Latitude $ max (°S) v200–v850 (10°N) m s1 v200–v850 (10°S) m s1 STJ (15°–25°N) m s1 STJ (15°–25°S) m s1

22.9 8.0 3.1 27.0 5.2 2.1 25.7 6.7

15.4 10.0 7.3 13.0 3.2 1.0 23.2 16.7

1.4 26.0 19.3 7.0 1.5 4.0 4.2 23.3

Source: from Oort and Yienger (1996). Note: negative signs denote southward transport.

October

Annual

8.2 16.0 10.0 7.0 0.8 1.8 8.3 21.6

9.2 12.0 7.4 9.0 1.8 1.2 13.1 17.1

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the summer hemisphere and the virtual disappearance of the cell in the latter. In the northern winter, meridional velocities in the northern Hadley cell are about twice those in the southern hemisphere, but in the northern summer the southern cell is ﬁve times stronger than that in the northern hemisphere, according to Mak and Liu (1993). This is attributable to the cross-equator ﬂow associated with the Asian summer monsoon. However, these patterns refer to values of v obtained by zonal averaging. Using meridional wind components for July 1957–December 1964, directly observed at 330 stations between 45°N and 45°S, Schulman (1973) shows that the monsoon circulation dominates the sector 40°–150°E whereas the remaining two-thirds of the global circumference have a nearly symmetrical Hadley cell pattern. Longitudinally there is considerable spatial variability in the occurrence of ascending air. It is concentrated over the tropical continents, in Amazonia, equatorial Africa and the “maritime continent” of Indonesia–Malaysia; in the intervening sectors there is large-scale descent. These longitudinal contrasts give rise to zonal (east–west) Walker circulations discussed in Section 5. Ascent is highly localized in “hot towers” (cumulonimbus cells) mainly within synoptic-scale disturbances (Riehl and Malkus, 1958, 1979). GCM experiments by Rind and Rossow (1984) show that the Hadley circulation is forced by two competing heat sources. These are solar radiation, which at the solstice peaks at 23° latitude, and latent heat released by near-equatorial precipitation. These are not in phase at the solstices; in the summer hemisphere they are in opposition, forcing circulation cells of opposite direction between the equator and 23° latitude. The spatial pattern of atmospheric heat sources and sinks shows strong seasonal contrasts, as noted above (pp. 129–3). The primary contribution to heating identiﬁed in most areas is the release of latent heat through cumulus convection. Comparing calculated apparent heat sources and moisture sinks over the western equatorial Paciﬁc warm pool, Yanai and Tomita (1998) show a vertical pattern of heating between 400–500 mb and drying between 600–700 mb, characteristic of cumulus convection. However, over most of the North Paciﬁc storm track in winter, initial sensible heat transfer to the atmosphere from the warm ocean surface is replaced downstream by condensational heating. Also, over much of Asia and North America in northern spring, sensible heat ﬂuxes are important, especially over the Tibetan Plateau and the western USA. These patterns help account for the principal features of the Hadley and Walker circulations, as well as some components of the monsoon circulations (see section 3.7.3). In addition to the annual shifts, the Hadley cells are affected by considerable variability on both long and short time scales. Anomalies in Hadley cell strength are inversely correlated with the strength of the low-latitude Walker circulations, and there are also correlations with the phase of the El Niño Southern Oscillation (Section 5). During El Niño (La Niña) the winter pattern features two strengthened (weakened) Hadley cells (Oort and Yienger, 1996) resulting from the intensiﬁcation (relaxation) of the latitudinal temperature gradient. Submonthly variability of the Hadley circulation is suggested by the fact that the standard deviation of the zonally averaged meridional wind component in the upper troposphere (200 mb) over the equator reaches 0.7 m s1 in summer and winter. A possible mechanism for submonthly ﬂuctuations is suggested below. The factors controlling the latitudinal extent of the meridional Hadley circulation have been examined in a number of studies. According to Hunt (1979) the poleward extent, which is demarcated by the latitude of the subtropical westerly jet core, is determined by the planetary rotation rate, which affects the momentum balance. Compared with a control case value of 22° latitude, in a hemispheric simulation with a general circulation model, the jet core and poleward extremity of the Hadley cell expand to 46° latitude for a ﬁvefold increase in rotation rate and contract to 10° latitude for a ﬁvefold decrease in rotation rate (Figure 3.22). In each experiment the subtropical jet core, the poleward limit of the Hadley cell, and the mean surface anticyclone are latitudinally coincident. The descent of air is necessitated by the requirement for the atmosphere to generate east–west pressure gradients in order to restrict the increase of the atmosphere’s relative angular momentum

146 Synoptic and dynamic climatology

Figure 3.22 Changes in the extent of the Hadley cell in relation to rotation rate. The panels show meridional stream functions (1012 kg s–1) for fast (top), control case (middle), and slow (bottom) rotation rates. (From Hunt, 1979)

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during a poleward displacement (Lorenz, 1967, p. 74). Local east–west gradients provide the necessary torque to prevent the zonal wind from attaining excessive values of latitudinal wind shear. Meridional shear values just equatorward of the subtropical jet are approximately 1.3 105 s1. In spite of the appealing simplicity of Hunt’s results, other studies imply that the extent of the Hadley cell also depends on the static stability (Schneider, 1977), the depth of the circulation, and the magnitude of the differential radiative heating (Held and Hou, 1980). The work of these authors provides the basis of a theory for a symmetrical Hadley cell. The width of the cell, the distribution of surface winds, the position of the upper-level jet and the vertically averaged ﬂuxes of heat and momentum (expressed non-dimensionally) can be speciﬁed as universal functions of the external thermal Rossby number: RE gH H /(r")2

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where r radius of the earth (6,000 km), g gravity (9.8 m s2), H 8 km is the depth of a Boussinesq atmosphere (i.e. where vertical variations in density are negligible), H a baroclinicity parameter, " earth’s angular velocity. Schneider’s model requires that the atmosphere adjusts its static stability to balance radiative cooling with a given heating distribution, which results in apparent dependence of the width of the Hadley cell on the static stability. In contrast, Held and Hou (1980) adopt a stable and stratiﬁed, dry, differentially heated, rotating Boussinesq ﬂuid where angular momentum is nearly conserved in the poleward ﬂow. They ﬁnd that here the width of the Hadley cell is proportional to the square root of both the horizontal temperature gradient and the cell’s depth and inversely proportional to the rotation rate. The theory is most appropriate at the equinoxes, when the heating is symmetrical about the equator (Williams, 1988). It predicts that for RE % 1, the Hadley cell extends approximately to the core of a tropical jet near the latitude (H) where the wind resulting from angular momentum conservation (uM r " sin2 /cos ) interacts with the westerly thermal wind of middle latitudes; this latitude is given by H (5/3RE )1/2 which is about 30° latitude, as observed. When RE increases, latitude H also increases. The Held and Hou model ignores latent heat release, which will tend to strengthen the Hadley cell and restrict the zone of ascending air but should have limited effect on the cell’s width or the strength of the westerly jet. The incorporation in the Held and Hou model of latent heat release during condensation does in fact reduce the meridional extent of the ascending air, according to Dodd and James (1997), and in contrast to other studies it does not weaken the circulation when the heating is away from the equator. In the real atmosphere, moist convection may raise the tropical tropopause, thereby modifying RE and the cell’s width, and so circulation-induced changes in cloudiness, and therefore the heating distribution will also be important. Nevertheless, each of the proposed relationships, as well as laboratory analog experiments with slow rotation, would imply an equator-to-pole cell for the rotation rate observed on Venus. In contrast, Rossow (1985) notes that observations of Venus’s atmosphere indicate strong zonal ﬂow and fully developed turbulent motions. Existing models neglect heat and momentum transfer due to eddies that can modify the mean zonal ﬂow and temperature distribution and the extent of the Hadley cell. Model experiments by Rind and Rossow (1984) where static stability, circulation depth, rotation rate, and radiative forcing are held constant still exhibit ﬂuctuations in Hadley cell extent. Thus a balanced mean meridional circulation appears to involve eddy processes, which must be appropriately parameterized in any model study, and also surface friction. The possible range of atmospheric circulations can be explored by GCM experiments where the controlling parameters – the rotation rate, latitudinal gradient of Coriolis, moisture state, surface drag and surface heating – are varied. Williams (1988) uses this approach to determine the basic controls of the dynamic components of the planetary circulation, namely the mix of meridional cells, jets, and horizontal eddies, and their size and strength.

148 Synoptic and dynamic climatology The rotation rate, "*("/"E), normalized by the terrestrial value ("E), is varied from 0 to 8. The GCM incorporates a zonally symmetric swamp surface, nine vertical levels and R15 to R42 spectral resolution (i.e. with romboidal truncation at wave Nos 15–42). Some of Williams’s main ﬁndings are as follows. 1

The strength of the Hadley cell, relative to the large-scale horizontal eddies that force the zonally averaged mean ﬂow (the quasi-geostrophic modes), depends on the moisture content; moist atmospheres are dominated by the Hadley modes, dry ones by quasi-geostrophic eddies (Figure 3.23). Nevertheless, the circulations resulting from localized heating in low latitudes for dry atmospheres resemble those in atmospheres with latent heating, having similar Hadley cells and tropical westerly winds.

Figure 3.23 Schematic summary of the characteristics of the quasi-geostrophic (QG), quasi-Hadley (QH) and natural Hadley (NH) circulations. The QG(QG ) case represents ﬂow for nonlinear baroclinic instability (on a midlatitude -plane; u¯ = mean zonal wind, —– —– = stream function, v′T′ = mean meridional eddy heat transport, v′M′ = mean merid—–– ional momentum transport, –w′M′ = mean vertical momentum transport, Kz: KE(P:KE = barotropic (baroclinic) energy conversion. Negative values shaded. (From Williams, 1988)

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The Hadley mode disappears in favour of a quasi-geostrophic barotropic mode if surface drag is eliminated. For moist atmospheres, low rotation rates ("* 0–0.25) set up a direct cell of almost hemispheric extent with a near-polar westerly jet (a “Natural Hadley” circulation; see Figures 3.23–4); low-latitude waves are generated by convective eddies and high-latitude ones by barotropic cascades in the polar jet. The jet moves to mid-latitudes as "* increases. For "* 0.5–1.0 the direct cell is narrow, with a strong tropical westerly jet and mid-latitude Rossby waves (a quasi-Hadley circulation); the Hadley cell is strong and there is a weak indirect Ferrel cell. For high rotation ("* 2–8), there is a tropical Hadley cell with tropical upper westerlies and several mid-latitude elements and a polar quasi-geostrophic (QG) element comprising narrow, weak cells and weak westerly wind maxima (Figure 3.24). Williams shows that the mid-latitude (QG) elements have poleward jet-traversing momentum ﬂuxes associated with planetary waves that are dispersing asymmetrically and favor equatorward propagation. The polar QG element has symmetric wave dispersion that produces jet-converging momentum ﬂux (see Figure 3.25).

The forcing of the average Hadley circulation has been demonstrated via a number of experiments with primitive equation models for a zonally symmetric regime forced by radiative heating. Analytical investigations of the role of axially symmetric heating about the equator indicate that an equator-to-pole atmospheric temperature gradient of 40°C (corresponding to that observed over the oceans) produces a Hadley circulation much weaker than the observed annual average condition if the forcing is centered over the equator (Held and Hou, 1980). Even a gradient of 100°C (corresponding to a state of planetary radiative equilibrium), is insufﬁcient in these circumstances. Nevertheless, when the thermal forcing is displaced a few degrees off the equator, the Hadley cell in the winter (summer) hemisphere is greatly ampliﬁed (reduced). The mass ﬂux in the low-latitude mean meridional circulation shows a good degree of symmetry only during equinoctial conditions in April (Peixoto and Oort, 1992), as shown in Figure 3.13. However, the asymmetry which exists between the hemispheres for most of the year gives rise to an annually averaged Hadley circulation that is signiﬁcantly larger than that forced by the annually averaged heating (Lindzen and Hou, 1988). Moreover the transport of angular momentum by the Hadley cell is primarily directed into the winter hemisphere, where it excites eddy instability and forms a strong subtropical westerly jetstream on the poleward margin of the Hadley cell. Lindzen and Hou point out an apparent discrepancy between their model and observations, namely the zonally averaged thermal maximum is located slightly north of the equator year-round. This difﬁculty appears to be related to the distribution of low-level convergence and latent heat released by convection that modiﬁes the zone of diabatic heating. In fact, latent heat release tends to occur south of the equator in the austral summer, especially over the southwest Paciﬁc Ocean. In a further study Hou and Lindzen (1992) show that heating in a narrow zone centered symmetrically on the equator strengthens the Hadley circulation up to ﬁvefold, relative to a non-perturbed case. In the atmosphere the heating concentration in the equatorial zone is effected by low-level moisture convergence, convection and latent heat release. For off-equatorial heating, the intensity of the Hadley circulation depends on the way in which the heating is latitudinally distributed – symmetrically about the heating maximum or preferentially from the winter hemisphere. There is strong Hadley cell intensiﬁcation in the latter case, and only modest heating concentration is required to match the calculated and observed intensities of the Hadley circulation. The results of Hou and Lindzen’s analysis are consistent with the observed broad latitudinal distribution of zonally averaged precipitation (Table 3.7), rather than a narrow ITCZ precipitation belt. For equatorial and off-equatorial cases where the redistribution of heating is restricted to a zone much narrower than that of the circulation, the circulation strength is not signiﬁcantly augmented.

Figure 3.24 Meridional distribution of mean zonal wind (m s1) for the moist version of the GCM for "* equal to (a) 4, (b) 1 (= terrestrial value), (c) 1/2, and (d) 1/8. (From Williams, 1988)

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Figure 3.25 As Figure 3.24 for the mean stream function (1013 g s1). (From Williams, 1988)

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152 Synoptic and dynamic climatology Nevertheless, the results imply that a weakening of the easterly wave disturbances (see section 6.5) and monsoon regimes that currently broaden the zonally averaged precipitation distribution would enhance the Hadley circulation strength and modify the jetstreams and wave transport in higher latitudes. Using a simpliﬁed version of the Goddard Laboratory for Atmospheric Sciences (GLAS) GCM, Hou (1993) simulates the effect of a shift of prescribed heating into the summer hemisphere on the extratropical circulation. In agreement with earlier work he ﬁnds an intensiﬁed cross-equatorial Hadley circulation into the winter hemisphere, enhanced upper tropical easterlies and to a lesser degree the winter subtropical jetstream. The mid to high latitudes of the winter hemisphere are warmed by increased dynamic heating related to eddy heat ﬂuxes associated with lowfrequency transient waves in middle latitudes and high latitude stationary waves (which may help organize transient eddies along storm tracks). In an extension of the steady-state frictionally controlled models of thermal forcing Hack et al. (1989) examine the evolution of meridional circulations in a frictionless balanced zonal ﬂow model. The width of the thermal forcing is chosen to be 4° (8°) latitude at latitude 30° (15°), respectively, in accordance with satellite observations of ITCZ cloudiness. The calculated maximum mass ﬂux from the winter hemisphere, for a narrow or wide zone of heating, is obtained when the heating is at 12° latitude (Figure 3.26). The maximum difference between the mass ﬂuxes of the two cells occurs with heating centered at 13° latitude. The sensitivity of the circulation to the asymmetry of the heating with respect to the equator is less than that obtained by Lindzen and Hou (1988), but is in the same sense. The air is drawn across the equator into the ITCZ, increasing the mass ﬂux in the winter hemisphere Hadley cell, by virtue of the inertial instability of the air in low latitudes. However, if the heating is displaced more than about 12° latitude, the transfer of air becomes less effective. Hack et al. (1989) note that this value is consistent

Figure 3.26 Calculated mass ﬂuxes with different thermal forcings. (From Hack et al., 1989)

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with a Rossby radius of 14° latitude according to the equatorial plane theory of Matsuno (1966). In a further study, using the same model formulation with time-dependent radiative heating, Mak and Liu (1993) conﬁrm the results of the earlier studies and show that the transitions between the dominance of the respective winter-hemisphere Hadley cells last only about a month in each case. There is also a strong zonal jetstream at the poleward margin of the winter cell, as a result of absolute angular momentum conservation in the poleward upper-level ﬂow. The latitudinal extent of the Hadley cells is controlled by the thermal gradient needed to sustain baroclinic shear that increases with latitude. Note that the Hadley circulation cools the tropics and warms the extratropical latitudes. The model simulates bands of disturbances on the poleward side of the winter-hemisphere Hadley cell that arise from instability in the baroclinic shear ﬂow. Mak and Liu (1993) propose that these disturbances (causing submonthly ﬂuctuations in v) result from symmetric instability of slowly evolving background ﬂow.

3.4 The Earth’s geography

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The preceding discussion has focused on the two-dimensional meridional circulation components. However, the three-dimensional structure involves the zonal Walker circulations (noted above) and monsoon circulations (section 3.7.3). Both play key roles in shaping the actual global general circulation. Considerable insight into the role of geography in shaping the Earth’s climate has been gained from experiments using General Circulation Models to simulate paleoclimatic conditions (Barron and Washington, 1984). There has not yet been a comprehensive suite of experiments, using different models, to examine the relative effects of land–sea proportion and distribution and of mountain barriers in a systematic way. Therefore the following account must be regarded as subject to future revision. Hay et al. (1990a, b) have used the NCAR Community Climate Model (CCM) to investigate the large-scale effects of continental location and orientation. In one set of experiments they use a north–south continent, 105° longitude wide, extending from pole to pole. The total land area corresponds to the present value. There is snow cover south of 70°S. Varying reliefs are used – low (750 m) plateau, high (1,500 m) plateau, and mostly low plateau but with 3 km mountains on the east or west side. In a related study they contrast an Earth with two 750 m high polar continents from 45° latitude to the poles, the southern one with snow cover poleward of 70°S, an Earth having a 750 m high tropical continent between 17°N and 17°S. All experiments are forced by mean annual solar radiation and a “swamp” ocean that can form and grow sea ice. Cloud cover and the hydrological cycle in the experiments are predictive. The land–sea proportions and mean elevations are as now, except for the elevation of Antarctica. The comparison of polar continents versus a tropical one shows that low-latitude continents give rise to an earth that is 8°C warmer than present for the mean global surface temperature (Figure 3.27). Tropical evaporation is much reduced by a low-latitude continent which decreases tropical precipitation. The Earth with polar continents has a mean temperature close to present and a strong hydrological cycle, especially in low latitudes. The continental interiors are dry, but the pole with a snow cover appears to create positive feedback for precipitation that would maintain an ice cap. By itself a polar continental location seems insufﬁcient to promote glaciation, suggesting the importance of moisture as well as temperature effects. The polar continents give rise to strong temperature gradients near their margins, but the jetstreams are rather weak (25 m s1). The mean sea level (MSL) pressure pattern in the tropics and subtropics is characteristic of a water-covered earth. The tropical continent case has a hot interior, and polar temperatures are around 4°C, which contrasts with the results of a seasonal-cycle energy budget model (Crowley et al., 1987) that generated

Figure 3.27 Results of simulations with the NCAR Community Climate Model to illustrate the effect of orography on global temperature (above) and precipitation (below) for an Earth with pole-to-pole continents for (a) 750 m plateau, (b) 1,500 m plateau, (c) low plateau with 3 km mountains on the east side; (d) as (c) with mountains on the west side. (From Hay et al., 1990a)

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polar ice. The subpolar lows are deep and the almost symmetrical jetstreams exceed 30 m s1 at 12 km altitude. In both polar regions there are weak circulations. The results suggest that at present the Earth’s climate is near to one extreme; mean temperatures in the southern hemisphere now (Table 3.5) are close to those in the snowcovered polar continent case. More signiﬁcantly, land–sea distribution is shown to modify the hydrological cycle’s intensity by limiting moisture availability (Barron et al., 1989). The experiments for pole-to-pole continents with differing relief (Hay et al., 1990a) clarify the effects of orography on global climate5. Higher land masses increase the mean MSL pressure by displacement of mass, and increase the contrast between high and low pressure cells. Increased elevation also reduces temperatures (Figure 3.27). Moisture transport eastward into the continental interior is enhanced by mountain ranges on either coast. West-coast mountains give rise to an equatorial wet belt, while east-coast mountains cause considerable continental aridity except for a narrow northern temperature belt. The north–south coasts eliminate any moisture source for the continent from the ocean to the east. All four conﬁgurations show zonal patterns of precipitation, soil moisture, and runoff, implying that Köppen’s (1931) idealized climatic pattern, suggesting a southwest–northeast slope of the arid zone boundary from 20° latitude on the west coast to 40°–60°E latitude in the east, may be determined primarily by present geography; this is especially true for the conﬁguration of the Tibetan Plateau and the Cordilleras of North and South America. Modeling studies with varying soil moisture support this suggestion for the southern continents (Cook, 1994). The effects of mountain barriers on climate have been extensively studied using several different GCMs – the GFDL model (Manabe and Terpstra, 1974; Manabe and Broccoli, 1990), the NCAR Community Climate Model (Ruddiman and Kutzbach, 1989). Model control cases have been run with and without present orography. The simulations show that mountain ranges indeed account for the dry present-day continental interiors of Asia and North America (Figure 3.28) (Kutzbach et al., 1993). In the absence of the mountain barriers, these regions experience moist climatic regimes (Manabe and Broccoli, 1990). The role of orography in the planetary wave structure is examined in section 4.2. There is also an extensive literature on paleoclimates for past epochs with radically different land and ocean distributions and orography (see Crowley and North, 1991) and postulated changes in orbital geometry (Kutzbach, 1994; see Crowley and North, 1991).

3.5 Climate system feedbacks

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The third set of processes that contribute to global and regional climate are internal to the climate system. They include trace gases and other atmospheric constituents, cloud cover, air–sea and land surface–atmosphere interactions, including the biosphere. Internal factors largely operate through feedback effects on the exchanges of energy between the atmosphere and surface. A feedback process occurs when one variable in a system triggers a change in a second variable that in turn affects the system. More generally, we can say that “information” on the state of the system feeds back to affect the system’s future state. This may act to amplify and destabilize, or to dampen and stabilize, the initial perturbation; these are referred to as positive or negative feedback, respectively. For example, with a temperature increase at the surface, the outgoing infrared radiation increases, offsetting the temperature rise – a negative feedback. If the soil warmed enough to release more carbon dioxide, that could enhance the greenhouse effect, thereby providing positive feedback. The quantitative analysis of feedback in climate model simulations can be performed following alternative deﬁnitions (Dickinson, 1985; Schlesinger, 1989) and, therefore, some care is needed with the literature on the topic. The relative importance of its primary contribution to long-term climate change has been estimated from climate model experiments by Hansen et al. (1984). Figure 3.29 indicates that the approximately 5°C global mean temperature change between the last glacial maximum

156 Synoptic and dynamic climatology

Figure 3.28 Simulations with the NCAR Community Climate Model showing the effects of orography on temperature and moisture balance in the continental interiors for present-day orography (M) minus no mountains (NM). (From Kutzbach et al., 1993)

and the present day can be attributed primarily to the water vapor–temperature feedback, changes in cloud cover (of uncertain magnitude), and snow/ice–albedo feedback. The initial forcing due to astronomical factors exerts only a minor inﬂuence. The feedback mechanisms operate as follows. Water vapor–temperature feedback A rise in air temperature permits an increase in saturation vapor pressure. The relationship, described by the Clausius–Clapeyron equation, is a non-linear, exponential one: d ln es L = v2 dT Rv T where es saturation vapor pressure, Lv latent of vaporization, Rv speciﬁc gas constant for water vapor. The importance of this relationship in the atmosphere is illustrated by the fact that plots of the atmospheric vapor pressure against air temperature in vertical air columns at different latitudes are parallel to the esT curve (Figure 3.30). Water vapor is an infrared-absorbing gas; indeed, it accounts for about two-thirds of the natural terrestrial “greenhouse” effect (21 K of the 33 K increase in mean surface temperature above the Te value of 255 K). Hence more of the outgoing radiation is trapped in the atmos-

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Figure 3.29 Contribution of different feedback processes to the glacial–modern differences in global mean temperatures. (From Hansen et al., 1984)

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phere, causing a further increase in temperature – a positive feedback. Evidence of increases in evaporation over the tropical oceans (10ºS–14ºN) by 16 percent (20 cm yr2) during 1949–89, and corresponding increases in atmospheric water vapor, suggest an additional energy input into the global atmosphere of 13 W m2, according to Flohn et al. (1992). The relationship between surface temperature and column moisture has been controversial. Lindzen (1990) argued that warming in the tropics might create drying of the middle–upper troposphere as a result of downdraft areas (decreasing the water vapor greenhouse). Analysis for 1988–93 shows, however, that surface temperatures and 500–200 mb speciﬁc humidity are signiﬁcantly positively correlated both locally and for tropical averages, 30°S–30°N (Yang and Tung, 1998). A positive coupling of surface temperature and mid-tropospheric humidity is also obtained from ERBE data by Inamdar and Ramanathan (1998). They calculate the normalized atmospheric greenhouse effect for the ice-free parts of the earth (representing 94 percent of the globe). The mean surface outgoing radiation is 415 W m2, compared with 274 W m2 outgoing at the top of the atmosphere, leaving to 141 W m2 trapped in the atmosphere – a greenhouse effect of 0.34. Values are only 0.10–0.15 over desert areas, compared with 0.35–0.40 over warm oceans with deep convection. Snow and ice – albedo feedback

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Snow cover has a spectrally integrated albedo of about 0.87 and bare ice 0.50–0.60, in contrast to tundra or ocean summer values of about 0.15–0.20 in high latitudes (Table 3.10). Rising temperatures reduce the extent of snow and ice, thereby exposing land or ocean surfaces of much lower albedo. This permits an increase in absorbed solar radiation at the surface, which in turn raises air temperature further through turbulent heat

158 Synoptic and dynamic climatology

Figure 3.30 Plots of atmospheric vapor pressure (mb) as a function of air temperature (°C) for mean atmosphere values at the equator (long dashes), 40°N (short dashes) and 75°N (dotted) at standard pressure levels (marked in hundreds of millibars on each curve), and the Clausius-Clapeyron es, T curve (C–C). (a) December–January–February, (b) June–July–August. (From Webster, 1994)

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Table 3.10 Typical all-wave albedo values of different surfaces during winter and summer.a Surface

Winter (snow-covered surfaces)

Summer

Open water Tropical rain forest Farmland, stony deserts Rice paddies Light-colored sand deserts Deciduous/coniferous forests Tundra New snow Melting old snow Young ice (no snow) First-year ice (no snow) Melting white ice Old melt pond

0.07–0.24b – 0.50 – – 0.33–0.36 0.82 0.87 – 0.21 0.50–0.60 – –

0.05–0.10 0.07–0.10 0.15–0.17 0.12 0.35–0.42 0.14 0.17 – 0.77 – – 0.56–0.68 0.15

Sources: modiﬁed after Henderson-Sellers and Hughes (1982), Henderson-Sellers and Wilson (1983), Kukla and Robinson (1980), Hummel and Reck (1979). Notes a Overcast sky conditions typically increase all-wave albedo values by 0.02–0.05. b The albedo of a water surface is strongly dependent on solar elevation angle and therefore on latitude. Values are greater than those cited poleward of 50° latitude in winter and 65° latitude in summer.

0

transfer to the atmosphere from the Earth’s surface. The positive feedback effects of snow–ice cover and water vapor operate as part of the annual temperature cycle, particularly in the marginal zones of the seasonal cryosphere. Model estimates suggest a sea ice–albedo feedback of about 0.18 W m2 K1 (Ingram et al., 1989), but the results are highly dependent on the parameterization of sea ice processes, including summer melt ponds, the fraction of open water in leads, and energy storage in brine pockets in the ice, in melt ponds and in the leads (Ebert and Curry, 1993), as well as on cloud interactions. Cloud cover has a direct effect on clear-sky short-wave radiation and reﬂected radiation over snow cover, and an indirect effect through longwave radiation feedbacks (Cess et al., 1991).

0

Cloud feedbacks

0

0 11

Cloud cover affects the surface radiation balance in complex ways that depend on the total cloud fraction and the type and altitude of the cloud layers. Global cloud cover has a cooling effect through the high albedo of clouds (45 to 50 W m2) and a contrary warming effect through the absorption of terrestrial infrared radiation (30 W m2) (see Table 3.11) (Ramanathan et al., 1989). The contribution of solar radiation, to the net effect is about 1.7 times that of infrared radiation, according to Hartmann and Doelling (1991). Overall, the effect of cloud cover relative to clear sky conditions is one of net cooling, averaging about 17 W m2, but there are signiﬁcant latitudinal and cloud level effects. Table 3.11 summarizes the global radiation budget for clear sky and mean cloud conditions and the average cloud-forcing components. In general, low and middle cloud layers have a net cooling effect (short-wave reﬂectance exceeds the cloud trapping of infrared radiation) and thin, high cloud a warming effect on the surface (due to the opposite effect). The global net cloud forcing is between 13 and 21 W m2 during the year. In the tropics, shortwave cloud forcing (60 to 70 W m2) dominates, whereas both short and long-wave cloud forcing are important in middle latitudes, and poleward of 60° latitude long-wave cloud forcing (~60 W m2) prevails (Kiehl, 1994). In polar latitudes in winter, cloud cover tends to produce a warming of the surface as a result of the downward infrared radiation

160 Synoptic and dynamic climatology Table 3.11 Components of the Global Radiation Budget for the Earth–atmosphere system April 1985

July 1985

October 1985

January 1986

Annual

Component Average SW absorbed (W m2) LW emitted (W m2) Net heating (W m2) CLWa (W m2) CSWb (W m2) Net cloud forcing (W m2)

236.5 234.5 2.0 31.3 45.1 13.8

234.4 237.5 3.1 30.1 46.7 16.6

243.0 234.1 8.9 32.2 50.1 17.9

243.3 231.9 11.4 30.6 51.7 21.1

239.3 234.5 4.8 31.1 48.4 17.3

Source: from Ramanathan et al. (1989), Harrison et al. (1990). Notes a CLW Cloud long-wave forcing. b CSW Cloud short-wave forcing.

increases as cloud amount increases, whereas the opposite is observed for lower surface albedos. Wendler (1986) refers to this as the “radiation paradox.” Biosphere feedbacks A further feedback effect is associated with shifts in vegetation zones and coastlines during glacial/interglacial cycles or regionally as a result of anthropogenically enhanced desertiﬁcation and deforestation. Typical surface albedos are shown in Table 3.10. Thus, lowered glacial sea levels result in less solar radiation absorption at the Earth’s surface. The reduction in forest cover and its replacement by grassland also increases surface albedo. Desertiﬁcation in the tropical savannahs has a similar effect, and the precipitation decrease in the Sahel in the 1970s and 1980s has been attributed to the postulated biosphere feedback (Charney, 1975). Additional effects involve changes in surface roughness and soil moisture (Garratt, 1993). The effects of changes in vegetation on global climate have been analyzed mainly through GCM sensitivity experiments. Most attention has focused on desertiﬁcation, especially of the Sahel, and on tropical deforestation, particularly of the Amazon basin. Early studies concentrated on the responses to a change in albedo (Charney, 1975) but changes in energy partitioning, especially in the evaporative term, as well as in roughness length and precipitation interception, also need to be considered (O’Brien, 1996; Zeng and Neelin, 1999). More recently, a biosphere or land surface module has been incorporated into sophisticated climate models as well as simpler statistical-dynamic models. Various onedimensional Surface Vegetation–Atmosphere Transfer Schemes (SVATS) have been used, including the Biosphere–Atmosphere Transfer Scheme (BATS), the Simple Biosphere (SiB) model and other derivations. BATS calculates the energy and moisture ﬂuxes of eighteen different surface types. Canopy–atmosphere and canopy–surface exchanges are treated. SiB treats the radiative, turbulent heat and momentum ﬂuxes between the vegetated surface and the atmosphere via three submodels. A summary of selected results from these experiments is shown in Table 3.12. Changes in land cover through human activity are recognized to be of at least regional, and possibly global, signiﬁcance. The globalmean radiative forcing due to changes in surface albedo since pre-industrial times is estimated to be about 0.2 W m2, with a similar contribution from biomass-burning aerosols (Shine and Forster, 1999), compared to +4 W m–2 for CO2 doubling. More complex feedbacks with the biosphere involve biogeochemical cycles. Higher atmospheric concentrations of carbon dioxide may augment photosynthesis, leading to increases in net primary production and carbon storage on land (a negative feedback). Methane (CH4), which has contributed about 20 percent (0.5 W m2) of the increase in radiative forcing due to anthropogenic trace gas increases since pre-industrial times, is

Global climate and the general circulation 161 11

Table 3.12 Model experiments of (1) Amazonian deforestation and (2) African desertiﬁcation Ts (K)

Ta (K)

(1) Deforestation NCAR CCM

–

1 to 3

COLA UKMO SDM

– – –

Model

0

(2) Desertiﬁcation COLA – COLA 0.3 to 1.2 SDM

1.1

P (cm/yr) E (cm/yr) Reference

0

20

2 2.1 1.2

64 30 18

50 20 19

0.5

55 11 to 55

26

14

16

– 0.7

–

Dickinson and Henderson-Sellers (1988) Nobre et al. (1991) Lean and Rowntree (1993) Varejao-Silva et al. (1998) Xue and Shukla (1993) Dirmeyer and Shukla (1996) Varejao-Silva et al. (1998)

Source: from Verajao-Silva et al. (1998). Notes NCAR CCM US National Center for Atmospheric Research, Community Climate Model; COLA Center for Ocean–Land–Atmosphere; UKMO UK Meteorological Ofﬁce; SDM Statistical dynamical model.

0 released from natural wetlands, bogs, and tundra in addition to releases from rice paddies, enteric fermentation in ruminants, biomass burning, gas drilling, coal mining, and anaerobic decay in landﬁlls (Aber, 1992). Warming in northern high latitudes may potentially deepen the seasonal active layer overlying permanently frozen ground (permafrost), thereby enhancing anaerobic methanogenesis in the peatlands of sub-Arctic and Arctic North America and Eurasia. Thawing of the permafrost itself may also release some of the methane stored as clathrates. Thus methane concentrations may increase in response to global warming, providing a positive feedback. It is also possible that drier regimes in some regions may lower water tables in wetlands, with the opposite result. 0

3.6 General circulation models Numerical models of the atmospheric circulation, commonly referred to as General Circulation Models (GCMs), were ﬁrst developed in the 1960s, building on work by Phillips (1956) and many others. The essence of an atmospheric GCM (AGCM) is that the basic equations describing the atmosphere’s large-scale motion and energetics are solved iteratively over a global grid with numerous vertical layers and a time step of a few minutes. For numerical weather prediction (NWP), AGCMs typically have high spatial resolution and are run for time periods of a few days, whereas in climate applications the spatial resolution is lower and the models are integrated over years. The behavior of the atmosphere is prescribed by the basic laws of hydrodynamics and thermodynamics which concern the following principles:

0

1 2 3 0

The conservation of mass and of water substance. The ﬁrst law of thermodynamics – that internal energy can change only through the addition/removal of heat or by the system performing work. Newton’s second law of thermodynamics – that the momentum of an air parcel is changed through the vector sum of the forces acting on it.

Using pressure (p) as the vertical coordinate, the momentum equation can be written: 11

V

V VⴢV fkV

t

p

DM

162 Synoptic and dynamic climatology where V is the horizontal velocity, is the vertical motion, f Coriolis parameter, k is a unit vertical vector, is the gradient operator, gz is the geopotential and DM is the dissipation of momentum by friction. The expanded equations for the zonal and meridional components of motion are:

冢

冣

冢

冣

tan & 1 1 p du v= fu F dt r r cos & and dv 1 p tan & fu F& u= dt r & where longitude, & latitude, and r the Earth’s radius; d·

u

v

= w dt t r cos & r &

z The mass continuity equation is: .V

=0

p

The equation for continuity of water substance is:

q

q = V.q E C Dq

t

p where q speciﬁc humidity, E evaporation, C condensation and Dq is the diffusion of moisture. The thermodynamic equation is:

冢

冣

T T R LE

T n V.T DH p

p c cp

t p where 0.286, cp is the speciﬁc heat at constant pressure, and DH the diffusion of heat. The terms Rn net radiation, LE latent heat of evaporation, C condensation, and the diffusion components are often referred to as the “model physics.” They are determined from equations for radiative transfer through the atmosphere, for the processes of evaporation/condensation and precipitation, and for the turbulent transfers of heat, momentum, and moisture from the surface. The above equations contain time derivatives of the independent variables and so are prognostic. In addition, there is the diagnostic equation for hydrostatic equilibrium:

p g

z which, combined with the ideal gas equation, p RT, gives: gp

p =

z RT These are known as the “primitive equations” because they ﬁlter out sound and gravity waves that are present if the complete “exact equations” are used. The retention of these

Global climate and the general circulation 163 11

Table 3.13 Stages in the development of general circulation models Stage

Characteristics

First generation

Grid point. Low spatial and vertical resolution. Idealized or simple geography. Prescribed SSTs, albedo and cloud. Run in perpetual season mode. Best results for northern hemisphere winter circulation Annual cycle. Hydrological cycle. Cloud calculated at a few levels. Diurnal cycle Spectral representation. Sigma coordinates replace height levels. Improved spatial and vertical resolution. Improved physics – radiation bands, wave drag. Swamp ocean (energy budget). Time transient experiments Coupled atmosphere – ocean (mixed-layer ocean). Improved physics – snow cover, simple sea ice; cloud physics; trace gases. Coupled AGCM–OGCM

Improvements Second generation

0 Third generation

0

0

0

0 11

effects in the exact equations permits the rapid growth of errors, and this largely accounts for the failure of the ﬁrst attempt at numerical weather prediction by Richardson (1922). GCMs have evolved steadily over the last thirty years and at least three generations of successive developments and improvements can be identiﬁed. Table 3.13 summarizes the major characteristics of these several stages. Key physical processes treated in atmospheric GCMs include radiative transfers, partitioned into short and long-wave components and interacting with atmospheric gases, aerosols and clouds; convective and stratiform cloud development and associated precipitation; convective adjustment and lapse rate modiﬁcation; surface and boundary layer ﬂuxes (heat, momentum, and moisture); and surface hydrology, including snow storage, soil moisture, and roughness. These physical processes interact with the coupled dynamical and thermodynamic equations, as shown schematically in Figure 3.31. However, many physical processes operate on scales that cannot be adequately treated by the ﬁnite differences resolved in GCMs. Examples of subgrid scale processes include cumulus convection, surface heat and moisture ﬂuxes, and terrain-induced vertical motion. Such processes need to be represented by parameterizations in which the unresolved processes and properties are linked to model variables by means of simple mathematical relationships (Meehl, 1984; Blackmon, 1986; Washington and Parkinson, 1986; Cubasch and Cess, 1990; McGufﬁe and Henderson-Sellers, 1997; Kiehl, 1992; Trenberth, 1992). The primitive equations (Lorenz, 1967) are solved for short time steps, either over a ﬁnite difference grid, or by using a spectral harmonic expansion of the scalar ﬁelds (see Appendix 4.1). Where there is a low-order spectral truncation, however, sharp gradients in a particular ﬁeld (and in the surface topography) give rise to oscillatory patterns of over- and underestimated values of the function (Figure 3.32). These are known as Gibbs oscillations and require special functions to reduce their effects (Navarra et al., 1994; Holzer, 1996; Lindberg and Broccoli,1996), otherwise spurious negative values may occur in properties that are always positive – such as water vapor content. The horizontal grid resolution in early GCMs used for climate studies was of the order of 5º–8º latitude and is currently between 1º and 2.5º latitude, corresponding to spectral truncations of R15–R20 and T63–T106, respectively (see Appendix 4.1). Coarse resolution in models can have important effects on the simulation of precipitation ﬁelds, for example. The vertical coordinate can be represented in height (z) coordinates, or in terms of pressure (Hack, 1992). A transformed pressure coordinate ( ) is now common because of its convenience:

p/ps

where ps is surface pressure. The surfaces follow the terrain boundary, thereby avoiding the problems that arise with constant z surfaces which intersect mountains. The surfaces

164 Synoptic and dynamic climatology

Figure 3.31 The coupling of physical processes in a GCM. (From Peixoto and Oort, 1992)

are closely spaced in the lower levels and more widely spaced in the troposphere and stratosphere. The upper boundary may be above 25 mb (~ 25 km) to avoid systematic errors which result when stratospheric processes are omitted. The ﬁxed surface boundary conditions are the distribution and height of land areas. Some models also prescribe surface albedo and roughness length according to land cover type, as well as climatological sea surface temperatures and sea ice extent. The eventual goal is to predict all of these properties via prognostic equations. In addition, the vertical velocity is zero at the highest and lowest surfaces. Model simulations use initial conditions – an array of wind, pressure, temperature, and moisture data at all grid points – to begin the forward integration of the prognostic equations. These initial ﬁelds must be balanced to avoid error propagation. The time integration of the equations cannot use simple time-step measurements with centered ﬁnite differences in space. Computational instability results if this procedure is applied to equations that represent advection of a given property. Instead, a “leapfrog” scheme is used where ﬁnite differences are centered in time (Gadd, 1981; Hack, 1992). For example: pn = pn2 2t

冢 p t 冣

n1

There is a limit on the time step that can be used which depends on the grid spacing and the maximum speed of advection that is being simulated. Simulations are performed in a variety of ways. Most early studies carried out a sensitivity analysis to assess the possible effect of changes in an individual factor or a complex of factors. Such sensitivity experiments have been used to examine the possible role in cli-

Global climate and the general circulation 165 11

0

0

0

Figure 3.32 Examples of Gibbs Oscillations caused by spectral trunctions. Above An idealistic square mountain. Below Longitudinal section at 90°E through the Himalayas. Scripps: orographic data; spectrally truncated orographies at T30 and T42. (From Navarra et al., 1994)

0

0 11

mate change of volcanic dust clouds, nuclear winter (smoke) scenarios, deforestation, desertiﬁcation, altered solar constant, changes in the extent of sea ice or snow cover, and so on (Barry, 1975). Recent work investigates the sensitivity to global changes in leaf area index (Chase et al., 1996). Simulations of the climates of past geological epochs such as the Cretaceous warm period, the last glacial maximum, and the Holocene thermal maximum are also carried out via sensitivity studies of changed boundary conditions (Schlesinger, 1988; Barry, 1997). True transient climate experiments have become possible only with coupled ocean–atmosphere models and greatly augmented computer capability (Manabe and Bryan, 1969; Meehl, 1992; Neelin et al., 1994). Models are now integrated 100 years or more to assess the evolution of global climate during progressive increases of greenhouse gas concentrations. Results are detailed in the assessments of the Intergovernmental Panel on Climate Change (IPCC) (Gates et al., 1992; Kattenberg et al., 1996). To assess low-frequency variability, several 1,000 year integrations using coupled atmosphere– ocean–land surface models have recently been performed (Manabe and Stouffer, 1996). Models are typically evaluated in terms of their ability to simulate the mean features and variances of global climate variables (pressure, wind, temperature, precipitation, etc.), their seasonal cycles, and their geographical distributions (Gates et al., 1990). The Köppen climate classiﬁcation, which combines temperature and precipitation categories, has proved a useful diagnostic tool in this regard. Lohmann et al. (1993) show how the area of large-

166 Synoptic and dynamic climatology scale climatic regions may alter under warming scenarios. More recently, the synoptic variability of these elements has been compared with observed synoptic conditions using empirical orthogonal functions (EOFs) (Crane and Barry, 1988) and catalogs of synoptic circulation patterns (Hulme et al., 1993, for the British Isles, and McKendry et al., 1996, for western North America) (see also section 7.4). Model improvements typically occur in an incremental manner, based on comparative assessments of changes in physical processes and grid resolutions (Boville, 1991; Marshall et al., 1997). Models are also now assessed for their ability to simulate intraseasonal variability such as blocking modes and the interannual variation of ENSO events. There are several major projects designed to intercompare model capabilities. They include the Atmospheric Model Intercomparison Program (AMIP), which intercompares various aspects of some twenty-ﬁve to thirty GCMs (Gates, 1992; Phillips, 1994; Lau et al., 1996) and the Program to Intercompare Land Process Schemes (PILPS) (Henderson-Sellers et al., 1993), as well as research group studies of individual model elements or regional climate simulation (Hay et al., 1992; McGinnis and Crane, 1994; Randall, 1996; Kaurola, 1997). Examples of studies of the effect of model parameterizations include the intercomparison of ice–albedo and cloud feedbacks (Cess et al., 1990, 1991, 1996). The latest series of intercomparisons is being performed under the Coupled Model Intercomparison Project. Control runs as well as integrations for increasing carbon dioxide from many global coupled models are being intercompared (Meehl et al., 2000a, b). An important issue in climate assessment and impact studies is the ability of GCMs to provide useful results for speciﬁc regions. Approaches to this problem include the use of high-resolution mesoscale models nested within a GCM and statistical downscaling of the GCM results by the use of relationships between large-scale parameters and regional climate conditions as currently observed (Giorgi and Mearns, 1991; von Storch, 1995; Hewitson and Crane, 1996). The former has found application in the treatment of orographic precipitation, see Figure 3.33 for example, while the latter is used in climate change scenarios. Operational numerical weather prediction (NWP) has a long history but the model physics, horizontal and vertical resolution, and other aspects are constantly being upgraded (Cullen, 1993; McPherson, 1994). This has limited the use of such outputs for climatological analyses. Recognition of this fact led to a plan to reanalyze the archives of synoptic, upper air, and satellite input data, incorporating subsequent corrections and standardized procedures for data assimilation, together with a ﬁxed model. Currently, this reanalysis is being performed in the United States by the National Centers for Environmental Prediction (NCEP) (Kalnay et al., 1996) and at the Data Assimilation Ofﬁce, NASA (Schubert et al., 1993), as well as by the European Centre for Medium Range Weather Forecasts (ECMWF), European Reanalysis Agency (ERA) in Reading, UK. Studies utilizing the available results are already under way (WCRP, 1998).

3.7 The global circulation – description The global circulation in the troposphere displays an essentially tripartite structure from poles to equator: a polar vortex, subtropical high pressure, and a weak equatorial lowpressure trough. At sea level the pattern is modiﬁed in that the high-latitude minima are over the subpolar regions, and in the northern hemisphere the land masses disrupt the zonal regularity (Figure 3.34). 3.7.1 Subtropical high pressure The most prominent and persistent features of the mean tropospheric circulation in both hemispheres are the subtropical high-pressure belts. Their locations vary seasonally as a function of the meridional temperature gradient in the troposphere (Flohn and Korff,

Global climate and the general circulation 167 11

0

0

0

0

0 11

Figure 3.33 The simulation of mean precipitation for January over Europe as represented in (a) observations 0.5° 0.5°) from Legates and Wilmott (1990); (b) mesoscale model nested in the NCAR CCM-1; (c) the CCM-1 (the MM-4 R15 truncation). (From Giorgi et al. 1990)

168 Synoptic and dynamic climatology

Figure 3.34 Mean sea-level pressure and winds for January and July. (After Liljequist, 1970)

1969). As the difference in equator–pole temperature in the 700–300 mb layer increases in the winter months the subtropical anticyclones shift equatorward, and vice versa (Figure 3.35). This ﬁgure also illustrates the asymmetry between the hemispheres, associated with the stronger southern hemisphere circulation, which results in a displacement northward of the annual average equatorial trough to 6°N. The axes of the subtropical anticyclone cells slope generally equatorward and westward with height, towards the warm air, as a consequence of the hydrostatic relationship (p. 162). The MSL subtropical highs in the northern hemisphere are best developed in July, when they attain their northernmost positions (Figure 3.34). The highest pressures in July exceed 1,027 mb in both the North Atlantic and the North Paciﬁc oceans. In the southern hemisphere the centers are weaker and show less seasonal variation in amplitude and location. The South Atlantic cell is stronger in winter (at 30°S) than in summer (at 35°S), whereas

Global climate and the general circulation 169 11

0

0 Figure 3.35 The meridional temperature for the 300–700 mb layer in the previous month plotted against the latitude of the center of the subtropical high-pressure belt in each hemisphere. A constant vertical lapse rate is assumed. (From Flohn and Korff, 1969)

0

0

that in the southeastern Paciﬁc varies little, with maximum mean monthly pressures of 1,022 mb. The seasonal range of latitude is also only 5° for the southeast Paciﬁc cell, although the ridge axis in the western South Paciﬁc ﬂuctuates over 10° latitude, with an extreme range of 20°. The occurrence and strength of the subtropical anticyclones present a number of problems, in particular the fact that they are stronger in summer over the northern hemisphere. The arid regions of the subtropical anticyclones experience high net radiative heat loss which is balanced by adiabatic warming in the descending air. There is little descent associated with the mean Hadley cell over the northern subtropics in summer, for example, yet North African precipitation is minimal. Considerable attention has been given to the ideas ﬁrst proposed by Charney (1975) concerning enhanced adiabatic warming through subsidence related to anthropogenically induced biosphere–albedo feedback as a result of overgrazing and desertiﬁcation, but this does not address the fundamental question. A recent hypothesis proposes that the cells over the eastern oceans and adjacent subtropical deserts are closely linked with the regions of monsoon heating over the continents to their east (Hoskins, 1996; Rodwell and Hoskins, 1996). Calculations of the observed vertical velocity in the middle troposphere from ECMWF data for June–July–August 1983–88, and the associated contributions of the diabatic heating and horizontal advection to the thermodynamic energy balance, show that in the principal areas of descent in the northern subtropics the horizontal advection contribution is about twice the adiabatic cooling term. Moreover, the spatial structure of descent regions more closely resembles that of the horizontal advection (Figure 3.36). The terms illustrated in the ﬁgure are: Q /cp = V.p T ( p/po)k / p

0 11

where the term on the left is the diabatic heating and the two on the right are the horizontal advection and the vertical advection, respectively. The result is represented in the idealized simulation of tropical heating in the Asian summer monsoon by Gill (1980), which indicates weak Rossby wave descent west of the heat source, but the recent work

170 Synoptic and dynamic climatology

Figure 3.36 Fields of vertical velocity and thermodynamic budget terms for June–August 1983–88 from ECMWF analyses. (a) Vertical velocity at 477 mb, with contour interval of 0.5 mb hr1. (b) Diabatic heating. (c) Horizontal advection (V.pT). (d) Vertical advection (( p/po) / p). (e) Pressure and horizontal winds on the 325K isentropic surface (40 mb contour interval; H = pressure > 590 mb. The energy terms are at 477 mb, with contour intervals 0.5 K day1. (From Rodwell and Hoskins, 1996)

provides a more comprehensive explanation. There are four components of the proposed mechanism: 1

2 3

4

Heating associated with the continental monsoons moves poleward in spring–summer, generating an enhanced sinking of air on the western and poleward margins. The descending air is fed by the mid-latitude westerlies, rather than as the sinking arm of a simple vertical plane (Hadley or Walker) cell. The descending air suppresses convection and permits enhanced radiative cooling, which acts as a positive feedback on the descent. Strong equatorward ﬂow is set up below the level of maximum descent. Theory shows that this is accounted for by the vorticity balance: v f w/ z and is equivalent to anticyclonic vorticity generated in the region being displaced westward as a consequence of the effect. The equatorward ﬂow causes cold upwelling in the coastal waters through Ekman drift away from the coast, further suppressing any convection.

Global climate and the general circulation 171 11

0

In the non-linear primitive equation model of Rodwell and Hoskins (1996) the prescribed forcing by monsoon heating induces a Rossby wave to the west over the subtropics which extends poleward into mid-latitudes and warms the atmosphere. The descending air originates on the southern ﬂank of the westerlies and adiabatic descent is thermodynamically balanced by cold air advection in the mean. The local orography upstream in northwest Africa (the Atlas–Ahaggar mountains) is considered to be important in the localization of adiabatic descent over the eastern Sahara–Mediterranean Sea (Semazzi and Sun, 1997) and a similar relationship is suggested between the Zagros mountains and descending air over the Kyzlkum desert to the southeast of the Aral Sea. In the eastern Mediterranean the equatorward ﬂow element of the dynamic climatology is also expressed in the summer occurrence of the northerly Etesian winds over the Aegean Sea. 3.7.2 Low-latitude circulation The vertical structure of winds in low latitudes displays a variety of patterns (Figure 3.37). In some sectors and seasons, easterlies are prevalent, although their speed decreases with height. In other situations, tropical easterlies are overlain by mid-latitude westerlies; these extend equatorward in the upper troposphere during the cold season. In the summer monsoon regimes, low-level westerlies are overlain by tropical easterlies. The causes of this diversity are examined below. We begin by describing the tropical easterlies, known in terms of surface observations as the trade winds.

0

The trades and their structure The trade wind systems of the maritime tropics extend over almost 25 percent of the earth’s surface (Figure 3.38) (Crowe, 1949, 1950, 1971). They were ﬁrst described by

0

0

0 11

Figure 3.37 Schematic vertical structure of low-latitude zonal winds. a, b The trades in summer. c The trades in winter overlain by westerlies. d Summer monsoon areas

172 Synoptic and dynamic climatology

Figure 3.38 The trade wind systems illustrated by schematic surface streamlines for January and July. Shaded areas denote relative directional constancy of all winds >3 m s–1 from the predominant quadrant (percent): 50 (horizontal lines), 70 (stipple), 90 (dark stipple). (From Crowe, 1950)

Edmond Halley (1686); both he and Hadley (1735) attempted to account for them within a theory of the global atmospheric circulation. The tropical easterlies are essentially barotropic, implying an absence of thermal forces, as noted by Rossby (1949, p. 181). They originate from large-scale subsidence and divergence in the eastern sectors of the subtropical high-pressure cells. Measurements in the central North Atlantic, 10°–15°N, 30°–40°W, indicate subsidence of 200 m day1. Brümmer et al. (1974) found that 15 m level winds are 60–80 percent of the geostrophic value and directed 27º across the isobars towards lower pressure. Figure 3.38 indicates ﬂow from the northeast (southeast) quadrant in the northern (southern) hemisphere. There is remarkable constancy of direction and steadiness in the speed; surface winds typically average 7 m s1. Figure 3.39 illustrates annual averages and standard deviations of the u and v components over the tropical Paciﬁc Ocean based on 5 million ship observations. The u component shows larger variability on the margins of the “core” regions, in the western equatorial Paciﬁc and in the South Paciﬁc, 20°–30°S. The v component is most variable about 5°–10°N in the central–eastern equatorial Paciﬁc. The air subsiding over the eastern parts of the subtropical oceans creates a marked trade wind inversion that was ﬁrst identiﬁed in 1856, through studies on the Peak of Teneriffe in the Canary Islands. The inversion base is typically encountered around 1,000–2,000 m altitude, although it is lower over the cold current off Northwest Africa. Soundings with high vertical resolution made in July 1987 at San Nicolas Island, California, where the sea surface is similarly cold and there is persistent stratocumulus, indicate a maximum inversion frequency near 860 mb (1,400 m) with strong inversions at 915 mb (850 m) (Schubert et al., 1995). However, similar soundings taken during June 1992 at Port Santo in the Madeira Islands (33ºN, 16ºW) show that the most probable

Global climate and the general circulation 173 11

0

0

0 Figure 3.39 Annual averages and standard deviations of the u and v components of surface wind over the tropical Paciﬁc Ocean, 1950–1972. (From Barnett, 1977)

0

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inversion height is near 800 mb (2,000 m) with the stronger ones at 860 mb. Likewise, soundings during November 1992–February 1993 at Kwajelein Atoll (9ºN, 168ºE) resemble those at Porto Santo. Aircraft dropwindsonde measurements in the wintertime Paciﬁc trades south of Hawaii show that, contrary to earlier ideas, the inversion height (about 850 mb) does not increase equatorward (Firestone and Albrecht, 1986). Rather, subsidence weakens and the frequency of inversions decreases somewhat downwind, allowing penetrating cumulus towers to transport moisture into the drier air above. Purely thermodynamic models of the trade wind boundary layer predict that it deepens with increasing local sea surface temperature and decreasing subsidence, implying a downstream slope of about 2,000 m per 1,000 km. In fact, there is typically a gentle slope of ~300 m per 1,000 km until the intertropical convergence zone is reached. Schubert et al. (1995) propose that the subtropical inversion is extended into the tropics dynamically. A small poleward movement of low-level equatorial air along isentropic surfaces, which generates a weak westerly ﬂow (~1 m s1), modiﬁes signiﬁcantly the static stability. The subtropical inversion is weakened, while a stable inversion layer is extended into the tropics. Hence the inversion height is determined by horizontally averaged values of sea surface temperature, divergence, and atmosphere structure above the inversion, and not by local conditions. In the central Paciﬁc north of the equator, deep convective activity

174 Synoptic and dynamic climatology peaks near 7°N, but during January–February 1979 there were still 50 percent of soundings with low-level inversions near the equator. The stability of the inversion is sufﬁcient to suppress convection, but destabilization can occur within a few hours of vertical stretching of the inversion layer, not by deepening of the boundary layer (Firestone and Albrecht, 1986). The seasonal expansions and contractions of the global trade wind belts are illustrated in Figure 3.38 (Crowe, 1950, 1965). In general, the margins extend poleward in summer and contract in winter, in association with shifts of the subtropical anticyclones. The trades also extend farther westward during the cold season as the high-pressure cells strengthen. However, the “core regions” are essentially quasi-permanent. Thus the northeast and southeast trade wind systems are seasonally out of phase; the former reach a maximum in January–February and a minimum six months later. Wyrtki and Meyers (1976) show that the southeast trades in the Paciﬁc Ocean are more extensive than the northeast trades (Figure 3.40). Barnett (1977) also ﬁnds that variations in the wind ﬁeld just north (south) of the northeast (southeast) trades in the Paciﬁc are seasonally in phase with trades in the opposite hemisphere. This demonstrates the close coupling of the wind systems with the seasonal shifts of the high-pressure cells. Monthly departures from the monthly means of the u-component winds over the Paciﬁc show little correlation between 5°–19°N and 1°–15°S, although the v components (towards the equator) are quite well correlated (Reiter, 1979). Surges in the meridional components of the North and South Paciﬁc trades are also well correlated with precipitation anomalies in the equatorial central Paciﬁc (the Line Islands) (Reiter, 1978). In the tropical Atlantic, however, the v-component anomalies in the northeast and southeast trades are independent of one another and there are no correlations in meridional wind components between the Atlantic and Paciﬁc Ocean trade winds (Reiter, 1979). Wyrtki and Meyers (1976) found no relationship (other than the annual cycle between the strength or area of the southeast and northeast trades in the Paciﬁc) (Figure 3.37), but the u components of the Paciﬁc trade winds do show a coupling between hemispheres on the interannual time scales (Barnett, 1977). This is best developed west of the dateline, away from the core regions of maximum u. In contrast, the largest variations in the v component are found over the eastern and central Paciﬁc and in the southwest Paciﬁc.

Figure 3.40 The monthly variation in area of the trade winds over the Paciﬁc Ocean. (Wyrtki and Meyers, 1976)

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Equatorial westerlies Westerly winds are present seasonally in the equatorial lower troposphere over much of the eastern hemisphere. Flohn (1960) describes such climatological equatorial westerlies across Africa, southern Asia and the Indian Ocean to New Guinea in the northern summer and primarily in the Australia–Indonesia sector in the southern summer (Figure 3.41). They originate in response to the poleward displacement of the equatorial trough over the continents during the summer monsoons, discussed below. Over the Indian Ocean sector they are sometimes characterized as southeasterly trades that become southwesterly in response to the Coriolis effect after crossing the equator, yet as Flohn (1960) points out southwesterly surface winds at 2°–3°S were noted by Halley (1686) from sailing ship reports. Cross-sections of the wind and temperature structure along longitude 90°E show the seasonal shift between easterly trades and equatorial westerlies. In the Australian sector, low-level westerlies develop ﬁrst over the western South Paciﬁc (0°–10°S, 160°E–170°W), responding to the strengthening of the North Paciﬁc easterly trades and cross-equatorial northerly ﬂow around 170ºE (Murakami and Sumi, 1982). Cross-equatorial ﬂow from the northern hemisphere Hadley cell then extends to 10ºS between 100ºE and 170ºW. A dynamic explanation of equatorial westerly ﬂow is offered by Tomas and Webster (1997). In regions where there is a strong pressure gradient from the winter to summer hemisphere, shear in the zonal wind required to balance the cross-equatorial advection of absolute vorticity ( f ) causes the wind to become westerly (see Figure 3.42). The westerly winds must also strengthen rapidly north of the zero contour of ( f ); there are corresponding westerlies in the southern hemisphere summer. The occurrences of westerlies in the Indian Ocean in July, south of the zero line of absolute vorticity, is a result of the additional inﬂuence of a west–east pressure gradient along the equator. There are also synoptic-scale wind burst events in the western equatorial Paciﬁc (Kiladis et al., 1994). These play an important role in El Niño processes; they are discussed in section 5.3.

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Figure 3.41 Equatorial westerlies in any layer below 3 km in January and July. (After Flohn, 1960; from Barry and Chorley, 1998)

176 Synoptic and dynamic climatology

Figure 3.42 Schematic illustration of the dynamic balance in the summer hemisphere about the contour of zero absolute vorticity (' = f) . Left The divergent component of wind, V. Center The rotational wind component V and low-level westerlies. Right the latitudinal proﬁle of relative vorticity and of zonal wind, u. (Tomas and Webster, 1997)

The near-equatorial trough Considerable confusion exists in the literature as to the nature of the equatorial pressure minimum and its associated climatic characteristics. The problem arises in part through attempts to reconcile several distinct phenomena within a single model. Thus zones of wind conﬂuence, mass convergence, frontal boundaries, cloud or precipitation maxima, and pressure minima are treated as if they were synonymous, or mutually consistent. Moreover, some of these features are more appropriately recognized as climatologically averaged variables rather than as synoptic entities. Mariners have long identiﬁed a low-latitude zone of light and variable winds – the Doldrums – which are principally located in the eastern parts of the equatorial Paciﬁc and Atlantic Oceans. This zone locally separates the trade wind systems of the two hemispheres, but elsewhere the trades may be convergent, or at least conﬂuent. Figure 3.43 shows the average position of the tropical conﬂuence in the Paciﬁc by the zero contour of the v component dividing northward () and southward () ﬂow. The boundary is close to the equator near New Guinea (140°E) but reaches 10°N in the eastern Paciﬁc (Barnett, 1977), closely following the mean location of the isotherms of maximum SST. Pronounced angular conﬂuence gives rise to the identiﬁcation of an Intertropical Convergence Zone (ITCZ), but in other sectors there is only a broad near-Equatorial Trough of low pressure. In simple models, trade wind conﬂuence within the equatorial trough is considered to create an ITCZ, with an associated maximum of cloudiness and precipitation. However, these are often quite distinct features. Moreover, when equatorial westerlies develop over land sectors, forming the respective summer monsoon ﬂows, the main convergence is then between trades on the poleward side and westerlies on the equatorial side. Sadler (1975) refers to this feature a “monsoon trough.” The axis of maximum cloudiness is often located within the easterly ﬂow several degrees equatorward of the line of wind shear. In the central equatorial Paciﬁc during December 1977–January 1978, Ramage et al. (1981) report that the North Paciﬁc trades were heated and moistened from below whereas their South Paciﬁc counterparts underwent low-level divergence and slight cooling in response to this SST gradient. While the strength of the low-level convergence was related to the trade wind strength, mid and upper-level circulations apparently modulated the distribution of bad weather. Nevertheless, low-level convergence was also unrelated to upper-level divergence. The convergence zone between Hawaii and Christmas

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0 Figure 3.43 The tropical wind conﬂuence in the Paciﬁc Ocean, based on the ﬁrst EOF of the v components of wind accounting for 67 percent of the variance. (Barnett, 1977)

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Island during the experiment was made up of alternating strips of convergence and divergence. Occasional dry zones of subsiding air occurred within the ITCZ, suggesting mesoscale structures. Convergence within the near-Equatorial Trough or along the ITCZ thus shows considerable spatial and temporal variation. Low-latitude convergence can be studied indirectly by identifying large-scale convective systems known as cloud clusters that extend several hundred kilometers horizontally and vertically into the upper troposphere. Indices of such tropical convective systems, include low values of outgoing long-wave radiation (OLR) (Gruber and Krueger, 1984), indicative of cold, high cloud-tops associated with cumulonimbus anvils and cirrus cloud shields, and deep highly reﬂective clouds (HRC) mapped from visible and infrared satellite images, respectively, to ﬁlter out low to middle-level cloud systems. Garcia (1985) produced a daily HRC data set for 1977–87 identifying deep “convective systems extending at least 200 km horizontally.” These two indices are intercompared by Waliser et al. (1993) for 1974–87 and it is concluded that the HRC maps delimit the deep convection within the tropical convergence zones better than the OLR data, and compare well with convective cloud areas identiﬁed on monthly time scales in the ISCCP data. A climatology of the ITCZ, based on the HRC data, is shown in Figure 3.44. The ITCZ in the Atlantic and eastern Paciﬁc remains entirely north of the equator, following a nearly sinusoidal pattern from near the equator in February–March, when it is weakest, to near 10°N in August–September, when it is strongest. Over Africa it migrates from 10°S in the austral summer to 8°N in the boreal summer. In the central Paciﬁc there is a clearly marked double structure, especially evident in April and December, with the summer hemisphere zone predominating. Over the Indian Ocean there are also two bands, one between about 2°S and 8°S, and the other associated with the Indian monsoon between 50°N and 20°N. The western Paciﬁc sector also reﬂects the inﬂuence of the Australasian monsoon, although the South Paciﬁc Convergence Zone (SPCZ), discussed below, is a complex phenomenon. Finally, over South America, the ITCZ is near 10°S in the austral winter but, after its northward movement to 10°N in July–August, it decays and reappears again south of the equator in the austral spring.

178 Synoptic and dynamic climatology

(a)

Figure 3.44 A climatology of the ITCZ: (a) mean monthly frequency of HRC days in January, April, July, and October, and (b) time–latitude plots for seven regions and a global average (see p. 179). The white line shows the monthly mean position of the ITCZ. (From Waliser and Gautier, 1993)

The meridional distributions of OLR, SST and mean sea-level pressure (SLP) across the equator in the Indian Ocean (55º–85ºE), eastern Paciﬁc (120º–90ºW) and eastern Atlantic (30º–0ºW) (Figure 3.45) show some striking contrasts. In the eastern Atlantic and Paciﬁc Oceans, in February, maximum SST, minimum OLR, and a broad minimum of SLP occur about 50ºN. In July, however, the convection (OLR) in these areas is equatorward of the SST maxima and the pressure minima and a similar pattern occurs over the Indian Ocean in February. There is a closer colocation of the three variables when

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Figure 3.44 (cont.)

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convection is weak (larger OLR) and when there is an insigniﬁcant cross-equatorial pressure gradient (Tomas and Webster, 1997). Over the Indian Ocean there are dual SST maxima. In July one is over the equator, while the OLR minimum is at 5ºN. In the Atlantic and Paciﬁc Ocean sectors the ITCZ is located north of the equator yearround. In the southwest Paciﬁc, however, there is a pronounced convergence zone and cloud band, referred to as the South Paciﬁc Convergence Zone, which extends southeastward from New Guinea to about 30°S, 160°W (Figure 3.44). This is primarily a feature of the austral summer, when a monsoon low-pressure trough is present over northern Australia and southeasterly trades ﬂow towards the center (Vincent, 1994). The western section of the SPCZ is located over the western equatorial Paciﬁc “warm pool” where surface temperatures exceed 29°C. Gradients of the sea surface temperature appear to generate pressure gradients which force the low-level winds, leading to convergence of moisture and cloud development (Kiladis et al., 1989). The southeastward extension of the SPCZ cloud band is associated with tropical–mid-latitude interactions. Some studies

180 Synoptic and dynamic climatology

Figure 3.45 The meridional distributions of convection as indicated by OLR (W m2) (thin solid line), SST (°C) (heavy solid line), maxima stippled, and mean sea-level pressure, SP (mb) (heavy dashed line). Upper Indian Ocean (55°–85°E). Middle Eastern Paciﬁc Ocean (120°–90°W). Lower Eastern Atlantic Ocean (30°W–0°). Left February 1992. Right July 1992. (From Tomas and Webster, 1997)

identify the role of disturbances propagating toward lower latitudes near New Zealand and strengthening the convergence zone between themselves and the south-east Paciﬁc high (Kiladis and Weickmann, 1992). In addition, Kiladis et al. (1989) note that storm tracks propagate from lower to higher latitudes when the ITCZ interacts with a trough in the mid-latitude westerlies. The zonally averaged meridional transports of energy in the Earth–atmosphere system show a zero value at about 8°S in February and 15°N in August (Figure 3.46). Riehl and Simpson (1979) point out that this intertropical zone always lies in the winter hemisphere, so that the net energy source between these latitudes is always exported toward the winter pole. Hence the equatorial trough (the line of zero meridional energy transport) demarcates the export of energy from the tropics to the summer and winter poles, respectively.

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Figure 3.46 Zonally averaged meridional transports of energy in the Earth–atmosphere system, illustrating the location of the equatorial trough. (From Riehl and Simpson, 1979)

The seasonal latitudinal shifts of the equatorial trough are only half those of the overhead sun (between the Tropics of Cancer and Capricorn). Riehl and Simpson propose that the energy requirement of the high-latitude winter hemisphere exercises a constraint on the displacement of the trough. The climatological structure of the equatorial trough in July differs between “oceanic” and “continental” sectors (Riehl and Simpson, 1979). Over Africa and Asia (30°W eastward to 150°E) temperature cross-sections show a pronounced low-level warm anomaly from 8° south to 15° north of the trough compared with mean conditions ±20° latitude about the trough (Figure 3.47). For the ocean sector (150°E eastward to 30°W), there are very weak temperature anomalies, but speciﬁc humidities are highest in the trough zone. Highest moisture contents over the continental sector are on the equatorward side of the trough, which is displaced northward. The vertical structure of the ITCZ from composites during ﬁeld experiments in the central Atlantic and central Paciﬁc indicates a warm core near the surface and at upper levels, with an intermediate cold core (Estoque, 1975). The cold core at middle levels and warm upper levels is conﬁrmed by dropsondes and soundings made over the eastern Paciﬁc during the First GARP Global Experiment (FGGE) (Fernandez-Partagas and Estoque, 1985). The structure resembles that of the classical easterly wave (see section 6.5), but the observational period featured only a vigorous ITCZ along 11ºN westward from the west coast of Mexico, with no wave disturbances. The upper-level warming is attributed to latent heat release in cumulonimbus towers.

182 Synoptic and dynamic climatology

Figure 3.47 Cross-sections of temperature anomaly (°C) across the equatorial trough zone in July; latitudes are relative to the trough. Temperature deviations are with respect to a mean sounding within ± 20° latitude of the trough. Above The “continental” sector from 30°W eastward to 150°E. Below The “oceanic” sector from 150°E eastward to 30°W. (From Riehl and Simpson, 1979)

Explanation of the structure of the ITCZ is not fully resolved, in part because of the variability of its characteristics in time and space. The asymmetry of its distribution with respect to the equator is particularly marked in the eastern tropical Atlantic and Paciﬁc (see Figure 3.44). It is suggested by Philander et al. (1996) that this is a result of two basic factors. First, is the modiﬁcation of the basic equatorial symmetry of wind conﬂuence and the Equatorial Trough by ocean–atmosphere interactions. These interactions are most effective where the thermocline is shallow. In the eastern tropical Atlantic and Paciﬁc the northeasterly trades maintain a shallow thermocline by modifying the sea surface temperature regime, and in turn the cold ocean surface maintains persistent low-level stratus cloud. Second, the bulges in the continental outlines of West Africa and northwestern South America exert a geographical control on the location of the interactions, thereby determining where they are most effective. These hypotheses have been tested with a coupled ocean–atmosphere experiment using the Geophysical Fluid Dynamics Laboratory (GFDL) model. There are two basic theoretical viewpoints. One involves an axisymmetric model where, given an ocean with a zone of maximum sea surface temperature (SST), a single ITCZ and precipitation maximum are formed over the warmest water. Pike (1971) couples an axisymmetric atmosphere with an axisymmetric ocean and shows that cold upwelling along the equator can displace the ITCZ in this situation. Based on moist static energy

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balance considerations, convergence in the equatorial boundary layer will also localize moisture ﬂux-convergence and precipitation over the warmest water, according to Neelin and Held (1987). Experiments with a simple coupled model by Wang and Wang (1999) further explore the maintenance of the equatorial cold tongue. They ﬁrst note that east–west climatic asymmetry is generated primarily by the presence of an eastern boundary to the ocean. Unstable interactions between the sea surface temperature gradient and the zonal winds, on the one hand, and counteracting stabilization caused by differential surface buoyancy ﬂuxes, on the other, set up the equatorial cold tongue. However, they invoke an important role for solar radiation forcing. Seasonal variations in the solar declination angle cause the solar forcing to be antisymmetric about the equator. Hence it can affect the cold tongue indirectly through changes in the intensity of the trade winds. The cold tongue interacts with the ITCZ, creating a mechanism for it to be self-maintaining in one hemisphere. The cold tongue varies in phase with the annual cycle of SSTs south of the equator. Consequently the band of highest SSTs and the ITCZ are maintained in the northern hemisphere, rather than responding to the annual radiation cycle. The meridional moisture gradient between the cold tongue and the ITCZ favours moisture convergence, helping to sustain the ITCZ when it is in the summer hemisphere. The second viewpoint envisages atmospheric processes controlling ITCZ characteristics. Moisture convergence may be driven by boundary-layer pumping, referred to as Conditional Instability of the Second Kind (CISK) by Charney and Eliassen (1964) and Bates (1973), or by convective activity that is organized as a result of zonally propagating equatorial waves in the tropical easterlies – or wave-CISK (Holton et al., 1971; Lindzen, 1974). In the latter model, maximum boundary-layer convergence, associated with four to ﬁve-day easterly waves in the tropical eastern Paciﬁc, should occur at 6° latitude, where the local Coriolis frequency and the wave frequency are equal. In either case, in an axially symmetric atmosphere, it is feasible to form an ITCZ in each hemisphere parallel to the equator, although both CISK hypotheses have been questioned on various theoretical grounds. More signiﬁcantly, observations of deep convection over the tropical Atlantic and Paciﬁc Oceans indicate that boundary-layer convergence forced by friction or easterly waves plays only a minor role in large-scale forcing of convection, contrary to both CISK hypotheses. Rather, various combinations of large-scale ITCZ forcing, cloud cluster feedback, and their diurnal modulations are the principal factors. Figure 3.48 summarizes the relative magnitudes of these forcings to the 850 mb vertical motion from composites of extensive data sets (McBride and Gray, 1980). In the eastern Atlantic, diurnal variations in low-level convergence arise as a result of subsidence from nocturnal radiative cooling of the zone of stratiform cloud to the north of the ITCZ. To explore this question, experiments have been carried out with GCMs for the idealized conditions of a water planet. Such experiments with the NCAR Community Climate Model (CCM) 1, with solar radiation ﬁxed for March 21, suggest that a primary determinant of the location(s) of the ITCZ is the particular formulation used for simulating atmospheric convection processes (Hess et al., 1993). These experiments use the moist convective adjustment (MCA) scheme, where a saturated unstable air column is adjusted locally to the saturated adiabatic lapse rate (SALR), conserving moist static energy; relative humidities that exceed 100 percent as a result of the convective adjustment are set equal to 100 percent and excess water vapor instantly rains out. For the CCM 1 simulations using the MCA scheme, an ITCZ forms over the zone of maximum SST even when the SST gradient is weak. With this MCA scheme, dry air above the boundary layer has to be moistened by strong vertical advection of moisture in order for convection to occur. Hence rising air with high moist static energy over the region of warmest water favors convection. Hess et al. (1993) also examine an alternative parameterization due to Kuo (1965) which assumes that the amount of precipitation is proportional to the moisture ﬂux-convergence in the air column. Cloud properties are predicted by a steady-state “plume type” of cloud model. The rainfall rate is predicted from this plume model and a rate is

184 Synoptic and dynamic climatology

Figure 3.48 The relative magnitude of forcing of 850 mb vertical motion in the ITCZ for three sectors and diurnal phase. (McBride and Gray, 1980)

also determined for each grid box; the ratio of these rates deﬁnes the cloud fraction in a grid box. With the Kuo scheme, the CCM 1 generates an ITCZ on each side of the equator at about 7° latitude for a wide variety of SST distributions, including the case where the maximum SST is located on the equator. This parameterization is very sensitive to weak convergence in the boundary layer and it appears that this convergence is a result of the effects of transient low-latitude disturbances. Thus bands of convection can develop away from the equator. Additionally, the model simulations show that the spectrum of equatorial waves is signiﬁcantly affected by the choice of convective parameterization. Dynamical arguments to account for the location of areas of heating and convection away from the equator are provided by two recent studies. Waliser and Somerville (1994) suggest that low-level convergence is maximized dynamically between 4º and 12º latitude. A heat source near the equator generates a pressure gradient anomaly across the equator which is balanced by geostrophic motion and friction associated with the lowlevel convergence and convection. If the heat source is displaced a small distance poleward, the frictional effect is enhanced on the equatorward side of the anomaly (where the Coriolis component is small), but further poleward displacement weakens the convergence as the Coriolis force on the equatorward side increases. Waliser and Somerville propose a positive feedback between latent heat release in the mid-troposphere and lowlevel convergence. An alternative hypothesis (Tomas and Webster, 1997) invokes inertial instability to generate off-equator convection because, in regions of strong cross-equatorial pressure gradients, the ITCZ is displaced poleward of an axis of zero absolute vorticity ( ƒ) (see Figure 3.42). Tomas and Webster (1997) propose three requirements for near-equatorial convection:

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The advection of absolute vorticity across the equator into the summer hemisphere by a strong cross-equatorial pressure gradient generates ﬂow that has anticyclonic (negative) absolute vorticity and is (locally) inertially unstable (e.g. Sawyer, 1949). Secondary meridional circulations cells make the system stable. The SST is near the maximum tropical value, but need not reach a local or absolute maximum. Poleward of the zero absolute vorticity axis, the atmosphere locally must be conditionally unstable. Deep convection is then set up, augmented by low-level moist static energy convergence.

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In the absence of 1, the convection region is determined by conditions 2 and 3. Figure 3.49 illustrates monthly mean positions of ( f ) = 0 at 850 mb for 1980–94, showing its poleward displacements, particularly in the Atlantic and Indian Ocean sectors, where it is about latitude 5º–10º in the summer hemisphere (Tomas and Webster, 1997). These sectors experience strong cross-equatorial pressure gradients and low-level divergent winds as a result of either differential land–sea heating, or a meridional SST gradient. Locally, values of ( f ) are less than zero and the ﬂow is inertially unstable. The poleward displacement of convection is limited as latitude () increases by the weakening of anticyclonic absolute vorticity advection (with cos ) and the concomitant increase (with sin ) of vortex-tube stretching, which generates cyclonic vorticity. Hence the convective zone cannot be displaced too far from the equator. Tomas and Webster also note that inertial instability sets up colocated divergent wind maxima and zero absolute vorticity away from the equator so that the divergent wind is accelerated (decelerated) on the equatorward (poleward) side of the zero ( f ) axis. There is a divergent/convergent doublet to the south/north of this zero axis in the summer hemisphere. In contrast, they point out

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Figure 3.49 Monthly-mean locations of axes of ( f) = 0 at 850 mb, 1980–94, based on ECMWF data. (From Tomas and Webster, 1997)

186 Synoptic and dynamic climatology that the Waliser and Somerville (1994) mechanism, discussed above, would give rise to maximum velocity divergence on the equator where f 0. The location of the ITCZ has important effects on the structure and strength of the mean Hadley cells and on the subtropical jetstreams in the model experiments, whereas in these respects neither the amount of precipitation in the ITCZ, nor the convective scheme that is adopted, has any signiﬁcant inﬂuence. Hess et al. (1993) ﬁnd that the jet maxima are located near 30° (40°) latitude when the location of the ITCZ(s) is over (off) the equator. The jet maximum is weaker for a single equatorial ITCZ, and the jet structure is less barotropic. These differences reﬂect the additional transport of relative westerly angular momentum as the air moves farther poleward. In the real atmosphere, it is likely that in many areas latitudinal gradients of SST determine the ITCZ location through the mechanism of enhanced boundary layer convergence of moisture. However, geographical and seasonal differences arise where SST gradients are weak. This is the case over the Indian Ocean and west Paciﬁc Ocean most of the time and likewise in the central Paciﬁc during the boreal spring. In such areas the ITCZ may be directly determined by convective processes in the boundary layer rather than by the SST gradients. The spatio-temporal variability in the ITCZ over the central equatorial Paciﬁc and Atlantic Oceans sometimes shows a sequence which involves: (1) undulations developing in the ITCZ cloud band; (2) disruption of the cloud band and its breakdown into several equatorial disturbances; (3) the evolution of one or more of these disturbances into tropical storms/cyclones; and (4) the movement of these storms westward and poleward, allowing the ITCZ cloud band to reform (see Plate 4). Ferreira and Schubert (1997) suggest that the breakdown is a convectively modiﬁed form of barotropic and baroclinic instability of the mean ﬂow. Simulations of barotropic processes, using the non-linear shallow water equations on a sphere, where the ITCZ is represented by a prescribed zonally elongated mass sink near the equator, generate a cyclonic potential vorticity (PV) anomaly with a reversed meridional gradient of PV on the poleward side. This zone becomes unstable in the presence of small disturbances and (in the model) it either undulates and breaks down into several cyclones or it changes into a single large cyclone. Ferreira and Schubert propose that the breakdown of the ITCZ accounts for the tendency for tropical cyclones to cluster in time and for their development poleward of the ITCZ and to the east of pre-existing storms (see section 6.5). Moreover the horizontal morphology of the ITCZ, which is wider north–south to the west of Central America, may help explain the local maximum of tropical cyclogenesis. It is interesting in this context to examine the climatology of the ITCZ (Figure 3.44) with its broader extent in the western and eastern equatorial Paciﬁc and western equatorial Atlantic, in relation to the major regions of tropical cyclone formation (Waliser and Gauthier, 1993). Equatorial ﬂow patterns Airﬂow in low latitudes is often poorly determined because pressure gradients are typically weak and the Coriolis parameter is small. However, some important theoretical considerations merit discussion. Above the boundary layer, in the absence of pressure and frictional forces, the synoptic conditions in low latitudes commonly satisfy the requirements for horizontal ﬂow to display inertial motion. Such motion involves a balance between centrifugal acceleration (V2/r) and Coriolis acceleration ( f V) i.e.: V2 fV r and thus V rf

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Plate 4 Sequence of images illustrating the breakdown and redevelopment of the ITCZ cloud band over the eastern tropical Paciﬁc Ocean, 2°–24°N, 108°–165°W. The infrared images are for 16.46 UTC on (a) July 26, (b) July 28, (c) August 3, and (d) August 12 1988. Hawaii is in the upper left corner and Baja in the upper right corner of each image. (a) A zonal band of convection around 10°N. (b) Cloud-band undulations (tropospheric easterly waves) that evolve into ﬁve tropical depressions on July 30. (c) Four of the depressions are now tropical cyclones. (d) The ITCZ has redeveloped. (From Ferreira, R.N. and Schubert, W.H., 1997, “Barotopic aspects of ITCZ breakdown,” J. Atmos. Sci., 54(2): 262–3)

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188 Synoptic and dynamic climatology It follows that, if the latitudinal variation of f is ignored, an air parcel will move horizontally in an inertial circle of radius r. This inertial motion is clockwise (counterclockwise) in the northern (southern) hemisphere. A parcel will move around a circle in 2/f, which corresponds to twelve hours at 90°, twenty-four hours at 30°, sixty-nine hours at 10° and 137 hours at 5° latitude. The characteristics of inertial ﬂow in low latitudes, taking account of the latitudinal variation of f, are treated by Wiin-Nielsen (1970; see Asnani, 1993, pp. 419–27). His solutions show that three types of trajectory may occur according to the initial latitude, direction, and speed of the air parcel. These trajectories result from a relationship: > V0 cos2 0 1/4 0 f02 < where V0 initial velocity, 0 initial angle with latitude circle (x axis), f / y, and f0 initial value of Coriolis parameter. Where the solution of the equation is positive, the trajectory repeatedly crosses the equator; where it is negative, the parcel remains in one hemisphere; and where it is identical to zero, the parcel approaches the equator asymptotically. Wiin-Nielsen also shows that, because the path is not actually circular, the time interval required for a parcel to return to the same latitude (in the equivalent phase of motion) differs considerably from 2/f depending on 0 and latitude. Patterns of steady-state ﬂow, identiﬁable on synoptic and climatological maps over Africa, have been classiﬁed in a simple scheme by Johnson and Mörth (1960) and Johnson (1965). Figure 3.50 illustrates their six suggested types. In four of them there is a pressure maximum or minimum along the equator. The duct pattern (a) is common over equatorial East Africa in the transition seasons, while the bridge (b) occurs over West Africa in July. The drift types (d-f) are observed over Indonesia and East Africa in January. Divergence computations following trajectories are sensitive to small errors in the initial winds (Gordon and Taylor, 1970; Ramage, 1971, pp. 85, 130). Near the equator the divergence is determined mainly by the curvature of the pressure gradient, whereas beyond 5° latitude the Coriolis terms are important. Flohn (1960) points out that in low latitudes westerly and poleward ﬂows are convergent, easterly and equatorward ﬂows divergent. The vertical acceleration can be expressed (Haltiner and Martin, 1957, p. 169) as:

p

w = 2u " cos R g

t

z where the ﬁrst term on the right is the vertical component of the Coriolis acceleration and the second term is the vertical component of the pressure force per unit mass; R the earth’s radius. Johnson and Mörth (1960) and Flohn (1960) postulate that the vertical Coriolis component is important near the equator, but even for large values of u it is only about 0.1 percent of g. The term appears to be sufﬁcient to account for the convergence of equatorial westerlies (Ramage, 1971, pp. 85, 130). As a consequence of these relationships, air approaching (moving away from) the equator undergoes horizontal velocity divergence (convergence) and therefore descends (ascends). The horizontal divergence is given by: div VH v cot /R For v 1 m s1, the divergence is 90 106 s1 at 0.1° latitude and 9 106 s1 at 1° latitude. An alternative interpretation from the conservation of potential vorticity is that, for air approaching the equator, f and therefore sin tend to zero and accordingly the vertical column shrinks, creating subsidence. Asnani (1993, p. 458) refers to this as the dynamic valley effect of the equator.Where the isobars cross the equator in the models of Figure 3.50 they display a cusp or bulge. For the cases of nearly zonal ﬂow the motion

Global climate and the general circulation 189 11

0

0

0

Figure 3.50 Idealized equatorial ﬂow patterns. (Johnson, 1965)

0

0 11

is quasi-geostrophic, although the pressure or height gradients are generally weak. For balanced ﬂow in geostrophic westerly winds in the equatorial planetary boundary layer, Kuo (1973) concludes that the ﬂow is maintained by vertical momentum transport; downward motion is required because there is cross-isobaric motion away from the equator both to north and to south. For a westerly ﬂow of 5 m s1 there would be subsidence of 0.7 cm s1. Johnson (1962) examines the temporal distribution of rainfall over East Africa (10°S– 4°N) and ﬁnds no evidence of mobile disturbances or zonal bands of precipitation. Rather the rainfall patterns indicate patchiness on a scale of 100–300 km with intervening areas that are dry or have only scattered precipitation. The rain episodes, with 8–12 mm per rain day, are superimposed on the broad regimes of wet and dry seasons, and there are no clear relationships between the precipitation areas and the circulation patterns.

190 Synoptic and dynamic climatology A major component of cross-equatorial ﬂow into the northern hemisphere in the boreal summer is accomplished by a low-level jetstream (LLJ) over East Africa. First identiﬁed and analyzed by Findlater (1966, 1969a, b, 1971, 1972), it has been examined observationally and modeled by a number of subsequent investigators (Hart et al., 1978; Bannon, 1979). The East African LLJ is an important component of the summer monsoon system over the Indian Ocean. It develops in May as a narrow band of strong, low-level southeasterly winds across the East African coast around 10°–5°S. During July–August streamlines at 1 km show an elliptical ﬂow from about 16°S, 70°E to the equator around 42°E and then northeastward to 16°N over India, as shown in Figure 3.51 (Findlater, 1971). Maximum speeds at 1–1.5 km are about 15 m s1 off northern Madagascar and 15–18 m s1 northeast of the Horn of Africa. There is also a splitting of the jet here, with one branch directed east-southeastward towards Sri Lanka. The LLJ maintains high constancy of direction, but varies greatly in intensity. Oscillations in intensity with a four or ﬁve-day period have been detected, according to Asnani (1993). The jet is best developed at night and in the early morning, suggesting boundary layer forcing. The ﬂow is forced by low-level velocity divergence in the southeasterly trades emerging from the South Indian Ocean high and is bounded in the west by the mountains of East Africa (Anderson, 1976; Bannon 1979). The variation of the term and the diurnal temperature structure in the lower troposphere also play important roles. The East African LLJ accounts for about half the cross-equatorial transport of water vapor in the western Indian Ocean (from 75°E to the East African coast). Moreover, active monsoon regimes over India appear to be preceded by a strengthening of the LLJ. 3.7.3 Monsoons The name “monsoon” derives from mausam, the Arabic word for season. The climatic concept refers both to seasonal rains and to seasonal changes of prevailing winds. The structure of the wind ﬁeld in low latitudes and the existence of a southwesterly monsoon ﬂow in the northern summer over the Atlantic and Indian Oceans was ﬁrst documented

Figure 3.51 (a) Monthly locations of the mean axis of the low-level jetstream over the western Indian Ocean and East Africa. (b) Mean velocity (m s1) of the jet at 1 km in July. (After Findlater, 1971; Barry and Chorley, 1998)

Global climate and the general circulation 191 11

by the astronomer Edmond Halley in 1686. Remarkably, his maps from ship observations correctly depict the southwesterly ﬂow in the Gulf of Guinea and southwesterlies originating about 2–3°S in the Indian Ocean (see Kutzbach, 1987; Webster, 1987). Climatologists have used various deﬁnitions to distinguish the monsoon areas of the world. Rather comprehensive criteria proposed by Ramage (1971) take account of three factors: 1 2 3

0

A shift of the prevailing wind direction by 120° or more between January and July, with the prevailing directions having an average frequency greater than 40 percent. A mean resultant wind exceeding 3 m s1 in at least one of the months. Fewer than one cyclone/anticyclone alternation occurring every two years in January or July within a 5° latitude–longitude area.

This deﬁnition largely corresponds to the zone where the ITCZ has a large annual oscillation and it encompasses most of southern Asia, northern Australia, West Africa and eastern Africa. However, this monsoon system interacts dynamically with the circulation over a far wider area of the tropics and subtropics. In addition, the term “monsoon” has subsequently been extended to describe summer conditions on the plateaus of the southwestern United States and northwest Mexico (Douglas et al., 1993; Tang and Reiter, 1984; Adams and Comrie, 1997; Barlow et al., 1998), although these areas do not meet Ramage’s criteria, and to the western Paciﬁc Ocean (Holland, 1995). The extent of seasonal monsoons shown in Figure 3.52 is based on the criteria of a seasonal wind reversal and a wet summer/dry winter rainfall regime (Webster, 1987a). The ﬁrst theoretical concept of the seasonal wind reversal was offered by Halley (1686), who proposed a continental-scale sea–land breeze system, due to differential heating of the land and sea, with upper-level return currents. Fifty years later, George Hadley (1735) pointed out that the airﬂows were northeast–southwest (southeast–northwest) in the northern (southern) hemisphere as a result of the rotational effect of the Earth on the air motion rather than being caused by the westward movement of diurnal solar heating, as proposed by Halley. Systematic study of the monsoon in southern Asia, with a view to documenting the nature of its variations and forecasting their behavior, began over a century ago with the statistical investigations of Blanford (1884), elaborated later by Walker (1914, 1923). However, signiﬁcant advances in understanding the dynamics of the monsoon have occurred only in recent decades through a combination of observational data, theoretical studies, and modeling. The observational component included several international ﬁeld programs; the International Indian Ocean Expedition, 1959–65, especially 1963–65 (Ramage and Raman, 1972a, b); the Indian–Russian Monsoon ’77 program in the Indian Ocean; the winter and summer Monsoon Experiment (MONEX) and the West African Monsoon Experiment (WAMEX) in 1978–79 (WMO/ICSU, 1980, 1981; Petrosyants and Belov, 1988); and Chinese programs on the Qinghai-Xizang (Tibetan) Plateau (QXPMEX) in summer 1979 (Xu, 1986). MONEX was carried out over the sector 40°–180°E, between about 20°S and 32°N, as part of the GARP Global Weather Experiment, 1978–79.

0

0

0

General characteristics and mechanisms

0 11

Monsoon climates are usually characterized in terms of onset/withdrawal dates, rainfall regime, break and active phases, and synoptic disturbances. These are obviously areaspeciﬁc. There are considerable differences between the summer monsoons of the Indian Ocean, West Africa, and eastern Asia, and in the austral summer, northern Australia. Nevertheless, a common element is the occurrence of equatorial westerlies at low levels associated with cross-equatorial ﬂow from the easterly trades of the winter hemisphere, and the development of low pressure over tropical continental areas in the summer hemisphere. In essence, the equatorial trough/ITCZ is displaced poleward and becomes a

192 Synoptic and dynamic climatology

(a)

(b) Figure 3.52 Domains of the principal global monsoon systems. (a) Boreal summer. (b) Boreal winter. The rectangle (dashed) outlines the classical monsoon region based on the criteria of a seasonal wind reversal and distinct wet summers/dry winters. Arrows indicate predominant surface airﬂow; areas of maximum seasonal precipitation are outlined. Land areas with maximum surface temperatures are cross-hatched and the coldest land surfaces are stippled. NET and SET northeast and southeast trade winds. In (a): EAM East Africa monsoon, ISWM Indian southwest monsoon, WafM West Africa monsoon, NAmSM North America summer monsoon. In (b): NEWM Northeast (Asian) winter monsoon, ANWM Australian northwest monsoon, NAmWM North American winter monsoon, AfWM African winter monsoon. (From Webster, 1987a)

monsoon trough. Figure 3.53 illustrates conceptually the joint effects of Coriolis deﬂection and a heated continent (Young, 1987). If there is a tropical land mass in the summer hemisphere, strong heating enhances the southwesterlies (Figure 3.53b), and these may be augmented further by precipitation and latent heat release over the land area (Figure 3.53c). If the continent is in mid-latitudes, however, there is no cross-equatorial ﬂow and only moderate inﬂow (Figure 3.53d). An equatorial continent will generate inﬂow only towards the heated interior (Figure 3.53f), whereas a land barrier to the west of an equatorial ocean will augment the cross-equatorial airﬂow (Figure 3.53e). In the case of an equatorial ocean area there will be a weak ITCZ in the summer hemisphere, a few degrees off the equator (Figure 3.53a). These ideas are quantiﬁed to some degree in experiments by Dirmeyer (1998) with a coupled land–biosphere version of the COLA GCM with prescribed sea surface temperatures. A ﬂat continent with savannah vegetation from 30°W to 30°E is placed in different

Global climate and the general circulation 193 11

0

0

0 Figure 3.53 Factors inﬂuencing monsoon winds. Land areas are shaded. Arrows show winds; strong winds are bold arrows. The panels illustrate schematically the effects of land–sea distribution, heating, precipitation, and topographic barriers (see text). (From Young, 1987)

0

0 11

latitude locations extending between 0° to 27°N and 27° to 53°N (in 4.4° latitude steps). For the subtropical continent, 13°–40°N, the width is also varied between 60°, 120° and 360° of longitude. Dirmeyer shows that latitudinal location is critical in establishing the seasonal precipitation regime. With land in the tropics there is heavy precipitation south of 20°N, with wettest conditions in the eastern half and dry conditions in the northwest, as observed in India. A low-latitude continent also results in a landlocked precipitation maximum. A mid-latitude continental location sets up a winter (Mediterranean-type) precipitation maximum, while a subtropical continent (13°–40°N) has an overall drier climate. In order to obtain heavy convective precipitation over the land in summer, the extension of a subtropical continent into the tropics is required. The meridional extent of the continent affects both the strength of the Hadley circulation and the seasonality of precipitation over land. In all cases the descending arm of the Hadley cell is locked over the land area. Increased zonal extent of the continent leads to a decrease in zonally averaged precipitation and an expansion of the low precipitation area in the western part of the continent. The inclusion of a tropical peninsula in the southeastern or southwestern sector also affects the precipitation distribution. With a peninsula in the southeast, as in Africa, the summer monsoon is enhanced, although the rains do not penetrate as far north. Nevertheless, in both cases the northwest is dry. Finally, 1,200 m high mountains are

194 Synoptic and dynamic climatology incorporated in the four cardinal directions in turn. Overall, precipitation amounts are increased (reduced) to the south and east (north and west) of the mountains. The inclusion of a mountain range leads to increased instability and precipitation, through the elevated heat source effect, and it creates shifts in airﬂow that produce greater moisture ﬂux convergence. Mountain uplift is not shown to be an important precipitation mechanism in the model. With the mountains in the south or east, there are enhanced tropical westerlies and moisture transport. With mountains in the north and west, the westerlies are kept in higher latitudes. The role of topography in the tropics in observed global climatic characteristics is reviewed by Meehl (1992). The basic forcing of the Asian summer monsoon is provided by the annual cycle of solar radiation interacting with the different heat capacities of the tropical oceans and land areas (Li and Yanai, 1996), and their respective geographical arrangements. The monsoon ﬂow, however, represents much more than a giant sea breeze circulation. Strong heating in spring creates a thermal low over the northern Indian subcontinent, but it is another month or so before the onset of the monsoon southwesterly ﬂow. Numerous empirical and modeling studies suggest that the extent of spring Eurasian snow cover modulates the intensity of the Asian monsoon during the subsequent summer, as represented by precipitation totals over India (Barnett et al., 1989; Yasunari et al., 1991). Recent work conﬁrms such an inverse relationship for 1979–92, which is stronger when El Niño years are excluded (Sankar-Rao et al., 1996). During winters with more snow over Eurasia the atmosphere is colder (warmer) than normal over the land (ocean). The troposphere over Asia north of India remains colder in summer following a winter/spring with more extensive snow cover and the monsoon is delayed and weaker. The winter (December–January–February) snow depth in European Russia shows a significant negative correlation with the subsequent Indian monsoon rainfall, according to Kripalani and Kularni (1999), while there is a positive relation between winter snow depth in central Siberia and monsoon rainfall; the sign of both these correlations reverses following the monsoon. They interpret the correlation structure in terms of the long-wave pattern: there is an anomalous ridge (trough) over Eurasia during the winter preceding a strong (weak) monsoon. Model simulations by Vernekar et al. (1995) and Dong and Valdes (1998) suggest that the use of energy for snow melt in spring causes cooler conditions over the Tibetan Plateau, resulting in decreased upward sensible heat ﬂux, a reduced meridional temperature gradient and thereby a weaker monsoon. Dynamically, the snow cover favours high pressure over India, a decreased Somali jet, and weakened low-level southwesterlies and upper-level easterlies. It seems that the snow cover may be largely an indicator of circulation anomalies that modify surface temperatures, and hence the land–sea temperature contrast may be primarily responsible for the monsoon strength (Meehl, 1994). The patterns of heating are complex because the seasonal variation of ocean temperatures lags both the incoming solar radiation and continental heating; moreover these variations are strongly dependent on latitude and also show large longitudinal differences. The large-scale heating in the atmosphere is also a function of altitude, as shown by vertical variations in the annual temperature oscillations (Verma and Sikka,1981). The amplitude of the annual oscillation shows two peaks, one in the lower troposphere due to sensible heating and the other around 300–400 mb as a result of latent heat release (Figure 3.54). Over southern Asia the lower maximum occurs in June and the upper one thirty to forty-ﬁve days later associated with monsoon precipitation from deep cumulus cloud over the Himalayas and southeastern Tibet (Flohn, 1968; Yeh and Gao, 1979). These characteristics indicate the complexity of the diabatic forcing and the additional role of dynamic factors. The large-scale circulation over the tropics and subtropics in summer and winter is shown in Figure 3.55 (Webster et al., 1977). In June, July, and August the 200 mb streamlines demonstrate a dominant anticyclonic outﬂow from the Tibetan Plateau, because of the effect of Plateau heating reversing the meridional thermal

Global climate and the general circulation 195 11

0

0

Figure 3.54 Proposed model of atmospheric heating in the Asian summer monsoon region. (From Verma and Sikka, 1981)

0

0

gradient. Easterlies extend from the western equatorial Paciﬁc, across southern Asia and Africa into the North Atlantic; these recurve across the equator and form the subtropical upper westerlies of the southern hemisphere. At 850 mb, equatorial westerlies and southwesterlies in the mean are well developed north of the equator only from central Africa to the Philippines. The distribution of sea surface temperatures and the large-scale topography ensure that the winter and summer monsoon circulations of southern Asia and the Indian Ocean are far from being mirror images of one another (Webster et al. 1977), as is apparent from the streamline maps of Figure 3.55. There is a modest anticyclonic outﬂow aloft over northwestern Australia, and the equatorial westerlies are conﬁned to the Australasian sector. Three topographic factors are important in this respect: the relative sizes of Asia and Australia; the location and form of the Asian mountains and Tibetan Plateau; and the mountainous islands (particularly Sumatra, Java, Borneo and New Guinea) comprising the “maritime continent” (Ramage, 1968). The large-scale shift between the winter and summer circulation regimes over southern Asia clearly involves major adjustments in the atmosphere. Key elements in this transition are: 1 2

0

3 4

11

The weakening and northward displacement of the westerly subtropical jetstream (STJ) and the appearance of the tropical easterly jet (TEJ) over southern India (see section 4.2). The associated reversal of the thermal gradient in the upper troposphere, which is directed northward in the cold season, but is directed equatorward over southern Asia in summer, owing to atmospheric heating over the Tibetan Plateau. Strong heating over the Indian subcontinent in the spring transition months establishing a south–north pressure gradient at low levels. Cross-equatorial motion of the southern hemisphere easterly trade winds in the Indian Ocean and their deﬂection to a southwesterly direction over the Arabian Sea.

196 Synoptic and dynamic climatology

Figure 3.55 Mean streamlines for (a) June–July–August and (b) December–January–February at 850 mb (upper) and 200 mb (lower). (From Webster et al., 1977, after Sadler, 1975, and Newell et al., 1972)

5 6

The regional enhancement of this cross-equatorial ﬂow over East Africa in the Somali low-level jet (LLJ) and its associated transport of moisture in the lower troposphere. The formation of an onset vortex over the Arabian Sea, with westerlies on its equatorward side triggering the start of heavy rains over central India (Krishnamurti et al., 1981). However, this feature does not develop in every year.

Global climate and the general circulation 197 11

(a)

0 (b)

0 Figure 3.56 Schematic illustration of (a) “active” and (b) “break” phases of the Asian summer monsoon (see text). (From Webster, 1987b)

0

0

The monsoon over India is characterized by active phases and breaks with little or no precipitation. Figure 3.56 illustrates the latitudinal distribution of cloud and precipitation during these phases. In the active phase there is a strong TEJ and maximum precipitation is over the central Indian peninsula. In the break regime there is subsidence over peninsular India, with cloud bands and precipitation to the north over the Himalayan foothills as well as to the south around 5°–12°N. Breaks tend to recur over approximately ten-to-twenty-day and forty-to-ﬁfty-day intervals. Sikka and Gadgil (1980) show that during 1973–77 bands of maximum cloudiness (and the co-located 700 mb trough) in the Indian Ocean repeatedly propagated northward from a few degrees north of the equator to the Himalayan foothills (25°–28°N) in ten to twenty days. The extended forty to ﬁfty day variations represent modulations of convective activity propagating eastward in low latitudes (see Madden–Julian Oscillations (MJO), p. 334). Divergent components of the wind (represented by velocity potential ﬁelds5) associated with an MJO enhance (weaken) the monsoon westerlies as the direction of the divergent wind component has a large westerly (easterly) component (Webster, 1987b; Webster et al., 1998). A broader picture of break/active phases within the northern summer monsoon is suggested by intraseasonal oscillations (ISOs) in ECMWF tropospheric wind data and OLR signatures (Wang and Xu, 1997). These ISOs are found to be phase-locked to the annual cycle, moving northward from the equator to the northern Philippines during May–July, and westward along 15°N during August–September to the Bay of Bengal. Wang and Xu propose that climatological monsoon singularities over this large region are linked with this sequence and identify four phases: 1

0 2 11

A peak wet phase over the South China Sea and the Philippines in mid-May followed by a dry phase of the western North Paciﬁc monsoon (see below), the Indian monsoon and the Meiyu/Baiyu rains of East Asia in late May–June. A peak of convection, more or less in phase, in mid-June, marking the onset of the Indian and western Paciﬁc monsoons and the Meiyu in central China, followed by monsoon breaks and the end of Meiyu over northern China in mid-July.

198 Synoptic and dynamic climatology 3 4

The maximum of the western Paciﬁc monsoon in mid-August, followed by the westward propagation of a dry phase, giving a second western Paciﬁc break, out of phase with that in India during mid-September. The ﬁnal active phase of the western Paciﬁc monsoon and withdrawal of the Indian monsoon.

Despite the temporal association of precipitation events over southern and eastern Asia implied in 2 above, the Meiyu cloud band variations appear to be an independent mode linked with the East Asian jetstream (Liang and Wang, 1998; Kang et al., 1999). It seems that a deﬁnitive picture of the southern and East Asian summer monsoons cannot yet be presented. Wang and Fan (1999) discuss various monsoon indices for South Asia based on convective activity indicated by OLR data. The monsoon rains over China typically last thirty to forty days, with totals decreasing northwards, although in northern China there is also rain from mid-latitude systems (Samel et al., 1999). Precipitation in southern China is augmented when low-level cold air from the north is overrun by the warm, moist southwesterly monsoon ﬂow. The Meiyu front is also strengthened when there is high pressure over the Sea of Okhotsk. Monsoon rainband precipitation in northern China is greater when there is strong heating over Eurasia and increased southeasterly ﬂow associated with a subtropical ridge over the China coast. Holland (1995) identiﬁes a unique summer monsoon occurring over the western equatorial North Paciﬁc. In the lower troposphere there is a monsoon trough or diffuse lowpressure area, a westerly monsoonal ﬂow extending eastward over the equatorial western Paciﬁc, and a conﬂuence between this ﬂow and the central Paciﬁc tropical easterlies (see Figure 3.57) In the upper troposphere there are a diffuse anticyclone and extensive tropical easterlies. This regime appears to be quite distinct from the Asian summer monsoon. Key drivers of the system are the lower tropospheric conﬂuence and upper difﬂuence, which enhance convection and maintain a feedback cycle with the monsoon circulation. Precipitation within the Asian summer monsoon is strongly determined by synoptic systems. The major categories of rain-producing systems are: monsoon depressions, subtropical cyclones, and tropical cyclones (Hamilton, 1979), with heat lows producing hot, dry weather. These are categorized schematically in terms of vertical motion, divergence, and weather in Figure 3.58, and are they now discussed in turn.

Figure 3.57 Schematic diagram of the principal lower tropospheric components of the summer monsoon in the western North Paciﬁc. (From Holland, 1995)

Global climate and the general circulation 199 11

0

Figure 3.58 The circulation systems which occur in monsoon climates, arranged schematically according to their divergence, vertical motion, and weather. Levels where nondivergence occurs are marked by heavy lines. (From Ramage, 1971)

0

0

0 Figure 3.59 Monsoon depression over southern Asia, 12.00 GMT, July 4 1957. Above 500 mb height contours (dam). Below Sea-level pressure (mb) and the Monsoon Trough (dashed line). Wind arrows (barb indicates 5 m s1) and precipitation areas (oblique shading). (Based on IGY charts of the Deutsche Wetterdienst, from Barry and Chorley, 1998)

0 11

Monsoon depressions. These low-pressure systems move west-north-west across India from the Bay of Bengal, steered by the upper easterly ﬂow. Records since 1891 show there are two to two and a half per month during June–September (Sikka, 1977). During 1891–1970 about four to ﬁve systems each season crossed 85°E, primarily between 18° and 27°N, although only about one-quarter of these reached 75°E (Mooley, 1973; Rao, 1976, p. 107). A few may also occur over the Arabian Sea, usually in June. Monsoon

200 Synoptic and dynamic climatology depressions have a lifetime of three to ﬁve days. The circulation is cyclonic for some 500–1,500 km radially, and extends to about 400 mb, with maximum development between 700 mb and 850 mb. There is anticyclonic circulation and divergence above 10 km. The surface pressure anomaly is typically less than about 12 mb and the winds are between 9 m s1 and 17 m s1 (Sikka, 1977). Their thermal structure is varied; some are cold cored at lower levels and warm cored above 500 mb, some are mainly warm cored, while others are neither (Keshvamurty, 1972; Krishnamurti et al., 1975, 1976). In many cases the axis tilts southwestward with height, with a cold lower troposphere overlain by a warm pool above the main precipitation area southwest of the surface center, to the left of its track (Figure 3.59) (Godbole, 1977; Douglas, 1992). Monsoon depressions occur on only 7 percent of days in July–August at 80°–85°E; consequently they contribute only 8–12 percent of the monsoon rainfall over the Ganges basin (Dhar and Bhattacharya, 1973). Nevertheless, they do modify the spatial distribution and timing of rainfall in individual years. Their formation tends to activate the monsoon trough, and, when two systems occur in close succession along a similar track, ﬂoods may result (Sikka, 1977). The importance of rainfall from these systems is illustrated by the fact that the ratio of mean precipitation on days with depressions, relative to the mean on other days, is 1.5 at Delhi (28.8°N, 77°E), but 6.9 at Ahmadabad (23°N, 72.7°E) (Mooley, 1973). Most of the disturbances originate from lows over the western tropical Paciﬁc, South China Sea and Southeast Asia. Krishnamurti et al. (1977) propose the following sequence: 1 2 3

A tropical storm approaches the coast of Vietnam, and pressures rise around 20°N. In the following week, pressures rise 5–7 mb over Indochina and Burma. A monsoon disturbance forms in the northern Bay of Bengal.

According to Ramage (1971, p. 46), there are three favorable preconditions. The monsoon trough extends along the Ganges valley from the northern Bay of Bengal. Over the southern part of the bay, rising air associated with convergent west-southwesterly ﬂow overlain by divergent east-northeasterly circulation accelerates. The resulting increase in low-level cyclonic vorticity south of the monsoon trough becomes concentrated into a vortex in the trough, causing uplift, latent heat release, and circulation intensiﬁcation. Strengthening of the monsoon southwesterlies over India and the central Bay of Bengal plays a major role by increasing cyclonic wind shear farther north, according to Sikka (1977). Hovmöller longitude–time plots of surface pressure changes show a low– high–low pattern characteristic of low-latitude downstream ampliﬁcation. The waves have 6° westward phase velocity (Krishnamurti et al., 1977). The failure of these systems to intensify into cyclones is attributed by Ramage (1971) to the limited heat source available over the northern Bay of Bengal and the presence of strong vertical wind shear. Subtropical cyclones. These systems were ﬁrst investigated in the eastern North Paciﬁc, where they originate from cut-off lows in the upper westerlies during winter–spring (Simpson, 1952; Ramage, 1962). In the Hawaiian Islands, where they tend to be longlived, they are known as Kona storms. They are best developed in the middle troposphere, where convergence between 600 mb and 400 mb is compensated for mainly by ascent and upper-level divergence. Also, beyond about 500 km radius, there is some low-level sinking (Figure 3.60). The inner 200 km constitutes a broad eye with only scattered clouds. A further type of subtropical cyclone is found over the northern Indian Ocean in summer (Miller and Keshvamurthy, 1968; Ramage, 1971; Hamilton, 1979; Hastenrath, 1991). These have a similar vertical arrangement of convergence and divergence, with surface westerlies and upper easterlies. Case studies show that the system’s core is cold at 700 mb and relatively warm at 500 mb. Figure 3.61 illustrates a north–south cross-section along 72°E and the corresponding kinematic analysis at 600 mb for a system near Bombay, India, in July 1963. These systems tend to form, intensify, and dissipate during a one-to-

Global climate and the general circulation 201 11

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0 Figure 3.60 Schematic radial cross-section of a subtropical cyclone, showing gross features of vertical motion and clouds. Divergence (convergence) is denoted by () signs. Regions of dry (moist) adiabatic temperature changes are shown by D (M). (From Ramage, 1971)

three-week period over the northeastern Arabian Sea. They are an important contributor to rainfall over western India. Precipitation falls mainly south and west of the cyclone center, to the left of the cyclone track. Since the development of a subtropical cyclone requires a deep, moist airstream, which is associated with the monsoon southwesterlies, the rains over western India are typically later than over the east of the country, where the mean ﬂow steers depressions from the east. According to Ramage, subtropical cyclones do not develop until a heat low has formed over the Great Indian Desert in northwestern India. He proposes (1966, 1971, pp. 60–7) that this heat low is the source of mid to upper tropospheric cyclonic vorticity which is exported towards the Arabian Sea. The developing subtropical cyclone in turn intensiﬁes the heat low through subsidence. Dissipation appears to result from the mid-tropospheric advection of dry air into the cyclone. Three subtropical cyclones studied by Miller and Keshavamurthy (1968) were all preceded by cyclonic activity over eastern India, and were associated with anticyclonic vorticity over the heat low and enhanced cyclonic vorticity over the Arabian Sea between 18°N and 21°N. Subtropical cyclones are also observed on the equatorward side of heat low troughs over the Sahara and Australia (Ramage, 1971), over the southern Bay of Bengal and Burma in April–May, and over Indochina and the South China Sea in summer.

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Heat lows. The deserts bordering the summer monsoon regions are dominated in summer by persistent, quasi-stationary heat lows or troughs. These are found, for example, over the Sahara, in an arc from the Horn of Africa through Arabia to northwest India, over Australia, and over the Great Basin of the United States. Their particular signiﬁcance in the context of the monsoon systems is their apparent role in exporting energy in the upper troposphere which helps sustain the monsoon system (Rodwell and Hoskins, 1996) and may also support the genesis of subtropical cyclones, as noted above. The structure of a heat low, or trough, features low-level converging air. However, little cloud generally develops as a result of the very low relative humidity. Moreover, subsidence

202 Synoptic and dynamic climatology (a)

(b)

Figure 3.61 Composite model of a subtropical cyclone over the Arabian Sea, showing (a) a kinematic analysis at 600 mb for July 2–10 1963; (b) the distribution of clouds, vertical motion and temperature along 20°N, and (c) along 72°E. In (a) the isotachs are in knots; each full wind barb represents 5 m s1. Rainfall exceeded 40 mm day1 along the coast between 21°N and 12°N. In (b) and (c) the vertical velocity vectors represent 40 cm s–1 (long) and 5 cm s–1 respectively. (From Miller and Keshavamurty, 1968, and from Ramage, 1971)

(c) occurs in the middle troposphere, associated with convergence in the upper tropospheric tropical easterly jet. This subsidence maintains an inversion level in the middle troposphere. During the transition seasons the heat low may be diurnal, forming by day through surface heating and weakening or dissipating at night through radiative cooling. There have been few detailed investigations of heat lows, largely because of the paucity of stations in desert areas. However, two ﬁeld campaigns have targeted the Arabian heat low in the context of the summer monsoon over southwest Asia. Measurements during

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MONEX in the summer of 1979 identiﬁed weak heat lows of about 1,004 mb over the Arabian Peninsula during 9–12 May (Blake et al., 1983). The motion was anticyclonic and divergent between 850 mb and 700 mb, with westerlies above 600 mb. During the day there was ascent up to 1 km and descent above, whereas, prior to sunrise, descent prevailed at all levels. Long-wave radiative cooling of 2°–5°C day1 throughout the troposphere is counterbalanced around midday by strong shortwave warming which is attributed to absorption by a dust layer extending to 600 mb. Adiabatic warming also occurs of up to 3°C day1 around 500 mb, associated with the downward motion. These two heating sources contribute to stabilizing the mixed layer. Smith (1986) demonstrates that in June 1981 the lower and middle layers are heated by radiative absorption and adiabatic descent (Figure 3.62). The heat low is approximately radiatively neutral or slightly positive at the top of the atmosphere. This implies that during the monsoon it maintains itself as an energy source to the surrounding environment. Horizontal advection of the mid-tropospheric excess heat over the northwest Arabian Sea may help in maintaining the low-level inversion which is generally attributed solely to large-scale subsidence. This inversion caps the low-level southwesterly jet ﬂow which transports the moisture towards the monsoon trough over India. Thus the Arabian heat low may play an important role in the monsoon system. Measurements of the surface energy budget over the Rub-al Khali Desert during June–July 1981 by Smith (1986) indicate peak daily values of the heating terms near the center of the heat low of the following order: Absorbed solar radiation 650 W m2 Net long-wave radiation 250 W m2 Net radiation 400 W m2 Sensible heat 250 W m2 Ground heat storage 175 W m2

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Figure 3.62 Controls of the structure of the Arabian heat low. (From Smith, 1986)

204 Synoptic and dynamic climatology However, the phases of these peaks are not symmetrical. For daily averages in summer the net radiation is balanced approximately by sensible heat. The day-night range of skin temperature is up to 50°C because sand is a poor heat conductor. Recent work in northwestern Australia suggests that the “classic” heat low, with lowlevel cyclonic convergence, a late afternoon pressure minimum, and divergent anticyclonic circulation aloft, occurs primarily in the absence of large-scale circulations that can lead to subsidence throughout much of the troposphere (Racz and Smith, 1999). Analysis with a hydrostatic primitive equation numerical weather prediction model indicates that, in northwestern Australia, a daytime sea breeze and nocturnal low-level jet play an important role in low-level convergence. Convective mixing in the afternoon weakens the cyclonic relative vorticity, which reaches its maximum in the early morning. The Asian winter monsoon In the northern hemisphere the intense polar anticyclones over eastern Siberia generate recurrent surges of cold, dry northerly airﬂow in winter over China and southeast Asia. In contrast, southward ﬂow out of central Asia is largely blocked by the mountain ranges of the Caucasus, the Elburz and Zagros in Iran, and the Hindu Kush–Karakorum– Himalayas. Weak northeasterly low-level ﬂow over the Indian subcontinent originates from air subsiding beneath the subtropical westerly jetstream, aided by the land–sea pressure gradient that is maintained by the land–sea temperature difference. High-pressure cells emanating from west of Lake Baikal near 40°N, 90°E tend to move southeastward across northern China to the Yangtze delta (track 1 in Figure 3.63b) while others affect northeastern China, the Yellow Sea, the Korean peninsula, and the Sea of Japan (tracks 3 and 4). Cells that move eastward to the south of 50°N cross Xinxiang Province and Mongolia and affect eastern China (track 2). During winters 1980–84, 64 percent of high cells moved on track 1, 27 percent on track 2, and 10 percent on track 3 (Ding and Krishnamurti, 1987). Outbreaks of cold Siberian air are associated with a sharp pressure rise which propagates rapidly southeastward over China. Pressure tendencies at stations along the path of a cold surge indicate that it can advance at 30 m s1, covering 2,000 km in twenty-four hours; this is up to three times the speed of the winds in the surge, implying that the mechanism involves the propagation of a gravity wave rather than cold air advection (Chang and Lau, 1980). Compo et al. (1999) suggest that they may represent a topographic Rossby (“shelf”) wave related to the eastward-sloping terrain of East Asia. About twenty such cold events affect some part of China in a given winter (Pan et al., 1985). Using NCEP/NCAR reanalysis data for 1979–95, Zhang et al. (1997) report an average of thirteen cold surges during October–April, of which two are typically strong events. Surges evolve over ﬁve to fourteen days and last about nine days. They may travel as far as Singapore (1°N, 104°E) within a couple of days, although their cooling effect is negligible in low latitudes. In northwestern China, Mongolia, and eastern China the temperature reduction is typically 10°–12°C, but drops of 20°C or more are common in coastal areas of southern China. The frequency of cold surges over the South China Sea (Hainan Island) is found to be low (high) during El Niño–low SOI (La Niña–high SOI) conditions (Zhang et al., 1997). However, the interannual variability in cold surges of the winter monsoon and ENSO is not yet understood, (see sections 5.2 and 5.4). There are robust correlations between surge events over East Asia and anomalies in low-level winds and convection over the Bay of Bengal, the eastern Indian Ocean, Indonesia, and the western Paciﬁc, according to Compo et al. (1999). Also, surges over the South China Sea are linked with enhanced convection over Indonesia, the Philippines, and the eastern Indian Ocean. Hence the surges are of more than regional importance.

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Figure 3.63 (a) The frequency of Siberian high-pressure centers calculated for 5° latitude–longitude boxes for October–March, 1955–79. (b) Schematic tracks of high-pressure cells over eastern Asia. (After Pan et al., 1985, and Ding and Krishnamurti, 1987)

206 Synoptic and dynamic climatology The Australian monsoon The austral summer monsoon in northern Australia is associated with westerly and northwesterly cross-equatorial airﬂow in the sector 100°–160°E, from 5° to 12°–15°S. A monsoon trough or shear line forms between these westerlies and trade wind easterlies to the south, with heat lows over northwestern and eastern Australia augmenting the forcing (McBride and Keenan, 1982). In northwestern Australia a regional heat low over Pilbarra generates onshore westerly ﬂow, identiﬁed as a pseudo-monsoon by Gentilli (1971), while in northern Queensland the shear line/trough is disrupted by the topography of the Great Dividing Range. Monsoon onset and withdrawal over northern Australia can be identiﬁed in terms of monsoon rains, lower-tropospheric westerly winds, or changes in upper-level winds (Suppiah, 1992). For the Indonesian sector, Tanaka (1992, 1994) ﬁnds a simple zonal pattern of monsoon advance and retreat when wind data are used. However, if cloud images of convective activity are used, the monsoon onset is shown to be ﬁfteen to sixty days earlier over New Guinea and northern Australia than over the adjacent sea areas. Figure 3.64 illustrates the monsoon season in 1978–79 deﬁned by the 850 mb zonal wind component at Darwin. During 1952–82 the mean onset and ending dates were December 24 and March 7 (Holland, 1986), but the seventy-four-day average season has a standard deviation of twenty-ﬁve days. Drözdowsky (1996) ﬁnds mean dates to be ﬁve or six days later, using surface 500 mb zonal winds for 1957–92. Much of the interannual variability is related to ENSO characteristics in the preceding spring (September–October–November) season. During warm (cold) events the summer monsoon trough shifts equatorward (southward) (Evans and Allan, 1992). During onset there is strong low-level convergence over northern Australia and the Arafura Sea to the north. Deep convection is often associated

Figure 3.64 Daily mean zonal winds at 850 mb over Darwin during October 1978–September 1979, showing the summer monsoon season and active and inactive periods. (From Holland, 1986)

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with an intersection of the northern and southern hemisphere Hadley cells, according to Davidson et al. (1983). At 200 mb the tropical easterlies are observed to strengthen over this sector, and a weaker summer subtropical jetstream shifts southward from 25°S to about 40°S (McBride, 1987; Hendon and Liebmann, 1990). These changes mark part of the annual cycle of the general circulation in the two hemispheres. In contrast to southern Asia, however, the upper-level circulation over Australia in summer is only weakly anticyclonic. As elsewhere, the Australasian summer monsoon shows characteristic intervals of active bursts and breaks. Typically there are one or two such cycles, each lasting about forty days during the westerly season, that appear to be linked with the planetary circulation, the SOI, and probably MJOs. Commonly there is a break regime in late January–midFebruary (Holland, 1986). Active phases are associated either with a broad area of convection from the equator to 15°S, 100°–140°E, or with monsoon depressions and tropical cyclones along a shear line over northern Australia (McBride, 1986). Monsoon cloud bands are often located offshore over 29°C water, and the monsoon ﬂow intensiﬁes following week-long episodes of deep convection which Mapes and Houze (1992) attribute to MCSs. Their aggregate circulations spin up an active phase of the monsoon. Break phases feature a shallow monsoon shear line displaced equatorward, although rainfall may occur, with westward-moving squall lines (cloud lines), as it does in the pre-monsoon period. These mesoscale systems include a northwest–southeast line of convective cells (sometimes with the well known Morning Glory cloud line signature) that may also have extensive stratiform cloud layers to the east, or a strong tropical squall line (Holland et al., 1986). During the cool season (April–October), 70–90 percent of the precipitation in northwest Australia comes from cloud bands that originate west of 120°E, while northeastern Australia has rains from cloud bands originating east of the same meridian (Wright, 1997). 3.7.4 The extratropics

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The westerlies

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Poleward of the subtropical high-pressure cells, the circulation in both hemispheres is dominated by a circumpolar vortex with associated westerly wind belts, sometimes referred to as the Ferrel Westerlies (Hare, 1960, 1962). For the zonally averaged u component, the westerlies at sea level extend from 35° to 65° latitude in both hemispheres in summer and from 33° to 67° in the northern hemisphere winter, and from 28° to 66° in the southern hemisphere winter (Figure 3.65). The low values of the zonal wind component at the surface in the northern hemisphere result from the variability in wind direction caused by the frequency of traveling storm systems, the land–sea contrasts, and orographic and frictional effects on airﬂow. For example, in the Scilly Isles, off southwest England, 46 percent of the winds are from between southwest and northwest, but 29 percent are from the opposite quadrant. In contrast, Kerguelen Island (49°S, 70°E) has an 81 percent annual frequency of winds from between southwest and northwest. The zonally averaged winds in the southern hemisphere are strongest near 52°S in January and July. However, they average 12–14 m s1 in both seasons over the South Atlantic and Indian oceans, compared with 8–10 m s1 over the South Paciﬁc (van Loon, 1972b). The wind maximum over the South Indian Ocean is closer to the equator than that over the South Paciﬁc in both seasons. Although the area of the southern westerlies is greatest in the austral winter, they are stronger at 45°–50°S in some places during summer (van Loon, 1972a; 1991). This is related to the poleward decrease in the annual temperature range over the oceans from 30°S to about 50°S (Figure 3.66) caused by greater cloudiness and heat storage spread through a deeper mixed layer in mid-latitudes and the location of the southern continents. The greater annual temperature range in the subtropics, and the poleward temperature

208 Synoptic and dynamic climatology

Figure 3.65 Proﬁles of the average west-wind component (m s1) at sea level in the northern and southern hemispheres during their respective summer (A) and winter (B) seasons. (After van Loon, 1964)

decrease, result in a steeper latitudinal temperature gradient in summer than winter. From 55°S to 65°S surface winds have maxima in the transition seasons (Figure 3.67). The velocity of the westerlies increases with altitude in accordance with the poleward gradients of 1,000–500 mb thickness, as shown by the thermal wind relationship (see p. 266). Figure 3.68 illustrates latitude–height cross-sections for each hemisphere, showing zonal winds and isotherms. In contrast to the barotropic structure of low latitudes, the mid-latitude troposphere is weakly baroclinic, with narrow baroclinic zones related to the polar front separating tropical and polar air masses and a northern hemisphere arctic front separating polar and arctic air. There are also upper tropospheric baroclinic zones in the subtropics associated with jetstream conﬂuence. The strength of the mean jetstreams is in part determined by their persistence in space and time. The location of the polar front jet, for example, varies over a wide latitudinal range seasonally, in contrast to the subtropical jetstream. It is also temporally and geographically variable, in association with traveling waves. The subtropical jetstream in northern winter is strongly constrained by the orographic inﬂuence of the Tibetan Plateau and the Himalaya. The mean locations of the 200 mb jetstream cores show a close relationship to the standing long-wave troughs downstream of the Rocky Mountains and the Tibetan Plateau and to the land–sea boundaries of eastern Asia and eastern North America (see Figure 4.8). In the southern hemisphere (Figure 3.69) the subtropical jetstream at 200 mb exceeds 40 m s1 over a 5°–10° latitude range from 90°E to 150°W, but in summer there is only a small core exceeding 25 m s1 around the dateline (Berberry et al., 1992). In winter the subtropical jet spirals poleward to merge with the subpolar jet over South America. The jet structure gives rise to differing seasonal conﬁgurations of absolute vorticity. Near New Zealand the 200 mb absolute vorticity ﬁeld is nearly ﬂat in winter between the polar and subtropical jets. The subtropical jet gives regions of maximum latitudinal gradient of absolute vorticity along the jet core; negative latitudinal gradients exist near 40°S from 105°E eastward to 160°W in winter, and there is a separate band around Antarctica in both seasons.

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Figure 3.66 Amplitude of the annual wave in zonally averaged temperature at the surface and 500 mb in both hemispheres. (From van Loon, 1972)

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Subpolar easterlies Poleward of the westerlies at sea level in both hemispheres are weak polar easterlies (see Figures 3.34 and 3.65). These represent the easterlies on the poleward sides of the Icelandic and Aleutian lows in the northern hemisphere and the subpolar trough in the southern hemisphere. There is no permanent high pressure over the Arctic Ocean, although polar anticyclones are common in winter and spring over the Beaufort, Chukchi, and East Siberian Seas sector (120°W to 140°E) (Serreze et al., 1993). Over Antarctica the topography of the ice sheet which rises to 4,000 m in East Antarctica and averages about 2,200 m elevation, makes it impossible to analyze MSL pressures poleward of about 70°S, except over the Ross and Weddell Seas. Indeed, the inﬂuence of the topography of Antarctica is evident in the zonal asymmetry of the southern hemisphere circulation (James, 1988) as described in section 4.3.

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Mean sea monthly level pressure maps for the globe were ﬁrst constructed by Alexander Buchan (1869), who described the zones of prevailing winds. The mean centers of high and low sea-level pressure were termed centers of action by Teisserenc de Bort (1883) because they represent the principal circulation features that help determine mid-latitude

210 Synoptic and dynamic climatology

Figure 3.67 The annual march of the zonally averaged MSL zonal geostrophic wind (m s1) (From van Loon, 1972)

weather and wind systems. Indeed, he described “anticyclones as regulators of weather.” The centers are clearly deﬁned in the northern hemisphere, whereas in the more oceanic southern hemisphere (see Table 1.1) the subtropical high and subpolar low-pressure areas form more or less continuous belts around the hemisphere (see Figure 3.34). There is, however, a fundamental difference between a mean high and a mean low-pressure center. The former represents a quasi-stationary system that changes little from day to day; the latter is an area into which deep cyclones move with high frequency (Petterssen, 1950). Thus mean pressure charts need to be compared with maps of the frequency and tracks of cyclone centers, the frequency of cyclogenesis/cyclolysis, and deepening rates for a proper understanding of their signiﬁcance (see section 6.4). Nevertheless, the mean subpolar lows are also stationary disturbances of the zonal ﬂow maintained by the atmosphere’s thermal structure, due to land sea contrasts, and by the effects of orography (Spar, 1950; Smagorinsky, 1953). The four principal centers of action in the northern hemisphere are the Icelandic and Aleutian lows, and the Azores and North Paciﬁc highs. Their long-term locations and central pressure averages, based on 5° × 5° gridded mean monthly data derived from the US Historical Weather Map Series for 1899–1978, are shown in Table 3.14, although these estimates are relatively crude, owing to limited data coverage in the early record, approximations caused by the data grids, and uncertainties when two or more locations had identical pressure values. It is interesting that in the North Paciﬁc the centers are

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Table 3.14 Long-term mean locations and central pressures of the northern hemisphere centers of action Feature

Central pressure (mb)

Location

Aleutian low Icelandic low Azores high North Paciﬁc high

1,002 1,001 1,024 1,024

56°N, 60°N, 33°N, 35°N,

168°W 36°W 35°W 143°W

Source: from Angell and Korshover (1982).

displaced longitudinally relative to one another, in contrast to the North Atlantic. However, there are major changes over the annual cycle, as discussed below. It must be noted that the vertical structure of high and low-pressure centers differs considerably. The Siberian winter anticyclone is a shallow, cold high; the hydrostatic equation (See Section 1, n. 1) implies that in a cold air column the isobaric surfaces are lowered with height. The equivalent case of a heat low, in summer over the southwestern United States for example, has isobaric surfaces that are raised with increased altitude within the warm air column. In contrast, the dynamically induced subtropical highs are warm at all levels. The axis of the highs in the northern tropics slopes westward and southward with increasing altitude, corresponding to the locations of warmer air in the upper troposphere. These highs are maintained by upper-level convergence and subsidence. The characteristics of each of the main centers of action are brieﬂy described. 3.8.1 The Siberian high

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In the winter half-year a shallow semi-permanent cold high extends over most of Asia, centered about 45°–50°N, 90°–110°E (Perry, 1971; Sahsamanoglou et al., 1991). For 1873–1988 mean monthly central pressure values exceed 1,027 mb from October through March, with highest mean monthly values of 1,037 mb in January. Daily values are often observed to be 1,050–1,070 mb (Ding, 1994). Walker (1967) notes that the sea-level pressures are problematic because many of the stations are in valley or basin locations. In winter these experience extremely low temperatures and persistent inversions, making the adjustment of barometric pressure to sea level dubious, since normal lapse rates are assumed. Nevertheless, high-pressure centers are observed to move out of the region across the Arctic coastline. Sahsamanoglou et al. report that 1,000–500 mb layer mean temperatures over the anticyclone average only 250–5 K during December–March, consistent with a cold high structure. Surface pressure values are inversely correlated with the 1,000–500 mb thickness, especially for November–February. Moreover the intensity of the high has weakened slightly since 1980, apparently in response to a warming trend. In the middle and upper troposphere there is a trough over eastern Asia (Gommel, 1963). The dynamic structure of the Siberian high features cold highs that move (1) eastward from southern Europe across central Asia; (2) southeastward from Scandinavia across the Urals; (3) southeastward from the Kara Sea; and (4) southward from the Taimyr Peninsula (Figure 3.63) (Ding et al., 1991; Ding, 1994). The Siberian high plays an important role in the winter climate of East Asia. It gives rise to frequent outbreaks of cold air over China as high-pressure cells move southeastward and the East Asian trough shifts eastward to near 140°E and deepens (Ding and Krishnamurti, 1987). Their analyses show that the development of a surface cold anticyclone typically involves northwesterly ﬂow in the middle and upper troposphere associated with the western limb of a trough off East Asia. This northwesterly ﬂow provides signiﬁcant cold air advection aloft which persists while surface high cells propagate southeastward.

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Figure 3.68 Latitude–height cross-sections of (a) mean zonal wind, u, and (b) meridional wind, v (m s1), (c) potential temperature, T (K), and (d) vertical motion, (102 Pa s1), for January 1987–89 (above) and July 1986–88 (below), from ECMWF analyses. The contour intervals are: 5 m s–1 for u, 0.5 m s–1 for v, 5K for T, and 1 × 10–2 Pa s–1 for . (From Trenberth, 1992)

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Figure 3.69 Mean locations of southern hemisphere 200 mb level jetstreams and mean absolute vorticity (contour interval 1 105 s1). (a) Winter. (b) Summer. (Berberry et al., 1992)

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The dynamics of the anticyclone can be understood from consideration of the proﬁles of relative vorticity and diabatic heating (Ding, 1994). Based on ﬁve cases, the inception of anticyclogenesis is associated with positive vorticity advection in the middle and upper troposphere. During this inception stage there is convergence in the lower and upper troposphere, with divergence in between. During the mature stage, relative vorticity is negative throughout the troposphere as a result of advection of anticyclonic vorticity in the mid-upper troposphere and low-level divergence. The heating proﬁles show a heat sink of about 2ºC day1 in the lower middle troposphere due mainly to radiative cooling, with a weak heat source in the upper troposphere related to horizontal advection. The adiabatic cooling induces subsidence in the mid-lower troposphere which results in upper (lower)-level mass convergence (divergence). In summer, most of Asia is covered by a broad low-pressure system centered over northwestern India. In early summer this is primarily a surface heat low, but the elevated terrain over much of the area makes the MSL pressures largely hypothetical. 3.8.2 The North Atlantic subtropical anticyclone

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The subtropical high-pressure center in the North Atlantic is a prominent center of action which, in combination with the Icelandic low, determines the circulation pattern over the North Atlantic (Tucker and Barry, 1984). Only exceptionally is this mean pattern reversed (Moses et al., 1987). Using sea-level pressure data for 1873–1980, Sahsamanoglou (1990) shows that the monthly mean central pressure exhibits maxima in July (1,026 mb) and January (1,024 mb) and minima in October (1,022 mb) and March (1,023 mb). The center is located mainly between 25°–40°N, 20°–45°W. There is a seasonal clockwise displacement of the center from about 30°N, 25°W (the Azores high) in December, westward to 40°W in July (the Bermuda high) and then northward to 35°N in September and eastward to 20°W in November, before returning southward. A correlation of the central pressure individually with the latitude and longitude of the center (Sahsamanoglou, 1990) indicates a tendency for the pressure to be higher when the center is farther northward; this also occurs for an eastward displacement during December–February, April and September–October. An independent study for 1899–1990 by Davis et al. (1997) provides daily frequencies of pressure values ≥ 1,020 mb over a 5° 5° latitude–longitude grid. This reveals two principal spatial modes of the anticyclone – a single maximum centered over the central Atlantic (30°N, 35°–40°W) in summer, and a winter pattern of weaker, dual maxima with cells over the southeastern United States (32°–35°N, 83°–85°W) and off northwest Africa (30°N, 22°W in January). The winter mode accounts for 26 percent of the variance and the summer one for 18 percent. There is, however, substantial variability in the frequencies of high pressure in the core region and around its fringes. Moreover, Davis et al. (1997) report a long-term decline in the frequency and intensity of the Azores high, which appears to represent a real net loss of mass over the North Atlantic. A similar conclusion is reached by Perry (1971) and Sahsamanoglou (1990). Central pressure in the Azores anticyclone increased from 1873 to 1914 (by 8.8 mb/100 yr), decreased from 1914 to 1963 (5.2 mb/100 yr), and then rose again. 3.8.3 Icelandic low

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The subpolar Icelandic low is centered between 55°–70°N, 10°E–60°W, with a monthly mean central pressure that varies between 994 mb in January and 1,008 mb in May (Sahsamanoglou, 1990). Like the Azores anticyclone, the center of the Icelandic low shows a seasonal movement, but in an east–west direction. From 60°N, 30°W during February–April the center moves to 70°W in July–August, returning to 30°W during September. However, in July there is a secondary frequency maximum of the low center

216 Synoptic and dynamic climatology appearing at 18°–30°W, while in March–April this secondary maximum appears at 0°– 10°E. The area from Iceland to the Kara Sea is characterized not only by a broad maximum of cyclone frequency in winter, but also by maximum deepening rates of 6.8 mb/12 hr near Iceland. In summer this area is represented by cyclone deepening rates of only 3.3 mb/12 hr and regional contrasts in cyclogenesis and cyclone occurrence are far less pronounced (Serreze, 1995). Between 10 percent and 15 percent of cyclone events in the Icelandic low represent cyclogenesis and 13–18 percent (depending on the month) involve cyclolysis (Serreze et al., 1997). About half of the cyclones within the Icelandic low develop north of 55°N, but others can be traced upstream to the Rocky Mountain areas of Alberta or Colorado. Winter cyclone events are shown to be twice as frequent during the positive mode of the North Atlantic Oscillation (see section 5.5) as during the negative mode. Unlike the Azores anticyclone the intensity of the Icelandic low remained almost constant between 1973 and 1980 (Sahsamanoglou, 1990). However, the central pressure decreased in winter in the late 1980s (Machel et al., 1998). The mean center drifted westward at 3.7° per year southward at 1.7° per year, with maximum rates during 1873–90 and 1910–30. During winter 1955–70 the center shifted 1°–2° southward of its 1885–1995 mean (59.2°N), moving back northward in the 1980s. Since the 1970s there has also been a signiﬁcant eastward displacement of the low in summer. 3.8.4 Aleutian low The Aleutian low resembles the Icelandic low in intensity. Its characteristics are reported using data for 1960–85 by Martynova (1990) and for 1891–1990 by Perevedentsev et al. (1994). It is deepest (approximately 995 mb) in December–January and weakens to about 1,010 mb in June. The center shifts from near 50°N in January to 60°–65°N in August, it is near the dateline in winter, but moves eastward to about 160°W in the autumn (Martynova, 1990). Interannually, the strength of the mean Aleutian low in winter months ﬂuctuates between a mode when the center is near 50°N, 170°W, with a central pressure below 1,000 mb, and another where the low is weaker and farther west (near 165°E), with high pressure over Alaska (Rogers, 1981). These modes correspond respectively to winters with below (above) normal temperatures in the Aleutians (western Canada), and the opposite pattern. This North Paciﬁc pressure oscillation is described in section 5.6. Overland et al. (1999) show that 37 percent of the wintertime interannual variability in the Aleutian low is on a time scale longer than ﬁve years. It was a prominent deep low after 1977, but returned to near average strength in 1989. 3.8.5 North Paciﬁc subtropical anticyclone The North Paciﬁc anticyclone, according to Martynova (1990) and the data of Perevedentsev et al. (1994), shows less latitudinal displacement, but more longitudinal displacement, than its Atlantic counterpart. From about 30°N in January–February it moves to 40°N in July. During this shift it strengthens from 1,021 mb to 1,026 mb. Its mean minimum intensity is reached in October (1,020 mb), as in the North Atlantic subtropical anticyclone. The longitudinal displacements are between about 135°W in November and 150°W in July (Hastenrath, 1991, p. 137). 3.8.6 The Arctic high Simple schemes depicting the global circulation usually show high pressure over the Arctic and subpolar easterly winds north of the subpolar low pressure centers. The concept can be traced to the theoretical ideas of Helmholtz (1888), who argued on dynamical grounds

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for a surface high beneath the cold tropospheric polar vortex. The ideas were further developed in the “glacial anticyclone” theory of Hobbs (1926, 1945), subsequently refuted for Greenland by Matthes (1946) and Matthes and Belmont (1950). In the virtual absence of data north of the Arctic Circle, synoptic analysts adopted these climatological concepts and extrapolated the pressure ﬁelds over the Arctic in preparing the US Weather Bureau historical series of daily northern hemisphere weather maps for 1899–1945. Nevertheless, arctic expeditions noted the high synoptic variability of the marginal seas and, as early as 1945, B.L. Dzerdzeevski noted frequent cyclonic activity in the Arctic Basin, especially in summer. Jones (1987) suggests a positive bias of about 4–6 mb prior to 1930 in the US Weather Bureau analyses over the central Arctic. The establishment of stations in the Canadian High Arctic by the early 1950s and the deployment of Arctic Ocean drifting buoys since 1979 have greatly improved the data coverage. More recent surveys using these data (Serreze and Barry, 1988; Colony and Rigor, 1992; Serreze et al., 1993) indicate that during winter there is more usually a ridge of high pressure over the Beaufort–Chukchi–East Siberian seas linking centers over Siberia and the Mackenzie–Yukon, with a trough over the Barents Sea. Serreze and Barry show highest winter anticyclone frequency (1979–85) over the Beaufort Sea between 75°N and 90°N, 140°W to 160°E. Cyclones from the North Atlantic regularly track into the Arctic in winter north of Novaya Zemlya (75°N, 20°–80°E) and south of Svalbard (75°N, 20°–40°E). These patterns largely persist through spring, although pressures are now highest (1,022 mb) off the Queen Elizabeth Islands, centered about 80°N, 130°W (Maxwell, 1980). There are no strong maxima of highs or lows over Greenland. MSL pressures are, of course, largely ﬁctitious, but upper lows do occasionally cross the ice sheet at the 700 mb level (Hamilton, 1958). Atmospheric pressure at all levels in the troposphere and stratosphere are higher (lower) in northern latitudes (south of 40ºN) in spring than in autumn; this difference between the transition seasons strengthens with altitude and the latitudinal pressure gradient intensiﬁes. Fleming et al. (1987) attribute this to the fact that tropospheric mean temperatures are lower in spring than in autumn in middle and higher northern latitudes. In the northern hemisphere the annual temperature cycle in the middle and upper troposphere lags about forty days behind that of incoming solar radiation. A major change occurs in summer (Figure 3.70). Anticyclones, with central pressures of about 1,025 mb, occur in a band about 75°–80°N eastward from near Novaya Zemlya to the Beaufort Sea. A mean low is usually present over the Canada Basin for at least one month during the period July–September. For August 1979–1985, the mean low over the Canada Basin (85°N, 120°W) had a central pressure of about 1,006 mb, comparable with the mean Icelandic low in summer. Moreover, for individual three-to-four-week periods the central pressure may average as low as 995 mb, as occurred in midAugust–mid-September 1980 (Serreze, et al., 1989). This represents a recurrent tendency, in about two years out of three, for a cyclonic system to move into the area, from northern Eurasia mainly, become stationary, and persist for up to a month (Serreze and Barry, 1988). However, this sector is characterized only by cyclolysis (Serreze, 1995). 3.8.7 Southern hemisphere centers of action

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The southern hemisphere is 81 percent ocean surface, and three-ﬁfths of the land area is concentrated between the equator and 30°S. The mean circulation is much less cellular than in the northern hemisphere. Nevertheless, important zonal asymmetries in atmospheric and oceanic characteristics are apparent in association with the topography of the Andes cordillera and eastern Antarctica, the relative sizes of the South Atlantic, Indian and Paciﬁc Oceans (approximately 2, 3 and 5 respectively), and the cold Benguela and Peru currents and associated upwellings off southwest Africa and western South America (Taljaard, 1967).

218 Synoptic and dynamic climatology

Figure 3.70 Mean sea-level pressure over the Arctic Ocean. (a) Annual. (b) For August 1979–85. The arrows indicate motion of drifting buoys on the sea ice. (From Serreze and Barry, 1988; Serreze et al., 1989)

The most striking element of mean sea-level pressure maps for the southern hemisphere is the pronounced circumpolar trough of low pressure, located between about 60°S and 65°S (van Loon, 1984; Figure 3.71). This climatological feature is produced by mature mid-latitude depressions that originate primarily in the subtropical South Paciﬁc and South Atlantic Oceans and spiral clockwise toward Antarctica with an additional contribution from high-latitude polar lows and cold air mesocyclones (van Loon, 1972b; Trenberth, 1981a; Carleton, 1992). However, cyclone tracks in the southern hemisphere are less well deﬁned than their northern hemisphere counterparts (Physick, 1981). The trough expands (contracts) and simultaneously weakens (intensiﬁes) in a semi-annual oscillation (see below). Mean zonally averaged pressures are lowest (980 mb) in September (spring); they average 983 mb in July–August–September and 986 mb in January–February–March (Hurrell et al., 1998). North of the circumpolar trough there is a zonal belt of subtropical high pressure. The zonally averaged subtropical ridge (STR) is centered on 28°S in June–July–August and 33°S in December–January–February. However, the three subtropical anticyclone centers are located farther south, with highest mean central pressures at 38°S in June–July–August and 46°S in December–January–February (Jones and Simmonds, 1993). The displacement of the mean STR towards the subtropics suggests greater persistence, associated perhaps with blocking and/or cold air surge events (Sinclair, 1996). In the winter half-year (May–October) the anticyclones have the following locations and mean central pressures: South Indian Ocean (40°S, 60°E; 1,023 mb); South Atlantic Ocean (40°S, 5°W; 1,022 mb) southeast Paciﬁc Ocean (40°S, 90°W; 1,020 mb). In the summer (November–April) they are 2–3 mb weaker, but occupy almost the same

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0 Figure 3.71 Elements of the circulation over the Southern Ocean (from Carleton, 1992). Variability of the mean seasonal axis of the circumpolar trough, 1972–77 (from Streten, 1980). Relative frequency with longitude of monthly mean low pressure centers in the trough (1972–77). Tracks (heavy arrowed lines: major; thin arrowed lines: secondary). Mean location of the continental anticyclone center (1972–77), September/February sea ice limits (Jacka, 1983)

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locations in the South Atlantic and South Paciﬁc, while the Indian Ocean center is now near 85°E. The subpolar lows are also deepest in winter. Central pressures average 976 mb in the Ross Sea, near 70°S, 150°W; 977 mb near 63°S, 100°E, and 980 mb near 65°S, 25°E. In summer the last two centers occupy almost the same locations and are only slightly weaker, while the South Paciﬁc center is displaced eastward near 82°E, with a central pressure of 980 mb. Over Antarctica monthly mean charts for the lower-middle troposphere (800–500 mb) indicate that an anticyclone is generally present over the high plateau of East Antarctica, with a cyclone over West Antarctica (Taljaard, 1972; Schwerdtfeger, 1970, 1984; LeMarshall et al. 1985). This pattern undergoes little seasonal variation. Some cyclones penetrate into the continental interior from the Ross Sea across Marie Byrd Land. However, daily 500 mb maps show that very few cyclones affect East Antarctica, although the high may split into two centers (Astapenko, 1964).

220 Synoptic and dynamic climatology 3.8.8 The semi-annual oscillation in the southern hemisphere The tropospheric circulation in the southern hemisphere features a well known semiannual oscillation (SAO) in the extent and strength of the circumpolar low-pressure trough (Schwerdtfeger, 1960; van Loon, 1967). The maximum amplitude of the SAO at sea level is observed at 55°–60°S. The amplitude is ≥ 3 mb in high latitudes, where it accounts for 50 percent or more of the variance in pressure (Simmonds and Jones, 1998). The annual wave dominates over the three continents in lower latitudes but the semi-annual component is evident in middle latitudes. The trough contracts and intensiﬁes between June and September, and again from December to March, and it expands and weakens from March to June and from September to December. Pressure difference maps between these seasons illustrate the changes in the trough and equatorward to 25°S. The June mean minus that for March shows the expansion of the trough over the Southern Ocean, especially the South Paciﬁc, contrasting with pressure rises in middle latitudes over the southern continents (Figure 3.72b) (van Loon and Rogers, 1984b). This oscillation is also apparent in the troposphere, and van Loon (1967) used the difference in zonal mean 500 mb temperature between 50°S and 65°S as a suitable index (Figure 3.73). Fifty-nine percent of the variance in this difference is attributable to the second harmonic, although the ﬁrst harmonic is heterogeneous in phase in the zonal means and so its contribution is reduced in the averaging (van Loon and Rogers, 1984a). The forcing of the oscillation arises from the differences in annual temperature cycle in the troposphere over the mid-latitude oceans and Antarctica (Schwerdtfeger,1960; van Loon, 1967; Mo and van Loon, 1984), and also between continents and adjacent oceans in the subtropics and lower mid-latitudes (van Loon, 1972b) (Figure 3.66). At 50°S ocean heat storage delays the seasonal maximum and minimum of SST by two or three months from the extremes of net surface heat ﬂux. Heat storage also inﬂuences the more rapid fall of SSTs from their maximum in late summer and their slower rise from the late winter minimum. The decrease in autumn is facilitated by deep vertical mixing, whereas the spring rise is slowed by the strong winter cooling of the surface layer. These contrasts in heat capacity and storage inﬂuence the longitudinal location and amplitude of the tropospheric waves, especially those between Australia and the Indian and South Paciﬁc Oceans (van Loon, 1967, 1972b). They are revealed in maps showing the differences in SLP between mid-season months, whereby pressures rise (fall) over the continents (adjacent oceans) between March and June, and fall (rise) over the same areas between June and September (Mo and van Loon, 1984). The resulting changes in the amplitude and longitude position of the waves are also manifest in the meridional wind (v component). A signal reminiscent of the SAO has been identiﬁed in the seasonal changes in latitude of the speed maximum associated with the Antarctic Circumpolar Current (Large and van Loon, 1989). The SAO is also believed to play a part in the asymmetric seasonal regimes (advance, retreat) of the Antarctic sea ice cover occurring via surface wind stress associated with the circumpolar trough (Enomoto and Ohmura, 1990). The seasonal marches of the SLP for islands in southern middle latitudes and the Antarctic are out of phase (Streten and Zillman, 1984), manifesting the SAO over subantarctic latitudes. The resulting intensiﬁcation (relaxation) of the gradients of SLP and zonal wind speed (Figure 3.67) during the equinoctial (solstitial) months produce twiceyearly changes in the preferred latitude locations and mean central pressures of the Antarctic circumpolar trough. These comprise two equatorward (in December, June) and two poleward (March, September) latitude extremes of the trough, when it is also weaker (stronger) (van Loon, 1967). These excursions are associated with the preferred patterns of extratropical cyclogenesis in middle latitudes and cyclone tracks into the “graveyards” (cyclolysis regions) close to Antarctica (Carleton, 1981; Howarth, 1983). Satellite imagebased studies of synoptic-scale cloud vortices in the southern hemisphere (Carleton 1981) show the strong latitude variations in cyclone occurrence that occur between early winter

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Figure 3.72 Mean sea-level pressure difference for (a) March minus December, (b) June minus March, (c) September minus June, and (d) December minus September, illustrating the semi-annual oscillation in the southern hemisphere. (From van Loon and Rogers, 1984)

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(June) and late winter (September), in association with the changes in the time-averaged circumpolar trough associated with the SAO. There is also a signature of the SAO in the patterns of formation and movement of cold-air mesocyclones over the southern oceans when considered on weekly to monthly time scales (Carleton and Carpenter, 1990; Carleton and Song, 1997). Numerical modeling conﬁrms the primary mechanism of the SAO to be the intensiﬁcation and relaxation of the temperature gradient asymmetrically between low and high latitudes (Meehl, 1991). Anomaly experiments to simulate the impacts on the SAO of leveling the Antarctic continent and removing the sea ice reveal inﬂuences of both but with a stronger sensitivity to the elevation of the ice sheet (Walland and Simmonds, 1998).

222 Synoptic and dynamic climatology

Figure 3.73 The difference in zonal mean 500 mb temperature between 50°S and 65°S (dotted line). Dashed line = ﬁrst harmonic, solid line = second harmonic. Their variance contribution is shown. (Meehl, 1991)

This is in accord with the apparent inﬂuence of East Antarctica in helping to generate baroclinic instability at high latitudes (Mechoso, 1980; James, 1988). Walland and Simmonds (1998) suggest that cloud-radiative forcing is the common factor involved in the results of both experiments, possibly indicating the importance of the higher-latitude cloudiness regime to the SAO over sub-antarctic latitudes. There are interannual and also decade-scale changes apparent in the strength of the SAO that are not explained by differences in data sets between study periods (van Loon and Rogers, 1984a; Mo and van Loon, 1984). The SAO may be linked to the ENSO: it is weaker when the westerlies are stronger and temperatures are below-normal in East Antarctica; as occurs during a warm event (Van Den Broeke, 1998b). The SAO was strong in the 1970s, when it was the dominant signal in middle and high latitudes of the southern hemisphere. The SAO weakened in the early 1980s and, in mid-latitudes, in the early 1990s. On a multi-year time scale, since about 1979 there has been a weakening and delay into November of the springtime phase of the SAO (Hurrell and van Loon, 1994), especially near Antarctica (Simmonds and Jones, 1998). Simmonds and Jones (1998) suggest that the variability of the SAO in the high-latitude temperature gradient could be related to the effects of sea ice extent on the atmospheric dynamics. The change in the springtime phase may have important implications for the intensity and longevity of the Antarctic “ozone hole” since that time. Its source is believed to be in the strengthened latitude gradient of pressure and temperature that accompanied trends to increased SSTs over lower latitudes and decreased SLP in the Antarctic circumpolar trough, notably in the Paciﬁc sector. This change in the temperature gradient is not distributed evenly throughout the year at each latitude (Meehl et al., 1998), and so the SAO has become modulated to produce peaks in approximately May and November, rather than in March and September. Possible “ﬁngerprints” of these changes are an observed intensiﬁcation of the poleward-directed ﬂuxes of sensible heat accomplished by the atmospheric eddies during the 1980s (van Loon and Kidson, 1993), and signiﬁcant cooling in May and June at stations in East Antarctica since the mid-1970s (Van Den Broeke, 1998b). Over the Antarctic continent the regional expression of the SAO is apparent in the annual temperature cycle which features a “coreless (or kernlose) winter.” This refers to the lack of a clear midwinter minimum; instead the temperature curve is almost ﬂat during the winter months after a steep decrease during autumn. Advection of mid-latitude maritime air and eddy heat ﬂux towards the interior via transient eddies help to delay the

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annual temperature minimum until early spring (van Loon, 1967; Meehl, 1991). Warm air with accompanying increases in cloudiness are advected into the region on the eastern side of a long wave trough (Stearns and Wendler, 1988). These changes affect dominantly wave No. 3 (Van Den Broeke, 1998a). Data from additional manned stations and also the automatic weather station network show this coreless winter to be widespread in the Antarctic (Bromwich, 1988; Allison et al., 1993), especially along the west coast of the peninsula (Van Den Broeke, 1998a). Moreover, it is present in most climate variables, particularly wind speed and precipitation (e.g. Turner et al., 1997).

3.9 Global climatic features There are some fundamental differences in the characteristics of the atmosphere between the tropics and extratropics that have profound signiﬁcance for their weather and climate (Charney, 1973; Asnani, 1993). These can be summarized as follows:

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1 In the tropical easterlies (mid-latitude westerlies), the atmosphere gains (loses) relative westerly angular momentum, owing to friction at the surface interface. The low-latitude gain is transferred to middle latitudes, where it is dissipated by friction. The westerlies would cease in about ten days without this continual resupply from low latitudes. 2 The Earth–atmosphere system in low latitudes has a positive net radiation balance, on average to about 35° latitude. Poleward there is a net deﬁcit; the tropical surplus is continuously offset by poleward meridional energy transfer in the atmosphere and oceans. 3 The lower and middle troposphere in the tropics (extratropics) is generally convectively unstable (stable). 4 In the tropics (extratropics) the troposphere is baroclinically stable (unstable) with respect to dry adiabatic processes. There are hyperbaroclinic (frontal) zones in the extratropics. 5 There is, respectively, weak (strong) vertical coupling in the tropical (extratropical) troposphere. Strong vertical coupling in the tropics requires moist adiabatic processes and condensation is necessary for synoptic-scale dynamic instability. Without condensation and latent heat release, vertical motion in the tropics is an order of magnitude smaller than in mid-latitudes, so that the motion in such cases is quasi-barotropic and quasi-nondivergent. 6 The Coriolis parameter, f, has a value of approximately 105 s1 in the Tropics and 104 s1 in middle latitudes. Relative vorticity () is of similar magnitude to f in the tropics but smaller than f in the extratropics. 7 The tropics have quasi-stationary seasonal wind regimes with high zonal wind constancy. There are superimposed slow oscillations in position and intensity. The extratropics are characterized by irregularly ﬂuctuating regimes of zonal and meridional circulation on submonthly time scales. 8 The extratropics (tropics) are affected by only westerly (both easterly and westerly) waves. 9 Tropical (extratropical) cyclones are warm (cold) core systems. Tropical systems are generally smaller in extent but have greater intensity than extratropical cyclones. 10 Average daily totals are of the order of 1 cm (3 cm) for precipitation areas in the extratropics (tropics). Diurnal precipitation regimes are strongly developed in the tropics and there is a semidiurnal (twelve-hour) pressure wave. 3.9.1 Global cloudiness and precipitation characteristics The cloudiest regions of the globe are associated with the extratropical storm tracks over the Southern Ocean around 60°S, the mid-high latitude North Paciﬁc and the North Atlantic

224 Synoptic and dynamic climatology (London et al., 1989). In these areas, cloudiness amounts average 80–90 percent in winter and summer. The summer cloudiness over the Arctic Ocean is similarly high. The least cloudy region is the Saharan–Arabian desert, where amounts are only 10–20 percent in winter and 20–30 percent in summer. Low amounts are also found over much of Australia and southwest Africa. There is a second equatorial belt of cloudiness associated with tropical cloud clusters along the ITCZ and over the tropical Indian Ocean. Figure 3.74 illustrates the average zonal distributions for summer and winter 1971–81, based on surface observations. The dominance of the Southern Ocean peak is especially striking. There is still considerable uncertainty in total global cloudiness. Hughes (1984) showed the discrepancies existing in publications up to the early 1980s. The statistics assembled under the International Satellite Cloud Climatology Project (ISCCP) showed more global cloud than earlier studies, probably as a result of more complete coverage of ocean areas, particularly the Southern Ocean. Global cloudiness is about 60 percent when mapped on a 250 km scale, compared with earlier estimates of 50 percent (Rossow and Schiffer, 1991). The initial ISCCP algorithms had difﬁculty in mapping cloud over snow and ice surfaces, although adjustments to address this have since been made (Rossow and Garder, 1993). Statistical analysis suggests that most variation in cloud cover takes place on space scales of 50–250 km and time scales of a few days (Rossow, 1993). Additionally, midlatitude land areas have large diurnal changes in cloud amount, especially in summer. There is typically more cloud in mid-afternoon, related to the maximum solar heating of the surface and convective cloud development. The distribution of cloud types has been investigated using surface observations (Warren et al., 1986, 1988). A statistical correction is made to minimize the effect on the observations of the obscuration of higher clouds by lower cloud layers. Figure 3.75 shows the results for land areas, for 1971–81, and ocean areas, 1952–81. Stratiform cloud is most common over land and ocean surfaces in higher latitudes during both seasons, while nimbostratus is predominant in higher middle latitudes. Cumuliform clouds predominate in the equatorial latitudes, although convective clouds show a secondary maximum over northern land areas around 55°N in summer. Clouds tops are generally highest in the tropics, but the high-level cirrus canopies that spread out above the convective towers bias the areal coverage. In contrast, extratropical cloud systems tend to be large-scale multilayered systems formed through slantwise (or slope) convection (Ludlam, 1966, 1980). Each system may cover a 106 km2 area and Stewart et al. (1998) consider that there are ten such systems at any one time over the Earth’s surface. The ISCCP data on cloud type and total cloud optical thickness are used by Lau and Crane (1995) to develop a composite classiﬁcation of cloud top height and thickness. The distribution of eight cloud top-thickness categories is mapped for October–March and April–September 1983–90, between 60°N and 60°S, and the seasonal climatology is shown to be consistent with the corresponding dynamical characteristics and atmospheric structure in different areas of the tropics, subtropics, and extratropics. The mean global distribution of precipitation based on station records for December–February and June–August is shown in Figure 3.76. The data have been adjusted for gauge undercatch but probably are still too low where much of the precipitation falls as snow. The Global Precipitation Climatology Center (GPCC) in Offenbach, Germany, is now producing monthly maps on a routine basis that blend station data and estimates derived from satellite infrared and passive microwave data (see section 2.2). They are also endeavouring to incorporate appropriate corrections for the numerous gauge types and wind-shielding devices employed by different national agencies. A number of major features are apparent in Figure 3.76: 1

The near-equatorial maximum is displaced into the northern hemisphere over the Paciﬁc and Atlantic Oceans. In northern summer it is located in the tropics in southern

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Figure 3.74 The distribution of total cloud amount (percent) for 75°N to 75°S derived from surface observations for 1971–81 for June–August (above) and December–February (below). High values are hatched, low values stippled. (From London et al., 1989)

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226 Synoptic and dynamic climatology

Figure 3.75 Percentages of zonally averaged cloud amount according to cloud type. (a) Land areas, December–February 1971–81. (b) Land areas, June–August 1971–81. (c) Ocean areas, December–February 1952–81. (d) Ocean areas, June–August 1952–81. Ci, Cirriform; As, Alto-forms; Cu, Cumulus; Cb, Cumulonimbus; St, Stratus and stratocumulus; Ns, Nimbostratus. (After Warren et al., 1986, 1988; from Grotjahn, 1993)

2 3

4

and eastern Asia. These patterns reﬂect the ITCZ occurrence and the Asian summer monsoon regime. There are prominent tropical–subtropical dry zones, extending into the eastern parts of the major oceans, which are associated with subsidence in the subtropical anticyclones. Mid-latitude west-coast areas have large totals in the respective winter seasons associated with the extratropical disturbances in the westerlies and augmented by orographic effects over the coastal mountains in North and South America. There are also large totals in the austral summer over the stormy Southern Ocean. Low precipitation in high latitudes and in winter in the interiors of the northern continents is attributable to low moisture content in the very cold air. Nearly all of this precipitation falls as snow.

3.9.2 The tropics Recent advances in ground-based, aircraft, and satellite measurements provide considerable information on the quantitative characteristics of tropical cloudiness and precipitation and help shed light on the regional variations in circulation processes. Over the eastern parts of the subtropical and tropical oceans, large-scale subsidence of about 40 mb day1

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Figure 3.76 Mean global precipitation (mm day1) for December–February and June–August. (After Legates, 1995)

occurs in the subtropical anticyclones, and cold coastal currents cause stratiform cloud to predominate (Figure 3.77; Klein and Hartmann, 1993) and only drizzle or light rain usually falls. The ﬁve regions with such conditions are summarized in Table 3.15. The seasonal cycle of cloud amount is closely related to that of static stability and inversion strength rather than to the intensity of the anticyclone or the trade wind divergence (Klein and Hartmann, 1993); stratus amount increases about 6 percent for each degree Kelvin increase in static stability, both seasonally and interannually. It also decreases with higher sea surface temperatures, but the relationship is not as strong. In the California region, where there is extensive summer stratiform cloud, the boundary layer is found to be well mixed. Observations during the First ISCCP Regional Experiment (FIRE) in 1987 show that the cloud base can be around 200–400 m, with the stratus only a few hundred meters deep.

228 Synoptic and dynamic climatology

Figure 3.77 Mean annual stratiform cloud (and fog) from surface observations, 1951–81. (From Klein and Hartmann, 1993) Table 3.15 Regional occurrence of subtropical marine stratus Coastal extent

Longitude

Season and Value (°C) of amount (%) of max. stabilitya max. stratus and month

California 20°–30°N Canary Islands 15°–25°N Peru 10°–20°S Namibia 10°–20°S Western Australia 25°–35°S°

120°–130°W JJA

67

June

22

DJF

45

February 18

25°–35°W

JJA

35

June

16

SON

17

October 13

80°–90°W 0°–10°E 95°–105°E

SON 72 SON 75 DJF 45

DJF 42 MAM 48 JJA 41

February 18 February 17 June 15

October 22 September 22 February 18

Season and Value (°C) of amount (%) of max. stability min. stratus and month

Source: from Klein and Hartmann (1993). Note a The difference between 700 mb and surface potential temperature.

The boundary is well mixed, with an almost uniform vertical moisture distribution. The air temperature is only about 0.5–1.0°C less than that of the sea surface; however, the convection is readily sustained by cloud-top radiative cooling (Albrecht et al., 1988, 1995). Off northwest Africa, during the Atlantic Stratocumulus Transition Experiment (ASTEX) in June 1992, the more broken cloud cover was found to be associated with a decoupled boundary layer. This develops initially during the day as solar radiation is absorbed by the clouds, and is assisted by occasional drizzle. Shallow cumulus ﬁrst develops in the subcloud layer (Figure 3.78), then, as the air ﬂows over warmer waters, the cloud layer becomes permanently decoupled from the surface mixed layer. It appears that transitional processes are occurring beneath the stratiform cloud deck apparent on satellite imagery. Downstream, in the tropical easterlies, the transition to trade wind cumulus is completed. The average vertical structure of the lower troposphere in the trade wind zones features several distinct layers (Figure 3.79). During fair weather there is a shallow surface layer overlaid by an isentropic mixed layer, to about 700 m altitude, which is maintained by mechanical mixing and dry convection. This in turn is capped by a cloud layer with trade

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Figure 3.78 The transition from stratocumulus to trade wind cumulus. (Albrecht et al., 1995)

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Figure 3.79 Structure of the lower tropical troposphere. (Sarachik, 1985)

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wind cumulus fed by moisture from the surface and mixed layer and buoyant ascent of almost 100 mb day1. Sarachik (1985) proposes a simple theory to explain this structure. Evaporation and heat transfer from the ocean provide buoyancy for the mixed layer, whereas buoyancy in the cloud layer is derived from condensation in the clouds. As clouds form, the cloud layer becomes conditionally unstable, with a lapse rate somewhat greater than the SALR to the base of the trade wind inversion (Figure 3.80). The inversion is maintained by the upward ﬂux of moisture and the evaporation of the trade wind cumulus, according to Betts (1973). The clouds moisten and cool the dry air descending across the inversion. The growth of the mixed layer is balanced by subsidence between the cumuli in the cloud layer and the mixed layer top approximates the lifting condensation level. Fairweather cumulus transports moisture upward and heat downward as a result of evaporation, convection, and condensation. This destabilizes the lower layers and serves to deepen the cloud layer (Betts, 1978). In spite of the heat transport by the cumuli, the re-evaporation of cloud droplets means that the clouds are not a signiﬁcant net source of heat for the layers between the cloud tops and the sea surface. As larger cumuli develop, the associated

230 Synoptic and dynamic climatology

(a)

(b)

Figure 3.80 (a) Atmospheric structure associated with the trade wind inversion for the ATEX triangle, February 6–12 1969. (From Augstein et al., 1973; Augstein, 1978.) (b) Vertical proﬁles of horizontal velocity divergence, speciﬁc humidity (q), air temperature, and total static energy (J g1) based on ATEX data for the central trades (solid line), the transition between divergent and convergent ﬂow (dashed), near the origin of the trade wind trajectory (dash–dot) and for ITCZ conditions during GATE (dotted) (based on measurements of Brümmer, 1978)

downdraughts serve to dilute the moist subcloud layer and create a more stable stratiﬁcation. Cloud layer growth is balanced by large-scale subsidence above the trade wind inversion. The long-term mean structure of the tropical troposphere overlying the convective cloud layer is driven by buoyancy and heating generated in deep cumulonimbus clouds. Figure 3.81 shows soundings illustrating these changes. The stability of the inversion layer

Global climate and the general circulation 231 11

0

Figure 3.81 (a) Undisturbed and (b) disturbed soundings in the trade wind boundary layer of the central equatorial Paciﬁc, 170°–180°W. Equivalent potential temperature (e) and saturation equivalent potential temperature (es) are plotted. The lifting condensation level (LCL) is indicated for a parcel originating at 975 mb. (From Firestone and Albrecht, 1986)

0

0

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is sufﬁcient to suppress deep convection, except where vertical advection can destabilize the boundary layer. Cloud downdraughts also modify the velocity ﬁeld by adiabatic warming of low-level air (Betts, 1978; Firestone and Albrecht, 1986). Rising and descending air currents in the cloud layer more or less balance. Mixing, by entrainment of warm dry air and cloudy moist air, is limited to the top of the inversion layer. These thermodynamic views of the trade wind inversion imply that it is maintained by a balance between large-scale subsidence and moist convective and radiative processes. This assumes that the boundary layer depth and structure are determined by local values of SST and large-scale divergence, but Schubert et al. (1995) argue that horizontal advection plays a key role. The inversion extends into the tropics from the subtropics as a layer of high potential vorticity. It is strongly coupled horizontally. Thus the inversion height is actually controlled by horizontally averaged space values of SST, divergence, and the atmospheric structure above the inversion. Research into tropical convection during the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean–Atmosphere Response Experiment (COARE) observation periods in the western Paciﬁc shows that three cloud types are widespread: trade wind cumulus, cumulus congestus, and cumulonimbus. Similar ﬁndings have been reported earlier for other tropical ocean areas. For each category, the cloud tops are near major stable layers located at altitudes of around 2 km, 5 km (linked with melting below the freezing level) and 15–16 km (the tropopause), respectively (Johnson et al., 1999). These stable layers are characterized by stratiform cloud shelves. Moreover, cumulus congestus accounted for 57 percent of the precipitating clouds and 28 percent of the convective rainfall. The cloud populations ﬂuctuate signiﬁcantly in association with thirty-to-sixty-day oscillations. In the eastern tropical Atlantic during the GARP Atlantic Tropical Experiment (GATE) in 1974 a distinctive convective and precipitation regime was observed (McBride and Gray, 1980). Moisture ﬂux convergence peaks around 07.00 local time, whereas the maximum rainfall is in the early afternoon. Convection is suppressed by thermal stability and, because of the vertical structure of the heating, up to eight hours is required for a squall line to develop. Mesoscale systems are observed to be organized into convective bands about 60 km apart (Frank, 1983). Over the course of a day, cumulonimbus anvils

232 Synoptic and dynamic climatology tend to merge, forming cloud clusters, and these may have a radial dimension of 2°–12° latitude. Deep, precipitating convection is buoyancy-driven. Betts (1997) indicates that in this convective mode about 20 percent of falling precipitation evaporates, forming unsaturated downdrafts which cause local stabilization. This can help to split the cloud layer vertical circulation and initiate the build-up of deep cumulonimbus. It is important to note that the freezing level in the tropics is typically located in mid-troposphere about the 550 mb level, so that the coalescence process plays the dominant role in precipitation formation. Convective development is pronounced in the oceanic ITCZ zones. Over the western tropical Paciﬁc and in the western Atlantic, in contrast to the GATE area, low-level mass convergence reaches a maximum about 07.00 local time and rainfall peaks at the same time, demonstrating the rapid coupling of moisture convergence and rain formation (McBride and Gray, 1980). Over the western equatorial Paciﬁc warm pool, where SSTs exceed 27°C, deep convection is frequent. However, there is considerable temporal variability, and, at times, large-scale controls inhibit convection and can give clear skies (Zhang, 1993). In January the frequency of deep convection generally increases as SSTs rise from 26°C to around 30°C, but this relationship is not apparent in April or, in general, along the ITCZ relative to other areas. Indeed, Zhang considers that the relationship frequently proposed between SSTs and convective activity holds better for their spatial association than for the temporal variations at an individual location. It is noteworthy that the seasonal latitudinal displacements of deep convection and low-level convergence in the central Paciﬁc are in phase with the solar radiation maximum, whereas the development of SSTs above 28°C lags the radiation by one to two months (Fu et al., 1994). Deep convection depends on the convective available potential energy (CAPE), which is the potential energy available for convection minus that needed to initiate it,6 but also on the vertical buoyancy proﬁle, particularly boundary layer instability. Thus deep convection can be forced by either surface wind convergence or high SSTs. The role of cumulus convection in large-scale tropical circulations presents complex problems. Convective clouds redistribute heat, moisture, and mass from the subcloud layer into the upper troposphere. Early studies of the equatorial trough by Riehl and Malkus (1958) and Riehl and Simpson (1979) proposed the upward transport of energy by undiluted cumulus towers penetrating into the upper troposphere. The latent heat release was presumed to be sufﬁcient to offset radiative cooling and to generate potential energy by raising high enthalpy air from the subcloud layer to the upper troposphere. Such parcel ascent requires external forcing by the trade wind ﬂow, generating Ekman pumping. Xu and Emmanuel (1989) point out that, in much of the tropics, the level of free convection (LFC) is generally low, so that cumulus convection is widespread. The large amounts of CAPE required for undilute cloud ascent cannot readily develop. In contrast, over the US Midwest in spring, capping inversions allow CAPE to accumulate without immediate release. Arakawa and Schubert (1974) postulate that cumulus clouds are actually dilute mixtures of mixed-layer and ambient air. Several studies now indicate that the “mixed layer” is a convectively well adjusted layer and is well mixed only with respect to virtual potential temperature (v) which measures the buoyancy of unsaturated air (Betts, 1982; Xu and Emmanuel, 1989).7 The equivalent potential temperature (e) is observed to decrease with height in the mixed layer, by about 5°C between 1,000 mb and 950 mb, so the buoyancy of cloud parcels (formed reversibly) depends on where they originate in the subcloud layer.8 Parcels lifted from the top of the subcloud layer are closer to neutral stability than those originating nearer the surface, according to Xu and Emmanuel (1989). They show that the magnitude of the buoyancy over the central and western equatorial Paciﬁc during July–September 1965–80 depends primarily on the mean surface e. The buoyancy tends to increase as the height of the LFC decreases (from 875 mb to 975 mb), and these data show a local maximum buoyancy near 600 mb of about 2 K. Negative buoyancies of up to 0.5 K are found below LFCs at 925 mb or higher. This analysis

Global climate and the general circulation 233 11

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0

0

excluded conditionally unstable conditions, which accounted for 15 percent, 25 percent, and 33 percent of all cases at Koror, Truk, and Majoro, respectively. Xu and Emmanuel ﬁnd that the troposphere of the area is near neutral to non-precipitating clouds originating in parcels displaced reversibly from the top of the subcloud layer during 67–85 percent of the time, conﬁrming earlier observations (Betts, 1982). Larger buoyancies can occur by lifting air from near the surface. Xu and Emmanuel conclude that moisture convergence is unable to generate kinetic energy in a conditionally neutral atmosphere. Thus the growth of large-scale disturbances occurs by redistribution of existing kinetic energy via baroclinic instability or by air–sea feedback mechanisms modifying the subcloud layer. Synoptic cloud patterns in tropical disturbances over the western Paciﬁc, based on data from the International Satellite Cloud Climatology Project (ISCCP) for 1983–90, are provided by Lau and Crane (1995). They indicate cloud structures like those observed in squall lines with low cloud-top altitudes in a zone of subsidence ahead of the leading edge of the convection, thick cloud extending to 150 mb in a 1° latitude-wide convective area and thinner high clouds in the trailing edge and outﬂow region. Wind proﬁler measurements made during twelve months of the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean Atmosphere Research Experiment (COARE) at 2°S, 147°E provide a valuable climatology of rainfall types over the warm pool (Webster and Lukas, 1992; Williams et al., 1994). Stratiform, convective, and mixed stratiform–convective types each account for about 30 percent of the total rainfall (160 cm falling in 313 hours), although the rate of stratiform cloud precipitation is half or less than that associated with the other two types. The remainder of the total is low-level “warm” rainfall. The ratio of convective to stratiform precipitation appears to inﬂuence the thermally direct zonal circulation cells in the tropics according to Raymond (1994). Calculations of idealized tropical convection forced by anomalous sea surface temperature gradients (Lindzen and Nigam, 1987) show that differential heating sets up boundary layer convergence, giving low-level ascent, and this is further strengthened by upper-level heating gradients and ascending motions that result from the distribution of stratiform precipitation. The annual cycle of convection, as determined from harmonic analysis of OLR and HRC data, provides a dynamically based deﬁnition of tropical climate regimes (Wang, 1994). Wang identiﬁes a “maritime monsoon” regime, associated with convergence zones in the western North Paciﬁc, South Paciﬁc and southwest Indian Ocean (Figure 3.82), that is characterized by a unimodal precipitation distribution with a summer maximum. In these sectors, surface westerly winds are found equatorward of the monsoon trough. In the convergence zones located between the trade wind belts over the central North Atlantic and North Paciﬁc there is a more or less persistent rainy season with a bimodal (transition seasons) pattern of variation. The Asian–Indonesian–Australian and West African summer monsoon areas have bimodal rainfall patterns. As noted by Wang, the

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Figure 3.82 Convection/precipitation regimes in the tropics. Regimes indicated are A arid, SA semi-arid, PC permanently convective, M summer monsoon and ITCZ trade wind convergence zone. Permanently convective (arid) regions are hatched (shaded), respectively. (Wang, 1994)

234 Synoptic and dynamic climatology peak rainy season shows an apparent eastward progression, almost along the equator, from 60°E in the Indian Ocean to 130°E south of New Guinea from July through February of the following year, reﬂecting the monsoon transition from boreal to austral summer (Figure 3.83). In this sector there is also an India–Indonesia–northern Australia displacement in convection, noted by Meehl (1987), and an analogous feature between Central and South America, identiﬁed in OLR data by Pulwarty et al. (1992). The propagation of convection eastward during July–October in the tropical North Paciﬁc, shown in Figure 3.83, may be associated with the seasonal change in the western Paciﬁc monsoon trough and the autumn peak of tropical cyclone occurrence. The view that convection drives large-scale circulations, such as the Hadley cell, through latent heat release in deep cumulonimbus has been challenged by Emmanuel et al. (1994). In fact, latent heat release is largely compensated for by radiative and adiabatic cooling. They argue that the equivalent potential temperature (e) of the subcloud layer is determined through the combined effects of ocean surface temperatures (assumed by Lindzen and Nigam, 1987, to play the dominant role), low-level winds, large-scale vertical motion in the convecting layer, turbulent entrainment at the top of the subcloud layer, and downdraughts of air caused by the evaporation of precipitation. These downdraughts lower e in the subcloud layer, and this in turn cools the free air. Interactions between disturbances and convection primarily result in “moist convective damping” which selectively damps high frequency oscillations. The paradigm advanced by Emmanuel et al. (1994) asserts that the vertical temperature gradient is controlled not by heating but by convection which is a response to instability. Their central tenet is that convective systems, in the tropics at least, are close to a statistical equilibrium with their environment. Nevertheless, departures from this equilibrium lasting a few hours can play an important role in the dynamics. For example, where an equatorial Kelvin wave (see section 6.5) moves westward through an area of convection, enhanced downdraughts associated with the ascending phase of the wave system lower subcloud e and in turn the temperature of the free air. A short time lag in the convective response transfers this effect westward behind the zone of maximum ascent, so that the convection overlaps into the cold phase of the wave. This creates a negative correlation of heating and temperature leading to wave damping. Concurrently, however, the baroclinic circulation of the disturbance leads to strengthened low-level easterlies east of the ascending air. These winds increase the surface heat ﬂuxes via a wind-induced surface heat exchange (WISHE) process and thereby set up warming, which is partly in phase with the temperature perturbation of the wave, thus generating an amplifying effect. Emmanuel et al. also conclude that the magnitude of observed CAPE values in the tropics undergoes only small variations that are not readily detected (except

Figure 3.83 Phase diagram for the annual mean minimum OLR. Thick solid lines indicate discontinuities of at least two months in the isochrones. Paths 1A, 1B, 2A, 2B, and 2C show phase propagations. Very dry regions are shown by the stippled areas; areas where the variance accounted for by the ﬁrst harmonic is less than 0.2 are shown hatched. (Wang, 1994)

Global climate and the general circulation 235 11

where air parcels are raised through different depths of the atmosphere). Conditional instability can arise in the upper troposphere, however, through the freezing of cloud condensate, which alters the lapse rate (Williams and Renno, 1993). 3.9.3 The extratropics The cloud and precipitation characteristics of middle and high latitudes over the oceans are strongly shaped by the occurrence of extratropical cyclones as well as mesoscale cyclones. These relationships are discussed in Chapter 6. Over land areas, in summer, mesoscale convective systems and isolated thunderstorms play the major role and there are strong diurnal cycles of precipitation. The distribution of mesoscale convective systems is shown in Figure 6.31. In winter, cyclone systems interact strongly with topography. It is inappropriate to treat here the wide variety and complexity of regional precipitation regimes. Details may be found in sources such as Trewartha (1981) or Lockwood (1974), and the regional volumes of the World Survey of Climatology (Landsberg, 1970–84).

0

3.10 Air masses The mean thermal conditions of the atmosphere discussed in section 3.2 obscure important features of the atmospheric structure and signiﬁcant synoptic variations. One framework for characterizing this structure is the air mass concept ﬁrst formulated by Bergeron (1928), followed by Schinze (1932) and Willett (1933), and subsequently applied in many regional investigations. An ideal air mass is a barotropic ﬂuid in which isobaric and isosteric (constant speciﬁc volume) surfaces do not intersect. This implies that the density (or temperature) ﬁeld is a unique function of pressure and that the geostrophic wind velocity remains constant with height. Air mass boundaries are hyper-baroclinic (frontal) zones where isobaric and isothermal surfaces intersect. The idealized criterion is generally too restrictive, and James (1969) proposed that an air mass could be considered as spatially homogeneous where standard deviation values for air temperature are less than 1.2°C. The atmospheric structure is conventionally presented as a function of height (pressure) and latitude in terms of isotherms and isotachs of zonal wind speed (see Figure 4.6). Figure 3.84 shows a schematic cross-section for the northern hemisphere, depicting quasi-barotropic air masses separated by a baroclinic frontal zone. The frontal zones have associated arctic and polar front jets, whereas the subtropical jetstream is related to an upper tropospheric baroclinic zone. An alternative presentation of atmospheric structure using isentropes was ﬁrst proposed by Sir Napier Shaw (1930). A schematic illustration of this type is given in Figure 3.85 (Hoskins, 1991). Shaw distinguished an Overworld where the isentropic surfaces are everywhere above the tropopause; a Middleworld, where they cross the tropopause, and an Underworld, where they intersect the surface. The tropopause intersections imply a direct route between the troposphere and stratosphere for air parcels moving isentropically. Hoskins points a number of interesting relationships between PV structure and tropospheric circulation. For a lower boundary with high (low) values, and a tropopause with low (high) values, there is an associated cyclonic (anticyclonic) circulation and decreased (increased) static stability in the troposphere. If the anomalies at the tropopause and lower boundary are not in opposition, the associated circulations tend to cancel one another. In addition, the mass-weighted PV integrated between two isentropic surfaces 1 and 2 is equivalent to the circulation on the surface (C ) around the mass of air:

0

0

0

0 11

冕

2

1

(PV) dm =

冕

2

1

C d

236 Synoptic and dynamic climatology

Figure 3.84 Air masses in relation to the structure of the troposphere and the tropopause. J = jetstream, A = arctic, P = polar, S = subtropical. (After Defant and Taba, 1954)

where dm is the mass element:

1 p

This C circulation is unchanged by heating processes or friction within the region, or by external forces (Hoskins, 1991). A climatology of winter season PV for the 315 K surface in the northern hemisphere (Brunet et al., 1995) shows a clear relationship with the mean stream function distribution of troughs and ridges, although the polar vortex is bounded by high PV gradients near 50°N, approximately along the 2 PV units contour. The strongest gradients are over eastern Asia and eastern North America in association with the mean jetstreams. On a global scale, the three segments of the tropopause can serve to distinguish between air with tropical, subtropical, and temperate characteristics (Figure 3.84; Defant and Taba, 1957). This tripartite division is well marked in the middle and upper troposphere but near the surface, especially in middle and high northern latitudes, there is considerable spatial and temporal variability as a result of the strong local contrasts in surface conditions. Moreover, analyses of temperature frequency distributions in the free air, over Budapest for example (Ozorai, 1963; see Barry and Perry, 1973, p. 184), show inconsistencies in the number of frequency maxima that are present at 500 m, 1,500 m, 3,000 m, and 5,000 m altitude in both January and July, implying that homogeneity in the vertical structure of air masses is uncommon. There are two procedures that can be used in air mass classiﬁcation. One would identify conservative atmospheric properties chosen to designate the net effects of the source area and subsequent dynamic and thermodynamic modications. For surface data, dew point temperature is a conservative tracer of air movement for dry adiabatic and diabatic processes, while wet-bulb potential temperature is quasi-conservative for dry and saturated adiabatic processes in the free air. Nevertheless, such an approach requires air trajectories to be determined regularly. The second procedure, which has been widely

Global climate and the general circulation 237 11

0

0 Figure 3.85 A schematic vertical cross-section of the isentropic structure of the atmosphere. Isentropes are shown at 30 K intervals from 270 K to 390 K (thin lines). The tropopause is shown by the solid line. The arrows indicate transports. The irregular equatorial boundary is deﬁned by the zero contour of potential vorticity. (From Hoskins, 1991)

followed, takes account of the lapse rate structure despite its spatio-temporal variability. Primary air mass types are considered to form over extensive areas that have broadly uniform surface conditions, high surface pressures and small pressure gradients with weakly divergent airﬂows. The primary division separates arctic (A), polar (P) and tropical (T) air, with a secondary division according to the maritime (m) or continental (c) nature of the source area. The term “polar” is ﬁrmly established in the literature but is geographically inappropriate; the Russian usage, “temperate,” is a more logical designation (see Figure 3.84). The six major air mass types are:

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Arctic Polar Tropical

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Continental cA cP cT

Maritime mA mP mT

The tropical air masses originate in the subtropical anticyclone cells, while cA and cP air masses originate in polar anticyclone cells and the latter are modiﬁed into mA and mP air masses, respectively, as changes ensue in the vertical structure during passage over ocean surfaces. The arctic types have been recognized particularly over North America in winter by Canadian meteorologists in the three front (four air mass) model (Godson 1950; Penner, 1955; Galloway, 1958; McIntyre, 1958). For climatological purposes the identiﬁcation of air masses can be based on several different criteria. One involves the decomposition of a frequency distribution of daily values of an appropriate air mass parameter (maximum daily temperature, dew point, etc.) into several component partial frequency distributions or collectives. A numerical method was developed by Essenwanger (1954), although Bryson (1966) shows that sufﬁciently accurate results can also be obtained graphically (see Figure 2.6). Alternatively, streamlines of

238 Synoptic and dynamic climatology mean resultant winds can be used to identify source regions according to the areas of streamline divergence. On this basis the major source regions in each hemisphere and their annual duration have been determined by Wendland and Bryson (1981) and Wendland and McDonald (1986) (Figure 3.86). The oceanic subtropical anticyclone sources are dominant in both hemispheres. In the northern hemisphere they cover at least 25 percent of the surface and for six months of the year they affect nearly 60 percent of the hemisphere. There is even greater dominance by the equivalent oceanic anticyclonic source areas in the southern hemisphere, whereas the northern land areas have several regional centers of varying importance. The regions of persistent polar anticyclones (Siberia, northwestern Canada, and the western Arctic Basin) have been traditionally regarded as sources of continental polar (cP) or arctic (cA) air mass formation (Wexler, 1937, 1951). Using a simple radiative cooling model, Wexler estimated that cooling of a maritime polar air mass to 3 km would require about twenty-six days. A revised calculation that incorporated turbulent ﬂuxes, subsidence, condensation, and ice crystal precipitation suggested that most cP properties are acquired in four days, but ﬁfteen days are needed for the complete development (Curry, 1983). The question as to whether this interval of time is typically available in arctic air can be examined in terms of the net mass ﬂux across 70°N. Using Oort’s (1982) data, Fultz (1986) ﬁnds that residence times are only four or ﬁve days for the lower troposphere and three days in the vertical mean, in striking contrast to the calculated requirements for full development. Such low residence times are conﬁrmed by studies of pollution events at Barrow. Raatz and Shaw (1984) note that pollutants travel from Eurasian source areas across the Arctic in seven to ten days during quasi-stationary anticyclonic situations. For the regions of Siberia and Canada (50°–70°N) dominated by polar anticyclones, Oort’s data for the 1,000–700 mb layer suggest winter residence times of ﬁve days and three days, respectively (Fultz, 1986). The discrepancy lacks a clear explanation. The omission of processes from Curry’s (1983) model that could signiﬁcantly speed up the air mass transformation seems unlikely. The mean residence times and model calculations could be reconciled if the air mass formation were concentrated in a limited part of the source area, with stronger exchanges over the remainder. Also, there may be biases in the observed wind data used to calculate residence times. However, Fultz concludes that the rapid exchange times imply that air masses must be shallow, low-level structures; air masses or frontal zones extending to the upper troposphere “must primarily be formed by dynamics and not by the sort of regional processes originally envisioned.” There are many studies of air mass modiﬁcation processes. Transformation of cA or cP air by diabatic heating and moisture transfer as it ﬂows offshore over relatively warm ocean surfaces has received much attention, as have the more gradual effects on mT air ﬂowing poleward over cooler surfaces. Nevertheless, there are other equally important processes at work in the free air. For example, air moving towards lower latitudes and conserving its absolute vorticity needs to increase its relative vorticity to offset the decrease in the Coriolis parameter. If the actual curvature is anticyclonic, the air column shrinks, causing adiabatic heating. However, if the trajectory is cyclonic a polar outbreak can maintain its depth into low latitudes (see Figure 3.87). In view of the importance of atmospheric optical properties for radiative transfer (Smirnov et al., 1994), renewed attention is being paid to air mass characteristics as a means of stratifying observed and modeled data. The marine atmosphere over the North Atlantic, for example, has been characterized in terms of the Canadian three front, four air mass model by Low and Hudak (1997). Using 850 mb and 700 mb wet-bulb potential temperature steps of 4°C and a two-day persistence criterion, the frequency of air mass categories was determined for fourteen subregions of the North Atlantic using a method analogous to the partial frequency analysis described above. Five main categories (mT, mP, mA, cA, and a transitional type with shallow arctic air, cmA) are identiﬁed, as well as seven combinations involving up to three adjacent air masses. Two subtypes of

0 (b)

0

0

0

0

11

Figure 3.86 Air mass source regions in (a) the northern hemisphere and (b) the southern hemisphere. The number of months per year each air mass affects the regions delimited is shown. (After Wendland and Bryson, 1981; Wendland and McDonald, 1986)

(a)

11

240 Synoptic and dynamic climatology

Figure 3.87 The effect of the air trajectory on the depth of polar air outbreaks in lower latitudes. (From Barry and Perry, 1973)

mT air for “fair” and “disturbed” conditions are also recognized using discriminant analysis of 700 mb wind speeds and column water vapor content. One of the most striking illustrations of the broad signiﬁcance of air mass characteristics is the demonstration by Bryson (1966) that the summer limit of arctic air dominance over North America coincides closely with the southern boundary of tundra vegetation. In North America the boreal forest zone is bounded by the winter and summer isolines of 50 percent arctic air occurrence. In Eurasia, Krebs and Barry (1970) also found that the arctic frontal zone in summer coincides with the boreal forest–tundra ecotone. Pielke and Vidale (1995) suggest that the air mass contrasts are themselves driven by differences in energy, moisture and momentum exchanges over the contrasting vegetation surfaces. However, further investigation of this question appears to be needed.

Appendix 3.1 Potential vorticity In order to understand and interpret this important dynamic quantity properly, it is necessary to trace the history of its deﬁnition and use. Thorpe and Volkert (1997) provide a useful account which is summarized here. 1. The term potential vorticity was introduced by Rossby (1940) apparently as a concept analogous to that of potential temperature. Rossby states that potential vorticity “represents the vorticity the air column would have if it were brought isentropically to a standard latitude and stretched or shrunk vertically to a standard depth or weight.” Rossby’s potential vorticity, PR ( f )

p0 f0 p

where the relative vorticity about an axis perpendicular to an isentropic surface, f the Coriolis parameter, f0 f for a chosen reference latitude, p the pressure depth of an air column in which the top and bottom have a potential temperature difference 0, and p0 the reference p selected for the same column. PR has dimensions of s1 and is conserved for dry adiabatic ﬂow in the absence of friction; thus it can be used as an air mass tracer. An isentropic relative vorticity can also be deﬁned by:

p (P f0) f p0 R

Global climate and the general circulation 241 11

2. An independent hydrodynamical approach to the vorticity of adiabatic motion was proposed by Ertel (1942) and applied to the study of cyclone dynamics by Kleinschmidt (1950, 1951; Thorpe, 1994). The Ertel adiabatic vorticity is deﬁned as: PE

1 a #

where a is the absolute vorticity vector. Making the hydrostatic approximation: PE ⯝ –g ( f )

0

p

PE has dimensions of K m2 s1 kg1. Comparing the above equation with that of Rossby’s, it can be seen that: PR = PE

[p0] [g0]

The term in brackets above can be evaluated using numerical values chosen by Rossby (1940); for f0 104 s1, p0 100 mb, and 0 4 K, the expression becomes 250 kg m2 K1. Using the potential vorticity unit (1 PV unit 106 m2 s1 K kg1), if PE 1 PV unit, then PR 1.5 104 s1. Thorpe and Volkert (1997) point out that, confusingly, the term “potential vorticity” is now commonly applied to Ertel’s expression, although its dimensions are not in vorticity units.

0

Notes 1

0

The radiation ﬂux density (J m2 s1) emitted by a black body is proportional to T 4 (Kelvin). The emission (F) integrated over the wavelength range for a hemispheric surface is expressed by the Stefan–Boltzmann law F = oT 4, where (the Stefan–Boltzmann constant) = 5.67 108 W m2 K4. For a black body, the wavelength of maximum emission is shown by Wien’s law to be proportional to the absolute temperature.

max = T 1 k 2

0

3

where k = 2898 m K. The vertically and zonally averaged momentum transports (m2 s2) are converted to angular momentum transport units (1018 kg m2 s2) by multiplying the values by 2r2 cos2 (Po /g) 2.56 cos2 where 2r = length of the appropriate latitude circle, r cos = distance to the axis of rotation, Po /g = mass per unit area (~104 kg m2) (Oort and Peixoto, 1983). To convert units of ºC m s1 to energy units (PW), multiply the values by 0.4 cos (= 2r cos cp (Po/g)). Divergent and rotational wind. The horizontal wind ﬁeld can be separated into a non-divergent rotational component and a divergent irrotational component. The non-divergent rotational part, (Vr ), where the horizontal wind vector VH is parallel to constant streamlines (), can be expressed: Vr k where k a unit vertical vector, a gradient operator, a horizontal stream function (units m2 s1), is deﬁned as: u

0

y

v=

x

and the relative vorticity as:

11

2

242 Synoptic and dynamic climatology The divergent irrotational part Vd can be expressed in terms of a velocity potential Vd where is deﬁned as

x

u 4

v=

y

and the horizontal divergence #VH 2. The (Stokes) mass stream function for the mean meridional circulation is determined from (Peixoto and Oort, 1992, p. 158):

[v] = g

2 r cos p

and

[w] = g 5 6

7

2 r 2 cos p

An earlier study of precipitation zones on a pole-to-pole continent, 45º longitude across, and one extending only equator-to-pole, on either side of the equator, was made by Robinson (1972) as reported in Lamb (1977, p. 301). Convective available potential energy (CAPE) refers to the buoyancy force in moist air integrated between the level of free convection (LFC) and the equilibrium level (EL). On a thermodynamic diagram such as the tephigram it is indicated by the area between the environmental temperature conditions and the path curve (a saturated adiabat) connecting the LFC and the EL (Bluestein, 1993, p. 444). Values of CAPE range from around 103 J kg1 (m2 s2) for moderate convection to maxima of about 5 103 J kg1. The maximum vertical velocity at the equilibrium level (Wmax) (assuming there is no entrainment of cooler, drier ambient air) is: Wmax = (2.CAPE)1/2. Thus Wmax is 50 m s1 for a CAPE value of 1,250 J kg1. For an air parcel with temperature and humidity mixing ratio corresponding to the mean values of the lowest 500 m, the work needed to lift the parcel to the LFC is termed the convective inhibition. The virtual temperature expressed by: Tv Tp

冤1 1 q/0.622 q 冥

where q humidity mixing ratio. The difference in virtual temperature of cold air and ambient air can be used to deﬁne buoyancy (Xu and Emmanuel, 1989). For a pseudoadiabatic process, a parcel is lifted adiabatically without sustaining its liquid water content. Here the virtual temperature: Tv (A) Tp

)/0.622 冤1 1 q(T q (T ) 冥 p

s

p

where Tp temperature of the parcel lifted adiabatically, and qs saturation mixing ratio. A parcel lifted adiabatically in a reversible moist adiabatic process retains all of the condensed water and this increases the effective density of cloud air. Consequently the Tv(R) of cloud air is lower for a reversible moist adiabatic process: Tv (R) Tp

8

冤1 q1 (T Q)/0.622冥 s

p

T

where QT total water content of the parcel. The differences found in the upper troposphere over the equatorial Paciﬁc between Tv(A) and Tv(R) were 2°–4°C (Xu and Emmanuel, 1989). The equivalent potential temperature is deﬁned as the potential temperature of an air parcel if all of its water vapor were condensed and the latent heat converted into sensible heat (Houze, 1993, pp. 28–9). This implies that the air is lifted dry-adiabatically to its lifting condensation level, then moist-adiabatically to a high altitude (with precipitation of condensed water as it forms), and ﬁnally is brought by dry-adiabatic descent to 1,000 mb. To a ﬁrst approximation, for saturated air:

Global climate and the general circulation 243 11

e ≈ e

Lq (T, p)/cpT

For practical purposes: e ≈ (1 Lq/cpT)

where q humidity mixing ratio (kg of water per kg of air), and L latent heat of vaporization. In the case of saturated air, q is the corresponding saturation value (qs). A detailed evaluation of errors in the calculations due to various computational approximations is given by Bolton (1980).

0

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Rossow, W.B. 1985. Atmospheric circulation of Venus. In: S. Manabe, ed., Adv. Geophys., 28A (Issues in Atmospheric and Oceanic Modeling, Part A. Climate Dynamics), pp. 347–79. Rossow, W.B. 1993. Clouds. In: R.J. Gurney, J.L. Foster, and C.L. Parkinson, eds, Atlas of Satellite Observatioms related to Global Change, Cambridge University Press, Cambridge, pp. 141–63. Rossow, W.B. and Garder, L.C. 1993. Validation of ISCCP cloud detections. J. Climate, 6 (12): 2370–93. Rossow, W.B. and Schiffer, R.A. 1991. ISCCP cloud data products. Bull. Amer. Met. Soc., 72(1): 2–20. Ruddiman, W.F. and Kutzbach, J.E. 1989. Forcing of late Cenozoic northern hemisphere climate by plateau uplift in southern Asia and the American west. J. Geophys. Res., 94: 18409–27. Sadler, J.C. 1975. The monsoon circulation and cloudiness over the GATE area. Mon. Wea. Rev., 122 (9): 369–87. Saha, K.R. and Bavadekar, S.N. 1973. Water vapor budget and precipitation over the Arabian Sea during the northern summer. Quart. J. Roy. Met. Soc., 99: 273–8. Sahsamanoglou, H.S. 1990. A contribution to the study of action centers in the North Atlantic. Intl. J. Climatol., 10: 247–61. Sahsamanoglou, H.S., Makrogiannis, T.J., and Kallimopolous, P.P. 1991. Some aspects of the basic characteristics of the Siberian anticyclone. Intl. J. Climatol., 11: 827–39. Samel, A.N., Wang, W.-C., and Liang, X.-Z. 1999. The monsoon rain band over China and relationships with the Eurasian circulation. J. Climate. 12 (1) : 155–81. Sankar-Rao, M., Lau, K.M., and Yang, S. 1996. On the relationship between Eurasian snow cover and the Asian summer monsoon. Intl. J. Climatol., 16 (6): 605–16. Sarachik, E.S. 1985. A simple theory for the vertical structure of the tropical atmosphere. Pure Appl. Geophys., 123: 261–71. Sawyer, J.S. 1949. The signiﬁcance of dynamic instability in atmospheric motions. Quart. J. Roy. Met. Soc., 75: 364–74. Sawyer, J.S. 1963. Notes on terminology and conventions for the high atmosphere. Quart. J. Roy. Met. Soc., 89: 156. Schinze, G. 1932. Troposphärische Luftmassen und vertikaler Temperaturgradient. Beitr. Phys. fr. Atmos., 19: 79–90. Schlesinger, M.E. (ed.). 1988. Physically based Modelling and Simulation of Climate and Climate Change, I. Reidel, Dordrecht, 624 pp. Schlesinger, M.E. 1989. Quantitative analysis of feedbacks in climate model simulations. In: A. Berger, R.E. Dickinson and J.W. Kidson, eds, Understanding Climate Change, Geophys. Monogr. 52, Amer. Geophys. Union, Washington DC, pp. 177–87. Schneider, E.K. 1977. Axially symmetric steady-state models of the basic state for instability and climate studies. I. Linearized calculations. J. Atmos. Sci., 34 (2): 263–79. II. Non-linear calculations. J. Atmos. Sci., 34 (2): 280–96. Scholeﬁeld, J.T., et al. 1998. The Mars Pathﬁnder Atmospheric Structure Investigation/Meteorology (ASI/MET) Experiment. Science, 278 (5344): 1752–8. Schubert, S.D., Rood, R.B., and Pfaendtner, J. 1993. An assimulated data set for earth science applications. Bull. Amer. Met. Soc., 74 (12): 2331–42. Schubert, W.H., Cieselski, P.E., Lu, C.G., and Johnson, R.H. 1995. Dynamical adjustments of the trade wind inversion layer. J. Atmos. Sci., 52 (16): 2941–52. Schulman, L.L. 1973. On the summer hemisphere Hadley cell. Quart. J. Roy. Met. Soc., 99: 197–201. Schwerdtfeger, W. 1960. The seasonal variation of the strength of the southern circumpolar vortex. Mon. Wea. Rev., 88: 203–8. Schwerdtfeger, W. 1970. The Climate of the Antarctic, World Survey of Climatology 14. Elsevier, Amsterdam, pp. 253–355. Schwerdtfeger, W. 1984. Weather and Climate of the Antarctic. Elsevier, Amsterdam, 261 pp. Semazzi, F.H.M. and Sun, L.-Q. 1997. The role of orography in determining the Sahelian climate. Intl. J. Climatol., 17: 581–96. Serreze, M.C. 1995. Climatological aspects of cyclone development and decay in the Arctic. Atmos.Ocean, 33: 1–23. Serreze, M.C. and Barry, R.G. 1988. Synoptic activity in the Arctic Basin, 1979–85. J. Climate, 1: 1276–95. Serreze, M.C., Barry, R.G., and McLaren, A.S. 1989. Seasonal variations in sea ice motion and effects on sea ice concentration in the Canada Basin. J. Geophys. Res., 94 (8): 10955–70.

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Understanding the characteristics of the large-scale circulation and climate requires both the analysis of observational data and interpretation of the physical and dynamical processes involved through theoretical analysis and modeling. In the following sections the spatio-temporal characteristics of the circulation in extratropical and tropical latitudes are discussed, together with consideration of their interactions. In section 3.2 the focus of general circulation transport mechanisms was on zonal mean ﬂow, meridional standing cells, and time and space eddies. The alternative paradigm, implicit in the classical work of Rossby, Namias, Palmén, and Riehl, among others, is that the high and low-frequency transient components can be separated from the time mean (zonal mean and stationary wave) components. Wallace (1987) points out that low-frequency dynamics, including important longitudinally dependent interactions between the time-mean ﬂow and transients, began to receive attention in the late 1970s. This chapter examines the outcomes of the extensive work that has been performed. The discussion begins with an account of the westerly vortex and jetstreams, followed by consideration of planetary waves, zonal and blocked ﬂows, and low-frequency variability and persistence.

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The extratropical circulation is dominated by the tropospheric westerlies. In the free atmosphere these constitute a broad ﬂow in each hemisphere around the respective polar low-pressure centers. Climatically, the extent to which a more or less symmetrical polar vortex exists depends on the season and level in the atmosphere (Figures 4.1 and 4.2). In general, the symmetry is greater in the southern than in the northern hemisphere, it is more developed at high levels (200–100 mb) and, for the northern hemisphere, it is more pronounced in summer. In the middle and upper troposphere of the southern hemisphere the polar vortex extends to about 30°S in summer (January) and 10° nearer the equator in winter (July) (van Loon, 1972b). In middle latitudes there is strong baroclinicity, but conditions are almost barotropic poleward of 60°S. At 500 mb the vortex center is displaced slightly towards the Ross Sea (180° longitude) in both summer and winter. Geopotential heights are lower over the colder south pole than the north pole, with a 400 gpm height difference at 500 mb. It must also be emphasized that in spite of the strong zonality of the mean circulation in the southern hemisphere, the daily variability of the height ﬁeld is closely similar to that in the northern hemisphere (van Loon, 1972b). The extent of the northern circumpolar vortex undergoes important temporal variations. Its size has been estimated by planimetry along a ﬁxed contour for mean monthly maps at 500 mb (Markham, 1985; Davis and Benkovic, 1992) and 300 mb (Angell and Korshover, 1978). Using the 546 dam contour, which is within the mid-latitude baroclinic zone in January, Davis and Benkovic (1994) obtain an alternate day record for January 1947–90. The mean latitudinal extent of the vortex, so deﬁned, is shown in Figure 4.3.

264 Synoptic and dynamic climatology

Figure 4.1 Mean geopotential heights of the 500 mb surfaces (a) January 15 and (b) July 15 1982–86 for the northern hemisphere. The contour interval is 30 m. (From Epstein, 1988)

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Figure 4.2 As Figure 4.1 for the southern hemisphere, (a) January 15 and (b) July 15 1982–86. The contour interval is 30 m. (From Epstein, 1988)

266 Synoptic and dynamic climatology

Figure 4.3 Mean latitudinal extent of the northern circumpolar vortex, deﬁned by the 546 dam contour for January 1947–90. (From Davis and Benkovic, 1992)

The principal trough is at 140°E off eastern Asia with another at 80°W and ridges at 10°W and 130°W. The mean position of the zonally averaged 546 dam shifted 1.2° latitude equatorward between 1947–65 and 1966–90, associated with an expansion of the vortex and ampliﬁed troughs over the central North Paciﬁc and eastern North America. There was also some contraction over western North America, implying a pattern of wave ampliﬁcation. Burnett (1993) suggests that these changes reﬂect a more frequent positive mode of the Paciﬁc/North American teleconnection pattern (see section 5.6) compared with an expanded vortex in the early 1960s and 1976. At 300 mb, however, the vortex contracted sharply in the 1980s (Angell, 1992). The structure and variability of the polar vortex can also be described by so-called elliptical diagnostics (Waugh, 1997). An ellipse is ﬁtted to the contour of a quasi-conservative tracer like potential temperature or potential vorticity. Parameters such as the equivalent latitude of a zonal circle enclosing the same area, the latitude and longitude of the ellipse center, and the aspect ratio and orientation of the ellipse can be determined. Waugh and Randel (1999) illustrate the technique for the Arctic and Antarctic stratospheric vortices, showing differences in their structure and seasonal evolution. It has proved hard to draw a distinction between atmospheric conditions in the troposphere over middle latitudes and those within the polar vortex. Climatological average values and variance spectra of daily upper-air sounding data (temperature, u and v wind between 850 mb and 100 mb) at Churchill, Manitoba (59°N, 94°W), Oimyakon, eastern Siberia (63°N, 143°E) and Wakkanai, Japan (45°N, 142°E), provide little evidence to separate the two zones (Müller et al., 1979). However, conditions within the polar vortex in winter are most strongly modulated by ultralong period circulation patterns (more than ten days), related to lower tropospheric cold air outbreaks, with high persistence in the ﬁfteen to twenty-day range. In summer, changes within the vortex are mainly associated with the Arctic Front over both short (less than ﬁve days) and long (ﬁve to ten days) periods. The strength of the westerlies is determined by the temperature gradient between the tropics and high latitudes, as a consequence of the thermal wind relationship. As shown by combining the hydrostatic relationship and the equation of state:

Large-scale circulation and climate 267 11

∂p pg = ∂z RTv where R the gas constant for dry air (287 J kg1 K1) and Tv virtual temperature; Tv ≈ T r/6, where r the humidity mixing ratio (g kg1). Thus a warm air column expands vertically, compared with a cold air column, having the same pressure at sea level. Hence in this case, at any level there is a horizontal pressure gradient from the warm air to the cold air. In analogy with the geostrophic wind relationship, air does not move directly along this pressure gradient but is turned 90° to the right (left) in the northern (southern) hemisphere as a result of the Coriolis force. The balanced state is referred to as the thermal wind, which blows at right angles to the mean thermal gradient (with a speed inversely proportional to the isotherm spacing), with cold air to the left (right) in the northern (southern) hemisphere, viewed downwind. This thermal wind is a hypothetical component of the actual wind velocity at upper levels. It can be analyzed directly from charts of the geopotential height difference, or thickness, between two standard pressure surfaces (e.g. 1,000–500 mb, 700–300 mb, etc.) as illustrated schematically in Figure 4.4. The thickness value is proportional to the mean temperature of the air column, as indicated by the following relationship:

0

0

(z2 z1) =

R g

冕

p1

p2

Tv

dp p

For example, the 1,000–500 mb thickness is:

冢

冣

RTv 1,000 = 20.3Tv ln g 500 The 1,000–500 mb thickness is 5,685 m for Tv 280 K (a tropical value) and 4,770 m for Tv 235 K (a polar value). Note that the 500 mb level is near the mid-point of the atmospheric mass and corresponds approximately to the steering level of baroclinic waves.

0

0

0 11

Figure 4.4 Schematic illustration of the pattern of 1,000–500 mb thickness and thermal wind. (From Barry and Chorley, 1998)

268 Synoptic and dynamic climatology Table 4.1 Mean temperatures at standard pressure levels 1963–73 (°C) p (mb)

80°S

65°

50°

December–January–February 100 40.8 -43.8 52.9 44.9 47.0 52.1 200 300 52.4 50.6 45.0 500 33.1 29.3 20.8 700 19.1 15.1 5.8 850 – 17.6 1.1 1,000 – 0.1 7.9 June–July–August 100 78.6 69.9 200 71.1 66.4 300 63.2 59.2 500 41.4 36.2 700 27.3 21.2 850 – 15.3 1,000 – 2.6

58.3 59.2 52.4 27.7 11.2 3.6

35°

20°

Eq.

20°

66.1 76.0 79.9 75.4 54.4 53.2 53.3 53.9 37.7 32.0 31.2 34.1 11.3 6.3 5.5 8.1

4.5 11.7 19.0

9.1 17.5 25.2

9.6 17.7 26.8

35°

63.0 55.8 44.8 19.5 7.5 3.8

14.7 23.6

4.9 4.4

12.9 22.0

17.2 26.4

19.7 26.6

65°

80°N

57.5 57.1 55.6 35.7 21.5 16.5 4.3 11.6

62.7 59.5 58.1 39.8 26.3 22.0 23.7

55.2 55.6 52.2 30.0 15.0 3.2 8.9

13.9

60.6 72.5 76.2 73.7 66.1 55.1 54.2 53.9 52.7 52.4 44.0 34.5 31.8 31.0 33.7 18.7 8.6 5.9 5.7 7.9 2.4 6.3 8.9 10.6 8.4

5.2

50°

16.6 22.6

53.5 52.1 41.1 14.7

46.7 48.1 45.1 19.9 0.8 4.6

9.4 11.8

42.7 43.2 46.6 23.8 8.9 3.7 2.3 7.9 0.2

Source: from Oort and Peixoto (1983).

In the northern hemisphere the tropics-to-pole temperature gradient increases considerably from summer to winter. Table 4.1 gives mean seasonal temperatures for selected levels and latitudes. Peixoto and Oort (1992, p. 143) show that the meridional gradient at low levels in northern mid-latitudes is 4°C/1,000 km in summer, but in excess of 8°C/1,000 km in winter. As a result, the upper westerlies, expressed as a zonal average around the hemisphere, strengthen twofold from summer to winter. In the southern hemisphere, in contrast, there is a much more complex pattern of seasonal change in the meridional temperature gradient (Figure 4.5). This is a result of the latitudinal distribution of the land masses and its effects on the heating of the atmosphere by the surface. Consequently there is no simple summer to winter strengthening of the zonal circulation. In contrast to the limited seasonal variation, in the extent of the circumpolar vortex, in the southern hemisphere there is considerable variation interannually (Trenberth, 1979; 1981a). Easterly winds dominate the circulation in low latitudes (Figure 4.6). The zonal components are only of the order 2–3 m s1, except over the equator in June–August, where they exceed 5 m s1 above 400 mb. The strongest tropical easterlies are found at 100 mb and above, between 5°N and 20°N. The occurrence of zonal mean easterlies in the tropical upper troposphere is generally attributed to deceleration of the zonal westerly winds on the equatorward margin of the subtropical jetstream. According to Feldstein and Held (1989), the absorption of wave disturbances near their critical latitude (see section 4.3, p. 302) implies momentum ﬂux divergence from midlatitude-generated eddies. Recent work challenges this interpretation. As shown in section 3.2 (p. 126), the zonal mean momentum ﬂux is normally decomposed into three components, owing to the mean meridional circulation, stationary eddies, and transient eddies. Lee (1999) points out that the transient eddy term [u′v′] represents the sum of the transient eddy momentum ﬂux [u′*v′*] and the transient meridional momentum ﬂux [u′][v′]. The contribution of the transient eddies to the zonal mean momentum ﬂux has been assumed to be small. Lee’s analysis of 200 mb wind data for 1980–95 shows that in the subtropics the transient eddies serve to decelerate the zonal mean zonal winds. However, for the deep tropics (approximately 10°N–10°S) the deceleration is attributable to the horizontal momentum ﬂux divergence in the transient meridional circulation. This circulation arises from the seasonal north–south shifts of the Hadley cells. Interestingly, Lindzen and Hou (1988) ﬁnd that the upper-tropospheric equatorial easterlies are strongest for a non-equinoctial

Large-scale circulation and climate 269 11

0

0

0

0

Figure 4.5 Seasonal change of the zonally averaged temperature gradient per 5° latitude at (a) the surface, (b) 500 mb in the southern hemisphere, and at (c) 500 mb in the northern hemisphere. (From van Loon, 1972a)

0 11

270 Synoptic and dynamic climatology

Figure 4.6 Meridional cross-section along 120°E for December 4 1959, observing temperature, zonal wind component (knots), and frontal zone. The polar front and subtropical jetstreams are evident. Jet maxima are shown. (From Hare, 1962)

circulation. This implies that a critical role in their maintenance is played by the earth’s obliquity (Table 3.3). The transient eddy momentum ﬂux actually accelerates the zonal mean zonal winds in the deep tropics. The acceleration is supported on the intraseasonal (thirty to seventy days) time scale by the Madden–Julian Oscillation (see p. 334). There are also intra- and interannual transient eddy ﬂuxes that feature eastward-propagating disturbances of zonal wave No. 2 (see section 4.3.1) and a two-to-four-year period of ENSO-like characteristics. Nevertheless, the deceleration by the transient meridional circulation far outweighs the accelerations by the transient eddy momentum ﬂuxes.

4.2 Jetstreams North–south cross-sections of temperature reveal that in each hemisphere the atmosphere is partitioned into two major barotropic zones separated by a narrow baroclinic or frontal zone (Figure 4.6); that is, there are so-called tropical and polar air masses on either side of

Large-scale circulation and climate 271 11

0

the polar front. Associated with the horizontal temperature gradient in the upper troposphere is a maximum of the zonal wind, or jetstream (Riehl et al., 1954, 1962; Reiter, 1996). In a barotropic atmosphere there are no horizontal temperature gradients, and height contours of the pressure surfaces have corresponding patterns in the vertical dimension. Hence there is no vertical shear of the geostrophic wind. Where there are horizontal temperature gradients, but the isotherms are parallel to the height contours (an equivalent barotropic atmosphere), the geostrophic wind direction is independent of height but the speed varies in the vertical. Such an atmosphere may feature warm highs/cold lows where the pressure anomalies and geostrophic wind speeds increase upward. In the case of cold highs/warm lows the opposite is observed. Following from the thermal wind relationship discussed above: VG2 VG1 =

0

0

0

0 11

冢冣

constant ∂T R ∂T p ln 1 ⬵ f ∂n p2 f ∂n

– where T mean layer temperature between pressure levels 1 and 2, VG the geostrophic wind velocity, and n is normal to the temperature gradient, implies that the difference in geostrophic wind speed between levels 1 and 2, or the vertical wind shear, is proportional to the horizontal gradient of mean layer temperature. This is the basis of the horizontal temperature gradient–jetstream association illustrated in Figure 4.6. A jetstream is a strong narrow current, typically a few hundred kilometers wide and several kilometers in depth, extending horizontally over distances of up to several thousand kilometers. The wind speeds are at least 30 m s1 (an arbitrary limit) and may exceed 100 m s1 near the tropopause. They were “discovered” during US air raids over Japan in 1944, according to Bryson (1994), and were named by C.-G. Rossby, who had previously studied jets in water. However, Seilkopf (1939) introduced the German term Strahlströme for upper tropospheric wind maxima, according to Reiter (1963), and Kington (1999) reports that in the 1870s Clement Ley (in England) assembled cirrus cloud motion reports to infer very strong upper winds. Early balloon measurements conﬁrmed such inferences, but there was no systematic study of jetstreams until the late 1940s. The contrast in the horizontal and vertical dimensions of jetstreams – the former exceed the latter by nearly two orders of magnitude – results in much larger vertical than horizontal gradients of temperature and wind speed (Reiter, 1963). The horizontal wind shears in a mid-latitude jetstream are of the order of 104 s1, compared with vertical shears of about 5 103 s1. The horizontal temperature gradient is ~ 6 105 K km1 compared with a vertical gradient of ~6 K km1. Nevertheless, it is the former which determines the vertical wind shear, as discussed above. In winter in each hemisphere the upper troposphere zonally averaged geostrophic wind maximum is about 40 m s1 and located in the subtropics near 30° latitude at 200 mb (see Figures 3.68 and 4.7) (Trenberth, 1992; Hurrell et al., 1998). However, there are major differences in higher latitudes (van Loon, 1972c). In the southern hemisphere the west wind maximum extends upward into the stratosphere in higher latitudes, intensifying with height to above 10 mb. This has no northern counterpart, although there are weaker “polar night” stratospheric westerlies. In the summer cases the zonal maxima are weaker in both hemispheres; they average 20 m s1 at 42°N in July, but are 50 percent stronger in January at 45°S, where they attain 32 m s1 (Trenberth, 1987). There are also strong upper tropospheric easterlies in the northern tropics in July. In summer in the northern hemisphere the average (1979–93) 200 mb zonal wind forms a virtually continuous spiral jet maximum (Figure 4.8). The jet cores are located just north of the Tibetan Plateau (40°N, 80°–100°E), with a 32 m s1 average, and over the St Lawrence estuary and Newfoundland (50°N, 60°W) where the maximum averages 34 m s1. The winter jets are 10°–15° nearer the equator and are about twice as strong, although the maximum off East Asia peaks at over 70 m s1 (Figure 4.8).

272 Synoptic and dynamic climatology

Figure 4.7 Zonally averaged mean zonal wind (m s1) for JFM (upper) and JAS (lower) from ECMWF data for 1979–93. Values over 5 m s1 are stippled and those under 5 m s1 are hatched. (From Hurrell et al., 1998)

Although the mean temperature gradient in the troposphere is directed from the equator to the pole, giving rise to a strong westerly thermal wind component, in summer this gradient is reversed in the tropical upper troposphere, particularly in the northern hemisphere. Hence the thermal wind relationship (section 4.1) dictates an easterly ﬂow in low latitudes and as a result of the small magnitude of the Coriolis parameter there is substantial vertical wind shear. The existence of a tropical easterly jetstream at 200–150 mb over India and northern Africa was ﬁrst identiﬁed in the early 1950s and it was subsequently analysed by Koteswaram (1958); Figure 4.9 provides an illustration for July 25 1955. The mean speed in the core over southern India (9–10°N) is about 30 m s1 at 14–15 km and the direction, but not the speed, shows high constancy. The summer reversal of the thermal gradient in the upper troposphere is associated with the occurrence of minimum temperatures in the equatorial upper troposphere, as well as with diabatic heating of the 500–200 mb layer over the Tibetan Plateau and Himalayan–Karakorum mountains. Strong radiative heating of the surface following the disappearance of the thin and discontinuous winter snow cover provides a source of sensible heat ﬂux to the atmosphere, and this is strengthened by latent heat release in cumulonimbus cloud development along the southern

Large-scale circulation and climate 273 11

0

0

0

Figure 4.8 Mean 200 mb zonal winds (m s1) for JFM (upper) and JAS (lower) from ECMWF data for 1979–93. Dashed lines show negative values. (From Hurrell et al., 1998)

0

0 11

ﬂanks of the mountain ranges and high plateau in midsummer (Flohn, 1968). Energy budget measurements in the area give a range of estimates for this heating. Table 4.2 provides estimates from station data during 1961–70. There is a large sensible heat ﬂux of about 220 W m2 over the arid western plateau in June; over the eastern plateau the sensible ﬂux is about half as large, but in July in this area it is supplemented by a larger contribution from latent heat of condensation. Subsequent calculations suggest lower amounts of heat transfer over western Tibet (Ding, 1994, chapter 5). For May 26–July 4 1979 Luo and Yanai (1984) determined a mean daily heating rate of about 3 K day1 for the 500–200 mb layer over eastern Tibet, comparable with the rate in the monsoon trough over Assam–Bengal to the south. However, Smith and Shi (1996) estimate net warming in summer of only 3 K day1 in the plateau boundary layer and net cooling in the upper troposphere through radiative divergence. They ﬁnd modest heating only over the western part of the plateau.

274 Synoptic and dynamic climatology

Figure 4.9 The tropical easterly jetstreams at 200 mb on July 25 1955. Streamlines (solid) and isotachs (dashed lines). Wind speeds are in knots, easterly negative, westerly positive; 2 kt ≈ 1 m s1. (From Koteswaram, 1958) Table 4.2 Estimates of net diabatic heating (Q) and Earth–atmosphere system energy budget (Qsys) over the Tibetan Plateau (daily values, W m2) F Q Qsys

M

A

M

J

J

A

S

42 25 43 21

60 58

94 88

109 100

101 98

74 75

44 49

O

N

D

Year

10 54 77 21 4 47 74 21

Source: after Yeh et al. (1979).

Over northern Africa a midtropospheric jetstream termed the African Easterly Jetstream (AEJ) is present at about 650 mb with winds of 12–15 m s1 from the Sudan to the West African coast near Dakar. It is primarily a response to low-level baroclinicity induced by the steep surface temperature gradient (about 1°C deg1 latitude) between the equatorial forests of the Congo and coastal West Africa and the semi-arid Sahel zone to the north. In addition, this temperature gradient is strengthened by the parallel meridional gradient in soil moisture (Cook, 1999). Newell and Kidson (1984) found that the AEJ is indeed stronger (weaker) and moves southward (northward) in dry (wet) years in the Sahel. Model results suggest that the AEJ is maintained by two diabatically forced meridonial circulations (Thorncroft and Blackburn, 1999). One is associated with surface heat ﬂuxes and dry convection in the Saharan heat low, which extends up to 700 mb, and the other is linked with deep convection in the ITCZ. The AEJ is both barotropically and baroclinically unstable, so that easterly waves grow at the expense of the AEJ. The thermal structure of the atmosphere discussed in section 4.1 does not “explain” the jetstreams. Their existence is due primarily to the meridional pressure gradient associated with that in the thermal ﬁeld and to transports of angular momentum. An early attempt to account for jetstream maxima was put forward by Namias and Clapp (1949). They related jet maxima to wind conﬂuence in a baroclinic zone. The zonally averaged wind maxima represent a composite of the polar front and subtropical jetstreams that are apparent on individual vertical cross-sections, especially at particular longitudes. Wind conﬂuence provides a qualitative explanation for a jet maximum, but conﬂuence itself is caused by some perturbation in the ﬂow that gives rise to the superimposition of ageostrophic motions on the geostrophic ﬂow. Figure 4.6 illustrates a jetstream associated with the polar front and the subtropical jet associated with an upper-level baroclinic zone located approximately over the subtropical anticyclone belt. These jetstream features are generally not distinct on mean sections, since the polar front jet is highly variable in intensity in time and space (it shifts north and south in association with waves in the

Large-scale circulation and climate 275 11

0

0

0

upper westerlies), whereas the subtropical jets are more stable and persistent. Commonly, the strongest winds occur when the trough of a mid-latitude wave penetrates into the subtropics and approaches the crest of a subtropical ridge (Krishnamurti, 1961). In the southern hemisphere, meridional vertical cross-sections in the Australia–New Zealand sector show both a subtropical and a mid-latitude wind maximum in January (summer) that is absent elsewhere and in winter. The extratropical jetstreams of the winter hemisphere are located poleward of regions of maximum convective activity (as shown by minima of tropical OLR values) and therefore upper tropospheric heating in the summer hemisphere. Thus, for example, in the boreal winter, the extratropical jetstreams over North America, Europe, and Asia are located northward of the zones of rainfall and of heating maxima over the Amazon, Central Africa, and Indonesia (Krishnamurti, 1979, p. 325); correspondingly, in the austral winter, the jets over South America, South Africa, and Australia are southward of heating areas over North America, North Africa, and the Asian monsoon (Yang and Webster, 1990). The heating ﬁeld in the summer hemisphere exerts a strong inﬂuence on both the location and the intensity of the subtropical and polar front jetstreams in the winter hemisphere. Yang and Webster show that the annual changes of intensity of the jetstreams are in phase with the maximum heating. The strength of the heating is also positively correlated with the interannual variability of jetstream intensity. On annual and interannual time scales, the linkage between the summer hemisphere heating and the winter hemisphere westerly jetstreams involves the upper-level meridional wind component. The outﬂow from the East Asian monsoon towards the Australian jet is stronger, for example, than the reverse coupling. In addition to these overall associations between the hemispheres, Yang and Webster (1990) demonstrate relationships between the extratropical jetstreams and the El Niño Southern Oscillation mode (see section 5.2). The Australian jet undergoes downstream strengthening (weakening) in El Niño (La Niña) years, whereas upstream, over western Australia, the westerlies are weaker (stronger), respectively. During El Niño years the summer hemisphere heating is shifted eastward. There is also an analogous effect on the Asian winter jetstream, with downstream strengthening (weakening) during El Niño (La Niña) years. However, in this case the upstream effects are lacking. Theoretical limits to the latitudinal proﬁle of geostrophic zonal wind are provided by constant angular momentum on the equatorward side of the velocity distribution and by constant absolute vorticity (CAV) on the poleward side. The limiting value of constant angular momentum is determined by the latitudinal change of the term cos2 2/cos2 1, since u/CE cos where latitude and CE the equatorial speed of the earth’s rotation; thus cos2 is a measure of angular momentum. Air displaced from 30°N to 40°N would increase its velocity by 99 m s1, for example. The CAV limit on the zonal wind proﬁle poleward side is determined by u (1 sin ) cos = CE (1 sin )

0

0 11

(Reiter, 1963). Figure 4.10 illustrates these theoretical limits and some observed upper tropospheric proﬁles of geostrophic zonal wind. For example, air at rest at 20° latitude would, if it were displaced 10° poleward and conserved its absolute angular momentum, accelerate eastward with a speed of 34 m s1 (see Table 3.3). Rossby (1947) proposed that lateral mixing of vorticity provides the redistribution of absolute angular momentum poleward of the polar front jets, with, theoretically, a sharply deﬁned equatorward border. On the anticyclonic side of a zonal jetstream a limiting condition is imposed by hydrodynamic stability, i.e. the Coriolis parameter, f, represents a critical value of lateral anticyclonic shear ( u/ y) for hydrodynamic instability at a given latitude. If the absolute vorticity ( f u/ y) becomes negative, the zonal ﬂow breaks and small disturbances can amplify

276 Synoptic and dynamic climatology

Figure 4.10 Theoretical limits to latitudinal proﬁles of upper tropospheric geostrophic zonal winds (see text) and some observed wind proﬁles. Zonal wind speed is expressed as a rate of equatorial speed, CE . (After University of Chicago, 1947; from Barry 1967)

rapidly. However, the observed latitudinal wind proﬁle in individual cases may depart from the theoretical one associated with geostrophic wind components. Rossby used this argument, expressed in terms of the conservation of absolute vorticity (CAV) (see section 4.3) to account for the observed mean velocity proﬁle on the poleward side of the jet maximum (Figure 4.10). Jetstreams characteristically have regional wind maxima both synoptically and in a climatological sense; these maxima may be quasi-stationary or progress slowly in association with extratropical cyclone systems. The entrance and exit zones of these jet cores are important in determining the distribution of upper-tropospheric divergence and relative vorticity. Figure 4.11 shows that, with respect to a straight westerly or easterly jetstream in the northern hemisphere, there are distinct patterns of convergence/divergence, relative vorticity (), and its time derivative ( / t). The major terms of the vorticity equation in general are: 1 d(f ) ·VH = 0 (f ) dt For straight ﬂow, is determined by the lateral wind shear, which is strongest on the cyclonic side. In the right entrance and left exit regions of the westerly jet maximum there is divergence, as a result of ageostrophic ﬂow components and associated ascending air in the troposphere, while in the other two quadrants there is convergence and sinking. Hence there are also transverse wind components with respect to the jet axis (Figure 4.11b). In the easterly jet the right entrance and left exit are also the quadrants where upper-tropospheric divergence is located. For corresponding westerly and easterly jetstreams in the southern hemisphere, divergence and ascent are located in the left entrance and right exit zones of the jet maxima. The vorticity maximum (minimum) located to the left (right) of the jet maximum in Figure 4.11c, implying positive (negative) vorticity

Large-scale circulation and climate 277 11

0

(a) (c)

0

(b) (d)

0

0

0 11

Figure 4.11 (a, b) The transverse ageostrophic circulations and (c) patterns of divergence/convergence associated with the entrance and exit regions of a straight jetstream maximum. (a, b) Vertical cross-sections along lines A–A′ and B–B′ showing the vertical motion and direct (indirect) transverse circulations in the jet entrance (exit) regions. (d) Patterns of relative vorticity and NVA/PVA corresponding to the straight jet streak in (c) (From Kocin and Uccelini, 1990)

advection in the left exit, right entrance (left entrance, right exit) sectors. Where the jetstream is associated with a wave trough or ridge, there are additional curvature contributions to the vorticity. For a jet maximum located in a wave trough there is a single vorticity maximum, giving a two-quadrant model with positive (negative) vorticity advection everywhere in the exit (entrance) region of the jet core. The maintenance of the climatological jetstreams is explained by accelerations in the jet entrance that are provided by the time-mean meridional ageostrophic ﬂow. The thermally direct circulation in the entrance region, with ascent on the equatorward side (Figure 4.11b), supplies kinetic energy to the jet. In the jet core and exit regions, however, ﬂuxes from transient eddies are involved according to Blackmon et al. (1977) and Hoskins et al. (1983). Blackburn (1985) suggests, from scale analysis, that the meridional plane vertical circulations inferred by Blackmon et al. (1977) may be invalid. The interpretation of ageostrophic ﬂow depends on whether the local value of the Coriolis parameter is used in the geostrophic balance relationship or whether a constant f plane approach is adopted. Blackburn shows that ageostrophic ﬂow based on the local Coriolis arises in part from rotational ﬂow, where air parcels move up and down isobaric surfaces in closed circulations, converting potential energy to kinetic energy. The rotational ﬂow gives rise to the wave retrogression in a non-divergent Rossby mode. He concludes that vertical circulations associated with the climatological jetstreams are mainly forced geostrophi-

278 Synoptic and dynamic climatology cally. They are secondary features maintaining thermal balance with the horizontal advection by the time-mean ﬂow.

4.3 Planetary waves 4.3.1 General characteristics The basic zonal circulation (wave number zero) in the mid-latitude troposphere is perturbed by a spectrum of superimposed waves. Contour maps of mean monthly geopotential height in the middle troposphere for the northern hemisphere show a well developed wave structure (Epstein, 1988; Harman, 1991). In January there are three mean waves over eastern North America, Kamchatka, and eastern Europe, with a prominent high-latitude ridge over Alaska (Figure 4.1a). The April map suggests four waves and a much weaker circulation. In July the subtropical highs have moved poleward and the circulation has weakened further; the principal trough is located over eastern North America (Figure 4.1b). The October map shows a return toward the winter intensity, but with four waves in high latitudes and ﬁve in low latitudes. An alternative, but less common, representation is a superposed plot of the trough and ridge axes for individual months (Stark, 1965). The mean locations and outliers are readily apparent. These maps can be summarized by a longitudinal plot, for a speciﬁc latitude, of the frequencies of mean monthly trough and ridge axes. Figure 4.12 illustrates this for 45°N (Harman, 1991). It conﬁrms some of the features noted above, but the July pattern shows a complexity not readily apparent in Figure 4.1. These distribution plots identify the month-to-month variability in the ridge and trough positions by the size of the respective peaks and the degree of symmetry about the indicated modes. The wave structure can be separated into its harmonic components by a Fourier analysis of the heights along a given latitude circle (see Appendix 4.1 for details). The wave

Figure 4.12 Longitudinal plots of the frequencies of mean monthly trough and ridge axes for 45°N in January, April, July, and October 1946–87. (From Harman, 1991)

Large-scale circulation and climate 279 11

0

0

0

0

pattern along a latitude circle is speciﬁed by its scale (zonal wave number, m indicates the number of waves along the latitude circle; m 2R cos /L where R earth radius, latitude angle and L wavelength), the phase angle, indicating the longitudinal occurrence, and the amplitude or intensity, in terms of the height departure from the latitude mean value. Wave number m 1 has a ridge and one trough, m 2 has two ridges 180° apart and two troughs, and so on. The nature of these different waves varies considerably over the wavelength spectrum. Wave No. 1 represents an eccentricity of the polar vortex; the circulation pole in the northern hemisphere is commonly displaced 5°–10° latitude between longitudes 150° and 160°W (La Seur, 1954). The eccentricity with respect to the geographic north pole increases, according to Barrett (1958), as the amplitude of wave No. 1 increases. Analysis of the wave structure for a series of years shows that the longitudinal patterns of the major wave components (m 1–4) recur regularly in monthly or seasonal averages. This reﬂects the fact that there are quasi-stationary planetary waves (Trenberth, 1980) related to forcing by orography and/or land–sea contrasts in diabatic heating. They have wavelengths above 5,000 km and are dynamically different from traveling synoptic waves in that the advection of earth’s vorticity greatly exceeds that of geostrophic vorticity (Bluestein, 1992, p. 62). Wave Nos 5–8 are free planetary waves in the zonal basic current and waves m 9–12, approximately, are associated with traveling synoptic disturbances. Eady (1950, 1953) ﬁrst suggested that the waves act continually to regenerate the basic zonal current. Calculations of kinetic energy transfers between wave numbers show that the cyclone waves largely maintain both the long waves (m 1–5) and the basic current, as well as supplying energy to shorter waves (m 11–15) (Saltzman and Fleisher, 1960). In simplest terms, the stationary planetary waves are represented by the deviations from zonal symmetry in the time-averaged circulation ﬁelds described above. These departures from zonal symmetry represent two rather different phenomena. One involves stationary waves, i.e. real waves with zero phase speed, and the other concerns climatological (statistical) wave-like structures that are shown on monthly mean contour maps (Ashe, 1978). Monthly mean height ﬁelds in fact represent a quasi-stationary wave. Rossby et al. (1939) pointed out that the theoretical determination of two or three stationary waves at 60°N and 3–6 at 30°N was in fair agreement with the observed dimensions of the subpolar lows and the subtropical high-pressure cells. The very long waves (m 1–3) tend to dominate in high latitudes, and m 6–7 in low latitudes, but it is important to note that the actual wavelength of m 7 at 30° latitude is approximately equal to that of m 3 at 60° latitude. The mean amplitude of these three waves and locations of the ridges at 500 mb for average winter conditions, 1964–77, at 50°N are shown in Table 4.3. It will be noticed that the ridge of m 3 at 60°W is near that for m 2. For January 1976 the component wave numbers are shown in Table 4.4. Wave No. 1 dominates at 75°N and 30°N, whereas in middle latitudes wave No. 3 is predominant. The circulation in January 1976 was characterized by fast mid-latitude westerly ﬂow and a pronounced threewave pattern at 700 mb with troughs located over eastern North America, eastern Europe, and the western Paciﬁc (Wagner, 1976). In summer there are two distinct regimes of standing waves (White, 1982). There is a subtropical regime consisting of lower troposphere oceanic highs overlain by 200 mb midTable 4.3 The amplitude of zonal wave numbers m = 1–3 for winters 1964–77 at 50°N Wave no. 1 2 3

0 11

Mean amplitude (m) 102 ± 17 93 ± 20 81 ± 22

Source: from Fogarasi and Strome (1978).

Longitude of ridges(s) 5°E ± 14° 81°W ± 27° (99°E) 60°E ± 42° (180°, 60°W)

280 Synoptic and dynamic climatology Table 4.4 Component wave numbers of northern hemisphere circulation at 500 mb in January 1976 North latitude

74° 60° 45° 30°

Zonal wave no. (% contribution) 1

2

3

4

5

6

81.4 8.7 36.9 65.3

10.6 2.8 8.7 2.2

7.3 72.6 48.8 13.4

0.2 11.1 4.6 9.2

0.2 3.8 0.6 2.6

0.2 0.6 0.2 4.3

Source: Fogarasi and Strome (1978).

Figure 4.13 Longitude–height cross-sections of geopotential height for the stationary waves. (a) 60°N, (b) 45°N. (c) 25°N. The contour interval is 50 m. (From Wallace, 1983, after Lau, 1979)

oceanic trough and continental heat lows overlain by upper-level anticyclones which represent a monsoonal response to thermal forcing. In higher latitudes there are weaker, smaller-scale waves than in winter, showing a barotropic structure. The structure of the stationary waves is most clearly revealed by longitude plots of geopotential height ( –z ) (Figure 4.13). In the northern hemisphere during winter the high latitudes show greater wave amplitude near–the tropopause and also a westward tilt with height. Corresponding mean temperature (T ) sections show a small longitudinal phase

Large-scale circulation and climate 281 11

0

0

0 Figure 4.14 Longitude–height cross-sections of temperature corresponding to Figure 4.13. The isotherm interval is 2 K. (From Wallace, 1983)

0

0 11

difference from the geopotential section (Figure 4.14). Theoretically, a westward tilt with height implies vertically propagating waves and upward eddy energy ﬂux, whereas the presence of maximum – wave amplitudes in the upper troposphere and minimal difference between ( –z ) and (T ) imply an equivalent barotropic structure with vertical trapping of waves (Shutts, 1987). In contrast to the northern hemisphere, the southern hemisphere wave structure is equivalent barotropic except in high latitudes (Karoly, 1985). This analysis updates earlier studies by van Loon and Jenne (1972) and van Loon et al. (1973). Southern hemisphere waves have amplitudes about half those of their northern hemisphere counterparts and tend to be of longer wavelength (Trenberth, 1979; LeMarshall et al., 1985). This is attributed to the predominantly zonal conﬁguration of land and ocean in middle and high latitudes and the greater mobility of the waves (Adler, 1975; Trenberth, 1981b). Upper tropospheric mean height ﬁelds, or mean stream functions, show a dominant wave No. 1 (Trenberth, 1980; Lejenäs, 1984). This has a mean longitude of the ridge about 140°W and a maximum amplitude throughout the year around 60°S (Quintanar and Mechoso, 1995). Nevertheless, the zonal wind is enhanced over the South Atlantic–South Indian Oceans and the jet splits near 40°S, 130°E, south of Australia, with a zone of blocking and weak zonal winds over New Zealand (Figure 4.8b). This structure is closely linked with the topographic asymmetry of Antarctica, since altitudes exceed 4,000 m in East Antarctica at 82°S, 75°E. Using a non-linear barotropic model, James (1988) shows

282 Synoptic and dynamic climatology that the orographic forcing due to Antarctica is mainly in wave No. 1 south of 40°S. The asymmetric distribution of sea-surface temperatures and the Antarctic Ocean Convergence in the Southern Ocean, as well as of the extent of Antarctic sea ice, have been linked with the pattern of wave No. 1 (Anderssen, 1965; Raynor and Howarth, 1979), but it seems more likely that they are all forced by the topography of the East Antarctic ice sheet (Watterson and James, 1992). However, in spite of the contributory role of orography, Quintanar and Mechoso (1995) argue the primary forcing of quasi-stationary wave No. 1 is from low latitudes, especially the Indian Ocean in June and October, based on Eliassen–Palm ﬂux vectors (see Appendix 4.2). They show poleward propagation of wave No. 1 from the subtropical Indian Ocean in October. At 50°S the annual mean 500 mb wave No. 1 accounts for 72 percent of the height variance, with an amplitude of 54 gpm; m 2 and m 3 both have amplitudes of only 23 gpm and each accounts for 13 percent of the variance (Trenberth, 1979). Karoly (1985), using data for 1972–82, ﬁnds higher contributions to the variance from m 2 and m 3 at the 300 mb level and 55°S. Wave No. 1 has an amplitude of about 85 m in winter and summer, m 2 is between 35 m and 40 m, and m 3 between 40 m and 50 m amplitude. Wave No. 3 is especially represented on ten-to-ﬁfty day time scales in middle and higher latitudes (Shiotani, 1990; Kidson, 1991). It is also prominent in the monthly averaged frequencies of cyclonic cloud vortices identiﬁed on satellite imagery (Carleton, 1979, 1981; Rogers and van Loon, 1982); year-round troughs are located over the three ocean basins. A wave train (m 5) set up by the Andes equatorward of 40°S is conﬁned to subtropical latitudes, and South Africa has a similar effect. However, neither source affects the circulation in high southern latitudes. The Andes is a high but narrow range, whereas South Africa has less relief (Trenberth, 1979). Analysis of daily 500 mb height data at 50°N for ten winters and three summers in terms of wave number frequency spectra indicates three variance maxima (Böttger and Fraedrich, 1980). There are ultralong stationary waves (m 1–4) with a greater than twelve-day period, eastward-propagating waves (m 5–6) with a ten-day period, and shorter synoptic waves (m 7–8 with periods of four to six days). A similar analysis at 50°S for May–July and November–January 1964–70 ﬁnds only two variance maxima (Fraedrich and Kietzig, 1983). They are associated with wave Nos 4–5, with periods in the six to twelve-day range, and wave Nos 6–7 traveling eastward with periods shorter than six days. Figure 4.15 illustrates the time–longitude progression of 500 mb traveling waves at 50°N for winter 1976–77 on a Hovmöller diagram. Space–time spectra of 250 mb geopotential heights at 60°E latitude in both hemispheres show maxima of variance in the thirteen to thirty-two-day range that are westward-propagating and of zonal wave No. 1 (Speth and Madden, 1983; Speth et al., 1992). The disturbances at 60°N have slightly longer periods (sixteen to twenty days) and are more prevalent, up to 38 percent of the time in December–February, than their southern hemisphere counterparts (thirteen to seventeen days and 28 percent occurrence in June–August). In all seasons there is considerable vertical coherence in the waves near 55°–60°S from the surface up to about 100 mb (Speth et al., 1992) The westward-propagating wave No. 1 episodes are seldom coincident in the two hemispheres. However, it seems that, although the disturbances are global, they are forced in one hemisphere (Lau, 1979; Held, 1983; Wallace, 1983). 4.3.2 Properties of waves It is worth pausing here to review brieﬂy the essential features of atmospheric waves. The “instantaneous” wavelike motion of the atmospheric circulation is readily illustrated by a plot superimposing the path of a single contour of the 500 mb height ﬁeld on successive days (Figure 4.16). The conﬁguration of a typical sinusoidal wave is described mathematically by its length ( ), amplitude (a), and velocity (c). The geographical location

Large-scale circulation and climate 283 11

0

0

0

Figure 4.15 Hovmöller diagram of 500 mb geopotential during winter 1976/77 at 50°N showing long (short) traveling waves by continuous (dashed) lines. (From Fraedrich and Böttger, 1978)

0

(longitude) at a particular time, t, is represented by a phase parameter. Following Atkinson (1981b, p. 104) a one-dimensional wave of constant proﬁle moving eastward (positive direction along the x axis) with constant velocity, c, can be expressed in terms of the meridional component of motion (v) as: v f (x ct) For pure sine and cosine waves: v a sin b (x ct)

0 and 11

v a cos b (x ct)

284 Synoptic and dynamic climatology

Figure 4.16 The superimposition of single contours of the 500 mb height ﬁeld on successive days, June 3–11, 1998, illustrating zonal and blocking patterns. The ensemble mean (7.0–11.0) is for June 10–14. (Climate Diagnostics Center, CIRES)

where a is the amplitude; b can be expressed as 2/ for a periodic sine or cosine wave. (The latter is identical, but shifted by /2.) Hence: v = A sin

2 (x ct)

v = B cos

2 (x ct)

and

Because the maximum amplitude of the wave need not occur at x 0, t 0, this phase shift can be incorporated by describing the peak at x0 when t 0: v = A cos

2 (x ct x0) .

These relationships are generally transformed in terms of exponentials involving complex numbers, i.e.:

Large-scale circulation and climate 285 11

sin =

ei ei ei ei ; cos = 2i 2i

where i √   1. The wave velocity (in the x direction) may also involve a complex form: c cr ici The real part cr and the imaginary part ci must both be real numbers. The wave velocity c is real if ci 0; it is imaginary if cr 0 but ci ≠ 0. Where ci 0 the wave amplitudes remain constant with time, so that the wave is neutral or stable. If ci ≠ 0 the wave can in certain conditions become unstable and amplify. cr measures the wave motion in the x direction for all cases. For unstable waves ci speciﬁes the rate of growth. In the atmosphere there are several pure types of wave motion. Sound waves caused by density variations are longitudinal (compression) waves where the trajectories of air particles are parallel to the direction of wave propagation. Gravity waves (e.g. mountain lee waves), which are driven by gravity restoring an external perturbation to the current, involve particle motion vertically and transversely (back and forward) as the waves propagate horizontally in the x direction (Figure 4.17). The planetary (Rossby) waves are horizontal- transverse, so that particles move meridionally (north–south) as the waves propagate horizontally in the x direction. These large-scale waves conserve absolute vorticity ( f ) by changes in the vertical component of relative vorticity of air motion () in response to latitudinal variations in the Coriolis parameter, f (the vertical component of the earth’s vorticity), i.e.:

0

0

d ( f ) = 0 dt

0

0

0 11

Figure 4.17 The instantaneous distribution of velocity, pressure and buoyancy perturbations in an internal gravity wave, viewed in the x–z plane. The phase of the wave is constant along all the slanting lines. Velocity and pressure perturbations have extrema along the solid lines and buoyancy perturbations have extrema along the dashed lines where velocity and pressure perturbations are zero. Small arrows show the pertubated velocities. Air parcels move parallel to the wave fronts (lines of constant phase) and over time the wave fronts move perpendicular to the parcel trajectories. The group velocity (showing direction of energy propagation) is upward, parallel to the air parcel trajectories. (From Durran, 1990)

286 Synoptic and dynamic climatology It must be stressed that the dynamics of large-scale waves, which involve motions in a very shallow atmospheric layer (10 km deep but several thousand kilometers in horizontal extent), are strongly inﬂuenced by the earth’s rotation. A useful index of this effect is provided by the Rossby number: R0 =

U 2"L

where U horizontal ﬂow velocity, " the earth’s angular velocity (7.3 105 s1), and L the characteristic length scale (the distance from ridge crest to wave trough). If R0 1 (i.e. if a ﬂuid element travels the distance L in a time less than the period of the earth’s rotation), so that L/U (1/"), the ﬂuid will be scarcely inﬂuenced by the earth’s rotation on this time scale (Pedlovsky, 1987). Large-scale waves have values of R0 % 1. Note that the Rossby number as deﬁned above is not appropriate in low latitudes because the formulation is in terms of the component of absolute vorticity perpendicular to the earth’s surface. A Rossby wave, assuming horizontal and non-divergent ﬂow (approximated at the 500 mb level), can be represented by: v = A cos

2 (x ct)

It has a wave speed c=U

冢冣 2

2

where U is the speed of the zonal current on which the wave is superimposed and f / y (the latitudinal variation of the Coriolis parameter). Note that the waves propagate slowly upstream (westward) with no change of shape; their speed of motion depends on the wavelength (Platzman, 1968). The Rossby wave has an angular speed along a latitude circle of: 2" m(m 1) where " the earth’s angular velocity. Another source of changes in wave structure is resonance. This occurs, for example, if the wave number is close to the barotropic stationary Rossby wave number (Austin, 1980). Its presence in individual zonal wave number plots is shown by 180° phase reversals; also, low-frequency anomalies are of global scale with resonant waves, but are localized when the waves are dissipative or dispersive (Held, 1983, p. 133). Resonant forcing of stationary planetary waves by topography is usually interpreted via the conceptual model of changes in relative vorticity caused by ﬂow over a mountain barrier, assuming conservation of potential vorticity and adiabatic motion: ( f ) = constant p where p depth of an air column in pressure units. The denominator now implies divergent motion. Upstream of the mountain, relative vorticity () is zero in the zonal westerly ﬂow (Figure 4.18). As an air column approaches the barrier it ﬁrst undergoes vortex stretching and lateral contraction, giving convergence and cyclonic rotation. As the air rises over

Large-scale circulation and climate 287 11

0

Figure 4.18 Schematic illustration of airﬂow over an extensive mountain range, assuming conservation of absolute vorticity. This model ignores upstream inﬂuences (see text).The solid line indicates a trajectory; A, B, and C illustrate the deformation of vortex tubes. (Buzzi and Tibaldi, 1977)

0

the barrier and shrinks, causing divergence, the relative vorticity becomes negative and the air curves anticyclonically southward. Downstream of the mountains, as the air column resumes its original depth, the air is at a lower latitude (smaller f value) and so the relative vorticity increases and the air moves poleward. However, this model fails for an easterly airstream approaching a barrier. The negative vorticity and increasing f, as the air moves up the mountains, would cause the air column to recurve eastward and it would be unable to cross the mountain range (Holton, 1993). The true explanation of airﬂow over topography with a large meridional extent involves the inﬂuence of the barrier in producing upstream lifting and volumetric expansion which produces anticyclonic vorticity (Smith, 1979a, b). Holton notes, however, that lifting extends only about a radius of deformation (NH/f ) upstream where H a standard atmospheric scale height and N the Brunt–Väisäla frequency. The latter refers to the natural frequency of vertical oscillations that are superimposed on large-scale ﬂow patterns:

0

0

冢g ∂∂z 冣

1/2

N=

where = potential temperature. It is of the order of 102 rad s1 in the troposphere, corresponding to an oscillation period (2p/N) of ten minutes. Stationary waves can exist for long wavelengths; the stationary wavelength, assuming Cartesian coordinates on a ﬂat earth, is: s = 2

0 11

冢冣 U

1/2

At latitude 45°, s is about 5,000 km for U 10 m s1, corresponding to m 6 and 7,500 km for U 20 m s1, corresponding to m 4 (see Table 4.5a).

288 Synoptic and dynamic climatology Table 4.5a The wavelength (km) of stationary Rossby waves for selected latitudes and mean zonal wind speeds Mean zonal wind speed (m s1) Latitude

5

10

20

60° 45° 30°

4,140 3,490 3,190

5,850 4,940 4,460

8,300 6,980 6,310

Source: after Haurwitz (1941).

Table 4.5b The relationship between zonal wind speed, U (m s1), and stationary zonal wave number, ms, with latitude Wave No. (ms ) Latitude

Earth circumference (km)

2

3

4

6

8

60° 45° 30°

20,000 28,380 37,600

29.0 90.1 150.7

12.9 40.1 67.0

7.3 22.9 37.7

3.2 10.0 16.7

1.8 5.6 9.4

Where the equations of motion are expressed in spherical instead of Cartesian coordinates, the relationship between wave number (m) and zonal wind speed, U, is:

冢

m(m 1) = 2 1

"R U sin

冣

where R the earth’s radius, latitude angle, and " the angular velocity of the earth (Haurwitz, 1941). At latitude 45° from this equation U 16 m s1 for m 6 and 36 m s1 for m 4; these values are substantially greater than those in Table 4.5b. Rossby’s formulation assumes that the zonal current is constant in the vertical and meridionally. The extratropics are, however, baroclinic so that wind speeds increase upward. Mathematical analysis shows that the stability of a baroclinic wave depends on its wavelength and the vertical wind shear. Figure 4.19 indicates that waves longer than about 3,500 km are unstable for vertical shear over 1 m s1 per km; such values of shear are common. The amplitude of unstable waves will double or triple within about two days. 4.3.3 Global wave modes Atmospheric pressure oscillations were ﬁrst examined around AD 1800 by P.S. Laplace. Extending his treatment of ocean tides to those in the atmosphere, he calculated lunar and solar gravitational forcing for a hypothetical isothermal atmosphere of uniform density (incompressible) having an “equivalent depth” of about 8 km. However, despite a twofold greater gravitational potential for lunar forcing, that effect is negligible. Laplace expected that solar thermal forcing would generate a twenty-four-hour pressure oscillation, whereas late nineteenty-century studies by Lord Kelvin (1882) and Hann (1889) showed the dominance of a twelve-hour pressure wave with an amplitude of about ±1.5 mb in the tropics. Also, the twelve-hourly wave was found to occur at a ﬁxed time near the poles, like a standing oscillation, whereas the low-latitude component traveled with the sun having a

Large-scale circulation and climate 289 11

0

0

0

Figure 4.19 The occurrence of stable and unstable waves in relation to wavelength and vertical wind shear. (From Atkinson 1981, after Wiin-Nielsen, 1973)

0

0 11

maximum (minimum) near 10.00 (22.00) LST. A global traveling pressure wave was indeed observed as a result of the eruption of Krakatoa in August 1883; it had a velocity (c) of 319 m s1 and, assuming c (gH)1/2 for a gravitational wave, the equivalent depth would be 10.4 km (Asnani, 1993). However, nuclear bomb tests in the 1950s gave a range of 285–310 m s1 for such global waves. Kelvin proposed that a free oscillation associated with the solar gravitational tidal potential could be enhanced by a factor of two through resonance in the atmosphere. Haurwitz and Möller (1955) rejected solar tidal forcing but suggested thermal forcing was enhanced by resonance. The role of resonance has subsequently been discounted and a mechanism to account for thermal forcing adopted. Chapman and Lindzen (1970) consider thermal forcings from radiative absorption by water vapor and ozone, as functions of latitude and height only, for twenty-four-hour and twelvehour periods. The diurnal oscillation which is driven mainly by the absorption of solar radiation by tropospheric water vapor is small because the energy mainly goes into trapped modes which do not reach the ground (Lindzen, 1967). The model accounts quite well for the observed semi-diurnal pressure amplitude, but not its phase, nor the polar standing oscillation. The phase difference is attributed to additional heating with maxima around 03.00 and 15.00 LST, associated with the observed semi-diurnal oscillations in precipitation and latent heat release in low latitudes by Lindzen (1978). However, van den Dool et al. (1997) argue that the moisture variation is an order of magnitude too small to explain the semi-diurnal oscillation. In NCEP six-hour reanalysis ﬁelds, they ﬁnd that m

290 Synoptic and dynamic climatology

(a)

(b) Figure 4.20 (a) Associated Legendre functions (Pnm sin ) versus latitude for P21, P31, and P41. (b) The corresponding patterns of geopotential height. The amplitude is arbitrary. (From Madden, 1979)

Large-scale circulation and climate 291 11

0

0

Figure 4.21 Schematic global ﬁelds of geopotential and velocity for modes (m, nm) = (1, 1) and (1, 3) of the Laplace tidal equations. L and H denote low and high-pressure centers, respectively. (From Ahlquist, 1982)

0

0

0 11

2 accounts for 30–50 percent of the variance of diurnal heights. For surface pressure it gives an equatorial amplitude of 1.47 mb, compared with 0.67 mb for m 1; these values are probably overestimated in the reanalysis by comparison with the estimates of Haurwitz (1965) of 1.16 mb and 0.6 mb, respectively. m 1 is largest at 18.00 and 00.00 hours, m 4 at 06.00 and 18.00 hours. Large-scale waves in the atmosphere exhibit a variety of spatial structures and frequencies. For the normal modes (Appendix 4.3), or free waves representing resonant states of the atmosphere, these depend on the earth’s rate of rotation and the scale height of the atmosphere (the equivalent depth of an isothermal atmosphere – approximately 10 km). In the real atmosphere the zonal wind modiﬁes this basic wave structure (Ahlquist, 1982; Madden and Speth, 1989; Speth and Madden, 1983). Figure 4.20(a) illustrates the associated Legendre functions Pnm versus latitude for P21, P31 and P41 (see Appendix 4.1), while Figure 4.20(b) shows the corresponding patterns of geopotential height or pressure (Madden, 1979). The superscript m denotes the zonal (longitudinal) wave number, and n the meridional index where (nm) is the number of latitudes between the poles at which the stream function of the wave is zero. Modes where (nm) is even (odd) are antisymmetric (symmetric) about the equator. The global geopotential and velocity ﬁelds for modes with (m, nm) (1, 1) and (1, 3) are shown in Figure 4.21 (Ahlquist, 1982). The well known planetary waves ﬁrst described by Rossby (1938) are -plane ( f/ y 0) and/or non-divergent approximations of the global waves. In the absence of zonal winds (i.e. U 0), these waves propagate westward with a local period of days n (n1)/2m. Haurwitz (1940) demonstrated that for U 0, with a constant longitudinal

292 Synoptic and dynamic climatology Table 4.6 Normal mode characteristics in the troposphere Meridional wave No. (n m) Zonal wave No. (m)

0

1

2

3

4

1 Period (d) Amplitude (mb)a Percent varianceb 2 Period Amplitude Percent variance 3 Period Amplitude Percent variance 4 Period Amplitude Percent variance

– – – – – – 2–2.5 – – 3–3.5 – –

4.6 0.5 38.5 3.5–5 0.3 33.8 4–5.5 0.4 35.0 5–7.5 0.3 28.2

7.5–20 0.6 24.2 6–13 0.6 15.3 6–14 0.6 21.2 6–14 ?

12–30 2.0 38.0 10–30 12–30 1.0 2.0 28.0 – 17 approx. – 25.5

5

17–50 2.0 –

Sources: from Ahlquist (1982); Venne (1989). Notes a Surface pressure amplitude at the latitude where the wave is a maximum. b Percent of variance explained by the normal modes in the ﬁlter passband for the 850–200 mb levels.

scale (i.e. m constant), westward propagation slows as the latitude scale decreases (i.e. (nm) increases). Westward propagation also slows as longitudinal scale decreases (i.e. m increases) with (nm) constant. Zonal wind effects are small for modes where the wave numbers are small. In the atmosphere, planetary waves m 1 and m 2 nearly always move westward, m 4 waves usually move eastward, while m 3 waves may move in either direction (Madden, 1979). The periods and amplitudes of the ideal modes calculated by Ahlquist (1982) are shown in Table 4.6. The periods are shorter (longer) in lower (higher) latitudes. It is apparent that while many normal mode waves have periods of two to thirty days, they are strongly excited. The amplitudes or surface pressure given in the table are for ideal modes and may therefore differ from those of the actual free modes in the atmosphere. At the 500 mb level, waves m 1–4 have annual mean amplitudes of about 5 gpm for the gravest symmetric meridional mode (nm 1), and 10 gpm and 20 gpm, respectively, for the next two meridional modes (nm 2 and 3) (Ahlquist, 1985). The amplitudes vary seasonally within about ±25 percent of the annual mean. Ahlquist ﬁnds signiﬁcant coherence and correlation between mode (1, 1) at 60°N and 60°S, but not for mode (1, 3) although it exists in each hemisphere. In another study the forced modes as represented in monthly mean 500 mb height ﬁelds, smoothed to retain only the annual, semi-annual, the third, and half of the fourth cycle per year, are projected mathematically on to the free modes (Tribbia and Madden, 1988). The height projections show that the amplitudes are generally about 10–15 m, typical of transient long Rossby waves. Mode (1, 0) is large (20–5 m) in boreal winter and spring at latitude 37°, with a phase near 90°E all year; mode (1, 1) is about 20 m in January and April; mode (1, 2) has an amplitude of 40 m in DJF when it is near 45°W and mode (1, 3) has a maximum of 20 m in November, when the phase is about 60°W; modes (2,0) and (2, 1) have small amplitudes relative to wave No. 1, whereas the (2, 2) mode is 10–15 m in boreal winter. The largest wave No. 3 mode (3, 2) is about 15 m in winter, when the phase is near 90°E. Analyses of global geopotential data for December–February indicate that the westward-moving (1, 1) ﬁve-day wave is in phase vertically in mid-latitudes and the tropics and has its largest signal-to-noise ratio in the tropics. It represents almost 40 percent of the passband ﬁltered variance (Venne, 1989). The subsequent ﬁrst symmetric modes (2, 1),

Large-scale circulation and climate 293 11

0

0

0

(3, 1), and (4, 1) are also in the four to seven-day time range and each account for about 30 percent of the ﬁltered variance in tropospheric heights. The second antisymmetric modes (1, 2), (2, 2), and (3, 2) which have longer periods are more weakly represented in the explained variance (Table 4.6). The second symmetric mode (1, 3) sixteen-day wave has amplitude peaks at about 70°N and 25°N and 50°S and 25°S, with those in the subtropics nearly 180° out of phase with those in higher middle latitudes (Venne, 1989). The (2, 3) and (3, 3) modes have a similar frequency and a similar structure; all of these second symmetric modes contribute substantially to the variance. The global characteristics of planetary waves are best characterized by the use of spherical harmonic analysis (Volland, 1988) (Appendix 4.1). The global wave modes and their theoretical and observed periods in the atmosphere are shown in Table 4.7. There is good agreement for the gravest Rossby normal mode 1, 1 and also reasonable similarity for the next antisymmetric mode about the equator (1, 3). The Rossby waves of largest longitudinal and latitude scales move most rapidly westward (Madden, 1979). Thus wave mode 1,1 travels around the earth in about ﬁve days, whereas that with a smaller latitudinal scale (1, 3) takes up to three weeks (Table 4.7). Zonal waves m 3 may propagate both eastward and westward and m 4 almost always eastward. Note that mode 1,0 is an eastward-propagating Kelvin (gravity) wave with a period of about thirty-two hours (Salby, 1984). The equatorially symmetric modes 2,0 and 3,0 have theoretical periods of 1.8 days and 2.1–2.4 days, respectively; the latter corresponds well with observations. An analysis of 500 mb height data for the International Geophysical Year (1957–58) provides a useful starting point to characterize the contribution of different ranges of wave number to the total variance of kinetic energy of the geostrophic wind (Eliassen and Machenhauer, 1969). Globally, at the 500 mb level, 39 percent of the total variance resides in the zonal mean state (m 0), zonal wave Nos m 1–4 account for 37 percent and m 5–12 for 22 percent. There is a large contrast between the hemispheres, as seen in Table 4.8. Firstly the zonal mean state predominates in the southern hemisphere, with a similar contribution in both seasons, whereas there is a considerable summer–winter contrast in the northern hemisphere. Secondly, there is a disparity between the contribution of the lower and higher-frequency waves only in the northern hemisphere winter. In the northern summer, and in the southern hemisphere in both seasons, the energy in the two spectral bands is almost equal. The relative contributions of the stationary and transient waves to eddy kinetic energy in the northern hemisphere (25°E–75°EN, 925 to 100 mb) has also been analyzed by Salby (1984). Table 4.9 shows that the transient contribution in the ﬁrst three zonal wave numbers dominates those of the stationary waves, especially in summer.

0 Table 4.7 Periods (days) of the planetary wave modes in the presence of westerly zonal winds in mid-latitudes and easterlies at the equator Meridional wave No. (n – m)

0 11

Zonal wave No. (m)

1

2

3

4

1 2 3 4

5 [5] 4–6 [3.8–4.5] 4 4–5

~ 10 [8.3–10.6] 7–8 7–8 8

7–21 [11.1–20] 14–15; 60 12–14 11–14

28–32 21 15.5 12.5

Sources: from Madden (1979); Salby (1984); Williams and Avery (1992). Note Square brackets indicate theoretical periods.

294 Synoptic and dynamic climatology Table 4.8 The contribution of zonal wave numbers for (n m) = 0 to 7 to the total variance of the kinetic energy of the 500 mb geostrophic wind (103 m2) Hemisphere

January

July

Northern m=0 1–4 5–8

469 307 139

112 61 72

Southern m=0 1–4 5–8

577 131 123

665 166 142

Source: from Eliassen and Machenhauer (1969).

Table 4.9 Relative contributions of zonal wave numbers to eddy kinetic energy, normalized to one, between 25°–75°N and 925–100 mb Winter

Summer

Wave No.

Stationary

Transient

Stationary

Transient

1 2 3

0.42 0.31 0.30

0.58 0.69 0.64

0.58 0.21 0.15

0.77 0.79 0.85

Source: from Salby (1984).

4.3.4 Orographic and thermal forcing There has been a long controversy over the relative roles of diabatic heating and surface topography in forcing responses by the atmospheric circulation. Barotropic model studies (Charney and Eliassen, 1949) initially indicated orographic forcing as predominating, while considerations of the similarity between 1,000–500 mb thickness patterns and 500 mb height contours pointed to the inﬂuence of thermal forcing (Sutcliffe, 1951; Bolin, 1952). Fundamentally, we should expect orographic effects to be more apparent in the northern hemisphere, related to the presence of both the extensive Rocky Mountains and the Tibetan Plateau, relative to the southern hemisphere, where only the Andes are present. Also, orographic effects should be evident in all seasons, whereas thermal forcing should vary its phase seasonally as a result of differences in land–sea heating. A model calculation of thermal forcing by ﬁxed diabatic heat sources and sinks for January and July was performed by Smagorinsky (1953), showing that both factors contribute to the stationary waves. An updated version of this analysis is shown in Figure 4.22. We should note that effects due to orography are not solely mechanical, but involve diabatic heating as a result of the release of latent heat in cloud systems. Moreover, diabatic heating itself is dependent on the circulation and is not geographically and seasonally ﬁxed. Topographic effects on airﬂow that can generate planetary waves include: (1) compression and expansion of air columns, leading to vortex stretching; this may be balanced by vorticity advection; (2) adiabatic heating and cooling due to rising and sinking air motions; this may be balanced by temperature advection; (3) secondary effects such as orographically induced precipitation releasing latent heat, and momentum transport in smaller-scale gravity waves. Orographically forced vertical motion (Wb ) is often approximated as: Wb = u

∂h ∂x

Large-scale circulation and climate 295 11

0

0

0

Figure 4.22 Thermal forcing and orographic forcing of stationary waves in winter (above) and summer (below). Solid lines show 500 mb heights and dashed lines sea-level pressure averaged for 40°–50°N. The topography is shown schematically (shaded). The upward/downward arrows show the integrated diabatic heating and cooling (surface–500 mb); the triangle size indicates the relative magnitude of the heating. (From Hoskins et al., 1989)

where u– is the low-level mean zonal wind and h the height of the terrain. However, this linearized expression neglects the contribution of eddy winds by introducing a prescribed ﬁeld of vertically averaged divergence (Ashe, 1978; Dickinson, 1980). A more complete expression is given by: 0 Wb = u

0 11

∂h ∂h v ∂x ∂y

Barotropic theory, for a plane where f/ y is constant with latitude, implies that westerly ﬂow over the upslope of a north–south mountain barrier undergoes shrinking of the air column and anticyclonic vorticity generation, with corresponding downslope generation of cyclonic vorticity by stretching (Smith, 1979a, b; Wallace, 1993). However, the response of westerly ﬂow to an orographic barrier has been shown to be wavelengthdependent by Hoskins and Karoly (1981). For wavelengths shorter than the stationary zonal wavelength, the dominant term in the barotropic vorticity equation (on a sphere) is zonal vorticity advection whereby shrinking generates an anticyclone over the mountains (see Figure 4.23). For long wavelengths, the dominant term sets up a cyclone in the same location. In the upper troposphere the stationary wavelength is ~ 7,000 km, however,

296 Synoptic and dynamic climatology

Figure 4.23 Schematic vertical sections illustrating the response of the atmosphere to westerly ﬂow over a mountain range. (a) and (b) are barotropic atmospheres; clockwise (counterclockwise) arrows indicate the generation of anticyclonic (cyclonic) vorticity. H, high pressure ridge; L, trough. (c) and (d) are baroclinic atmospheres; the circled cross (dot) indicates poleward (equatorward) ﬂow. W, warmest air; c, coldest air. H and L are the mean sea-level pressure ridge and trough; the sloping lines show their vertical tilts. k is the wave number and ks the stationary wave number. (From Hoskins and Karoly, 1981)

so that the ﬁrst case is more likely. For a baroclinic model there is adiabatic cooling (warming) on the windward (leeward) slopes. For high mountains (> 2 km), upslope cooling tends to be balanced by a poleward ﬂow of warmer air, and vice versa on the downslope. This forcing also generates anticyclonic conditions over the mountains. Hence the two theoretical models, “fortuitously,” both generate anticyclonic circulations (Dickinson, 1978). The solution of the Charney–Eliassen barotropic model on a plane (i.e. no latitudinal variation of f/ y), with uniform westerly zonal wind ﬂowing over a schematic topography along 45°N, is illustrated in Figure 4.24. The response has been split into the part due to the topography of the western hemisphere and the part due to that of the eastern hemisphere. Each wave train shows rapid eastward decay and limited interference with the other (Held, 1983). The interacting effects of different major topographic barriers is illustrated by perturbation experiment with a linearized baroclinic model of Hoskins and Karoly (1981). Using smoothed earth orography and mean zonal ﬂow for the northern hemisphere winter, they simulated wave trains at 300 mb from the Himalayas and Rocky Mountains, and to a small extent from Greenland (Figure 4.25). Examining the effects of each mountain area separately, it appears that the Himalayas generate almost all of the downstream perturbations at 120°E, 160°E and 130°W, and about half of a ridge at 75°E. The Rocky Mountains generate a ridge at 40°W, a trough at 20°E and half of the trough at 75°E. However, the upstream ridge and downstream trough set up by the Rocky Mountains are modiﬁed by the effects of the Himalayas to produce a ridge at 100°W, 40°N, and a trough at 65°W. The results of model assessments of orographic forcing must be interpreted with care in view of the simpliﬁcations introduced by the representation of the topography by spherical harmonic functions (Hoskins, 1980) (see Appendix 4.1). For a T42 truncation, for

Large-scale circulation and climate 297 11

0

0 Figure 4.24 Uniform westerly ﬂow of 17 m s1 over schematic topography along 45°N from the Charney–Eliassen barotropic model with dissipation over ﬁve days, showing the response of the total height (m) (continuous line), which closely resembles the 500 mb January waves, and the responses due to the topography of the eastern and western hemispheres separately. (From Held, 1983)

0

0

Figure 4.25 Simulated wave trains at 300 mb, using a linearized baroclinic model, smoothed Earth orography and winter mean zonal ﬂow. (From Hoskins and Karoly, 1981)

0 11

298 Synoptic and dynamic climatology example, the Rockies have a maximum altitude of 2,120 m compared with 3,100 m in a 1° grid input grid. The Himalayas–Tibet reach 5,250 m in T42 compared with 6,100 m in the input data. The usual effect of harmonic representations is to lower the altitude of a mountain range and increase the width, especially with narrow ranges. The importance of ﬁne model resolution in the horizontal and vertical domains is stressed by Jacqmin and Lindzen (1985), who consider that deﬁciencies in these respects give misleading estimates of the sensitivity of model responses to forcings. Calculations with a high resolution spectral primitive equation model, linearized about observed zonal wind and temperature ﬁelds for the northern winter, show that the response to topographic forcing in mid-latitudes exceeds that from thermal forcing. Wave Nos 1 and 2 are inﬂuenced by the Himalayas and Rocky Mountains at 30°–45°N and wave No. 3 by the northern Rockies. In the mid-troposphere, the topographic response is insensitive to changes in the zonal wind. However, thermal forcing plays a larger role in the interannual variability of the waves, which amounts to less than 40 gpm in amplitude at 500 mb for combined topographic and thermal factors. The vertical structure of planetary waves provides important information on their ability to transmit energy to high altitudes. The vertical propagation of stationary Rossby waves depends on the zonal wind exceeding a critical zonal wind speed Uc (Charney and Drazin, 1961; Shutts, 1978). If 0 < U(z) < Uc for all heights, z, then a local heat source gives rise to waves that tilt upstream with height and show a rapid phase change with respect to the heat source. If U(z) > Uc , or U(z) < 0 for all z, then vertical wave propagation is prohibited and there is a sharp 180° phase change in the lower troposphere. For either case, low-level anticyclones become cold lows aloft, as over eastern Siberia in winter. If 0 < U < Uc for z < zc and U > Uc for z > zc , then planetary waves are trapped below zc (Shutts, 1987). The vertical amplitude and phase of the low-frequency waves can be analyzed in terms of harmonic (Fourier) components (Eliassen and Machenhauer, 1965; van Loon et al., 1973; Dickinson, 1980; Wallace, 1983). These studies show that zonal wave Nos 1 and 2 tilt westward with height in winter, and the westerly winds in the troposphere and stratosphere enable energy to be transmitted up to 30–60 km. In July, in contrast, wave Nos 1 and 2 tilt eastward and wave No. 3 is essentially vertical. The easterly stratospheric winds above 20 km cause rapid attenuation of the waves. Linearized quasigeostrophic models of large-scale forcing indicate that thermal forcing plays a major role, relative to the (untrapped) orographic modes (Shutts, 1987b). Thermally generated waves also make the primary contribution to poleward energy transport (see section 3.7). However, thermal forcing involves both stationary heat sources and the effects of atmospheric ﬂow over different surfaces in modifying the heating. Shutts (1987b) proposes that thorough interpretation of the forcings requires non-linear effects to be incorporated where free-mode structures are taken into account. Non-linear free modes, including isolated free modes such as the Modon (McWilliams, 1980), are likely to dominate the instantaneous circulation patterns where thermal and/or vorticity advection is strong. In some sense, the atmosphere tends to be close to stationary free mode behavior. The forcing of planetary waves by diabatic heating is not well determined. It involves inputs of sensible heat, latent heat release through condensation, and solar and infrared radiative heating. In winter the largest sensible heat ﬂuxes to the atmosphere in the northern hemisphere are found off the east coasts of Asia and North America, with cooling over the continents. The vertically integrated sensible heat contributed to each mid-latitude wave (m 1, 2, 3) is about 10 W m2, corresponding to 20 percent for each wave of the excess heating over the oceans compared with the land areas (50 W m2), according to Dickinson (1980). He notes that there is also three to ﬁve times more precipitation over mid-latitude oceans than over the continents in winter. For each of waves 1, 2, and 3 the Fourier amplitudes of precipitation in winter are about 0.8 mm day1, corresponding to an integrated lower tropospheric heating of 20 W m2.

Large-scale circulation and climate 299 11

0

0

0

Figure 4.26 Mean heating of the troposphere below 500 mb in the northern hemisphere due to the asymmetric portions of sensible and latent heat (the mean zonal component removed) in January and July. The maps are low-pass ﬁltered. Units are 106 K s1 (艑5 W m2). (From Ashe, 1979)

0

0 11

The maximum excess of heating over ocean versus land areas is approximately 100 W m2. Figure 4.26 illustrates the patterns of heating due to the turbulent heat ﬂuxes in January and July. In July the large longitudinal contrasts in the tropics are associated with variations in rainfall amount and the land–sea phase has reversed. The Sahara is not a heating source in either season. Solar radiation absorbed by the atmosphere is estimated to be of the order of 30 W m2 over land, compared with 50 W m2 over the oceans, giving a difference of only ~20 W m2 (Dickinson, 1980). The net effect of infrared ﬂuxes overall is probably close to zero. However, the heating/cooling of the lower troposphere from surface infrared emission may give land–sea differences comparable with those due to sensible and latent heating.

300 Synoptic and dynamic climatology Thermal forcing involves not only heating by the surface primarily from sensible heat transfer, but also heating in the atmosphere by latent heat release. However, both may be balanced by horizontal advection or vertical motion. In mid-latitudes, with large baroclinic gradients, heating at any level is generally balanced by horizontal temperature advection (Smagorinsky, 1953; Hoskins and Karoly, 1981). A mid-tropospheric heat source, for example, is usually balanced by advection of polar air and the trough is then located to the east of the heat source. Low-level heating may be balanced in part by zonal advection where the temperature gradient is in the same direction. In this case, also, the trough is east of the heating. In contrast, Hoskins and Karoly point out that in the tropics low-level heating is balanced by vertical motion. The vorticity generation for waves of long wavelength causes stretching that is balanced by poleward motion (the term). Hence the surface trough is now located west of the heat source. It should also be noted that boundary layer mixing causes damping of thermal forcings, while at higher levels damping may result from radiative processes or the dissipative effects of transient eddies. Experiments using general circulation models with and without mountains conﬁrm the strong control of the mountain and high plateau of eastern Asia and western North America on the northern hemisphere wave pattern in winter (Manabe and Terpstra, 1974). Comparing the circulation in January with present-day mountains, no mountains and an intermediate “half-mountain” case, using the NCAR Community Climate Model, Kutzbach et al. (1989) show that mountains cause the planetary waves to increase in amplitude, with the low-level ﬂow being progressively blocked or diverted around the barriers (Figure 4.27). In July monsoon-like circulations develop near the Tibetan and Colorado plateaus in response to higher barriers. 4.3.5 Propagation of wave trains The response of Rossby waves (Platzman, 1968) to topography is inﬂuenced by both meridional dispersion and vertical dispersion. Meridional effects arise from the earth’s spherical geometry and the latitudinal variation of mean ﬂow. Barotropic ﬂow models on a sphere suggest that the Tibetan Plateau and Rocky Mountains set up wave trains propagating equatorward into the tropics. Such patterns can be analyzed by ray tracing, which is based on kinematic wave theory and geometrical optics (Hoskins and Karoly, 1981; Held, 1983; James, 1994). A ray path indicates the direction of energy propagation away from a source region. The propagation takes place with a speed equal to the magnitude of the group velocity. Zonal wave number (m) and wave frequency are conserved along a ray path, whereas the meridional wave number (k) evolves (Karoly, 1978). According to Hoskins and Karoly (1981): k (K s2 m2)1/2 where the total steady wave number:

冢 [u]冣

1/2

Ks =

M

M the meridional gradient of potential vorticity (on a Mercator map), [u] the longitudinally averaged zonal wind. Ks is analogous to a refractive index in optics; rays bend towards (away from) a maximum (minimum) of Ks (James, 1988). A source localized in latitude produces two rays for each zonal wave number (m) less than the stationary value (ms) that propagate away from the source, while zonal wave numbers m > ms are meridionally trapped near the source (ms ( /U)1/2). For a source localized in latitude and longitude, there are two rays for each m < ms that correspond to the two possible meridional wave numbers k ± (ms2 – m2)1/2. For m positive (negative) there is poleward (equatorward) propagation.

Large-scale circulation and climate 301 11

0

(a)

0

0 (b)

0

(c) Figure 4.27 The 250 mb circulation in January simulated by the NCAR Community Climate Model for (a) no mountains (NM) (b) “half-mountains” (HM) and (c) present-day mountains (M). (From Kutzbach et al., 1989)

0 11

302 Synoptic and dynamic climatology Ray paths are refracted towards larger (ms m2)1/2 and so towards larger ms . Held (1983) observes that at the latitude where k ms , k → 0, and cy (the meridional group velocity) → 0; an incident poleward-propagating wave train is reﬂected and continues to propagate eastward. At a critical latitude where the mean zonal wind is zero, k → , but cy → 0, and rays are refracted into this region. For zonal ﬂows with uniform angular rotation the rays are great circles. For Rossby waves on a sphere, ray paths are curved as a result of the meridional variation of . Figure 4.28 illustrates calculations by Karoly (1978) for ray paths of Rossby waves forced at 30°N in a zonal ﬂow of 15 m s1 westerly at the equator with constant angular velocity (termed “super-rotation”); the ray paths are great circles between the source and the antipodean point. Note that the speed of energy propagation is a maximum for the nearly zonal propagation of m 6. For a realistic jetstream wind proﬁle, Figure 4.29 shows ray paths for a 10°N source in the 300 mb zonal ﬂow. The two wave number regimes in low and middle high latitudes create contrasting ray paths for high and low zonal wave numbers, respectively (Hoskins and Karoly, 1981).

4.4 Zonal index The state of the westerlies is commonly described by the difference in pressure, or geopotential height, between two latitude circles (usually 35° and 55°) expressed as a geostrophic wind speed (Vg). Vg =

1 ∂p · f ∂x

Figure 4.28 Rays paths of steady waves forced at 30°N in a zonal westerly ﬂow of 15 m s1 at the equator with constant angular velocity. (From Karoly 1978)

Large-scale circulation and climate 303 11

0

0 Figure 4.29 Ray paths of steady waves forced at 10°N, in 300 mb winter jetstream ﬂow, peaking at 30°N. The rays are labeled according to wave number. The total wave number (K = n2 m2) is large equatorward of the jet maximum and small on the poleward side. Rays propagating equatorward are not shown, as there is a critical line near the equator. (From Karoly, 1978)

0

0

0 11

where 1.2 kg m3, f 1.11 104 s1, and p/ x zonal pressure gradient; 1 mb 102 Pa ( 102 kg m1 s2). The geostrophic westerly wind measured in this way is an average around the hemisphere or some sector of it (Forsdyke, 1951). This zonal index was ﬁrst investigated by Rossby et al. (1939). It was recognized that the character of the large-scale circulation varies in association with the strength of the westerlies. Rossby and Willett (1948) identiﬁed ﬂuctuations in the zonal index with a four to six-week time scale during which the westerlies intensify and the circumpolar vortex expands, followed by a decrease in westerly circulation and its breakdown into cellular patterns. High index, or zonal, circulations typically display elongated subtropical highs, deep subpolar lows and rapid west–east movement of cyclones, whereas low index circulations have cellular blocking highs (Willett, 1948). Figure 4.30 illustrates composite maps of surface pressure for ﬁfteen winter months of each mode and highlights the pressure anomaly patterns. However, this view of two extreme modes was recognized to be overly simplistic. Riehl et al. (1952) demonstrated four distinct low index states with different synoptic patterns at the surface and 700 mb. The westerlies of the northern hemisphere have a strong annual cycle of intensity (Winston, 1954). A statistical analysis of the hemispheric zonal index (35°–65°N) of surface pressure since 1899 shows that the annual cycle peaks in October at 7.5 mb (1.7 m s1) and has its minimum of 2.5 mb (0.6 m s1) in May. The index has an absolute range for mean monthly values varying from 16.5 mb (3.7 m s1 geostrophic westerly wind) in January 1978 to 11.5 mb (2.6 m s1) in February 1947 (Kozuchowski, 1993). The seasonal trend in the strength of the westerlies is often removed from the zonal index. There are also the effects of the longitudinal asymmetry of the planetary waves. A moderate index may represent a combination of strong zonal ﬂow in one sector and wave or cellular patterns elsewhere. Indices for limited sectors (Allen et al., 1940) can overcome this problem, but the movement of individual pressure systems across the boundaries can introduce spurious oscillations in a sector index. The degree of departure from circular

304 Synoptic and dynamic climatology

Figure 4.30 Composite of mean monthly surface pressure (mb) for ﬁfteen high-index winter months (upper) and ﬁfteen low-index winter months (lower) during 1950–76. Pressure departures (dashed lines) are at 2 mb intervals. (Arai, 1981)

Large-scale circulation and climate 305 11

zonal ﬂow (waviness) can be described by a meander index (M) representing the ratio of the circumference of a selected geopotential height contour to the area it encloses (Mörth, 1977; Jones and Mörth, 1978). For each 10° meridian, the intersection of a chosen height contour is determined both from the pole equatorward and from 25° latitude poleward, in order to include closed contours or slanting trough/ridge features that are inside or outside the circumference of the main vortex contour. M (ai ) (bi )

0

0

0

where ai distance equatorward from the pole along meridian i to the grid point with a smaller height value than the chosen contour, b the corresponding distance poleward from 25° latitude; bi (yn ai) where yn the number of grid points along meridian i excluding the pole, and standard deviation. The effect of eccentricity of the vortex is incorporated by summing the index values for each meridian 180° apart around the hemisphere. The circumpolar vortex, especially in winter, is often eccentric with respect to the pole, as reﬂected in the dominance of wave No. 1; the circulation pole may be displaced 10° latitude from the geographical in the sector 160°–170°W (La Seur, 1954), which also affects a ﬁxed-latitude hemispheric index. For this reason Riehl et al. (1952) made use of zonal proﬁles by plotting geostrophic westerly wind for 5° latitude belts versus time. This is useful for displaying northward/southward displacements of relative wind maxima. The recurrent ﬂuctuations in the westerlies between high and low index, and subsequent recovery, termed an index cycle, were ﬁrst investigated systematically by Namias (1950). He showed that such ﬂuctuations of thirty to ﬁfty days’ duration are especially characteristic of late winter and suggested that they are caused by the accumulation of a polar reservoir of cold air. This subsides as a series of extensive outbreaks of cold air occurs, disrupting the zonal westerlies and creating a low index with strong meridional ﬂow components. The meridional thickness gradient over land areas does reach a peak in February, according to Miles (1977). The main cold air outbreaks leading to a transition from high to low index occur over northeast Asia, northwestern North America, and Greenland–Iceland, according to Defant (1954). The transition of a low to high index pattern features a rise in surface pressure and tropospheric warming over the subtropical western North Atlantic, in winter especially, and also over the western Paciﬁc. Eventually a more zonal jetstream develops in higher latitudes and the polar vortex contracts. Namias’s theory implies a negative correlation between the index and eddy activity, although this was not found by Wallace and Hsu (1985). The regularity of changes in hemispheric circulation was analyzed by Kletter (1962) using time–latitude proﬁles of 850 mb zonal wind for 1955–59. Figure 4.31 illustrates schematically the most frequently

0

0 11

Figure 4.31 The most frequently occurring sequences of 850 mb circulation regimes, 1955–58, in the northern hemisphere. (From Kletter, 1962; Barry and Perry, 1973)

306 Synoptic and dynamic climatology occurring sequences of circulation regime and it is clear that the evolution is considerably more complex than pictured by the simple index cycle. The “characteristic cycle” is comparatively rare. There have been many suggestions of periodicities with three to four weeks’ duration in the index (Panofsky and Wolff, 1957; Webster and Keller, 1975), but Julian (1966) found no evidence of strong covariance in the three to eight-week range between polar, temperate and subtropical indices at 700 mb. Nevertheless, in the southern hemisphere, Kidson (1988) reports index cycle components of twelve and a half to ﬁfty days’ duration at 500 mb that are signiﬁcant at 60°S in winter. These represent transitions from a single to a double jetstream structure. During the 1979 Global Weather Experiment the transition from zonal ﬂow to a blocking mode over the Atlantic sector was associated with an increase in the poleward ﬂux of heat in mid-latitudes, according to Kidson (1985). Consequently the cycle, which lasts about twenty-ﬁve days, may help to regulate the latitudinal temperature gradient between 50° and 70°N. Earlier, Lorenz (1952) and Mintz and Kao (1952) investigated the predictability of the zonal index and Lorenz found lag correlations between the poleward transport of relative angular momentum averaged about a latitude circle, [uv], and the mean zonal wind, indicating a relationship between meridional and zonal motions. Zonal and meridional indices are not signiﬁcantly correlated on time scales of days to weeks, although Wahl (1972) notes a strong positive correlation on an annual scale, when the meridional index lags the zonal ones by twenty-four days. Laboratory experiments with a rotating annulus (Fultz and Kaylor, 1959; Hide, 1970) have shown that rotating ﬂuids commonly exhibit regular periodic time variations in the ﬂow pattern. This behavior, referred to as vacillation, resembles index cycles of westerly circulation in the atmosphere. Vacillation is deﬁned by Hide as comprising pulsations in amplitude which may include changes in wave number, the progression of a large distortion around the wave pattern, or a wavering of the shape of the pattern. Because they are spatially and temporally periodic, these ﬂows may be predictable. However, in different experiments, the same external conditions can give rise to different regular ﬂows, suggesting that regular ﬂows are intransitive (Hide, 1985) (see Figure 4.46). The criteria necessary for steady or vacillating waves are still unknown, according to Hide. The question arises as to whether the circulation in middle latitudes exhibits “random” ﬂuctuations or whether there are preferred modes. The ﬁrst alternative involves “transient variability of linear wavelike perturbations about a climatological mean basic state” whereas the latter may be represented by “discrete transitions of the polar vortex,” i.e. an index cycle, according to Wallace et al. (1991). These alternatives were already recognized by Berggren et al. (1949), but a deﬁnitive resolution of the problem is still awaited. The question is important in terms of the mechanisms involved and because it could be of value in extended-range forecasting. Using a simple numerical geostrophic model, Lorenz (1962) simulated index cycles that had a chaotic, rather than a periodic, nature. That is, the time series contain a degree of regularity (Lorenz, 1986). This seems to accord with the suggestion of Wallace and Hsu (1985) that index ﬂuctuations are perhaps a “statistical residue of dynamically unrelated events at different longitudes.” For 1899 to 1990, monthly index values show spectral peaks of two to three, six and twelve months, as well as a quasi-biennial (~ 2.2 yr) and approximately thirteen to ﬁfteen-year periodicity, according to Kozuchowski (1993), although the last two are not stable. Over the period of record the index has been higher than average between 1899 and 1938, and from 1972 to 1990, and lower during 1939 to 1971. Figure 4.32 illustrates the ﬂuctuations of the 500 mb index (55°–35°N) for winters 1947–94. It is apparent that sequences of easterly anomalies show a tendency to recur (1948–52, 1972–76, 1989–94) and there is a statistically signiﬁcant one-year lag correlation of 0.34. There are also abrupt interannual reversals in 1976 and 1988. The reason for interannual persistence in zonal wind could be related to persistence in anomalies of sea surface temperature in the North Paciﬁc (Ting et al., 1996).

Large-scale circulation and climate 307 11

0

0

Figure 4.32 The 500 mb zonal geostrophic wind index (55°–35°N) for winter 1947–94 (m s1), from NCEP analyses. The indicated year refers to January of each winter season. (From Ting et al., 1996)

0

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Robinson (1991), using a global primitive equation model, also found high and low index states. Forcing of momentum ﬂuxes by synoptic eddies is shown to be strongly correlated with the index and leads it slightly, i.e. the synoptic eddies sustain the index against dissipation. In contrast, forcing by low-frequency eddies is less well correlated with the index and these eddies tend to erode the index. Robinson shows that blocking is not a critical feature. Blocks may occur with extreme high and low indices, which is contrary to the earlier view of Namias (1950). Robinson’s model does not reproduce a split jet, as reported by Kidson (1988) for the southern hemisphere. Further examination of the role of eddy forcing through wave–zonal mean ﬂow interaction1 has been based on the NCEP reanalysis (with R30 truncation) for 1979–95 (Feldstein and Lee, 1998). They ﬁnd that although unﬁltered eddy forcing lacks signiﬁcant feedback, high-frequency eddies (less than ten days) do prolong anomalies of the zonal index against dissipation by low-frequency and “cross-frequency” (high low frequency) eddy forcing and surface drag. The spatial structure of the zonal wind relative to the mean zonal index at 500 mb is illustrated in Figure 4.33. The regression is for geostrophic zonal wind (u– ) and westerly (positive) zonal index. There is considerable coherence in u– values across the subtropics with the mean westerly component at 35°N, whereas large negative regression coefﬁcients are found around 60°N, associated with the winter jetstreams over eastern North America, the North Atlantic, and the Aleutian Islands (Ting et al., 1996). Thus the anomalies of zonal wind at 35° and 55°N are out of phase. Interestingly, although the Atlantic and Paciﬁc climatological jets apparently ﬂuctuate “in phase,” the winter-mean ﬂuctuations of the two jets for winters 1967–94 are uncorrelated. The relationships between regional climatic anomalies and zonal indices have been the subject of many investigations, but recent studies can draw on lengthy records, enabling identiﬁcation of multi-decadal ﬂuctuations in circulation characteristics. Sector indices for the eastern North Atlantic and northern Europe, for example, show few episodes of persistently low or high index during the period 1899 to 1992 (Jonsson and Börring, 1994). There was an interval with low index during winters 1955–70 over the North Atlantic (45°–65°N, 5°–40°W), followed by stronger westerly circulation after 1970 in winter and

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Figure 4.33 Regression of the December–February geostrophic zonal wind against the zonal wind index. Anomaly amplitudes are obtained by multiplying the regression coefﬁcients by the standard deviation of 2.7 m s1. The contour interval is 1 m s1; negative contours are dashed. The right-hand panel shows the corresponding zonal mean (m s1). (Ting et al., 1996)

spring. The zonal indices for these two sectors are shown to be positively (negatively) correlated with winter (summer) temperatures in southern Scandinavia. Increases in zonal index generally correspond with higher temperatures over Europe, except in Scandinavia in summer. The strongest correlations between zonal index and temperature are found over the British Isles in winter and over northern Germany, Denmark, and Poland in spring. In the southern hemisphere, several circulation indices have been calculated using station data. A Trans-polar Index (TPI) based on monthly pressure values at Hobart, Tasmania, and Stanley, Falkland Islands, was tabulated for 1931–60 by Pittock (1980) and has been extended to cover seasonal values for 1895–1997 by Jones et al. (1999). It is a measure of wave No. 1, or the eccentricity of the polar tropospheric vortex. Carleton (1989) shows links between the TPI and Antarctic sea ice extent. During austral summer there are negative correlations (0.3 to 0.5) between the TPI and temperatures in southern South America (Jones et al., 1999). Pittock also calculates an index of the tropical South Atlantic zonal circulation for 1941–60 using sea-level pressure departures from normal at Ascension Island and St Helena minus those at two stations in coastal Brazil. Zonal and meridional indices for the New Zealand sector were introduced by Trenberth (1976), based on pressure differences between Auckland and Dunedin (ZNZ) and Hobart and Chatham Island (MNZ). Seasonal values for 1853–1997 (ZNZ) and for 1878–1997 (MNZ) are presented by Jones et al., as well as corresponding zonal and meridional indices for 1903–97 (ZSAAP) and 1895–1997 (MSAAP) over southern South America and the Antarctic Peninsula, based on Stanley–Orcadas and Punta Arenas–Stanley, respectively. They show that surface temperatures in southern South America and New Zealand depend primarily on the local meridional circulation, with correlations of 0.4 to 0.7 for MNZ. However, temperature trends in New Zealand are unrelated to meridional ﬂow intensity or its frequency. It appears that the temperature rise since the mid-1970s is caused by warming of the Southern Ocean, rather than by decadal variations in atmospheric circulation.

4.5 Zonal and blocking ﬂow modes 4.5.1 Synoptic characteristics The mid-latitude westerlies of both hemispheres exhibit a wide variety of ﬂow behavior. Over sectors spanning about 90° of longitude there can be strongly zonal (west–east) ﬂow, well developed waves, or meridional (north–south) ﬂows associated with a ridge of high pressure. Interruption of the westerly circulation is termed blocking. There are numerous

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deﬁnitions of blocking, and many statistical analyses have been made of its temporal and spatial characteristics (Rex, 1950; Geb, 1966; Dole and Gordon, 1983; Wallace and Blackmon, 1983; Knox and Hay, 1985; Dole, 1996). Synoptic descriptions of blocking identify a stationary warm anticyclone, persistent for ﬁve days or more, in the westerly wind belt. The blocking high may develop from a ridge extending poleward from the subtropical anticyclone, or it may form in high latitudes, as over Scandinavia, with limited effect on the zonal index, since the westerlies are enhanced upstream (Berggren et al., 1949; Rex, 1950). The blocking high or ridge has an equivalent barotropic structure, forming a closed anticyclone at lower levels and a ridge in the upper troposphere. In the “dipole pattern” (Rex blocking), a “cut-off” cold low usually develops about 30°– 40° latitude equatorward of a high-latitude anticyclone. The jetstream splits into a northern branch poleward of the high, and a branch equatorward of the cutoff low with weak winds in between (Figure 4.34). Difﬂuence in the westerlies upstream of the block is matched by conﬂuence downstream. Disturbances in the mid-latitude westerlies are steered around the block, often on the poleward side. Another blocking pattern has a high-latitude anticyclone ﬂanked at lower latitudes by cyclones to east and west. This pattern is termed an omega block from its resemblance to the Greek letter ". The identiﬁcation of blocking patterns has been performed by manual analysis of synoptic charts at the surface and upper levels (Elliott and Smith, 1949; Rex, 1950; Geb, 1966; Treidl et al., 1981) and by the use of objective criteria (Dole, 1978; Illari et al., 1981; Lejenäs and Økland, 1983; Shukla and Mo, 1983; Knox and Hay, 1985; Schilling, 1986; Lupo and Smith, 1995a). For example, Dole (1978) identiﬁes blocking as a persistent, large positive anomaly of 500 mb height. Illari et al. (1981) examine the weekly variation of a selected 500 mb height contour between 60°W and 30°E (a meandering index). Lejenäs and Økland arbitrarily deﬁne blocking as occurring locally and instantaneously when the 500 mb height difference between 40°N and 60°N is negative and a high-pressure cell is poleward of a cut-off low. Tibaldi and Molteni (1990) modify this criterion in order to exclude cases of cut-off lows that are anomalously displaced poleward. Shukla and Mo recognize blocking as an anomaly, of 500 mb geopotential height ≥200 gpm in winter or ≥100 gpm in summer, that persists at a grid point for seven days or less. Knox and Hay (1985) examined a selected sample of 1,200 positive anomalies of ﬁve-day mean 500 mb heights ( k) stratiﬁed seasonally. A blocking signature is deﬁned according to the magnitude of the anomaly, weighted according to latitude. They identiﬁed a blocking signature as occurring when “the absolute value of the distance between

k [the height anomaly] and k1 is less than 1905 km”; this corresponds to a threshold speed of 4.4 m s1 at 60° latitude. Having examined the nature of zonal and blocking ﬂows, we consider next their geographical occurrence. Knox and Hay (1985) analyzed anomaly centers at 500 mb over the northern hemisphere for 1946–78, using the criteria described above. The sectors with maximum frequency of blocking signatures are: 1 2 3 4

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10°W in the North Atlantic. 75°W over northeastern Canada. 60°E near the Ural mountains of Eurasia. A broad sector from 140°W to 180°W over Alaska and the northeastern Paciﬁc Ocean.

These results generally match earlier ﬁndings. Rex (1950) identiﬁed the major regions in the northeast Atlantic and eastern North Paciﬁc sectors, using analyses for 1932 to 1950, and these were conﬁrmed by Treidl et al. (1981) for 1945–77. These two sectors coincide with regions where the annual temperature range is minimal. Johansen (1958) points out that the mean temperature of the 1,000–700 mb layer varies by only 6°–10°C. There are larger variations in the 700–500 mb layer, implying that oceanic effects damp the temperature range in the lower troposphere, strengthening anticyclonic tendencies. Dole

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Figure 4.34 Schematic illustrations of 500 mb contours for (a) February 15 1983, the dipole (Rex), and (b) the omega patterns of blocking. (From Dole, 1996)

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and Gordon (1983) found persistent positive and negative anomalies of 500 mb heights in winter corresponding to sectors 1, 3, and 4 above. Knox and Hay show that the frequencies are highest in spring, especially over eastern Canada and the North Atlantic, and lowest in autumn, except at 20°W and 60°–70°E. They also identify a high Arctic maximum, clockwise from 90°W to 40°E, which is prominent in spring and summer, and is associated with warm anticyclones that migrate poleward. The blocking maximum over northeastern Canada that peaks in spring is surprising in light of the mean upper air trough at 70°W. It is explained by variations in the intensity and location of the mean trough. Tibaldi et al. (1994) ﬁnd, from 500 mb ECMWF analyses for December 1980–November 1987, that European blocking centered around 10°–20°E has a pronounced spring maximum, whereas the eastern North Paciﬁc maximum is in winter, with an autumn minimum. Their deﬁnition is based on a modiﬁed version of that by Lejenäs and Økland (1983) with a requirement of ﬁve consecutive blocked days in a given sector. Results based on Tarleton’s (1986, 1987) deﬁnition for 500 mb height anomalies are shown in Figure 4.35; blocking is common in three preferred sectors in the northern hemisphere: the eastern North Paciﬁc–western North America, the northeastern North Atlantic–western Europe, and northern Eurasia. However, only the ﬁrst two locations are identiﬁed using absolute heights. Anomaly-based results emphasize the oceanic situations and understate orographically related blocking areas such as western north America and western Europe. In the southern hemisphere both deﬁnitions show the New Zealand sector as being the primary location for blocking. The northern hemisphere pattern also shows seasonal differences between winter–spring on the one hand and summer– autumn on the other (Tibaldi et al., 1994). Cold-season blocking anticyclones are more intense, persistent, and larger than their summer counterparts, according to Lupo and Smith (1994). Other deﬁnitions produce similar, but not identical, results. For example, Dole (1983) ﬁnds positive and negative height anomalies co-located over ocean areas. Precise reasons for such deﬁnition-related differences are not yet clear. For the southern hemisphere, several studies ﬁnd that the New Zealand sector (160°– 180°E) is a primary area of blocking, often with a split jet (van Loon, 1956; Lejenäs, 1984; Trenberth and Mo, 1985). Other areas are in the South Atlantic east of South America (50°–70°W), and in the southern Indian Ocean east of South Africa (40°E). The overall frequency is about half that in the northern hemisphere, and 55 percent of the cases identiﬁed by Lejenäs (1984) lasted only one day. However, the method used is apparently sensitive to the accuracy of the southern hemisphere charts. Comparison of this procedure with the use of positive height anomalies for 500 mb ﬁelds during December 1978–November 1979 suggests that more frequent blocking ridges, without a dipole cutoff low, occur near South Africa and South America (Lejenäs, 1987). In the analysis of Tibaldi et al. (1994) the number of blocking days in the Australian sector is comparable in the austral summer and autumn with that in the European sector in the corresponding boreal seasons. Blocking occurrences in the southern hemisphere differ in several ways from those in the northern hemisphere. Coughlan (1983) notes that there are three main preferred regions in the southern hemisphere, rather than two, as found by Rex (1950) and Treidl et al. (1981) for the northern hemisphere, and the mean latitude is about 45°S, compared with 56°N. However, the majority of events, and particularly those lasting a week or more, all occur in the southwest Paciﬁc (Lejenäs, 1984). There is a late winter–early spring maximum in both hemispheres, but in the southern hemisphere there is weaker seasonality and also a secondary autumn maximum in the Indian Ocean region. The three dominant blocking regions in the southern hemisphere are positioned just downstream of the land masses, but they do not appear to be a result of orographic forcing. Coughlan notes that they correspond instead with sectors where the sea surface is warmest and a west–east positive gradient of temperature is observed upstream of the warm anomalies (with respect to a zonal average).

312 Synoptic and dynamic climatology

Figure 4.35 Frequency of blocking in each hemisphere based on 500 mb height anomalies. (From Tarleton, 1987)

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The blocking indices so far discussed incorporate, directly or indirectly, some measure of the zonal mean circulation. For this reason they are unable to address the question as to whether blocking frequency is inﬂuenced by zonally symmetric ﬂow. Accordingly, Kaas and Branstator (1993) devised an index for this purpose which is based only on 500 mb eddy ﬁelds; they use opposite anomalies of the meridional wind component (>10 m s1 or <10 m s1) at locations within ±1,000 km of each grid point (Pi ) selected along a latitude circle (positive anomalies west of Pi, negative anomalies to the east). Coherent space–time anomaly clusters are identiﬁed via a Euclidean distance metric. The index delimits the familiar maxima of blocking (10 percent of days for winter months) centered over the west coast of North America and from northwest of Ireland to northern Scotland. In both regions, strong winter blocks are accompanied by weak zonal winds at 50°–60°N and strong ones near 30°N. They suggest that localized blocking is more likely when the mean zonal wind is in an anomalous state, resembling the structure of the leading EOF of zonally averaged winds. 4.5.2 The climatology of zonal and blocking ﬂow patterns The locations of the centers of action and the intermediate tracks of traveling cyclones differ considerably between high index (zonal) and low index circulations. Figure 4.36 illustrates that, with high index conditions in winter, cyclones are more frequent in the central eastern North Atlantic and northern North Paciﬁc, whereas with low index they are more frequent in the Mediterranean and mid-latitudes of the North Paciﬁc. Anticyclones tend to shift from ocean to mid-continent areas, and from middle to higher latitudes, as the zonal index goes from high to low values (Bradbury, 1958). Corresponding differences in cyclonic and anticyclonic activity are less pronounced in summer, when the overall ﬂow in the northern hemisphere is weaker. The best indication of the contrast between zonal and blocking patterns is provided by studies of monthly or seasonal climatic anomalies. Typically, in western Europe, zonal circulation gives mild, stormy winters and rather cool, changeable or wet summers. Blocking conditions, in contrast, typically give rise to warm, sunny summer weather, as shown in Figure 4.37 for July 1976. A persistent ridge from the Azores anticyclone gave record high temperatures in southern England (Green, 1977). Depending on the location of the anticyclone, northerly and easterly airﬂows give cold winters in western Europe, often with recurrent snowfalls and long-lasting snow cover. An extreme type of low index regime occurs occasionally in winter in the North Atlantic sector. It involves a reversal of the normal pressure pattern, with an anticyclone over Iceland and low pressure over the Azores. Based on monthly sea-level pressures at Stykkisholmur and Ponta Delgado between 1867 and 1980, there is a 6 percent frequency of this “reversal pattern,” with two thirds of these cases occurring in the cold season, October through March (Moses et al., 1987). For positive and negative pressure departures of at least one standard deviation from normal, there were forty cases (3 percent of the total). This pattern may be part of a hemispheric anomaly, with subpolar high pressure and a deep low-pressure belt across the mid-latitude North Paciﬁc and Atlantic. Shutts (1987a) refers to this as a “severe winter pattern” (for western Europe). The winters of 1947–48 and 1962–63 were of this type (Figure 4.38).

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4.6 Blocking mechanisms Theories on the origins of blocking can be distinguished according to those concerned with the initial development and those that relate to the maintenance of an established block. Blocking onset has been attributed to:

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The non-linear interaction of a traveling wave and a topographically forced wave (Egger, 1978).

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Figure 4.36 Distribution of anticyclones and cyclones during high/low index patterns. (From Bradbury, 1953)

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Rossby wave dispersion (Hoskins et al., 1977; Webster, 1982). The ampliﬁcation of planetary waves through resonance effects of topography or heating (Tung and Lindzen, 1979). Constructive interference between stationary planetary waves (Austin, 1980), especially downstream of orographic barriers. Instability of a three-dimensionally varying basic ﬂow (Frederiksen, 1982). Transport of eddy vorticity in a region of jetstream splitting (Shutts, 1983; Neilley and Dole, 1991).

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Figure 4.37 Summer weather conditions over western Europe associated with blocking. Mean 500 mb contour chart (16 dam spacing) for July 1976, showing jetstream locations and above-normal rainfall areas (shaded). (Green, 1977)

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Figure 4.38 Sea-level measure anomalies associated with the North Atlantic pressure reversal of January 1963 which gave rise to severe winter conditions in Great Britain. (Moses et al., 1987)

316 Synoptic and dynamic climatology In diagnostic terms, the onset of blocking coincides with a minimum of eddy kinetic energy and a relatively low value of mean kinetic energy (Smith, 1973). Comparison of blocking and transient ridges over the North Atlantic and North Paciﬁc in winter indicates that blocks show a zonal scale of 60° longitude in the vorticity pattern; there is a 40 percent reduction in the local 500 mb zonal wind and a 20 percent decrease in the zonal mean, according to Hartmann and Ghan (1980). The major difference between blocks and transient ridges over the Paciﬁc is a reduced eastward advection of vorticity in the blocking ridges; these results point to barotropic mechanisms. In contrast, in the Atlantic sector, blocking ridges are more baroclinic than transient ones and indicate the need for a large conversion of potential to kinetic energy. The ampliﬁcation of planetary waves that occurs during the initiation of blocking implies an increase in wave energy. Tanaka (1991) notes that in principle this may result from either a downscale cascade of energy from the zonal ﬂow, or an upscale energy transfer from synoptic or shorter-scale waves. The index cycle of Namias (1950) illustrates energy transfer downscale from the zonal ﬂow into large-scale waves, although Tanaka points out that the growth rates in themselves are small. Instabilities of the zonal mean state may involve baroclinic instability whereby zonally available potential energy is transferred into planetary wave energy. Also, barotropic instability in a zonal jet transfers zonal kinetic energy into planetary waves (Schilling, 1986). The contrary upscale transfer of energy may occur owing to non-linear wave–wave interactions with barotropic energy conversions. Such transfers represent a major energy source for planetary wave ampliﬁcation (Hansen, 1988; Kung et al., 1989). The zonal and wave modes of circulation identiﬁed by Sutera (1986) (see p. 325), have been examined in terms of the energy sources involved in the transitions from one mode to the other by Hansen (1988). Based on thirteen winter cases in four years, the low-amplitude (zonal) circulation (mode 1) and the large-amplitude waves, m 2–4 (mode 2) each persist ten to eleven days and the transitions between them last typically two to four days. The main source of the approximately 50 percent increase (1.7 105 J m2) in both kinetic energy and available potential energy during the mode 1 → 2 transition involves non-linear barotropic interactions between intermediate scale waves. The energy calculations are for 20°–80°N, 1,000–10 mb. During the mode 2 → 1 transition, kinetic energy is dissipated primarily by upscale transfer from waves 2–4 to wave No. 1. The apparently conﬂicting results of numerous studies suggest that different energy sources may set up blocking modes having a similar structure and behaving in a similar manner (Tanaka and Kung, 1989). Thus a low-frequency free eigen mode can be excited by various energy sources. That the eigen vector has a dipolar nature implies that positive and negative anomaly patterns should have similar structures, as conﬁrmed by Dole (1986). The theory of Rossby wave dispersion, generalized to a sphere, proposes that a train of waves initiated by an anomaly source in low latitudes propagates into middle latitudes of the winter hemisphere. This is because the upper westerlies are closest to the equator there. However, Frederiksen and Webster (1988) point out that the theory implies that wave trains will shift longitudinally in response to displacements of tropical heating anomalies. This is not in accord with observations or modeling results. Moreover, the theory assumes a zonally averaged basic state, whereas Arkin and Webster (1985) show important variations with longitude in the time-averaged zonal ﬂow and in the transient eddies relative to that basic state. The observed vertical structures of mature anomalies are also found to be different from those generated by local diabatic heating anomalies (Dole, 1986). Dole remarks additionally that the anomalous circulations “grow and decay while the external forcing remains nearly ﬁxed.” The idea that blocking occurs through simple interference between stationary planetary waves has been explored by Austin (1980). She concluded from observational data and a simple model of interaction between planetary and baroclinic waves that blocking anticyclones are initiated by constructive interference between stationary planetary waves

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0 Figure 4.39 Constructive interference between stationary planetary waves, as shown by the normal phases of the waves at 500 mb, compared with the longitudes of initial jet splitting. (From Austin, 1980, based on Rex, 1950, and Eliassen, 1958)

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(m 1–3) with normal phase but very large amplitudes (Figure 4.39). As discussed in section 4.3, stationary planetary waves are forced by land–sea thermal contrasts and largescale orography. Wave ampliﬁcation may occur as a result of surface anomalies (sea surface temperatures, snow cover) or through a change in propagation conditions in the stratosphere. Austin proposes that blocking in the North Atlantic sector involves interference between large-amplitude wave Nos 1 and 2 at 500 mb, whereas Paciﬁc blocking requires large amplitude wave Nos 2 and 3, with wave No. 1 small. She notes that in summer the 500 mb mean waves are displaced 15° eastward of the January position, causing more continental blocking, in line with observations. Contrary to these results, Quiroz (1987) found that traveling, mainly retrogressive, waves were signiﬁcant in twenty out of twenty-four blocking cases. The resonance theory, elaborated by Tung and Lindzen (1979), indicates that amplifying waves are resonant with topographic forcing and differential land–sea heating. If the energy is constrained horizontally and vertically, as in a wave guide, the wave can become resonant. They show that the necessary conditions are readily satisﬁed for m 4 and much less so for m 1 or 2. The role of synoptic/planetary-scale interactions during blocking onset involves four key elements, according to Tsou and Smith (1990). They are: (1) a quasi-stationary planetary-scale ridge at 500 mb; (2) a developing precursor cyclone at the surface located 40°–60° longitude upstream of the block, corresponding to about half a planetary wavelength; (3) an associated amplifying short-wave ridge at 500 mb; (4) a strong jet maximum on the upstream ﬂank of the developing short-wave ridge. Interestingly, a rare occurrence of continental blocking over Saskatchewan, Canada, in April 1980, when a ridge over western North America ampliﬁed into a blocking anticyclone, also contained these key features (Lupo and Bosart, 1999). In this event, the time-mean thermal wind provided lower tropospheric thermal advection and anticyclonic vorticity advection, leading to subsidence which created abnormal warmth, but synoptic-scale cyclones played an important role in forming and maintaining the block. The instability theory advanced by Frederiksen (1982), Frederiksen and Webster (1988), and Frederiksen and Bell (1990) takes account of the three-dimensional instability of a

318 Synoptic and dynamic climatology three-dimensionally varying basic ﬂow. Both climatological mean and instantaneous synoptic ﬂows have been studied. Using a ﬁve-level, quasi-geostrophic model, with spherical geometry, applied to the development of a blocking event in January 1979, Frederiksen and Bell (1990) examine the growth rates and structures of the fastest-growing modes.2 Largescale barotropic dipole or multipole instability modes are found to be quasi-stationary and undergo rapid growth. Blocking onset in the central North Atlantic occurred, with rapid cyclogenesis off eastern North America and large-scale wave-train modes propagating eastward. This stage involves both barotropic and baroclinic instability, whereas mature blocking is characterized by barotropic instability. The breakdown of the westerlies through the growth of localized disturbances is dependent on the wavelength of the Rossby wave traveling in the westerly ﬂow. For a short wave in strong zonal ﬂow the disturbance moves downstream, whereas a large-amplitude wave in weak zonal ﬂow can grow in situ over a few days (Illari et al., 1981). Illari et al. (1981) refer to fast-moving unstable disturbances that increase in space rather than time as displaying “convective instability” and ones that grow in time as having “absolute instability.” Topographic instability, proposed by Charney and DeVore (1979) and Tung and Lindzen (1979), appears to play a small role in any of these stages. The fastest-growing monopole cyclogenesis-instability modes are initially focused in the climatological storm tracks over the North Paciﬁc, North Atlantic, and Siberia, but subsequently the cyclogenesis modes in the Atlantic split into wave trains to north and south of the block. Given the initiation of blocking, the next consideration is: how is it maintained? Frederiksen and Webster (1988) suggest two possible mechanisms. One involves the concept of multiple ﬂow equilibria (Charney and DeVore, 1979) although, as has been pointed out, this proposition may be only hypothetical. A second mechanism is based on the “modon” theory of a solitary Rossby wave. The role of a modon, or vortex pair, in blocking has been examined by Haines and Marshall (1987), based on non-linear solutions of the equivalent barotropic vorticity equation.3 Vortex pairs can be excited by an appropriate vorticity forcing function; for example, synoptic systems that propagate in a difﬂuent jet, as described above (Figure 4.40). However, the results of Frederiksen and

Figure 4.40 Schematic illustration showing how transient eddies reinforce the dipole pattern of blocking. (Shutts, 1983)

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Bell (1990) suggest that the modon mechanism may be unrealistic, because blocks are constantly changing and being regenerated by dynamic processes; they are not stationary. Numerical model studies demonstrate that blocking can develop in experiments with no mountains, although the duration of blocks in such cases is shorter than observed (Mullen, 1987). Kikuchi (1971) notes that the dynamic effect of mountains appears to cause blocking occurrences in the sector clockwise from 150°E to 150°W and from 30°E to 30°W. In the sectors 30°–150°E and 30°–150°W, however, blocks seem related to dynamic and thermodynamic forcings. Scorer (1979) drew attention to Rossby’s relation for vorticity conservation along a streamline: y constant

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where = relative vertical vorticity, f / y, and f the Coriolis parameter. This implies that blocking anticyclones can form downstream of an orographic barrier, or any other extensive ﬂow perturbation. For example, during the winter 1978–79 blocking in the North Atlantic and North Paciﬁc developed in two instances downstream of extensive regions of intense cyclogenesis (Hansen and Chen, 1982). The role of transient eddies in producing blocking through the transport of vorticity was ﬁrst proposed by Green (1977), based on a study of blocking during the European drought of summer 1976. Shutts (1983) notes that continual replenishment of mass in a blocking anticyclone is required in order to overcome frictional dissipation or mass removal by the mean ﬂow. The location where a jetstream split forms is a region of deformation within which synoptic eddies transfer vorticity to the block, with anticyclonic (cyclonic) eddies becoming absorbed into the poleward (equatorward) parts of the pattern. Shutts suggests that this eddy propagation sets up a region of high eddy enstrophy (onehalf the square of the relative vorticity) with net downgradient energy ﬂux. Figure 4.40 illustrates schematically how transient eddies reinforce the dipole pattern of vorticity. Shutts (1983, 1986) uses the non-linear barotropic vorticity equation to show that blocking modes can be generated by introducing a “wave maker” to excite small-amplitude waves superimposed on simple ﬂow ﬁelds. The split jet can be enhanced by the westward motion of planetary-scale waves 1 and 2. Lejenäs and Madden (1992) show that some 20–40 percent of 500 mb blocks in the North Atlantic and North Paciﬁc during 1950–79 were associated with a westward-propagating wave No. 1 which has a sixteen to twenty-day period and maximum amplitude at 60°N. A westward-propagating wave No. 2, which has maximum amplitude at 40°N, occurred within 20° longitude of the blocked ﬂows for an additional 10–20 percent of the blocks. The results lend support to the eddy straining mechanism proposed by Shutts. These ideas are extended to the inception of blocking events by Neilley and Dole (1991). Using composite analyses of eighteen wintertime blocks in the eastern North Atlantic, they show that the development of large-scale ﬂow anomalies is preceded (six days earlier) by a general increase in synoptic eddy activity over a large part of the hemisphere. As wave activity over the North Paciﬁc and western North America propagates eastward, eddies approach the upstream margin of the developing block and the largescale ﬂow anomalies rapidly intensify. This suggests that feedbacks between the eddies and the large-scale ﬂow serve as a self-sustaining mechanism for the ﬂow anomalies. The net forcing of the transient eddies on the time-mean zonal ﬂow over the central North Atlantic (calculated from the divergence of the zonal ﬂow E vector; see Appendix 4.2) indicates that the eddies tend to weaken the westerlies. The eddies also become meridionally elongated as they move into the area of difﬂuent large-scale ﬂow over the west-central Atlantic in accordance with Shutts’s model (1983, 1986, 1987a; see Figure 4. 41). Subsequently the storm track in the Atlantic shifts northward towards Iceland and eddy activity weakens over the east-central Atlantic as the block develops. The climax of the blocking event during February 12–16 1983 is a pronounced dipolar block over western Europe and the British Isles (see Figure 4.34a). The Ertel potential vorticity (Q)

320 Synoptic and dynamic climatology

Figure 4.41 Meridional elongation of eddies moving into an area of difﬂuent large-scale ﬂow (“eddy straining”) over the west-central Atlantic associated with a split jetstream and a dipolar block north of Scotland (see Figure 4.40), February 15 1983 (Shutts, 1986). The arrows show the Eliassen–Palm ﬂux vectors which point in the direction of westerly momentum transport, superimposed on the mean 300 mb stream function ﬁeld centered at 30°W (British Crown copyright)

distribution for February 15 (Figure 4.42) shows that a narrow tongue of low values extends from the Canary Islands to Greenland and then eastward into the anticyclone. High Q values are present over 40°–50°N, 0°–10°W, associated with an upper cut-off low (Figure 4.34a), and a tongue of high values over the western Atlantic pushes southeastward to form a cut-off low on the next day. Another case study of a North Atlantic blocking cycle conﬁrms the role of anticyclonic vorticity advection at 500 mb in the formation and maintenance of the blocking anticyclone (Lupo and Smith, 1995b). A climatological analysis of blocking onset over northern Europe and over the Gulf of Alaska during 1955–84 shows that baroclinic wave activity in the two and a half to sixday range is enhanced along the upstream storm track about ﬁve days before the block is fully established (Nakamura and Wallace, 1990). This wave activity then separates into a northeastward branch along the western margin of the blocking anticyclone and a southeastward one around an anomaly of low frequency (over six days) cyclonic circulation developing south of the block. Decay conditions are associated with a tendency to suppressed upstream wave activity. It seems that baroclinic wave activity helps to maintain the climatological 500 mb ridges located over the eastern oceans of the northern hemisphere. It now appears that the onset of blocking is linked with antecedent explosive

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Figure 4.42 Transport of Ertel potential vorticity (plotted as Q1/4 to show the gradients in mid-latitudes and the subtropics) for the blocking pattern shown in Figure 4.41. The contour interval is 1 103. The map illustrates the northward transfer of subtropical air of low potential vorticity into a blocking high over western Europe. (From Shutts, 1986; British Crown copyright)

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cyclogenesis over the western North Paciﬁc and western North Atlantic. These cyclogenetic events occur about once every ﬁve to seven days. However, statistical analyses fail to identify a clear relationship (Colucci and Alberta, 1996). The most frequent precursor of blocking onset within ﬁve days (76 percent of 222 cases of explosive cyclogenesis during seven winters) is anomalously strong planetary-scale southerly ﬂow and weaker westerly ﬂow at 500 mb over the antecedent cyclone. However, the dynamical mechanisms involved in blocking onset remain to be resolved. Colucci and Alberta (1996) suggest either interaction between synoptic-scale waves and “preconditioned” anomalously ampliﬁed planetary waves or anomalous planetary-scale advection of synoptic-scale vorticity associated with the upstream cyclone. A diagnostic analysis of synchronous blocking in the North Atlantic and North Paciﬁc sheds some light on scale interactions (Lupo, 1997). Such simultaneous blocks are comparatively rare – about 7 percent of all the blocking days identiﬁed by Lejenäs and Økland (1983). By partitioning the planetary- and synoptic-scale components of 500 mb height tendency, non-interaction and corresponding scale interactions, Lupo (1997) demonstrates no dynamic connection between the paired blocks. Hence “local” mechanisms appear to be responsible for the synchronous developments. In particular, upstream cyclogenetic events contribute to the formation of blocks, and whereas vorticity advection is important for low-amplitude development, temperature advection is the primary factor in

322 Synoptic and dynamic climatology high-amplitude wave blocking events. As shown by Hartman and Ghan (1980), there are important differences between blocks forming over the North Paciﬁc (56°N, 160°W) and Europe (54°N, 10°E). In a study by Nakamura et al. (1997) composites of 250 mb Ertel potential vorticity (see Appendix 3.1) for the thirty strongest events during 1965–92 are derived and the synoptic eddies are removed by ﬁltering. In the North Atlantic, upstream of European blocks, there is a quasi-stationary wave train, which has no counterpart over the western Paciﬁc. Nakamura et al. suggest that, for European blocks, wave activity ﬂux is derived from the upstream Rossby wave train. Vorticity ﬂux convergence from transient synoptic eddies supports 75 percent of the ampliﬁcation of Paciﬁc blocks but only 45 percent of the European blocks. The barotropic model of Shutts (1986) cannot explain the relative warmth of blocking highs. Green (1977) proposes that subsidence is forced by differential eddy vorticity in the vertical, whereas Shutts (1983) suggests that tropical air (with low potential vorticity) is advected in southerly ﬂow upstream of the block as each cyclone approaches from the west. Mullen (1987) examines the net forcing of blocking by synoptic eddies. Temporal ﬁltering is used to separate transient eddy transports of heat and vorticity from longer time-scale effects during observed and modeled blocking events. Anticyclonic eddy forcing is in a quadrature relationship (one-quarter wavelength upstream) with the blocking high, as found by Austin (1980). Mullen shows that the thermal anomalies of the block are – —– maintained by advection of time-mean temperature by the time-mean wind (V # T ); heat transports by the eddies act in the opposite direction to decrease zonal asymmetry in the thermal ﬁeld. The eddy heat transports thus perform a similar role for climatological-mean and blocking ﬂows. In contrast, the vorticity transports by the synoptic eddies are dissimilar for blocking and climatological ﬂows. For the latter, the eddies tend to shift the jetstream axis poleward. Upstream of a block the eddy vorticity transport has the same tendency, but at the block itself the eddy forcing acts to displace the blocking pattern westward. Trenberth (1986) found the same result in an independent analysis of southern hemisphere blocks. Less attention has been paid to the decay of blocking anticyclones. As noted above, Nakamura and Wallace (1990) ﬁnd climatologically that baroclinic wave activity upstream of a decaying block is suppressed; anomalies are of opposite polarity to those during the onset phase, and presumably this applies also to the eddy transports of vorticity and heat.

4.7 Low-frequency circulation variability and persistence The tendency of the planetary circulation to display low-frequency variability, with time scales of several weeks to several years, and with distinct geographical distributions, is an important problem in climate dynamics and long-range prediction. Theoretical aspects of this problem are now attracting the attention of dynamic meteorologists, but we begin with an overview of the empirical nature of the variability. Beyond the life span of a family of mid-latitude cyclones, about ten to ﬁfteen days, detailed predictability of weather conditions is lost. The theoretical limit to such predictability marks the threshold of what is called low-frequency variability of the atmospheric circulation. Wallace and Blackmon (1983) analyzed northern hemisphere daily 500 mb height data for winters 1962–63 to 1979–80 to study its spatiotemporal characteristics. Three time scales were isolated by ﬁltering the data, using (1) a band-pass ﬁlter to distinguish baroclinic waves, (2) a low-pass ﬁlter to emphasize ﬂuctuations longer than ten days, and (3) a thirty-day average. The spatial variance characteristics of each ﬁeld were compared with those of the original unﬁltered data (Figure 4.43). Category (1) depicts a zone resembling that of storm tracks over the oceans, whereas (2) and (3) show high variance in regions of the North Atlantic, North Paciﬁc, and Siberian Arctic that have been identiﬁed as centers of blocking activity by Knox and Hay (1985). The similarity of (2) and (3) suggests that the geographical pattern of variance is reasonably constant

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Figure 4.43 Variance of 500 mb height data for winters 1962/63–1979/80; 10 m contour intervals. (a) Unﬁltered twice daily data. (b) Band-pass ﬁltered to show two and a half to sixday baroclinic waves. (c) Low-pass ﬁltered for periods over ten days. (d) Thirty-day mean value. The contribution of the mean annual cycle to the December–February values has been removed. (From Wallace and Blackmon, 1983)

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at time scales beyond ten days. The baroclinic waves were also found to contribute less to the total variance than the low-frequency components. The great majority of the atmospheric variability in the northern hemisphere 500 mb heights is attributable to the 10–90 time scale (Blackmon et al., 1977). The low-frequency ﬁelds are determined principally by the planetary waves (0 ≤ m ≤ 6) at high latitudes and synoptic-scale waves (7 ≤ m ≤ 12) in middle latitudes. In contrast, in the southern hemisphere the transient eddies with periods less than a week play a much larger role in mid-latitudes. Jones and Simmonds (1993) indicate that the contributions from medium and short-wave (7 ≤ m ≤ 12 and 13 ≤ m ≤ 18, respectively) to MSL pressure variations peak around 50°–60°S in the hemispheric storm track. The bandpass ﬁltered (two and a half to six-day) component of the daily sea-level pressure data for 1972–91 contributes about 25 percent of the variance,

324 Synoptic and dynamic climatology and locally 40 percent between 30° and 60°S. The low pass (over ten-day) frequencies contribute to the larger share, with maximum values equatorward of 30°S and in high latitudes, where the contribution of planetary waves (0 m 6) becomes increasingly important. The processes involved in establishing the major modes of atmospheric behavior have been identiﬁed by the analysis of a multilevel, quasi-geostrophic model (Frederiksen and Bell, 1987). Four classes of motion system are distinguished as the fastest-growing modes on a climatological basic state for January 1978: 1 2 3 4

Rapidly propagating cyclogenesis lasting two to six days, corresponding to shallow disturbances with maximum amplitudes in the storm track zones over the North Atlantic and North Paciﬁc. A dipole mode in the meridional direction, with maximum amplitude at 300 mb, corresponding to onset-of-blocking patterns of larger scale and lasting seven to eleven days. Barotropic modes of intermediate scale, lasting eleven to seventeen days and resembling mature anomaly patterns over the Atlantic and Paciﬁc. Large scale, long-lasting (more than seventeen days) equivalent barotropic modes resembling the major northern hemisphere teleconnection patterns.

These four classes closely match the variance patterns found by Wallace and Blackmon (1983), shown in Figure 4.43. Analysis of a blocking event in the North Atlantic in January 1979 identiﬁed the periods of corresponding instability modes growing on the instantaneous synoptic ﬂows as being in the ranges one to ﬁve, ﬁve to ten, ten to twenty, and over twenty days, respectively. Before considering further the geographical features of low-frequency variability, we need to examine the behavior of particular modes of circulation in the time domain. There are three primary characteristics of circulation modes or “weather regimes” – recurrence, quasi-stationarity, and persistence (Michelangeli et al., 1995). Recurrence can be identiﬁed by locating atmospheric states that have the largest probability of occurrence. Examples include the work of Molteni et al. (1990) and Kimoto and Ghil (1993). Cluster analysis affords a further practical approach (Mo and Ghil, 1988; Cheng and Wallace, 1993). Michelangeli et al. (1995) use a dynamic clustering procedure with iterative partioning in order to obtain stable clusters. The data are agglomerated around randomly chosen seeds and the partition is found which minimizes the sum of the variances within each cluster with respect to their centroids; the Euclidean distance metric is used as a similarity measure. A classiﬁability index is developed, based on the correlation between the cluster centroids obtained from different seeds. Quasi-stationarity of regimes occurs when the large-scale motion is stationary in a statistical sense. The large-scale patterns are maintained despite feedback from transient eddies; this is termed non-linear equilibration. Michelangeli et al. (1995) compare the recurrent and quasi-stationary patterns in 1949–92 winter 700 mb maps for the northern hemisphere, sampled on alternate days. Scale separation is achieved by an EOF analysis for both the Paciﬁc and the North Atlantic–European sectors. Only three clusters for the Paciﬁc sector and four for the Atlantic can be distinguished from a ﬁrst-order Markov process having the same covariance as the data at lag 0 and lag 1 (two days). Identiﬁcation of quasi-stationarity of regimes follows the procedure of Vautard (1990). A state vector containing the large-scale variables of ﬂow is deﬁned as the vector containing the projection coefﬁcients on to the ﬁrst few EOFs. The composite time tendency of any large-scale pattern is calculated as the mean of the instantaneous tendencies associated with adjacent patterns within a speciﬁed radius. This mean is weighted inversely with distance. About 100 convergent solutions were identiﬁed for 2,974 iterations, but only 10–20 percent of them were retained as signiﬁcant, based on reproducibility. The patterns obtained by the

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two approaches differ in their temporal characteristics. The recurrent patterns show a slow, systematic evolution, or drift, in many cases. This may involve ampliﬁcation of an anomaly, for example, but in other cases such evolution may signiﬁcantly change the pattern over a few days. Persistence of regimes can be deﬁned from patterns of anomalies (above/below a speciﬁed threshold) that persist for a given time interval, as illustrated by Dole and Gordon (1983). There are a large number of investigations of persistence. For example, Horel (1985a) examined daily northern hemisphere 500 mb maps for winters 1965–66 to 1981–82, using time series of pattern correlations. This neglects their amplitude and spatial conﬁguration. Based on correlations between pairs of days exceeding 0.5, there were ﬁftyeight quasi-stationary regimes lasting seven days or more. These runs accounted for 25 percent of all days, and a third of them began in December. The most common regime features a wave No. 3 pattern in mid-latitudes superposed on zonally symmetric anomalies of opposing signs between middle and polar latitudes. The circulation is also more persistent than red noise over two to seven-day intervals on 25 percent of winter days, although this persistence varied considerably from year to year. In comparison with individual time series of station weather (Oerlemans, 1978), there are few “breaks” in the hemispheric patterns of height anomalies, i.e. transitional situations are more common than a switch in mode. Horel also concludes that persistence originates through planetary-scale, not regional, processes. A further analysis of 500 mb height ﬁelds, by application of Oerlemans’s technique for deﬁning breaks in a time series, their transition speed, and amplitude, is described by Yang and Reinhold (1991). Grid-point values of 500 mb heights for 1946–84 are ﬁltered to retain scales longer than ten days after removal of the seasonal cycle. The “quality” of a break is deﬁned by the ratio of the amplitude of the shift relative to the root mean square of the variance. The quality is 1.0 when the amplitude is equal to the ﬂuctuations (“noise”) about an ideal break function. The frequency of breaks with a quality of 3.5 and a height change of 200 gpm at 45°N, 170°W over the thirty-year record has a peak at 5.4 days for the transition time. This is conﬁrmed at other locations in mid-latitudes, with distinct longitudinal maxima around 10°–20°W and 160°–180°W, corresponding to the maxima in frequency of persistent anomalies reported by Dole and Gordon (1983) (see section 4.5). Breaks are relatively rare events, such as occur following explosive cyclogenesis. The frequency of six-day transitions suggests the possibility of baroclinic instability as a mechanism, although breaks occur over a wide range of time intervals (or speeds). There is need for caution in interpreting analyses of single variables; 500 mb height data spanning 15°–75°N for four winters (detrended for variability longer than ninety days) are used by Sutera (1986) to examine the frequency characteristics of both zonal geostrophic wind and the amplitude of planetary waves 2, 3, and 4. The probability density distribution of height variance associated with these waves is found to be bimodal in each winter, and of the four winters together, whereas the corresponding distributions of zonal wind are unimodal but slightly skewed, suggesting interannual variability (Figure 4.44). Mean 500 mb height maps for the days representing each of the two modes of height variance resemble a high-index pattern and a large-amplitude wave pattern. The difference maps between modes 1 and 2 in each winter are analogous to patterns obtained by Charney et al. (1981) using a different objective criterion for blocking. In an extension of Sutera’s study, Hansen (1986) used daily 500 mb height data for March 1980–May 1984 to compute wave amplitude for Fourier wave Nos 2–4. The results were ﬁltered to remove high-frequency (of ﬁve days or less periods) and low-frequency (annual and interannual) variability. A frequency histogram again reveals bimodality. The 500 mb ﬁeld is strongly zonal in Mode 1 and has prominent troughs over the western North Paciﬁc and eastern North America in Mode 2 (Figure 4.45). Their relative frequencies are 54 percent and 46 percent, respectively. It is noteworthy that days subjectively classiﬁed as showing “Rex blocks” (20 percent of the total) do not generally occur in Mode 2 events; the latter

326 Synoptic and dynamic climatology

Figure 4.44 Probability density distribution of (above) 500 mb height variances for four winters and (below) the corresponding variances of zonal wind. (From Sutera, 1986)

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328 Synoptic and dynamic climatology are ampliﬁed wave patterns. Rex blocks can occur, however, on days designated as Mode 1 with overall zonal ﬂow at 500 mb. In spite of the apparently considerable evidence for bimodality, there are other results that cast doubt on the idea. Using 500 mb northern hemisphere height data for thirty-nine winters (with ten-day low-pass ﬁltering), Wallace et al. (1991) analyze the spatial covariance, the spatial anomaly correlation, and the statistical distance (RMS difference) between each map and all others in phase space. There is no evidence of bimodality, although the frequency distributions show that large positive spatial covariances and positive anomaly correlations are much more frequent that negative ones. In other words, there are more positive anomaly analogs than antilogs (or “mirror images”). Van den Dool (1991), however, ﬁnds that antilogs are almost as common as analogs for 500 mb ﬂow patterns over eastern North America, except for very deep lows which lack antilogs. While these asymmetries may suggest regime-like behaviors, they may alternatively represent subtle non-linearities in the dynamical behavior of the atmosphere at low frequencies. The relative persistence of zonal and blocking patterns is a subject of considerable debate. Using a measure of day-to-day persistence in 500 mb ﬂow over western Europe, Madden and Lejenäs (1989) found the degree of persistence to be inversely proportional to the strength of the zonal wind at 55°N for 20°W–40°E. However, contrary to these results and traditional ideas, hemispheric zonal ﬂows have been found to be more persistent than blocking regimes over the central Paciﬁc (Horel, 1985b; Legras and Ghil, 1985). Strong 500 mb zonal ﬂows are more persistent than either random or red noise processes, according to Horel (1985a). He also identiﬁed a North Atlantic “seesaw mode” (m 1) (see section 5.6) and an enhanced polar vortex pattern (m 3) among the diverse patterns that occur in extended runs during winter months. These conﬂicting ﬁndings may depend on the size of domain and the particular regions selected. The persistence properties of circulation anomalies do differ regionally. Blocked patterns in the central North Paciﬁc tend to occur with zonal ﬂow across the west coast of North America, while in the North Atlantic an omega pattern of blocking is common. In contrast, ﬂow regimes are less persistent over East Asia and eastern North America, according to Dole and Gordon (1983). Positive and negative anomaly patterns of 500 mb heights in the northern hemisphere during 1963–64 to 1976–77 are found to have a nearly constant probability of persisting for an additional day beyond ﬁve days, i.e. persistence is independent of the duration of a run (Dole and Gordon, 1983). For moderate magnitude and duration events occurring south of the Aleutians, west of the British Isles, and near Novaya Zemlya, the frequency of positive and negative anomalies is about equal. Positive anomalies are rather more frequent for long duration, large-magnitude events. An important ﬁnding by Dole and Gordon is that these persistence patterns can be described by a simple non-linear autoregressive model and, contrary to Charney and DeVore’s (1979) theoretical ideas (described below), there is no evidence of multiple quasi-equilibria. Under the assumption that the statistics of westerly circulation are contained in a normally distributed continuum from intense zonal ﬂow to blocking ﬂow, considerations of symmetry would imply that both extremes should exhibit persistence tendencies. This idea has been tested by Tarleton (1986, 1987). The geostrophic ﬂow is determined for a channel deﬁned by a pair of 500 mb height contours at intervals of 10° longitude. Following a rank ordering of the ﬂow intensity (from high to low), the statistics are divided into halves. Tarleton calculates the frequency of high and low index ﬂows for thirty years of daily 500 mb data at two-day intervals, using actual heights and height anomalies in both hemispheres. The autocorrelation of these categories of ﬂow index at lags of two days to six days shows signiﬁcantly greater persistence for blocking than for zonal ﬂows. This implies that strong blocking and zonal ﬂows are not symmetrical with respect to their temporal characteristics. There is theoretical and empirical evidence that intense zonal ﬂow becomes unstable. Theoretical considerations show that on the equatorward side of a westerly jetstream the

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ﬂow becomes unstable where anticyclonic shear exceeds the Coriolis parameter (see section 4.2). When the critical value of dynamic instability is reached, meridional motion will tend to develop. Moreover, under these conditions a small initial perturbation rapidly ampliﬁes and a low-index, blocking regime may ensue. Lindzen (1986) concluded from theoretical and statistical considerations that blocking regimes should not be regarded as more persistent than other atmospheric circulation anomalies of corresponding geographical scale. This proposition continues to be re-examined. For example, the persistence of blocking events as deﬁned by the subjective criteria of Rex (1950) and by the deﬁnition of Dole (1983) for positive height anomalies is compared by Liu (1994). Positive height anomalies at 45°N represent a ridge extending northward from the subtropical anticyclone, whereas positive anomalies at 60°N typically feature a block centered near 50°N with negative height departures in lower latitudes. Liu ﬁnds that day-to-day changes in geopotential heights are less than average over the block, and greater than average to the north of it, as a result of the jetstream and eddies being displaced poleward. Blocking and strong zonal ﬂow modes are found to have similar height anomaly patterns, of opposite sign, as well as similar average persistence, resembling a ﬁrst-order Markov processing in each instance. The existence of distinct regimes of blocked and zonal ﬂows is important for prediction purposes. Rather than purely statistical behavior, it implies some underlying orderly dynamical process that Ghil (1987a) characterizes as “order in chaos.” The idea that there are several possible circulation modes (i.e. multiple ﬂow equilibria) for a unique set of boundary conditions has intrigued meteorologists. The concept of transitive and intransitive ﬂow behavior was introduced to general circulation studies by Lorenz (1969a); a transitive or ergodic system is one which all initial states lead to the same solution, in terms of its statistical properties. An intransitive system has two or more sets of statistics, depending on the initial states. An almost intransitive system has two or more sets of statistics during ﬁnite periods of its evolution from different initial states (Figure 4.46). It is unclear which category describes the climate system. Using a set of simple equations for thermal convection, Lorenz (1963) demonstrated that the system shifted randomly between a pair of steady states. The behavior of a system of three (or more) variables

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Figure 4.46 Schematic illustration of a transitive, intransitive, and almost intransitive system in a climate system with time-independent forcing according to E.N. Lorenz. (From US National Research Council, 1975)

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Figure 4.47 The behavior of three variables in a model of thermal convection over a long time interval and the directions of the corresponding empirical orthogonal functions (EOFs 1, 2, and 3). (a) The time variations of one variable, Y. (b) and (c) Projections of the trajectory in phase space on to the Y, Z plane and X, Y plane, respectively. Numerals in (b) and (c) denote successive epochs 1,000 time units apart. (From Lorenz, 1963)

over a long time interval can be displayed as the trajectory of one variable, Y, in phase space on to the X, Y- or Y, Z-planes (Figure 4.47). The path of a trajectory converges, as a result of dissipation, to a subset with an inﬁnite number of layers of dimension two (or more). All points on this “attractor” are stable in the direction(s) perpendicular to the layer it is in, but unstable in directions tangential to the attractor. Trajectories may approach one another closely, but will still diverge. The long-term behavior of the trajectory for a system with three (or more) variables has been termed a strange attractor (Ruelle, 1980). Daily climatic data have been used to illustrate the possible existence of strange attractors (Essex et al., 1987). Numerical experiments by Charney and DeVore (1979) with a simple equivalent barotropic model showed two stable stationary solutions (zonal and blocked ﬂow) rather than the single solution that had been expected. More elaborate, but still simple, versions of this approach indicate that, when the forcing of the ﬂow is allowed to dissipate over realistic time scales, stable solutions break down and periodic oscillations or aperiodic solutions may develop (Ghil, 1987a). Figure 4.48a and c shows the time behavior of variable x, with respect to the long-term mean x–, for solutions to forced dissipative models with non-linear dynamics. A periodic solution in time, when displayed in three-dimensional phase space (variables x, y, z), can approach a limit cycle (Figure 4.48d), whereas a convergent solution appears as a ﬁxed point in

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Figure 4.48 Long-term behavior of solutions to forced dissipative models of non-linear dynamics. (a) and (c) How variable x behaves over time, t; x denotes time-mean. (b) and (d) Phase–space maps of the attractor set where (b) is a ﬁxed point convergence, (d) is a limit cycle, and arrows show the directions of stability and motion. (From Ghil, 1987)

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phase space (Figure 4.48b) (Ghil, 1987a). Aperiodic solutions are more complex than those in Figure 4.48. Ghil (in Radok, 1987, p. 141) summarized these achievements in understanding the structure of atmospheric circulation modes and their temporal behavior by the following statement: 0 this modern mathematical and computational work validates a lot of ideas which have been around the long-range forecasting community for a long time. In other words, regimes are just Grosswetterlagen [see section 7.4], and some of these persistence times are the synoptic periods of Multanovski.

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Low-frequency modes of circulation variability have been attributed to at least three different dynamical processes: multiple equilibria, or stationary solutions, of a barotropic or baroclinic model (Charney and DeVore, 1979); Rossby wave propagation from low latitudes in response to thermal forcing (Hoskins and Karoly, 1981); and barotropic instability of a zonally asymmetric circulation (Simmons et al., 1983). Charney and DeVore’s theory predicts bimodality of the mean zonal wind and the planetary wave amplitude; as noted above (p. 226), observations conﬁrm the latter but not the former (Hansen, 1986; Sutera, 1986; Hansen and Sutera, 1987). The frequency distribution of wave amplitude can be compared with hemispheric circulation regimes identiﬁed by clustering techniques. For example, Mo and Ghil (1988) analyze a non-linear barotropic model with orographic forcing for the northern extratropics. In the phase space generated by the ﬁrst two EOFs of the model ﬁelds they show a multimodal probability density function. For 500 mb northern hemisphere height ﬁeld data, six clusters, obtained using pattern correlations, represent 62 percent of the sample. Two stationary clusters (a zonal and a blocked pattern) have persistences greater than ten days; another persistent mode resembles the Paciﬁc–North American pattern of Wallace and Gutzler (1981) (see section 5.10). Transient clusters (of wave trains) occur in the two and a half to six-day band. The analysis supports the existence of bimodality of wave amplitudes.

332 Synoptic and dynamic climatology A different approach to deﬁning circulation regimes involves identifying clusters of atmospheric states in a ﬁve-dimensional phase space generated by the leading EOFs of eddy geopotential ﬁelds (Molteni et al., 1990). A cluster is deﬁned about each local density maximum of points in the phase space, applying a Euclidean distance measure. The study uses ﬁve-day mean eddy ﬁelds of 500 mb height for thirty-two winters (December– February), 1952/53–1983/84 for 20°E–90°EN. The EOFs were computed on a larger sample of October–March for the thirty-two years so that the ﬁrst EOF includes the seasonal cycle (Molteni et al., 1988). Six clusters, illustrated by the average height ﬁelds of their centroids (Figure 4.49), are identiﬁed by Molteni et al. (1990). The largest, C1, representing 45 percent of the 576 pentads, approximates the winter climatology. The other ﬁve clusters (17 percent of the ﬁelds) represent anomalous ﬂow regimes: C3 corresponds to zonal ﬂow with below-average wave amplitude; clusters C1 and C2 contain features characteristic of positive Paciﬁc–North American anomalies (see section 5.10) and C5b has a negative-Paciﬁc–North American signature; C4 suggests blocking over Europe whereas C5 has strong ridges over Europe and the eastern Paciﬁc. Molteni et al. remove the redundancy in these patterns by determining three rotated EOFs from a linear combination of the ﬁve original EOFs. A new ﬁve-dimensional density–cluster analysis, performed on the 576 three-dimensional vectors deﬁned by the rotated EOF coefﬁcients, generated eight local density maxima; the corresponding eight clusters contain 92 percent of the 576 ﬁelds (40 percent in the ﬁrst one). The new clusters (three of which are subgroups that can be combined to give a total of six) closely resemble the original ones but include more of the ﬁelds sampled. Probability density estimates for the total wave amplitude computed for the ﬁrst sixteen and last sixteen winters both show a clear bimodality (near 70 gpm and 90 gpm). From the rotated EOF coefﬁcients, this variability appears to be associated with wave No. 3. New clusters 1 and 3 represent the low-amplitude class and 2, 4, 5, and 6 the high-amplitude class. (The new clusters are numbered according to their similarity with the ﬁrst set.) Transition frequencies calculated between the clusters show about 50 percent persistence for clusters 1 and 3 (low-amplitude waves), 40 percent for clusters 4 and 5, and only 30 percent for cluster 2 (high-amplitude). Among 275 transition cases, 74 percent are to or from cluster 1. Transitions from cluster 1 to high-amplitude modes occur more frequently toward a regime of large, negative Paciﬁc–North American index than its positive counterpart. Two main asymmetrical transition cycles were noted: from cluster 1 to 5 (wave ampliﬁcation), then either to cluster 3 (low amplitude but negative Paciﬁc–North American index) and back to cluster 1; or to cluster 4 and then to cluster 2 (positive Paciﬁc–North American index, high amplitude) before returning to cluster 1. This type of analysis can be carried out on GCM ﬁelds (Molteni and Tibaldi, 1990) in order to diagnose their biases and particularly their ability to predict regime transitions.

4.8 Intraseasonal oscillations The preceding discussion of low-frequency variability focused on the tendency for the circulation to exhibit large-scale episodic spatial structure (zonal/blocking modes) and considered the persistence characteristics of these regimes. In this section attention turns to the oscillatory behavior of the atmosphere on the intraseasonal time scale, i.e. approximately ten to 100 days. While there are connections between the episodic and oscillatory components (Ghil, 1987a, for example), it is convenient here to treat them separately. Intraseasonal oscillations (ISOs) are of considerable importance because of the signiﬁcant percentages of the variance which they account for in tropospheric winds and cloudiness, relative to synoptic-scale disturbances (under ten days), especially in the tropics. The discussion covers the tropics and extratropics, although studies of tropical oscillations have received much more attention and now span the last three decades.

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Figure 4.49 Anomaly patterns for the centroids of six clusters of mean eddy ﬁelds at 500 mb from rotated EOF analysis of data for winters 1952/3–1983/4. (From Molteni and Tibaldi, 1990)

334 Synoptic and dynamic climatology 4.8.1 Tropical intraseasonal oscillations The primary oscillation identiﬁed in dynamical ﬁelds in the tropics is the forty to ﬁfty (or thirty to sixty) day Madden–Julian Oscillation (MJO). The signal was ﬁrst identiﬁed through spectral analysis of lower stratospheric winds at Kanton (3°S, 172°W), where a spectral peak of forty-one to ﬁfty-three days was reported (Madden and Julian, 1971). Similar characteristics were also found in zonal wind, pressure, and temperature data from distant stations in the tropics, showing cross-correlation peaks and a high degree of coherence. Surface pressure oscillations suggest an eastward-propagating wave, with the characteristics of a standing oscillation in the vertical plane that originates in the Indian Ocean. At 150 mb, the u component of wind at tropical stations shows that the oscillation is strongest in December–February and is most pronounced over the Indian Ocean and from the Date Line east to South America. Indeed, the oscillation is now established as a global-scale disturbance, concentrated in zonal wave No. 1, that propagates eastward with periods in the thirty to sixty-day range (Madden and Julian, 1972, 1994; von Storch and Xu, 1990). Figure 4.50 illustrates a conceptual zonal plane model of the disturbance over the eastern hemisphere; the letters denote temporal progression of low (A) to high (E) pressure at Kanton; negative sea-level pressure anomalies at Kanton, shown at the bottom of the ﬁgure, are shaded. The oscillation is represented in various climatic variables such as winds, cloud amount and OLR (Weickmann et al., 1985), as well as in global relative angular momentum. Spectral analysis of ﬁfteen years of OLR data, for example, shows a dominant peak at forty to ﬁfty days, with additional peaks about twenty to thirty and seventeen days (Anyamba and Weare, 1995). These peaks are distinct away from the equator in the western hemisphere. The OLR data conﬁrm the equatorial dipole at 80°E and 160°E. A study using extended EOFs of the time–height modes of u and v components and convective heating and drying from rawinsonde data for 0°–10°S conﬁrms the importance of a forty-one-day oscillation in u, accounting for 48 percent of the variance (Fraedrich et al., 1997). There are westerly (easterly) anomalies in the upper (lower) troposphere, indicative of an internal Kelvin wave. The heating–drying index has a corresponding forty-day oscillation, accounting for 41 percent of the variance, with a mid-tropospheric maximum. The second pair of EOFs represents a twenty-four-day upper tropospheric zonal wind variation (14 percent of the variance), while the second pair for convective heating–drying represents a thirteen-day oscillation with a structure similar to the forty-day one. Associated with the MJO is a spectrally broader convection signal that seems to be concentrated in the eastern hemisphere, where the climatological mean pattern indicates convection. It propagates eastward at about 5 m s1 with periods between thirty-ﬁve and ninety-ﬁve days (Hendon and Salby, 1994). The convective signal, identiﬁed in daily OLR data for 1979–89, shows a concentration in zonal wave Nos 1–3 over the Indian Ocean–western Paciﬁc Ocean (Salby and Hendon, 1994). In the Indian Ocean–western tropical Paciﬁc the eastward movement of cloud complexes, for example, shows a pattern closely similar to the propagation of the oscillation (Figure 4.49). The zonal wind oscillations in the tropical upper troposphere resemble a thermally forced Kelvin wave4 trapped within the tropical easterlies by the change in sign of the Coriolis parameter at the equator (Webster, 1972, 1973; Holton, 1973). The low-latitude forcing is attributed to the release of latent heat by precipitation and the effects of subtropical steady forced motions. The longitudinal heating distribution in low latitudes appears to give rise to a slowly varying Kelvin wave response with a time scale of about one month, but several aspects of the atmospheric response to local heating and convection remain to be explored (Madden and Julian, 1994). Salby and Hendon (1994) propose that in the eastern hemisphere there is a Kelvin structure in the equatorial convective anomaly, with ﬂanking subtropical Rossby gyres, which are all propagating eastward as a forced response at about 5 m s1. In the western hemisphere, there is mainly a Kelvin component response radiating from the convective anomaly at about 10 m s1. The convection

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Figure 4.50 Schematic diagram of the mean spatiotemporal variations in the forty to ﬁfty-day MJO in the zonal plane. The letters at the left indicate time evolution of the station pressure at Kanton (2°S, 172°W). A corresponds to low pressure and E to high pressure at Kanton, with intermediate times denoted by the other letters. The mean pressure disturbance is shown at the foot of the ﬁgure; negative anomalies are shaded. The cumulus and cumulonimbus clouds indicate regions of enhanced convection. (From Madden and Julian, 1972a)

336 Synoptic and dynamic climatology anomaly is positively correlated with a vertically integrated tropospheric temperature anomaly and with surface convergence when the MJO is amplifying, implying the generation of eddy available potential energy; this is converted into eddy kinetic energy in the upper troposphere (Hendon and Salby, 1994). When the MJO is decaying, however, the temperature anomaly and convection are out of phase with the surface convergence. It is striking that anomalous 850 mb convergence and 200 mb divergence are in phase with the anomalous convection throughout the MJO life cycle. In contrast, the spatial association of surface convergence and the tropospheric temperature anomaly, indicating convective latent heat release, undergo a progressive evolution. This evolution suggests that moisture convergence in the boundary layer is essential in organizing the convection. The occurrence of boundary-layer frictional convergence some 40°–50°E of the zonal convergence at 850 mb, when convection is amplifying, supports the hypothesis of a frictional wave–CISK mechanism, according to Hendon and Salby (1994). New details concerning extratropical–tropical interactions are continuing to emerge. Matthews and Kiladis (1999) ﬁnd that when the Asian jet and upper tropospheric easterlies over the western Paciﬁc warm pool are shifted westward, during northern winters, high-frequency transient waves can propagate equatorward to enhance the MJO. The waves enhance convective variability in the central Paciﬁc ITCZ, and this can project onto ISO time scales. Satellite OLR data near the equator also indicate eastward-propagating cloudy areas with a scale of zonal wave Nos 1–2. They comprise several eastward-moving super cloud clusters (SCC) of about 103 km dimension that are made up of smaller clusters moving westward with a one to two-day lifetime as illustrated in Figure 4.51. During active ISO phases, large persistent cloud systems occur in the late afternoon (14.00–19.00 LST) and develop maximum extent of cold cloud tops between 00.00 and 06.00 LST (Chen and Houze, 1997). These do not decay until the second day, and so a two-day periodicity (“diurnal dancing”) is set up. The westward-propagating elements are the envelopes of many convective systems, which may be a combination of a two-day inertio-gravity wave with the superposed one-day convective regime, rather than merely representing the footprint of westward-moving clusters (Nakazawa, 1988; Chen and Houze, 1997). Analogous tropical intraseasonal convection anomalies (TICA) in the equatorial Indian and Paciﬁc Oceans, with a minimum scale of 30° longitude and a minimum lifetime of twenty days, as identiﬁed from OLR data, show a variety of behaviors (Wang and Rui, 1990). Over a ten-year period, out of 122 such TICAs originating in the west-central Indian Ocean or off equatorial West Africa, seventy-seven (63 percent) moved eastward, while twenty-seven moved northward during June–August, and eighteen (weak systems) moved westward. The seventy-seven eastward-moving systems also show varying behavior (Figure 4.52); thirty-two remained within ±15° of the equator, twenty-ﬁve moved eastward initially but near 100°E they moved northeast or southeast; and the other twenty moved eastward and northward over India and/or the western Paciﬁc. The second group that move southeastward near 100°E occur during November–April, apparently related to the Australian monsoon. The corresponding ones moving northeast are primarily associated with the Asian summer monsoon, while the third group, which combines eastward and northward movements, occurs predominantly in May and October. A case study for December 1978–February 1979 over the western tropical Paciﬁc (Sui and Lau, 1992) identiﬁed two intraseasonal oscillations between 0°S and 10°S, propagating eastward from the Indian Ocean. One was associated with monsoon onset and surface westerlies in northern Australia. The oscillations are enhanced as they become quasi-stationary over the western Paciﬁc warm pool, where they develop a strong lower tropospheric westerly jet. Westerly wind bursts with a spatial scale of 500–400 km and lasting two to ten days, during November–April especially, were recognized earlier by Keen (1987). Cloud areas moving northward at about 1° latitude per day and eastward at 5° longitude per day are characteristic of “active” phases of the Indian monsoon, as has been

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Figure 4.51 Large-scale eastward-propagating cloud systems in the tropics. Supercloud clusters (SCC) propagate within larger clusters or intraseasonal variability (left). The SCC contains smaller westward-moving clusters (right).

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338 Synoptic and dynamic climatology

Figure 4.52 Contour plot of the total number of occurrences of strictly eastward-moving cloud complexes in 2° by 2° grid boxes for 1975–77, 1979–85. The heavy line and arrows indicate the central paths; the contour interval is 0.8 per 10 years in (a) and (c), and 0.6 per ten years in (b). (a) Strictly eastward motion. (b) Splitting over the eastern Indian Ocean. (c) Moving eastward and northward over southern Asia. (From Wang and Rui, 1990)

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conﬁrmed by several studies. Madden and Julian (1994) show that maximum (minimum) cloudiness and precipitation over India coincide with maximum (minimum) zonal wind at 150 mb over Chuuk (Truk) (7°N, 152°E), consistent with a stronger (weaker) circulation in the equatorial plane. Spectral peaks of about forty days in summer rainfall affect southern India (Hartmann and Michelsen, 1989). Active periods are associated with the eastward movement of an equatorial low and a trough progressing northward. Corresponding thirty to ﬁfty-day modulations of monsoonal westerlies occur in Australia and are associated with monsoon onset events as well as active periods (Hendon and Liebmann, 1990). There are also some mid-latitude manifestations of these tropical oscillations in the upper troposphere. Analyses at 250 mb show eastward propagation of OLR anomalies, following areas of upper-level divergence (Weickmann et al., 1985). They are strongest over the warmer waters of the Indian and western Paciﬁc Oceans, particularly in the summer hemisphere, and negligible over the cooler eastern Paciﬁc and Atlantic. When cloudiness is present over Indonesia (100°–140°E) the upper tropospheric circulation consists of paired tropical anticyclones located near the convective area, with twin cyclones to the east, as illustrated in Figure 4.53. The pattern is more or less reversed when cloudiness extends eastward to the dateline. The circumpolar vortex is expanded in the regions of equatorial cloudiness and subtropical anticyclones and contracted in the less cloudy cyclone regions. In addition to the MJO spectral band in the tropics, a twenty-four to twenty-eight-day mode in OLR variability is equally strong during 1974–86 in the sector from 170°W to 90°W, according to Ghil and Mo (1991). Analysis of area-averaged rainfall over the tropical Asia–Australia monsoon sector 60°E–120°W and the extra-monsoon sector 120°W–60°E shows a twelve to twenty-four-day mode during 1979–80 (Chen et al., 1995). At 200 mb this latter mode has a zonal wave No. 1 structure in circulation parameters and propagates eastward at 25 m s1, especially over the sector 120°W–60°E. This mode and the MJO cause a seesaw oscillation in intraseasonal variation between these two sectors of the tropics.

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Figure 4.53 Schematic relationship between OLR areas of cloud and clear skies and 250 mb circulation during stage of maximum cloudiness over Indonesia (corresponding to stage H in Figure 4.50). (From Weickmann et al., 1985)

340 Synoptic and dynamic climatology 4.8.2 Intraseasonal oscillations in the extratropics Intraseasonal oscillations outside the tropics have only recently been examined, but the results are intriguing. Ghil and Mo (1991) study the ten to 120 day band, using singular spectral analysis (section 2.5) on northern hemisphere 700 mb daily data for December 1949–December 1986 and southern hemisphere 500 mb data for June 1972–September 1984. The height ﬁelds were ﬁrst analyzed for the dominant spatial patterns of variability, using EOFs and rotated EOFs (section 2.4); then the leading principal components were examined in the time domain via singular spectrum analysis (SSA) (see p. 73). Singular spectrum analysis is appropriate for identifying non-linear oscillations in time series that are noisy and of limited length. The traveling and standing components of the oscillations are distinguished by using Hovmöller time–longitude plots of geopotential height anomalies. In the northern hemisphere there are important oscillations near forty-eight days and twenty-three days. The former is primarily represented in zonal wave No. 2 and has both traveling and standing components. The twenty-three-day mode resembles that identiﬁed in the winter season over the western hemisphere by Branstator (1987) and the ﬁfteen to thirty-day retrograding wave disturbances that create high/low index modes over the North Paciﬁc (Kushnir, 1987). The twenty-three-day mode is westward-propagating, has equivalent barotropic structure in the troposphere, and its amplitude increases upward. For November–March it can account for 25 percent of the variance of tropospheric geopotential heights, based on twenty-one years of daily data analyzed by complex EOFs. Further analysis of northern hemisphere 500 mb height data for 1946–88 ﬁnds sixty to seventy-day as well as thirty to forty-day oscillations (Zhang et al., 1997). While the latter show a PNA-like pattern and account for only about 13 percent of the local variance, the sixty to seventy day mode (about 20 percent of the variance) involves an alternation between anomalies of opposite sign over the polar cap and mid-latitudes of Europe to North America and the Paciﬁc–East Asia. It is best developed in winter and spring. The anomalies propagate northward over Greenland and the Ural mountains and southwestward over Europe and the North Atlantic to North America.

Appendix 4.1 Spectral harmonic functions The longitudinal departures of a height contour from the zonally averaged value for a given latitude circle are described by harmonic or Fourier functions (see section 2.5). The spatial structure of large-scale waves at standard pressure levels in the atmosphere is represented most conveniently by spherical harmonic functions. The stream function $ associated with a given n wave has a shape which is expressed by:

冤冢2t 冣冥 P (sin &)

$(t, , ) = Am,n sin m r

m n

where Am,n is an arbitrary constant, longitude, t time, period, and Pnm (sin &) is a Legendre polynomial of order m and degree n. A global ﬁeld of geopotential, , can be expressed in spherical harmonics: =

兺兺 P m

m

n

n

m n

(sin ) e im

where Pnm (sin ) is an associated Legendre function, normalized to unity, of order m and degree n, longitude, and latitude. These Legendre functions represent solutions to the non-divergent vorticity equation on a sphere (Haurwitz, 1940). The associated Legendre functions for m 1, 2, 3, and 4 are illustrated in Figure 4.20a, and the corresponding latitudinal structure of the geopotential height ﬁelds is shown in Figure 4.20b.

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0

Note that the associated Legendre functions themselves are (antisymmetric) symmetric about the equator if (n m) is odd (even). With |m | n, m is the zonal (west–east) wave number, (n – m) the meridional wave number, i.e. the number of nodal zero values such that $ vanishes along a meridian between (but not including) the north and south poles (Madden, 1979). If m 1 and n 1, no north–south wave exists. The gravest mode is that with the largest latitudinal scale. As m increases, the number of zonal waves increases, and the number of meridional waves decreases. The spherical harmonic series is generally truncated; the truncation may be triangular or rhomboidal (Figure 4.54). In a triangular truncation, every position and direction on the sphere is treated identically. Spherical functions with m 0 are called zonal harmonics and express the mean zonal ﬂow. Here the meridional coordinate is geographical latitude, &.

Appendix 4.2 Eliassen–Palm ﬂux A vector quantity, E, based on Eliassen and Palm (1961), serves as a diagnostic measure of small-amplitude waves superimposed on a mean zonal ﬂow. The vector E displayed on cross-sections in a meridional plane characterizes the northward eddy ﬂuxes of angular momentum and heat (Edmon et al., 1980). For a plane with pressure, p, as a vertical coordinate, the quasi-geostrophic approximation for E, the Eliassen–Palm ﬂux, is:

0

冦

E = u′v′ ,

f v′′ p

冧

where u and v are zonal and meridional ﬂow components, f the Coriolis parameter ( f (y) where y northward distance), potential temperature, p is a measure of static stability, and is negative for a stable atmosphere; the overbars denote a zonal mean and the primes are departures from the means. 0

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Figure 4.54 Illustration of rhomboidal and triangular wavenumber truncations of a spherical harmonic series (for corresponding degrees of freedom). (From Hoskins, 1980)

342 Synoptic and dynamic climatology The direction of E shows the relative importance of the eddy heat and momentum ﬂuxes. The sign of the vertical component of E is positive when the arrow points toward high pressure (downward). The divergence of E: ·E =

∂(u′v′) f ∂((′) ∂y p ∂p

is zero for steady, conservative ﬁnite-amplitude wave disturbances of the zonal wind. For atmospheric cross-sections, the terms in both equations incorporate r cos, where r the Earth’s radius and latitude to allow for spherical geometry. Edmon et al. (1980) show that # E represents the magnitude of transient eddy processes at each latitude and vertical level; it is a direct measure of the total eddy forcing of the zonal-mean state. The divergence of E is also shown to be proportional to the northward ﬂux of quasi-geostrophic potential vorticity, which involves only horizontal advection. Cross-sections illustrating the contribution of transient waves in winter and summer (1966–77) are shown in Figure 4.55. The upward E ﬂux, especially in winter, has a pattern resembling that for simulated cases of non-linear baroclinic wave instability. The equivalent cross-sections for stationary eddies (not shown) have E ﬂuxes less than half those of the transient ones. Extensions of the basic approach have been used to study local interactions between transient eddies and the time-mean zonal ﬂow (Hoskins et al., 1983). Trenberth (1986) deﬁnes appropriate horizontal components of E by: 1 2 (v ′ u ′2) i u ′v ′ j 2 Arrows of E directed from weak to strong values of the zonal velocity indicate positive net growth of eddy energy. The divergence ( # E) and curl (k # E) describe the eddyinduced accelerations of the zonal and meridional wind components due to barotropic processes. For the barotropic case, the group velocity of the transient eddies relative to the local time-mean ﬂow is parallel to E; the shape and horizontal tilt of the eddy can be inferred from the major or minor axis of the anisotropic part of the eddy stress tensor which subtends with the zonal direction an angle equal to twice the corresponding angle for the E vector (Hoskins et al., 1983). The E ﬂux is a zonally averaged quantity, but an extension of this diagnostic tool enables determination of quasi-geostrophic stationary waves on zonal ﬂow (Plumb, 1985). To illustrate this, Figure 4.56 shows the wave activity ﬂux, Fs , for the northern hemisphere stationary wave ﬁeld at 500 mb averaged over ten winters. There are two distinct wave trains, one from eastern Asia across the North Paciﬁc and a weaker one eastward from eastern North America, with a smaller feature emanating from western North America. The vertical component, shown by contours in Figure 4.56, is directed upward (Figure 4.57). It is noteworthy that there is no evidence for the propagation of stationary waves out of the tropics. Moreover the apparent origins of the wave trains are not closely related to orography, at least in the case of the Rocky Mountains. Plumb (1985) suggests that areas of strong diabatic heating gradients are important sources of the wave trains, supplemented by interactions with transient eddies within the major hemispheric storm tracks.

Appendix 4.3 Normal modes Internal and surface wave motions in a ﬂuid made up of two or more layers of different density can be described by a set of equations known as the shallow water equations. These are applicable to ﬂuids where the horizontal scales are large compared with the

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Figure 4.55 The contribution of transient waves to vertical and meridional ﬂuxes for (a) winter and (b) summer 1966–77. The Eliassen–Palm vectors (arrows) are in units of (a) 1.25 1015 m3 and (b) 5 1015 m3 for the arrow scale in the horizontal direction; a vertical arrow of the same length in the vertical component is in m3 k Pa 80.4. The units of m3 are equivalent to dimensions of energy/pressure. (From Edmon et al., 1980)

344 Synoptic and dynamic climatology

Figure 4.56 Total wave ﬂux by the 500 mb stationary wave ﬁeld in winter (1965–75); horizontal component shown by arrows (see scale, lower right); vertical component shown by contours plotted at values (n 1/2), where is the contour interval 4.12 102 m2 s2; contours solid for n = 0. (From Plumb, 1985)

Figure 4.57 Longitude–height cross-section of wave ﬂux along 45°N. (Scales are in the inset.) Schematic topography is shown. (From Plumb, 1985)

Large-scale circulation and climate 345 11

vertical scale. Thus the hydrostatic equation is valid (Gill, 1982). In a closed basin system with two layers of different density there are two modes of oscillation, corresponding to the number of degrees of freedom. These modes are termed the normal modes of oscillation; they behave independently of one another. Gill (1982, pp. 119–21) shows that, for a ﬂuid system with two layers of different densities that do not mix, the structure of the two modes is determined by a quadratic equation: ce4 gHce gg*H1 H2 0

(1)

where the total ﬂuid depth H H1 H2, the subscripts 1 and 2 referring to the upper and lower layers, respectively; g* is the reduced gravity g(2 1)/ 1; and ce is a wave speed. For each of the two solutions (eigen values) ce2 of this equation there is a normal mode structure expressed by the equation:

0

h (x, y, t) z*(x, y, t)

(2)

where h is the upward displacement of the density interface, z* is the perturbed position of the free surface, and is an eigen vector which is independent of x, y, and t and has an appropriate value. For ocean waves, the small vertical density differences permit approximations to be made; g*/g ≈ 0.03. This leads to two distinct roots, ce2 in equation 1. The larger root:

0

冢

c12 ≈ gH 1

g*H1H2 gH2…

冣

and z H1 ; ≈ h H2 0

the ratio of the horizontal velocities of the ﬂuid layers: u2 g*H1 =1 u1 gH… As g*/g → 0 in the limit, the solution corresponds to the surface gravity wave for a ﬂuid of uniform density. This is conventionally termed the barotropic mode. The smaller root (for small g*/g) is:

0

c12 =

H 冢g*HH H 冣冢1 g*H gH … 冣 1

2

1 2

2

and H gH u z* ≈ 2; 2 ≈ 1 h gH u1 H2 This is termed the baroclinic mode. For the ocean, c1 is about 2–3 m s1, corresponding to an equilibrium depth He ce2/g of 0.5–1 m. In the atmospheric two-layer model c1 is about 10–20 m s1 and H ≈ 10–50 m. If the lower layer is thick relative to the upper layer (H2 ! H1), the equation for the smaller root becomes c1 ≈ g*H. Internal waves travel more slowly than surface waves because: g* % g

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346 Synoptic and dynamic climatology

Notes 1

The E vector technique gives information on wave structures, propagation, and net forcing of time-mean ﬂows by transient eddies. It is an extension of Eliassen–Palm ﬂux vectors for zonalmean ﬂows (see Appendix 4.2). Following Trenberth (1986), the components of the zonal ﬂow E vector (in pressure coordinates) are given by:

冤(v′ 2 u′ ) u′v′, f v′′ S 冥 2

Eu =

2

3 4

2

where u and v are the ﬂow components, primes denote departures from time-mean values, = potential temperature, and S is a measure of the horizontally and temporally averaged static stability. The x component of the Eu vector is related to the horizontal asymmetry of the eddies, the y component to horizontal phase variations. The divergence of the E vector ·Eu > 0 implies a positive net (eastward) forcing of the mean ﬂow (i.e. augmentation of the westerlies). “For time-dependent ﬂows the perturbation equations for instability literally describe the linear error growth of disturbances superimposed on the basic ﬂow. However, . . . the rapid growth of particular instability modes was a precursor to subsequent dynamical development” (Frederiksen and Bell, 1990). Quasi-geostrophic ﬂow is equivalent barotropic if there is no vertical phase tilt and zero thermal advection (Hoskins and Pearce, 1983, p. 389). Equatorial Kelvin waves are limited to low latitudes, are symmetrical about the equator, and affect only the u component of motion (Figure 5.27). They propagate eastward and downward. For easterly basic ﬂow, the Kelvin wave has a phase speed slow enough for it to appear quasi-stationary (Webster, 1983). The wave is a solution of the planetary wave equations on a plane (where there is no latitudinal variation of the Coriolis parameter (f ), i.e. ( f / y = = 0), assuming constant basic zonal ﬂow. The amplitude of the wave is proportional to exp ( 0 y2/2c) where 0 is the rate of change of f at the equator and c = the zonal phase speed relative to the mean ﬂow. Temperature oscillations lead oscillations of u by a quarter of a cycle, i.e. they are in quadrature (Lindzen, 1967; Holton and Lindzen, 1968; Parker, 1973).

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Manabe, S. and Terpstra, T.B. 1974. The effects of mountains on the general circulation of the atmosphere as identiﬁed by numerical experiments. J. Atmos. Sci., 31 (1): 3–42. Markham, C.G. 1985. A quick and direct method for estimating mean monthly global temperatures from 500 mb data. Prof. Geogr., 37: 72–4. Matthews, A.J. and Kiladis, G.N. 1999. The tropical–extratropical interaction between highfrequency transients and the Madden–Julian Oscillation. Mon. Wea. Rev., 127 (5): 661–77. McWilliams, J.C. 1980. An application of equivalent modons to atmospheric blocking. Dyn. Atmos. Ocean., 5: 43–66. Michelangeli, P.-A., Vautard, R., and Legras, B. 1995. Weather regimes: recurrence and quasistationarity. J. Atmos. Sci., 52 (8): 1237–56. Miles, M.K. 1977. The annual course of some indices of the zonal and meridional circulation in middle latitudes of the northern hemisphere. Met. Mag., 106: 52–66. Minz, Y. and Kao, S.-K. 1952. A zonal index tendency equation and its application to forecasts of the zonal index. J. Met., 9: 87–92. Mo, K.C. and Ghil, M. 1988. Cluster analysis of multiple planetary ﬂow regimes. J. Geophys. Res., 93 (D): 10927–52. Molteni, F. and Tibaldi, S. 1990. Regimes in the wintertime circulation over northern extratropics. II. Consequences for dynamical predictability. Quart. J. Roy. Met. Soc., 116: 1263–88. Molteni, F., Sutera, A., and Tronci, N. 1988. EOFs of the geopotential eddies at 500 mb in winter and their probability density distribution. J. Atmos. Sci., 45: 3063–80. Molteni, F., Tibaldi, S., and Palmer, T.N. 1990. Regimes in the wintertime circulation over northern extratropics. I. Observational evidence. Quart. J. Roy. Met. Soc., 116: 31–67. Mörth, H.T. 1977. A new approach to indexing the circumpolar upper wind circulation. I. Clim. Monitor, 6 (5): 158–62. Moses, T., Kiladis, G.N., Diaz, H.F., and Barry, R.G. 1987. Characteristics and frequency of reversals in mean sea level pressure in the North Atlantic sector and their relationship to long-term temperature trends. J. Climatol., 7: 13–30. Mullen, S.L. 1987. Transient eddy forcing of blocking ﬂows. J. Atmos. Sci., 44: 3–22. Müller, K., Buchwald, K., and Fraedrich, K. 1979. Further studies on single station climatology. 1. The summer conﬂuence of subtropic and polar front jet. 2. The two northern Cold Poles. Beitr. Phys. frei. Atmos., 52: 362–73. Nakamura, H. and Wallace, J.M. 1990. Observed changes in baroclinic wave activity during the life cycles of low-frequency circulation anomalies. J. Atmos. Sci., 47: 1100–16. Nakamura, H., Nakamura, M., and Anderson, J.L. 1997. The role of high- and low-frequency dynamics in blocking formation. Mon. Wea. Rev., 125 (9): 2074–93. Nakazawa, T. 1988. Tropical superclusters within intraseasonal variations over the western Paciﬁc. J. Met. Soc. Japan, 66: 823–39. Namias, J. 1950. The index cycle and its role in the general circulation. J. Met., 7: 130–9. Namias, J. and Clapp, P.F. 1949. Conﬂuence theory of the high-tropospheric jetstream. J. Met., 6: 330–6. National Research Council 1975. Understanding Climate Change: A Program for Action, National Academy of Sciences, Washington DC, 239 pp. Neilley, P.P. and Dole, R.M. 1991. Interactions between synoptic-scale eddies and the large-scale ﬂow during the development of blocking over the North Atlantic Ocean. In: Proc. International Symposium on Winter Storms. Preprint Volume. Amer. Met. Soc., Boston MA, pp. 50–5. Newell, R.E. and Kidson, J.W. 1984. African mean wind changes between Sahelian wet and dry periods. J. Climatol., 4: 27–33. Oerlemans, J. 1978. An objective approach to breaks in the weather. Mon. Wea. Rev., 106: 1672–9. Oort, A.H. and Peixoto, J.P. 1983. Global angular momentum and energy balance requirements from observations. Adv. Geophys., 25: 355–490. Panofsky, H.A. and Wolff, P. 1957. Spectrum and cross spectrum analysis of hemispheric westerly index. Tellus, 9: 195–200. Parker, D. 1973. Equatorial Kelvin waves at 100 millibars. Quart. J. Roy. Met. Soc., 99: 116–28. Pedlovsky, J. 1987. Geophysical Fluid Dynamics. Springer-Verlag, New York, 710 pp. Peixoto, J.P. and Oort, A.H. 1992. Physics of Climate. American Institute of Physics, New York, 520 pp. Pittock, A.B. 1980. Patterns of climatic variation in Argentina and Chile. I. Precipitation. Mon. Wea. Rev., 108: 1347–61.

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Sinclair, M.R. 1997. Objective identiﬁcation of cyclones and their circulation intensity and climatology. Wea. Forecasting, 12: 595–612. Smagorinsky, J. 1953. The dynamical inﬂuence of large-scale heat sources and sinks in the quasistationary mean motions of the atmosphere. Quart. J. Roy. Met. Soc., 79: 342–66. Smith, E.A. and Shi, L. 1996. Reducing discrepancies in atmospheric heat budget of Tibetan Plateau by satellite-based estimates of radiative cooling and cloud–radiation feedback. Met. Atmos. Phys., 56: 229–60. Smith, R.B. 1979a. The inﬂuence of mountains on the atmosphere. Adv. Geophys., 21: 87–230. Smith, R.B. 1979b. Some aspects of the quasi-geostrophic ﬂow over mountains. J. Atmos. Sci., 36 (12): 2385–93. Smith, R.F.T. 1973. A note on the relationship between large-scale energy functions and characteristics of climate. Quart. J. Roy. Met. Soc., 99: 693–703. Speth, P. and Madden, R.A. 1983. Space–time spectral analysis of northern hemisphere geopotential heights. J. Atmos. Sci., 40 (5): 1086–100. Speth, P., May, W., and Madden, R.A. 1992. The average behavior of large-scale westward-traveling disturbances evident in the southern hemisphere geopotential heights. J. Atmos. Sci., 49 (2): 178–85. Stark, L. 1965. Positions of the monthly mean troughs and ridges in the northern hemisphere, 1949–63. Mon. Wea. Rev., 93: 705–20. Sui, C.-H. and Lau, K.-M. 1992. Multiscale phenomena in the tropical atmosphere over the western Paciﬁc. Mon. Wea. Rev., 120: 407–30. Sutcliffe, R.C. 1951. Mean upper contour patterns of the northern hemisphere: the thermal synoptic viewpoint. Quart. J. Roy. Met. Soc., 77: 435–40. Sutera, A. 1986. Probability density distribution of large-scale atmospheric ﬂow. Adv. Geophys., 29: 227–49. Tanaka, H.L. 1991. A numerical simulation of ampliﬁcation of low-frequency planetary waves and blocking formations by the upscale energy cascade. Mon. Wea. Rev., 119: 2919–35. Tanaka, H.L. and Kung, E.C. 1989. A study of low-frequency unstable planetary waves in realistic zonal and zonally-varying basic states. Tellus, 41A: 179–99. Tarleton, L.F. 1986. Some characteristics of 500 mb blocking and zonal ﬂows in the southern hemisphere. Second International Conference on Southern Hemisphere Meteorology (Wellington, New Zealand). Extended Abstracts, Amer. Met. Soc., Boston MA, pp. 274–7. Tarleton, L.F. 1987. “Persistence Characteristics of 500 mb Blocking and Zonal Flows in the Middle Latitudes of the Northern and Southern Hemispheres.” Ph.D. Dissertation, University of Colorado, Boulder CO. Thompson, P.D. 1961. Numerical Weather Analysis and Prediction, Macmillan, London, 170 pp. Thorncroft, C.D. and Blackburn, M. 1999. Maintenance of the African Easterly Jet. Quart. J. Roy. Met. Soc., 125: 763–86. Tibaldi, S. and Molteni, F. 1990. On the operational predictability of blocking. Tellus, 42A: 343–65. Tibaldi, S., Tosi, E., Navarra, A., and Pedulli, L. 1994. Northern and southern hemisphere variability of blocking frequency and predictability. Mon. Wea. Rev., 122 (9): 1971–2003. Ting, M.-F., Hoerling, M.P., Xu, T.-Y. and Kumar, A. 1996. Northern hemisphere teleconnection patterns during extreme phases of the zonal-mean circulation. J. Climate, 9 (10): 2614–33. Treidl, R.A., Birch, E.C., and Sajecki, P. 1981. Blocking action in the northern hemisphere: a climatological study. Atmos.-Ocean, 19: 1–23. Trenberth, K.E. 1976. Fluctuations and trends in indices of the southern hemisphere circulation. Quart. J. Roy. Met. Soc., 102: 65–75. Trenberth, K.E. 1979. Interannual variability of the 500 mb zonal mean ﬂow in the southern hemisphere. Mon. Wea. Rev., 107 (11): 1515–24. Trenberth, K.E. 1980. Planetary waves at 500 mb in the southern hemisphere. Mon. Wea. Rev., 108: 1378–89. Trenberth, K.E. 1981a. Interannual variability of the southern hemisphere 500 mb ﬂow: regional characteristics. Mon. Wea. Rev., 109: 127–36. Trenberth, K.E. 1981b. Observed southern hemisphere eddy statistics at 500 mb: frequency and spatial dependence. J. Atmos. Sci., 38 (12): 2585–605. Trenberth, K.E. 1986. An assessment of the impact of transient eddies on the zonal ﬂow during a blocking episode using localized Eliassen–Palm ﬂux diagnostics. J. Atmos. Sci., 43 (19): 2070–88. Trenberth, K.E. 1987 The zonal mean westerlies over the southern hemisphere. Mon. Wea. Rev., 115: 1528–33.

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5

Global teleconnections

5.1 Pressure oscillations and teleconnection patterns The study of hemispheric and global-scale oscillations in sea-level pressure has a centurylong history. Inverse pressure variations over southeastern Australia and southern South America were ﬁrst noted by Hildebransson (1897) in his studies of centers of action. Low-frequency pressure seesaws were conﬁrmed by Lockyer (1906) and, undoubtedly, these results provided the basis for the extensive investigations of Sir Gilbert Walker between 1909 and the 1930s. Several large-scale pressure patterns were distinguished by Walker in an attempt to isolate predictors useful in long-range forecasting. Through studies of the temporal correlation of monthly mean sea-level pressure at various locations around the world he discovered three large-scale oscillations of pressure and associated temperature and precipitation anomalies. The circulation modes are identiﬁed according to the strongest simultaneous negative correlations with a given location at some remote distance, 3,000–6,000 km away. The three patterns identiﬁed by Walker (1924) and Walker and Bliss (1932) were: 1 2 3

The North Atlantic Oscillation (NAO), involving the Icelandic low and Azores high. The North Paciﬁc Oscillation (NPO), involving the Aleutian low and North Paciﬁc high. The Southern Oscillation (SO) between the southeast Paciﬁc high and the equatorial trough in the Indian Ocean–Indonesian region (Figure 5.1).

These pressure oscillations imply changes in the strength (or anomalous components) of surface wind. The term teleconnection was introduced by Ångström (1935) in the context of patterns of climatic ﬂuctuations; Bjerknes (1969) later used it to describe patterns of atmospheric response to a remote surface forcing. The 1990s saw renewed interest in teleconnection patterns between the pressure oscillations described by Walker and more distant global anomalies. Their global characteristics and causes have been explored using various spatial analysis techniques (Horel, 1981; Wallace and Gutzler, 1981; Barnston and Livezey, 1987; Mo and Ghil, 1987, 1988; Kushnir and Wallace, 1989; Rogers, 1990; Cheng and Wallace, 1992) and atlases of teleconnections have been published (e.g. O’Connor, 1969; Namias, 1981; Kousky and Bell, 1992). Two aspects of teleconnection patterns need to be considered. First, the nature of the patterns that are identiﬁed by various classiﬁcation techniques. Second, mechanisms that may be responsible for their occurrence. The principal statistical approaches used to identify the modes of atmospheric circulation are (1) correlation analysis of teleconnections and (2) principal component analysis (PCA), or empirical orthogonal function (EOF) analysis, often combined with cluster analysis. These methods are ﬁrst brieﬂy described.

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0 Figure 5.1 Simultaneous correlations (×10) of annual mean sea-level pressure with that at Darwin, Australia, based on a composite assessment of several sources. The ﬁgure shows the Southern Oscillation pattern. (From Trenberth and Shea, 1987)

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The correlation ﬁeld is a statistical construct representing the “net” result of various underlying constituent patterns. Thus the outlying features of a teleconnection pattern may blur other response modes. Barnston and Livezey (1987) note that the reference location for a teleconnection pattern has an artiﬁcially high correlation compared with the remote centers and that the teleconnections tend to be selected according to the strength of the negative correlation with the subjectively selected reference location, while neglecting the spatial extent of the pattern. Nevertheless, simple composite analyses of the positive and negative modes of Walker’s three primary teleconnection patterns have proved very informative (van Loon and Rogers, 1978; Rogers, 1981b). The statistical description of concurrent or time-lagged weather relationships between different parts of the globe by the use of correlation coefﬁcients and multiple regression equations was pioneered by G.T. Walker between 1910 and the 1930s. Walker, as a mathematician, was aware of many of the limitations of linear correlation methods and their application to data that possess autocorrelation in time and space, although his empirical studies were subsequently criticized (Montgomery, 1940, for example). The use of correlation patterns involves two principal problems: the multiplicity of correlations and the existence of autocorrelation in bivariate time series (Brown and Katz, 1991). The ﬁrst problem concerns the bias caused by the limitation in the selection of factors to those with the highest correlations. Walker recognized the need to adjust the probability levels in determining the true signiﬁcance of many correlations; otherwise there is an increased likelihood of assuming a relationship exists when the correlation is actually negligible (a Type I error). Brown and Katz demonstrate the need to adopt an appropriately conservative threshold of signiﬁcance. The so-called Bonferroni approach assumes that to achieve the same probability level for multiple tests (o) as for an individual test (), tests of the individual correlations should employ the criterion 0 /k ; for example, for k ten pairs of correlations, an overall test level of 0 0.05 would require that the individual

360 Synoptic and dynamic climatology test level be 0.005. The modern “bootstrap” procedure of cross-validation by Monte Carlo replication is a more sophisticated alternative (Ephron and Gong, 1983). The second problem of autocorrelation in time series can generate apparent lead and lag relationships where none exists; smoothing of a time series has a similar effect. Brown and Katz suggest that an autocorrelated time series be transformed into an uncorrelated time series (“pre-whitening”) by the use of a ﬁrst-order autoregressive model. A similar method is to take the “ﬁrst differences” of values in a time series (xi xi1) that is known to be highly temporally correlated, such as monthly values of sea ice extent (e.g. Carleton, 1989). These ﬁrst differences may then be tested for serial autocorrelation to conﬁrm that the series is now comprised of independent observations. Alternatively, an adjustment can be incorporated to account for the effective number of independent samples through a so-called variance inﬂation factor (Katz, 1988). The recommended approach that takes into account multiplicity and autocorrelation for a desired 0 0.05 is 1 (0.95)1/k (Brown and Katz, 1991). Teleconnections are established by constructing one-point correlation maps for all grid points. Wallace and Gutzler (1981) propose their summarization by a teleconnectivity ﬁeld which selects the strongest negative correlations in these one-point maps. The teleconnectivity at grid point i is deﬁned: Ti = | (rij) minimum for all j | where rij is the correlation of point i with all other j grid points. Large values of Ti are usually part of a standing oscillation involving one or more remote areas. 5.1.2 EOF and clustering methods The use of empirical orthogonal functions (EOFs, or principal components) to obtain the most efﬁcient possible representation of a data set has become commonplace in meteorology since the mid-1960s. An outline of the basic aspects of EOF analysis and references to detailed descriptions are given in section 2.4. Only the salient features of this approach need to be noted here. In the analysis of a time series of pressure (or height) ﬁelds, a matrix of grid-point pressure values is converted into a matrix of covariance or correlation coefﬁcients between the pressure ﬁelds. The principal component analysis (PCAs) yields: a set of principal components (PCs), orthogonal to one another, representing normalized time series; eigen values which describe the normalized variance attributable to each principal component; and eigen vectors (or loading vectors) describing the spatial patterns associated with each principal component (Horel, 1981). Each eigen vector can be scaled by the square root of the eigen value to obtain coefﬁcients (loadings) relating the PC to the original time series. The PCs can be linearly transformed (rotated) so that the variance of the squared correlation coefﬁcients between each rotated PC (RPC) and each of the original time series is maximized – the varimax solution. This solution is also independent of the spatial domain of the analysis (Kaiser, 1958). This important feature overcomes the domain-dependent sequence of ordinary orthogonal PC patterns identiﬁed by Buell (1975; see also Richman, 1993). The component scores obtained by PCA, representing the projection of the original time series data on to the PC axes, can be clustered to obtain a classiﬁcation. A summary of the procedures used to group similar objects together is contained in section 2.4. Several independent studies, using correlation pattern analysis (Wallace and Gutzler, 1981) and rotated principal component analysis (Horel, 1981; Barnston and Livezey, 1987; Kushnir and Wallace, 1989; Rogers, 1990), have identiﬁed the principal northern hemisphere tropospheric teleconnection patterns. From monthly mean 700 mb height ﬁelds, Barnston and Livezey show two north–south dipole patterns located in the eastern Paciﬁc and the western Paciﬁc, and two uncorrelated patterns each with three centers – the Paciﬁc–North America and Northern Hemisphere Tropics pattern, and a northern Asia

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pattern. Wallace and Gutzler’s analysis of 500 mb ﬁelds for winter months depicts ﬁve main patterns (Figure 5.2) over the west Atlantic, east Atlantic, Eurasia, west Paciﬁc, and Paciﬁc/North America (PNA). Rotated PCA by Horel (1981) of 500 mb heights for ninety winter months (December–February 1950/51–1979/80) and a separate analysis for ninety summer months (June–August 1959–79) gives ten patterns for each season (Figure 5.3). For winters, patterns associated with RPCs 1, 2, 3, and 5 resemble the PNA, east Atlantic, west Paciﬁc and west Atlantic teleconnections, although pattern 2 in the subtropics is displaced eastward. They account for 26 percent of the variance. RPCs 4, 6, 7, 8, and 9 are not apparently teleconnection patterns. In summer a number of RPCs appear to be related to the grid periphery. However, RPCs 4, 5, 8, and 9 are regional ﬂuctuations analogous to, but displaced northward of, the winter ones and accounting for only 13 percent of the variance. When the same RPC procedure is performed for ﬁve-day mean wintertime sea-level pressures, ﬁve modes are identiﬁed by Hsu and Wallace (1985), including the North Atlantic Oscillation and the Paciﬁc/North American. These two spatial patterns were identiﬁed by Kushnir and Wallace (1989) as the dominant modes of interannual variability at 500 mb during 1946/47 to 1984/85. Hsu and Wallace (1985) indicate that these two patterns show a barotropic structure whereas the other three – one over northern Asia, the North Paciﬁc Oscillation and one over Tibet–China – differ considerably at 500 mb from the sealevel patterns in their shape and polarity. Before describing these teleconnection patterns, however, we ﬁrst examine the pressure oscillations themselves.

5.2 The Southern Oscillation and El Niño

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The Southern Oscillation (SO) comprises a standing atmospheric wave that involves a west–east vertical circulation (“Walker”) cell between an area centered on Indonesia and the eastern Paciﬁc Ocean. The concept of a thermally driven mass circulation, with rising air over the “maritime continent” of Indonesia–Malaysia, divergent westerly upper tropospheric ﬂow across the Paciﬁc, descent over the eastern Paciﬁc and low-level easterly ﬂow along the equator, was developed by Schell (1956), Troup (1965), Bjerknes (1969), and Julian and Chervin (1978), among others, and later conﬁrmed observationally (Streten and Zillman, 1984). An EOF analysis of pressure data shows the Southern Oscillation to be a preferred mode of tropical circulation (Kidson, 1975). The Southern Oscillation is most simply measured by the sea-level pressure difference between Tahiti (Papeete) and Darwin, Australia (Troup, 1965; Parker, 1983) or Djakarta, Indonesia (Berlage, 1957), although the term introduced by Walker was not meant to imply that the pattern is conﬁned to the southern hemisphere. The immense spatial scale of the Southern Oscillation, as represented by simultaneous correlations of MSL pressure with that at Darwin, is illustrated in Figure 5.1. It is the major contributor to variance in climatic ﬁelds globally on interannual time scales. A positive Southern Oscillation Index (SOI) represents a strong southeastern Paciﬁc high with anomalously low pressure centered over Indonesia–northern Australia, well developed low-level easterlies and strong convection over Indonesia; during low (negative) SOI the region of ascending air and convection shifts eastward to the central Paciﬁc and this sets up low-level westerlies over the western Paciﬁc (Figure 5.4). During negative SOI regimes, warm surface waters (temperatures 28°C) extend eastward from their normal location between 10°N and 10°S in the western Paciﬁc as the trade winds relax, and the normal cold upwelling waters off Peru and Ecuador (Figure 5.4c and d) are replaced by a well developed current of warm equatorial water ﬂowing southward along the coast (Rasmussen and Carpenter, 1982). The associated atmosphere–ocean links are discussed below. Various indices are used to describe the SO and to identify its modes. Selected atmospheric and oceanic variables exhibit a characteristic pattern of variations in time, as illustrated in Figure 5.5. The principal indices used to identify the different modes of the SO are: sea-level pressure anomalies at Darwin (12.4°S, 130.9°E) and Tahiti (17.5°S,

362 Synoptic and dynamic climatology

(a)

(b) Figure 5.2 Simultaneous correlation patterns in winter monthly mean 500 mb height data, 1962/63–1976/77. (a) Centers of the ﬁve strongest patterns: EU Eurasian, WP West Paciﬁc, PNA Paciﬁc North American, WA West Atlantic, EA East Atlantic. The + and – signs denote the sign of the correlation within each pattern. The light lines show the wintertime mean 500 mb height contours. (b) Arrows and shaded areas denote strong negative correlations between distant locations; heavy shading > 0.75 and light shading > 0.6. Correlations are plotted ×100 and arrows point the direction(s) of the correlations. (After Wallace and Gutzler, 1981; from Wallace and Blackmon, 1983)

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Figure 5.3 Summary map of the loading vectors associated with the ﬁrst ten RPCs, derived from (a) ninety winter months and (b) ninety summer months, for 500 mb height ﬁelds. The respective seasonal mean 500 mb height contours are indicated. (From Horel, 1981)

364 Synoptic and dynamic climatology

Figure 5.4 Schematic models of (a) non-ENSO and (b) ENSO mode vertical circulation cells in equatorial latitudes. (c) Sea-surface temperature anomalies corresponding to (a), (d) and (e). Changes in sea level and thermocline depth corresponding to (a) and (b). (Wyrtki, 1985; from Barry and Chorley, 1998)

149.6°W), usually expressed as the anomaly at Tahiti minus that at Darwin (the SOI1); sea surface temperatures in the equatorial Paciﬁc east of 180° longitude; rainfall in the equatorial central Paciﬁc; and zonal wind anomalies also in the equatorial central Paciﬁc. Figure 5.5 shows a close similarity between these different variables. An enhancement of the climatological-mean high pressure over Tahiti and low values over Darwin are highly correlated with a cooler sea surface in the central eastern equatorial Paciﬁc, reduced rainfall in the equatorial central Paciﬁc and stronger easterlies – referred to as the High/Dry mode of the SO and its converse as the Low/Wet phase. Modern usage refers to the related “warm” (El Niño) or “cold” (La Niña) events described below.

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Figure 5.5 Monthly values of seven indices of the Southern Oscillation. (a) SST, mean anomaly 6°–2°N, 170°–90°W; 2°N–6°S, 180°–90°W; 6°–10°S, 150°–110°W. (b) Rainfall, mean percentage of cube root at up to six stations in central equatorial Paciﬁc. (c) Pressure anomaly at Darwin. (d) Pressure anomaly at Darwin minus that at Tahiti. (e) Mean zonal wind anomaly 5°N–7°S, 150°E–150°W. (f) As (c) but smoothed using ﬁlter (0.25, 0.5, 0.25). (g) As (e) but smoothed as in (f). (From Wright, 1985)

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The basic indices are sometimes contaminated by local scale or transient disturbances. In order to extract the SO signal from pressure data, Trenberth (1984) calculated a ratio of the SOI, deﬁned as the difference between the monthly MSL pressure values at Tahiti and Darwin, each normalized by the respective standard deviations, divided by a noise index, deﬁned as the sum of the normalized pressures at Tahiti and Darwin. The signal-to-noise ratio can be further ampliﬁed by applying a low-pass eleven-point ﬁlter to the series to remove ﬂuctuations with a period less than about a year. Figure 5.6 illustrates the clear inverse relationship between sea-level pressure anomalies at Darwin and Tahiti ﬁltered in this manner. An alternative to averaging sea surface temperatures over a grid box is to analyze the longitude of the 28.5°C (warm pool) isotherm between 4°S and 4°N, as illustrated in Figure 5.7. The extreme nature of the 1982–83 warm event (Caviedes, 1984) is readily apparent. The 1997–98 warm event gave rise to extreme seasonal anomalies of temperature and precipitation (the upper quintiles of the distribution) over about 80 percent of the contiguous United States. One-ﬁfth of the country experienced ﬁfty-year record winter temperature anomalies. Even so, Harrison and Larkin (1998b) emphasize that these percentages are not exceptional compared with other non-El Niño years. Figure 5.5 shows also that the various SO indices exhibit signiﬁcant persistence; for 1946–81 the autocorrelation of seasonal values of the Darwin–Tahiti pressure index is 0.63 for a one-season lag and 0.40 for a two-season lag, while sea surface temperatures

366 Synoptic and dynamic climatology

Figure 5.6 Sea-level pressure anomalies at Darwin and Tahiti, 1950–99. Monthly values are smoothed with an eleven-point ﬁlter (Courtesy Dr K.E. Trenberth)

Figure 5.7 Variation of the longitude of the 28.5°C isotherm of sea surface temperature for 4ES–4EN for January 1951–June 1991. Values are three-month running means. The forty-year mean longitude is 175.6°W. (From Diaz and Kiladis, 1992)

for the equatorial Paciﬁc from the Galapagos to longitude 180° show corresponding autocorrelations of 0.76 and 0.41, respectively (Wright, 1985). There are smaller, but statistically signiﬁcant, negative autocorrelations for a six-season lag in these same indices. This persistence is high between July and February, and least in April, which creates a barrier to seasonal predictability (see section 5.4). The correlations between indices are

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also greatest from September through February. In effect, the SO pattern has a tendency to become established around April, although this is by no means a ﬁxed recurrence date. The SO is closely linked with the occurrence of sea-surface temperature anomalies in the eastern equatorial Paciﬁc, particularly off Ecuador–Peru. During the austral summer, warm equatorial water moves southward along the west coast of South America as the southeasterly trades slacken. Typically this weak warm current reaches 5°–6°S by late December or early January, hence the name El Niño, or Christ child. Every few years, however, this water is anomalously warm (2°–4°C above normal) and penetrates along the coast of Peru, replacing the usual cold, nutrient-rich Humboldt Current and thereby leading to disastrous effects on marine life. Heavy precipitation at this time also causes severe ﬂooding and mudslides in the normally arid coastal zone. The phenomenon was ﬁrst studied in its local and regional context (Bjerknes, 1966, for example). In the nineteenth century, reductions in guano production by island bird colonies during El Niños had major impacts on the Peruvian economy by cutting exports of guano fertilizer. Anchoveta ﬁshmeal, used as animal feed, became economically important in Peru during the 1950s–60s until overﬁshing, exacerbated by the 1972–73 El Niño, virtually eliminated the industry. Various criteria are used to deﬁne ENSO events. Wright et al. (1988) use the MSL pressure at Darwin only, while van Loon and Madden (1981) emphasise MSL pressure in the zone from Darwin to Cocos Island (12°S, 97°E) and rainfall at stations in the equatorial South Paciﬁc. Rainfall indices are commonly determined from the cube root of monthly amounts, which serves approximately to normalize the frequency distribution (Wright, 1984). Changes in SST patterns and the distribution of deep cloud masses, as an indicator of precipitation occurrence, can be readily monitored from satellite infrared data (see Chapter 2). Rasmusson and Carpenter (1982) deﬁne ENSO events in terms of sea surface temperatures off the Peruvian coast. Kiladis and van Loon (1988) use the SOI combined with an SST anomaly index for the eastern equatorial Paciﬁc. A multivariate ENSO Index (MEI) has been developed by Wolter (1987; Wolter and Timlin, 1993). It combines information on six observed variables over the tropical Paciﬁc – sealevel pressure, zonal and meridional components of surface wind, sea surface and air temperature, and total fractional cloudiness. The spatial ﬁelds of these variable are simpliﬁed by clustering and the MEI is determined by the ﬁrst unrotated principal component of the six ﬁelds combined. Time series of the index are published on the Web by the NOAA Climate Diagnostics Center (http://www.cdc.noaa.gov). The usage of the term “El Niño” has evolved over time. The original regional deﬁnition has generally been superseded through the recognition that such events are commonly part of a Paciﬁc-wide warming pattern extending from the coasts of Ecuador and Peru to the date line. Trenberth (1997) indicates that an appropriate quantitative deﬁnition for El Niño is provided by the index introduced by NOAA in April 1996. This is based on a ﬁve-month running mean of anomalies of sea surface temperature (SST) of ±0.4°C or more lasting for at least six months in the “Niño 3.4” region (5°N–5°S, 120°–170°W). The annual mean SST for the Niño 3.4 box is 26.8°C, with a standard deviation of 0.77°C; monthly means range from 26.4°C in NDJ to 27.6°C, in April. The positive mode, with a negative SOI, and its large-scale manifestations, is referred to as an El Niño–Southern Oscillation (ENSO) (warm event); the other non-ENSO pattern (cold event) is now termed La Niña (the girl) because the atmospheric and oceanic conditions are essentially opposite to those associated with El Niño (Aceituno, 1992; Enﬁeld, 1989; Philander, 1990). However, episodes of low SOI values and El Niño events have both occurred separately (Deser and Wallace, 1987). For the period of January 1950–March 1997 Trenberth (1997) identiﬁes 31 percent of months as El Niño (ﬁfteen events), and 23 percent as La Niña (ten events), with neither being present for 45 percent of the time. The relationship between El Niño events and the SO is apparently not ﬁxed (Wright et al., 1988). Diaz and Pulwarty (1992) suggest that during 1882–1938 negative SOI

368 Synoptic and dynamic climatology events may have reached maximum intensity late in year 0 of El Niño onset, continuing into year 1, whereas during 1939–88 the SOI was strongest earlier in year 0. The largescale SO was absent during the interval 1925–35, according to Trenberth and Shea (1987), although El Niño events occurred in 1925, 1930 and 1932. Nearly half of the last 112 years can be characterized as cold or warm events; Diaz and Pulwarty list twenty-seven El Niño (warm) years and twenty-one cold ones between 1877 and 1988. Because ENSO events differ in strength, listings provided by various authors commonly differ according to the criteria adopted. Quinn et al. (1978) distinguish strong El Niño events as having monthly sea-surface temperature anomalies > 3°C range and weak ones in the 1.0°–2.5°C range. Weak events may also appear relatively late in the year. El Niño events have major climatic impacts regionally and worldwide, through ENSOrelated teleconnections (Glantz et al., 1991; Allan et al., 1996). Based on cases between 1950 and 1975, the anatomy of these warm episodes is illustrated by a composite ENSO developed by Rasmusson and Carpenter (1982) (Figure 5.8). 1

2 3 4 5

Strongly positive SST anomalies develop in the eastern equatorial Paciﬁc and along the coast of South America from the equator to about 12°S. The anomalies tend to reach a maximum between April and June, with a second peak around the following December. Weaker ocean surface warming extends across the equatorial Paciﬁc westward to the dateline, reaching a peak around December; mean SST anomalies for six events exceeded 1°C eastward of 150°E. The normal east–west gradient of MSL pressure in the tropical South Paciﬁc collapses, leading to negative SOI values which peak around September. The rainfall belt, normally located over Indonesia, is displaced eastward towards the usually dry central Paciﬁc. This begins in April–May, culminating around December. Westerly wind anomalies develop at the surface in the western equatorial Paciﬁc, on the western edge of the region of anomalous rainfall over the Paciﬁc.

A more fully documented composite based on the ten warm events between 1950 and 1991 is now available (Harrison and Larkin, 1996, 1998). Harrison and Larkin distinguish six phases associated with the following statistically robust features of sea level pressure, wind and sea surface temperatures: 1

2 3

4

5

Pre-ENSO, March (1) to November (1): strong westerly anomalies over the equatorial Indian Ocean; weak surface warming in the tropical and subtropical western and central South Paciﬁc. There is a negative pressure anomaly over southeast Australia–New Zealand. Ante-ENSO, December (1) to February (0): initial warming of the equatorial Paciﬁc surface from the dateline to the South American coast. Onset, March (0) to June (0): signiﬁcant warming of the eastern and central equatorial Paciﬁc; sea-level pressure anomalies are negative in the eastern equatorial Paciﬁc and the subtropical southeast Paciﬁc, and positive over northern Australia and the Arabian Sea. Peak, July (0) to December (0): maximum equatorial Paciﬁc warmth and low-level convergence over the central equatorial Paciﬁc; westerlies on the equator from 150°E to 130°W, easterlies over Indonesia and in the ITCZ over the eastern equatorial Paciﬁc; strong westerlies and negative SST anomalies in the North Paciﬁc near 170°E, 40°N. The pressure anomalies of the onset phase continue with the Arabian high weakening and a new positive anomaly appearing in the subtropical western North Paciﬁc. Decay, January (1) to April (1): the warmth in the equatorial Paciﬁc decays near South America but persists in the central sector; after January (1) the North Paciﬁc

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Figure 5.8 Composite SST anomalies illustrating the development of the canonical El Niño. (a) March–May of the ENSO year, the peak phase off South America. (b) August–October, with anomalies developing in the central Paciﬁc. (c) December–February, the mature phase. (From Rasmusson and Carpenter, 1982)

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6

westerlies weaken; the easterlies persist over Indonesia. The eastern equatorial negative pressure anomaly is the only robust feature. Post-ENSO, May (1) to August (1): no signiﬁcant equatorial SST anomalies; easterlies persist over Indonesia.

A further composite description of warm and cold events in the Paciﬁc based on the Comprehensive Ocean Atmosphere Data Set (COADS) for 1946–85 and outgoing longwave radiation (OLR) data for 1974–89 provides some additional information (Deser and Wallace, 1990). There is an equatorward expansion and intensiﬁcation of the ITCZ over the eastern Paciﬁc during warm events. There is a net increase of deep convection over the tropical Paciﬁc, and enhanced convection in the ITCZ is accompanied by a net increase in surface wind convergence. However, moisture convergence extends through a deeper layer in the western equatorial Paciﬁc than in the ITCZ over the eastern Paciﬁc. Corresponding to these associations, high correlations are found between MSL pressure at Darwin, SSTs and precipitation in the central equatorial Paciﬁc, and zonal wind in the western equatorial Paciﬁc, particularly between July and November (Wright et al., 1988). A composite analysis of eighteen warm events (1902–76) shows a precipitation anomaly averaged at three stations in the “maritime continent” of 2.2 mm day1 compared with 3.2 mm day1 at three equatorial island stations in the central Paciﬁc; the mean anomaly of this dipole is 2.7 mm day1 (Lau and Sheu, 1988). The corresponding precipitation anomalies during sixteen cold events are 2.8 mm day1 and 1.9 mm day1, with a mean dipole anomaly of 2.3 mm day1. Both the warm and cold ENSO events appear to be “phase-locked” with the annual cycle, showing a tendency to develop during March–June and lasting at least a year. The extreme modes have a tendency to follow one another. Composite anomalies of global temperature and precipitation show opposite signs in seasonal anomalies between the year prior to the event (year 1) and year 0 for both cold and warm events, although this relationship is not invariable (Deser and Wallace, 1987; Diaz and Kiladis, 1992). Moreover, since the late 1970s, by comparison with earlier decades, the paradigm of a quasi-periodic annually phase-locked ENSO cycle has failed to provide an adequate representation of ENSO complexities (Wallace et al., 1998). Anomalies of March–April wind and pressure over the Atlantic Ocean for ten El Niño years (and its opposite phase) show inverse pressure tendencies in the subtropical anticyclones over the South Paciﬁc and South Atlantic Oceans and associated wind ﬁelds (Covey and Hastenrath, 1978). During the positive SO phase in March–April, when eastern equatorial Paciﬁc waters are cold, there are cold surface waters associated with northeasterly trades in the tropical North Atlantic, but positive anomalies in the tropical South Atlantic. Hastenrath et al. (1987) show that the near-equatorial low-pressure trough is displaced southward at this season, but well northward in July–August, associated with enhanced meridional displacements in positive SO phases. These northward shifts favour precipitation in the Sahel and the Caribbean–Central America. During negative SO (El Niño) years the excursions of the equatorial trough tend to be suppressed. The tropical Atlantic Ocean resembles the tropical Paciﬁc in having an equatorial lowpressure trough, trade wind systems, and a convergence zone, as well as the cold Benguela Current off southwest Africa analogous to the Peru current. Anomalies similar to those during El Niño in the Paciﬁc may occur, as in 1984, for example (Horel et al., 1986). In this phase, heavy rains can affect the usually arid coast of Namibia, southwest Africa. In the tropical Atlantic, however, the anomalies have no east–west negative correlation patterns; rather there are meridional displacements of the ITCZ. There is also no eastward movement of a convergence center, such as occurs over the western Paciﬁc. The upper ocean in the equatorial Atlantic responds in phase with the seasonal variation of the tropical easterlies, not interannually as in the Paciﬁc. Philander (1990, p. 88) notes that the east-to-west slope of the thermocline in the equatorial Atlantic is steepest from

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July through December, when the winds are strongest. These ocean basin contrasts are related in part to the smaller east–west extent of the tropical Atlantic (one-third of the Paciﬁc) and to the land–sea arrangement and its effects on currents. The Southern Oscillation displays distinct evidence of leads and lags in its spatial evolution, that must be seen as a variant of the normal annual cycle of pressure over the South Paciﬁc Ocean. From March to June mean pressure falls east of New Zealand, reﬂecting the half-yearly pressure wave in southern temperate latitudes (van Loon, 1972), and rises over Australia; in the subtropics, southerlies strengthen and the trades weaken. The seasonal pressure trend from June to September reverses, with pressure falling over Australia and rising east of New Zealand. The subtropical ridge strengthens over the Paciﬁc, with faster trades and northerly wind components over the western South Paciﬁc (van Loon, 1986; van Loon and Shea, 1987). In June–August of the year preceding a warm event, a weaker than normal trough east of New Zealand is often a precursor (van Loon and Shea, 1985). The northerly anomalous geostrophic winds in the southwest Paciﬁc form positive anomalies of sea-surface temperature which persist into the austral spring. Heating by this warm surface intensiﬁes the South Paciﬁc Convergence Zone as it extends southeastward and, as a result, below-normal pressure develops over the Tasman Sea. During the succeeding austral summer (January of the El Niño year) the anomalous wind component becomes westerly in the trade-wind belt of the Paciﬁc Ocean south of 10°S, as illustrated by the sequence in Figure 5.9. During the warm event year there is an ampliﬁcation of the normal annual cycle of trade winds between 15°S and 30°S, and of the westerly trough in the South Paciﬁc Ocean, compared with the preceding year (van Loon, 1984). The concept of a simple standing oscillation between Indonesia and the eastern equatorial Paciﬁc has given way to the view that ENSO is a propagating system (Barnett, 1985, 1988; Xu and von Storch, 1990). Barnett (1984) characterized the SO as “one part of a larger propagating wavelike phenomenon in the sea level pressure ﬁeld that appears ﬁrst over the Indian Ocean.” Correlation analysis suggests that the pattern of pressure anomalies reﬂects the motion of a wave train that begins in the Paciﬁc, crosses South America and ends in the southern Indian Ocean, with a phase that reverses from the year preceding the warm event to the El Niño year itself (van Loon, 1986). Composite analysis of eight warm events between 1951 and 1985 shows that anomalies of a given sign develop sequentially over a roughly two-year interval in a counterclockwise trajectory from southern Asia to the tropical Indian–Paciﬁc ocean, to the eastern Paciﬁc and North Paciﬁc, before returning to Asia (Barnett, 1988). Further information is available from a Principal Oscillation Pattern (POP) analysis of monthly mean sea-level pressures for 15°–40°S during 1951–58 and 1972–83 by Xu and von Storch (1990). This diagnostic technique extracts standing or oscillating spatial patterns from a dynamically complex multicomponent system. The time series, ﬁltered in time and space to retain the speciﬁc scales of interest (x(t)), is assumed to be generated by a ﬁrst-order autoregressive process: x (t 1) Ax(t) forcing where A is a matrix, whose real (or complex) eigen vectors are called POPs; real valued POPs represent standing patterns, and pairs of complex-valued POPs represent oscillatory patterns that often migrate in space. POPs describe the normal modes of a linear stochastic process. They do not comprise an orthogonal set of patterns and are not necessarily independent. There is also no information from the analysis about the variance (von Storch et al., 1990). Xu and von Storch (1990) ﬁnd an oscillatory POP pair with an oscillation period of about thirty-seven months. It accounts for 48 percent of the variance in the SOI, or 73 percent for the 1972–88 data. A sequence of patterns is identiﬁed:

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…→ P1 → P → P1 → P2 → P1 →

372 Synoptic and dynamic climatology

Figure 5.9 Anomalies of sea-level pressure and anomalous wind components in the South Paciﬁc Ocean for the year preceding (1) and the year of (0) a warm ENSO event for (a) May–June–July (1), (b) November–December (1), January (0), (c) May–June–July (0) and (d) November–December (0), January (1). (From van Loon and Shea, 1987)

P1 represents a positive SOI mode which is replaced after seven to nine months by P2, associated with westerly wind anomalies in the western tropical Paciﬁc. About sixteen months later, there are easterly anomalies over the eastern tropical Paciﬁc and southerly anomalies near the dateline (P1). By twenty-seven months there are easterly wind anomalies over the western tropical Paciﬁc associated with positive pressure anomalies north of New Zealand (P2) with a return to P1 by thirty-six to thirty-seven months. These results support earlier studies of southern hemisphere pressure ﬁelds using composites based on the state of the SO and the annual cycle (van Loon and Shea, 1985, 1987). The warm and cold events represent features superimposed on an annual cycle of convective activity and the west–east circulation cell linking the Indian and southwest Paciﬁc sectors of the tropics (Meehl, 1987a). During a cold event the Indian summer monsoon is usually strong, and there is also lower pressure, increased rainfall, and higher

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0 Figure 5.10 A schematic model of zonal and meridional circulation cells in the Indian and Paciﬁc ocean sectors. (From Meehl, 1987)

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SSTs to the west of the South Paciﬁc Convergence Zone (SPCZ). To the east of the SPCZ the descending arm of an intensiﬁed zonal Walker circulation sets up higher surface pressure and less rainfall (Figure 5.10). During the following October–January–April, the convective maximum moves southeastward across southeast Asia to northern Australia, maintaining its strength, partly owing to the warmer ocean surface, which also induces a southwestward displacement of the SPCZ. The South Paciﬁc high now weakens, and lower pressure in the SPCZ is associated with anomalous cyclonic ﬂow nearer the equator. Westerly wind anomalies at the surface are typical of the transition to a weak annual cycle (Figure 5.8), which in the extreme is a warm event. During years with a weak cycle, conditions across the region are the opposite of those described. During a warm event

374 Synoptic and dynamic climatology the ITCZ and SPCZ are both displaced equatorward, becoming merged near the dateline (Kiladis and van Loon, 1988). Links between the Walker and Hadley circulations in the equatorial western Paciﬁc are apparent from a comparison of indices of the zonal and meridional wind components in a sector approximately 10°S to 10°N, 150°E to 140°W (Barnett, 1984). Changes in anomalous surface winds in the Walker cell lead those in the Hadley cell by two to six months. Thus, strengthening in the Walker cell between January and April is followed by weakening of the Hadley circulation in September–October (and vice versa). The mechanism appears to involve forcing exerted by anomalous convergence and precipitation within the shifting mode of the Walker cell. The relationship is not apparent, however, in northern winter. Time series analyses of various SO indices reveal that the variance is concentrated around 2.3 years (a quasi-biennial oscillation, or QBO, in surface climate variables) and between three and six years (Berlage, 1957; Trenberth, 1976) (see Table 5.1). When singular spectrum analysis2 is used to ﬁlter out the two to three-year variability in the SO, a four to six-year ENSO cycle is isolated (Keppenne and Ghil, 1992). Further study by Barnett (1991) using ﬁlters to distinguish quasi-biennial and three to seven-year periods Table 5.1 Twentieth-century warm and cold events in the Paciﬁc Warm events

Cold events

1902 1904 1911 1913 1918 1923 1925 1930 1932 1939 1951 1953 1957 1963 1965 1969 1972 1976 1982 1986 1991 1997

1903 1906 1908 1916 1920 1924 1928 1931 1938 1942 1949 1954 1964 1970 1973 1975 1988 1995 1998

Note: only the zero year is tabulated when the SOI changes sign and central-eastern equatorial Paciﬁc SST anomalies became strongly positive (warm event) or negative (cold event). The events are identiﬁed on three criteria up to 1976: SSTs in the equatorial Paciﬁc, sealevel pressure at Darwin (and Tahiti from 1935), and rainfall anomalies in the equatorial Paciﬁc dry zone. The events in 1977 and subsequently are based on the ﬁrst two of these criteria (Kiladis and Diaz, 1989). Source: updated after van Loon (1984); Diaz and Kiladis (1992).

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in near-global SST and SLP data indicates that ENSO time scales involve annual and QBO periodicities of twenty to thirty and thirty-six to eighty months. Barnett suggests that both data sets indicate that the ENSO phenomenon is attributable to a non-linear interaction of the two longer time scales. There is also a traditional SO in the SLP records and El Niños in the SST data, in both frequency bands. The quasi-biennial mode of ENSO variability is phase-locked with the annual cycle of surface winds in the eastern equatorial Indian Ocean (Rasmusson et al., 1990). This component provides some regularity to ENSO occurrences, although its amplitude is variable and some phase changes were recorded during 1950–87. Warm and cold events apparently reﬂect opposite phases of the biennial component, modulated by the low-frequency (four to ﬁve-year) mode. Records of 115 El Niño events from Peru during AD 1524–1987 compiled by Quinn et al. (1987; Quinn and Neal, 1992) show an average periodicity of almost four years, with several episodes (45 percent of cases) recurring at about nine-year intervals. However, the recurrence shortened to 3.1 year during 1824–1941 (Diaz and Pulwarty, 1992). Tree ring records from the southern Great Plains and Sierra Madre Occidental, which correlate well with the SOI, support this. Southern Oscillation extremes of either sign have an average interval of 6.04 year for 1699–1850, but only 3.71 year for 1850–1971 (Stahle and Cleveland, 1993). The intensity of the SO signal also seems to vary over longer time scales. This is illustrated by the correlation of annual mean pressure at Darwin and Tahiti; it was 0.75 during 1940–80, but only 0.38 for 1883–1940 (Elliott and Angell, 1988). Older data may be less reliable, but it seems more probable that the southeast Paciﬁc pole of the Southern Oscillation is subject to changes in location and/or intensity. Moreover, while the SO consists of a dominant east–west standing wave, it also has other components. Historical reports since the early 1600s in Peru and Chile indicate that El Niño activity was pronounced in the periods 1607–24, 1701–28, 1812–32, 1864–91, and 1925–32, but weak in the intervening years. The 1982–83 event, like those of 1925–26 and 1891, is categorized as having been very strong; other strong events occurred in 1940–41, 1957–58, and 1972–73. Very strong events have occurred only eight times in ﬁve centuries, with none of them recurring within twenty years (Enﬁeld, 1992). Low-frequency, especially decadal, ENSO modes are identiﬁed by a Fourier transform analysis of the records for 1876–1995. Brassington (1996) shows that from the 1870s to the 1930s very severe ENSOs recurred with signiﬁcant seven to eight and thirty-ﬁve year periods in addition to quasibiennial and quasi-quadrennial (four to ﬁve and ﬁve to six-year) periodicities. Spectral power is also identiﬁed at about thirty-ﬁve to forty-ﬁve years in records of 18O from ice cores in the Quelccaya ice cap, Peru, and in El Niño, but with low coherence between ENSO and 18O (Diaz and Pulwarty, 1994). The use of historical records of Nile ﬂoods at Cairo as a proxy for SOI-related climatic activity since AD 622 is proposed by Quinn (1992) and receives some statistical support from Diaz and Pulwarty (1992). The annual maximum ﬂow of the Nile registers the summer monsoon rainfall over the Ethiopian Highlands, with below-normal ﬂood levels occurring during low (negative) SOI phases. Spectral analysis of the composite record suggests cycles of around ninety, forty to ﬁfty, and twenty-two to twenty-four years in SOI-related activity (Anderson, 1992; Diaz and Pulwarty, 1992), suggestive of lowfrequency solar forcing. There is also evidence of fewer and weaker El Niño events during episodes of strong solar activity. Using the data of Quinn et al. (1987) for AD 1525–1988, spectral analyses by Diaz and Pulwarty (1992) show that the period of ENSO events is three to four years when solar activity and the Quasi-Biennial Oscillation (QBO) are weak, but lengthens to four or ﬁve years during high solar activity and a well developed QBO. Enﬁeld (1992) conﬁrms this and by conﬁning the analysis to strong (and very strong) events, ﬁnds corresponding modal intervals of 8.5 years for low solar activity and 12.8 years for high solar activity. Analyses of several ENSO indices indicate a possible regime shift in 1976/77 (Zhang et al., 1997). SSTs in the tropical Paciﬁc and off the west coast of the Americas are

376 Synoptic and dynamic climatology higher during 1977–93 than during 1950–76. However, it is also feasible that a cooling trend occurred during 1943–55, followed by gradual warming, rather than a regime shift. Nevertheless, the frequency and duration of ENSO increased over the last 20 years of the century, with 1990–95 having the longest sustained high pressures recorded at Darwin during 1892–1995 (Rajagopalan et al., 1997). Rajagopalan et al. suggest an average return period for this ﬁve-year event of 350 years (based on non-parametric statistics and a Markov model), and suggest that the event could occur with 0.28 probability in the 1882–1995 record. However, a linear autoregressive moving average (ARM) analysis by Trenberth and Hoar (1996, 1997) which is a more appropriate statistical model, focuses on the size of the SOI anomalies and shows that both the post-1976 anomalies and the 1990–95 event have less than a 0.05 percent chance of occurring in the 1882–1995 record. It is still debatable whether the event could be greenhouse effect-induced, as proposed by Trenberth and Hoar. Latif et al. (1997) argue against this view on the grounds that the decadal mode of SST variability is dominant in the 1990s. This mode has SST anomalies of the same sign in all three tropical oceans. There is a “horseshoe” anomaly pattern centered in the western equatorial Paciﬁc, with branches extending northeast and southeast into the subtropics. This pattern is unlike that associated with ENSO anomalies in the eastern Paciﬁc (see Figure 5.8).

5.3 ENSO mechanisms The climatological state of ocean temperatures in the equatorial Paciﬁc is determined by three dynamical effects (Bjerknes, 1969; Cane, 1992). These involve: 1 2 3

Horizontal advection of cold water westward from the west coast of South America by the easterly trades. Upwelling along the equator resulting from the divergence of the westward surface current (Figure 5.11). Waters move poleward in each hemisphere under the inﬂuence of the Coriolis deﬂection (to the right/left in the northern/southern hemisphere). The upward displacement of the thermocline off the west coast of South America as the upper warmer-water layers are forced westward. This is accompanied by cold upwelling and a reduction in ocean heat storage (Wyrtki, 1985).

The relative importance of these three processes is still uncertain.

Figure 5.11 Schematic model of the processes contributing to the maintenance of a tongue of cold water in the eastern equatorial Paciﬁc. (From Niiler, l982)

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A model to account for El Niño conditions was proposed by Wyrtki (1975). During periods with strong trade winds (high SOI) the ocean level is raised (~20 cm) in the western Paciﬁc by the accumulation of water against the coasts of East Asia and New Guinea–Australia. Coastal wind-induced divergence off Ecuador and Peru and the shallow westward-tilting thermocline facilitate the normal upwelling (Figure 5.4e). As the trade winds relax, warm water from the western Paciﬁc returns eastward, and the Peruvian upwelling is suppressed as the thermocline becomes deeper. Wyrtki (1985) observes that positive height anomalies (h) are associated with a depression of the thermocline and an increase in mixed layer temperature, as represented by a selected isotherm depth (D):

0 h = D 11

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冢QQ冣

where Q/Q is the relative density difference between two layers (~ 5 to 6 103). The slow build-up of water level in the western ocean during easterly years is rapidly reversed during an ENSO cycle. For example, the 1982–83 ENSO was associated with an eastward ﬂux of warm water (about 40 Sv) in the upper layer of the equatorial Paciﬁc, 15°N–15°S, according to Wyrtki’s hypothesis. He proposed that the relaxation of the trade winds prompts the eastward displacement of water by triggering a Kelvin surge. This warm water is deﬂected towards the subtropics by the west coast of the Americas and so out of the tropics. Attractive though this simple model may be, it fails to explain the history of the 1986–87 El Niño, which was not preceded by a build-up of warm tropical water (Miller and Cheney, 1990). Moreover, during the extreme 1982–83 ENSO event, heat content was enhanced only between 5°N and 5°S, not 15°N–15°S. Most strikingly, in 1982–83 warm waters appeared not along the South American coast but along the equator in the austral winter, from where they spread eastward. The warm and cold ENSO modes show a persistent tendency to alternate from one to the other. Bjerknes (1969) remarked, “Just how the turnabout between trends takes place is not yet quite clear.” The mechanism appears to involve adjustments in the subsurface ocean thermal structure associated with internal ocean waves (Neelin et al., 1998). Nevertheless, the record El Niño of 1997–98 was terminated primarily by the rapid and unexplained onset in May-June 1998 of strong trade winds driving cold upwelling (McPhadden, 1999). The pattern of sea surface temperature change in the equatorial Paciﬁc shows a general tendency for the warmest area (28.5°C) to migrate eastward from the west-central Paciﬁc (about 150°E) during the development of ENSO events and to retreat westward during the decaying phase. However, there are differences between events in the pattern of warming. Three SST patterns can be deﬁned on the basis of temperatures in the zone 4°N–4°S, 120°E–80°W (Fu et al., 1986). Pattern A has much warmer surface water in the east-central Paciﬁc, below normal west of 160°E (1957, 1965, 1972, and 1982); pattern B is warm mainly in the eastern Paciﬁc, below normal in the west (1976); and pattern C is warm almost everywhere (1963, 1969). The equatorial westerlies extend farther east during strong events of pattern A than during weaker ones like 1976. This may reﬂect a positive feedback between the westerlies and warmer surface water. The warmest areas also appear to be associated with strong convection. The warming of the ocean surface layer is determined primarily by the advection of heat by wind-driven ocean currents (Barnett, 1984). Vertical heat exchange between the ocean and atmosphere plays only a minor role in the heat budget of the central equatorial Paciﬁc (Ramage and Hori, 1981). Indicative also of this oceanic heat advection is the high correlation found by Barnett between anomalies of surface wind SSTs near the equator and in the southwest Paciﬁc (0.74 in January–February and 0.86 in August–September). An important related aspect of ocean–atmosphere interaction concerns the postulated association between higher sea surface temperatures and enhanced convective activity and

378 Synoptic and dynamic climatology precipitation proposed by Bjerknes (1969). Observations over the tropical Indian and Paciﬁc Oceans show that convection is closely controlled by SSTs, being much more active for SST 27.5°C (Graham and Barnett, 1987). However, Graham and Barnett’s suggestion of a decrease in the activity beyond about 28°C has not withstood further analysis. The frequency and intensity of deep convection increases with SSTs between about 26°C and 30°C (especially in January), but this relationship is less apparent within the ITCZ and at times can be overridden by other factors over the warm pool in the western Paciﬁc (Zhang, 1993). Large-scale vertical motion at 850 mb across the tropical Paciﬁc matches the patterns of HRC and there is also a strong correlation on a seasonal basis between maximum upward motion and maximum sea surface temperatures according to Zimmerman et al. (1988). They ﬁnd that warming of the eastern Paciﬁc surface strengthens the Hadley circulation, as postulated by Bjerknes (1966), but there is no evidence of changes in the zonal gradient of equatorial sea surface temperatures affecting east–west circulation cells. Observed sensible and latent heat ﬂuxes turn out not to be correlated with SSTs and precipitation in the manner hypothesized by Ramage (1977). A possible resolution of this contradiction, involving a simple model linking low-level convergence and the moist static energy, has been offered by Neelin and Held (1987). Precipitation in the model depends on moisture convergence, not heat ﬂuxes. High SSTs do cause high rainfall areas, but no change in heat ﬂuxes into the atmosphere is required. Variations in precipitation are caused almost entirely by moisture convergence, which is dependent on SST, through the effects of SST on large-scale moist stability. Within a shallow equatorial boundary layer, surface wind convergence, and therefore precipitation, is proportional to the Laplacian of sea surface temperature (2 SST), according to Barnett et al. (1991). Figure 5.12 shows how condensation heating can set up a secondary circulation that feeds back on the structure of the planetary boundary layer (PBL).

Figure 5.12 A schematic model of coupling between an SST gradient which sets up convergence in the planetary boundary layer (PBL) and to ﬁrst order dictates the position of the precipitation zone. Condensation heating sets up a secondary circulation cell that feeds back positivity on the PBL structure. (From Barnett et al., 1991)

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Recent oceanographic research highlights the important role of internal waves in the upper layer of the equatorial Paciﬁc for ENSO mode transitions (Cane, 1992). Atmospherically forced Kelvin waves (Figure 5.13a), propagating rapidly eastward, are trapped along the equator because the eastward motion of water on either side of the equator is deﬂected equatorward by Coriolis forces in each hemisphere. Figure 5.13a illustrates a downwelling equatorial Kelvin wave, moving eastward in the upper layer and westward below the thermocline. Hence the wave-induced currents are counter to the mean westward-moving South Equatorial current and the underlying Equatorial undercurrent ﬂowing eastward (Mysak, 1986). Kelvin waves at the thermocline travel at about 1–3 m s1 and have a Rossby radius of deformation3 of the order of 125–250 km. They cross the Paciﬁc Ocean in about three months. The ocean Kelvin waves are forced by variations in zonal wind stress anomalies over the western equatorial Paciﬁc. Two potential mechanisms have been identiﬁed. The ﬁrst involves anomalous westerly winds in the western/central Paciﬁc (Harrison and Schopf, 1984). Observations of post-1950 El Niño events show that, in nine out of ten cases, a burst of west winds of a few weeks’ duration preceded coastal warming off South America by one to four months (Luther et al., 1983). A climatological analysis for 1958–87 indicates that equatorial westerlies in the western Paciﬁc have a maximum frequency of almost 20 percent in November–December, but low steadiness, typically lasting ﬁve to seven days (Chu et al., 1991). These episodes may result from a pair of tropical storms north and south of the equator (Keen, 1982). Spells of fourteen days or more peak in March–April, and these are more likely to generate a Kelvin wave. Verbickas (1998) reports seven WWB events in 1982 and eight during January–September 1997. Westerly windburst (WWB) episodes in the western tropical Paciﬁc have a varied character. Harrison and Giese (1991) identify 155 episodes of westerly wind anomalies >5 m s1 (relative to the mean monthly value) affecting two or more western Paciﬁc island stations. The composite characteristics for events centered between 0°S and 7°S is that they have maximum westerly winds of 8 m s1, a meridional extent (half amplitude) of 3° and a duration of ten days if centered near the equator, or up to 5° extent and sixteen-day duration if centered near 5°–7°S. About 40 percent of the cases relate to named cyclones (Keen, 1982) and half of all cases are associated with ENSO events. A further study of WWBs for 1980–89, using ECMWF 1,000 mb winds, applies Harrison and Giese’s classiﬁcation and ﬁnds that almost half (47 percent) of the patterns have maximum westerlies centered between 5°S and 10°S, and 37 percent between 3°N and 7°N (Hartten, 1996). They are most common in December–February. Also, WWBs tend to occur around 10°N in July and move southward over a twelve-month period, with no complementary northward progression. There is large interannual variability, with a tendency for more/less WWBs to occur during low/high SOI. The second mechanism for generating Kelvin waves invokes near-resonant forcing by Madden–Julian Oscillations (see section 4.8). The dominant Kelvin wave period is about seventy days and the zonal wavelength is 13,000–14,000 km (the width of the Paciﬁc Ocean), while the MJO has low frequency components around sixty days, according to Hendon et al. (1998). West of the dateline the two have similar eastward phase speeds below 5 m s1. Using a simple Kelvin wave model, forced by the observed intraseasonal variations in zonal stress, Hendon et al. simulate 80 percent of the observed intraseasonal variance in the depth of the 20°C isotherm – indicating vertical motion associated with Kelvin waves. The predominant forcing takes place in the western equatorial Paciﬁc and the Kelvin waves travel eastward, initially at less than 5 m s1, increasing to >10 m s1 east of the dateline. Westerly windbursts still play a signiﬁcant role in forcing oceanic Kelvin waves, individually and collectively, within the MJO spectrum. Their amplitude is about 0.02 N m2 with a longitudinal extent of 2,000 km, whereas the MJO has an extent of 7,000 km but an amplitude of only about 0.015 N m2 (Hendon et al, 1998). Equatorial westerly winds in the western Paciﬁc tend to elevate the ocean thermocline north and south of the equator in the west, owing to latitudinal shear both northward

Figure 5.13 (a) Side and (b) rear views of a downwelling equatorial Kelvin wave pulse in a two-layer ocean generated by relaxation of the easterly trade winds. The rear view shows a Gaussian-shaped trapping region. (c) A westward-traveling baroclinic Rossby wave in a two-layer ocean showing the velocity pattern and thermocline structure. The wave is transverse, with current ﬂuctuations perpendicular to the direction of phase propagation. (d) The transformation of a ﬁrst baroclinic mode Kelvin wave into poleward-traveling coastal Kelvin waves and reﬂected equatorial Rossby waves. The wave guides are approximately delimited by dashed lines; re = equatorial internal Rossby radius, and r = mid-latitude value (~ 35 km at ± 25° latitude, Emery et al., 1984). (From Mysak, 1986)

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and southward, creating cyclonic vorticity. In the eastern Paciﬁc, maximum thermocline displacements are along the equator (Chao and Philander, 1993). However, westerly windbursts are neither necessary nor sufﬁcient conditions for ENSO events, according to Luther et al. (1983). Moreover, based on a study for 1980–85 over the Indian and Paciﬁc Oceans, the frequency of westerly bursts increases after ENSO onset and such bursts are not restricted to the western equatorial Paciﬁc (Murakami and Sumathipala, 1989). When Kelvin waves encounter the eastern boundary some of the wave energy is transmitted polewards via coastally trapped waves, extending the equatorial waveguide to higher latitudes, in each hemisphere, and some is reﬂected westward as a more slowly moving Rossby wave front (Figure 5.13c). It is worth noting that coastal upwelling does not shut off; it continues in the 50–100 m subsurface layer, but these upwelling waters are not abnormally warm. The coastal Kelvin waves disperse (“radiate”) energy into baroclinic Rossby waves that propagate slowly westward along the subtropical thermocline between 15° and 30° latitude (Figure 5.13b). The Rossby waves travel westward, in response to the geostrophic balance, much more slowly than the Kelvin wave motion, taking two years to cross the Paciﬁc at 15° latitude and up to nine years at 30°N or 30°S (McCreary, 1983). These time scales may help determine the irregularity of ENSO events. Nevertheless, the faster Rossby waves at lower latitudes transport energy and mass, delivered by the Kelvin waves, in the reverse direction. At the western boundary the mass ﬂux transported by the Rossby waves is moved equatorward in the boundary currents and then returned eastward via the Kelvin waves. Kessler (1991) demonstrates that westwardtraveling Rossby waves need to be equatorward of 8° latitude in order for reﬂected Kelvin waves to have signiﬁcant amplitude. Satellite altimetry data clearly identiﬁed such equatorial Kelvin waves prior to and during the 1986–87 and 1991–95 El Niños (Miller et al., 1988; Kessler and McPhadden, 1995; McPhadden et al., 1998). In 1997, however, reﬂected Rossby waves were not initially present, and eastward advection of warm water was forced by MJO-induced WWBs (McPhadden, 1999). The western coastal boundary of the tropical Paciﬁc is discontinuous, with seven major island complexes between 10°N and 10°S extending from about 120°E to 160°E. However, Clarke (1991) and du Penhoat and Cane (1991) show that the Asia–Borneo–Indonesia and New Guinea–Australia land masses reﬂect most of the Kelvin wave energy that would be reﬂected eastward by a continuous meridional boundary; for example, half of the incident energy of a mode m 1 Rossby wave (symmetric about the equator) would be reﬂected eastward as a Kelvin wave by a solid north–south wall, compared with 34 percent for the island boundary. The amplitude of the reﬂected Kelvin wave is 85 percent of that for a solid boundary (for m 1 incoming) and correspondingly is 74 percent for Rossby wave mode m 3 incoming. The sea-level amplitude, as determined by satellite altimetry data for 1986–87, shows that mode 2 (Rossby waves) peaking at 5°N and 5°S account for about 75 percent of the mode 0 Kelvin wave amplitude (White and Tai, 1992). The circumstances that prevailed in the Paciﬁc when the strong, protracted El Niño of 1939–41 and 1942–44 succeeded a seven-year interval without an event are described by Biggs and Inoue (1992). There were two episodes of wave reﬂection at the eastern and western boundaries. Waves arrived ﬁrst at the coasts of South America and eastern Australia and later at the west coast of Mexico and in the northwestern Paciﬁc. It seems that interaction between upper ocean conditions and surface winds is critical for the transition between modes. Coupled with these features of equatorial ocean dynamics, the warm ENSO mode requires the availability of an equatorial reservoir of warm water (Cane, 1992). This water is transferred eastward by Kelvin waves to initiate a warm event. The anomalous warm water in the east then sets up westerly wind anomalies, forcing Kelvin waves that depress the thermocline further in the east, thereby requiring a compensatory shallow thermocline elsewhere. Westerly wind forcing, as described above, and westward-propagating Rossby wave packets accomplish this. The thermocline thus becomes shallower and SSTs are

382 Synoptic and dynamic climatology lowered. “Cold” Kelvin waves are now reﬂected from the western boundary, and several years are necessary for the warm water pool to be recharged. Biggs and Inoue (1992) propose that an extensive deep warm pool in the western equatorial Paciﬁc in the late 1930s was the energy source of the El Niños of the early 1940s. Nevertheless, the 29°C warm pool as measured in January–February 1986 between 8°S and 5°N, near 150°E, had an average mixing layer depth of only 30 m and the thermocline at 60 m (Lukas and Lindstrom, 1991). Low salinities due to net excess of precipitation minus evaporation determine the density structure. Westerly winds may cool the mixed layer by increasing evaporation and through entrainment of cooler water from below and thereby cause the warm pool to move eastward. The role of atmospheric pressure anomalies in the evolution of the ENSO cycle is suggested by the characteristics of the propagating oscillatory patterns across the South Paciﬁc described earlier. An hypothesis linking the SO and the South Paciﬁc Convergence Zone (SPCZ) is outlined in Figure 5.14, based on van Loon and Shea (1985, 1987) and Kiladis and van Loon (1988). For example, northerly wind anomalies near the dateline, that develop with the strengthened subtropical high of a cold event, set up positive SST anomalies within the SPCZ. This intensiﬁes convective activity in the SPCZ, leading to negative pressure anomalies (von Storch et al., 1988). Westerly wind anomalies then develop over the western equatorial Paciﬁc and may trigger El Niño onset, as outlined above. Figure 5.14 also shows the sequence initiated by a warm event. Xu and von Storch (1990) illustrate the applicability of this hypothesis in a hindcast analysis for 1974–88. During a warm event, when the South Paciﬁc high is weak, the SPCZ shifts north and eastward, merging with the ITCZ over the central Paciﬁc and enhancing convection (Trenberth, 1976; Yarnal and Kiladis, 1988). Nevertheless, other prediction schemes consider the tropical wind ﬁeld and the role of equatorial waves (Graham et al., 1987). This suggests that there is a large, complex interacting system involved in ENSO phenomena.

Figure 5.14 Schematic relationships between the SO and SPCZ. (From Xu and von Storch, 1990)

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The current paradigm for the warm/cold transition is characterized as a “delayed oscillator mechanism,” with the oscillation memory provided by the westward propagation of Rossby waves through wind stress, and the reﬂection of a Kelvin wave from the western boundary to the eastern Paciﬁc. This mechanism provides the basis for coupled atmosphere–ocean models of SST anomalies in the eastern equatorial Paciﬁc (Graham and White, 1988; Zebiak and Cane, 1987; Battisti and Hirst, 1989; Schopf and Suarez, 1988, 1990). ENSO behaves as a delayed oscillator because the slow dynamic adjustments of the ocean never catch up with changes in the wind ﬁeld. The Zebiak–Cane model simulates the characteristics of ENSO SST anomalies when forced by composite ENSO wind anomalies. When initiated with an imposed 2 m s1 westerly wind anomaly of four months’ duration, it reproduces realistic irregular three to four-year SST anomalies, with peaks of varying amplitude, that tend to be phase-locked to the annual cycle and reach maximum amplitude at the end of the calendar year. The model has greater forecast skill than climatology out to ﬁfteen months’ lead time, and beyond six months is substantially better than persistence (Cane, 1992). In an enhanced version of the Zebiak–Cane model, Jin et al. (1994) ﬁnd that seasonal cycles generate quasi-periodic motion or irregular oscillations in ENSO structure. ENSO can also be entrained by non-linearities into synchrony with the annual cycle, leading to longer-period oscillations. They also note that the inherent frequency of the system may lock onto a sequence of annual subharmonics of the external frequency and this leads to a transition to chaos through the overlapping of the non-linear resonances – a behavior referred to as “the devil’s staircase.” A detailed review of coupled model experiments of tropical air–sea interaction is presented by Neelin et al. (1992, 1998). However, these models overlook the oceanic complications associated with a continuing oscillation of several years’ duration (Chao and Philander, 1993). Zonal wind ﬂuctuations must excite a spectrum of superimposed ocean waves, making it impossible to identify explicitly individual Kelvin and Rossby waves. Newer ocean models have been developed (Jin, 1997; Wang and Fang, 1996), but there is as yet no single consensus model (Neelin et al., 1998). The role of free Rossby/Kelvin waves is downplayed by Zhang and Levitus (1997). They emphasize the importance of changes in the depth of the thermocline for SST anomalies in the eastern and central Paciﬁc. Based on composites of ocean temperatures and surface wind ﬁelds for ﬁve warm and ﬁve cold events during 1966–99, they argue that the interannual variations in the coupled system evolve through the slow propagation of subsurface thermal anomalies around the tropical Paciﬁc basin. These subsurface anomalies take two years to cross the Paciﬁc. The above models postulate that ENSO is triggered in the eastern Paciﬁc, but some recent modeling studies focus on forcing in the western and central Paciﬁc. Moore and Kleeman (1997) show that perturbations in a coupled ENSO model grow most rapidly in the western and central equatorial Paciﬁc. Error growth in the ocean can originate in association with different combinations of upwelling (U), thermocline (T) displacement, and ocean heat advection (A), but the resulting SST patterns appear to have a preferred response to these various processes acting in the mixed layer. In the atmosphere, error growth is initiated by SST anomalies, but requires SSTs > 28°C, deep penetrative convection, and latent heat release through moisture ﬂux convergence. Recall that the Clausius– Clapeyron relationship is more effective at the higher temperatures in the western compared with the eastern equatorial Paciﬁc. Including only the observed seasonal cycle, the perturbation energy growth associated with the three terms together reaches a maximum in the model during JAS. However, the effect of T is largest in AMJ, U in JAS, and A in OND. The ENSO cycle can modify perturbation growth. During the different phases of an El Niño (or La Niña) event the mechanisms creating perturbation growth vary geographically in their relative contributions to growth, thereby forcing longitudinal differences in the evolution of ENSO. Moore and Kleeman also show that the patterns of wind perturbation over the tropical western Paciﬁc Ocean in their model resemble observed tropical cyclone pairs on opposite sides of the equator. Such cyclone

384 Synoptic and dynamic climatology pairs are associated with westerly windbursts, and they propose that westerly and easterly windbursts are a preferred response to stochastic variability in the coupled system. The termination of an El Niño warm anomaly is generally preceded by the return of the thermocline to shallow depths in the central and eastern equatorial Paciﬁc (Harrison and Vecchi, 1999). The “delayed oscillator” model depends on this process for the removal of positive SST anomalies. The reason for the rise of the thermocline is thought to be either a direct result of wind forcing or equatorial Kelvin wave forcing in the ocean. The southward shift of westerly wind anomalies, from a distribution that is symmetrical about the equator in October– November to one that is centered south of the equator in December–January, is simulated in an oceanic GCM by Harrison and Vecchi. This southward shift is a response to the normal seasonal displacement of waters warmer than 28°C south of the equator by December–January. The southward movement of warm water, which is present in both “normal” and El Niño years, seems to be responsible for the thermocline returning to shallow depths. Analyses of air–sea interaction in the equatorial Atlantic (Zebiak, 1993) show the existence of a coupled mode resembling ENSO. It is farther west in the Atlantic basin than its Paciﬁc counterpart and less robust. However, the oscillation periods are similar; the difference in basin size is apparently offset by the strength of the air–sea coupling and differences in zonal structure. The tendency for decadal scale modulations of ENSO to occur was noted in section 5.2. Mechanisms to explain these modulations are still not yet understood. Two processes involving linkages from mid-latitudes into the tropics have been suggested. One invokes the generation of wind anomalies in mid-latitudes that extend into low latitudes and force the ocean circulation (Barnett et al., 1999). The other proposes the formation of SST anomalies, by latent heat ﬂux anomalies in mid-latitudes, and their advection southward within the subsurface branch of a shallow meridional ocean cell in the North Paciﬁc (Gu and Philander, 1997). A different ocean teleconnection is suggested by Kleeman et al. (1999), in which wind speed anomalies in the northeastern subtropical Paciﬁc create LE ﬂux anomalies. These provide a positive feedback for the decadal oscillation, while a delayed negative feedback results from anomalous horizontal advection and overturning. Observational data are so far inadequate to resolve these arguments. Analysis of irregular ENSO oscillations via stochastic forcing of a non-linear dynamical coupled model suggests that ENSO can respond to different mechanisms in various stages of its evolution, depending on the basic state and the non-linear dynamics (Wang et al., 1999). ENSO oscillations may be (1) irregular, when stochastic forcings act on a stable basic state; (2) unstable and non-linear (a stable limit cycle), when stochastic forcing perturbs the limit cycle; or (3) bi-stable, when stochastic forcing causes multi-equilibrium states to oscillate irregularly between warm and cold stable states. White and red noise are shown to be more effective in stochastic resonance than band-limited white noise in modifying the non-linear oscillations. Strong white noise forcing can, however, destroy resonant or non-linear oscillations.

5.4 Teleconnections with ENSO Meteorological and oceanographic variables in many areas of the world show high correlations with the SO in its core regions of the Indian Ocean–Paciﬁc Ocean. These teleconnections both lead and lag events in the Paciﬁc sector. For example, Wright (1986) reports that warm water in the southeast Paciﬁc, accompanied by low pressure, low cloudiness, and weak southeast trades in December–February is associated with a low pressure value at Darwin in the following twelve-month April to March period. In contrast, warm waters in the equatorial Atlantic in December–February are associated with high twelvemonth pressure at Darwin in the following April to March. The effects of an ENSO warm event on the circulation over the North Paciﬁc were ﬁrst outlined by Bjerknes (1966, 1969). According to his hypothesis, the warm equatorial

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ocean surface produces enhanced convective activity, and the release of latent heat in the cloud systems augments the poleward ﬂuxes of heat and momentum in the Hadley circulation, which, in turn, lead to an intensiﬁcation of the extratropical jetstreams and an enhanced Aleutian low. Symmetrical effects are observed over the western tropical Paciﬁc, where the intensity of the Australian subtropical jet is also increased (Nogués-Paegle and Mo, 1988). An important association with the western North Atlantic–Caribbean Sea is shown by the reduction in frequency of tropical cyclones during El Niño years (Gray and Sheaffer, 1991). For the Atlantic cyclone seasons of 1900–88, the average number of tropical storms and hurricanes for El Niño years is only 5.4, compared with 9.1 in non-El Niño years; moreover, the corresponding ratio for major hurricanes is 1:3. The mechanism responsible for the decreased activity during El Niño events involves the occurrence of an anomalous upper tropospheric westerly ﬂow over central America and the Caribbean. This is a result of enhanced deep convection in the eastern equatorial Paciﬁc. The anomalous ﬂow creates unfavorable conditions of vertical wind shear for hurricane formation in the western Atlantic and Caribbean. Climatic anomalies associated with extremes of pressure in the Indian Ocean sector were recognized in the late nineteenth century by meteorologists working in India and Australia (Diaz and Kiladis, 1992). Walker (1923) subsequently demonstrated that droughts in India and Australia accompany a negative SOI, as well as cool, rainy winters in the southeastern United States. The variability in annual precipitation is one-third to one-half greater in areas immediately affected by ENSO events and those linked by consistent teleconnections than in other parts of the world, according to Nicholls (1988). Subsequently there have been many investigations of global and regional patterns of precipitation anomalies associated with ENSO (Stoeckenius, 1981; Behrend, 1987; Ropelewski and Halpert, 1987; Lau and Sheu, 1988; Kiladis and Diaz, 1989). The evolution of global anomalies of precipitation during warm events (June–August of year 0 to December–February of year 1) is illustrated in Figure 5.15. The maps show the composite anomaly for warm minus cold events so that W wetter and D drier than normal during warm events, with the opposite signs for cold events. Summer droughts are indicated for India, particularly over the peninsula (Kiladis and Sinha, 1991), and the Ethiopian Highlands (Quinn, 1992), and over Australia in the following seasons. Figure 5.15d demonstrates the widespread extent of positive anomalies in the tropics during December– February of year 1 following warm events. Over the Americas the strongest signals are above-average winter and spring precipitation over southern South America in year 0 of a warm event. In North America cool, wet winters over the south and southeastern United States follow the warm ENSO mode. A study of precipitation anomalies associated with SO phase shows that in ﬁfteen out of nineteen regions around the globe there are opposite anomalies between high and low SO and that the relations were consistent in thirteen out of nineteen years, with high SO mode between 1885 and 1983 (Ropelewski and Halpert, 1989). The precipitation–SO relationship also occurs in the same seasons for both high and low SO in thirteen of the ﬁfteen regions. Long-term, global-scale associations of air temperature with “warm” and “cold” ENSO events, and the seasons of maximum association, have been documented by Halpert and Ropelewski (1992). They ﬁnd that the temperature anomalies associated with ENSO tend to be of opposite sign for the two phases, especially in the tropics, where they have the same sign as the local anomaly of SST. Outside the tropics, the SO–temperature association may only be evident regionally for one phase. Japan and western Europe/North Africa exhibit an SO–temperature relationship only during cold events, while the southeastern United States has an association only during warm events, despite having drier than normal conditions in cold events. Ten ENSO events between 1950 and 1988 have been differentiated according to their duration. Using the persistence of positive SST anomalies, Tomita and Yasunari (1993)

Figure 5.15 Composite differences for warm minus cold ENSO events for precipitation (a–h) and temperature (i–p) in the eastern hemisphere and the Americas for the seasons SON 1, DJF 0, SON 0 and DJF 1, where the event occurs in year 0. W wetter, D drier than normal; A above and B below normal temperature. Solid symbols represent signiﬁcant differences at the 1 percent level, open symbols at the 5 percent level. (From Kiladis and Diaz, 1989)

388 Synoptic and dynamic climatology identify one group (1951, 1953, 1963, 1965, 1972, and 1982) where the event lasts about a year and includes one boreal winter, and a second group (1957, 1968, 1976, and 1986) which includes two boreal winters and persists for more than two years. Another categorization, by Wang (1995), distinguishes changes in the onset phase and SST anomaly patterns between the events of 1957, 1965, and 1972, on the one hand, and 1982, 1986/87, and 1991, on the other, which moved eastward to set up anomalous westerlies in the western equatorial Paciﬁc. In the earlier group, the onset phase (in November–December of year 1) featured a large anomalous anticyclone over eastern Australia. These cases also showed warming off South America for three seasons prior to warming in the central Paciﬁc. In the later group there was an anomalous low over the Philippines during the onset phase which generated westerlies. Coastal warming occurred only after that in the central Paciﬁc. These differences appear to reﬂect the control of the background SSTs in the Paciﬁc. A sharp interdecadal warming in the equatorial Paciﬁc waters took place in the late 1970s, with cooling in the northern and southern extratropical latitudes. Associated changes occurred in the onset cyclone, the western Paciﬁc westerly anomalies, and the southeast Paciﬁc trades which resulted in different modes of warming in coastal South America and the central Paciﬁc (Wang, 1995). Composite analysis of El Niño and La Niña events for northern winters 1950–96 indicates non-linearity in their anomaly patterns. There is a 35° longitude phase shift in eddy 500 mb height anomalies, respectively westward/eastward, during warm and cold events (Hoerling, et al., 1997). The wave trains appear to originate in different parts of the tropics since the positive anomalies of tropical rainfall occur east (west) of the dateline during warm (cold) events. However, it is also noted that the composite SST anomalies are not exact inverse counterparts of one another. A fundamental question concerning these worldwide teleconnections with the ENSO signal is: what mechanisms link the regional-scale forcing of sea surface temperature anomalies in the equatorial Paciﬁc to circulation anomalies that extend into the extratropics and vertically throughout the troposphere (Tribbia, 1991)? Simple models of the circulation response to an equatorial heat source reproduce the main elements of the zonal Walker cell, the north–south Hadley cell, and low-level cyclonic ﬂow anomalies to the west of the local heating source (Heckley and Gill, 1984) (Figure 5.16). Observational wind data for September–October 1972 and September 1982 match this model quite well near the equator (Barnett, 1984). The anomalous equatorial heat source generates lowlevel convergence and upper-level divergence (a thermally direct circulation). This upperlevel divergence serves as a source of vorticity for a wave train which becomes a stationary pattern. Horizontal propagation of the anomalies from a vorticity source region to midlatitudes has been analyzed by extending the wave equation of C.-G. Rossby (p. 286) to a sphere, where the latitudinal variation of the Coriolis parameter causes the latitudinal changes in the direction of energy propagation and the wavelength. Hoskins and Karoly (1981) use wave ray paths to trace the propagation of wave energy, initially poleward and then recurving to cross the equator. A critical limit is reached where the zonal phase speed of a Rossby is equal to the zonal ﬂow; waves cannot propagate through this region. Figure 5.17 illustrates that waves with smaller wave numbers (1–3) can penetrate further poleward before they are reﬂected (Lau and Lim, 1984). It takes between seven and fortytwo days for wave energy to propagate from an equatorial source to its critical latitude for wave Nos 6 and 1, respectively, for a zonal ﬂow of 10 m s1 and phase speed of 120 m s1. For zonally uniform westerly background ﬂow, wave trains of dispersing stationary Rossby waves move eastward following great-circle paths. Tribbia (1991) points out that the rays emitted from a stationary wave source are absorbed in regions of mean easterly ﬂow. In the northern hemisphere summer the easterlies extend into the subtropics. Thus larger extratropical responses may be expected during the respective winter seasons. Moreover, cross-equatorial Rossby wave propagation is possible where upper-level extratropical westerlies extend across the equator, as in the eastern equatorial Paciﬁc and

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Figure 5.16 Schematic illustration of equatorial low-level ﬂow and vertical motion associated with asymmetrical heating north of the equator over a region of about 40° of longitude. For a steady state, after at least ten days, the circulations in the two hemispheres are almost independent. In the model the wave front propagates westward at about 8° longitude per day. (From Heckley and Gill, 1984)

Atlantic sectors during the austral summer (Kiladis and Mo, 1998). These regions are termed “westerly ducts” by Webster and Holton (1982). Subsequent work indicates limitations in these interpretations. The mid-latitude circulation anomalies are predicted by the theories of Gill (1980) and Hoskins and Karoly (1981) to shift in location, in response to the longitude of the equatorial SST anomaly, but a lack of such sensitivity is apparent (Simmons et al., 1983; Geisler et al., 1985). This arises because the disturbances produced by a vorticity source are able to extract energy from the mean upper-level ﬂow and thereby amplify. Simmons et al. (1983) show that in the northern hemisphere winter, when there are large-amplitude stationary long waves, the circulation structure that does this most efﬁciently closely resembles typical teleconnection patterns. Moreover the extraction of energy is greatest off the east coasts of the continents in subtropical jet exit regions. Tribbia (1991) points out that although vortex stretching is the principal vorticity source for the anomaly patterns, this contribution is small off the east coasts, remote from the SST anomaly. The locally strong vorticity gradients off the east coasts dictate that convergence of the transport of mean vorticity by the divergent wind, induced by the SST anomaly, becomes an important term (Sardeshmukh and Hoskins, 1985). Hence there is no inconsistency in the fact that the anomalies associated with ENSO events are in the central eastern Paciﬁc. In summary, the teleconnection mechanism according to Tribbia (1991) involves the following steps:

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A locally warm ocean surface sets up anomalous low-level convergence (see Figure 5.16), enhancing precipitation. Enhanced precipitation increases mid-tropospheric release of latent heat, generating anomalous upper-level divergent ﬂow and, through non-linear relationships, absolute

390 Synoptic and dynamic climatology

a.

b. Figure 5.17 Ray paths on a sphere for a zonal basic ﬂow of 10 m s1 for wave Nos 1–8 from an equatorial heat source. (a) Non-divergent ﬂow. (b) Divergent motions with a phase speed of 120 m s1. (From Lau and Lim, 1984)

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vorticity. This divergent component of ﬂow is illustrated by the mean velocity potential at 200 mb (Figure 5.18) (see Chapter 3, note 3). Vorticity transport created by the anomalous upper-level outﬂow excites instability of the barotropic ﬂow, extracting energy from the climatological mean ﬂow off the east coasts of Asia and North America. Recent work suggests that vertical wind shear is required to complete the link between upper-level divergent ﬂow, which creates vorticity by vortex stretching, and the energy conversion to the vertically averaged ﬂow. A train of dispersing, almost stationary, Rossby waves emanates from this region of energy extraction.

Thus tropical forcing sets up a geographically ﬁxed circulation pattern. For an initial cold anomaly, the subsequent anomalous upper-level inﬂow in step 3 above also excites circulation instability. Additional variations in the forcing are introduced by the north–south seasonal migration of the regions of upper tropospheric outﬂow over South America and central Africa (Rasmusson, 1991). Tropical convection over the “maritime” continent of Indonesia– Malaysia, which promotes divergent outﬂow into the Walker and Hadley circulations, also oscillates seasonally (Meehl, 1987a). These seasonal ﬂuctuations cause variations in the intensity and location of the vorticity sources and the ensuing large-scale circulation patterns. As noted earlier, there is an eastward progression of tropical convective activity from India (in July) to northern Australia and the southwest Paciﬁc for the austral summer

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Figure 5.18 Divergent component of ﬂow at 200 mb as illustrated by the mean velocity potential for December–February 1986/87–1988/89 (above) and June–August 1986–88 (below); contour interval is 1 × 106 m2 s1. (From Rasmusson, 1991)

monsoon (Meehl, 1987a). The SPCZ in turn may link the tropical anomalies with higher southern latitudes. The linkage between the South Asian summer monsoon and ENSO is still being explored. However, it was recognized over forty years ago that variations in Indian monsoon activity appear to lead those in the Southern Oscillation rather than the converse (Normand, 1953). Monsoon anomalies lead tropical SSTs by about six months (Yasunari, 1991). Monsoon precursors of ENSO are very epoch-speciﬁc (Kumar et al., 1999). Such correlations occurred in 1951–90, but not during 1911–50, nor after 1991. From GCM experiments, the Asian monsoon appears to be coupled to the Southern Oscillation via an upper-level circulation couplet which acts as a radiating node for teleconnection signals to be transmitted from the monsoon area into the extratropics (Lau and Buc, 1998). Weak Asian monsoons are associated with a warm eastern equatorial Paciﬁc Ocean and a negative SO, with low pressure over Tahiti and the reverse for a strong monsoon. There are two wave trains associated with the monsoon ﬂuctuations. One extends over northeast Asia via the Aleutians to North America and the other extends from northwest Europe via Siberia to northern India. Webster and Yang (1992) point out that the summer monsoon is developing rapidly in spring at a time when zonal equatorial pressure gradients are minimal and lagged correlations with SOI are decreasing rapidly. The center of convective activity (as depicted by minimum outgoing long-wave radiation) shifts northwestward

392 Synoptic and dynamic climatology

Figure 5.19 Gradients in latent heat (LE) and radiative ﬂux convergence (Rn) between the main branches of the South Asian summer monsoon and the near-equatorial circulations. The zonal (“transverse monsoon”) has gradients that are one-third larger than meridional (“lateral monsoon”) or the Walker circulation. (From Webster, 1995)

from the western equatorial Paciﬁc “warm pool” region in winter to East and South Asia in June–July while the areas of strong radiational cooling (maximum OLR) over North Africa and the Middle East remain stationary (Webster, 1995). Figure 5.19 illustrates the boreal summer pattern of zonal (“transverse monsoon”) and meridional (“lateral monsoon”) heating gradients due to latent heat (LE) and radiative ﬂux convergence (Rn) between the main branches of the South Asian monsoon and the near-equatorial circulations. The link between the Asian monsoon and north African–Arabian deserts is apparent. Empirical analyses and coupled model studies all indicate a “predictability barrier” in the northern hemisphere spring for the tropical ocean–atmosphere system; that is to say, forecasts for time periods straddling this season have no skill. An explanation of this decrease in forecast skill, and the observed corresponding decline in lagged SOI correlations (Figure 5.20), has been sought in two hypotheses. The ﬁrst (Figure 5.21a) suggests that there is an inherent predictability limit of the coupled ocean–atmosphere system which is set by the fragility of the near-equatorial (ENSO) circulation in the boreal spring and its susceptibility to random perturbations. This hypothesis is examined by Torrence and Webster (1998), using historical SST and pressure data. They show that the strength of the predictability barrier is controlled by the degree of phase locking of ENSO to the annual cycle. Anomalies of the SST and the SOI show a slow decrease in autocorrelation within each series over several months. However, deﬁning persistence in terms of the ﬁxed-phase correlation between pairs of months, there is a sharp decrease in persistence in April for the SOI and in June for Niño 3 SST. Statistical modeling suggests that the persistence barrier occurs because boreal spring represents a time of transition from one state of the atmosphere to another with a low signal/noise ratio. There are signiﬁcant variations in the degree of persistence on interdecadal scales. For 1871–1920 and 1960–90, ENSO variance is large and the persistence barrier is strong. In contrast, ENSO variance is low during 1921–50 and the barrier is weak. Such ﬂuctuations in persistence may simply result from stochastic variability in the wind forcing on SST persistence, according to Flügel and Chang (1999). Weiss and Weiss (1999) examine the statistics of persistence in ENSO, particularly its annual phase-dependence. A persistence barrier may arise where the frequency of a sine wave function is a biennial cycle, or one of its harmonics. Their analysis indicates that the barrier in the SOI series and Niño 3 region SSTs is statistically distinguishable from that of a red-noise process. They also conclude that the barrier was weak from about 1915 to 1945, strong in the 1960s and early 1970s, and weakened in the late 1970s.

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Figure 5.20 Lagged correlations of the mean monthly SOI show the rapid decline during boreal spring (boxed area). (From Webster and Yang, 1992)

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The second hypothesis (Fig 5.21b) invokes the role of external inﬂuences, particularly an inverse coupling in the strength of the South Asian monsoon and the Paciﬁc trades. Another factor could be the extent of Eurasian snow cover in spring. Anomalously strong/weak monsoons are associated with stronger/weaker summer trade winds over the Paciﬁc Ocean. Moreover, annual cycle composites of outgoing long-wave radiation for strong/weaker monsoons show coherent patterns over southern Asia and the tropical Indian Ocean during the previous winter–spring seasons. The Asian monsoon and Walker circulation appear to have a six-month phase difference and to be selectively interactive, according to Webster and Yang (1992). In spring the developing monsoon dominates the near-equatorial Walker circulation. In the boreal autumn–winter the monsoon is weakest, with convection near the equator. The Walker circulation is now strongest and may dominate the winter circulation. Figure 5.22 illustrates the composite variation of the monthly SOI over twenty months during strong, normal and weak monsoons between 1871 and 1992 (sixteen in each category) (Lau and Yang, 1996). These several studies are complementary in suggesting that a combination of inﬂuences are responsible for the springtime predictability barrier (Figure 5.21c). Moreover a weak Asian monsoon is associated with strong tropical predictability from May to August but a sharp decline for predictions from September to April, whereas a strong monsoon is associated with a recovery of predictability from September to April (Lau and Yang, 1996.) Linkage of the two hypotheses is apparent, since Torrence and Webster (1998) ﬁnd from wavelet analysis that interdecadal variations in ENSO are correlated with changes in the strength of the Indian monsoon. According to Frederiksen and Webster (1988), remote linkages to the extratropical circulation associated with anomalous tropical heating represent one type of teleconnection mechanism. Visual evidence of the extratropical teleconnection with convective activity in the central and eastern equatorial Paciﬁc is given by the appearance of elongated

(b)

Figure 5.21 Schematic representations of hypotheses to account for the decrease in boreal spring tropical correlations and the associated spring predictability barrier. (a) Hypothesis 1, showing oscillations of ENSO between two regimes (upper). The vertical amplitude of the annual cycle (upper) determines the “robustness” of the system. There is a weak restraint in boreal spring (middle) but a strong restraint in boreal summer (lower). (b) and (c) Hypothesis 2 invokes coupled variability in monsoon and Paciﬁc Ocean trade wind strengths. The annual cycle and ENSO are regarded as distinct systems. (d) A uniﬁed hypothesis combining internal and external inﬂuences. (Webster, 1995)

(a)

(d)

(c)

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11 Figure 5.22 Composite variation of the monthly SOI for sixteen cases each of strong, normal, and weak monsoon. The normalized Indian monsoon rainfall index values (Parthasarathy et al., 1991) for each category are shown in parentheses. (From Lau and Yang, 1996)

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plumes of moisture and cloud (“moisture bursts”) in satellite imagery (Iskenderian, 1995). These bands connect with the subtropical jet on their poleward edge. A study of four cold seasons suggests that these features comprise a transient Hadley circulation (McGuirk et al., 1987). However, the cloud bands in southwesterly ﬂow occur ahead of upper-level troughs that propagate into the tropics over the eastern North Paciﬁc in winter from the exit region of the East Asian jet (Kiladis and Weickmann, 1992a, b; Kiladis, 1998). Extratropical teleconnections are discussed more fully below (section 5.8). The occurence of positive SST anomalies in remote ocean areas (the South China Sea and the Indian Ocean) three to six months after maximum positive anomalies in the tropical Paciﬁc are linked with the effects of changes in atmospheric circulation and energy ﬂuxes by Klein et al. (1999). They suggest that the circulation changes lead to reductions in cloudiness or evaporation, which increases the absorption of solar radiation and warms the surface. In the tropical North Atlantic the SST increase is attributed to weaker trade winds reducing evaporation. Such linkages provide evidence for a so-called “atmospheric bridge.” A second type of teleconnection mechanism involves extratropical effects on low latitudes. Observations and theory show that mid-latitude disturbances can propagate into low latitudes in regions of equatorial westerlies (Webster, 1983). During the northern winter, westerlies in the upper troposphere of the eastern Paciﬁc are sufﬁciently strong to permit propagation across the equator into the southern extratropics (Kiladis and Weickmann, 1992b, 1997). Interactions in the lower troposphere are particularly important in the western Paciﬁc during northern winter, when surges of cold air penetrate into the tropics (Arkin and Webster, 1985). Their antecedents include the intensiﬁcation of the East Asian jetstream (Lau and Lau, 1984) and the development of organized wave trains over the extratropical North Atlantic some six or seven days earlier (Joung and Hitchman, 1982; Compo et al., 1999). If the cold surge is strong, the enhanced subsidence may suppress convection farther east in the tropics. However, in the case of weak or moderate surges, moisture and energy may be advected eastward by upper westerlies and, ultimately, towards extratropical latitudes in longitudes of the central and eastern Paciﬁc (Lau et al., 1983). Two cases of cold advection into the tropical Paciﬁc during northern winter that forced convection within the eastern Paciﬁc ITCZ and the SPCZ are illustrated by Kiladis and Weickmann (1992). A third type of linkage – acting within the tropics – is suggested by Fredericksen and Webster (1988) on theoretical grounds. Satellite observations of OLR reveal intraseasonal

396 Synoptic and dynamic climatology variations of tropical convection, particularly in the Indian Ocean, on time scales of forty to ﬁfty days (the forty- to ﬁfty-day oscillations; see section 4.9). Strong changes in the eastward propagation of the convection patterns into the Paciﬁc, between ENSO warm event and non-ENSO years, may imply that the forty- to ﬁfty-day oscillations help trigger this lower-frequency ENSO (Lau and Chan, 1986). This hypothesis has been modiﬁed by the result of modeling (Zebiak, 1989), which suggests that intraseasonal variations may be important at certain times but, on average, do little to alter the evolution of ENSO as predetermined by the initial conditions. The biennial signals that are apparent in the ocean–atmosphere system of the tropical Indian and Paciﬁc Oceans, and involve modulations of the annual convective cycle (Meehl, 1993, 1997), may be another example of intratropical linkage. A biennial signal has been identiﬁed in a Tropic-wide Oscillation Index (TOI) deﬁned by the ﬁrst EOF of normalized rainfall anomalies over 20°S–40°N (Navarra et al., 1999; Miyakoda et al.,1999). Teleconnections within the tropics are examined using observations (gridded precipitation for 1961–94, SSTs, ECMWF 500 mb heights, MSL pressure, and vertical velocity) and corresponding ECHAM-4 model runs. The TOI best characterizes the ENSO/Asian monsoon oscillation in July–September for years with large SST anomalies and is an effective precursor of SST and vertical motion in the eastern equatorial Paciﬁc, representing El Niño intensiﬁcation in the following November–December– January. Whereas the teleconnection pattern of pressure is a dipole across the Paciﬁc Ocean to Indonesia, the patterns of teleconnection for SST, precipitation, and vertical motion show a horseshoe pattern from the SPCZ, through Indonesia, and northeastward into the western North Paciﬁc. Omitting eight out of thirty-four years with near-normal SSTs and Walker circulation over the equatorial Indo-Paciﬁc Ocean (45°E–180°–82°W), the TOI for July–September shows a biennial period, apart from 1971. Moreover, in line with earlier work, wet/dry Indian monsoons can be projected ﬁfteen months previously (May the previous year) with good success, based on SST anomalies in the eastern equatorial Paciﬁc Ocean. It should be noted that the two states of coupling/decoupling of ENSO and the monsoon deﬁned by the Walker circulation (SST) index in these studies are distinct from the two types of ENSO proposed by Tomita and Yasunari (1993).

5.5 Extratropical teleconnection patterns The primary teleconnection patterns are now routinely identiﬁed by the Climate Analysis Center, NOAA, using a rotated principal component analysis as proposed by Barnston and Livezey (1987). Indices are calculated for each calendar month based on 700 mb monthly height anomalies for January 1964–July 1994; they are determined from the height anomaly ﬁelds for the three-month period centered on a given month. Time series are displayed as standardized amplitude (mean zero, variance 1.0) determined simultaneously for each calendar month such that the combined sum of their products with the corresponding pattern eigen vector explains the maximum spatial structure of the observed monthly height anomaly ﬁeld. The major patterns and their seasonal occurrence in the northern hemisphere are as follows: North Atlantic Oscillation (NAO) East Atlantic (EA) pattern East Atlantic Jet (EA-Jet) pattern West Paciﬁc (WP) pattern East Paciﬁc (EP) pattern Paciﬁc/North America (PNA) pattern North Paciﬁc (NP) pattern

All months September to April April to August All months All months except August–September All months except June–July March to July

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Paciﬁc Transition pattern East Atlantic–Western Russia (Eurasia, EU) pattern Scandinavia (SCAND) pattern Polar/Eurasian pattern Tropical/Northern Hemisphere (TNH) pattern Asian Summer pattern

May to August September to May All months except June–July December to February November to January June to August

The EA and SCAND patterns, as well as two others – over southern Europe and the northern Atlantic (SENA), and over the Bering Sea–central North Paciﬁc Ocean (BER) – were ﬁrst identiﬁed in an RPCA of sea-level pressure data for 1899–1986 by Rogers (1990). Monthly NAO and Eurasian (EU) indices have been reconstructed back to AD 1675 by canonical correlation analysis based on observed station pressure, temperature and precipitation values, and multiple proxy records (Luterbacher et al., 1999). The predictive skill is highest in autumn and winter, but the indices are considered reliable except for summer before about AD 1750. Figure 5.23 illustrates the modes for ﬁve primary patterns in the northern hemisphere, based on ten-day mean 700 mb heights (Barnston and Livezey, 1987). Several of these patterns are now discussed in more detail.

5.6 North Atlantic Oscillation

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The North Atlantic Oscillation (NAO) involves a negative correlation in winter months between sea-level pressures in the subtropical Atlantic high and the Icelandic low. For example, a correlation of 0.76 was obtained for the winters of 1963–77 between the pressures at 65°N, 20°W and those in the region of the Azores, with analogous negative correlations at 500 mb (Wallace and Gutzler, 1981). The gradient of sea-level pressure between the Azores high and the Icelandic low ranges from 9 mb/1,000 km in December–January to 5.5 mb/1,000 km in May–August (Mächel et al., 1998). The NAO is strongest in winter and weakest in autumn, but it is unambiguously evident each month of the year (Rogers, 1990). An NAO index can be deﬁned, following Walker (1924), as the normalized mean sea-level pressure anomaly for Ponta Delgada, Azores, minus that for Akureyri, Iceland (Rogers, 1984). A time series of this index for winters since 1879 suggests quasi-decadal oscillations and a trend toward negative values from the early twentieth century to the 1960s, followed by more recent positive peaks and a general positive mode since 1980 (see Figure 5.24) (Koslowski and Loewe, 1994). The NAO index has recently been extended back to 1820, using pressure data from Gibraltar and a composite south-west Iceland series (Jones et al., 1997). Their record identiﬁes intervals of lower correlations between southwest Iceland and the Azores, Lisbon, or Gibraltar during 1821–60 and 1951–95, especially during spring, summer, and autumn, implying variability or shifts in the pressure centers. Wavelet analysis of the 315 year reconstructed NAO and EU indices by Luterbacher et al. (1999) indicates low-frequency periodicities. There are signiﬁcant periods in the NAO (Azores–Iceland) index around ninteen to twentythree years and ﬁfty to sixty-eight years in spring, and ﬁfty-four to eighty-eight years in summer. The annual (April–March) mean NAO has a ﬁfty-four to sixty-eight-year period. In the EU (Great Britain–Black Sea) index, the periods are sixteen to twenty-two years in spring, and six or seven years and twenty-two to twenty-eight years in summer. There is a general positive correlation between the NAO and EU indices in autumn and winter: strong (weak) Atlantic westerlies occurring with strong (weak) northerly ﬂow over central Europe, except during the mid-nineteenth century. From an EOF analysis of hemispheric MSL pressure anomaly maps for January, it appears that the NAO is essentially represented by the ﬁrst eigen vector pattern (Kutzbach, 1970). Thus it has hemispheric signiﬁcance. Comparisons between composite anomalies for the Southern Oscillation and the NAO for 1900–83 suggest a weak tendency for strong Atlantic westerlies to occur simultaneously with high SOI and dry conditions in the equa-

398 Synoptic and dynamic climatology

Figure 5.23 The primary northern hemisphere teleconnection patterns identiﬁed by rotated PCA, using ten-day mean 700 mb height patterns. The modes are: (a) PNA in January. (b) TNH in January. (c) EU in January. (d) NAO in April. (e) Subtropical zonal in July (see text). (Barnston and Livezey, 1987)

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Figure 5.24 Variations of the normalized NAO index for winters 1874–2000, based on tabulations provided by J.C. Rogers. The indices are based on the monthly sea-level pressure difference between Ponta Delgada, Azores, and Akureyri, Iceland. The pressure data are normalized by calculating the monthly anomalies from the 1874–1999 mean divided by the mean monthly standard deviation for the same period. The standardized value for iceland is then subtracted from that for the Azores, so that positive indices represent strong westerlies. The ﬁgure shows the individual winter values and an unweighted nine-year moving average.

torial Paciﬁc and for Atlantic blocking during the low SOI/wet (ENSO) mode (Rogers, 1984). However, the two oscillations are more frequently unassociated. Rogers notes that spectral estimates for the NAO show peak energy at 7.3 years, compared with about six years for Darwin pressures. The percentage of surface area north of 20°N over which pressure differences are statistically signiﬁcant between extremes of the NAO and SO is 37 percent for the NAO and 29 percent for the SO, but the signiﬁcant area in common is only about 10 percent during 1900–42 and 1943–80, and only 3 percent of the surface area is involved for the two oscillations for both time periods. Cross-spectral analysis of NAO and ENSO indicates that the coherence between them is dependent on both the frequency band and the year (Huang et al., 1998). There is signiﬁcant coherence between the NAO and SSTs in the Niño region (see p. 367) in nineteen out of twenty-seven El Niño years during 1900–95. Nine of these cases were associated with ﬁve to six-year period events and ten of them with a two to four-year period. During these events the initial winter had a dominant positive Paciﬁc North American teleconnection pattern (see section 5.9). In the eight cases with little or no NAO–ENSO coherence, SST anomalies in the Niño 3 region were weak. These winters had a characteristic strong negative NAO teleconnection pattern. The NAO pressure anomalies are apparent not only at the surface. There are corresponding height anomalies of the same sign at the 500 mb level, as illustrated in Figure 5.25 showing differences in heights between winters with strong and weak NAO indices (Rogers, 1984). As noted by Walker and Bliss (1932), a deep Icelandic low gives rise to strong advection of cold air over Bafﬁn Bay–west Greenland and mild southwesterly ﬂows over northwestern Europe (Figure 5.26). The NAO creates a characteristic seesaw pattern of winter temperature anomalies in western Greenland and northern Europe that was ﬁrst recognized in Greenland and Denmark in the eighteenth century. Loewe (1937) and van Loon and Rogers (1978) have detailed the seesaw modes and their relationship to circu-

400 Synoptic and dynamic climatology

Figure 5.25 The difference in height (m) at 500 mb between winters when the NAO index is above and below normal, based on the period 1947–83. Eleven winters were above the normal NAO index, and fourteen below. (From Rogers, 1984)

lation anomalies (Figure 5.26). When Greenland is warmer than normal (“Greenland Above”), pressure anomalies average 9 mb over Denmark Strait, 3 mb over southern Europe and 6 mb over the North Paciﬁc Ocean. Almost the reverse pattern is observed when Greenland is colder than normal (“Greenland Below”); the pressure anomalies are 6 mb north of Iceland and 5 mb in the North Paciﬁc. Strong (weak) NAO winters correspond to the seesaw modes Greenland Below (Greenland Above), respectively, although the match is imperfect as a result of differing deﬁnitions. Figure 5.27 illustrates the anomaly patterns of pressure, winds, temperatures, and relative humidity for strong and weak NAO winters (Kapala et al., 1988). Storm tracks are strongly concentrated from eastern Canada northeastward to Iceland and the Norwegian Sea for positive NAO, whereas for the negative mode they are highly variable: many move into the Labrador Sea, others into northwest Europe, and some through the Mediterranean and into eastern Europe and Russia. An association between the NAO and ocean circulation in the western North Atlantic has recently been suggested. Taylor and Stephens (1998) ﬁnd that almost 50 percent of the variance in annual mean latitude of the Gulf Stream between 65°W and 79°W is predictable from NAO intensity. A positive NAO mode with stronger westerly and trade winds gives rise to a more northerly path of the Gulf Stream two years later. Persistence explains a further 10 percent of the variance. The cause of the time lag is uncertain but may represent the time needed for the ocean gyre at 35°N to adjust. Some of the unexplained variance seems to be attributable to the Southern Oscillation (Taylor et al., 1998). Two years after ENSO events the mean latitude of the Gulf Stream is 0.2° farther northward than in years without an event. A slight southward shift following La Niña years is not statistically signiﬁcant. An analysis of the composite patterns of eddy sensible heat associated with NAO events for the time period 1948/49–1979/80 ﬁnds the anti-phase behavior of the Icelandic and

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Figure 5.26 (a) Pressure anomalies for the “Greenland Above” mode (warmer than average in West Greenland, colder than average in Norway). (b) Corresponding anomalies for the “Greenland Below” mode. (From van Loon and

402 Synoptic and dynamic climatology

Figure 5.27 Departures from “normal” for (a) strong and (b) weak NAO winters. Shown are sealevel pressures (isolines, dashed: negative departures), wind vectors (arrows), sea surface and air temperatures (over 0.2°C dark gray, under 0.2°C light gray), C cool, W warm, w wet, d dry for relative humidity anomalies. (c) Sea-level pressure (mb) and wind velocity for “normal” NAO winters, 1950–89. (From Kapala et al., 1998)

Aleutian lows to be less pronounced, with the largest variations between GA and GB centered on longitudes of the Icelandic low (Carleton, 1988b). In that region there is a marked change in the heat transport by the quasi-stationary waves and by the transients (traveling lows and highs). Transport by the quasi-stationary waves (transients) is considerably smaller (greater) in GA compared with GB modes of NAO. The circulation and temperature anomalies also have important effects on sea ice conditions. Ice extent in the Baltic Sea (Koslowski and Loewe, 1994) is inversely related to that in Davis Strait and to iceberg frequency off Newfoundland in the seesaw years (Rogers and van Loon, 1979). The seesaw modes have occurred in half of all winter seasons since 1840, based on observations at Oslo and Jakobshavn, when the temperature anomalies differed by at least 4°C. The “Greenland Below” mode was most frequent before 1924 and during the early 1970s and 1980s. The “Greenland Above” mode was most frequent in the 1870s, the 1940s, and during 1976–86. In addition, van Loon and Rogers note that other months occur when both Greenland and northern Europe have above/below average temperatures. The “both above” mode was most frequent between 1926 and 1939 and contributed to the 1920s warming over the northern North Atlantic (Rogers, 1985). These “both above/below” patterns each have frequencies of about 10 percent during the winter months, leaving 30 percent of months falling into none of these four categories. The GA and GB

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modes of the NAO also favor different storm track locations (Rogers, 1999). With the GB mode they are concentrated west of Portugal, whereas with the GA mode they are more often, but not uniquely, concentrated in the northeast North Atlantic. An extreme version of the GA mode of the NAO occurs when a high is centered over the Iceland area and low pressure is found near the Azores. These reversals in the normal gradient occurred in 6 percent of months during 1873–1980, with two-thirds of the eightytwo cases in the cold season (Moses et al., 1987). They are most common in February– March. The highs are commonly between 70° and 80°N, 20° and 40°W, and the lows about 45°–50°N, 20°–40°W. For twenty-two cases where the pressure departures exceeded one standard deviation at Stykkisholmur, Iceland, and Ponta Delgada, the pressure departures ranged from 8.6 to 23.5 mb at the former and 6.7 to 18.1 mb at the latter station. In January 1963, for example, the maximum departures were 24 mb and 10 mb (see Figure 4.38). This pattern closely resembles a composite map of pressure anomalies for the coldest Januaries in northwest Europe. The patterns are associated with minimum values of the zonal index over the North Atlantic (Makrogiannis et al., 1982). These reversals were most frequent in the late nineteenth century and in the 1960s (Moses et al., 1967); none occurred in the 1900s, 1930s, or 1970s, paralleling the frequency of GA winters identiﬁed by van Loon and Rogers (1978). Studies by Hurrell (1995) and Jones et al. (1997) show that NAO has had strongly positive values since 1980, especially in winters 1993, 1989, and 1995, when the index had the highest values on record. Northern Europe experienced wetter and warmer than normal conditions, whereas the opposite occurred in southern Europe and the Mediterranean (Hurrell and van Loon, 1997). Winters 1995/96 and 1996/97 interrupted the run of mild winters in northeast Europe, however.

5.7 North Paciﬁc Oscillation The North Paciﬁc Oscillation (NPO) involves a subtropical–subpolar pressure oscillation analogous to the NAO. The related winter temperature seesaw between western Canada 0

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Figure 5.28 Composite pressure difference map for “Aleutians below” minus “Aleutians above” Januaries. (From Rogers, 1981)

404 Synoptic and dynamic climatology (Edmonton) and western Alaska (Dutch Harbor, St Paul), featuring an Aleutians Abovenormal (AA) temperature mode and an Aleutians Below-normal (AB) mode has been analyzed by Rogers (1981a). The AB mode is characterized by an eastward elongated Aleutian low with strong westerlies in the central North Paciﬁc and strong polar easterlies over Alaska and the Beaufort–Chukchi–Bering seas, as well as a deep Icelandic low. The AA mode features a weaker Aleutian low displaced 25° to the west off Kamchatka, and a high over northwest Canada –Alaska; the Icelandic low is now weaker. Thus warmer winters over western Canada are associated with low pressure in the Gulf of Alaska. This AB pattern also has more extensive ice in the Bering Sea. The pressure difference map for AB Januaries minus AA Januaries (Figure 5.28) shows main centers of 12 mb over the Gulf of Alaska and 8 mb over northwestern Europe. Positive differences (also statistically signiﬁcant) are located over the northwestern United States and Kamchatka. In contrast to the NAO, the NPO is essentially a regional-scale oscillation and matches Kutzbach’s (1970) second eigen vector pattern for winter MSL pressure anomalies. The AA and AB modes of the NPO are also identiﬁed by the rotated PCA of ﬁve-day mean sea-level pressure data for twenty-nine winters (Hsu and Wallace, 1985). However, the corresponding 500 mb ﬁelds are considerably different. The AA mode features a strong blocking ridge over the Aleutians, whereas the AB mode has a strongly zonal circulation over the North Paciﬁc. There are no apparent trends in frequency of the two NPO modes. There were thirteen AB winters and eleven AA winters during 1906–76; the NPO occurs in 43 percent of winter months. From an RPCA of gridded northern hemisphere pressure data from 160°E eastward to 40°E for 1899–1986, Rogers (1990) identiﬁes three major patterns of variability in the North Paciﬁc. They are: the NPO, a north-central Paciﬁc pattern and a Bering Sea pattern. The reproducibility of the NPO is quite low between 1899–1945 and 1946–86 in January and February, although it is strong in December and March. The other two patterns are more consistent except for the Bering center in January. It is possible that some of the patterns are not unique modes of variability or that they are subject to spatial and temporal variations in the distribution of anomalies. It is suggested by Gershunov and Barnett (1998) that the NPO modulates ENSO signals over North America. Their results are based on a two-way classiﬁcation of high and low NPO with El Niño and La Niña events. During high (low) NPO phases, El Niño (La Niña) signals are strong and stable, whereas they are weak, spatially incoherent and unstable during El Niño–low NPO and La Niña–high NPO combinations. They propose that a deeper Aleutian low displaces Paciﬁc storms southward while the El Niño-enhanced moisture contributes to the storms from the eastern tropical Paciﬁc. In contrast, with a weaker Aleutian low during La Niña storms are steered northward, increasing precipitation in the Paciﬁc Northwest, and giving fewer storms and drier conditions in the US southwest.

5.8 Zonally symmetric oscillations The north–south MSL pressure oscillations in the North Atlantic and North Paciﬁc appear to be coupled to one another such that the intensity of the Icelandic and Aleutian lows in winter is inversely related. This zonally symmetric pattern was noted by Lorenz (1951), Kutzbach (1970), van Loon and Rogers (1978), Trenberth and Paolino (1981), and Wallace and Gutzler (1981), using different approaches and data sets. Rogers (1990) notes that the North Atlantic and North Paciﬁc patterns each have a mode with a zonal cyclone track in mid-latitudes. Thompson and Wallace (1998) show that the leading EOF of wintertime geopotential heights in the northern hemisphere comprises an Arctic Oscillation (AO) between the Arctic Ocean and a zonal ring in middle latitudes, particularly over the oceans; land–sea contrasts weaken the zonal symmetry. Figure 5.29a illustrates the AO signature at 1,000 mb, based on the leading principal component of November-April monthly mean

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height anomalies for 1947–97. Here it accounts for 22 percent of the variance at sea level. The other panels show regression maps on this pattern for tropopause pressure, 50 and 500 mb heights, 1,000–500 mb thickness and surface air temperature anomalies. The pattern is robust throughout almost a century of sea-level pressure data and dominates both intraseasonal and interannual variability, provided that interdecadal trends are removed from the data. The AO is also identiﬁed, especially in winter–spring, in phase alternations of 500 mb anomalies over the northern polar cap versus the mid-latitudes of Europe–North America and the North Paciﬁc–east Asia. Zhang et al. (1997) ﬁnd oscillations of sixty to seventy and thirty to forty days’ duration, using multichannel singular spectrum analysis of ﬁve-day mean 500 mb height ﬁelds for 1946–88. The anomalies are shown to propagate poleward along the Ural mountains and over Greenland and southwestard over Europe and the North Atlantic toward North America. Nevertheless, these patterns account for only 20 percent of the local variance. Importantly, the variance of winter surface air temperatures accounted for by the AO index (39 percent) is over twice that due to the NAO index (17 percent). The correlation of November–April temperatures over Eurasia is 0.55 with the AO, compared with only 0.23 with the NAO. Thompson and Wallace demonstrate a deep coupling within the polar vortex through the troposphere and lower stratosphere. Figure 5.29 indicates the presence of both a deep, zonally symmetric, barotropic signature and a more wavelike baroclinic signature in the troposphere, as shown by the thickness and 500 mb patterns. It is noteworthy that LeDrew et al. (1991; LeDrew and Barber, 1994) proposed a coupling between the stratospheric polar vortex and late summer cyclones over the Beaufort Sea (see Figure 6.6), but he did not explore this conceptual model. Baldwin et al. (1994) and Perlewitz and Graf (1995) also demonstrate links between the stratospheric polar night jet, the tropospheric circulation and surface temperature. In the southern hemisphere there are generally out-of-phase relationships at 500 mb between high and low latitudes, and also for mid-latitudes versus low and high latitudes (Mo and White, 1985). The anomalies are essentially barotropic (Rogers and van Loon, 1982). The sea-level pressure ﬁeld in middle and high latitudes of the southern hemisphere also shows a strong seesaw tendency, which was referred to as the Antarctic Oscillation by Gong and Wang (1998). Subsequently they deﬁne an Antarctic Oscillation Index (AAOI) as the difference in normalized zonal-mean sea level pressure between latitudes 40°S and 60°S (Gong and Wang, 1999). This zonally symmetric oscillation is characterized by the ﬁrst EOF of monthly sea-level pressure. It is a year-round feature, with EOF 1 accounting for between 17 percent (March) and 33 percent (December) of the variance. Thompson and Wallace (2000) make a comprehensive study of the southern hemisphere counterpart of the Arctic Oscillation using monthly circulation data poleward of 20°S for 1957–97. They show that the structure of monthly mean ﬁelds in the southern hemisphere is an annular mode represented by the leading principal component (Figure 5.30). The percentage of variance in all calendar months explained by PC1 is 27 percent for the zonally varying 850 mb heights, 45 percent for the 1,000–50 mb zonal-mean zonal wind and 47 percent for the zonal-mean 1,000–50 mb geopotential height. The corresponding values for the northern hemisphere are: 20 percent, 35 percent and 45 percent. The meridional scale of the annular modes in the southern hemisphere is almost identical throughout the troposphere and lower stratosphere. As Thompson and Wallace point out, the similarity and robustness of the annular modes in both hemispheres are notable, given the contrasts in land–sea distribution and in their planetary wave structure (see section 4.3). Coupling of the respective tropospheric modes with the stratospheric circulation is evident only in late austral spring (boreal winter) in the southern (northern) hemisphere, when the patterns amplify upward into the stratosphere. This occurs during times of the year when strong zonal ﬂow in the stratospheric polar vortex favours interactions between the planetary and wave-mean ﬂow. In the southern hemisphere this represents the time of breakdown of the vortex, whereas in the northern hemisphere it occurs when the vortex is strongest.

406 Synoptic and dynamic climatology

Figure 5.29 Regression maps for monthly anomalies of tropopause pressure, 50 mb geopotential height (z50 ), 1,000–500 mb thickness (z500–z1000 ), 500 mb geopotential height (z500 ) surface air temperature, and with the ﬁrst EOF of sea-level pressure (z1000 ) for November–April 1947–97 (the AO index). Contour intervals (negative values dashed) expressed in units per standard deviation of the AO index are: 12.5 m for z1000, 20 m for z500 and 10 m for z500–z1000; as labeled for z50 surface air temperature; and 5 mb for tropopause pressure. Extreme values are labeled in the corresponding units. (From Thompson and Wallace, 1998)

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Figure 5.30 Southern hemisphere (left, a and c) and northern hemisphere (right, b and d) structures of the hemispheric circulations associated with their respective annular modes (the AAO and the AO). Zonal-mean geostrophic wind (m s1) (a and b) and lowertropospheric geopotential height (in meters per standard deviation of the respective time series) (c and d) are regressed on the standardized indices of the AAO and AO. The contour interval is 10 gpm for height and 0.5 m s1 for zonal wind. (From Thompson and Wallace, 2000)

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The AAOI was higher in the 1980s than in the 1960s to early 1970s, indicating a strengthening of the zonal circulation (Gong and Wang, 1999). This is conﬁrmed by Thompson et al. (1999), who show a linear trend during 1968–97 in the leading PC of the 850 mb height ﬁeld in all months of the year except June. A different zonal structure, with a mode near the equator and anti-phase relationships between the hemispheres, has been identiﬁed from 250 mb global stream functions analysed for winters 1978/79–1988/89 (Hsu and Lin, 1992). Figure 5.31 shows seven waveguides associated with eddy activity. After removing this zonal structure, interhemispheric teleconnections were found in the low-frequency (over thirty days) domain, with dipoles straddling the equator near 90°W and 20°W and others between the tropics

408 Synoptic and dynamic climatology

Figure 5.31 Seven wave guides at 250 mb in winter inferred from lag correlation maps for base points located in areas of maximum teleconnectivity. These relate to ten to thirty-day eddy activity. (From Hsu and Lin, 1992)

and mid-latitudes across the exit zones of the mid-latitude jetstreams in the Paciﬁc (Figure 5.31). These exit zones represent regions where perturbations are likely to grow.

5.9 The southern hemisphere EOF analysis of SLP and tropospheric height has helped to determine the dominant modes of low-frequency variability of the atmospheric circulation in the southern hemisphere. These teleconnections also reveal associations with the tropical ENSO, to varying degrees, depending on the eigen vectors studied and seasons considered. The zonally varying pattern Several authors (Rogers and Van Loon, 1982; Mo and White, 1985; Kidson, 1988b, 1991; Shiotani, 1990) have described a dominant teleconnection pattern characterized by the anomalies of SLP, 500 mb height and zonal winds which are out of phase between low and middle and middle and high latitudes. These zonally asymmetric anomalies comprise a barotropic “seesaw” pattern centered on 60oS and upon which zonal wave No. 3 anomalies are superimposed (Mo and White, 1985; Shiotani, 1990). The extreme modes of the seesaw, representing out-of-phase variations of geopotential height between middle and high latitudes in one case, and between the tropics and middle latitudes in the other (Karoly, 1990), are also characterized by concomitant variations in temperature gradients, cyclonic activity, and the eddy transports (Rogers, 1983; Shiotani, 1990). These patterns of zonally varying anomalies also exhibit some association with the ENSO, and are such that the trade winds are stronger in the cold phase, or La Niña (weaker: El Niño), and the westerlies north (south) of about 45oS are correspondingly stronger (weaker) (Trenberth, 1981; Rogers and van Loon, 1982). The Trans-polar Index (TPI) and wave No. 1 Eigen vector analysis of southern hemisphere SLP and 500 mb height data reveals a prominent teleconnection pattern separate from that associated with ENSO, involving an out-of-phase relationship of the pressure/height anomalies between the Australasian and South America sectors (Rogers and van Loon, 1982). The variability in the eccentricity of planetary wave No. 1 that is implied can be depicted by a Trans-polar Index (TPI), or pressure anomaly difference for Hobart,

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Australia, 147.3°E, 43°S, minus Stanley, Falkland Islands, 58°W, 51.6°S (Pittock, 1980a, b). A negative TPI implies a shift of the polar vortex towards Australia, which is more likely during June–September. Rogers and van Loon (1982) conﬁrmed the presence of the TPI as EOF 2, secondary to the zonally asymmetric pattern and Connolley (1997) ﬁnds an analogous result in a coupled GCM. The Trans-polar Index has been correlated signiﬁcantly with annual rainfall and temperature on the southern continents, and with variations in the extent of ice in the Scotia Sea, Antarctica (Pittock, 1980a, b, 1984; Rogers and van Loon, 1982). Positive (negative) values of the index are correlated with the variations in sea ice conditions in the Scotia Sea: mild (severe) ice years being associated with the trough in the Australia (South America) sector. Other workers (Streten, 1983; Carleton, 1989) have shown statistical associations between TPI and sea ice extent and concentration in the Antarctic south of Australia, and in the Ross Sea. These are linked physically by the effects of changes in zonal wind speeds associated with the TPI on the sea ice cover. Conﬁrmation of these results, and development of a plausible physical model to explain them, awaits the application of a longer-term and more extensive data set. The TPI and southern westerlies are positively correlated with the SOI when TPI leads by up to one year (Pittock, 1984), although there is some dependence in the strength of this association on the choice of time period studied (Carleton, 1989; Villalba et al., 1997). This may imply a long-term variation of the “poles” of the oscillation. The TPI association with ENSO probably also relates to the appearance of signiﬁcant circulation anomalies in the storm track area near New Zealand in the period leading up to a “warm event” (Trenberth and Shea, 1987). The Paciﬁc–South America pattern Mo and Ghil (1987) applied EOF analysis to the Australian daily hemispheric 500 mb height analyses for the period June 1972 to July 1983. The ﬁrst EOF conﬁrms a previously established tendency for southern hemisphere heights to be out of phase between lower and higher latitudes (Rogers and van Loon, 1982; Mo and White, 1985); the second EOF resembles a quasi-stationary three-wave pattern that is related to blocking events (Trenberth and Mo, 1985; Sinclair et al., 1997). In particular, Mo and Ghil (1987) also found a strong wave No. 3 component in the third EOF of the winter data that they called the Paciﬁc–South America (PSA) pattern, because of its resemblance to the PNA of the northern hemisphere. However, the two teleconnections (PSA, PNA) do not necessarily occur at the same time (Mo and Ghil, 1987). The PSA comprises a wave train of alternating anomalies that extends southeastwards from the subtropical Paciﬁc to the Antarctic Peninsula. A subsequent EOF analysis by Farrara et al. (1989) retained all wave numbers in the anomalies, including those of wave No. 5 and greater that were discarded by Mo and Ghil (1987). This resulted in the PSA comprising EOF 2, rather than EOF 3, of the winter 500 mb height anomalies, with EOF 1 remaining as the zonally symmetric anomalies identiﬁed in Mo and Ghil (1987). Kidson (1988a) and Berberry et al. (1992) have also identiﬁed a PSA-like pattern in southern hemisphere anomaly ﬁelds that have been ﬁltered to retain time scales of between ten and ﬁfty days. Southern extratropical teleconnections with the El Niño–Southern Oscillation The tropical ENSO phenomenon links directly with the southern extratropics in the Paciﬁc Ocean sector (Zillman and Johnson, 1985; van Loon and Shea, 1985). In the lead-up (approximately twelve to eighteen-month lead) to a warm ENSO event (El Niño) the seasonal cycle of the trough in the Tasman Sea is strongly enhanced relative to its intensity in a non-ENSO year (van Loon, 1984). As a result, extratropical cyclone activity in the New Zealand region is typically increased (Trenberth and Shea, 1987). This is associated with a greater frequency of blocking near New Zealand and enhanced troughing downstream in longitudes of the Ross Sea during El Niño. Accordingly, there are more outbreaks of

410 Synoptic and dynamic climatology cold air towards lower latitudes of the southwest Paciﬁc. There are also large changes in cyclone frequency elsewhere in the southern extratropics associated with ENSO (Sinclair et al., 1997). These include increases (decreases) in the number of wintertime cyclones during an El Niño over the Indian Ocean and the Amundsen Sea (near Wilkes Land, and the subtropical eastern Paciﬁc). Interestingly, the patterns of greatest cyclonic activity during the La Niña phase of ENSO are virtually the opposite to those in the El Niño, suggesting a more or less linear response of the southern hemisphere cyclone eddies to ENSO. The inﬂuence of the Southern Oscillation, particularly the El Niño phase, is evident in the pressure and temperature anomaly ﬁelds, and also in the sea ice conditions of the Antarctic and sub-antarctic (Carleton, 1988a, 1989; Smith and Stearns, 1993; Gloersen, 1995; Simmonds and Jacka, 1995). In particular, synoptic studies of atmospheric circulation variations in the southeast South Paciﬁc and Antarctic Peninsula region link the PSA pattern with the ENSO (Marshall and King, 1998; Carleton et al., 1998). The center of action comprising the Amundsen Sea low exhibits a strong out-of-phase relationship with ENSO, such that the “mean” low is stronger (weaker) during La Niña (El Niño) events, resulting in positive (negative) temperature anomalies in winter on the Antarctic Peninsula (Marshall and King, 1998). Hemispheric-scale maps of the 500 mb height anomalies composited for sets of “warm” and “cold” winters on the Antarctic Peninsula bear a strong resemblance to the extreme phases of the PSA pattern. Similarly, for two winters characterized by strong anomalies in the intensity of the Amundsen Sea low (1988: weak; 1989: strong), Carleton et al. (1999) ﬁnd that the frequencies of occurrence of cold-air mesocyclones in the Bellingshausen and Amundsen seas were greatly increased in 1989 relative to 1988. The mesocyclone strong increases in 1989 accompanied an area of widespread cooling of the sea surface and greater sea ice extent in the May through September period that were, apparently, tied to the greater intensity of the “mean” Amundsen Sea low and the more frequent cold-air outbreaks associated with this feature during that winter. These contrasted with weaker seasonal changes in the SST and also with less extensive sea ice in the Bellingshausen/Amundsen seas, for the 1988 winter. The variations in longitude position of the Amundsen Sea low that are related to its intensity variations (i.e. a displacement westward towards the Ross Sea when weaker; displaced eastward toward the Antarctic Peninsula when stronger, e.g. Carleton and Fitch, 1993), dominate the interannual variations of moisture ﬂuxes into West Antarctica (Bromwich et al., 1995). Values of the moisture convergence into this region increase (decrease) when the low is displaced to the west (east) of its long-term mean position. The mechanism by which the intensity variations of the Amundsen Sea low are linked with ENSO likely involves the eddy momentum ﬂux convergence patterns associated with the subtropical jet (STJ) and polar front jet (PTJ). The two jetstreams vary out of phase with each other according to the phase of ENSO, whereby the STJ is weaker (stronger) and the PFJ stronger (weaker) in cold La Niña (warm: El Niño) events (Chen et al., 1996; Cullather et al., 1996).

5.10 Tropical–extratropical teleconnections As noted earlier (section 5.1), in addition to the dipole teleconnections in the North Atlantic and North Paciﬁc sectors, there are independent patterns each with three centers – in Eurasia, the northern tropics, and the Paciﬁc/North Atlantic. Tropical–extratropical teleconnections in 500 and 200 mb height ﬁelds during northern winter, identiﬁed by simultaneous and lagged correlation statistics for 1963–81, are the Paciﬁc–North American (PNA), a Tropical/Northern Hemisphere (TNH) pattern, and a mixed one of these two labeled West Paciﬁc Ocean (WPO) (Mo and Livezey, 1986). The three cumulatively account for about one-third of the variance of the mean northern hemisphere winter, and a larger fraction over the North American sector. The PNA pattern

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features in both low-pass (seasonal) and high-pass (monthly) ﬁltered data, but the seasonal connection with the tropics is limited to ENSO years. Further analysis of tropical heating, inferred from SST and OLR data, indicates that positive projections are likely for all three circulation modes (at 700 mb) during strong ENSO winters (Livezey and Mo, 1987). The absolute strength of the TNH is shown to be directly related to SST anomalies in the central Paciﬁc, for example. The PNA pattern refers to the relative amplitudes of the ridge over western North America and the troughs over the central North Paciﬁc and eastern North America (Leathers et al., 1991). A strong (weak) ridge–trough pattern is designated as a positive (negative) PNA regime (Figure 5.32). An index of PNA strength proposed by Wallace and Gutzler (1981) uses a linear combination of standardized 700 mb height anomalies at grid points nearest the four mean centers of the anomaly ﬁeld; Leathers et al. use a different variant for three centers. During the positive mode of the PNA (Figure 5.33a) with a strong Aleutian low and a strong ridge over western Canada, there is a well developed storm track from Asia extending eastward into the central Paciﬁc between 40°N and 50°N and then northeastward to the Gulf of Alaska. For the negative phase (Figure 5.33b), cyclones track northeastward along the East Asian coast to the Bering Sea, with a second area near the west coast of Canada (Lau, 1998; Klasa et al., 1992; Ueno, 1993). The PNA is generally in the positive mode during a warm ENSO event (see Horel and Wallace, 1981) and in the negative mode during the cold phase of ENSO. In autumn, winter, and spring the PNA is a principal mode of circulation variability in the middle troposphere. It has strong effects on winter temperatures in the United States, with warm anomalies in the northwest and cold outbreaks in the southeast under positive PNA patterns (Leathers et al., 1991). The frequency of the two modes of the PNA pattern is not symmetrical. Using EOFs of winter 500 mb height data to identify the modes, Dole (1986) found more occurrences of low or moderate amplitude positive PNA than negative PNA, but for larger amplitude anomalies the opposite is true. Predictability experiments suggest that the atmosphere is more barotropically stable during episodes of positive PNA (Palmer, 1988). This is reﬂected in the tendency for a composite of negative PNA mode pentad-mean ﬁelds of 500 mb height over the northern hemisphere during winters 1952–84 to have lower variability than a similar composite of positive mode events.

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Figure 5.32 Schematic plot of dominant low-frequency (thirty-day) teleconnectivities at 250 mb in winter. (After Hsu and Lin, 1992)

412 Synoptic and dynamic climatology

Figure 5.33 The composite sea-level pressure ﬁelds for (a) positive and (b) negative modes of the Paciﬁc/North American pattern. (From Wallace and Gutzler, 1981)

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This teleconnection depends on the location and strength of the jetstream over East Asia which extracts energy from the mean ﬂow (Nakamura et al., 1987) (see section 5.4). In winter the PNA is signiﬁcantly associated with the East Asian jet, Asian land temperatures, and tropical SSTs in the central Paciﬁc (Leathers and Palecki, 1992). In contrast, only mid-latitude variables are involved in spring: SSTs in the western North Paciﬁc and Asian land temperatures. In summer and autumn there is little PNA variance associated with any of these variables. The role of synoptic eddies in forcing the tropospheric PNA pattern has been examined by Klasa et al. (1992) for twenty-ﬁve winters. By analysis of the conversion of eddy kinetic energy from barotropic eddies into the mean ﬂow (through Eliassen–Palm ﬂux vectors), and also the eddy vorticity forcing for positive and negative PNA modes, they show that the strongest anomaly of eddy forcing is collocated with large amplitude PNA centers in the Paciﬁc. The eddy forcing has a six to ten-day time scale. During well developed PNA patterns, the conversion of synoptic-scale eddy kinetic energy into the mean ﬂow is most important within jet exit zones. For PNA positive, the maximum eddy-mean ﬂow conversion is over the eastern Paciﬁc, while for PNA negative the maximum is displaced westward into the central Paciﬁc. The primary source of kinetic energy for the major teleconnection patterns is the conversion of the KE of the basic ﬂow to the KE of the response, according to Li and Ji (1997), rather than that supplied directly by the external forcing. Using the barotropic vorticity equation, linearized about the 300 mb level zonally varying climatological ﬂow for the northern hemisphere winter, they analyze the location of “efﬁcient” forcing modes – which lead to the growth of anomalies within about ﬁve days – and the preferred response patterns. The sources of efﬁcient forcing are localized in the subtropics, south of the major jetstream maxima, and over the Arctic. The subtropical loci give responses resembling various observed teleconnection patterns: forcing over Central America and the western Atlantic gives a North Atlantic pattern (Hsu and Lin, 1992); forcing over North Africa sets up a south Eurasia pattern; forcing over South Asia and the western Paciﬁc gives an East Asia–Paciﬁc pattern (Nitta, 1987), while central Paciﬁc forcing leads to the Paciﬁc–North America pattern. In addition to these, the Arctic forcing gives propagating responses from northern Canada to the western Atlantic and from northern Asia to the western Paciﬁc. Hoskins and Ambrizzi (1993) documented a zonal wave train from forcing in North Africa to response centers over southern Asia. However, Li and Ji (1997) note that the PNA pattern does not correspond to a wave guide and they also point out that the possible path of Rossby wave rays (Hoskins and Karoly, 1981) may vary according to the wave number involved. Teleconnection patterns do not appear to be determined solely by the dispersion of wave energy. It must be emphasized that the tropical SST forcing of interannual climate variability in the extratropics accounts for less than the total variability of wintertime mean surface air temperature. Horel and Wallace (1981) show that the correlation between the SOI and extratropical height ﬁelds in the northern hemisphere is stronger at 700 mb and 300 mb than at the surface and is stronger for North America in winter. Studies show that extratropical seasonal climate anomalies are a result of the combined effects of ENSO states and zonal index anomalies on teleconnection patterns and that anomalies of u are essentially independent of tropical SST variations (Hoerling et al., 1995; Ting et al., 1996). ENSO explains an important part of the seasonal variance over the North Paciﬁc and central Canada. In contrast, anomalies of the stationary waves associated with u anomalies produce “centers of action” over the Paciﬁc–North America and North Atlantic–Eurasia regions, where they account locally for 30–40 percent of the interannual variability (IAV) of 500 mb heights. Moreover, Ting et al. (1996) ﬁnd that the IAV over many parts of the northern hemisphere extratropics is largely independent of both the variations of ENSO and the zonal index.

414 Synoptic and dynamic climatology

5.11 Teleconnections and synoptic-scale activity The principal teleconnection patterns identiﬁed by Wallace and Gutzler (1981) and others are associated with characteristic spatial distributions and levels of activity of the 500 mb synoptic-scale storm tracks as indicated by RMS statistics of two and a half to six-day band-pass ﬁltered heights (Lau, 1988). For example, the dipole modes of the western Paciﬁc and western Atlantic teleconnection patterns in the northern hemisphere are accompanied by changes in wintertime storm track intensity and mean zonal wind over the western oceans. There are similar variations in storm track intensity over Siberia associated with the northern Asian teleconnection pattern noted by Esbensen (1984). Eddy activity is strengthened over western Siberia (50°–60°N) when 500 mb heights are above normal over Mongolia. In contrast, the modes of the Eastern Atlantic and Paciﬁc/North American patterns, which display more of a wave-like character, are associated with latitudinal displacements of the storm tracks and jetstreams over the eastern oceans, rather than with intensity changes, Lau (1988) also ﬁnds that enhanced (weakened) 500 mb eddy activity over the North Paciﬁc and North Atlantic is associated with strengthened surface westerly (easterly) winds to the south of negative (positive) sea-level pressure anomalies, respectively. Patterns of composite sea-level pressure differences for several of the storm track modes resemble North Paciﬁc and North Atlantic oscillation patterns. Northern hemisphere circulation regimes, as characterized by the pattern classiﬁcation of Dzerdzeevski (see section 7.3.3), are found to differ considerably during extreme (warm and cold) modes of the ENSO sequence (Fraedrich et al., 1992). There is enhanced (reduced) zonality in winters following the warm (cold) event. A warm event forces the North Paciﬁc storm track into a more zonal arrangement, especially in the eastern part. Overall, warm event winters have a reduction in the eight to ten-day “residence time” of meridional circulation types. An extension of this analysis for nine warm and nine cold events shows that the latter have more variance in the 500 mb heights along 50°N owing to transient eddies relative to stationary eddies (Fraedrich and Müller, 1994). Thus, for height variance in periods of ﬁfteen days or more, cold event winters have most variance in zonal wave Nos. 3 and 4 whereas warm event winters show no peak in quasi-stationary wave No. 3.

5.12 Time-scale aspects of teleconnections The analysis of ﬁelds of simultaneous pressure values provides no information about their temporal evolution, although lag correlations and band-pass ﬁltering are useful in this regard. The time scale dependence of 500 mb circulation patterns examined by lag correlations suggests that ten to thirty-day features represent wave trains. They display zonally oriented curved ray paths that migrate with the reference point, whereas patterns that last longer than a month are mainly ﬁxed north–south dipoles situated over the oceans (Blackmon et al., 1984a). The zonally oriented wave-like patterns do not actually propagate zonally; rather, downstream centers develop and intensify as the ones upstream decay, providing an eastward dispersion of energy (Blackmon et al., 1984b). The spatial structure of low-frequency variability differs considerably according to the time scale. An analysis of monthly 700 mb height anomalies for winters 1949/50 through 1976/77, where the intermonthly and interannual signals are obtained by ﬁltering, ﬁnds important differences in the teleconnection patterns on these two time scales (Esbensen, 1984). The intermonthly ﬁelds resemble the map of Wallace and Gutzler (1981) with prominent western Paciﬁc, western Atlantic and Paciﬁc–North American (PNA) centers and a further dipole over northern Asia.4 The interannual ﬁeld, in contrast, displays patterns that are considerably more global in extent; they are the PNA, the Eurasian, the Zonally Symmetric Seesaw, and the North Paciﬁc dipole (Figure 5.34). These differences in pattern of teleconnectivity suggest that the associated dynamical and physical mechanism may

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Figure 5.34 Teleconnection ﬁelds in winter for (a) the intermonthly signal and (b) the interannual signal. Hatched regions indicate correlations greater than 0.6 and dark shaded regions greater than 0.7. Solid arrows denote patterns supported by teleconnectivity and one-point correlation maps, open arrows are based primarily on the latter. (From Esbensen, 1984)

416 Synoptic and dynamic climatology also differ. Analysis of connections between tropical and mid-latitude circulations at 250 mb conﬁrms that the mechanisms do indeed differ according to time scale (Mo and Kousky, 1993). On the intraseasonal scale there is a substantial zonally symmetric connection in both summer and winter, comprising a forty-eight-day oscillation. Anomalies of OLR show this to be related to convection over the tropical Paciﬁc. In contrast, Mo and Kousky (1993) identify a PNA wave-train mode in the boreal winter extending northeastward from the Paciﬁc into western North America, and then southeastward to the subtropical Atlantic. The mid-tropospheric winter circulation in the northern hemisphere has been analyzed with daily 500 mb height data for November–April 1946–84, ﬁltered to distinguish periods of ten to sixty, sixty to eighty, and over 180 days, by Kushnir and Wallace (1989). The interannual (over 180 days) variability displays modal structure – the NAO and PNA patterns – which account for much of the total height variance in these sectors (Figure 5.35c). In the sixty to 180 day intraseasonal band, however, only the western Atlantic

Figure 5.35 Teleconnectivity of wintertime 500 mb height ﬂuctuations for (a) ten to sixty-day periods, (b) sixty to eighty days, (c) longer than 180 days. Extrema show correlation values with decimal point omitted; bold contours indicate r = 0.4 in (a) and 0.5 in (b) and (c). The contour interval is 0.1. (From Kushnir and Wallace, 1989)

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dipole pattern identiﬁed by Wallace and Gutzler (1981) rises above the background continuum. In the ten to sixty-day interval no geographically ﬁxed patterns are apparent in the spatial distributions of variance, or teleconnectivity, or the coefﬁcient of anisotropy (measuring the shape and orientation of transients). There are zonally oriented wave trains, particularly over the continents, and weak north–south dipoles over the oceans, as illustrated in Figure 5.35a for teleconnectivity. The dipole patterns occur downstream of the climatological jetstream maxima. A general issue that needs to be recognized is the tendency of mid-latitude SST anomalies to recur in successive winters without being present in the intervening summers (Namias and Born, 1970). A re-emergence of anomalies from beneath the summer mixed layer has been shown to take place via entrainment in the following autumn–winter as the mixed layer deepens (Alexander and Deser, 1995). Conﬁrmation that SST anomalies of given sign in the central Paciﬁc, and of opposite sign off the west coast of North America, are sequestered in the summer thermocline and re-emerge in autumn is provided by Alexander et al. (1999). The shallower mixed depth in the eastern Paciﬁc allows an earlier return than in the western Paciﬁc. Re-examination of this process (Zhang et al., 1998) shows that the primary reason for the SST signal is the fact that the dominant SST anomaly mode has a similar spatial structure all year, with maximum amplitude in summer. The pattern is more persistent from one summer to the next than from one winter to the next. However, the persistence from summer to winter in the anomaly pattern is greater than that of the local SST anomalies at their primary centers. It is unclear why the pattern does persist. West of 140°W at 40°N there is the same polarity of anomaly all year, but a winter-to-summer reversal to the east of this longitude. Zhang et al. suggest that negative SST anomalies east of 140°W in winter favor a positive PNA and this circulation reinforces negative SSTs to the west. Southerly ﬂow in the eastern Paciﬁc tends to raise SSTs there. Meehl et al. (1998) using a global coupled ocean–atmosphere–sea ice model, simulate variability in the nine to twenty-year range and show that the thirteen to ﬁfteen-year time scale in the model is set approximately by the average circuit of the ocean gyre circulations. Mehta (1998) ﬁnds twelve to thirteen year periods in SSTs for 1882–1991 in the tropical South Atlantic but not in its northern counterpart. There are three modes of variability in tropical Atlantic SSTs. In the South Atlantic decadal mode, anomalies form in situ and travel from the subtropical South Atlantic along the eastern boundary into the tropics. They reside there several years and they tend to travel southward along the western boundary into the subtropical South Atlantic. There is also an energetic North Atlantic mode with anomalies from the extratropics making a clockwise rotation around the ocean boundary. Analysis of SST and sea-level pressures (SLPs) by White and Cayan (1998) distinguishes periods in the three to seven, nine to thirteen, and eighteen to twenty-threeyear range. During 1900–89 the largest SST and SLP anomalies occur in the extratropics and near the eastern boundaries of the oceans. Peaks of a tropical warm phase are noted in 1900, 1920, 1940, 1960, and 1980. During 1955–94, they identify global reﬂection symmetries about the equator and global translation symmetries between ocean basins. Sea-surface temperatures in the tropical and eastern ocean areas are above (below) average in association with stronger (weaker) extratropical westerlies. Positive (negative) SST departures in the west-central subarctic and subantarctic frontal zones covary with stronger (weaker) subtropical and subantarctic gyres in the North Paciﬁc and North Atlantic and with basin and global SSTs that are about 0.1 K above (below) normal. Changes in the frequency of major hemispheric circulation regimes are recognized to be implicated in climatic change. Corti et al. (1999) examine the structure of the ﬁrst two EOFs of monthly-mean 500 mb height ﬁelds for November–April 1949–94. The probability density function of the atmospheric state vector, as described by the loadings of the monthly-mean heights onto the ﬁrst and second EOFs plotted in two-dimensional phase space, identiﬁes four maxima. The spatial structures of these clusters resemble

418 Synoptic and dynamic climatology several well known circulation modes. One represents the 500 mb height ﬁeld associated with the cold ocean, warm land (COWL) pattern in 1,000–500 mb thickness anomalies discussed by Wallace et al. (1995, 1996). The second and third clusters are related to the positive mode of the NAO and the negative mode of the PNA, while the fourth is well correlated with the negative phase of the Arctic Oscillation. These clusters are still evident when ENSO years are removed from the data. Corti et al. conclude that changes in the frequency or duration of the principal circulation regimes in the northern hemisphere over the second half of the twentieth century have contributed substantially to hemispheric temperature trends. These circulation changes may, nevertheless, be a response to anthropogenic forcing. In the southern hemisphere, monthly mean patterns as well as low-pass ﬁlters suggest the existence of a zonally symmetric pattern that features a change in the sign of the anomalies near 60°S and also of a zonal wave (n 3) in middle to high latitudes (Mo and White, 1985; Mo and Ghil, 1987). For the intraseasonal (ten to sixty-day) time domain, extratropical teleconnectivity maps at 200 mb indicate short-wave (3,000 – 4,000 km) negative correlations with slow-moving zonal wave patterns in winter (Berberry et al., 1992). A wave train that appears to originate in the South Indian Ocean (45°S) splits into two in the vicinity of Australia, with the subpolar and subtropical jetstreams serving as wave guides (Figure 5.36). The two wave trains merge near South America, with equatorial propagation towards northeast Brazil and the subtropical South Atlantic. In summer the wave patterns are more meridional (equatorward) and geographically ﬁxed.

5.13 Interannual to interdecadal oscillations Several recent observational and coupled GCM studies suggest the occurrence of a range of multi-year coupled ocean–atmosphere oscillations. Most of these are still incompletely documented, owing to the short available record length, but it seems worth while at least to point out their major characteristics and suggested mechanisms involved. The occurrence of variability on interannual to interdecadal time scales has been examined by various spectrum analysis techniques (see section 2.6) for global ﬁelds of air temperature (Mann and Park, 1994), and sea-surface temperature (Moron et al., 1998), as well as for the SOI (Allen and Smith, 1994) and other variables. The global analysis of sea-surface temperatures since 1901 by Moron et al. (1998) identiﬁes a number of oscillations: (1) a thirteen- to ﬁfteen-year seesaw oscillation between the Gulf Stream region (Bermuda to Cape Hatteras) and the North Atlantic Current sector south of the Denmark Strait; (2) a quasi-decadal oscillation over the North Atlantic (also Tourre et al., 1999) as well as over the South Atlantic and Indian Oceans; (3) a seven to eight-year oscillation involving the subtropical and subpolar gyres in the North Atlantic; and (4) interannual oscillations of sixty to sixty-ﬁve, forty-ﬁve, and twenty-four to thirty months, particularly in the tropical Paciﬁc Ocean. In the Paciﬁc Ocean from 20°S to 58°N Zhang et al. (1998) ﬁnd a quadriennial (ﬁfty-one-month) oscillation in SSTs accounting for about 20 percent of the variance. It represents a standing wave in the tropics but there is propagation northeastward from the Philippines Sea and then eastward along 40°N. There are also weaker interdecadal and QBO signals. 5.13.1 Quasi-biennial oscillations The existence of a twenty-ﬁve and a half to twenty-four-month (quasi-biennial) oscillation was ﬁrst noted in North American temperature data (Clayton, 1885) and subsequently in many climatic records. These include snow cover (Voeikov, 1895), sea-surface temperatures off Norway (Helland-Hansen and Nansen, 1920), surface pressure in mid-latitudes (Shapiro, 1964), blocking (Boehme, 1967), and other surface and tropospheric climate parameters (Landsberg et al., 1963). The discovery in the 1950s of a twenty-six-month

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Figure 5.36 Teleconnection patterns in the southern hemisphere for (a) winter and (b) summer. The correlation values are ×100; values below 50 percent are shaded. Points of maximum negative teleconnection are connected. (From Berberry et al., 1992)

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420 Synoptic and dynamic climatology oscillation in easterly and westerly phases of the equatorial stratospheric winds – the stratospheric QBO (Veryard and Ebdon, 1961; Naujokat, 1986) – focused much interest on the question of stratospheric–tropospheric interactions. The stratospheric oscillations have a maximum amplitude about 25–30 km and propagate downward at ~1 km/month. Lindzen and Holton (1968) proposed a two-way interaction between the mean ﬂow and momentum ﬂuxes from waves in the troposphere. Waves on all scales from gravity waves to planetary waves appear to play a role (Dunkerton, 1997). Momentum is transferred upward from the equatorial tropopause to a critical layer below 40 km where the phase of the oscillation in the zonal winds is triggered. The amplitude of the momentum ﬂuxes appears to play a primary role in determining the oscillation frequency, but this can vary with changes in the thermal relaxation time and the horizontal diffusivity. The momentum transfer to the mean ﬂow causes the critical layer to move downward. The response to momentum eddy ﬂux convergence is a ﬂow acceleration which has a limited latitudinal extent. Haynes (1998) suggests that the internal dynamics of longitudinally symmetric motion in a rotating, stratiﬁed atmosphere, with thermal relaxation, may be a sufﬁcient cause of the equatorially conﬁned QBO. A possible inﬂuence of the equatorial stratospheric QBO phase on the Southern Oscillation and the northern hemisphere winter 700 mb height ﬁeld was suggested by van Loon and Labitzke (1987, 1988; Labitzke and van Loon, 1989). The data record is rather short, but a statistical association exists between the eleven-year cycle in 10.7 cm solar ﬂux and northern hemisphere tropospheric conditions for the westerly phase of the QBO: high solar ﬂux, westerly QBO and high SOI for 1951–88. The relationship with northern hemisphere winter climate failed in January–February 1989, apparently because the Paciﬁc cold event of 1988–89 superseded solar–QBO forcing (Barnston and Livezey, 1991). Barnston et al. (1991) ﬁnd a preference for the TNH and WPO patterns during the easterly QBO phase at 45 mb for 1951–89 in response to anomalies in the Southern Oscillation. During the westerly phase there is a preference for the PNA pattern. Changes in the height of the tropopause with QBO phase, that affect the potential for convergence in the troposphere, may provide the forcing for the responses in mid-latitudes. However, a physical mechanism for the QBO to mediate the effects of the solar cycle, or the extratropical effects of the Southern Oscillation, remains to be established. Biennial oscillations in the tropical troposphere appear to be linked with variability in the Southern Oscillation and ocean–atmosphere coupling (Brier, 1978; Barnett, 1991; Yasunari, 1989; Meehl, 1997). The South Asian monsoon plays an active role in the tropospheric biennial oscillation (TBO), according to Meehl (1997). He proposes mechanisms for both ocean–atmosphere (OA) and land–atmosphere (LA) couplings on a biennial scale. The OA coupling involves a biennial alternation in the intensity of local convection during the season of local convective maximum, arising from SST anomalies. The LA coupling involves a corresponding alternation in monsoon strength associated with the relative land–sea (meridional) temperature contrast. Linkages between the two mechanisms, operating over the tropical Indian and Paciﬁc sectors, require that anomalies of land surface temperatures in southern Asia and SST anomalies in the tropical Indian Ocean and eastern equatorial Paciﬁc vary roughly in phase over the annual cycle. Observations indicate that the air–sea coupling tends to be best developed during the season when the seasonal convective cycle is also strong at some location in the Indian–Paciﬁc sector, as postulated by Meehl (1993). Moreover the upper ocean heat content is known to have sufﬁcient memory to maintain SST anomalies for about a year. Figure 5.37 illustrates schematically the evolution of the TBO over a two-year period. Beginning in the boreal winter (DJF) of year 0, anomalies of SST and convection, set up previously, are associated with an anomalous ridge over central Asia, giving warm, dry conditions. By boreal spring (MAM0), the weak Australian monsoon has left a warm sea surface north of Australia and a relatively cool surface in the tropical Paciﬁc. As the ITCZ moves northward, convection is weak over the Paciﬁc and stronger over Indonesia,

Global teleconnections 421 11

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0

Figure 5.37 The idealized evolution of the tropospheric biennial oscillation over a two-year cycle. Year 0 (1) refers to years with a strong (weak) Asian monsoon. The cycle begins in DFJ0, counter-clockwise to SON1. (From Meehl, 1997)

maintaining the pattern from DJF0. In the following boreal summer the anomalously warm land surface of South Asia enhances the land–sea (meridional) temperature contrast, creating a strong Asian monsoon. The maximum of convection shifts southeastward during SON0, leaving a relatively cool moist land surface in South Asia. Enhanced evaporation persists there through DJF1 and there is a strong Australian monsoon, associated with the warm sea surface. The east–west Walker circulation suppresses convection in the central tropical Paciﬁc and in East Africa. Over Asia a mid-latitude trough advects cold air and causes increased snow cover. This pattern is maintained into the next boreal spring (MAM1). Now SST anomalies become positive in the tropical Paciﬁc, and negative over Indonesia, with corresponding convective anomalies. The cool South Asian land surface weakens the land–sea (meridional) temperature gradient and the summer monsoon (JJA1). By autumn (SON1) South Asia is relatively dry and warm, setting the stage for the TBO to continue.

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5.13.2 Antarctic Circumpolar Wave

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A set of atmospheric variables (sea-level pressure and meridional wind stress) and ocean variables (sea surface temperature and sea ice concentration) for the Southern Ocean all show evidence of anomalies propagating eastward in the circumpolar southern westerlies at about 6 km/day. White and Peterson (1996) term this feature the Antarctic Circumpolar Wave (ACW). The overall wave No. 2 pattern has a period of four to ﬁve years and propagates around Antarctica in eight to ten years. It is best developed in the South Paciﬁc sector. The anomalies at 56°S have ranges of up to 8 mb in MSL pressure, 0.03N m2 in meridional wind stress, 1.6°C in sea surface temperature, and 350 km in the sea ice margin. Lagged cross-correlations indicate that positive (negative) anomalies of sea surface

422 Synoptic and dynamic climatology temperature (SST) follow high (low) pressure anomalies by about one year (90° phase) and are about 180° out of phase with equatorward (poleward) anomalies in meridional wind stress and the sea ice margin. The SST anomalies seem to originate in the western subtropical South Paciﬁc and propagate southeastward before moving eastward in the Southern Ocean. It should be noted that the observational record in this region spans little more than a decade and that its overall quality is uncertain in the case of atmospheric analyses. A case study for 1982–94 identiﬁes the major source of the ACW in the western subtropical South Paciﬁc (Peterson and White, 1998). Here anomalies develop in SST and moisture content and these move, together with SLP anomalies, into the Southern Ocean, where they migrate eastward in the Antarctic Circumpolar Current. Parts of the interannual SST anomalies branch northward into the South Alantic and South Indian oceans. These return to the tropics some six to eight years after appearing in the lowlatitude Paciﬁc. Remarkably, an ACW has been simulated in an extended integration with the Max Planck Institute coupled model (Christoph et al., 1998). Christolph et al. suggest that, while the SST and sea ice anomalies are advected eastward in the Antarctic Circumpolar Current, the pressure and meridional wind stress anomalies appear to be standing waves that are ampliﬁed or weakened as the SST and associated surface heat ﬂux anomalies move in and out of phase with the waves. White and Peterson postulate that the ACW may originate through a teleconnection between ENSO-related precipitation (and resultant latent heat) anomalies in the central and western tropical Paciﬁc and the atmosphere over the Southern Ocean. However, Christoph et al. ﬁnd that ENSO forcing explains only 30 percent of the variance at most. They propose that a standing wave pattern associated with the Paciﬁc–South American (PSA) oscillation (see section 5.9) generates surface heat ﬂux anomalies. These ﬂuxes

Figure 5.38 Schematic relationship between the coupled components in a speciﬁc phase of the ACW. The contours show SST anomalies (negative dashed), H and L denote high and low-pressure centers, Q and Q represent upward and downward heat ﬂuxes, and the open arrows marked show meridional wind stress maxima. The bold arrows indicate the eastward progression of SST anomalies; the other components undergo a standing oscillation. (From Christoph et al., 1998)

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warm (cool) the eastern (western) margins of the pressure anomalies, as illustrated schematically in Figure 5.38. Then the SST and wind stress anomalies drive the ﬂuctuations in the sea ice margin. The oceanic components encircle the Southern Ocean in about twelve to sixteen years, according to the model. The winter sea-level pressure and 500 mb heights in high southern latitudes characteristically feature a zonal wave No. 3 barotropic pattern (Mo and White, 1985), with a geographic distribution that favours the ACW regime. The wave No. 3 pattern coupled with the circumglobal ocean wave causes locally reappearing energy peaks at four or ﬁve-year intervals. 5.13.3 Quasi-decadal oscillations A number of studies of climate records in the northern hemisphere point to the presence of decadal and multidecadal ﬂuctuations. Meehl et al. (1998), using a global coupled ocean–atmosphere–sea ice model, simulate variability in the nine to twenty-year range and show that the thirteen to ﬁfteen-year time scale in the model is set approximately by the average circuit of the ocean gyre circulations. Mehta (1998) ﬁnds twelve to thirteenyear periods in SSTs for 1882–1991 in the tropical South Atlantic but not in its northern counterpart. There are three modes of variability in tropical Atlantic SSTs. In the South Atlantic decadal mode, anomalies form in situ and travel from the subtropical South Atlantic along the eastern boundary into the tropics. They reside there several years and tend to travel southward along the western boundary into the subtropical South Atlantic. There is also an energetic North Atlantic mode, with anomalies from the extratropics making a clockwise rotation around the ocean boundary. Analysis of SST and sea level pressures (SLPs) by White and Cayan (1998) distinguishes periods in the three to seven, nine to thirteen, and eighteen to twenty-three-year range. During 1900–89 the largest SST and SLP anomalies occur in the extratropics and near the eastern boundaries of the oceans. Peaks of a tropical warm phase are noted in 1900, 1920, 1940, 1960, and 1980. During 1955–94 they identify global reﬂection symmetries about the equator and global translation symmetries between ocean basins. Sea surface temperatures in the tropical and eastern ocean areas are above (below) average in association with stronger (weaker) extratropical westerlies. Positive (negative) SST departures in the west-central subarctic and subantarctic frontal zones covary, with stronger (weaker) subtropical and subantarctic gyres in the North Paciﬁc and North Atlantic and with basin and global SSTs that are about 0.1 K above (below) normal. In the North Atlantic region these seem to be related to several factors: the ocean thermohaline circulation (THC), the formation of North Atlantic deep water (NADW), Arctic ice export through the Fram Strait, and associated fresh-water anomalies in the northern North Atlantic, like the Great Salinity Anomaly (GSA) of the late 1960s (Mysak et al., 1990; Mysak and Venegas, 1998). Bjerknes (1963, 1964) ﬁrst developed a model of variations in the surface heat balance of the North Atlantic on short and long time scales. He proposed that, on the two to ﬁve-year scale, strong zonal ﬂow at 35°N is associated with large turbulent heat ﬂuxes and, since oceanic heat transport responds slowly, there is cooling of the surface layers. Over ﬁfty-year periods, wind speeds and surface temperature in the Icelandic low area are also negatively correlated. On this time scale, cooling due to upwelling is weak when the Icelandic low is weak. Warm water transported in the Irminger Current branch of the North Atlantic Current lags the Icelandic low strength by a few years, so that these opposing inﬂuences are out of phase. The mechanisms responsible for such long-term trends are still under debate. Kushnir (1994) notes that decadal-scale variations in North Atlantic SSTs for 1900–87 show a distinct dipole pattern centered in the Iceland–Labrador Sea area and east of Bermuda. This pattern, he suggests, provides evidence for the role of the THC in the northwestern Atlantic–Greenland Sea. A coupled model study of the North Atlantic, however, indicates that interaction of the extratropical atmosphere and the wind-driven subtropical ocean gyre can account for the dipole pattern in quasi-decadal ﬂuctuations

424 Synoptic and dynamic climatology observed in winter temperatures east of Newfoundland and off the southeastern United States (Deser and Blackmon, 1993; Grötzner et al., 1998). A relationship analogous to that reported for the North Atlantic by Bjerknes is also observed in the North Paciﬁc (Latif and Barnett, 1996). It involves interdecadal variations in sea surface temperatures in the subtropical gyre of the North Paciﬁc, heat transport by the Kuroshio Current, and the strength of the Aleutian low. A pan-Atlantic decadal oscillation (PADO) has been proposed, based on a zonally averaged linear dynamic ocean–atmosphere model of the tropics (Xie and Tanimoto, 1998). Sea surface temperature anomalies are related to evaporation and the surface advection of heat. There are zonal bands of SST and wind anomalies with alternating polarities from the subtropical South Atlantic to Greenland. In the model, extratropical wind forcing sets up the observed decadal oscillations in SST. Low-frequency SST anomalies form an anti-symmetric dipole about the equator between 20°N and 20°S, whereas poleward of 20°N there is an equatorward-propagating perturbation. Rajagopalan et al. (1998) ﬁnd a strong coherence between the NAO and the SST difference between the northern and southern tropical Atlantic. Since both these tropical ocean regions are separately correlated with the NAO index over this time band, it is posssible that ocean–atmosphere interaction affects climate variability in the North Atlantic. Using the ECMWF–Hamburg (ECHAM-3) atmospheric model coupled with the largescale geostrophic (LSG) ocean model in a 700 year control integration, Timmerman et al. (1998) ﬁnd evidence of an approximately thirty-ﬁve-year period of oceanic meridional overturning and matching anomalies of North Atlantic SSTs in the region 20°–70°W, 30°–50°N. A possible model of the interactions involved, based on a lag regression analysis, is shown in Figure 5.39. A similar thirty-ﬁve-year oscillation is identiﬁed in the North Paciﬁc sea surface temperatures for 120°E–160°W, 20°–40°N despite the absence of GSA-type events in the Paciﬁc.

Appendix 5.1 Partitioning between equatorially symmetric and antisymmetric components The spatial symmetry of climatological anomaly ﬁelds about the equator is often a useful diagnostic tool for examining manifestations of the annual cycle and its variations. For

Figure 5.39 Schematic model of interactions that may produce an interdecadal cycle in the North Atlantic. The negative feedback loop begins with a negative SST anomaly, leading to a weakened NAO. This causes reduced fresh water transport, which creates positive surface salinity (SSS) anomalies in the northwest Atlantic, enhancing deep convection. This subsequently strengthens the THC and poleward heat transport, giving positive SST anomalies, completing the cycle. (Timmermann et al., 1998)

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example, the thermal ﬁeld over a tropical ocean in response to solar radiation forcing should be symmetrical about the equator, with a semi-annual regime in response to the corresponding second harmonic in solar forcing; the amplitude of response would be a maximum at the equator. There would also be an annual response in the thermal (and pressure) ﬁeld, antisymmetric about the equator, with a minimum amplitude at the equator, increasing toward the tropics. The latter regime is characteristic of the eastern Paciﬁc Ocean; the former resembles the annual variation over continental Africa, the tropical Indian Ocean, and the western Paciﬁc (Wang, 1993). For any monthly mean anomaly ﬁeld (x, y) in a given month, the symmetric (s) and antisymmetric (a) components are: Fs(x, y) =

1 [ f (x, y) F(x, y)] 2

11 Fa (x, y) =

1 [ f (x, y) F(x, y)] 2

where x, y are respectively longitude and latitude distance. Wang (1993) notes that partioning of the continuity equation (in pressure coordinations) yields:

0

∂us ∂va ∂a =0 ∂x ∂y ∂p and ∂ua ∂vs ∂a =0 ∂x ∂y ∂p This suggests a requirement of mass conservation for a linkage between the a (or s) part of the meridional wind with the s (or a) part of the zonal wind.

0

Notes 1

Three principal versions of the SOI are in use (Climate Analysis Center, 1986): Troup (1965): sea-level pressure anomalies at Tahiti minus Darwin are divided by the standard deviation for that month of the difference time series. This index is generally multiplied by 10. Trenberth (1984): the Tahiti and Darwin anomalies are normalized by the standard deviations of the respective anomaly time series. Twelve-month mean standard deviations are 0.931 for Tahiti and 1.003 for Darwin. The (Tahiti–Darwin) difference of the standardized anomaly is then computed. CAC (1986) follows the Trenberth procedure and then further normalizes the difference by the standard deviation of the TN minus DN difference time series. (TN and DN are the standardized anomalies for Tahiti and Darwin, respectively, based on the 1951–80 base period.) This gives a time series with a zero mean and unit variance. CAC (1986): tabulates monthly values for 1935–85. The Troup and CAC values are closely similar. CAC is now the Climate Prediction Center at NCEP

0

2

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Singular spectrum analysis is an application of PCA to a univariate time series, i.e. time lags replace the spatial direction. Unlike spectral analysis, the functions are determined empirically, not a priori. Oscillatory modes have pairs of nearly equal eigen values; their temporal EOFs and PCs have the same time scale of oscillation and are approximately 90° out of phase. The root of the eigen value represents the singular value of the corresponding temporal PC (Keppenne and Ghil, 1992). The method is equivalent to the extended EOFs described by Weare and Nasstrom (1982).

426 Synoptic and dynamic climatology 3

The Rossby radius of deformation, LR = c/f, where c = wave speed, f = the Coriolis parameter, deﬁnes the distance where the amplitude becomes negligible. For a stratiﬁed ﬂuid, the Rossby radius of deformation, LR, is

冢

LR = gh

4

冣

1/2

)

f

where h the vertical extent of the disturbance and / the degree of density stratiﬁcation (Hasse and Dobson, 1986, p. 71). In the ocean LR ~ 50 km and in the atmosphere LR ~ 800 km. Time series of ﬁve main teleconnection indices deﬁned by Wallace and Gutzler (1981) are now routinely updated so that low-frequency ﬂuctuations in these teleconnections can be examined (Bell and Halpert, 1993). (ftp://ftp.ncep.noaa.gov:/pub/cpc/wd52dg/data.indices/tele_index.nh)

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Synoptic systems have a horizontal scale of about 1,000–2,000 km and a lifetime of ﬁve to seven days. In mid-latitudes a weak low (high) pressure system typically has a central MSL pressure of less (greater) than about 1,008 (1,016) mb. In low latitudes, wave disturbances are more usually identiﬁed in streamline patterns because of the weak pressure gradients and the semi-diurnal pressure oscillation, except in the case of tropical cyclones. In view of their importance to mariners, identiﬁcation and tracking of storm systems over the tropical oceans began in the mid-nineteenth century. W.C. Redﬁeld (1831), for example, traced the parabolic paths of hurricanes over the western North Atlantic–Gulf of Mexico and, about the same time, Henry Piddington (1842) documented the motion of tropical storms in the Bay of Bengal–Arabian Sea. He also introduced the term cyclone (from the Greek kyklon, meaning revolving). Its counterpart, anticyclone, was ﬁrst used by Sir Francis Galton in 1863 (Khrgian, 1970).

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The nature and causes of storms became a major scientiﬁc controversy in the 1830s–1840s in North America and Great Britain, prompting efforts to compile meteorological reports and to systematize observations. A crucial step was the development of telegraph networks in the 1840s. Reviews and assessments of these early observations and theories are provided by Khrgian (1970), Kutzbach (1979), and Fleming (1990). Monmonier (1999) presents a fascinating history of the early meteorological charts prepared for Europe for the year 1783 by H.W. Brandes (1820) and of storm events over the eastern United States in the 1830s–1840s analyzed by Espy (1841), Redﬁeld (1843), and Loomis (1846). Identiﬁcation of the role of air currents with differing properties in mid-latitude weather systems became possible when synoptic weather maps were routinely prepared in the 1850s–1860s. Sir Francis Galton (1863) analyzed daily streamlines and rain areas, as well as the distributions of temperature and pressure over Europe for the month of December 1861, but his unique pioneering effort was not followed up. The “meteorograms” produced by seven British observatories for 1869–80, showing daily time traces of dry and wet bulb temperatures, vapor pressure, air pressure, wind speed and direction, and rainfall in orthogonal coordinates on a single 5-day chart (see Bergeron, 1980, ﬁgure 2, for example) were also badly underutilized, in part owing to the lack of understanding of their value. In the view of Bergeron (1980), emphasis on the study of the relation of weather to surface isobaric patterns by R. Abercromby (1878), Julius von Hann (1901), and others delayed the analysis of air trajectories and the recognition of fronts for up to ﬁfty years. The characteristics and life cycle of frontal cyclones were not described until 1919 by the ‘Bergen school’ of meteorologists in Norway (Bjerknes, 1919; Friedman, 1989, pp. 150–201). Their model, discussed below, remained little modiﬁed until the 1950s (Namias, 1983). The availability of upper air balloon soundings, aircraft observations, and radar measurements, followed later by data from satellites, Doppler radar, and boundary-layer

442 Synoptic and dynamic climatology 1

proﬁlers has greatly extended our three-dimensional view of mid-latitude cyclones. Anticyclones have received much less attention, primarily because they present fewer forecasting problems.

6.2 Climatology of cyclones and anticyclones There is a long history of studies on the characteristics of synoptic systems, beginning with classical work on mid-latitude cyclones (Mohn, 1870; Loomis, 1874) and anticyclones (Loomis, 1887; Russell, 1893; Rawson, 1908, 1909). A comprehensive view was presented for the northern hemisphere by Petterssen (1950), using manual analyses of the frequencies of cyclone/anticyclone centers, as well as of cyclogenesis/anticyclogenesis and rates of alternation between high and low-pressure values. Such information is an essential adjunct to the interpretation of mean pressure or height ﬁelds (Klein, 1958). It is well known that climatological low-pressure systems tend to represent areas through which deep lows frequently move whereas high pressure cells tend to be quasi-stationary or slow-moving within the corresponding climatological anticyclone centers. The ﬁrst detailed investigations of cyclone/anticyclone centers were based on the 1899–1939 Historical Weather Maps of the US Weather Bureau, either in their entirety (Petterssen, 1950) or selectively for 1909–14 and 1924–37 by Klein (1957). However, these charts are known to be unreliable in high latitudes until the 1950s, owing to the paucity of stations, and there are gaps in the station network in other areas. As longer time series and more reliable pressure and geopotential height ﬁelds have become available in the form of gridded values, the earlier analyses have been updated and improved upon. Algorithms to identify cyclones and anticyclones based on pressure or height data are described by Murray and Simmonds (1991) and Serreze et al. (1993), for example. The algorithms permit searches to be made for blocks of grid points to detect local pressure minima/maxima. Jones and Simmonds (1993, 1994) also introduce tests of curvature to avoid the inclusion of weak systems. The delimitation of anticyclone centers is commonly ambiguous because of slack pressure gradients and the tendency for weak maxima, that may shift irregularly over time, located within the highest closed isobar. System centers are tracked in space and time in order to obtain data on genesis/lysis and rates of cyclone deepening/ﬁlling. An assessment of the relative performance of three automated procedures for identifying cyclones and determining their tracks shows that the scheme of Murray and Simmonds (1991) identiﬁes the largest number of systems and of tracks (Leonard et al., 1999). An alternative procedure, proposed by Sinclair (1994, 1996, 1997) is to calculate geostrophic relative vorticity, although this is better suited to cyclones than anticyclones, as the latter have light winds and a wide separation between the loci of pressure maxima and relative anticyclonic vorticity maxima. For the southern hemisphere, analyses of ﬁfteen years of pressure data provide extensive statistics of the climatology of synoptic systems (Jones and Simmonds, 1993, 1994), updating the early work of Taljaard (1967). Figure 6.1 compares the zonally averaged behavior of mean sea-level pressure, anticyclone system density, and anticyclone mean central pressure on a seasonal basis. Interestingly, the anticyclone mean central pressure maximum at 38°S in JJA and 44°S in DJF is located 8°–10° south of the mean subtropical ridge (STR) of high pressure and 5°–8° south of the maximum zone of system density. The strongest anticyclones occur poleward of both the ridge and the maximum zone of system density. The poleward side of the STR is affected by frequent and intense lows, whereas the equatorward side generally has undisturbed ﬂow. Anticyclones are most numerous over the eastern subtropical oceans, with fewer over the southern land areas (excluding Antarctica). Genesis occurs over the southwestern Atlantic and Indian Oceans and over the Australian Bight and the Tasman Sea. Systems generally move eastward and somewhat equatorward, decaying near the oceanic centers of the time-mean anticyclones. There is a bifurcation in the distribution pattern of system density

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Figure 6.1 The zonally averaged distribution with latitude of highest mean sea-level pressure (MSLP), anticyclone system density (SD), and anticyclone mean central pressure (MCP) in the southern hemisphere. (Jones and Simmonds, 1994)

in winter from east of Tasmania to 150°W; in this sector there are maxima around 30°S and 45°S. Southern hemisphere cyclones as analyzed from weather maps for 1975–89 are characterized by a year-round frequency maximum in the circumpolar trough between about 60°S and 70°S (Jones and Simmonds, 1993). During winter and the transition seasons this maximum is fed by two branches spiralling towards it; one originates in the Tasman Sea and the other in the South American sector (Figure 6.2). However, when centers are computed by identifying local minima from 1,000 mb-level geostrophic vorticity, a different picture emerges. Sinclair (1994) uses ECMWF data for 1980–86 for this purpose, avoiding the bias towards slower/deeper systems in the traditional approach. This analysis thereby takes account of mobile vorticity centers in middle latitudes. These are fairly uniformly distributed and give rise to a primary eastward track between 45°S and 55°S, which also includes heat lows and lee troughs over or near the three land masses. A further maximum lies off East Antarctica, where there are well known katabatic outﬂows. There is a secondary maximum in winter–spring associated with the subtropical jetstream near 40°S east of New Zealand. Intense cyclones occur near New Zealand, east of South America and in the southern Indian Ocean in winter. Extension of the vorticity analysis to cyclogenesis shows that cyclones typically form in preferred areas in middle latitudes – near the jetstream baroclinic zones and to the east of the southern Andes year-round, as well as off the east coasts of Australia and South America in winter. Systems forming over the oceans intensify over strong gradients of sea surface temperature. Rapid cyclogenesis is particularly concentrated east of South America, southeast of South Africa, south of Australia, and near New Zealand. For the northern hemisphere, a twenty-year (1958–77) climatology of cyclones based on more consistent data than the earlier studies is available. Whittaker and Horn (1984) performed manual frequency counts over 5° latitude–longitude boxes for mid-season months using surface pressure charts. Figure 6.3a, b presents their maps of cyclone frequency and cyclogenesis. Whereas Klein (1957) makes counts at speciﬁc times, Whittaker and Horn tabulate a system only once in a given box. The principal ﬁndings of the analysis are as follows:

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Figure 6.2 (a) Cyclone system density, 1975–89 in the southern hemisphere. (b) Density of cyclogenesis for winter. (From Jones and Simmonds, 1993)

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Figure 6.3 (a) Cyclone frequency for 5° latitude and longitude boxes and (b) frequency of cyclogenesis in the northern hemisphere (corrected for latitudinal-scale change) for January and July (1958–77). (From Whittaker and Horn, 1984)

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1

2 3 4

In January the primary maxima are in the western North Atlantic, with an extension westward towards the Great Lakes, a broad zone in the western and central North Paciﬁc, with a peak about 45°–50°N extending into the Gulf of Alaska, where there is a secondary peak, and a subsidiary maximum over the north-central Mediterranean. In April the pattern is similar but with a decrease in the frequency of centers and a northward shift over the North Paciﬁc, where the activity now begins to the west over China along 50°N. In July the frequencies are further reduced and the hemispheric maximum is over eastern Canada at 55°N. The other main focus is from the western Paciﬁc to the western Aleutian Islands, with a subsidiary area over China. The October pattern resembles winter, except that the Atlantic maximum is off southeast Greenland, the main Paciﬁc center is in the Gulf of Alaska, and there is little activity in the Mediterranean.

Trends in cyclone frequencies in the northern hemisphere for 1958–97 are examined at the 1,000 and 500 mb levels by Key and Chan (1999), using the NCEP reanalyses. They show that, for 60°–90°N, closed lows increased in frequency at 1,000 mb in all seasons, but they decreased in frequency at 500 mb except in winter. In mid-latitudes, the frequency of lows decreases at 1,000 mb, with increases at 500 mb, except in winter. For 0°–30°N, lows became more frequent at both levels in winter and spring and at 500 mb only in summer and autumn. Agee (1991) used three previous analyses of cyclone and anticyclone frequency to examine trends in relation to intervals of warming and cooling in the northern hemisphere. Based on the works of Parker et al. (1989) on annual 500 mb cyclone and anticyclone frequency over the western hemisphere for 1950–85, Zishka and Smith (1980) on surface cyclone and anticyclone frequency over North America and adjacent oceans for January and July 1950–77, and Hosler and Gamage (1956) on surface cyclones in the United States for 1905–54, Agee suggested that warming (cooling) periods are accompanied by increases (decreases) in frequencies of both cyclones and anticyclones. The ﬁndings of Key and Chan (1999) indicate greater complexity. They also found no signiﬁcant difference in cyclone frequencies between El Niño and La Niña years. In both North America and Europe cyclone frequencies are poorly correlated with the NAO. Prior to the availability of pressure data from the Arctic drifting buoy program, which began in 1979, information on pressure systems over the Arctic Ocean was limited to the one or two manned drifting stations. These operated continuously from 1950 to 1991 under the North Pole Drifting Station Program of the Soviet Union, supplemented by a small number of US stations mainly in the 1950s and 1960s. Consequently the statistics based on pre-1979 data must be treated with caution (Jones, 1987; Serreze and Barry, 1988). The spatial distribution of systems identiﬁed by Serreze et al. (1993; Serreze, 1995) during 1973–92, shows that in winter months the cyclone maximum near Iceland extends northeastward into the Norwegian–Barents Sea (Figure 6.4). In the summer half-year this tendency is almost absent. In winter the rate of cyclone deepening and the frequency of deepening events peak in the area of the Icelandic low, southwest of Iceland, with a separate maximum in the Norwegian Sea (Serreze et al., 1997). Cyclogenesis is common in these areas, as well as in northern Bafﬁn Bay. Deepening rates are up to 6.8 mb (12 h1) for the Greenland Sea–North Atlantic sector. The combined effects of ice-edge baroclinicity, orographic forcing, and rapid boundary layer modiﬁcation in off-ice airﬂows are probably involved. Additionally, these same locations show high frequencies of systems ﬁlling and cyclolysis, implying that this sector of the Arctic is a dynamically active one with alternating regimes. In summer, high-latitude cyclones are considerably weaker. There is a frequency maximum over the central Arctic Ocean but it is characterized by cyclolysis, as are Bafﬁn Bay and Davis Strait, the Icelandic Low area and the Norwegian Sea. In summer,

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Figure 6.4 Cyclone events over the northern hemisphere north of 30°N for 1966–93. Systems lasting twenty-four hours are counted once in a 1,200 km (1,250 km north of 60°N) search area and counts are adjusted to a 60° reference latitude. Contour interval is 100: areas with over 300 are stippled. (Serreze et al., 1997)

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Figure 6.5 Estimates from a Q-vector analysis of the contributions to 850 mb vertical motion of differential vorticity and thickness advection and diabatic heating in a persistent lowpressure system over the Arctic Ocean during August–September 1990. (From Le Drew, et al., 1991)

deepening rates in the Greenland Sea are only 3.3 mb (12 h1), although this area remains the leading sector. Cyclogenesis in summer is common over central and eastern Eurasia and northwest Canada. The low over the Arctic in summer is a deep, cold-cored barotropic structure that is continually regenerated by thickness and vorticity advection associated with mature systems moving from northern Siberia via the Kara and Laptev seas. LeDrew (1988, 1989) uses the Q-vector formulation of the omega equation (Hoskins et al., 1978) to examine the contributions of differential advection (of vorticity and thickness combined) and diabatic heating to vertical velocity in ﬁve depression systems within the Polar Basin in June, July, and September 1979. A Q vector represents the rate of change of potential temperature gradient in the direction of low-level ageostrophic ﬂow and towards ascending air, assuming frictionless adiabatic motion (see Appendix 6.1). Q-vector convergence denotes cyclonic vorticity generation. Advection of cold (warm) air with the Q vectors implies frontogenesis (frontolysis). LeDrew ﬁnds much weaker vertical circulation at 850 mb within the Polar Basin than in North Atlantic cyclones. Advective processes contributed 38–53 percent of total 850 mb vertical circulation, latent heat 12–59 percent, sensible heat 28 percent to 51 percent and friction 4 percent to 42 percent. The ratio of diabatic heating to advective effects averaged 0.8 for trajectories over the central basin, 1.2 over the Chukchi–Beaufort seas and 1.4 over the Barents–Laptev seas. Further analysis by LeDrew et al. (1991) of the persistent low over the Canada Basin from August 15 to September 11 1988 (Figure 6.5) shows a mean vertical velocity of 0.8 cm s1 at 850 mb (based on ten days through the twenty-seven-day interval). Diabatic heating contributed 62 percent, advection 29 percent and friction 9 percent of the total vertical velocity. Surface heat ﬂuxes associated with areas of more open ice cover – forced in part by the cyclonic activity (Serreze et al., 1990) – may provide a

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Figure 6.6 A schematic model of atmosphere–surface coupling with cold low development over the Beaufort Sea in late summer. (From LeDrew and Barber, 1994)

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feedback to help maintain the cyclone. However, LeDrew et al. note that the calculations do not permit a ﬁrm conclusion to be reached. Ageostrophic cold air advection also is important. The cyclone initiation in mid-August may be related to cooling in the stratosphere and the circulation transition to a wintertime barotropic westerly vortex (Figure 6.6). Anticyclogenesis has received less attention except for that occurring in polar air (Curry, 1987) and blocking events. A study for the northern hemisphere identiﬁed 1,250 events during 1984 (Colucci and Davenport, 1987). Cases were deﬁned by a twenty-four-hour MSL pressure change averaging 2.7 mb with a closed isobar (8 mb spacing) appearing on two successive 12.00 UTC charts. For the western hemisphere, anticyclogenesis is concentrated over Alaska–western Canada, associated with cold air outbreaks, and over southeastern Canada, where cold anticyclones that have moved southeastward over snowcovered areas re-intensify. Zishka and Smith (1980), however, identify an area of winter anticyclogenesis over western Texas, Oklahoma, and Kansas in response to cold air advection in the rear of Colorado lee cyclones. These highs are usually shallow mobile systems. In summer a similar process operates farther north, over southern Alberta. Boyle and Bosart (1983) examine the transformation of a polar anticyclone over Alaska into a warm dynamic system off the east coast of the United States over a seven-day period in November 1969. The system is initially conﬁned to the layer below 850 mb, although vertical motion associated with the anticyclogenesis extends through the troposphere. The system ﬁrst moves southward towards the Gulf coast and then recurves northeastward. In the ﬁrst stages, upper-level vorticity advection and cold air advection

450 Synoptic and dynamic climatology 1

lead to subsidence downstream over the anticyclone. The anticyclone moves southward towards the region of maximum descent, forced by the cold advection. It is supported to the west by a jet streak maintaining a thermal gradient. In the later stages, warm advection in the lower middle troposphere west of the anticyclone forces an upper-level ridge.

6.3 Development of cyclones 6.3.1 Historical background Three ideas on cyclone development were current in the nineteenth century (Kutzbach, 1979; Bergeron, 1980; Namias, 1983). The American meteorologist J.P. Espy proposed that low-pressure centers were driven by rising warm air associated with latent heat release and sustained by lateral outﬂow aloft, a situation which resembles tropical storms. Julius von Hann objected to this “conventional hypothesis” on the grounds that mountain observatories showed the air to be warmer above anticyclones than above cyclones, as was later conﬁrmed by W.H. Dines and others. Von Hann suggested that cyclones and anticyclones derive their energy as eddies feeding off the zonal westerly current (c.f. L.F. Richardson’s dictum, p. 140). A third group, represented by H.W. Dove, R. FitzRoy, W. Blasius, and H. von Helmholtz, recognized the necessity for the juxtaposition of contrasting air masses in the formation of weather systems. Subsequently Napier Shaw and his associates identiﬁed the complementary spiral motions of air trajectories in cyclones and anticyclones. These last two concepts were incorporated in the frontal cyclone model of the Bergen school (Bjerknes, 1920; Bjerknes and Solberg, 1922), building on the dynamic concepts of Vilhelm Bjerknes and the three-dimensional synoptic analyses performed at Leipzig, 1913–17 (Friedman, 1989, p. 88; Eliassen, 1994). Using a dense station network in Norway and inferred upper air conditions (based on cloud motion and hydrometeors), the Bergen meteorologists showed that cloud and rain bands were associated with lines of wind convergence and also that temperature (or density) differences between air masses are concentrated at frontal discontinuities. The polar front theory of the life cycle of extratropical cyclones formulated by the Bergen school dominated synoptic meteorology from 1920 until the 1950s, although its formal adoption in the United States was slow (Namias, 1983; Newton and Newton, 1994). The principal idea of the theory is that cyclones form, mature, and decay along the polar front. The frontal boundary develops a small wave and incipient cyclonic circulation. Because the leading edge of the cold air in the rear of the system moves faster than the air in the warm sector, the cold front catches up with the warm front. The air in the warm sector is then lifted off the surface, forming an occlusion – a process identiﬁed in 1919 by Tor Bergeron (1980; Friedman, 1989, pp. 212–23). The dynamical mechanism involves the conversion of the potential energy stored in the atmospheric thermal gradients into kinetic energy. When occlusion occurs, the cyclone weakens as the low-pressure center becomes detached from the warmest air. Thus cyclonic shear across an initially quasistationary front leads to unstable growth of the incipient pressure perturbation, with the familiar frontal wave sequence evolving over a period of three to ﬁve days. As upper air observations became more numerous the important role of divergence in the upper troposphere on cyclogenesis was recognized by Scherhag (1934), Sutcliffe (1939), and Bjerknes and Holmboe (1944). The fall of pressure in a deepening surface low is made possible because air ascends in the low and diverges aloft. Such upperlevel divergence is typically located on the eastern limb of an upper trough in the westerlies, and so cyclogenesis is favored in that sector. The spatial pattern of convergence/ divergence in the upper troposphere is also strongly coupled with the distribution and structure of jetstreams (see section 4.3). Major theoretical advances occurred in the 1940s along two different lines of thought. We note ﬁrst the concept of baroclinic instability, formulated by Charney (1947) and

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Figure 6.7 Baroclinic instability model. (From Barry, 1967)

0 Eady (1949). It envisages that cyclones may form as a result of the breakdown of a zonal jetstream when the horizontal temperature gradient or the vertical wind shear reaches certain critical limits. Small perturbations can grow at the expense of potential energy in the mean motion when warm (cold) air undergoes a small upward (downward) displacement in air moving poleward (equatorward) ahead of (behind) an upper trough in the westerlies. Figure 6.7 illustrates this concept for (a) short and (b) medium-wavelength disturbances. In (a) the slope of the air motion exceeds that of the isentropic () surfaces and, since the rising air has lower than the subsiding air, the potential energy is increased and there is no wave growth. In medium wavelength systems (b), the potential energy is reduced by air of lower (higher) sinking (rising) and moving equatorward (poleward). The north–south slope of the air motion, as determined by Fleagle (1957, 1960), is:

0

=

( f /2HS2) 1 ( f 2 /2H 2S2)

where the slope of the isentropic () surfaces, ( f/ y), H the depth of the atmosphere (10 km), S static stability, and the wave number ( 2/L where L wavelength). The maximum conversion of potential to kinetic energy takes place where /2 and L 2H(2S/f )1/2. The maximum isentropic slopes are in mid-latitudes and, since /f increases equatorward, causing → , maximum baroclinic instability tends to occur climatologically in higher mid-latitudes. Theoretically there is a critical latitude, with baroclinic stability tending to prevail equatorward of 35°. Despite the simplicity of the Charney and Eady models, they demonstrated that synoptic disturbances with a horizontal scale of a few thousand kilometers can amplify with a doubling time of about twenty-four hours and propagate eastwards. The second line of advance in the 1940s was provided by the analysis of development in cyclones and anticyclones by Sutcliffe (1939). He proposed that surface development be diagnosed in terms of the difference in divergence/convergence within an air column, and he showed that development (D) occurs with ascending air and low-level stretching creating cyclonic vorticity (Hoskins, 1994):

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D = ·VU ·VL = p

∂2 ∂p2

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where VU and VL refer to the upper and lower-level wind velocities, and is the vertical motion in pressure units. Subsequently, Sutcliffe linked the horizontal divergence with vorticity considerations, following the ideas of Rossby et al. (1939). In particular, vertical motion is linked with the advection of vorticity by the thermal wind. Sutcliffe (1947) related development to the imbalance between changes in horizontal thermal gradients and the vertical difference in vorticity caused by differential advection with height. Thermal advection was analyzed via thickness charts (Sutcliffe and Forsdyke, 1950):

冤冢

冣冢

z zL z zL 1 ∂ [u L] = 2 u u v u ∂t f x y

冣冥

where zu zL denotes the difference in geopotential between an upper and lower isobaric level, or thickness; u– and v– are layer-average wind components and 2 is the Laplacian operator. The development theory (Sutcliffe, 1947) can be related to an approximate form of the omega equation for vertical motion, expressed in terms of vorticity and thermal advection (Hoskins et al., 1978), while Sutcliffe’s earlier work (1939) is consistent with the Q-vector formulation (Hoskins, 1994). 6.3.2 Modern views The traditional view of the frontal cyclone has been gradually modiﬁed. Ideas on the nature of occlusions have also undergone substantial change. Cold fronts propagate faster than warm ones, suggesting that one front overruns the other, but few studies of this have been made. Mass (1991) points out that there appears to be “a lack of consistent and well-deﬁned procedures for deﬁning fronts and for analysing surface synoptic charts.” Figure 6.8 illustrates the variety of analyses by specialist participants in a workshop on surface analysis (Uccellini et al., 1992). Analysts disagree on the type, location and even the existence of fronts. Moreover, analyzed fronts are not necessarily located where there are strong temperature gradients. Problems occur in the charting of shallow zones of temperature contrast that are related to topography (cold air damming and lee troughs) or to discontinuities in the surface type (coastal fronts; snow and ice boundaries on land or in the ocean) and difﬁculties also arise from the modiﬁcation of fronts by mountain barriers (Williams et al., 1992). The Bjerknes model was developed for North Atlantic systems. However, Petterssen and Smeybe (1971) showed that not all cyclones develop as frontal waves within a baroclinic zone. These Type A storms are prevalent over the North Atlantic Ocean and they evolve towards a classical occlusion (Petterssen et al., 1962). A second group (Type B) develops over North America, east of the Rocky Mountains, when a pre-existing upper trough advances over a zone of low-level warm advection that is weakly baroclinic. Initially there is strong upper-level vorticity advection on the forward side of the trough; this decreases as the system develops, while thermal advection increases when the storm transports cold air southward, enhancing the temperature gradient (Petterssen and Smeybe, 1971). Work by Locatelli et al. (1995) and Hobbs et al. (1996) provides a schematic model of cold-season frontogenesis east of the Rocky Mountains. The scheme involves an outﬂow of arctic air east of the mountains forming an arctic front and a dry lee trough, associated with dry air with low equivalent potential temperature (e ) crossing the mountains and overriding warm, moist, high e air from the Gulf of Mexico. Instability in the trough generates a rain band analogous to a warm front. The arctic air moves southward west of the trough or low center, causing lifting of warmer air but little precipitation. The ﬂow of low e air may represent an upper cold front advancing eastward over the trough and setting up a rain band along its leading edge, ahead of the trough. Type B systems are now thought to be widespread in their occurrence. Some polar lows resemble the Type B cyclones.

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Figure 6.8 Surface data analysis over North America for 21.00 UTC, February 13 1991, illustrating the variability of subjective analyses. The solid lines are fronts analyzed by participants in a professional workshop; troughs and squall lines are dashed. Heavy lines are the fronts or troughs from the National Meteorological Center analysis. Light lines are objectively analyzed isobars (4 mb). (From Uccellini et al., 1992)

Modiﬁcations of the Bergen model of frontal cyclone analysis since 1945 have followed two primary directions. The ﬁrst approach considers the synoptic analysis of frontal characteristics and the second treats the dynamical relationships involved in frontogenesis and frontolysis. For example, three-dimensional frontal analysis, involving the plotting of contours of frontal location at different isobaric levels (frontal contour analysis), by Canadian synopticians (Crocker, 1949; see also Palmén, 1951) and the associated threefront (four air-mass) model (Penner, 1955; Galloway, 1958) took account of the strong latitudinal temperature gradient in North America, especially in winter. This gradient commonly gives rise to cyclones developing simultaneously along the arctic and polar frontal zones with an associated double jetstream structure (McIntyre, 1958). A valuable aspect of this scheme was the recognition of the trough of warm air aloft (trowal) (Morris, 1972). However, the potential drawbacks of a rather rigid framework for analysis were also recognized (Longley, 1959). The complexity of frontal weather patterns, attributable in part to the vertical motion of air relative to a front, was identiﬁed in the 1930s by Bergeron (1937), but the terminology of anafronts (katafronts) with air ascending (descending) over the frontal surface proposed by Bergeron came into widespread use only in the 1950s and 1960s with the application of Doppler radar to detect such motions. This technology allowed conveyor belts (see Figure 6.9) to be mapped and incorporated into frontal models (Browning, 1990, 1994; Carlson, 1991, chapter 12). They are typically a few hundred kilometers wide and about a kilometer deep. The associated “slantwise” ascent/descent gives rise to characteristic distributions of cloud and precipitation

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Figure 6.9 Conveyor belts and precipitation in a developing frontal cyclone. The following features are represented. (a) Sea level pressure and fronts. The unshaded triangles indicate a cold front aloft. Between the two well marked cold fronts is a diffuse surface cold front indicated by a single triangle. The low is moving northeastward (arrow). (b) Cloud (stippled), showing a cloud head in the west and the polar front cloud band in the east. Precipitation is shown as follows. Solid lines in the cloud head, cross-hatched where convective (moderate–heavy); broken lines show warm conveyor belt (light) precipitation; broken cross-hatching: patchy, moderate mid-level convective precipitation; solid shading: narrow cold front bands of heavy rain. (c) Conveyor belts: W1, main warm conveyor belt (WCB). W2, lower part of WCB separates and rises in the upper cloud head. CCB, cold conveyor belt; difﬂuent ﬂow giving cloud head precipitation. J, upperlevel jet core; W1 and CCB westward ﬂow is an ageostrophic circulation at the jet exit. (d) Dry intrusion. Dry air descends from the upper troposphere upwind and rises towards the cyclone center, overrunning a shallow moist zone (SMZ in (c)), associated with W2, and gives rise to the dry slot (panel (b)). (From Browning, 1994)

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0 Figure 6.10 A schematic view of the life cycle of a marine frontal cyclone. (I) Incipient system. (II) Cold front fracture. (III) Bent-back warm front frontal T bone. (IV) Warm core seclusion. Upper sequence shows sea-level isobars, fronts, and satellite cloud signature (stippled). Lower sequence depicts warm (solid) and cold (dashed) air currents. (From Shapiro and Keyser, 1990)

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areas in frontal cyclones and also redistributes heat and moisture over long distances. More recently the combination of satellite and aircraft data has led to the identiﬁcation of split cold fronts south of the cyclone center and the back-bent warm front/T-bone pattern (Shapiro and Keyser, 1990; Shapiro and Grell, 1994). Figure 6.10 illustrates schematically a revised view of the life cycle of a marine frontal cyclone depicting these features. The process whereby relatively warm air is left behind in the center of circulation is termed “seclusion” by Shapiro. Fronts are associated with gradients of temperature, dew point, wind velocity, barometric tendency, and vertical motion. For example, a study of ﬁve cold fronts over Brittany, France, during the winter 1987–88, using rawinsonde and acoustic sound detection and ranging (SODAR) data, found that across the front there was a mean temperature drop of 2°C, a reduction of surface wind speed of about 6 m s1 and a 60° rotation in the direction of the wind (Leﬂoch and Amory-Mazaudier, 1998). A low-level jet averaging 27 m s1 between 600 and 1,000 m was also present over Brittany. Studies of frontal dynamics in the 1960s analyzed the ﬁeld properties of such parameters to develop objective, physically meaningful criteria for frontal deﬁnition. Potential temperature at 850 mb

456 Synoptic and dynamic climatology 1

was selected as a thermal parameter for frontal identiﬁcation by Renard and Clarke (1965). The divergence of the gradient of potential temperature is: 2 | | # n | | #n

where n is a unit vector in the direction of . The ﬁrst term on the right-hand side is the directional derivative of the magnitude of the gradient of potential temperature along its gradient. It is proportional to the horizontal shear of the thermal wind. The second term represents the tangential curvature of the potential isotherms. Renard and Clarke use the axis of maximum ||, which is coincident with a zero value of the ﬁrst term, as the locus of the baroclinic zone. Figure 6.11 illustrates, for a one-dimensional case, the meaning of the variation of the terms: X, | X| , and | X|#nx , respectively, with nx . Note that a zero value of the term | X|#nx corresponds to both maximum and minimum |X| (see Figure 6.11b and c). Maximum |X|, which may be of the order of 1 K km1, is an index of frontal intensity: the term |X|#nx is of the order of ±0.2 K (100 km)2. It must be noted that the unit vector in the direction of the gradient is ill deﬁned when | | is small or changes direction abruptly, as occurs in the conveyor belt zone ahead of a cold front (Hewson, 1998). Alternative approaches, based on other thermal characteristics, include that of HuberPock and Kress (1989), who use 850–500 mb thickness instead of 850 mb temperature. Their technique has been applied by Hoinka and Volkart (1992) and Zwatz-Meisinger and Mahringer (1990), but the fronts are still manually drawn. Steinacker (1992) uses 850 mb equivalent potential temperature and identiﬁes where | 2/ s2| is maximized, where s is a tangent to a streamline. Sanders (1999) proposes the use of potential temperature analysis in areas of complex terrain, in lieu of a surface temperature analysis. This allows non-frontal baroclinic zones to be distinguished. A front is identiﬁed where a wind shift

Figure 6.11 The relationship between (a) X , (b) |X | and (c) |X |·nx for the one-dimensional case where parameters vary with nx. (From Renard and Clarke, 1966)

Synoptic systems 457 11

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Figure 6.12 Above Plan view of idealized contours of a thermodynamic variable in the vicinity of a straight cold front with no along-front thermal gradient. Arrows represent vector differentials of , | , and ||||. Below Graphical plot showing the variation of | / x| and its scalar differentials in the cross-front direction. Points on the x axis denote where 3 / x3 0. Lines A and B delineate the baroclinic zone, and line B (and the ringed dot) shows the front. The term (m) is a positive fractional number (m) of grid lengths (). (From Hewson, 1998)

coincides with the warm edge of a baroclinic zone. Sanders also notes that most cold fronts lack a pronounced temperature contrast and advocates their mapping as baroclinic troughs. Hewson (1998) developed a procedure to plot fronts objectively based on the concepts discussed above. The following elements are used: a line adjacent to a baroclinic zone across which the magnitude of the thermal gradient changes most abruptly; the rate of change of the thermal gradient across a front exceeds a speciﬁed threshold; and the thermal gradient in the adjacent baroclinic zone also exceeds a threshold value. Figure 6.12 illustrates schematically the way in which the scalar differential values of a thermodynamic variable vary across a simple cold front where there is no along-front thermal gradient.

458 Synoptic and dynamic climatology 1

The baroclinic zone is deﬁned by the vertical lines A and B in the lower ﬁgure and line B demarcates the front. The Laplacian of , or 2/ x2, has a minimum value at the front and here also the gradient of the Laplacian, or the function 3/ x3 0. Hewson also develops additional diagnostics for cases of straight fronts with an along-front thermal gradient, and for curved fronts which may or may not have an along-front gradient. A frontal zone can also be identiﬁed from the vertical rate of change of geostrophic relative vorticity (g), which is proportional to the Laplacian (2T) (Kirk, 1966b). For quasi-geostrophic vorticity: f

∂g ∂p

R = 2T p

where R the gas constant for dry air, f the Coriolis parameter, and 2T =

∂2T ∂2T ∂x2 ∂y2

This Laplacian term is determined by the spacing and curvature of the isobars (Kirk, 1970). A corresponding approach based on the divergence of the gradient of potential temperature was developed by Clarke and Renard (1966). This term (2) can be broken down into components that are proportional to the horizontal shear of the thermal wind and the tangential curvature of the potential isotherms. Creswick (1967) extended their work using wet-bulb potential temperature. Much recent attention has focused on the use of potential vorticity analysis as a diagnostic tool (Hoskins et al., 1985); Thorpe (1990) illustrates its application to frontal cyclone study. Other workers addressed the nature of front–jet coupling (Bleeker, 1958), and a modern example is given in Figure 6.13 showing

Figure 6.13 Conceptual model showing the ageostrophic circulations associated with low- and upper-level jets in the vicinity of a cold front. (Sortais et al., 1993)

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the transverse, indirect, ageostrophic circulation associated with upper and low-level jets at a cold front (Sortais et al., 1993). Yet a further approach to the dynamics of baroclinic systems makes use of semi-geostrophic theory (Hoskins, 1971; Hoskins and West, 1979). 6.3.3 Cyclogenesis

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The key aspects of cyclogenesis involve: (1) horizontal divergence of air in the upper troposphere, in excess of low-level convergence, thereby permitting surface pressures to decrease; (2) the existence of a wavelength of maximum ampliﬁcation for typical frontal perturbations; (3) a frontal zone of strong baroclinicity and vertical wind shear. The role of upper-level divergence in cyclonic development was ﬁrst formulated by M. Margules and later extended by J. Bjerknes and by R.C. Sutcliffe (Bjerknes and Holmboe, 1944; Palmén and Newton, 1969, p. 134 ff.). Bjerknes’s “pressure-tendency” equation (from the hydrostatic and continuity equations) states that the pressure change with time can be expressed:

冢冣

∂p = g ∂t

0

冕

∞

0

H· V dz g

冕

∞

V ·H dz gw

0

where the terms on the right-hand side are: ﬁrst, the integrated horizontal divergence of the wind velocity (V), second, the advection of density (), and, third, the vertical motion. In practice, the ﬁrst term is about an order of magnitude larger than the second one, and the third term is of similar magnitude to the ﬁrst but opposite in sign. A surface pressure tendency of ±1 mb hr1 corresponds to an average divergence of ±0.3 106 s1

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Figure 6.14 Schematic illustration of the effects of streamline curvature in upper tropospheric ﬂow on cyclone development in the lower troposphere. (a) Ageostrophic wind components along the streamline at upper levels cause convergence (divergence) as a result of deceleration (acceleration) from supergeostrophic ﬂow in the ridges to subgeostrophic ﬂow in the trough. (b) Corresponding maximum (minimum) centers of relative cyclonic (anticyclonic) vorticity and associated regions of negative, or anticyclonic, vorticity (NVA) and positive, or cyclonic, vorticity advection (PVA) for an upper level wave. (From Shapiro and Kennedy, 1981; Kocin and Uccellini, 1990)

460 Synoptic and dynamic climatology 1

whereas measured values of >105 s1 are typical for individual levels. The calculation of surface pressure changes from observed winds is generally unreliable, owing to the usual compensation of convergence/divergence between the upper and lower troposphere and “noise” in wind soundings due to small-scale eddies. Pedder (1981) illustrates procedures to adjust these kinematic estimates. In spite of these practical problems of estimating divergence, the basic concept is useful in understanding that surface cyclogenesis is favored below the eastern limb of an upper level trough (Bjerknes and Holmboe, 1944). The upper ﬂow accelerates in this region as the air passes from cyclonic curvature (where the gradient wind is subgeostrophic) towards anticyclonic curvature (where the gradient wind is supergeostrophic). This acceleration causes upper-level divergence and hence rising motion and a surface pressure fall (Figure 6.14). In this way we see that cyclone development is related to the long wave structure and, hence to Rossby’s treatment of absolute vorticity conservation. The vorticity equation can be written: d( f ) = ( f ) (u·V) dt where relative vorticity (positive cyclonic) and H#V is the horizontal divergence of velocity. The role of patterns of vorticity advection and thermal advection in the upper troposphere in cyclogenesis was formulated by Sutcliffe (1947; see Hoskins, 1994) and Petterssen (1955; Petterssen et al., 1962), in particular. In synoptic terms, the upper-level ﬂow locally accelerates towards a jetstream maximum (core) and decelerates ahead of it (Figure 6.14). The entrance region of a jet streak is associated with a transverse ageostrophic component directed toward the cyclonic-shear side as shown in Figure 4.11, with the converse in the exit region. Case studies and composite average patterns of jet maxima conﬁrm the tendency for rising (sinking) motion on the anticyclonic (cyclonic) side of the jet entrance and the converse in the exit region. Consequently, this has an important inﬂuence on the associated patterns of vertical motion, cloud, and precipitation distribution. These relationships are apparent even in a mean sense, as shown by studies of Koteswaram (1958) and others for the Tropical Easterly Jetstream over southern Asia and West Africa. A perspective view of the life cycles of extratropical frontal cyclones in the context of planetary and synoptic waves is shown in Figure 6.15. A 200 mb subtropical wave (m~3) and jetstream and a 300 mb polar wave (m ~ 6) and jetstream each have suspended potential vorticity anomalies. The western system with a trailing cold front and indeterminate warm front is within the meridional anticyclonic shear south of the polar jet; the T-bone bent-back warm-front occlusion is below the vertically aligned jets and potential vorticity anomalies and is characterized by non-shear; the eastern system is a classical Norwegian warm-front occlusion evolving north of the subtropical jetstream within meridional cyclonic shear. The curvature of the upper contours modiﬁes the distribution of divergence/convergence in the jet entrance and exit zones, as illustrated in Figure 4.11. Rapid cyclogenesis events have received considerable attention over the last decade or so, particularly owing to events like the President’s Day storm, February 18–19 1979, and the QE II storm, September 10–11 1978 (Bosart, 1981; Gyakum, 1983). Sanders and Gyakum (1980) coined the term “bomb” to refer to storms that deepen explosively with pressure falls of at least 24 mb/24 hr (1 Bergeron) and up to 60 mb/24 hr. It is worth noting that, in contrast, anticyclogenesis is considered rapid if pressure rises by 5 mb/24 hr and maximum rates are only about 13 mb/24 hr (Colucci and Davenport, 1987). Leaving aside the mesoscale polar lows, rapid deepening occurs primarily over warm ocean currents in the North Atlantic and North Paciﬁc (Sanders, 1986; Uccellini, 1990). Figure 6.16 illustrates this distribution for 1976–82. However, rapid cyclogenesis is not limited to the oceans or coastal areas, such as the east coasts of the United States and Australia; it can occur

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Figure 6.15 Frontal cyclone life cycles in relation to planetary and synoptic-scale waves. Upper plane (light shading): 200 mb subtropical wave No. ~3 and jetstream (open arrow) with associated, suspended PV anomalies. Middle plane (heavy shading): 300 mb polar wave No. ~6 and polar jetstream (open arrow) and associated, suspended PV anomalies. Lower plane: three characteristic surface-level frontal cyclones; open frontal symbols indicate occlusion aloft. Left cyclone: a cause of anticyclonic shear south of the polar front jetstream with weak trailing cold front. Middle cyclone: non-shear case beneath vertically aligned polar and subtropical jetstreams featuring T-bone polar occlusion and bent-back warm frontal seclusion. Right cyclone: a case of cyclonic shear north of the subtropical jetstream representing the Norwegian model with backbent polar warm frontal occlusion. (From Shapiro and Grell, 1994)

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in the cold seasons over the east-central United States, for example. A key element is a “preconditioned” lower troposphere which features baroclinic development, cyclonic vorticity, and reduced static stability (Bosart, 1994). The development of these systems involves a rather short interval (twenty-four to thirtysix hours) of extremely rapid deepening which tends to begin when the exit region of an upper tropospheric jet moves over an area of difﬂuent ﬂow downstream of a trough axis (Uccellini, 1990). The contributions of thermal advection, diabatic heating, particularly from latent heat release, and the reduction in static stability due to low-level air motion over a warm ocean surface appear to vary in different cases (Kocin and Uccellini, 1990). However, there are certainly non-linear synergistic interactions between them, according to Uccellini. One of the most characteristic elements of rapidly developing cyclones is an asymmetric pattern of clouds and precipitation situated on the poleward side of the surface low. This feature is attributable to a rather cold and moist easterly ﬂow moving through the storm, with an ascending motion, and to a warm southerly ﬂow that rises over the warm front. These “conveyor belts” are illustrated in Figure 6.9. A related, explicitly Lagrangian, analysis has been carried out by Wernli and Davies (1997); they trace the movement of potential vorticity anomalies, as well as changes in speciﬁc humidity

462 Synoptic and dynamic climatology 1

Figure 6.16 Distribution of maximum deepening positions of explosive cyclones, 1976–82. (From Roebber, 1984)

and potential temperature in relation to ensembles of air parcel trajectories. Typically, banded mesoscale cloud and precipitation structures are embedded within the conveyor belts. The origin of such structures remains under discussion; suggestions include boundary-layer friction effects, gravity waves, and conditional symmetric (or inertial) instability (CSI), resulting from excess buoyancy acquired when air parcels are displaced along surfaces of constant absolute momentum (Bennetts and Hoskins, 1979). Small-scale processes associated with precipitation as well as the evaporation/sublimation and melting of raindrops and snowﬂakes can also modify frontal cloud and rain systems and may help to initiate and maintain multilayer cloud structures (Stewart et al., 1998). Recent studies of cyclogenesis in the western North Atlantic indicate cases that depart signiﬁcantly from the Norwegian model. Neiman and Shapiro (1993) analyse a winter storm that deepened 60 mb in twenty-four hours over 20°C Gulf Stream waters where the turbulent ﬂuxes approach 3,000 W m2 compared with mean January values of up to 400–500 W m2. They report a relative westward development of the warm front in the polar air, with a bent-back occlusion form, and a warm core frontal seclusion within the post-cold front cold airstream (see Figure 6.10). Different types of rapid maritime cyclogenesis can be identiﬁed from satellite imagery. This approach is especially valuable in forecasting over oceanic data sparse areas (Bader et al., 1995). Building on the information from the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA), Evans et al. (1994) examine imagery for ﬁfty rapid cyclogenisis events over the western North Atlantic during the 1970s and 1980s. Four types of cyclogenesis are distinguished and these are shown schematically in Figure 6.17. The “emerging cloud head” forms on the poleward side of a cirriform cloud band along the polar front. The development takes place in the left exit zone of the jetstream, downstream of the upper trough. As Figure 6.17a shows, there are two jet streaks and the cloud head forms in association with streamline difﬂuence and the phase adjustment of the wave trough and equatorward jetstream.

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Figure 6.17 Schematic diagram of cloud signatures for four types of rapid maritime cyclogenesis, showing their evolution. (a) Emerging cloud head. (b) Comma cloud. (c) Left exit (of jetstream). (d) Instant occlusion. The pairs of diagrams indicate early (left) and twelve to twenty-four hours later (right) stages of evolution. (From Evans et al. 1994)

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464 Synoptic and dynamic climatology 1

The cloud head signals rapid surface deepening, with the surface low situated near the equatorward edge of the cloud head. The comma cloud (Figure 6.17b) has no interaction with a polar-front cloud band. Development involves the expansion of a cloud cluster into a “baroclinic leaf,” typically in the left exit region of a jet streak associated with a difﬂuent upper-level trough. The longitudinal axis of the leaf tends to rotate cyclonically as cyclogenesis occurs and the cloud system assumes a comma shape. A surface low forms on the southwest edge of the comma cloud. In a variant of left-exit cyclogenesis, a baroclinic leaf of relatively warm stratiform cloud forms poleward of an existing polar front cloud band with colder, higher cloud tops (Figure 6.17c). The leaf, showing cyclonic rotation, merges with the main cloud band and a surface low develops below the intersection of the leaf and the cloud band. The instant occlusion category (Figure 6.17d) involves the merging of a “cold-air cloud cluster,” characterized by open cellular convection, with a polar front cloud band in a region of conﬂuent upper ﬂow. Rapid deepening of the surface low takes place on the equatorward edge of the cold-air cloud cluster. Evans et al. ﬁnd that in the instant occlusion the longitudinal axis of the cloud head is typically parallel to the upper tropospheric ﬂow, whereas in the left-exit category it is oriented perpendicular to the upper ﬂow direction. A study of twelve frontal cyclones in the North Paciﬁc shows that they doubled in size over a four-day period (Grotjahn et al., 1999). The lows were tracked by a wavelet analysis. This ﬁnding appears to contradict the assumption of some theories of cyclone development. It remains to be determined whether this result is a broadly representative one and whether it holds true in the upper troposphere. Secondary cyclogenesis within a primary frontal cyclone is a common phenomenon. During the ﬁeld campaign FRONTS 92, March–May 1992, one wave per day was observed over the North Atlantic, of which half showed deepening. Secondary cyclones may form in various locations within a primary system, as illustrated in Figure 6.18, although the processes involved in their formation are not well understood. Parker (1998) notes that during FRONTS 92 cold front waves were the most common type, whereas col waves were most likely to undergo development. Secondary lows are commonly shallow systems, implying that boundary layer processes play an important role. The mechanisms involved in secondary cyclogenesis seem to differ qualitatively from those in the primary systems. Proposed theoretical models invoke either a dry or a moist frontal instability mechanism. Dry instability can occur within a warm band ahead of a cold front or in a narrow, low-level zone of positive potential vorticity (PV). The width of the zone determines the wavelength and growth rate – narrower zones giving shorter, faster-growing waves (Parker, 1998). The Charney–Stern instability criterion (Charney and Stern, 1962) requires that the basic state PV gradient and the equivalent PV gradients, representing the boundary gradients, have regions of opposite sign for normal-mode dry instability of quasi-geostrophic systems. Wave growth ﬁrst involves barotropic conversions, with energy acquired from the kinetic energy of the basic state (the wind shear across a front, for example); then, if the perturbation has enough vertical depth, baroclinic growth occurs. Laboratory studies with a rotating two-layer tank of water suggest that, given a shallow depth of buoyant ﬂuid layer relative to the total ﬂuid depth, waves grow mainly through barotropic instability for a large Richardson number (Ri), which is a measure of the ratio of the PE of the basic state to its kinetic energy1 (Grifﬁths and Linden, 1981). For small Ri and a large depth of buoyant ﬂuid, baroclinic processes are dominant. The advection of an upper-level PV anomaly over a surface front can also trigger secondary cyclogenesis (Thorncroft and Hoskins, 1990). Moist instability may develop when latent heat release generates an unstable PV zone. Latent heat release by deep convection is of considerable importance in small-scale systems. The intensity of the October 15–16 1987 storm in southern England may have been a result of such heating, according to Hoskins and Berrisford (1988). However, many details remain to be resolved. It is not certain whether frontal waves are equally common at all scales, or whether there

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Figure 6.18 Schematic illustration of four locations within a parent (primary) frontal cyclone where secondary cyclogenesis may occur. (From Parker, 1998)

is any scale dependence of the dynamical processes and wave structure. For an idealized front separating air masses of different density, Orlanski (1968) showed that unstable modes are possible for all values of the along-front wave number.

6.4 Storm tracks 6.4.1 Climatology 0

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The conventional Lagrangian approach to deﬁning storm tracks involves tracing the movement of low pressure centers. Such manual investigations of cyclone tracks in midlatitudes began when synoptic weather maps were systematically prepared in the 1850s and 1860s. Early studies were performed by E. Loomis (1874) for North America, H. Mohn (1870) for Norway, V. Köppen (1880) and J. van Bebber (1891) for Europe, and N.A. Rykachev (1896) for Europe, including European Russia. Monthly maps of cyclone paths over the United States began to be published in Monthly Weather Review in 1875 and for the northern hemisphere four years later. Loomis (1885) was the ﬁrst to assemble information on cyclone paths over the northern hemisphere, but a comprehensive analysis was possible only in the mid-twentieth century (Petterssen, 1950; Klein, 1957). Petterssen drew attention to the importance of the zones where there is a high rate of alternation between high and low-pressure centers, which he termed pressure ducts. Recently, tracking algorithms have been developed for digital pressure data (Murray and Simmonds, 1991; Jones and Simmonds, 1993; Serreze et al., 1993). The main results of these studies are now summarized. For winter in the northern hemisphere (Figure 6.19): 1

0 2 11

Major tracks extend from the east coasts of the northern hemisphere continents northeastward across the oceans: in the North Atlantic, systems either turn northward into Bafﬁn Bay or, more frequently, continue northeastward to Iceland and the Norwegian–Barents Sea; in the North Paciﬁc, systems move from eastern Asia towards the Gulf of Alaska. Cyclones form or redevelop east of the Rocky Mountains in Alberta and Colorado and move eastward towards the Great Lakes and Newfoundland before turning northward towards Greenland and Iceland.

466 Synoptic and dynamic climatology 1

Figure 6.19 Cyclone tracks in the northern hemisphere (a) January, (b) July, 1958–77. Solid (dashed) lines indicate primary (secondary) tracks; main cyclogenesis areas are stippled. (From Whittaker and Horn, 1984)

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A Lagrangian climatology of North Atlantic storm tracks illustrates a further novel methodology (Blender et al., 1997). Cyclonic minima, deﬁned from ECMWF 1,000 mb height data at T106 resolution (~ 1.1° 1.1°) over 3 3 grid points, were tracked at six-hour intervals for winters 1990–94. Cluster analysis of the track data deﬁned three groups of storms that are persistent for at least three days – quasi-stationary (representing 56 percent of the total), northeastward-moving (27 percent), and eastward-moving (16 percent). Those moving northeastward from the east coast of North America about 40°–50°N toward the Norwegian Sea have a clear life cycle in terms of height anomaly and height gradient, whereas the zonal group has only a weak cycle, and the stationary systems none. Analysis of the zonal (x), meridional (y), and total displacement over time (t) for each group demonstrates that mean-squared displacements of the cyclones follow a power law scaling: dx2(t) dy2(t) ≈ t where is about 1.6 for the traveling systems, in line with other scaling analyses of geophysical ﬂows, and 1.0 for stationary systems, indicative of random walk type of behavior. The persistence of a northeastward storm track regime averages about ﬁve days (three to eight-day range), while the zonal regime has a slightly shorter duration. In the southern hemisphere, in contrast to the northern, storm tracks are virtually circumglobal, with little seasonal variability (Figure 6.20). The track density in winter is a maximum between 50°S and 60°S in the South Atlantic and South Indian oceans and south of 60° in the South Paciﬁc, with a secondary maximum near 40°S across the Paciﬁc, according to Sinclair (1997). This analysis for winters 1980–94 shows the frequency of centers per 5° latitude circle per month and translation vectors. The maxima shown are in higher latitudes than in earlier studies by the same author, where the grid spacing favored detection at lower latitudes. Cyclogenesis is most frequent downstream of the

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Figure 6.20 Cyclone track density in the southern hemisphere for winters (April–September) 1980–94 (contours show one center per 5° latitude circle per month) and average translation vector (From Sinclair, 1997)

468 Synoptic and dynamic climatology 1

east coast of South America and southeast of South Africa, extending south of Australia in a band into the South Paciﬁc around 55°S. A weakness of such analyses is the fact that changes in the intensity of the system and its rate of movement have to be taken into account independently. An alternative framework for the diagnostic analysis of the atmospheric circulation uses the analysis of the variance of the geopotential height ﬁeld. By high-pass ﬁltering of the data to extract variance in the two to six-day range, the behavior of positive/negative height anomalies can be examined. These are observed to propagate along zonally oriented wave guides. There are close overall similarities between the two sets of patterns. Differences between them are caused by zonal variations in the climatological mean ﬂow, which may displace the cyclone (anticyclone) relative to the corresponding anomaly center (Wallace et al., 1988). For example, as an area of negative (positive) height anomaly moves eastward from east Asia it enters a region where the mean 1,000 mb height gradient becomes more strongly southward directed. Hence the cyclone (anticyclone) center is displaced northward (southward) of the baroclinic wave guide. Figure 6.21a shows the divergent movement of cyclone

(a)

(b) Figure 6.21 Storm tracks determined by traditional methods (a) from surface weather maps and (b) from high-pass ﬁltered 1,000 mb height data for two selected locations (40°N, 70°W and 35°N, 135°E) on the major storm tracks in the northern hemisphere. (a) shows paths of cyclones (dashed arrows) and anticyclones (light solid arrows), also contours of the mean winter 1,000 mb heights (30 m intervals). (b) depicts paths of negative (dashed arrows) and positive (light solid arrows) height anomalies, also contours of the mean winter 700 mb height ﬁeld (60 m intervals). Both diagrams show the movement of high-pass ﬁltered anomalies inferred from lag-correlation maps (heavy arrows). (Wallace et al., 1988)

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Figure 6.22 Schematic relationships between the southern hemisphere mean jetstream (heavy solid arrows), storm track (broad stippled arrow), and high-frequency eddy statistics. Maxima in the various quantities are shown against the latitude scale. The double broken lines denote the trough/ridge axes, the temperature perturbations (cold/warm) are indicated by the bold dashed line, and the hatched zone demarcates cloud cover. (Trenberth, 1991)

and anticyclone centers, whereas the positive/negative height anomaly tracks are almost identical. In the southern hemisphere, high-pass ﬁltering (two to eight-day range) of 300 mb height ﬁelds for 1979–89 shows a highly zonal storm track from 45°W eastwards to 150°W, centered about 50°S in January (austral summer), with a maximum concentration over the South Indian Ocean. In July there is a more asymmetric pattern with a primary track from the South Atlantic through the Indian Ocean around 45°–50°S, curving poleward to 65°S at 160°W. There is no pronounced equatorward displacement of the storm track in winter, as occurs in the northern hemisphere, and the occurrence of maximum mid-latitude meridional temperature gradients during austral summer determines the degree of storm track activity and the tendency to zonal symmetry. The observed location of the primary storm track just downstream and poleward of the polar jetstream maxima (Trenberth, 1991) is accounted for by linear baroclinic theory for the observed basic state of the atmosphere in the southern hemisphere, according to Frederiksen (1985). However, James and Anderson (1984) emphasize the role of moisture entrainment into the low-level westerlies over the mid-latitudes of the South Atlantic, downstream of the source in the Amazon basin, as responsible for the large increase there of transient eddy activity. The relation between the mean jetstream, the storm track, and the associated highfrequency eddy statistics for the zonally symmetric circulation in the southern hemisphere is illustrated schematically in Figure 6.22. Maximum height variance (z′2), indicating a high rate of alternation, is located along the storm track, whereas perturbations of the vorticity (′2) are greatest just equatorward of the track as a result of the variation of the Coriolis parameter and consistent with the geostrophic relationship. Accordingly, perturbations of the meridional wind (v′) are displaced correspondingly, but zonal wind perturbations (u′) have maxima north and south of the storm track. The perturbations are elongated meridionally, thusv′2 <u′2, and have a characteristic eastward-bowed shape of trough and ridge axes. This generates momentum convergence from eddies into the storm track  (v′′ > 0). The perturbations of temperature, T ′, are greatest at low levels and are highly correlated with the east–west variations in v′. Also, maximum perturbations

470 Synoptic and dynamic climatology 1

of moisture, q′ and vertical velocity, ′, are closely related and are located in lower latitudes in association with the patterns of v′ and T ′. It is noteworthy that the positive/negative height anomalies identiﬁed by high-pass ﬁltering of 1,000 mb height anomaly data in the northern hemisphere display the characteristics of ﬁnite-amplitude baroclinic waves: over the oceans the waves are elongated in the east–west direction, have a mean wavelength of 4,000 km, tilt westward with height, and show a mean eastward phase propagation of 12–15 m s1 (Wallace et al., 1988). However, there are signiﬁcant differences east of the Rocky Mountains and the Tibetan Plateau. The height anomalies move eastward following distinct wave guides which are associated with maximum variance of the height ﬁelds. In contrast, cyclone (anticyclone) paths in the northern hemisphere tend to be orientated southwest–northeast (northwest–southeast), as illustrated in Figure 6.21. This is attributable to the fact that as baroclinic waves move eastward they encounter changes in the mean basic state. It is well known that, on a day-to-day basis, cyclones tend to be steered by the mean tropospheric ﬂow pattern in which they are embedded. Nevertheless, a cyclone may itself modify the steering pattern through thickness and vorticity advection as it approaches maturity (Palmén and Newton, 1969, chapter 11). On the time scale of weeks, steering patterns are greatly inﬂuenced by the thermodynamic effects of heat sources (sinks) which act to generate cyclonic (anticyclonic) vorticity in preferred locations. The long wave structure may be reinforced or damped by such heating and it is this pattern that determines the overall steering mode for intervals of perhaps two to four weeks. Wave-guides themselves can be deﬁned in several different ways (Wallace et al., 1988). First, as described above, they are identiﬁed as regions of strong variability in the highfrequency height anomaly ﬁelds (Blackmon, 1976). If the standard deviation values of geopotential height are converted into geopotential stream function, &, by using an f 1 weighting of the geopotential height, the maximum variability is shifted about 5° equatorward in closer agreement with results obtained using vorticity and kinetic energy. Second, bands of strong teleconnectivity in the high-pass ﬁltered data, determined from composite maps, show wave-like ﬂuctuations. Third, using lag correlation maps, the phase propagation of the high-frequency ﬂuctuations shows vectors parallel to the wave guides. Figure 6.23 presents an idealized representation of 1,000 and 500 mb wave guides for winter conditions in the northern hemisphere, showing consistency between the three methods. The traditional mapping of cyclone/anticyclone tracks yields information relevant to determinations of the wind ﬁeld and the sequence of weather conditions over given locations which is appropriate for many synoptic climatological purposes. However, it is the combined effect of the height anomalies in all frequency bands that make up the observed circulation pattern at a given time. As pointed out by Trenberth (1991), cyclonic vorticity advection and associated bad weather arise from the advent of a negative height anomaly or the departure of a positive height anomaly. 6.4.2 Processes The forcing and maintenance of storm tracks clearly merits explanation. There are two basic hypotheses concerning the development of storm tracks. One considers that the meridional temperature gradients formed by land–sea contrasts induce planetary wave structures through heating and orographic effects that are modiﬁed by transient inﬂuences. The second idea involves a self-organizing mechanism whereby eddies feed back onto the time-mean ﬂow. In this view, the storm track ends downstream through destruction of the eddy energy. Recently it has been demonstrated that the statistics of extratropical synoptic eddies can be derived from the assumption that they are stochastically forced disturbances evolving on a baroclinically stable background ﬂow (Farrell and Ioannou, 1993). Further, Whitaker and Sardeshmukh (1998) deduce the observed

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Figure 6.23 Idealized representation of northern hemisphere wave guides in winter, based on high-pass ﬁltered height ﬁelds. (a) 1,000 mb. The axes of high teleconnectivity having wave-like high-frequency ﬂuctuations are denoted by heavy arrows (dashed where less pronounced); the contours are the 50 m (inner) and 40 m (outer) standard deviations and the arrows (scale at lower right) are 1,000 mb phase propagation vectors. (b) Corresponding 500 mb patterns. The contours are 60 m (inner) and 50 m (outer) standard deviations. (Wallace et al., 1988)

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wintertime statistics of the zonally varying synoptic eddies that are associated with the observed zonally varying background ﬂows. They use a two-level hemispheric quasigeostrophic model, linearized about the observed mean ﬂow (for 400 and 800 mb winds, 1982–95), and forced by Gaussian white noise. The synoptic eddy covariance is linked with the spatial structure of the background ﬂow and with the covariance of the stochastic forcing by a ﬂuctuation–dissipation relationship; this relationship implies that the tendency of eddies to decay is balanced by forcing. The model reproduces most major features of the climatological winter storm tracks over the North Paciﬁc and North Atlantic as well as some aspects of their seasonal cycle and interannual variability. Using a similar modeling approach, Whitaker and Dole (1995) examine the sensitivity of storm track organization to zonally varying large-scale ﬂow. They identify two competing processes that are associated with the locations of a local baroclinicity maximum and a horizontal deformation minimum. If the equilibrium state comprises a zonally symmetric temperature ﬁeld and a barotropic stationary wave, the storm track is just downstream of a minimum in horizontal deformation in the upper jet entrance zone. However, as zonal variations in baroclinicity increase, the storm track is displaced to the jet exit region just downstream of a baroclinicity maximum. With ﬂows intermediate between these two cases, there are storm track maxima in both the jet entrance and exit zones. Midlatitude cyclones, at least off the east coasts of Asia and North America, develop and intensify primarily through baroclinic instability associated with diabatic heating. Hoskins and Valdes (1990) show that the major North Atlantic and North Paciﬁc tracks, identiﬁed using high-pass ﬁltered 250 mb height data for winters 1979–84, are centered somewhat eastward and poleward of the regions of maximum column-averaged diabatic heating. Figure 6.24 shows column-averaged mean values of 50 W m2 or more over the western oceans. This heating is mainly attributable to sensible heat ﬂuxes in these locations during outﬂows of cold continental air, supplemented by latent heat released in frontal cloud systems. Hoskins and Valdes show that the storm tracks are characterized by a baroclinic instability parameter due to Eady (1949); the maximum growth rate is

BI (day1) = 0.31 f

| |

V N 1 z

where the Brunt–Väisäla frequency: N (g )1/2/( z) is the static stability parameter (N 102 s1). Maxima of BI exceeding 0.6 day1 at the 780 mb level are found over the western North Paciﬁc and Atlantic oceans, implying ampliﬁcation of systems by factors of 2–3 day1. Figure 6.24 also shows poleward and vertical heat ﬂuxes at 700 mb located at the upstream ends of the storm tracks, with baroclinicity minima at the eastern ends of the tracks. The horizontal eddy transports of heat in mid-latitude storms act to reduce baroclinicity and therefore storm tracks might be expected to shift in time and space as systems move through an area, yet this is not observed because vorticity ﬂuxes help to offset the effect. Rather, the storm tracks tend to be self-maintaining as a result of the diabatic heating patterns primarily caused by the storm tracks. The vectors of the E ﬂux ( v′2u′2 ,   u′v′ ) shown in Figure 6.24 diverge from the storm tracks, indicating a tendency for cyclonic (anticyclonic) circulation on the poleward (equatorward) ﬂanks, which serves to force the mean westerly ﬂow by counteracting the destructive effects of the eddy heat ﬂuxes on the baroclinicity. Orlanski (1998) conﬁrms the increase in the barotropic component of the zonal jet due to the second term of the E ﬂux (Figure 6.25a). He also notes that the ﬁrst term displays a quadruple pattern of vorticity forcing, with cyclonic (anticyclonic) forcing located to the northwest and southeast (southwest and northeast) of the maximum in the  ( v′2 u′2) pattern. The quadruple pattern is also more or less in phase with the trough–ridge system in the stationary ﬂow (Figure 6.25b). The combined effect of the two terms of E is to tilt the storm track axis into a southwest–northeast orientation (Figure 6.25c). Hoskins and Valdes use a linear stationary wave model, with representa-

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Figure 6.24 Winter storm tracks in the northern hemisphere for December–February 1979–84, based on the two to six-day variance of the 250 mb height ﬁelds (thin contours at 15 m intervals) and the Eliassen–Palm ﬂux vectors shown by arrows (see text). Also shown are the 700 mb horizontal temperature ﬂux  (v′T  ′) (thick dashed contour, 10 K ( ′T ′) (thick dotted contour, 0.2 K pa m s1), the 700 mb vertical temperature ﬂux  s1), and the column mean diabatic heating (thick solid contour, 50 W m2). (Hoskins and Valdes, 1990)

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tive forcing in the storm track regions, to show that the mean diabatic heating off the east coasts of the northern continents in winter provides the necessary environment for storm track maintenance, overriding the thermal effects of the eddies. Nevertheless, the low-level winds that arise as a result of cyclone passages set up wind stresses that help to strengthen the Gulf Stream and Kuroshio currents, thereby in turn providing the initial diabatic heating and baroclinicity for the atmosphere. There are some unusual features in the seasonal occurrence of baroclinic wave activity over the northern hemisphere. Nakamura (1992) ﬁnds a strong midwinter peak in the frequency of 250 mb height variability under six days over the North Atlantic (70°–30°W), whereas over the North Paciﬁc (160°E–160°W) there are maxima in November and March–April (Figure 6.26). However, the double peak is only weakly evident in the middle troposphere and barely identiﬁable at the surface. There is also a larger seasonal shift in the latitude of storm activity in the Paciﬁc than in the Atlantic. Nakamura reports a positive correlation between jetstream strength and baroclinic wave activity for winds up to about 45 m s1. At higher speeds, which occur in midwinter over the western North Paciﬁc, the correlation reverses. It seems that barotropic feedback from the baroclinic waves is weakened when the upper tropospheric westerlies are very strong (> 50 m s1). Stronger winds cause the phase speed of the waves to increase, but the steering level

474 Synoptic and dynamic climatology 1

Figure 6.25 Schematic illustration of the correlation velocity patterns, the induced circulation and vorticity tendency. (a) The meridional ﬂux of momentum, showing a dipole circulation. (b) The variance of the v velocity minus the variance of the u velocity and the induced quadropole circulation. (c) The total response of the eddy forcing. (From Orlanski, 1998)

lowers from about 3 km for a 250 mb wind of 40 m s 1 to 2 km for a 65 m s1 wind. This implies trapping of the waves near the surface. Wave activity may also be suppressed because strong advection rapidly transfers developing waves downwind of a baroclinic region, thereby limiting their ability to amplify. It is shown by Nakamura and others that maximum baroclinic wave amplitude at 250 mb is located downstream of its surface-level counterpart over the North Atlantic and Paciﬁc. Nevertheless, other processes may be involved, because the wave amplitude in the Paciﬁc is not always pronounced even when the jetstream wind speed is optimal. Nakamura proposes that atmospheric moisture, which is higher during the transition seasons, may be a contributory factor. Climatological storm tracks are commonly identiﬁed by maxima in the variance of geopotential height. We have seen that large-amplitude, high-frequency eddies occur preferentially downstream of the major stationary wave troughs at 500 mb, giving rise to stationary storm tracks (Blackmon et al., 1977, for example). However, the planetaryscale waves oscillate in position. Therefore it is important to understand how traveling storm tracks may move in association with these planetary-scale waves. Low-frequency (seven to ninety days) and high-frequency (less than seven days) components of geopotential height can be separated by taking Fourier components of gridded height values in the frequency domains, for example. Cai and van den Dool (1991) apply this separation to twice-daily 500 mb heights for winters 1967–68 to 1976–77, 22°–90°N. At 50°N the time-averaged amplitude of the stationary waves in the 500 mb heights is mainly

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Figure 6.26 Latitude time section of the annual march of baroclinic wave amplitude in 250 mb heights averaged (a) for the western North Paciﬁc, 160 E°–160° W, and (b) for the western North Atlantic, 70°–30° W. The plot was derived from a thirty-one-day running mean envelope of six-day high-pass ﬁltered daily height data. The data are plotted at ﬁve-day intervals, with a contour interval of 10 m. (From Nakamura, 1992)

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concentrated in zonal wave Nos 1 and 2, the amplitudes of the low-frequency waves are similar for wave Nos 1 to 4 and then decrease slowly, while the smaller contribution of high-frequency waves is largest for wave Nos 5 to 8. The low-frequency variability is about twice that of the high-frequency component and represents regions of recurring high-amplitude anomalies in the central North Atlantic, Gulf of Alaska, western Siberia, and northern Hudson Bay. The high-frequency component has a background value of about 40 gpm and maxima of 70–80 gpm in elongated zones resembling the storm tracks of the North Atlantic and North Paciﬁc. The high-frequency transient eddies reinforce the barotropic component of the stationary waves, i.e. they lose energy barotropically to the stationary waves, whereas the low-frequency eddies gain energy from the stationary waves. According to Cai and van den Dool (1991), the low-frequency waves act to organize the high-frequency wave troughs. By analyzing the RMS heights of the high-frequency eddies within moving coordinates that are related to the low-frequency waves (wave Nos 1–4) along 50°N, they show for 1,800 cases that a traveling storm track exists statistically downstream of each trough of a planetary-scale traveling wave. For zonal wave Nos 1 and 2 there are two storm tracks, one centered about 45°N and the other about 55°N. The vorticity ﬂux of the high-frequency eddies in the traveling storm tracks serves to reinforce the low-frequency waves and retards their propagation.

476 Synoptic and dynamic climatology 1

Model studies support the idea that storm track anomalies are driven by, and through, feedback effects and may also modify large-scale, low-frequency circulation anomalies. Using a series of GCM integrations, Branstator (1995) shows that the distribution of storms can be altered by the barotropic component of the low-frequency perturbations through the steering of synoptic systems by the mean winds. Alternatively, storm tracks can be reorganized by changes in the location or intensity of baroclinic zones. However, because the climatological distribution of storms is not random but has distinctive spatial structure, large-scale circulation pattern anomalies can redistribute storm tracks such that anomalous momentum ﬂuxes may feed back positively on to the large-scale anomalies for some, but not all, of the primary circulation modes observed in the northern hemisphere.

6.5 Satellite-based climatologies of synoptic features The cloud ﬁelds organized in association with synoptic-scale circulation features are the most obvious characteristics of satellite images (section 2.2; Conway and the Maryland Space Grant Consortium, 1997). The early TIROS platforms conﬁrmed ground-based observations (Kuettner, 1959) that the atmosphere is organized primarily into banded structures, such as fronts, jetstreams, and long waves (Whitney, 1966; Viezee et al., 1967; Anderson et al., 1969; Kornﬁeld et al., 1967; Kletter, 1972; Streten, 1968a, 1973, 1978; Erickson and Winston, 1972; Downey et al., 1981). In addition, these images showed the prominence of vortical (rotational) features associated with cyclones in tropical and extratropical latitudes and, in some cases, anticyclones (Oliver, 1969; Chang and Sherr, 1969; Troup and Streten, 1972; Streten and Troup, 1973; Dvorak, 1975). Cyclone centers are identiﬁed by the presence of a cloud vortex, the conﬁguration and tightness of the central cloud relating to successive stages in the Norwegian model of frontal cyclone development. Over ocean areas of the subtropics, high-pressure systems are characterized by regions of only low-level cloudiness or, in visible channel imagery, by “sunglint.” Sunglint is direct solar radiation reﬂected from a smooth sea surface back to the satellite, and indicates the very light winds or calm conditions found within high-pressure areas. While cloud patterns in the tropics are often organized linearly, comprising the ITCZ (Gadgil and Guruprasad, 1990; Waliser and Gautier, 1993; Waliser et al., 1993), they also exhibit an embedded globular, or clustered, appearance arising from the convection associated with highly reﬂective clouds (HRCs) (Garcia, 1985; Grossman and Garcia, 1990; Hastenrath, 1990; Evans and Shemo, 1996). Thus cloud ﬁelds show structure on a range of spatio-temporal scales, detected largely as a function of satellite sensor spatial resolution (Hopkins, 1967; Houghton and Suomi, 1978; Sui and Lau, 1992). These scaling characteristics imply important scale interactions of cloud physical processes, particularly the moisture budget and cloud radiative forcing (Miller and Katsaros, 1992; Weaver and Ramanathan, 1996; Stewart et al., 1997). The earliest TIROS images conﬁrmed the general synoptic model of the mid-latitude cyclone, proposed some forty years previously by the Bergen school (cf. Bjerknes and Solberg, 1922; Boucher and Newcomb, 1962; Boucher et al., 1963; Leese, 1962; Minina, 1964; Widger, 1964; Sherr and Rogers, 1965; Barr et al., 1966; Brodrick, 1969; Kondrat’ev et al., 1970). Only relatively minor modiﬁcations and reﬁnements occurred to that basic model in the subsequent period (Katsaros and Brown, 1991), when satellite VIS and infrared images were acquired at increasing spatial and temporal resolutions, in combination with intensive ﬁeld programs to study extratropical cyclones at mesoscales, such as CYCLES (CYCLonic Extratropical Storms) and ERICA (Experiment on Rapidly Intensifying Cyclones over the Atlantic). These modiﬁcations include the so-called “T-bone” structure exhibited by some intense maritime cyclones (section 6.3). However, operational meteorological satellite imagery has also shown the existence of previously unknown, or little known, features at synoptic and subsynoptic scales (Nagle and Clark,

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1968; Chang and Sherr, 1969; Troup and Streten, 1972; Streten and Kellas, 1973); most notably, the cold air mesoscale cyclone (“polar low”), the “instant occlusion” cyclone, the Mesoscale Convective Complex (MCC), and the very long cloud bands linking the tropics and higher latitudes and known generally as TECBs: tropical–extratropical cloud bands (Kuhnel, 1989, 1990; Wright, 1997) or as “moisture bursts” in the eastern North Paciﬁc (McGuirk et al., 1987, 1988; McGuirk and Ulsh, 1990; Iskenderian, 1995). While the spatial resolutions of satellite data have improved greatly since 1960 (section 2.2), the role of human analysts to interpret the cloud ﬁelds organized at synoptic and subsynoptic scales in VIS/IR data, and to infer their basic physical associations with tropospheric humidity, stability, vertical motion, and horizontal wind speed, has changed little. However, quantitative evaluations became possible in the 1980s and 1990s with the use of operational atmospheric sounders; notably the TOVS for retrieval of vertical proﬁles of layer temperature and humidity at mesoscales (see section 2.2), and the Nimbus7 Total Ozone Mapping Spectrometer (TOMS) used to derive stratospheric ozone concentrations. These data permit inferences about the important physical processes associated with synoptic system development and, accordingly, help improve their prediction (Velden et al., 1991; Velden, 1992; McGuirk, 1993; Barsby and Diab, 1995). Moreover, microwave radiometry in dual polarization now permits more direct examination of processes within and below the clouds over ice-free ocean surfaces (section 2.2). These include precipitation rates, near-surface wind speeds, and the column-integrated cloud liquid water and water vapor. The following summarizes the development of satellite-based analyses of synoptic cloud systems in the tropics and extratropics. The stages are not necessarily mutually exclusive in time; for example, stages 2 and 3 occurred more or less concurrently during the 1970s. 1

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The recognition and basic interpretation of synoptic cloud features for the enhancement of meteorological analyses in data-sparse areas (Boucher et al., 1963; Alvarez and Thompson, 1965; van Loon and Thompson, 1966; Blackmer et al., 1968; Zillman and Martin, 1968). The development of generic and genetic classiﬁcations of cloud signature evolution to help identify associations with the conventional tropospheric ﬁelds for case systems (Leese, 1962; Nagle and Serebreny, 1962; Brodrick, 1964; Brooks and Shenk, 1965; Chang and Sherr, 1969; Brodrick and McClain, 1969; Oliver, 1969; Kondrat’ev et al., 1970; Sekioka, 1970; Dvorak, 1975; Evans et al., 1994). Composite (multi-case) studies of the meteorological ﬁelds associated with speciﬁc synoptic cloud features, useful for “bogusing” in numerical analyses (Elliot and Thompson, 1965; Shenk and Brooks, 1965; Barr et al., 1966; Rogers and Sherr, 1966, 1967; Brodrick, 1969; Nagle and Hayden, 1971; Troup and Streten, 1972; Streten and Kellas, 1973; Kelly, 1978; Sovetova and Grigorov, 1978; Junker and Haller, 1980; Carleton, 1987; Smigielski and Mogil, 1995). Determination of the characteristic regimes and variability (i.e. “climatology”) of speciﬁc cloud features on synoptic and subsynoptic scales (fronts, cyclones, TECBs, ITCZ, mesoscale cyclones and polar lows, the MCC) based upon the identiﬁcation of their areas of formation and decay, and tracking system movements (Streten and Troup, 1973; Carleton, 1979, 1981a, b, 1985a, 1995, 1996; Carrasco and Bromwich, 1996; Carrasco et al., 1997a, b; Fitch and Carleton, 1992; Forbes and Lottes, 1985; Heinemann, 1990; Reed, 1979; Turner and Thomas, 1994; Turner et al., 1996; Waliser and Gautier, 1993). Associations of the synoptic climatologies developed in 4 above with climate system features; for example, SST anomaly distributions, teleconnections with ENSO and NAO, polar sea ice and snow cover extent and their variations, and the transport of eddy sensible heat (Carleton, 1981c, 1983, 1985b, 1987, 1988a, b, 1996; Carleton and Whalley, 1988; Kuhnel, 1989; Iskenderian, 1995).

478 Synoptic and dynamic climatology 1

6 7

8

Attempts to automate the classiﬁcation of cloud systems and their intensity, to reduce the subjectivity of manual methods (stages 2 and 3 above) (Burfeind et al., 1987; Arnaud et al., 1992; Pankiewicz, 1995; Evans and Shemo, 1996; Velden et al., 1998). Application of mesoscale information from newer satellite sensors to help determine the structure of, and physical processes associated with, the cloud systems appearing in VIS/IR images; particularly the use of passive microwave and sounder data on precipitation, humidity, geopotential thickness, winds, stratospheric ozone (Velden, 1989, 1992; Alliss et al., 1993; Carleton et al., 1995; Carleton and Song, 1997; Claud et al., 1992, 1995; Heinemann et al., 1995; Katsaros et al., 1989; McMurdie and Katsaros, 1991; Rao and MacArthur, 1994; McGaughey et al., 1996; McMurdie et al., 1997; Petty and Miller, 1995; Song and Carleton, 1997; Miller and Petty, 1998; Carleton and Song, 2000). Assimilation of the satellite synoptic and mesoscale information in stage 7 into realtime numerical analysis and prediction models (Zillman et al., 1990; Velden et al., 1992; Hewson et al., 1995; Marshall and Turner, 1997).

6.5.1 Physical processes of satellite-viewed organized cloud ﬁelds Clouds are tracers of atmospheric energy, moisture, and stability. Most often, they become evident in satellite VIS/IR images when air rises through a given depth of the troposphere, and the adiabatic expansion of air parcels results in cooling to the dewpoint temperature (TD). As this occurs, the outgoing long-wave radiation (OLR) in the infrared window region decreases and the albedo (visible reﬂectance) increases. The thickness of the cloud and its form (either cumuliform or stratiform) are dependent on the depth of the moist air and the atmospheric stability (Kruspe and Bakan, 1990). Over ocean areas moisture is not a limiting factor, so the presence and form of the clouds are determined mostly by the atmospheric stability and larger-scale vertical motion patterns (see Zillman and Price, 1972). However, over land the characteristics and heterogeneity of the surface help to determine the availability of moisture and the partitioning of the net radiation into the latent and sensible heat ﬂuxes, which are important in cloud development – at least for boundary-layer clouds – and the ease with which air parcels can be moved above the condensation level by either forced or free convection (Weston, 1980; Pielke and Zeng, 1989; Gibson and Vonder Haar, 1990; Wetzel, 1990; Rabin et al., 1990; Raymond et al., 1994; Cutrim et al., 1995; Betts et al., 1996; Doran and Zhong, 1995; Lyons et al., 1993; Rabin and Martin, 1996; Brown and Arnold, 1998). Plate 5 shows enhanced infrared black-body GOES images of much of the region centered on North and Central America on a day in June 1988. The images are taken twelve hours apart and show phenomena on a range of spatial scales. The following features are particularly evident: 1 2 3 4

A larger diurnal range of the surface temperature over land compared with the ocean (compare the gray tones between the two images). Nocturnal thunderstorm activity (see image at 06.00 UTC) over the southwest and upper Midwest United States (the enhanced cloud tops). Shower and thunderstorm activity in the ITCZ, located in northern South America and the eastern tropical Paciﬁc. Areas that are clear or have only low stratiform clouds over the eastern portions of the subtropical oceans, associated with strong subsidence.

The associations between atmospheric stability and the satellite-observed cloud form were demonstrated by Tang et al. (1964) and Shenk and Brooks (1965). These authors showed that, over the ocean outside the tropics, air moving equatorward (poleward) is potentially colder (warmer) than the surface over which it is traveling. Thus cloud ﬁelds

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Plate 5 GOES-E enhanced thermal infrared images of the North America and Central America regions on June 25 1988 at (a) 06.00Z, and (b) 18.00Z. The high cold cloud tops associated with convective and frontal systems are particularly evident (see text). (NOAA)

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Figure 6.27 The Guymer-type synoptic cloud model of an extratropical cyclone (southern hemisphere perspective). (From Guymer, 1978)

tend to be dominantly cumuliform (stratiform) in response to the different atmospheric stability regimes that result. When cold air moves equatorward sensible heat and latent heat are directed strongly upward; however, when warmer air moves poleward, the sensible heat is directed towards the surface. These differences in stability and heat ﬂux were shown for the different satellite-viewed sectors of mature extratropical cyclones over the southern oceans by Zillman and Price (1972). Their satellite synoptic “model” (Figure 6.27) was subsequently elaborated by Kelly (1978) for incorporation into the Australian Bureau of Meteorology’s numerical analysis and prediction scheme. The heavy reliance on satellite VIS/IR imagery for the synoptic analysis of the southern oceans by the Bureau of Meteorology has a long history (Zillman and Martin, 1968; Troup and Streten, 1972; Streten and Kellas, 1973; Streten and Downey, 1977), and is still important today (Zillman et al., 1990; Seaman et al., 1993; Leighton, 1994). Given sufﬁcient moisture, clouds will develop whenever lower-level convergence of air occurs. Excluding the more local effects of surface features (topography, contrasting land covers) on cloud development, widespread upward motion and surface pressure falls are generated when divergence in the upper troposphere exceeds convergence in the lower troposphere. On average, the magnitude of the vertical motion decreases as the speed divergence (or convergence) of the horizontal wind component increases away from the level of non-divergence located at about 600 mb (see sections 2.4.4–5). Dines compensation ensures that the divergence and convergence have opposite sign above and below the level of non-divergence. On the forward (back) side of a mid-tropospheric wave, warm

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(cool) air rises and cools (sinks and warms), providing the energy for continued ampliﬁcation of the wave (slantwise convection). However, in both sectors of a wave the sign of the vertical motion induced by adiabatic processes is insufﬁcient to counteract the temperature change due to the combination of advective and also diabatic (radiation absorption and loss, latent heating) processes. The last is positive, or warming, for condensation and when the resulting thick clouds absorb solar radiation and impede the infrared emission to space. The diabatic component is negative, or cooling, when clouds evaporate or when infrared losses from the surface increase. As illustrated in Figure 6.14, divergence is favored in the forward, or exit, region of a jetstream maximum, and on the eastern side of an upper trough because of the negative relationship of the divergence with the vorticity in the horizontal plane. Thus upper divergence (convergence) and ascent (subsidence) are favored on the forward (back) side of an upper trough by the advection of vorticity. Accordingly, satellite-viewed cloud ﬁelds are characteristically deeper (shallower) on the forward (back) side of the trough. On synoptic time and space scales, where traveling medium-wavelength, or baroclinic, waves predominate (wave Nos approximately 7–10), the quasi-horizontal motion of air parcels cuts across surfaces of constant potential temperature, or isentropes. Potentially warmer

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Plate 6 DMSP visible channel mosaic (5.4 km resolution) of the Europe/western Russia sector, February 22 1978. A “shadow band” associated with a jetstream is clearly evident overlying the snow-covered land. (NISDC)

(b)

Figure 6.28 Atmospheric circulation over the Australasian sector at 00Z on November 8 1992. (a) SLP. (b) 500 mb height. (From the Commonwealth Bureau of Meteorology, Melbourne)

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(colder) air rises and cools (sinks and warms) ahead of (behind) a trough, enhancing the ampliﬁcation of the wave. This process accompanies synoptic “development,” which in middle latitudes is evident on satellite VIS/IR imagery by the following two features (Burtt and Junker, 1976): (1) a brightening of the clouds, indicating rising cloud tops (increased albedo, lowering cloud top temperature), and (2) increasing anticyclonic curvature to a cloud band (indicating divergence aloft). A satellite-observed feature associated with (1), especially over the ocean, is enhanced convection, which may precede the formation of some “polar lows” and “instant occlusions” (below). Typically associated with (2) is the formation of the “open wave” stage of an extratropical cyclone. This satelliteviewed stage, termed a “baroclinic leaf” (Evans et al., 1994; Smigielski and Mogil, 1995), is particularly prominent in cases of rapid cyclogenesis (Jaeger, 1984), including the formation of synoptic cyclone “bombs” (Bottger et al., 1975). The changes in sign of the relative vorticity and divergence between the equatorward and poleward sides of an upper-tropospheric jetstream result in opposing patterns of vertical motion: from ascent on the equatorward side to subsidence on the poleward side. Thus the jetstream axis is often evident in satellite VIS images by a sharp boundary, or “shadow band” (Whitney, 1966; Anderson et al., 1969), that demarcates the higher cloud on the warm side of the jet from the cold side of the jet that has lower clouds or is clear (Plate 6), or by the cirrus streamers on the warm side of the jet that terminate at the jet axis. In infrared images a similar feature to the shadow band is a marked discontinuity in the TBB across the jet axis (Martin and Salomonson, 1970), which can be used to identify the jetstream in the absence of conventional data (Togstad and Horn, 1974). In synoptic weather analysis, and increasingly also for synoptic climatological studies, much use is made of satellite remote sensing in more than one spectral band, and from more than one platform (Carleton et al., 1995; Claud et al., 1995). This is illustrated here for the Australian sector of the Southern Ocean on a day in November 1992. The passive microwave images from the SSM/I illustrate the wealth of additional new mesoscale information that can be retrieved over data-void ocean areas. Figure 6.28 a and b shows, respectively, the broad-scale ﬁelds of SLP and 500 mb height at this time. A high-amplitude trough extended well into central Australia from a cyclonic system near Adelie Land, Antarctica. A secondary low had formed on the equatorward end of the front in the Great Australian Bight. Given the high amplitude of the trough, and the presence of the subtropical high just west of Western Australia, strong cold air advection was occurring west of the trough. To the east of the trough, moisture of subtropical origin was moving poleward and ascending, resulting in a broad meridionally oriented cloud band ahead of the main frontal system. The SLP map is derived from observations from surface land stations, ships, and ocean drifting buoys. Additional, so-called “bogus,” information is also provided by VIS/IR cloud imagery from polar orbiting and geostationary satellites (Plate 7a). The latter include lower-level winds over the ocean (which help improve the isobaric analysis via application of the geostrophic wind rule), derived from automated tracking of cumuliform clouds in successive satellite images at high temporal resolution (Wylie et al., 1981; Nieman et al., 1993, 1997; Ottenbacher et al., 1997). The constant pressure analyses (e.g. Figure 6.28b) are model-generated ﬁelds of geopotential height, or thickness, and isotachs. They also incorporate considerable amounts of satellite information, particularly from the NOAA TOVS. The TOVS provide information on layer-mean temperatures (directly related to the thickness) and moisture for a range of cloud cover conditions, and may even be extended over ice-covered surfaces in polar regions. However, the TOVS retrievals are most reliable for the middle to upper troposphere (Kopken et al., 1995). Automated tracking of cirrus-level clouds on successive geosynchronous images (lower latitudes) or images from polar orbiters (higher latitudes) helps yield supplementary information on jetstream winds (e.g. the small jet maximum west of the upper trough along longitude 120oE and centered at about 45oS in

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Figure 6.28b. Thus the 500 mb analysis shown in Figure 6.28b shows strong vertical consistency with the manually constructed SLP chart (Figure 6.28a). Processes occurring within and below the clouds have to be inferred from visible and infrared images. To expand upon the synoptic situation for November 7–8 1992, the SSM/I retrievals of column-integrated water vapor (IWV), integrated cloud liquid water, solid (ice) precipitation and liquid precipitation (Petty, 1994), and near-surface wind speeds, are presented in Plate 7b–f. Successive orbits leave a data-void area in and south of the Australian Bight, which unfortunately excludes part of the secondary wave cyclone in that region. However, the broad-scale ﬁelds are consistent with those depicted in Figure 6.28a, b. For example, the IWV (Plate 7b) conﬁrms the considerable latitudinal extent of the cold, dry air to the west of the high amplitude trough in the Australian region, and the warm and moist air to the east. (The very high values around Antarctica in every SSM/I image are spurious; they are due to high microwave emission from sea ice.) Highest values of water vapor are conﬁrmed as occurring in the meridionally oriented cloud band to the east of the trough, which is also the region of greatest cloud liquid water content. Areas of liquid (rain) and solid (snow, graupel) precipitation are determined using the P37 and S85 indices (Plate 7d, e).2 Since P37 values < 0.8 indicate increasing probability of rain (section 2.2), these occur in the frontal cloud band east of the trough and also in the upstream frontal system located well south of Western Australia. Scattering of the microwave signal by large ice particles (Plate 7e) occurs in the deeper, or higher and colder, parts of cloud systems. While there is almost certainly precipitation also occurring in the cold air cumulus over the Southern Ocean west of the meridional trough, the coarse resolution of the SSM/I and the phenomenon of “beam ﬁlling,” whereby individual convective elements go unresolved, do not allow these showers to be detected (section 2.2). Finally, Plate 7f shows the near-surface (19 m height) wind speed retrieved from backscattering of the microwave signal by the ocean surface. Comparisons of SSM/I winds with those derived from buoys and other remotely sensed wind speed measurements over the ice-free oceans, such as Geosat (Mognard and Katsaros, 1995a, b) show high reliability; the major exception is in areas of large cloud liquid water or heavy precipitation. Thus one should ignore the wind speed values located between about 45°–55°S in the eastern cloud band (Plate 7f), as well as those contaminated by sea ice. Accordingly, the SSM/I data yield highest wind speeds (16–22 m s1) just west of the secondary low in the Australian Bight. Lowest wind speeds of about 2–6 m s1 occur on either side of the long-wave trough at higher latitudes. Satellite-based climatologies of synoptic and sub-synoptic cloud features in the extratropics consist of three main types. These are: 1

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Composite (average) statistical “models” of the cloud system features (e.g. jetstream maxima, cyclonic vortices, cloud bands) on a movable grid (Cox, 1969; Businger, 1990), based on the analysis of many cases having broadly similar satellite-observed characteristics, and in which either conventional atmospheric data or other satellite information (e.g. SSM/I) are averaged (Martin and Salomonson, 1970; Troup and Streten, 1972; Streten and Kellas, 1973; Carleton, 1987; Katsaros et al., 1989; Rao and MacArthur, 1994; Rodgers et al., 1994; Petty and Miller, 1995; Song and Carleton, 1997; Miller and Petty, 1998). The time-averaged regimes of circulation features, particularly synoptic and subsynopticscale cyclonic vortices. This involves mapping the formation, maturity and dissipation areas of many systems over weekly, monthly, or seasonal time scales, and also the tracks of those systems (Streten, 1968b, 1974; Streten and Troup, 1973; Carleton, 1979, 1981a, b, 1996; Carleton and Carpenter, 1990; Carleton and Fitch, 1993; Carleton and Song, 1997; Carrasco et al., 1997a, b; Forbes and Lottes, 1985; Turner and Thomas, 1994; Turner et al., 1996; Yarnal and Henderson, 1989a, b).

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For subsynoptic-scale systems, particularly MCCs and cold-air mesocyclones, the composite synoptic anomaly ﬁelds (e.g. 500 mb height, SLP, vorticity, surface–air temperature difference) associated with those features (Maddox, 1983; Businger, 1985, 1987; McAnelly and Cotton, 1989; Sinclair and Cong, 1992; Fitch and Carleton, 1992; Carleton and Fitch, 1993; Carleton and Song, 1997).

The above three categories comprise the basic framework for examining synoptic– dynamic climatologies of satellite-observed cloud systems in the extratropics, on a range of spatial scales (below). 0 6.5.2 Synoptic-scale extratropical cloud vortices 11

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Statistical models of extratropical synoptic cyclones have been developed, based on the composite ﬁelds of SLP, 500 mb height, wind speed, sea–air temperature difference, and vorticity, in different stages of cloud system development (Troup and Streten, 1972; Streten and Kellas, 1973; Zillman and Price, 1972; Kelly, 1978; Junker and Haller, 1980; Carleton, 1987; Seaman et al., 1993; Lau and Crane, 1997). These subjective (manual) classiﬁcation schemes of cloud vortices rely on assigning discrete stages of development in frontal cyclogenesis to a process that occurs over a time continuum. Fortunately the satelliteviewed cloud signatures associated with each stage (incipient → developing → maturity → dissipation → decay) are relatively distinct, especially for vortices occurring over the ocean. The successive stages of cloud vortex evolution also tend to occur at successively higher latitudes in the extratropics, on average. The incipient, or open-wave stage (W in the Troup and Streten, 1972, scheme), is marked by the appearance of a bulge, thickening, or anticyclonic turning of an existing frontal cloud band; the developing stage (B stage) is signalled by a “dry slot” which develops to the rear of the cloud band. Composites of the SLP and tropospheric height anomalies (i.e. synoptic climatology Type 1, above) for these early stages of cyclonic development (Troup and Streten, 1972; Carleton, 1987) show strong baroclinicity: the minima in these ﬁelds are displaced towards the colder air with increasing height in the system. Cyclone maturity (C stage) is heralded by the dry slot taking on a distinct cyclonic curvature, either hook-shaped or spiral in shape (Troup and Streten, 1972; Carleton, 1987). It is at this stage that the center of cyclonic rotation is easiest to discern, and central surface pressures are usually at their lowest (Sovetova and Grigorov, 1978; Junker and Haller, 1980; Carleton, 1987; Smigielski and Mogil, 1995). Moreover, microwave radiometry shows that the most rapidly deepening cyclones typically have heaviest rain rates in their northeastern quadrant, associated with the highest latent heat release (Petty and Miller, 1995; Miller and Petty, 1998). Disorganization of the central cloud vortex structures – for example, a “loosening” of the spiral cloud marking the C stage, or ﬁlling-in of the central cloud vortex – marks the onset of the dissipation (D stage). The D stage is usually more protracted than the preceding stages of development of extratropical cloud vortices, and can endure for up to several days (Troup and Streten, 1973; Carleton, 1987). There are also variations in the morphology of the D-stage that indicate strong differences in the associated SLP and tropospheric height departures from “climatology.” Streten and Kellas (1973) showed that D vortices can exhibit either a symmetrical or an asymmetrical frontal cloud band – the latter being the more intense system, and sometimes associated with “reintensiﬁcation.” Ultimately the D-stage vortices may be observed to enter a protracted “decay” (E stage), marked by the loss of a single frontal band and the appearance of a swirl of generally lower-level clouds. At this stage the vortex is coldcored throughout. However, when located over ice-free ocean areas in higher latitudes, such as the south-east South Paciﬁc and northern North Atlantic, the weak static stability means that cyclogenesis on a mesoscale is favored within these pre-existing centers of cyclonic circulation when PVA maxima also occur (Zick, 1983). Accordingly, multiple

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mesoscale vortices that evolve and rotate within E-stage vortices at higher latitudes comprise the so-called “merry-go-round” conﬁguration of mesocyclones (below). Satellite-based climatologies of synoptic-scale extratropical cloud vortices (synoptic climatology Type 2, above) have been developed almost exclusively for the southern hemisphere (Streten and Troup, 1973; Carleton, 1979, 1981a, b, 1983). The basic features which they reveal have been conﬁrmed and extended by recent studies using long-period grid-point analyses (Jones and Simmonds, 1993; Sinclair, 1994, 1995). The few studies undertaken for the northern hemisphere are typically of shorter duration and identify associations between the different stages of cyclonic development and climate teleconnections, such as the North Atlantic Oscillation (Carleton, 1985a, 1988b, 1996), or cryosphere variations (Carleton, 1987). In the southern hemisphere, frontal wave cyclogenesis (W and B stages in the Troup and Streten classiﬁcation scheme) predominates in the western parts of ocean basins in all seasons (Streten and Troup, 1973, Carleton, 1979). This emphasises a three-wave pattern in the time-averaged synoptic ﬁelds. Cyclonic maturity (C stage), dissipation and decay (D and E stages) occur to the southeastward, at progressively higher latitudes. The Antarctic circumpolar trough comprises mostly C and D/E vortices, although a considerable amount of mesoscale cyclogenesis also occurs at higher southern latitudes in all seasons (Carleton, 1992; Turner et al., 1993). The average frequencies of cloud vortices are greatest for the winter season. Highest densities of “late stage” (C, D, E) cloud vortices occur near Wilkes Land, in the Bellingshausen/Amundsen seas, and northeast of the Weddell Sea. These coincide with the areas of highest snow accumulations (>300 kg m2 a1, or 300 mm a1) in coastal Antarctica (Carleton, 1992), and emphasize the importance of synoptic cyclones for transporting moist air from lower latitudes on to the ice sheet (Bromwich, 1988). The satellite-observed successive stages of frontal cyclone development are also linked with the efﬁciency of poleward transport of the eddy sensible heat, occurring via their predominance in particular latitude zones. Carleton and Whalley (1988) show that the “early” (W, B) stages of cloud vortices transport the most heat polewards, on average, with strong eddy convergence of sensible heat into the circumpolar trough in the short-lived C stage. Moreover, there is an ENSO signal in the statistics of the eddy heat transport that is effected by cyclones, at least for the winters (June through September) of 1973–77. For that period, about 36 percent of the meridional heat transport by the vortices was explained by the SOI, with greater (lesser) ﬂuxes effected by the cyclones when the SOI is low: El Niño (high: La Niña). Interannual variations in the spatial patterns of extratropical cyclones of the southern hemisphere are large, and show dependence on the El Niño Southern Oscillation, particularly in the South Paciﬁc sector (Streten, 1975; Berlin, 1991). Sinclair et al. (1997), using grid-point analyses, show a more or less linear response of synoptic cyclones to ENSO. In the winter of an El Niño warm event these are reduced (increased) over the Indian Ocean, Australasia, and the Amundsen Sea (near Wilkes Land and the subtropical eastern Paciﬁc), and vice versa for the La Niña cold event. The change in intensity of the Amundsen Sea low associated with ENSO is an important component of the PSA (Paciﬁc–South America) teleconnection pattern, which is discussed in the context of lowfrequency circulation variations in section 5.9. 6.5.3 Tropical cloud clusters and vortices The classiﬁcation systems designed to identify and describe the time evolution of the cloud vortices associated with rotating storm systems originating in the tropics are based upon pattern recognition in VIS/IR imagery like those for extratropical cyclones (Oliver, 1969; Dvorak, 1975; Lander, 1990). The classiﬁcation systems of Oliver (1969) and Dvorak (1975) describe the intensiﬁcation (decreasing surface pressure, increasing surface wind speeds) associated with the progression from highly reﬂective cloud clusters of tropical depressions that exhibit little organization through the increasing circularity

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and size associated with tropical storms, and the appearance and structure of a relatively cloud-free eye (lower albedo, higher black-body temperatures) in full-blown hurricanes, typhoons, and tropical cyclones. Moreover the TBB data from geosynchronous platforms show that the cloud tops associated with tropical vortices, as well as smaller cloud clusters, undergo strong intradiurnal variations associated primarily with cloud-top radiational processes (Muramatsu, 1983; Steranka et al., 1984; Mapes and Houze, 1993; Machado et al., 1993). These are conﬁrmed from passive microwave data (Rodgers and Pierce, 1995). Because of their development primarily over ocean areas, and their potential for destruction along populated coastal areas, tropical cloud vortices appearing on VIS/IR imagery have always been highlighted in the synoptic analysis. Thus, unlike the situation with extratropical cloud vortices, the synoptic chart is usually a reliable indicator of the location of tropical cyclones – at least, in their more organized stages of development. The locations of tropical cyclogenesis, tracks of movement, and approximate central surface pressures (i.e. synoptic climatologies) are readily compiled from synoptic charts that include the interpretation of satellite images – as in the Mariners Weather Log monthly series – rather than from the imagery alone (Evans and Shemo, 1996). The emphasis in satellite remote sensing of tropical cloud systems has been mainly in the following areas:

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Identifying the initial stages of development of tropical waves in VIS/IR imagery. For example (Velasco and Fritsch, 1987; Laing and Fritsch, 1993a, b) many tropical vortices originate from Mesoscale Convective Systems forming over or near elevated terrain, such as the highlands of Mexico and West Africa. These MCSs may become more organized upon moving westward over ocean areas where SSTs exceed about 27oC and atmospheric conditions (especially low-level convergence and upper divergence) are favorable (Gray, 1968; Lau and Crane, 1997). Identifying and tracking the development of eastward-moving “superclusters” associated with the thirty to sixty-day oscillation, as revealed in Hovmöller analyses of outgoing long-wave radiation (OLR) (Nakazawa, 1986; Lau and Chan, 1988). The basic characteristics of superclusters, such as cloud-top temperatures, cloud system size and propagation speed, have relied upon the analysis of VIS/IR images from geosynchronous platforms (Nakazawa, 1988; Mapes and Houze, 1993). Associations between the extent of supercluster activity in the western tropical Paciﬁc, and the ENSO in the central and eastern Paciﬁc, have been suggested (Lau and Chan, 1988; Nakazawa, 1988). Passive microwave observations (e.g. SMMR, SSM/I) of rain rate, cloud water, and ice scatter, and radar altimetry (e.g. Geosat, TOPEX-Poseidon) of surface wind speeds help to depict and predict the intensiﬁcation and movement of tropical cyclones (Porter, 1990; Fishman, 1991; Alliss et al., 1993; Rodgers and Pierce, 1995; Rodgers et al., 1991; Velden et al., 1992; Peng and Chang, 1996). These data reveal that the most intense systems tend to have greater rain rates and more ice associated with increased latent heat release in the eye-wall clouds (Rao and MacArthur, 1994; Rodgers et al., 1994). In addition, the mesoscale wind vectors retrieved by scatterometers (Seasat, ERS-1) can be used in PBL models to generate detailed SLP ﬁelds (Hsu and Liu, 1996). Using TOVS-derived temperatures to determine tropical cyclone intensity from the strength of the warm pool in the upper troposphere (e.g. 250 mb) that results primarily from latent heat release in the eye wall (Velden and Smith, 1983; Velden, 1989). Moreover, the application of ozone observations from the Nimbus-7 TOMS adds information on the stratosphere–troposphere exchange of air and its relation to the dynamics of tropical cyclones in different regions (Rodgers et al., 1990; Stout and Rodgers, 1992).

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6.5.4 Mesoscale cyclones (mesocyclones, “polar lows”) Visible and infrared imagery shows the relatively frequent occurrence of subsynopticscale cyclonic cloud vortices over the ocean in the cold airstreams to the rear of major synoptic cyclones (Anderson et al., 1969; Whitney and Herman, 1968; Martin, 1968; Chang and Sherr, 1969; Zillman and Price, 1972). These systems became known as “comma clouds” because of their characteristic signature, and are observed to develop out of enhanced convection in response to the positive vorticity advection (PVA) associated with short waves or jetstream maxima (Harrold and Browning, 1969; Ninomiya, 1989; Craig, 1993; Evans et al., 1994). Comma clouds are the satellite-observed signature of some “polar lows” that generate much of the organized post-cold frontal shower activity and pressure/wind discontinuities noted in surface weather observations but which the basic Norwegian model of the middle-latitude cyclone could not adequately explain. Before their conﬁrmation as discrete subsynoptic systems in satellite imagery, “polar lows” were often identiﬁed on synoptic charts as “back-bent occlusions” or as polar “trofs” (Reed, 1979) that were associated with the main frontal cyclone. Broadly similar systems occur, only with less frequency, over the Great Lakes and interior continental areas during winter (Mullen, 1982; Forbes and Merritt, 1984; Mills and Walsh, 1988). The interpretation of satellite imagery revealed that a comma cloud may either remain separate from the cold frontal cloud band that precedes it (typically where a large horizontal distance separates the two features) or it may “catch up” with the cloud band and initiate frontal cyclogenesis as an “instant occlusion” (Plate 8). The term “instant occlusion,” or “instant frontogenesis” (Anderson et al., 1969), was coined for this phenomenon because of the large temporal gap between successive visible band images from the early polar orbiters, which gave the appearance of sudden and rapid development of the mature stage of a frontal cyclone. Because frontogenesis occurs simultaneously with cyclogenesis in instant occlusions, these systems were believed to manifest the Type B cyclogenesis mode advanced by Petterssen and Smebye (1971) (Streten and Troup, 1973). In this mode the strong low-level temperature advection typically associated with a developing frontal wave (or Type A development) is absent or weak, and cyclogenesis proceeds mainly from the import of baroclinicity (i.e. PVA) in the mid to upper troposphere. Consequently, thermal advection is weak to begin with, but intensiﬁes as the system evolves (i.e. frontogenesis and cyclogenesis occur concurrently in the Type B model). Case and composite analyses of the instant occlusion (Browning and Hill, 1985; Carleton, 1985a; Businger and Walter, 1988; Pearson and Stewart, 1994) conﬁrm that this cyclogenesis can occur quite rapidly, and often leads to a deep extratropical cyclone. Studies of cold-air cyclone systems conducted for north-west Europe and the west coast of North America (Harley, 1960; Lyall, 1972; Mansﬁeld, 1974; Monteverdi, 1976; Mullen, 1979, 1983) conﬁrmed the generally strong baroclinicity of comma clouds throughout the middle and upper troposphere. This was particularly apparent in the composite statistical “models” of southern hemisphere extratropical cloud vortices developed by Troup and Streten (1972), and in which the comma cloud (their Type A vortex) was the most strongly represented subsynoptic-scale vortex type. This system is associated with negative departures of SLP and tropospheric height, implying a cold-cored vortex. The acquisition of higher resolution imagery with successive generations of polar orbiters, such as the DMSP, showed that mesoscale cloud vortices occurred over a wide range of length scales, down to about 100 km diameter. The characteristic sizes of mesocyclones and synoptic-scale cyclones over the southern oceans, identiﬁed using DMSP infrared imagery, show a statistically signiﬁcant difference (Carleton, 1995). The modal class of mesocyclone cloud vortex has a diameter of around 240–460 km, and 680–880 km for frontal cyclones; there is a lack of vortices in the diameter range 460–680 km. This is suggestive of a possible “spectral gap” in cloud vortex size for the southern oceans. However, a similar satellite-based analysis of extratropical cloud vortices over the northern

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Plate 8 DMSP infrared image of an instant occlusion about to be initiated on a frontal cloud band associated with a North Paciﬁc extratropical cyclone (year and date unknown). The area of enhanced convection associated with a cold-air mesocyclone is merging with the band at approximately 36°N, 164°E. (From Carleton, A.M., 1985, “Satellite climatological aspects of the ‘polar low’ and ‘instant occlusion’,” Tellus, 37A (5): 436–7, 438–40)

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hemisphere oceans suggests the absence of such a spectral gap: frontal cyclones appear to be smaller and mesocyclones larger than their southern hemisphere counterparts (Carleton, 1996). The higher resolution satellite data also conﬁrm that comma clouds are one of several classes of oceanic mesoscale cyclones, albeit often the largest. Cold-air mesocyclones located deep within the cold air far from the jetstream, often develop just equatorward of the sea ice edge. These mesocyclones are known under a variety of names, including “arctic lows,” “arctic hurricanes,” or “true polar lows” (Emanuel and Rotunno, 1989; Businger, 1991; Rasmussen, 1981; Rasmussen et al., 1996). They have a spiraliform rather than comma cloud signature (Rabbe, 1987; Nordeng and Rasmussen, 1992), sometimes with a clear “eye” reminiscent of a tropical cyclone (Rasmussen, 1989). The signature differences between comma cloud and spiraliform mesocyclones suggest the importance of different physical processes, or at least different relative contributions of the same processes, in their formation (Carleton, 1996). Diagnostic case studies of Arctic lows have mostly been undertaken for the East Greenland and Norwegian seas (Shapiro et al., 1987; Shapiro and Fedor, 1989; Rasmussen et al., 1992; Douglas et al., 1995). The spiraliform signature type seems to occur more frequently there than in the Bering Sea of the western Arctic, the Sea of Japan, or in the Subantarctic – apparently because of the strong SST gradients located close to the sea ice edge, which enhance the surface–atmosphere ﬂuxes of latent and sensible heat when cold air moves out across the ice edge in winter. Case studies show a lack of deep baroclinicity associated with these spiraliform systems (e.g. Rasmussen, 1979, 1981). Rather, the baroclinicity tends to be conﬁned to a shallow surface layer that has moved out over warmer water to form a “boundary layer front” (Fett, 1989; Shapiro and Fedor, 1989). This shallow layer is surmounted by a barotropic cold core low in the upper troposphere in which air is subsiding and warming adiabatically (Bresch et al., 1997). The latter process effectively limits the mesocyclone to the lower to midtroposphere in most cases. Convection occurring along the boundary layer front is enhanced by the addition of heat and moisture from the upper ocean by the process of ASII (Air–Sea Interaction Instability) (Emanuel and Rotunno, 1989). ASII is the highlatitude variant of CISK (Conditional Instability of the Second Kind), which predominates in the tropics (see p. 183). CISK involves the organization of simple cumulus into a cyclonic center that becomes self-sustaining through latent heat release in the clouds, and divergence aloft, with subsidence surrounding the growing vortex. The diabatic processes encourage frictional convergence of the wind at lower levels, or the “spin up” of a cyclonic vortex such as a tropical cyclone. In ASII the presence of cold and stable, rather than warm and conditionally unstable, air prevents the deep convection typiﬁed by CISK (Bratseth, 1985). Moreover the boundary layer front near the ice edge is clearly also different from the barotropic conditions associated with tropical system development. The latent heat release within the convective clouds associated with spiraliform “polar lows” is believed to be responsible for the “warm pool” often observed in the 1,000–700 mb thicknesses (Rasmussen, 1981). However, a competing theory is that this feature results more from a seclusion type of process, similar to that observed in some synoptic cyclones (Montgomery and Farrell, 1992). Both the “warm pool” and the multiple spiral cloud bands associated with polar air vortices are reminiscent of tropical cyclones, although the latter are larger systems because of the weaker Coriolis force in lower latitudes. The recognition of the two broad classes of cold air mesocyclone (comma cloud, spiraliform) on the basis of satellite image interpretation (Plate 9) is pivotal to the cloud vortex classiﬁcation systems developed subsequently for use in climatological studies (e.g. Forbes and Lottes, 1985; Carleton, 1985a; Carleton and Carpenter, 1989, 1990; Yarnal and Henderson, 1989a, b). Composite models of these systems in the northern hemisphere, developed through the averaging of SLP and tropospheric height anomaly ﬁelds for multiple cases (Carleton, 1987), conﬁrmed the more baroclinic (barotropic) character of the comma clouds (spiraliform systems). However, for mesocyclones in subantarctic lati-

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Plate 9 DMSP infrared images of (a) a spiraliform mesocyclone and (b) a comma cloud mesocyclone, in the Labrador Sea during January 1979 (exact dates unknown). (From Carleton, A.M., 1985, “Satellite climatological aspects of the ‘polar low’ and ‘instant occlusion’,” Tellus, 37A (5): 436–7, 438–40)

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(b) Figure 6.29 Composite SSM/I retrievals of (a) integrated water vapor (kg m2 ) and (b) nearsurface wind speed (m s1) for twelve comma-cloud mesocyclones in the mature stage over the southern oceans during 1992. The infrared-observed position of the mesocyclone is at the center of each grid. Left mean values. Right standard deviation. (From Song and Carleton, 1997)

tudes during the cold season, passive microwave data, and also the TOVS-derived geopotential heights, suggest that some degree of baroclinicity may be present in both signature types (Carleton et al., 1995). Antarctic mesocyclones observed in the summer are more barotropic (Turner et al., 1993) because they either occur over cold ocean or ice surfaces where the air is very stable, or where air moving out from the Antarctic ice sheet undergoes column stretching and an increase in its cyclonic vorticity (Carrasco and Bromwich, 1997a; Heinemann, 1990). These systems are important snowfall producers for some coastal areas, such as the western Ross Sea, in all seasons (Rocky and Braaten, 1995). Song and Carleton (1997) determined the prominently recurring patterns of SSM/Iderived water vapor and surface wind speeds for different development stages of southern ocean “comma cloud” mesocyclones, using the “storm-following” technique of Businger (1990). The mean patterns for the mature stage of mesocyclones (Figure 6.29) show a latitude gradient of water vapor with evidence of the ingestion of warm air into the storm center, and strong cyclonic shear of wind speed from the northwest to southeast quadrants. The standard deviation ﬁelds of the retrieved ﬁelds show remarkably small variation, suggesting that some degree of conﬁdence can be placed in the mean mesocyclone microwave patterns. However, the sample of cases used in the composites should be increased to conﬁrm their representativeness more generally. The climatological spatial distributions of cold air mesocyclones (comma cloud polar lows) were known ﬁrst for the southern hemisphere, given the heavy reliance on the interpretation of satellite cloud data for synoptic analysis (Streten and Troup, 1973; Carleton, 1979). Subsynoptic-scale cyclones occur more frequently with increasing latitude over the

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11 Figure 6.30 Monthly frequency distribution of “polar lows” in the Norwegian and Barents seas, based on synoptic data for the period 1978–82. (From Wilhelmsen, 1985)

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southern oceans, possibly comprising up to 50 percent of all cyclogenesis events in the winter season. This has helped to revise our impression of the subantarctic trough as being a predominantly cyclolytic region, to one in which cyclogenesis also frequently occurs (Carleton, 1992). However, southern hemisphere cold-air mesocyclones are most abundant around the equinoctial months (March, September/October) with a secondary peak in midwinter (July) (Lyons, 1983; Carleton, 1995, 1996; Carleton and Song, 1997). This seasonal frequency pattern resembles the semi-annual oscillation (SAO) in tropospheric temperatures, pressure/heights and wind speed over subantarctic latitudes (van Loon, 1967; van Loon and Rogers, 1984). The SAO gives rise to extrema in the latitude locations of the circumpolar trough, such that it is closer to (further from) Antarctica in the equinoctial (solstitial) months. This contrasts strongly with the simpler annual pattern of polar low development in the North Paciﬁc and North Atlantic, in which maximum frequencies occur during the winter season, although there is a secondary minimum evident in February (Wilhelmsen, 1985; Businger, 1987) (Figure 6.30). The minimum occurs around the time of maximum latitude displacement of the westerly circumpolar vortex, and its baroclinic zones, away from the regions favorable to polar low formation. Carleton and Carpenter (1990) and Turner et al. (1996) indicate that spiraliform mesocyclones are more frequent than comma clouds in the Subantarctic, but estimates differ as to their relative abundance. Favored areas for the formation of polar mesocyclones in the southern hemisphere are in longitudes of the Ross Sea, the Weddell Sea, and the Amundsen–Bellingshausen seas (Bromwich, 1991; Carrasco and Bromwich, 1994, 1996; Carrasco et al., 1997a, b; Heinemann, 1990, 1996; Turner and Thomas, 1994; Turner et al., 1996). In all these areas cold air frequently sweeps out from the Antarctic ice sheet or sea ice zone, sometimes via the high-speed katabatic winds in the boundary layer, which are evident in satellite infrared images as warmer “plumes” of air overlying the colder ice surfaces (Bromwich, 1989, 1991; Bromwich et al., 1993). These are also favored areas for the stagnation of old cold-cored synoptic lows within which mesocyclones may develop and move cyclonically as a so-called “merry-go-round” formation type (e.g. Zick, 1983; Forbes and Lottes, 1985). Composites of the SLP and tropospheric height ﬁelds associated with “outbreak” times (i.e. synoptic climatology Type 3, above) conﬁrm these synoptic-scale associations of mesocyclones, although there are regional differences (Fitch and Carleton, 1992; Carleton and Fitch, 1993). For example, mesocyclones in the Ross Sea tend to develop in the enhanced thickness gradient (i.e. baroclinic southerly thermal wind) of a cold-cored low located to the northeast, whereas those in the

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Bellingshausen–Amundsen seas predominantly occur well within a cold-cored low (i.e. barotropic atmosphere). The interannual variability of cold-air mesocyclone frequency is large, in accordance with the variations undergone by the larger synoptic circulation. For example, the southern winters of 1988 (1989) were characterized by large numbers of mesocyclones in the Ross Sea (Amundsen–Bellingshausen seas). These major changes in mesocyclone frequencies between the two years accompanied strong shifts in the planetary waves and, hence, in the preferred longitudes of cold-air outbreaks and the greatest extent of the antarctic sea ice (Carleton and Fitch, 1993). In 1988 (1989) an enhanced trough, given by negative departures of the monthly-averaged SLP and upper tropospheric heights, was located in the Ross Sea (Amundsen) sectors. There may also be a “signal” of the El Niño Southern Oscillation (ENSO) in these interannual mesocyclone variations over middle and higher southern latitudes. The 1988 (1989) winter followed an El Niño (La Niña) phase of ENSO. The intensity of the Amundsen Sea “mean” low varies according to the phase of ENSO (Chen et al., 1996; Cullather et al., 1996), and this inﬂuences the longitudes of frequent cold-air outbreaks and surface air temperature anomalies over the Antarctic Peninsula (Marshall and King, 1997). The low is weaker (stronger) during El Niño (La Niña), and accompanies an out-of-phase association in the intensity of the STJ and PFJ of the South Paciﬁc region: the STJ (PFJ) is stronger (weaker) in the El Niño, and vice versa for La Niña. Moreover, Carleton and Carpenter analyzed southern hemisphere DMSP infrared mosaics for mesocyclones during the southern winters (June through September) of 1977–83. In the winter leading up to the development of the major ENSO warm event of 1982–83, large numbers of mesocyclones occurred south-east of Australia and around New Zealand, consistent with the ampliﬁcation in the mean of the seasonal cycle of the Tasman Sea trough during ENSO events (van Loon and Shea, 1985). Conversely, in the winter before the warm event (winter 1981), mesocyclone activity was reduced markedly in the New Zealand region, while far greater frequencies occurred about 90o of longitude to the west. While this pattern is consistent with the reduced annual cycle of the Tasman trough in year 1 of warm events (van Loon and Shea, 1985), satellite studies of mesocyclone activity during additional ENSO events (both “warm” and “cold” extremes) need to be undertaken to conﬁrm these synoptic and teleconnective associations. The only climatological study of the “instant occlusion” mode of satellite-observed extratropical cyclogenesis is for the southern oceans and covers ﬁve winters (Carleton, 1981b). Highest frequencies of this phenomenon occur in the latitude zone 45o–50oS, which is the zone of maximum overlap of the frontal wave (Troup and Streten, 1993, W category) systems over lower middle latitudes and the comma cloud (A type) vortices over higher-middle latitudes, in this season. The analysis of higher resolution DMSP imagery for the Ross Sea sector (Carleton, 2001) conﬁrms the occurrence of instant occlusions north of 60oS. Spatially, most instant occlusions occur to the southwest and south of Australia, which is west of the area in the southern hemisphere characterized by the highest frequency of blocking (the Australia–New Zealand sector). Frontal waves approaching the block are slowed, increasing the opportunity for the interaction with coldair comma clouds approaching from the west. Synoptic climatologies of cold-air mesocyclones using satellite data number fewer for the northern hemisphere and are more recent than those in the southern hemisphere. This reﬂects, at least in part, the better coverage by conventional synoptic data. Reed (1979) developed the ﬁrst satellite-based synoptic climatology of comma clouds for the eastern North Paciﬁc using two years of infrared mosaics from the NOAA Scanning Radiometer. Yarnal and Henderson (1989a, b) analyzed DMSP infrared images for six cold seasons (November through March) to document the synoptic climatology of mesocyclones over the entire North Paciﬁc. Their study conﬁrms the strong seasonality of mesocyclone occurrences in the northern hemisphere documented in other studies (Wilhelmsen, 1985; Lystad, 1986; Businger, 1987; Ninomiya, 1989). Despite the strong intraseasonal and interannual

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variations in frequency and patterns of mesocyclogenesis, the majority of systems tend to develop off the Asian land mass in the zone of strong baroclinicity associated with the jetstream, then track southeastwards to the mid-Paciﬁc and subsequently northeastward into the Aleutian mean low cyclolytic region. Interestingly, the winter preceding the major El Niño event of 1982–83 recorded the smallest number of cold-air mesocyclones for the North Paciﬁc basin as a whole. Whether this climatological feature of mesocyclones is typical of ENSO warm events, or whether the unusual strength of the 1982–83 event atypically inﬂuenced the development patterns, remains to be determined. The synoptic climatological context of cold air mesocyclones in the North Atlantic region (Forbes and Lottes, 1985; Carleton, 1985a) reﬂects a tendency for these systems to develop in groups as “outbreaks” associated with anomalous atmospheric circulation – typically highly meridional wave patterns. Accordingly, there also appears to be a link with the dominant atmospheric teleconnection pattern of this region, the North Atlantic Oscillation (NAO), on seasonal and interannual time scales (Carleton, 1985a). There were marked changes in the regions of mesocyclone formation, tracks, and dissipation between two winters of contrasting sign in the index used to characterize the NAO (1974/75: positive; 1976/77: negative). These changes were similar to the composite shifts in the planetary waves associated with this teleconnection pattern described for the middle and higher-latitude regions of the North Atlantic sector by Rogers and van Loon (1979). Carleton (1988b) subsequently showed that the satellite-observed changes in frequency and locations of both the synoptic and mesoscale cyclones between these two years are consistent with the composite mean patterns of sensible heat transport for the respective extremes of the NAO. That is, there were considerably more (fewer) cloud vortices in the positive (negative) mode of NAO, and the peak frequency was shifted poleward (equatorward) in those winters. Clearly, again, the data for more years should be studied to conﬁrm these possible NAO–mesocyclone associations. The synoptic climatology of cold-air mesocyclone outbreaks in the North Atlantic and North Paciﬁc regions has also been described where the satellite data were not the primary data source indicating the occurrence of these systems (Businger, 1985, 1987; Ese et al., 1988). These studies conﬁrm that the conditions necessary for cold-air mesocyclogenesis involve an area of negative heights in the mid to upper troposphere that moves equatorward prior to the development of mesocyclones, a high-amplitude tropospheric wave pattern, and the rapid advection equatorwards of cold air just to the west of the anomalous trough. Moreover, these conditions are similar to those necessary for meso-cyclogenesis over some higher southern latitude regions (Fitch and Carleton, 1992; Carleton and Fitch, 1993; Turner and Thomas, 1994; Carleton and Song, 1997), particularly the Bellingshausen–Amundsen Sea sector and the Weddell Sea. 6.5.5 The Mesoscale Convective Complex

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A corollary to the cold air mesocyclone that predominates over land in the warm season is the Mesoscale Convective Complex (MCC) (Maddox, 1980). The MCC is a class of multicelled thunderstorm organized as a cluster, and contained within the broader phenomenon of Mesoscale Convective Systems (MCSs) (Plate 10). MCSs include MCCs and also thunderstorms organized linearly as squall lines, in both the tropics and the middle latitudes (Zipser, 1982; Lau and Crane, 1995). In the tropics they make up much of the ITCZ, which is particularly prominent over land and in the summer hemisphere (Machado and Rossow, 1993; Machado, et al., 1993). The tropical squall line or squall cluster has a line of convective cells extending laterally some hundred(s) of kilometers. It is accompanied by a strong wind squall and heavy rainfall, which is followed by a wide band of steady precipitation from upper-level stratiform cloud (Cotton and Anthes, 1989, p. 595). MCCs were ﬁrst identiﬁed as discrete systems for the US Great Plains (Fritsch and Maddox, 1981). They deliver very heavy rainfall and also hail, cause ﬂooding and strong

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Plate 10 GOES-E enhanced infrared image showing a large MCS over the central United States (Arkansas–Missouri–Illinois region) on August 13 1982. (NOAA)

winds, including derechos, and even spawn tornadoes (Maddox, 1980). It is estimated (Fritsch et al., 1986) that MCSs (i.e. MCCs and other convective weather systems) are responsible for about 30–70 percent of the precipitation falling on the Great Plains in the April through September period, and even more during the summer (June–August). While the convective cells contribute intense rain and hail showers, precipitation also falls from the deep upper stratiform layer formed by the cloud anvils. Similar systems to those in the Great Plains have now been cataloged for other midlatitude and also tropical and subtropical regions (Velasco and Fritsch, 1987; Miller and Fritsch, 1991; Laing and Fritsch, 1993a, b). For example, it is now believed that much of the precipitation falling in the southwest United States summer rainfall singularity (or “monsoon”) of July and August is associated with organized thunderstorm complexes having much in common with the MCCs of the central United States (Perry, 1990; McCollum et al., 1995). Moreover, MCCs occurring in the low latitudes may provide an important “seed” circulation for some tropical cyclones (Velasco and Fritsch, 1987; Laing and Fritsch, 1993a, b). Like cold-air mesocyclones, MCCs were ﬁrst characterized using satellite images – speciﬁcally, those obtained in the thermal infrared (IR) wavelengths, which have been enhanced to accentuate the lowest temperatures associated with the highest cloud tops. An example of this type of image is shown in Plate 5, which is a view from GOES-E for the early morning of June 25 1988. The highest and coldest clouds are identiﬁed by the enhancement which repeats the gray scale in cloudy areas. Notice how these very

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Plate 11 GOES-W enhanced infrared image showing an MCC in central Arizona during the summer “monsoon” season on August 12 1982. The system developed over the elevated topography of the Mogollon Rim, and both enlarged and moved to the northwest in the ensuing several hours. (NOAA/National Weather Service)

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cold cloud tops occur in both the tropics (associated with the ITCZ) and over land (e.g. over northern Arizona and the upper Midwest) at this time. Mesoscale convective systems tend to peak in intensity (i.e. they exhibit the greatest horizontal extent of the cold cloud shield and lowest cloud top temperature) at night, when the vertical development of the system is enhanced by cooling aloft through longwave radiation loss from the thunderstorm tops, and by an intensiﬁed low-level jet (LLJ) advecting moist air poleward in the planetary boundary layer (Anderson and Arritt, 1998). Accordingly, the cloud-radiative forcing due to MCSs may comprise a crucial determinant of the Earth–atmosphere energy balance over a wide range of latitudes in the warm season. Mesoscale convective systems interact dynamically and thermodynamically with their surroundings. They result from the larger-scale atmospheric environment, and also help to modify it via strong and widespread vertical transports of heat and moisture (cf. Maddox, 1983; Read and Maddox, 1983). Like cold-air mesocyclones, MCSs may be concentrated temporally and spatially as “outbreaks” due to the persistence of synoptic conditions favorable to their development (Wetzel et al., 1983; Leary and Rappaport, 1987). Mesoscale convective complexes typically endure for around ten to twelve hours, although a small proportion of systems may persist for up to two or even three days (Wetzel et al., 1983). The individual thunderstorms comprising an MCC have lifetimes of just an hour or two. Maddox (1980) developed a set of criteria for identifying MCCs from satellite-enhanced infrared images, and for characterizing their successive stages of development (Table 6.1).

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Table 6.1 Criteria for identiﬁcation of a Mesoscale Convective Complex based upon analyses of enhanced infrared satellite imagery Criterion

Physical characteristics

Size

Cloud shield with continuously low infrared temperature ≤ 52°C must have an area > 50,000 km2

Initiation

Size deﬁnition ﬁrst satisﬁed

Duration

Size deﬁnition must be met for a period of six hours or more

Maximum extent

Contiguous cold cloud shield (infrared temperature ≤ –52°C) reaches maximum size

Shape

Eccentricity (minor axis/major axis) ≥ 0.7 at time of maximum extent

Temination

Size deﬁnition is no longer satisﬁed

Source: Maddox (1980); modiﬁed by Anderson and Arritt (1998).

Like the frontal cyclone and cold-air mesocyclone, MCCs exhibit a well deﬁned temporal evolution in the imagery that can be used to classify them into genesis, mature, and dissipation stages. The synoptic ﬁelds within which an MCC is embedded also change between these different stages (Maddox, 1983). The Maddox criteria emphasize the meso-alpha (i.e. larger subsynoptic) scale of the MCC, and include the areal extent of the coldest cloud tops, the duration of the system, and its shape. In the enhanced infrared imagery the coldest cloud tops are located in the central part of the system, as shown in the example for the southwest United States (Plate 11). At maturity the area covered by the coldest cloud tops (often <52°C) reaches its largest extent and the MCC is at its most elliptical (around 0.7: Table 6.1). Augustine and Howard (1988) modiﬁed Maddox’s original criteria by excluding weaker systems, whose cloud-top temperatures may be lower than 32°C but not reach the “critical” threshold temperature of 52°C established by Maddox. Other authors (McAnelly and Cotton, 1989; Anderson and Arritt, 1998) have made more minor modiﬁcations to the basic satellite-based criteria deﬁning MCCs. The longer horizontal axis of an MCC is parallel to the direction of movement of the system, which is to the right (left) of the upper steering wind in the northern (southern) hemisphere. Accordingly the MCC exempliﬁes a class of right-moving storm in the northern hemisphere. For middle-latitude systems this means that the MCC typically moves from north of west to south of east, but always into a region of warm, moist, and unstable air. This net motion is the vector difference of the direction of movement of new thunderstorm cells forming on the warm side of the complex (the so-called propagation component), and the advective component, which is the mean motion of cells comprising the system (Corﬁdi et al., 1996). In effect the MCC moves with the “thermal wind.” The availability of satellite-enhanced infrared images from geosynchronous platforms has meant that MCCs have been identiﬁed, and synoptic climatologies of their occurrence compiled, for large areas of the tropics and middle latitudes. As with the study of coldair mesocyclones, these regional and larger-scale climatologies of MCCs have typically taken three forms: 1

Composite analyses of standard meteorological ﬁelds, including precipitation (Kane et al., 1987; McAnelly and Cotton, 1989), stratiﬁed according to the characteristic temporal stages of development of MCCs denoted on the infrared images (Maddox, 1983).

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Mapped distributions of MCCs for a particular season or multi-year composite of seasons, such as those obtained by tracking the movements of the “centroid locations” (central point of the coldest cloud-top temperatures) of many such systems (Rodgers et al., 1983, 1985; Augustine and Howard, 1988, 1991; Anderson and Arritt, 1998). Composites of the larger-scale synoptic ﬁelds within which are embedded MCCs for time periods of their occurrence (Augustine and Howard, 1991).

The system-composite studies of MCCs (category 1 above) permit the temporal evolution of these storms to be analyzed. For example, the composite precipitation patterns of MCCs over the US Great Plains generated by McAnelly and Cotton (1989) reveal that the heaviest precipitation occurs about 50–100 km equatorward of the cloud-shield centroid. Moreover, these types of studies help reveal the dominant forcing mechanisms of MCCs. The MCCs that form in middle latitudes in summer show some baroclinic attributes, although they are largely thermally driven (Fritsch and Maddox, 1981). Thus many MCCs exhibit a warm core in the mid-troposphere resulting from latent heat release within the clouds, and a cold upper troposphere due to radiation loss from the cloud tops (Fritsch and Maddox, 1981). The second group of satellite-based climatological studies of MCCs emphasizes the locations, tracks, and dissipation areas of MCCs for different regions; initially North America (Rodgers et al., 1983, 1985; McAnelly and Cotton, 1989; Augustine and Howard, 1991). Over the US Great Plains there is an intraseasonal variation in the locations of MCC development such that systems are generally displaced to the southward (northward) in the late spring and early summer (mid to late summer). This pattern is connected with the heating of the land mass and retreat of the upper westerlies of the circumpolar vortex as the warm season progresses. Interannual variations in MCC development are apparently a response to larger-scale atmospheric circulation patterns (Rodgers et al., 1985) and, possibly also, teleconnections with ENSO. Velasco and Fritsch (1987) extended the use of GOES-E data to the study of MCCs in North and South America, as well as the tropical zone between. These authors found broadly similar features associated with MCCs in the middle-latitude areas of both continents, except that the cloud shield is about 60 percent larger in the Argentinian systems. The frequency of MCCs showed strong variations between the two years that they studied (May 1981–May 1983), and these appear connected with the major ENSO event of 1982–83. In middle southern latitudes more than twice the number of MCCs formed in the El Niño year (1982–83) compared with the non-El Niño year (1981–82). There were also considerably more MCCs that formed over the anomalously warm water off the Peruvian coast associated with the El Niño event. In common with the Brazilian rainforest region of South America (Velasco and Fritsch, 1987), relatively few MCCs develop over the equatorial rain-forest of central Africa despite the large amount of deep convection occurring there and the presence of the ITCZ. This observation emphasizes the differences between MCSs and MCCs (cf. Machado et al., 1992). Satellite-based climatologies of MCCs have now been undertaken for almost all regions of the globe. Comparisons of the infrared-derived signatures of MCCs in these different regions with those occurring in the Americas suggest strongly that they are all the same class of phenomenon. A global summary developed by Laing and Fritsch (1997) considers MCCs occurring over South and Central America (Velasco and Fritsch, 1987), the United States (Augustine and Howard, 1991), Africa (Laing and Fritsch, 1993a), India (Laing and Fritsch, 1993b), and the western Paciﬁc Ocean and adjacent areas (Miller and Fritsch, 1991); they also extend the analysis to Europe. Approximately 400 MCCs occur annually around the globe, with two-thirds in the northern and one-third in the southern hemisphere, related to the land–sea fractions. The great majority (91.6 percent) form over land areas. The systems are predominantly nocturnal, with the cold cloud shield attaining its maximum extent of about 3 × 105 km2 in the early morning hours. They persist for

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Figure 6.31 The global occurrence of MCCs and regions of widespread, frequent deep convection, indicated be outgoing long-wave radiation (ORL). The shaded areas indicate OLR minima. The OLR data (W m2) are from the ERBE for (a) July (north of the equator) and January (to the south) 1985–86 and (b) June–August (north of the equator) and December–February (to the south) 1974–78. (From Laing and Fritsch, 1997)

about ten hours, but the oceanic systems tend to end later in the morning than those over land. In summer there is a positive correlation (0.57) in mid-latitudes between cloud area and duration, illustrating the fact that systems with large cold-cloud shields tend to be more persistent. The geographical distribution of MCCs (Figure 6.31) shows two preferred zones of occurrence: (1) on the peripheries of minima in outgoing long-wave radiation and (2) in the lee of mountain ranges and high plateaus, relative to the prevailing mid-tropospheric airﬂow. Seasonally, the region of activity over the continents shifts between 35°S in the austral summer and north of 50°N in July, and this tendency is matched by the occurrence of midnight lightning, reported by Goodman and Christian (1993) and Barry et al. (1994). Higher frequency passive microwave sensing, particularly the 85 GHz dual-polarized channels of the SSM/I (section 2.2), readily lends itself to the study of MCS-type features (Adler et al., 1991; Mohr and Zipser, 1996a), because these channels are sensitive to the scattering of microwave radiation by large ice particles and snow aggregates in the upper parts of precipitating cumulonimbus clouds (McGaughey et al., 1996). Mohr and Zipser (1996b) used the 85 GHz channels to determine the basic distribution and characteristics

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of MCSs (i.e. all classes of mesoscale convective system) occurring over land and sea in the tropical zone. They found the greatest frequency of MCSs over tropical South America, tropical Africa, and the western Paciﬁc “warm pool,” The smallest (largest) MCSs occur over equatorial land regions, including tropical Africa (subtropical oceans). MCSs over land (ocean) tend to have the coldest (warmest) brightness temperatures. Comparisons of the sunset and sunrise orbital data of the SSM/I revealed diurnal variations of MCS frequency and size that show dependence on their occurrence over land or sea domains and, accordingly, differences in the extent of ice scattering (McGaughey et al., 1996). The third group of MCC studies comprises those in which the synoptic environments within which the MCC is embedded can be characterized. From those studies, the dominant forcing mechanisms of MCCs are determined to be as follows: 1

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Weak static stability due to their presence in, and movement towards, warm and moist air. Occurrence on the lee side of major mountain ranges, whereby air columns moving into the plains leading away from the mountains are forced to undergo vertical stretching to conserve potential vorticity, with convergence at lower levels and divergence aloft. Additionally, the slope of the plains can aid the development of LLJs and also enhance the ascent of moist air at low levels, both of which are important in MCC development (e.g. Miller and Fritsch, 1991; Leary and Rappaport, 1987). An upper short-wave trough that is evident at the surface as a weak cold front or stationary front located to the westward or northwestward (Maddox, 1980). A subtropical jetstream in the warm air that curves anticyclonically as the MCC develops (Maddox, 1983). This advects subtropical moisture into the region, and has associated divergence aloft, with convergence at lower atmospheric levels. An LLJ concentrated between about 950 mb and 850 mb that advects heat and moisture within the boundary layer into the storms on the warm side of the complex. The LLJ is typically diurnally varying, being strongest just below the top of the boundary layer when the surface-based inversion is strongest (i.e. at night and in the early morning). Accordingly the LLJ helps to initiate new thunderstorms on the warm side of the MCC, in association with the weak stability generated by the outﬂow boundaries either from existing or from recently deceased thunderstorms within the complex. It also helps provide a clockwise turning of the wind with height (northern hemisphere), which denotes warm advection into the MCC. Both of these signify a baroclinic atmosphere. Modeling work for the US Great Plains (McCorcle, 1988; Chang and Wetzel, 1991; Zhong et al., 1996; Wu and Raman, 1997) suggests that horizontal contrasts in land surface conditions, particularly soil moisture and vegetation, may increase the instability and thereby enhance the development and intensity of the LLJ and, potentially, also, the development of MCCs, under favorable broaderscale circulation conditions.

6.5.6 Tropical-extratropical cloud band connections

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The very long bands of deep cloud that develop on synoptic time scales, and which connect convection in the lowest latitudes with cyclonic circulations over higher latitudes (Plate 12), were also ﬁrst identiﬁed in satellite imagery (Oliver and Anderson, 1969). The lower-latitude branch of a tropical–extratropical cloud band connection (Erickson and Winston, 1972) originates either in clusters of convection or in a tropical cyclone that has begun to dissipate (Sekioka, 1970; Gray and Clapp, 1978; Davis, 1981); however, it seems that the movement equatorwards of a middle-latitude trough provides the initial impetus for development of such a connection (McGuirk et al., 1987; McGuirk and Ulsh, 1990). Once initiated, the cloud-band feature transports moisture and energy rapidly polewards and eastwards via the STJ (Thepenier and Cruette, 1981), the PFJ over middle

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Plate 12 DMSP infrared mosaic (5.4 space km resolution) showing a tropical–extratropical cloud band connection in the western North Paciﬁc during October 1977 (exact date unknown). The cloud band extends from a typhoon which is dissipating, through several extratropical cloud vortices, and terminates in the Bering Strait at 60°N. (From Carleton, A.M., 1985, “Synoptic cryosphere–atmosphere interactions in the northern hemisphere from DMSP image analysis,” Intl. J. Remote Sens. 6 (1): 245)

latitudes (Zwatz-Meise and Hailzl, 1980), and, sometimes, with a feature resulting from the merger of the two (Reiter and Whitney, 1969). The poleward terminus of a tropical–extratropical cloud band is usually a cyclonic cloud vortex; however, it is not uncommon for a series of cyclonic vortices in different stages of development to extend back along the band in middle latitudes (Plate 12). These tropical–extratropical cloudband connections manifest a preferred mode of the atmospheric cross-latitude transport of energy and moisture, and conﬁrm that it does not take place equally across all longitudes. Rather, this transport occurs rapidly and along relatively narrow zones that can be as short-lived as a couple of days (Gray and Clapp, 1978; Thepenier and Cruette, 1981; Davis, 1981). Tropical cloud-band connections are known by a variety of names. While Kuhnel (1989) referred to them more generally as Tropical–Extratropical Cloud Bands (TECBs), they are also differentiated on a regional basis. Several authors (McGuirk et al., 1987, 1988;

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McGuirk and Ulsh, 1990; Iskenderian, 1995) term the cloud bands in the central and eastern North Paciﬁc “moisture bursts” and “tropical cloud plumes,” while those originating in the Arafura Sea northwest of Australia are known as “northwest Australia cloud bands” (Downey et al., 1981; Tapp and Barrell, 1984; Bell, 1986). The cloud band in the South Paciﬁc, or SPCB, is the most frequently occurring and persistent TECB, especially in the November through May period (Kuhnel, 1989). This feature was prominent in the early analyses of multi-day composite visible images of the southern hemisphere used for developing the ﬁrst satellite cloud climatologies and for determining their associations with the atmospheric circulation (Kornﬁeld et al., 1967; Streten, 1968a, 1970, 1973; Miller and Feddes, 1971). The SPCB links the area of enhanced convection over the maritime continent with frontal systems in the southeast Paciﬁc Ocean (Plate 13), and is involved in the SST variations associated with the ENSO over tropical and subtropical latitudes (Trenberth, 1976). The role of the SPCB in transporting energy, momentum, and moisture poleward has been studied extensively for case periods (Huang and Vincent, 1985). In Kuhnel’s (1989) study it was present on the infrared imagery on 778 days in ﬁve years, but showed substantial interannual variability associated with the Southern Oscillation Index (SOI) consistent with other non-satellite studies (Trenberth, 1976). Streten (1978) found a quasi-periodicity in SPCB location of around twenty to twenty-ﬁve days, similar to the time scales of zonal indices and kinetic energy in the southern hemisphere. The association of the SPCB variations with ENSO was conﬁrmed and extended by Berlin (1991), who studied the SPCB using daily NOAA hemispheric infrared mosaics for the period June 1 1980 through May 31 1984. This includes the major warm event of 1982–83. She conﬁrmed the spring and summer frequency maximum of this band, and also found that the seasonal-scale shifts were more evident than a forty to ﬁfty-day oscillation, at least for the tropical reach of the cloud band. Berlin (1991) also found the frequencies of extratropical cyclones in the South Paciﬁc region to vary inversely with the frequency of cloud-band days, suggesting a net balance in the contribution to the total poleward ﬂuxes of heat and momentum by the standing and transient eddies, represented by the cloud-band and cyclonic vortices, respectively. Interannually, an eastward movement in the cloud band axis commenced in the southern spring of 1982 and reached its most eastward location in the summer of 1982–83, coincident with the ENSO warm event. This pattern is similar to that found for the 1972–73 El Niño event by Streten (1975). Also occurring during the 1982–83 event was an increase in the frequency of satellite-observed extratropical cyclone activity over the South Paciﬁc (Berlin, 1991). There are fourteen separate TECBs for the globe, seven in each hemisphere, sometimes having different seasons of occurrence (Kuhnel, 1989). While TECBs are typically a coolseason phenomenon, some have a late autumn maximum, and still others, like the SPCB and that over southern Africa, may occur relatively frequently in summer (Erickson and Winston, 1972; Harrison, 1984). This suggests that TECBs fulﬁll slightly different functions with respect to the atmospheric general circulation. Erickson and Winston (1972) studied the occurrence of TECBs in the western North Paciﬁc, and showed that the frequency of cloud bands increases in the late summer and early fall seasons, coincident with the build-up of the westerly circulation in middle latitudes. Since many tropical storms and typhoons are observed to decay upon interacting with these cloud bands at these times, the implication was that TECBs facilitate the rapid poleward transport of energy and moisture out of the tropics and into the extratropics of the northern hemisphere. The “moisture bursts” of the central North Paciﬁc that develop near the dateline, propagate across North America (McGuirk et al., 1987, 1988; McGuirk and Ulsh, 1990), and often extend into the northern North Atlantic (Thepenier and Cruette, 1981), are even more transient features. Half the bursts develop and dissipate over a period of between two and four days. Depending on the latitude at which moisture bursts enter North America, they may bring heavy precipitation into either the US west coast or across Texas

504 Synoptic and dynamic climatology 1

Plate 13 Infrared mosaic of the southern hemisphere from the NOAA SR (Scanning Radiometer), 8 km resolution, for June 17 1975. The image shows a well developed SPCB extending from the area near northeast Australia south and eastwards into the Bellingshausen/ Amundsen seas. (NOAA)

and the southern Gulf states (e.g. Dickson, 1973). When penetrating the interior regions in midwinter, dominated by air of Arctic origin, moisture bursts can be associated with severe ice storms (Plate 14). McGuirk et al. (1987) studied moisture bursts from GOESW imagery for the cool seasons (November–April) of 1975–76, 1977–78, 1981–82, and 1982–83. They found a substantial interannual variation in the frequency and longitude locations of moisture bursts that suggested an association with the ENSO teleconnection. The hypothesis of McGuirk et al. (1987) was tested in a ten-year climatology developed for the October through May periods of 1974–84 by Iskenderian (1995). That period recorded 1,062 “tropical plume” events. Composite analysis of streamline data showed that cloud plumes tend to occur during the preferred times of westerly wind ducts in lowlatitudes, which develop as a low latitude trough extends towards the region of plume origin, and a trough from the southern hemisphere also extends northwards. Tropical plumes also exhibit substantial intraseasonal variability, from a maximum in the October and May months but a short minimum in February and March. Moreover, there is a tendency for plume frequency to vary out of phase between the eastern Paciﬁc and central Atlantic on a seasonal basis. While Iskenderian (1995) conﬁrmed the strong decrease in plume events during 1982–83 found by McGuirk et al. (1987), no similar decrease was found for the 1986–87 ENSO warm event. In fact, plume frequencies were above average in the eastern Paciﬁc for that season. Thus the true association of moisture bursts and ENSO remains to be clariﬁed.

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Plate 14 GOES-E infrared images showing a moisture plume extending from the eastern North Paciﬁc into the central United States. In moving above arctic air near the Earth’s surface the moisture plume helped to produce a heavy ice storm over southern Illinois and Indiana on February 14 1990. (NOAA/National Weather Service)

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The northwest Australia cloud band is a potent rainfall producer for the inland areas of central and southeastern Australia in winter (Kininmonth, 1983; Wright, 1988), unlike the rainfall typically associated with cold frontal passages in this region. Diagnostic case studies of the northwest Australia cloud band (Bell, 1986) indicate it to be comprised mostly of middle and high-level clouds generated ahead of an elongated trough in the westerlies. Air parcels moving poleward on the forward, or eastern, side of the trough ascend more or less moist isentropically, leading to deepening clouds which give extensive pre-frontal precipitation over subtropical and lower middle latitudes of Australia. The probability of precipitation is increased poleward along the cloud band owing to increasing cyclonic vorticity and also slantwise convection.

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6.6 Synoptic-scale systems in the tropics Progress in understanding synoptic behavior in low latitudes spans the last ﬁfty years, and a number of signiﬁcant milestones of this period can be identiﬁed. The ﬁrst landmark contributions describing synoptic-scale waves in the tropical easterlies were based on time series analysis of single station sounding data and the use of streamline maps as a key diagnostic tool (Riehl, 1945, 1954; Palmer, 1950, 1952). It was recognized that the three-dimensional distribution of mass divergence and the vertical moisture proﬁle are important determinants of the occurrence of cloud cover and precipitation. A second major step came in the 1960s with the greatly expanded spatial view provided by satellite imagery, which brought recognition of the wide variety of tropical disturbances. Merritt (1964) identiﬁed ﬁve satellite cloud patterns, only one of which resembled the easterly wave (Frank, 1969); the others had vortical cloud distributions and included upper lows. The value of satellite remote sensing was further enhanced with the advent of operational geostationary satellites over the equator in 1974 (see section 2.2), enabling analyses to be made of diurnal cloud and convection regimes. A third phase, beginning in the late 1960s, involved the use of spectral analyses of meteorological data to identify wave periods, propagation, and wavelength characteristics. The fourth phase, which began almost concurrently and is still continuing, is represented by the mounting of international ﬁeld programs combining measurements from ships, aircraft, and satellites. These have included the Line Islands Experiment (LIE) in the central equatorial Paciﬁc in March–April 1967; the Atlantic Tropical Experiment (ATEX) in the central equatorial Atlantic in February 1969; the Barbados Oceanographic and Meteorological Experiment (BOMEX) in the western tropical Atlantic during May–July 1969; and the Venezuela International Meteorological and Hydrologic Experiment (VIHMEX) during June–October 1969 and May–September 1972. Subsequently there were more intensive campaigns organized as contributions to the Global Atmospheric Research Program (GARP), coordinated by the World Meteorological Organization and International Council of Scientiﬁc Unions (ICSU). Programs in the tropics included the GARP Atlantic Tropical Experiment (GATE) off West Africa in June–September 1974, the Monsoon Experiment (MONEX) over Southeast Asia–Indonesia–northern Australia in January–February 1979, the Arabian Sea–western Indian Ocean in May–June 1979 (WMO-ICSU JOC, 1976) and the linked West African Monsoon Experiment (WAMEX) in summer 1979. Subsequently there has been the Tropical Ocean Global Atmosphere (TOGA) Coordinated Ocean Atmosphere Response Experiment (COARE) over the West Paciﬁc warm pool in November 1992– February 1993 (Lukas et al., 1995). A crucial component of GARP is the assembly of comprehensive and consistent atmospheric ﬁelds through the Global Data Assimilation System (GDAS) operated at the primary international centers for operational weather prediction. This development has laid the foundations for intensive analyses of global circulation characteristics and the role of disturbances in combination, inter alia, with satellite-derived information on clouds, deep convection, and precipitation (Garcia, 1985; Rasmusson and Arkin, 1993; Mohr and Zipser, 1996a). In parallel with the enhanced observational capabilities and data sets, advances in statistical techniques and in theoretical work and modeling studies also played essential roles. The spatiotemporal characteristics of low-latitude circulation and weather have been more clearly deﬁned by the statistical analysis of time series, as well as by intensive diagnostic analysis of case studies. Nevertheless, it needs to be pointed out that the period and wavelength of waves identiﬁed in spectral analyses of the variance of wind, temperature, cloud cover, and so on, do not always coincide with the corresponding values determined from synoptic information. Burpee (1974), for example, reports a period of four and a half days and a wavelength of 3,800 km from spectral analysis of meridional wind and pressure data, but three and a half days and 3,100 km, respectively, based on analysis of forty-three waves over West Africa. He also found stronger signals by composite analysis than using spectral analysis.

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6.6.1 Global waves in the tropics Global waves that have been identiﬁed in the tropics on synoptic time scales include a ﬁve-day traveling wave (Deland, 1964; Wallace and Chang, 1969; Misra, 1972; Madden and Julian, 1972; Madden and Stokes, 1975; Madden, 1978) as well as a ﬁve-day standing oscillation; the latter was ﬁrst noted over India in the late nineteenth century by J. Eliot (Asnani, 1993) and subsequently reported by Frolow (1941) from central Africa to Central America across the Atlantic Ocean, and also over the equatorial Paciﬁc Ocean (Palmer and Ohmstede, 1956). For the 1957–58 International Geophysical Year, Misra (1972) analyzed sea-level pressure data at seventy-six stations between 20°N and 20°S. The data show four to ﬁve-day oscillations from a westward-moving zonal wave No. 1, in accordance with earlier work by Deland (1964). Madden and Stokes (1975) support this ﬁnding from a cross-spectral analysis of a seventy-two-year summer series of surface pressure data at 25°N, 90°W, 25°N, 150°W, and 25°N, 30°E. The wave is not evident in the winter series although this could be the result of a lower signal/noise ratio. Wallace and Chang (1969) indicate a phase speed of 100 m s1 in the surface pressure wave and an amplitude of about 1 mb at the equator. The signiﬁcance of these features for direct modulation of weather and climate is evidently modest, but they may have important dynamical interactions in the tropics and extratropics. Several waves having long wavelengths and periods have been identiﬁed in low latitudes. They appear to play an important role in the stratospheric quasi-biennial oscillation, the Walker circulations and thirty-to-sixty-day oscillations in the troposphere. Analytical studies of these waves by Matsuno (1966) and Longuet-Higgins (1968) are summarized by Webster (1983) and Asnani (1993). The pressure and velocity distribution of a theoretical westward-propagating inertiogravity wave of wave No. 1 is shown in Figure 6.32 (Matsuno, 1966). An important feature of the equatorial region (equatorward of about 15° latitude) is that inertio-gravity waves are reﬂected as they try to propagate poleward by virtue of the increase of f poleward, deﬂecting the ﬂow towards the equator. They have a frequency: [f 2 c02 m2]1/2

0

where c0 wave speed, m zonal wave number, and thus the minimum frequency is equal to f, the Coriolis parameter. The reﬂected inertio-gravity waves are trapped in an equatorial duct and tend to propagate zonally; they have a maximum period of about two days (Young, 1987). The duct has a width proportional to the Rossby radius of deformation (c0/ )1/2.

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Figure 6.32 The pressure and velocity distribution for a theoretical westward-propagating gravity wave, meridional wave No. 1. (From Matsuno, 1966)

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Figure 6.33 Schematic diagram of equatorial Rossby wave (left), Kelvin wave (center), and mixed Rossby–gravity wave (right). Lower level shows isobar (H, L centers) and winds; mid-level shows warm (W) and cold (C) centers and vertical motion. Typical phase and energy propagation vectors are indicated. (From Young, 1987)

Other important equatorial waves are the Rossby wave and the mixed Rossby–gravity wave. The Rossby wave is symmetric about the equator, with strongest zonal winds along the equator, and the circulation is almost geostrophic (see Figure 6.33). Note that the phase and energy propagate slowly westward. The second type resembles a hybrid between the Rossby and the inertia-gravity wave. It is asymmetric with respect to the equator and features strong ageostrophic and vertical motions. The phase propagation is westward at about 23 m s1, but energy propagates eastward (see Figure 6.33). The ﬁgure illustrates the cross-isobaric ﬂow components near the equator. The mixed Rossby–gravity wave has a horizontal wavelength of about 10,000 km and a period of four to ﬁve days in the lower equatorial stratosphere (Wallace, 1973). A further important wave in the equatorial lower stratosphere is the Kelvin wave (Figure 6.33). It features almost geostrophic zonal motion on both sides of the equator and strong cross-isobaric ﬂow. It has a period of about ﬁfteen days, propagating eastward at 25 m s1, with a wavelength around 30,000 km. The amplitude of the u component is about 8 m s1 (Wallace, 1973). The Kelvin wave transports energy and westerly momentum upward, whereas the mixed Rossby–gravity wave transports easterly momentum upward. These act together through wave-mean ﬂow interactions to produce the Quasibiennial Oscillation in the equatorial lower stratosphere. 6.6.2 Characteristics of tropical waves The literature on tropical waves and depressions has helped to create the impression that there exists a wide variety of synoptic disturbances in low latitudes. Among the models described are: the easterly wave over the Caribbean (Riehl, 1945), the tropical Atlantic (Carlson, 1969) and North Africa (Burpee, 1972, 1974); monsoon depressions over India (Duggupaty and Sikka, 1977); the subtropical cyclone (Ramage, 1962); and wave disturbances over the equatorial central Paciﬁc (Palmer, 1952), western Paciﬁc (Reed and Recker, 1971), and eastern equatorial Atlantic (Thompson et al., 1979). Nevertheless, as Riehl (1979, p. 321) has emphasized, it is unlikely that there are as many physical mechanisms for these storms as there are for superﬁcial differences. The essential element, except along the subtropical margins, is the conversion of latent heat into potential energy and then into kinetic energy. This conversion is concentrated in mesoscale convective systems (MCSs) which show a wide variety of organizational patterns – as squall lines (Aspliden et al., 1976; Houze et al., 1981; Zipser, 1970), cloud clusters (Mohr and Zipser,

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Figure 6.34 The basic types of zonal wind structure observed in the tropics. The proﬁles are for: 76°W (Caribbean easterly wave), 12°W (African monsoon cyclone), 75°E (Indian monsoon depression), 135°E (western Paciﬁc ITCZ wave), 170°E (central Paciﬁc ITCZ wave). (From Krishnamurti, 1979)

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1996b), and vortical cloud systems (Merritt, 1964) – and generally occupy areas of only about 2°–5° latitude radius. Within a given MCS there are individual convective cells covering about 2,000 km2 (Henry, 1974). The apparent variety of tropical disturbances seems to be a result of the variations in external controls; the vertical wind structure differs seasonally and interannually (Riehl, 1973) as well as geographically. Figure 6.34 illustrates the different basic structures that have been reported to occur in different sectors over the belt 5°–25°N during northern summer. The contrasting proﬁles of vertical wind shear are readily apparent. The classical easterly wave structure (Figure 6.35) pictures the trough tilted eastward with height and identiﬁes maximum cloud cover, convection, and precipitation ahead of the moving wave trough (Riehl, 1954). This relationship is interpreted in terms of the distribution of low-level convergence; if the easterlies are moving faster than the wave, then conservation of potential vorticity requires the air ahead of the wave to gain cyclonic vorticity to offset the decrease in f, thus the air converges and ascends, i.e., for adiabatic motion, potential vorticity conservation requires:

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∂( f ) =K ∂p

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An increase of cyclonic relative vorticity following the motion implies increased convergence, since: ∂( f ) = ( f ) ·VH ∂t

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At higher levels, where the easterlies are weaker, the air moves eastward relative to the wave, and anticyclonic vorticity and divergence result ahead of the trough. The structure supports ascending air and convection in association with a deeper moist layer. In the rear of the trough the opposite structure and descending air are found. However, observational analysis of many disturbances in the tropical Atlantic shows a different picture. For July 1975 traveling wave disturbances surveyed by Shapiro (1986) show a westward

510 Synoptic and dynamic climatology 1

Figure 6.35 The structure of the classical easterly wave in the Caribbean. Vertical time section at San Juan, Puerto Rico, July 11–13 1944 and corresponding 5,000 ft (1,640 m) winds, surface weather, and twenty-four-hour pressure change, July 12 1944 (from Riehl, 1954)

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tilt of the trough with height, with a 90° westward phase shift of meridional velocity and vorticity at 200 mb, relative to the lower troposphere, equivalent to a one-day lead. The pattern resembles that in developing tropical cyclones. For summer–autumn 1968 a survey of thirty-three Atlantic disturbances between 11°N and 15°N shows increased cloud cover on the wave axis (Carlson, 1969), while a more extensive analysis for summers 1960–64 indicates no preferred location with respect to the wave (Burpee, 1972). However, in a subsequent study of summers 1968 and 1969, Burpee (1974) reports maxima of precipitation and thunderstorm frequency west of the trough (succeeding ridge) for systems to the south (north) of 12.5°N. Analyses of GATE observations show the maximum convection and squall lines to be located just ahead of the trough axis (Aspliden et al., 1976; Reed et al., 1977) and this is conﬁrmed by Meteosat infrared and water vapor channel data and ECMWF analyses for June–September 1983–85 over the eastern tropical Atlantic and West Africa (Duvel, 1990). In contrast to the classical interpretation, Duvel attributes the vertical distribution of convergence and divergence to feedback processes from the vertical motion and convection induced by diabatic heating. Signiﬁcantly, he ﬁnds that over 70 percent of the intraseasonal variance in circulation, cloud, and water vapor parameters in the eastern tropical Atlantic is concentrated in the one to eight day frequency band. Wave development in low latitudes requires deep easterlies throughout the troposphere. When upper-level westerlies are present, tropical wave activity is substantially reduced. Deep easterlies are prevalent over West Africa, for example, whereas upper westerlies are not uncommon over the Caribbean and northern Venezuela (Riehl, 1973), as shown in Figure 6.34. As pointed out by Frank (1969), the Caribbean is not an ideal region in which to identify a “pure” easterly wave. The structure of wave disturbances in relation to the vertical shear of the zonal wind has been examined in several modeling studies. For example, Holton (1971) concluded that westerly vertical wind shear (a trade-wind regime), produces the classical easterly wave structure, tilting eastward with height, whereas the trough axis tilts westward with easterly wind shear. The diabatic heat source for the waves in Holton’s model is speciﬁed and there is no feedback from the wave to the heating. Shapiro et al. (1988) use a linear primitive equation model which incorporates a basic state zonal wind in gradient thermal wind balances, a forced westward-propagating disturbance, and diabatic heating centered at 400 mb. The tilt of the trough is found to depend on interactions between the wave and the environment, involving not only the vertical wind structure but also the latitude of the disturbance. For westerly shear in the lower middle troposphere and maximum heating at 19°N, westward tilt with height above 700 mb is determined by a downward ﬂux of energy below 200 mb towards the surface. An eastward tilt can occur below 400 mb with heating at 9.4°N; here the westward phase shift is focused just above the maximum heating level. A major source area of tropical waves for the North Atlantic is the sector from 0°W to 100°W in northern Africa. Disturbances in the tropical easterlies over the eastern North Atlantic were recognized in the 1930s by Piersig (1944). They appear to have their origins on either side of the mid-tropospheric African Easterly Jetstream (AEJ) located at about 15°N. They tend to develop either around 5°–15°N, 20°E downstream of the Ethiopian Highlands, or around 20°–25°N, 5°W–5°E, downstream of the Hoggar mountains (Reed et al., 1988), although it is not thought that topography plays any major role in wave initiation, in contrast with the generation of diurnal disturbance lines to the south (Eldridge, 1957; Aspliden et al., 1976). Figure 6.36 illustrates wave occurrence in 850 mb streamline and vorticity analyses during summer 1985. Systems formed in these regions may subsequently merge downstream. African waves have their greatest amplitude near the coast of West Africa, between 600 and 700 mb, in association with the 12–15 m s1 AEJ around 15°N. There is a secondary maximum in wave amplitude at 200 mb. For June–September 1983–85, 40–50 percent of the spectral amplitude of integrated water vapor, and over 60 percent near the West African coast at 15°N, occurs in the 2.8–5.1

512 Synoptic and dynamic climatology 1

Figure 6.36 Wave structure over West Africa; 850 mb streamlines and relative vorticity (dotted lines) (105 s1), from ECMWF analysis for 12.00 GMT, August 29 1985. H/L denotes vorticity maxima/minima; a full barb on the wind arrow denotes 5 m s1. Wave K formed in the proximity of the Hoggar mountains. (From Reed et al., 1988)

day band (Duvel, 1990). The 850 mb v wind in this frequency interval also has maximum wave amplitude near the coast at 20°N; this amplitude is stronger in August–September than in June–July. The amplitude of waves over West Africa in summers 1968 and 1969 was about 1.5 m s1 for the surface meridional wind at 19°N and 1 mb for sea-level pressure at 18°N, determined spectrally. Synoptic composites of forty-three waves gave a stronger amplitude reaching 3 m s1 for v and 1°C for temperature at 700 mb. In GATE cases (Reed et al., 1977), ascent averaged 5 mb/hr at 700 mb ahead of the trough. However, the associated cloud amount was only about 40 percent and the precipitation averaged 10–20 mm/day. The waves move westward at about 8 m s1 along zonal paths. The waves studied during GATE were cold-core systems below 650 mb and warm-core above 250 mb. Particularly on the equatorward side of the AEJ, the waves show a southwest–northeast tilt which is consistent with growth through barotropic energy conversion (Norquist et al., 1977). Thorncroft and Hoskins (1994) ﬁnd in a primitive equation simulation that the most unstable wave No. 10 grows by conversion of zonal to eddy kinetic energy ﬁve to six times

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Figure 6.37 Schematic illustration of the possible zones of convective activity in relation to a positive vorticity anomaly (PVA) on the African Easterly Jet (AEJ). (a) Longitude height section, looking northward, of an idealized PVA and vertical motions (short arrows) and meridional wind (· and x). (b) Possible distribution over dry surfaces at 20°N. (c) Possible distribution over moist surfaces at 10°N. (Thorncroft and Hoskins, 1994)

more effectively than by converting eddy available potential energy. Linear instability at the jetstream level is dominated by the north–south contrasts between positive (negative) gradients of potential vorticity on the ﬂanks (in the core) of the jet. However, the negative gradient of zonal mean quasi-geostrophic potential vorticity in the jet core also causes the instability to be destroyed by the energy conversion. The distribution of convection appears to be closely dependent on surface moisture conditions as well as on vorticity anomalies along the AEJ. Figure 6.37 illustrates this schematically for a meridional-height cross-section through a positive vorticity anomaly on the AEJ. At 20°N above a dry boundary layer, convection is possible only in southerly airﬂows east of the vorticity anomaly above the jet; at 20°N above a moist boundary layer, however, it can occur ahead of the wave trough, despite northerly airﬂow, as well as in the rear of the trough in the southerlies. These patterns may explain the observational evidence of convective activity

514 Synoptic and dynamic climatology 1

west of the easterly wave trough around 10°N but east of it around 20°N (Burpee, 1974; Duvel, 1990). Inclusion of latent heating effects with wave–CISK type convection in the Thorncroft and Hoskins analysis has little effect on the growth rate, but does increase the phase velocity of the waves and change the normal mode structures by augmenting baroclinic instability. It is also found that when the jetstream is farther north the baroclinic instability increases relative to the barotropic, although the growth rate and wave frequency are not affected. Thorncroft and Hoskins propose that waves grow linearly for about six days, then baroclinic instability and non-linear growth takes over. Downward propagation of Rossby waves is strongly damped by boundary layer effects, so that the waves have maximum amplitude in the mid-troposphere. During May–November there are on average (1967–89) about 90–100 tropical disturbances over the North Atlantic, and tropical systems originating from northern Africa typically account for about 60 percent of the total number of depressions and also the same percentage of the approximately ten systems per year that subsequently intensify into tropical storms (Avila, 1990). Disturbances in the tropical western Paciﬁc show a period of about six days and a wavelength of 2,500 km at 850 mb, based on an analysis of the variance in relative vorticity for 1980–87 (Lau and Lau, 1990). They propagate west-northwestward and are elongated southwest–northeast, implying the conversion of barotropic energy from the time-mean circulation to the transient eddies. The maximum activity is equatorward of the mean trade winds and occurs in association with a “monsoon trough” orientated westnorthwest from about 5°N, 165°E into the South China Sea. The disturbances dissipate over eastern Asia. An earlier comprehensive study ﬁnds that waves forming in the eastern tropical Paciﬁc, west of Panama, also travel west-northwestward with a period of 5.7 days and a wavelength of about 2,700 km (Nitta et al., 1985a, b); their structure is similar to that of systems in the western Paciﬁc. Using infrared imagery and ECMWF analyses for June–July–August 1980–83 and 1985–89, Takayabu and Nitta (1993) identify tropical depressions west of 160°E, particularly in El Niño years, propagating westward with periods of three to ﬁve days. Wave convergence and convection are tightly coupled, and the structure corresponds to the easterly waves described by Reed and Recker (1971). However, in La Niña years, differences in the large-scale environment (zonal wind, vertical wind shear, and SST distribution) lead to mixed Rossby–gravity waves developing along the equator. Near the dateline these have a large amplitude over a sector spanning about 4,000 km. Liebmann and Hendon (1990; Hendon and Liebmann, 1991) ﬁnd four-to-ﬁveday-period Rossby–gravity waves only in October–November and within about 30° longitude of the dateline. Their peak amplitude is near 7.5° latitude. They have wavelengths of 7,000–10,000 km and propagate westward at 15–20 m s1. Their occurrence is attributed to the warm ocean surface (≥28°C) and the double ITCZ structure (see Figure

Figure 6.38 Schematic illustration of “tropical depression” (TD) and mixed Rossby–gravity (MRG) wave disturbances. The lower tropospheric circulation, high/low pressure anomaly centers and areas of convection cloud are indicated. (From Takayuba and Nitta, 1993)

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3.44). Although the meridional wind components are small (~1 m s 1 at 850 mb), the waves account for over 50 percent of the variance of OLR. These waves appear to be convectively coupled, although convection is only loosely linked with the wave convergence (see Figure 6.38). 6.6.3 Tropical cyclones Distribution

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Tropical cyclones are the most severe category of large-scale weather system, bringing winds of up to 33–50 m s1 or more, torrential rain, and coastal storm surges. In the North Atlantic they are referred to as hurricanes and in the western North Paciﬁc as typhoons. They develop over warm tropical oceans, evolving over four to seven days from weak depressions into named tropical storms with highest sustained winds (averaged over one minute or more) of at least 18 m s1 and becoming a tropical cyclone if the winds intensify to 33 m s1 or more and an eye forms. Figure 6.39 shows their global distribution, relationship to ocean surface temperatures of at least 27°C, and typical paths. Only exceptionally do they occur over land, or cold ocean waters, or within about 5° of the equator (Gray, 1968, 1979). The seasonality is related to both oceanic and atmospheric conditions. Tropical storms in the North Atlantic and in the eastern and western North Paciﬁc are most frequent in late summer, whereas those in the southwest Paciﬁc and southwest Indian Ocean peak in midsummer (January). Those in the southeast Indian Ocean peak in March and January. In contrast, the storms in the Bay of Bengal and (more rarely) the Arabian Sea occur in spring and autumn. Vertical wind shear is large over these two areas in northern summer, as it is over the central Paciﬁc, and this appears to be a powerful constraint on tropical cyclogenesis (Gray, 1968). Annually, there are on average about ﬁfty-ﬁve tropical storms and cyclones in the northern hemisphere and twenty-seven in the southern hemisphere (Lander and Guard, 1998). The northwest Paciﬁc sector alone experiences about one-third of the global total (twenty-seven systems) (see Table 6.2). The global total has varied between seventy-ﬁve in 1986 and 103 in 1971. An analysis of Atlantic systems for 1967–93 indicates that of ﬁfty-nine tropical waves, on average, per season, twenty become tropical depressions but only nine (four) intensify to the tropical storm (cyclone) stage (Avila and Pasch, 1995). Systems of African origin represent about 60 percent of each intensity category, the remainder developing over the tropical Atlantic or Caribbean. African waves are also the main contributor to storms in the eastern North Paciﬁc, according to Avila and Clark (1989), although they are not a necessary precursor of tropical cyclogenesis in the eastern Paciﬁc (Molinari et al., 1997). The proportion of waves of African origin that develop in the Atlantic varies

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Figure 6.39 Frequency of hurricane genesis for a twenty-year period (isopleths) and primary tracks (arrowed). Areas with sea surface temperature exceeding 27°C in the warmest month are shaded. (After Gray, 1979, from Barry and Chorley, 1998)

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Table 6.2 Mean annual frequency of tropical storms and cyclones, 1966–95 Measure

Western N. Paciﬁc

Eastern N. Paciﬁc

N. Atlantic

N. Indian

S. hemisphere

Global

Mean

27.1

15.8

9.8

4.8a

27.2a

84.9a

4.5

3.8

3.4

2.4

3.8

9.8

Standard deviation

Source: from Lander and Guard (1998). Note a 1969–95.

greatly from year to year (Frank and Clark, 1980). Avila and Pasch distinguish “African” years if the ratio of African waves to total waves in the Atlantic is 0.7 or more, and “nonAfrican” years when the ratio is 0.5 or less; otherwise years are termed average. During 1967–95 twelve years are categorized as African, eight as non-African, and nine as average. Hurricanes of category 3–5 intensity on the Hurricane Disaster Potential (now the Safﬁr/Simpson) Scale (Simpson, 1974) are twice as likely to occur in African years as in non-African years. In this context “Out of Africa” takes on a signiﬁcant new meaning! Non-African years, such as 1972, 1977, 1983, and 1986 coincide with El Niño years, implying an unfavorable large-scale environment for cyclogenesis over the tropical Atlantic (Avila and Clark, 1989). Tropical cyclone activity in the western North Atlantic varies almost independently of that in the other ocean basins, whereas there are weak but signiﬁcant rank correlations (0.3–0.4) between the annual frequency of cyclones in the two North Paciﬁc regions and between each of them and the southern hemisphere. More important, cyclone frequency in the Atlantic is correlated (0.52) with the average ENSO index for boreal spring to autumn, and with the stratospheric QBO for June to September (0.44) (Lander and Guard, 1998). More cyclones are observed in the Atlantic during the west phase of the QBO, as was observed in 1995, for example (Landsea et al., 1998). For the other ocean basins such correlations are lacking. The 1979 season exempliﬁes an African year: there were eighty-ﬁve tropical waves, characterized by a trough or cyclonic curvature in the easterly trades, with maximum amplitude in the lower troposphere (Avila and Pasch, 1992). Of these eighty-ﬁve systems, ﬁfty-two originated over Africa and twenty-seven became closed depressions. The African disturbances accounted for seven of the eight named storms. Forty-three waves of African origin also moved into the eastern North Paciﬁc, where seven intensiﬁed into named storms. In addition, one Paciﬁc storm developed out of nine ITC disturbances originating in the Caribbean, one formed from an Atlantic wave, and another formed from an eastern Paciﬁc wave. In the non-African season of 1992 there were sixty-nine Atlantic waves. Nine tropical depressions formed, four of which originated from Africa; only two out of six storms, and one out of four hurricanes, had an African origin. Structure The structure of a mature tropical cyclone has several characteristic features (Miller, 1967; Palmén and Newton, 1969; Frank, 1977, 1982; Anthes, 1982; Pielke, 1990). The mean radius, as deﬁned by the outer closed surface isobar, is 350 km in the western North Atlantic and 500 km in the western North Paciﬁc (Merrill, 1984), which is much smaller than a typical extratropical cyclone. However, the radius ranges from less than 100 km up to 1,100 km in extreme cases. Surface winds spiral towards the center, with a 10–20 km-wide annulus of maximum winds (in near-cyclostrophic balance) surrounding the central eye, which may have a diameter of between 15 km and 100 km (Figure 6.40). The wind

Synoptic systems 517 11

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Figure 6.40 Schematic plan view and cross-section of a mature hurricane. The low-level wind streamlines and cloud bands based on radar echo patterns are shown above. (From Willoughby et al., 1984.) The lower ﬁgure shows the vertical motion and cloud structure. (From Musk, 1988)

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maximum coincides with a cloud wall and the most intense convection (Ramage, 1995, p. 242). Winds are weak within the eye and there is often a break in the middle–high cloud decks. The storm’s circulation may extend throughout the troposphere to 14–15 km altitude. Inﬂow is pronounced in the lower levels and, according to Gray (1979), may extend to 7 km altitude. In the mid-troposphere there is more or less tangential ﬂow, while outﬂow occurs above 8 km, with a maximum typically around 12 km. The upper circulation appears to be cyclonic in some cases, with outﬂow beyond about 300–500 km radius, especially on the poleward side of the storm center. Aircraft and satellite data show the storm comprises spiral cloud and rain bands, an annular ring of subsidence around the cirrus shield capping the storm, and an outer convective band at about 800 km from the center (Plate 15). In contrast to subtropical cyclones and tropical depressions, the hurricane is warm-cored as a result of the concentrated moist adiabatic ascent of air and the latent heat released in the eye wall and rain bands. The warmth is ampliﬁed within the eye by subsiding air, giving rise to temperature anomalies of 10° to 20°C.

518 Synoptic and dynamic climatology 1

Plate 15

GOES-E image of Hurricane Gilbert on (above) September 12 (visible) and (below) September 15 (IR) 1988. The eye and spiral cloud bands are clearly visible. (From Carleton, A.M., 1991, Satellite Remote Sensing in Climatology, Belhaven Press, London, p. 147)

Synoptic systems 519 11

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0

This subsidence creates an inversion in the mid-lower troposphere (850–500 mb), according to Willoughby (1998). The dry air above this inversion may be resident in the eye following its formation and it subsides only a few kilometers; the lower air is moist, owing to frictional inﬂow below the eye wall, sea surface evaporation, and moist downdrafts. Willoughby notes that the central pressure in an intense system may be 50–100 mb below that outside the vortex, but only 10–30 mb of this drop occurs between the eye wall and the center. This represents the contribution of warming due to subsidence. The storm acquires most of its energy from the inﬂow of latent heat, supplemented by sensible and latent heat transfer from the warm ocean surface. Sensible heat counteracts adiabatic cooling caused by the rapid pressure decrease of 50–100 mb as the air ﬂows towards the eye. For example, 27°C air at 1,000 mb pressure moving to a storm center of 900 mb would theoretically cool 9°C without heat transfer from the ocean surface (Pielke, 1990). Central pressure and wind intensities are closely related. A category 3 cyclone on the Safﬁr/Simpson scale has winds of 49–58 m s1 and a central pressure of 945–64 mb; a category 5 cyclone (such as Hurricane Camille in 1969 and Hurricane Gilbert in 1989), which can cause catastrophic damage, has winds exceeding 69 m s1 and a central pressure below 920 mb. Hurricane Andrew, which struck southern Florida in 1992, was rated as a category 4 cyclone, although it was the most costly weather disaster in the United States. Holland and Merrill (1984) propose using three related but weakly correlated parameters to describe tropical cyclones: size, intensity, and strength. “Size” is measured by the radius of the outermost closed isobar or by the axisymmetric radius of gale-force winds. “Intensity” is deﬁned by the maximum winds or central pressure. “Strength” is determined from the average relative angular momentum of the low-level circulation within 300 km radius. Systems of similar intensity may have very different sizes and strengths (Merrill, 1984). Large cyclones develop most commonly in October in the North Atlantic and northwest Paciﬁc near latitude 30°N. Cyclogenesis

0

0

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Several factors regulating the development of tropical cyclones are illustrated by Figure 6.41, as noted above. Warm tropical waters are typically at least 50–60 m deep, so that wind mixing does not bring cool water to the surface (Gray, 1979). However, the relationship with sea surface temperature (SST) is not straightforward, since two Atlantic hurricanes formed in 1980 over waters where the SSTs were only 23° and 20°C (Ramage, 1995, p. 260). Small horizontal gradients of SST are also important. Storms tend to develop from existing waves or depressions where cyclonic vorticity is locally concentrated. This condition may exist within a monsoon trough, in the ITCZ, in waves in the tropical easterlies, or at the trailing edge of a cold front that has penetrated to low latitudes. The disturbance needs to be at least 4°–5° from the equator so that the Coriolis parameter is large enough to support ﬂow curvature. A moist, warm atmosphere that is close to saturation and able to generate towering cumulus and cumulonimbus clouds is also a prerequisite. Deep convection can be enhanced by low-level convergence (Ekman pumping in the boundary layer) and by upper-level mass divergence. Important features of the upper circulation in summer are the tropical upper-tropospheric troughs (TUTTs) orientated westsouthwest–eastnortheast in the North Atlantic and North Paciﬁc oceans and westnorthwest–eastsoutheast in the South Atlantic and South Paciﬁc. At 200 mb over the South Paciﬁc in January the TUTT extends from 30°S, 105°W to the equator at 175°W; in the South Atlantic the TUTT stretches from 30°S, 15°W across South America to the equator at 75°W. In the North Atlantic in July the TUTT spans 35°N, 30°W to 22°N, 95°W and in the North Paciﬁc from 35°N, 145°W to 22°N, 130°E (Sadler and Wann, 1984; Ramage, 1995, pp. 57–61). The western ends of the TUTTs appear to provide upper-level support for tropical cyclogenesis. However, Ramage

520 Synoptic and dynamic climatology 1

Figure 6.41 Schematic illustration of secondary circulations resulting from convective heating in the inner core and momentum forcing in the outer region, and their potential effects on cyclone intensity, size, and strength. (From Holland and Merrill, 1984)

(1995, p. 263) suggests that 85–90 percent of western North Paciﬁc tropical cyclones form in the surface monsoon trough and only 10–15 percent develop where the TUTT overlies the trade winds. Synoptically, the TUTT consists of a series of upper cold lows, but it is most readily identiﬁed in upper-level vector wind analyses (Sadler, 1975). Such upper lows occasionally trigger tropical cyclogenesis, according to Sadler (1976). Divergence east of an upper cold low induces a low-level trough and, as convection builds deep cumulus, the heat released by condensation forms a ridge east of the cold low, which begins to shrink. A low-level depression now forms, with convective cloud concentrated to the east of the vortex. The upper tropospheric ﬂow becomes distorted and the lowlevel depression strengthens into a tropical storm overlain by a high-pressure cell in the upper troposphere. The dynamic process involves intense convection setting up low-level convergence, with the inﬂow generating cyclonic spin-up (Hastenrath, 1991, p. 226). This follows from the vorticity equation (see p. 47). For a tropical cyclone to develop, a pressure drop of 25–30 mb is needed (Ramage, 1971). The difference between the central pressure in a mature storm and values in the undisturbed surroundings is in the range 50–100 mb, i.e. central pressures of 960–910 mb; the pressure gradient is typically 1–2 mb km1. The extreme pressures are made possible by the latent heat release in the cloud bands, especially the eye wall cloud, and by adiabatic warming of subsiding air in the eye. The inward spiraling of air, with conservation of absolute angular momentum about the cyclone axis at distance r: Mr = vT r

f r2 2

where vT tangential velocity and r is the radial distance, would imply inﬁnite wind speeds at the center. Thus a limiting tangential velocity is achieved at some radial distance, and here the air is forced upward and outward. Composite vertical cross-sections of radial winds in intensifying storms and hurricanes (Holland and Merrill, 1984) show two low-level inﬂow maxima in the inner and outer

Synoptic systems 521 11

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sectors of the storm, a secondary inﬂow near 400 mb, and an extensive outﬂow region between 300 and 100 mb. The inﬂow at low levels within 6° radius imports angular momentum, strengthening the cyclone and offsetting frictional effects; it also promotes moisture ﬂux convergence. The inﬂow from the outer environment supplies the angular momentum needed to increase cyclone size. The secondary upper tropospheric inﬂow provides the vertical wind shear from cyclonic to anticyclonic winds. The high-level outﬂow removes air with high potential temperatures to the outer part of the storm and can thereby effect changes of cyclone intensity. Figure 6.40 illustrates schematically the effects of the various forcings on cyclone structure. The upper circulation is often asymmetric, in contrast to the lower levels. There is typically a major poleward outﬂow jet, ahead of a trough in the subtropical westerlies, and a subsidiary equatorward outﬂow, as demonstrated in the northwest Paciﬁc (Sadler, 1976) and the southwest Paciﬁc (Holland and Merrill, 1984). The import of angular momentum needed for intensiﬁcation of a tropical cyclone is very minor relative to that required for its initial growth and subsequent maintenance. Merrill (1984) calculates that the requirements are as follows: Angular momentum import across 8° radius (1017 kg m2 s1) Cyclone growth Poleward motion Surface stress Intensiﬁcation

0

0

0

0 11

2.86 1.94 0.90 0.08

Gray (1998) argues that lower-tropospheric wind surges play the critical role in triggering cyclone development from an initial convective disturbance. Zehr (1992) and Gray, in particular, identify a two-stage process. A ﬁrst antisymmetric wind surge lasting only six to twelve hours strengthens the convective activity within a large active cloud cluster, establishing a mid-level circulation and convective vortex. During the subsequent one to three days the broad system characterized by only weak to moderate convection undergoes little change. Then a second wind surge triggers a blow-up of deep convection and transforms the lower levels of the disturbance from a cold core to a warm core. This occurs because the inertial stability of the mid-level circulation, set up by the ﬁrst surge, concentrates the penetration of the second surge into the disturbance within the lower troposphere. Mechanically forced convergence and ascent moisten the upper levels in the inner core. Buoyancy sustained by cumulonimbus clouds near the storm center then begins to concentrate convection around the eye wall, and this is maintained by unstable, selfsustaining convergence. The storm becomes isolated from its external environment, and internal physical processes are dominant until the system encounters a cooler sea surface, strong vertical wind shear, or undergoes landfall. The wind surges originate in a variety of ways: as surges in the trade wind or monsoon ﬂow; as a cross-equatorial surge related to a winter-hemisphere baroclinic cyclone; from convergence induced by an easterly wave or by an upper-level trough (in the subtropics); or from convergence resulting from the low-latitude penetration of a cold front. The mean structures of western Paciﬁc and western North Atlantic tropical cyclones are compared with those of summer season cloud clusters in Figure 6.42 (McBride, 1981). These composites are each based on about eighty disturbances. The main difference over a 4° latitude radius between the cloud clusters and cyclones is in the relative magnitudes of the upper warm core, moisture anomalies, and tangential wind velocities. Using a “seasonal genesis parameter” (SGP) proposed by Gray (1979), which can be represented as the product of a dynamic potential and a thermal potential, McBride ﬁnds that the large differences in SGP between the cloud clusters and the cyclones are determined by the dynamic potential. The latter term is the product of a vorticity parameter, the Coriolis

Figure 6.42 Mean structure of eighty-seven cloud clusters in the western Paciﬁc and forty-six in the western Atlantic (left) and of a composite western Paciﬁc typhoon (147 cases) and Atlantic hurricane (seventy-three cases) (right). The clusters are all embedded in deep easterly ﬂow and moving westward at about 6 m s1. The typhoons have a mean location of 13°N, 136°E and the hurricanes 23°N, 73°W . (a) Difference between the mean temperature within 3° radius of the center of the cluster and 3°–7° radius to east and west of the cluster. (b) Height anomalies (m) of the 200 mb, 500 mb and 900 mb surfaces versus radius from the center. (c) Differences of speciﬁc humidity (g kg1 ) determined as in (a). (d) Radial wind (m s1 0 at 4° radius. (e) Vertical velocity (mb day1) for 0–4° radius. (f) Tangential wind speed at 4° radius. (From McBride, 1981)

1

Synoptic systems 523 11

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11

parameter, and a vertical wind shear parameter. The thermal potential for deep convection appears to play only a climatological role. Comparison of non-developing versus developing cloud clusters shows that the differences between them are rather subtle (McBride and Zehr, 1981). Both categories are warm-cored in the upper troposphere and the convective instability and moisture anomalies are similar in each case. In the developing cases the warm core has a larger horizontal extent but the critical differences are in the dynamic parameters. Developing clusters are in areas where the low-level relative vorticity is about twice that in non-developing cases; the difference between 900 mb and 200 mb relative vorticity over a 6° radius is 2.7 versus 0.7 105 s1, respectively. The conclusion is that cyclogenesis is dependent on a cloud cluster being located in a favorable large-scale environment, particularly one where upper-level anticyclonic vorticity overlies lower-level cyclonic vorticity. In a further study of developing and non-developing cloud clusters in the western Paciﬁc, Lee (1989) conﬁrms the importance of large-scale low-level convergence, a stronger middle and low-level cyclonic circulation, and mid-level moisture in the developing cases. The formation of tropical cyclones in subtropical latitudes and in the off season often involves a hybrid baroclinic process (Bosart and Bartlo, 1991). This is common in the southern hemisphere, where the upper westerlies penetrate to low latitudes. Upper-level troughs play a role by supporting outﬂow aloft and ascending motion.

0 Scale interactions

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The concept of a cooperative interaction between a cluster of deep cumulus and an incipient tropospheric vortex was put forward independently by Ooyama (1964) and Charney and Eliassen (1964). The central idea is that a cumulus cluster located in a region of vortical ﬂow generates buoyancy which serves as a driver for the vortex to develop. Ascent of air in the cumulus towers causes vortex stretching in the lower troposphere and therefore is a source of relative vorticity. The Charney and Eliassen theory considers the latent heat release by the cumulus convection to be proportional to the moisture ﬂux convergence in the boundary layer. Intensiﬁcation of the vortex enhances this moisture convergence and the release of latent heat, thereby generating positive feedback. This organization of convection leading to the growth of instability in a moist, conditionally unstable atmosphere is termed Conditional Instability of the Second Kind (CISK) to distinguish it from the simple instability responsible for the initiation of cumulus clouds. The linear model of Charney and Eliassen contains unstable modes, but the growth rates are relatively uniform over a range of horizontal scales. Moreover the fact that mid-tropospheric heating would eventually stabilize the atmosphere is neglected (Smith, 1997). The necessity for non-linear scale interactions is stressed by many investigators, although the precise mechanisms seem elusive. The concept of non-linear cooperative interactions between the cumulus and cyclone scales is proposed by Ooyama (1982). A depression organizes the clouds into a consistent pattern. Cloud entrainment forces a deep lower tropospheric inﬂow and shallow upper-level outﬂow. The formulation of cooperative intensiﬁcation by Ooyama (1964, 1982) differs in several fundamental ways from the “classical” CISK model, according to Smith. Deep cumulus clouds are able to entrain mid-tropospheric air and transfer it to upper levels. The heating rate is proportional to the boundary layer convergence and ascent, but it also depends on the degree of convective instability (i.e. on the convective available potential energy, or CAPE; see Chapter 3, n. 5) through a variable entrainment parameter. Also, the radial entrainment in the middle layer leads to convergence in the mid-troposphere which is required for the spinup of the vortex (Ooyama, 1982). Ooyama’s non-linear model allows the local Rossby radius of deformation (see Chapter 5, n. 3) to shrink as the inertial stability of the vortex increases in its inner core, thereby reducing the scale separation between deep cumulus and the tangential circulation in the vortex. It is important to note that Ooyama’s concept

524 Synoptic and dynamic climatology 1

addresses the transformation of a vortex to hurricane intensity and scale, given the prior organization of cumulus convection by vortical ﬂow resembling a tropical depression. His results also indicate that the transfer of latent and sensible heat from a warm ocean is critical for vortex intensiﬁcation, although energy budget calculations suggest these terms represent only 9–16 percent of the total heat source (Palmén and Newton, 1969, table 15.4). A related theory of hurricane intensiﬁcation by Emmanuel (1986) also involves interactions between a vortex and a warm ocean. Emmanuel views the energy cycle of a mature tropical cyclone as a Carnot cycle that converts heat energy acquired from the ocean into mechanical energy. The thermodynamic efﬁciency, (Ts To)/Ts , where Ts sea surface temperature and To is the mean temperature of the upper-level outﬂow. Emmanuel states that is typically one-third. Finite-amplitude instability is attributed to wind-induced surface heat exchange (or WISHE). Latent heat ﬂux increases markedly with increasing surface wind speeds, and this seems to be critical in hurricane intensiﬁcation. The actual sea–air temperature difference in the central area is small, but according to Emmanuel (1991) the near-surface air is undersaturated. The surface heat transfers increase the large-scale radial gradient of equivalent potential temperature in the boundary layer, and this gradient is communicated to the troposphere by convection. The concurrent increase in the large-scale gradient of buoyancy leads to convergence above the boundary layer, which enables the vortex to spin up. Thus cumulus convection redistributes heat acquired from the ocean surface upward and outward to the upper troposphere, where it is exported, or lost by radiation. Smith (1997) suggests that the only difference from Ooyama’s model is that the cumulus convection pattern is organized by the boundary layer entropy gradients rather than directly by boundary layer convergence. Rotunno and Emmanuel (1987) simulate realistic hurricanes, starting from a conditionally neutral ambient atmosphere. However, the simulated storm intensities regularly attain the theoretical minimum sustainable central pressures whereas only a few observed storms attain such intensities. The arguments of Gray (1998) concerning the dominant role of mechanical rather than thermodynamic factors in tropical cyclone spin-up are in disagreement with the theories of air–sea interaction.

Appendix 6.1 The Q-vector formulation The Q-vector is a form of the omega equation for large-scale vertical motion, developed so as to combine the generally opposing effects of vorticity advection and thermal advection (Hoskins et al., 1978). The omega equation expresses the synoptic-scale vertical motion (represented by the three-dimensional Laplacian of omega – term A) as the sum of the advection of absolute geostrophic vorticity by the geostrophic wind (term B) and the Laplacian of temperature (or thickness) advection (term C):

冢

2 f o2

冣

冤

冥

冤

冢

∂2 ∂ 2 ∂ Vg·(g f ) 2 Vg· 2 = fo ∂p ∂p ∂p

A

B

冣冥

(1)

C

where fo the (spatially averaged) Coriolis parameter, vertical motion in pressure coordinates, 2 the Laplacian operator, = geopotential and static stability. This equation assumes no effects from diabatic heating, surface friction, or orography. Term A is proportional to , the upward motion (see Bluestein, 1992). Billingsley (1997) shows that this relationship can readily be appreciated by considering the Laplacian of omega in only the zonal direction: x2() =

∂(∂) ∂x (∂x)

Synoptic systems 525 11

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( )/( x) is the rate of change of with x (or the gradient of the omega curve with increasing x) and the second derivative is the Laplacian of (i.e. its curvature). Where is a simple sine wave, the ﬁrst derivative is a cosine wave and the second derivative (the Laplacian) corresponds to a negative sine wave (). The relationship between omega and its Laplacian is generally more complex. Nevertheless, for synoptic-scale systems, well deﬁned and dominant areas of forcing largely determine the vertical motion ﬁeld. Term B in the omega equation represents the vertical variation of vorticity advection by the geostrophic wind. Where cyclonic (anticyclonic) vorticity advection increases with altitude (p) there is upward (downward) motion, in the northern hemisphere case. The static stability parameter ( ) implies an inverse relationship between the static stability and the magnitude of the forcing. The absolute vorticity advection is sometimes evaluated at 500 mb where vorticity tends to be nearly conserved. However, calculation of the differential advection in a layer such as 700–300 mb is preferable. The Laplacian of the thickness advection (term C) implies that warm (cold) forces upward (downward) motion. There is again an inverse relationship with static stability. Terms B and C are generally of opposing sign and the net result is a rather small residual. For this reason, alternative approaches have been proposed. Trenberth (1978) rearranges terms A, B, and C to combine the thermal and vorticity effects. Term A can be expressed as:

0 A ≈ 2fo

∂ug

冢∂x∂ ∂p ∂∂y ∂v∂p ∂v∂p 冣 g

g

g

g

(2)

by ignoring the deformation terms, which are mainly important near frontal boundaries or jetstreams (Carlson, 1991, p. 186; Martin, 1998). The preceding term can also be written as an approximation of Trenberth’s formulation in the form:

0

2fo V · ( f ) p500 T p g

where the thermal wind VT represents the advection in the layer 1,000–500 mb and the vorticity advection by the thermal wind is at a mean level of 700 mb, for example p500 500 mb. This equation enables the sign and approximate magnitude of the vertical motion at 700 mb to be estimated. The Trenberth version of the omega resembles the formulation of the development equation by Sutcliffe (1947). Practical applications are described by Carroll (1995), although Billingsley (1997) suggests that the method should be abandoned because of its neglect of deformation terms that are important near fronts and jets which can now be readily computed. A second approach takes account of transverse (ageostrophic) circulations normal to a frontal zone or jet streak and the vertical advection of geostrophic momentum. The differential vorticity advection term (B) and the thickness advection term (C) can be replaced by a combined Q-vector expression, which contains no explicit advection terms, plus a diabatic heating contribution (Hoskins et al., 1978):

0

i.e. A = 2·Q

R 2 H Cp

(3)

Equation 3 demonstrates that when the ﬁeld of Q-vectors is convergent (divergent), there is upward (downward) motion, . On constant pressure surfaces:

0 11

冤

Q=

冢

冣

冢

∂Vg ∂ ∂Vg ∂ , · · ∂x ∂p ∂y ∂p

冣冥

526 Synoptic and dynamic climatology 1

Figure 6.43 A schematic illustration of two-dimensional cross-front circulation, showing (a) frontogenetic and (b) frontolytic situations. The Q vectors point in the direction of the low-level ageostrophic motion and towards ascent. In (a) there is an enhancement of the gradient and development; in (b) the converse is true. (From Hoskins and Pedder, 1980)

A procedure to estimate Q-vectors is discussed by Sanders and Hoskins (1990); they also illustrate typical conﬁgurations of Q-vectors and vertical motion for schematic patterns of surface highs and lows, upper-level ridges and troughs, and frontogenetic and frontolytic situations. The Q-vector deﬁnes the rate of change of the potential temperature gradient moving with the geostrophic wind. Q-vectors are directed with the low-level ageostrophic ﬂow (assuming frictionless adiabatic motion) and indicate a frontogenetic tendency. Advection of cold (warm) air in the same direction as the Q-vectors implies an enhanced (weakened) potential temperature gradient indicative of frontogenesis (frontolysis), respectively (Hoskins and Pedder, 1980; Billingsley, 1998). Thus there is frontogenesis when Q is oriented within 90° of p , giving a thermally direct circulation, and frontolysis when Q is within 90° to 180° of p , giving a thermally indirect circulation (see Figure 6.43). p is the potential temperature gradient vector following geostrophic motion. LeDrew (1988), for example, uses the Q-vector technique to evaluate the relative importance of the dynamic properties of the advected airﬂow compared with surface effects for ﬁve lowpressure systems in the Arctic.

Notes 1

The Richardson number (Ri) is a measure of the importance of buoyancy forces (represented by the static stability, or the square of the Brunt–Väisälä frequency) to inertial accelerations. Thus:

Ri =

(g ∂ ln ) ∂z

)

(∂ |V |)2 ∂z

Flow becomes turbulent for small Ri. In the context of frontal waves, Ri can also be regarded as the ratio of the potential energy of the basic state to its kinetic energy.

Ri =

gH (1 2) (U2 U1)2

Synoptic systems 527 11 2

0

where U and are respectively the velocities and densities of the two layers, H is the total channel depth, and – is a m–ean density (Orlanski, 1968.) Passive microwave-derived precipitation indices. P37 and S85 are indices of the likelihood of rain occurrence and cold-cloud precipitation, respectively, based on passive microwave radiances retrieved by SMMR and/or SMM/I. P37 is obtained from the ratio of the difference between the brightness temperatures observed at 37 GHz for vertical and horizontal polarizations, divided by the expected clear-sky difference. The rain–no rain threshold is approximately 0.8, with rain probability increasing as P37 decreases (Petty and Katsaros, 1992). S85 is an analogous ratio of polarized brightness temperature differences at 85 GHz (Claud et al., 1992). It is a scattering-based index of cold-cloud precipitation (graupel, hail, snow aggregates) applicable to convective situations, that can be used over land or ocean. Values of S85 of about 30–60 are good indicators of ice hydrometeors associated with convection (McMurdie et al., 1997).

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Uccellini, L.W., Corﬁdi, S.F., Junker, N.W., Kocin, P.J., and Olson, D.A. 1992. Report on the Surface Analysis Workshop held at the National Meteorological Center, March 25–28 1991. Bull. Amer. Met. Soc., 73 (4): 459–72. van Bebber, J. 1891. Die Zugstrassen der barometrischen Minima nach den Bahnenkarten der Deutschen Seewarte für den Zeitraum 1875–1890. Met. Zeit., 8: 361–6. Van Loon, H. 1967. The half-yearly oscillations in middle and high southern latitudes and the coreless winter. J. Atmos. Sci., 24: 472–86. Van Loon, H. and Rogers, J.C. 1984. Interannual variations in the half-yearly cycle of pressure gradients and zonal wind at sea level on the southern hemisphere. Tellus, 36A: 76–86. Van Loon, H. and Shea, D.J. 1985. The Southern Oscillation. IV. The precursors south of 15oS to the extremes of the oscillation. Mon. Wea. Rev., 113: 2063–74. Van Loon, H. and Thompson, A.H. 1966. A note on southern hemisphere analysis incorporating satellite information. Notos, 15: 91–7. Velasco, I. and Fritsch, J.M. 1987. Mesoscale convective complexes in the Americas. J. Geophys. Res., 92: 9591–613. Velden, C.S. 1989. Observational analyses of North Atlantic tropical cyclones from NOAA polar orbiting satellite microwave data. J. Appl. Met., 28: 59–70. Velden, C.S. 1992. Satellite-based microwave observations of tropopause-level thermal anomalies: qualitative applications in extratropical cyclone events. Wea. Forecast., 7: 669–82. Velden, C.S. and Smith, W.L. 1983. Monitoring tropical cyclone evolution with NOAA-satellite microwave observations. J. Clim. Appl. Met., 22: 714–24. Velden, C.S, Goodman, B.M., and Merrill, R.T. 1991. Western North Paciﬁc tropical cyclone intensity estimation from NOAA polar-orbiting satellite microwave data. Mon. Wea. Rev., 119: 159–68. Velden, C.S., Hayden, C.M., Menzel, W.P., Franklin, J.L., and Lynch, J.S. 1992. The impact of satellite-derived winds on numerical hurricane track forecasting. Wea. Forecast., 7: 107–18. Velden, C.S., Olander, T.L., and Zehr, R.M. 1998. Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecast., 13: 172–86. Viezee, W., Endlich, R.M., and Serebreny, S.M. 1967. Satellite-viewed jetstream clouds in relation to the observed wind ﬁeld. J. Appl. Met., 6: 929–35. von Hann, J. 1901. Lehrbuch der Meteorologie, 1st edition, Leipzig. Waliser, D.E. and Gautier, C. 1993. A satellite-derived climatology of the ITCZ. J. Climate, 6 (11): 2162–74. Waliser, D.E., Graham, N.E., and Gautier, G. 1993. Comparison of the Highly Reﬂective Cloud and Outgoing Long-wave Radiation datasets for use in estimating tropical deep convection. J. Climate, 6: 331–53. Wallace, J.M. 1973. General circulation of the tropical lower stratosphere. Rev. Geophys. Space Phys., 11: 191–222. Wallace, J.M. and Chang, C.P. 1969. Spectrum analysis of large-scale wave disturbances in the tropical lower troposphere. J. Atmos. Sci., 26 (5): 1010–25. Wallace, J.M., Lim, G.-H., and Blackmon, M.L. 1988. On the relationship between cyclone tracks, anticyclone tracks and baroclinic wave guides. J. Atmos. Sci., 45 (3): 439–62. Weaver, C.P. and Ramanathan, V. 1996. The link between summertime cloud radiative forcing and extratropical cyclones in the North Paciﬁc. J. Climate, 9: 2093–109. Webster, P.J. 1983. Large-scale structure of the tropical atmosphere. In: B.J. Hoskins and R.P. Pearce, eds, Large-scale Dynamical Processes in the Atmosphere, Academic Press, London, pp. 235–75. Wernli, H. and Davies, H.C. 1997. A Lagrangian-based analysis of extratropical cyclones. I. The method and some applications. Quart. J. Roy. Met. Soc., 123: 467–89. Weston, K.J. 1980. An observational study of convective cloud streets. Tellus, 32: 433–8. Wetzel, P.J. 1990. A simple parcel method for prediction of cumulus onset and area-averaged cloud amount for heterogeneous land surfaces. J. Appl. Met., 29: 516–23. Wetzel, P.J., Cotton, W.R., and McAnelly, R.L. 1983. A long-lived mesoscale convective complex. II. Evolution and structure of the mature complex. Mon. Wea. Rev., 111: 1919–37. Whitaker, L.M. and Horn, L.H. 1984. Northern hemisphere extratropical cyclone activity for four mid-season months. J. Climatol., 4 (3): 297–310. Whitaker, J.S. and Dole, R.M. 1995. Organization of storm tracks in zonally varying ﬂows. J. Atmos. Sci., 52 (8): 1178–91.

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Whitaker, J.S. and Sardeshmukh, P.D. 1998. A linear theory of extratropical synoptic eddy statistics. J. Amos. Sci., 55 (2): 237–58. Whitney, L.F. Jr. 1966. On locating jet streams from TIROS photographs. Mon. Wea. Rev., 94: 129–38. Whitney, L.F., Jr. and Herman, L.D. 1968. The Nature of Intermediate-scale Cloud Spirals. ESSA Tech. Rept NESC-45, Washington DC, 69 pp. Widger, W.K., Jr. 1964. A synthesis of interpretations of extratropical vortex patterns as seen by TIROS. Mon. Wea. Rev., 92: 263–82. Wilhelmsen, K. 1985. Climatological study of gale-producing polar lows near Norway. Tellus, 37A: 451–9. Williams, R.T., Peng, M.S., and Zankofski, D.A. 1992. Effects of topography on fronts. J. Atmos. Sci., 49 (4): 287–305. Willoughby, H.E. 1998. Tropical cyclone eye thermodynamics. Mon. Wea. Rev., 126 (12): 3053–67. Willoughby, H.E., Marks, F.D., Jr, and Feinberg, F.J. 1984. Stationary and propagating convective bands in asymmetric hurricanes. J. Atmos. Sci., 41: 3189–277. Wright, W.J. 1988. The low latitude inﬂuence on winter rainfall in Victoria, south-eastern Australia. I. Climatological aspects. J. Climatol., 8: 437–62. Wright, W.J. 1997. Tropical–extratropical cloudbands and Australian rainfall. I. Climatology. Intl. J. Climatol., 17: 807–29. Wu, Y. and Raman, S. 1997. Effect of land-use pattern on the development of low-level jets. J. Appl. Met., 36: 573–90. Wylie, D.P., Hinton, B.B., and Millett, K.M. 1981. A comparison of three satellite-based methods for estimating surface winds over oceans. J. Appl. Met., 20: 439–49. Yarnal, B. and Henderson, K.G. 1989a. A satellite-derived climatology of polar-low evolution in the North Paciﬁc. Intl. J. Climatol., 9: 551–66. Yarnal, B. and Henderson, K.G. 1989b. A climatology of polar low cyclogenetic regions over the North Paciﬁc Ocean. J. Climate, 2: 1476–91. Young, J.A. 1987. Physics of monsoons: the current view. In: J.S. Fein and P.L. Stephens, eds, Monsoons. Wiley, New York, pp. 211–43. Zehr, R. 1992. Tropical Cyclogenesis in the Western North Paciﬁc. NOAA Technical Report, NESDIS 16, Washington DC, 181 pp. Zhong, S., Fast, J.D., and Bian, X. 1996. A case study of the Great Plains low-level jet using wind proﬁler network data and a high-resolution mesoscale model. Mon. Wea. Rev., 124: 785–806. Zick, C. 1983. Method and results of an analysis of comma cloud development by means of vorticity ﬁelds from upper tropospheric satellite wind data. Met. Rundsch., 36: 69–84. Zillman, J.W. and Martin, D.W. 1968. A sharp cold frontal passage at Macquarie Island in the Southern Ocean. J. Appl. Met., 7: 708–12. Zillman, J.W. and Price, P.G. 1972. On the thermal structure of mature Southern Ocean cyclones. Aust. Met. Mag., 20: 34–48. Zillman, J.W., Griersmith, D., LeMarshall, J., and Gauntlett, D.J. 1990. Remote sensing applications in the Australian Bureau of Meteorology. Intl. J. Remote Sens., 11: 1979–97. Zipser, E.J. 1970. The Line Islands Experiment: its place in tropical meteorology and the rise of the fourth school of thought. Bull. Amer. Met. Soc., 51 (12): 1136–45. Zipser, E.J. 1982. Use of a conceptual model of the life-cycle of mesoscale convective systems to improve very short-range forecasts. In: K.A. Browning, ed., Nowcasting, Academic Press, London and New York, pp. 191–204. Zishka, K.M. and Smith, P.J. 1980. The climatology of cyclones and anticyclones over North America and surrounding ocean environs for January and July 1950–77. Mon. Wea. Rev., 108: 387–401. Zwatz-Meise, V. and Hailzl, G. 1980. Interpretation of so-called shear bands in satellite images. Arch. Met. Geophys. Bioklim., B28: 299–315.

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7

Synoptic climatology and its applications Roger G. Barry and Allen H. Perry

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7.1 Synoptic pattern classiﬁcation There is a long history of interest in classifying the pressure patterns on synoptic weather maps into a small number of categories for the purpose of studying local climatic conditions stratiﬁed on a meaningful basis. Arbitrary monthly averages conceal much information as to the nature of the controlling weather processes, while three or six-hourly synoptic observations usually give too much data for convenient summarization. The determination of categories of atmospheric circulation types prevailing in a particular area or region is the ﬁrst stage in a synoptic climatology analysis. In addition, a synoptic typing scheme offers a descriptive tool for summarizing typical modes of the atmospheric circulation with the potential for reducing data volume (Key and Crane, 1986). The assumption that is implicit in the categorization procedure is that the weather conditions associated with a type are more or less homogeneous, and that the differences among types are relatively marked. Identiﬁcation of discrete categories of pressure patterns or circulation types, as they are commonly called, presents several problems:

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Atmospheric modes are continuous, so that the delimitation of any boundary between classes is extremely difﬁcult. Although patterns may switch abruptly, on other occasions a gradual evolution takes place. Pressure systems can be of variable intensities, and decisions have to be made as to when to include or neglect minor features such as a small weak low or transitory ridge. There may be seasonal variation in type characteristics. Generally weather systems are of greatest intensity in winter, when equator–pole heat differences are greatest.

Considerable effort has been expended on deriving appropriate classiﬁcations of weather systems, and Smithson (1988) notes that some researchers regard this topic as the principal purpose of synoptic climatology. The variables most commonly analyzed in synoptic climatological studies are the MSL pressure ﬁeld and 700 mb or 500 mb contour ﬁelds. Daily pressure maps covering most of the northern hemisphere are available from 1899 and for the North Atlantic sector since 1873. Height data (500 mb) essentially begin from 1946 for the northern hemisphere. However, Lambert (1990) identiﬁes discontinuities in the US National Meteorological Center (NMC) 500 mb ﬁelds due to analysis changes in 1953, 1955, 1962 and 1978; those in 1962 are particularly serious. A reanalysis project for the entire record from 1957 to 1996 is in progress at the National Center for Environmental Prediction (Kalnay et al., 1996). This uses a consistent scheme, T62 (210 km) model resolution and an augmented data base. A similar reanalysis project has been carried out by the European Centre for Medium Range Weather Forecasts. Daily surface pressure and upper air charts for the southern hemisphere are available from 1957 (the International

548 Synoptic and dynamic climatology 1

Geophysical Year), with different series in the 1960s and 1970s from South Africa and Australia. Even in the 1980s, global analyses were often in error in the southern hemisphere and of low quality south of 30°S (Trenberth and Olsen, 1988). Other variables that can be analyzed in a similar manner include satellite cloud patterns and diagnostic products of numerical analyses such as the ﬁelds of vertical velocity, horizontal divergence, and relative vorticity. The spatial scale of synoptic climatological studies tends to be rather arbitrarily determined. Some classiﬁcations refer to a small region – the British Isles, the European Alps; others to a large sector – western Europe and the eastern North Atlantic is the area covered by the well known Grosswetterlage scheme (Baur, 1951; Hess and Brezowsky, 1977). This has subsequently been tabulated for 1899–1992 (Gerstengarbe and Werner, 1993). Hemispheric patterns have been categorized independently by Dzerdzeevski (1968) and by Vangengeim (1935), and later modiﬁed by Girs (1974). Savina (1987) illustrates the patterns recognized in the Dzerdzeevski scheme and tabulates daily types for the northern hemisphere over the period 1899–1985. The Vangengeim–Girs catalogue for 1891–1970 is detailed by Girs (1974). When the objective is to investigate conditions in speciﬁc areas, however, there is no simple way of determining a priori what sector is most critical. In a study of the response of glacier mass balances in British Columbia and Alberta to the atmospheric circulation, Yarnal (1984b) ﬁnds that the best scale for analysis of the 500 mb circulation pattern may change with season and differ between the locations being studied. Brinkmann (1999a) shows that, contrary to the expectations that, weather conditions are primarily determined upstream in the westerlies, a grid “window” centered east of her study area – the Lake Superior basin – gave superior classiﬁcation results. The number of map types, identiﬁed using a correlation coefﬁcient measure of similarity for the overall grid and for latitudinal and longitudinal sectors, was larger with the center of the grid window moved 7.5° longitude eastward, than the same distance westward. With the window extending farther east, the classiﬁcation contained more distinct well deﬁned types that provided a better description of the climatic elements. Another consideration is the number of different synoptic categories recognized. This is necessarily an arbitrary choice, although since pressure patterns display a particular range of behavior (from zonal to meridional and blocked patterns), there is not an inﬁnite variety of possible conﬁgurations of the pressure ﬁeld over a speciﬁc geographical area. However, within this constraint, there remains a large number of overlapping possibilities. Two practical concerns help determine the solution to this question. First, if the time period under study is limited, we may specify that there be an adequate number of cases (say more than ten) representing each group. Alternatively, or as a further criterion, we might permit groups to be designated only if they constitute more than a certain percentage (say 5 percent) of all cases. Such subjective decisions do in fact have an outcome on the ﬁnal classiﬁcation scheme, even if the rest of the procedure is objective (Key and Crane, 1986; Yarnal and White, 1987; Yarnal et al., 1988). A different approach to this question was taken by Fliri (1965). For the European Alps he was able to compare the results of classiﬁcations by F. Lauscher (nineteen types), W. Gressel (twenty-three types), and M. Schüepp (thirty-three and 121 types) in terms of the standard deviations of temperature and cloudiness for each classiﬁcation, compared with their climatological standard deviation values. Fliri shows that, as expected, the standard deviations diminish as the number of types increases, but there is no appreciable reduction beyond about thirty types. An historical overview of the development of synoptic typing methods was given by Barry and Perry (1973) and updated in short progress reports by Barry (1980) and Perry (1983). El-Kadi and Smithson (1992) review methods of classifying pressure patterns used by climatologists. Yarnal (1993) has provided a comprehensive analysis of atmospheric circulation classiﬁcation procedures and hence we shall only brieﬂy review manual, subjec-

Synoptic climatology and its applications 549 11

Table 7.1 Classiﬁcation of synoptic climatology 1

Global scale

1.1 Subjective approach Description of seasonal changes of pressure and ﬂow ﬁelds Characteristics of typical circulation patterns (zonal–meridional–blocked) 2

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Continental scale

2.1 Subjective approach Classiﬁcation of surface pressure and ﬂow ﬁelds and important action enters (Grosswetterlagen). Delimitation of zonal and meridional circulation patterns Fixing preferred positions of high and low-pressure enters Classiﬁcation of air masses and fronts 2.2 Objective approach Classiﬁcation of the surface characteristics and parameters of pressure and ﬂow ﬁelds (pressure gradients, relative vorticity ﬂow durations) Classiﬁcation with the help of mathematical and statistical methods (e.g. principal component analysis, orthogonal polynomials)

0

3 Regional scale 3.1 Subjective approach Grouping of similar pressure and ﬂow ﬁelds (e.g. airﬂow, pressure ﬁeld climatology) Classiﬁcation of air masses and fronts 3.2 Objective approach Classiﬁcation of the surface characteristics parameters of pressure and ﬂow ﬁelds (pressure gradients, relative vorticity ﬂow durations) Classiﬁcation with the help of mathematical and statistical methods (e.g. principal component analysis, orthogonal polynomials)

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4

Local scale

4.1 Subjective approach The deﬁnition of surface weather types by means of measured weather parameters (complex climatology) 4.2 Objective approach Correlations between diverse local weather parameters and statistical summaries of local weather types Source: partly after Wanner (1980).

0 tive approaches and automated objective classiﬁcations. In addition to considering methods of classiﬁcation we shall include examples of different scale studies, illustrating many of the categories proposed by Wanner (1980) shown in Table 7.1. Synoptic classiﬁcations can be deﬁned in terms of the climatological phenomena addressed as well as their spatial applicability (Kalkstein et al., 1996), as illustrated in Figure 7.1. These various approaches are explained below.

7.2 Subjective typing procedures 0 11

Until the 1960s synoptic classiﬁcations were developed through subjective manual procedures. Indeed, these studies began almost as soon as weather maps were ﬁrst produced in the late nineteenth century, by meteorologists of the day like Abercromby (1883) and Van Bebber and Köppen (1895). By studying long series of daily weather map sequences

550 Synoptic and dynamic climatology 1

Figure 7.1 The categorization of synoptic approaches according to spatial scale and climatological phenomena. (Modiﬁed from Kalkstein et al., 1996)

the researcher gains familiarity with the more commonly recurring patterns of synoptic weather map features. By noting differences, similarities, and preferred modes of atmospheric circulation it is possible to abstract the most salient and frequently occurring patterns and designate a limited number of map pattern types. One major contentious problem has always been how many types to categorize. The recognition of numerous types will reduce the variability of weather conditions within each category, but unless a very long record is considered, most type categories will be represented by only a few cases, which makes their characterization difﬁcult. Conversely if few types are distinguished then each category inevitably contains a wide variability of weather conditions. Although subjective methods are still being used for particular applications (e.g. Sturman et al., 1984; Sumner and Bonnell, 1986; Carleton, 1987; Mock et al., 1998; Jacobheit et al., 1999), for a long period of record such analyses tend to be very timeconsuming. Also, as Key and Crane (1986) have pointed out, the subjectivity involved makes replication of the results difﬁcult, because the identiﬁcation of types by any two analysts may well differ. Even a single analyst may be inconsistent in the categorization of transitional situations and weak patterns, leading to “internal drift” in a type catalogue spanning a long time period. Tests by Frakes and Yarnal (1997b) indicate a replication rate approaching 90 percent in winter, when patterns tend to be well deﬁned, compared with only 60 percent in summer, when pressure gradients are weak. However, in his original work Lamb (1950) selected the years from 1899 to 1947 in random order to minimize the risk of any gradual changes in the subjective manual typing. We can broadly classify the approaches used in identifying synoptic types under three headings: 1 2

The static weather map, where the location of selected features of the pressure ﬁeld is emphasized. Kinematic classiﬁcation, where the large-scale movement of pressure systems or airﬂow is considered.

Synoptic climatology and its applications 551 11

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3

Weather element classiﬁcations. These originated in the Soviet Union in the 1920s (Federov, 1927; Lydolph, 1959), and such studies are generally described as “complex climatology,” i.e. dealing with weather complexes. The classical approach to air masses can also be viewed in this same context.

Of these three, kinematic classiﬁcations, where the synoptic weather map is viewed in terms of airﬂow and the movement of pressure systems, have yielded some of the bestknown and most widely used classiﬁcations, and several of these will be considered in some detail in section 7.4, since they have been widely employed in research and copied in other parts of the world. One new variant for kinematic analysis is the use of trajectory clustering (Dorling et al., 1992). Stohl and Scheiﬁnger (1994) calculate fortyeight-hour back trajectories from Sonnblick Observatory, Austria, at 850 mb and then develop a weather pattern classiﬁcation by distinguishing nine directional clusters. The geometric distances between the trajectory positions are minimized and the inter-group distances maximized. More weight is assigned to the last part of each trajectory. Previously, trajectories had been examined only for already speciﬁed airﬂow types, whereas here they are used to deﬁne the types. Attempts have since been made to combine regional classiﬁcations of weather elements with the kinematic descriptions. These involve complex statistical procedures, as described below.

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7.3 Objective typing procedures With the advent of high-speed computers and digital data sets of sea-level pressure and geopotential height ﬁelds there has been a transformation in procedures for preparing catalogs of synoptic types in the 1980s and 1990s (Yarnal, 1993). Initially numerical techniques were applied rather uncritically but recent experimentation with various options available to researchers has provided useful practical guidelines of so-called “objective” typing methods. There are basically two approaches that can be adopted in developing a classiﬁcation: 0 1 2

A determination of pattern similarity based on correlation methods. The use of one of a range of statistical techniques to extract components of the ﬁelds, perhaps combined with a clustering approach to obtain pattern types.

Blasing (1975) considers that neither technique seems universally superior to the other. “Map pattern typing appears to have distinct advantages over principal component analysis at least when used for descriptive as opposed to predictive purposes.” However, “lack of orthogonality between patterns can be a disadvantage if further statistical analyses are to be performed.” 0 7.3.1 Correlation-based methods

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Correlation methods were developed by Lund (1963). Examination of the similarity of pressure patterns over an area is achieved by correlating grid-point pressure values for each possible part of maps. The pattern (known as the key day) which has the largest number of maps correlated with it, using an arbitrary threshold of r > 0.8, is selected as Type A. After abstraction of these days the date with the next highest number of related maps is designated Type B, and so on. On completion of the process, each case is rechecked to see that it is assigned to the key-day group with which it has the highest correlation. The Lund correlation method typically classiﬁes 60–80 percent of the maps analyzed. Although Petzold (1982) has developed a technique to improve signiﬁcantly the percentage of maps classiﬁed by this method, the fact that not every pattern can be classiﬁed is considered by many climatologists to be a serious drawback of Lund’s methods,

552 Synoptic and dynamic climatology 1

and Willmott (1987) suggests that correlation in any form is not generally a satisfactory measure of the similarity between weather maps. The map pattern correlation approach has been quite widely used, for example with 700 mb and 500 mb height data to classify tropospheric ﬂow patterns in the western United States (Barry, 1973; Paegle and Kierulff, 1974), and to derive synoptic patterns over southeastern Australia by Jasper and Stern (1983). On a larger scale, Blasing and Lofgren (1980) have used a pattern recognition algorithm based on the use of the correlation coefﬁcient to identify recurrent types of seasonal sea-level pressure anomalies over the North Paciﬁc–western North American area, and this work was later extended (Blasing, 1981) to analyses of characteristic type patterns in summer for the whole northern hemisphere. Winter circulation anomalies over the North Paciﬁc Ocean were classiﬁed, using a threshold correlation coefﬁcient of 0.5, to deﬁne quasi-stationary regimes (Horel and Mechoso, 1988). An alternative procedure using sums of squares, rather than inter-map correlations, was proposed by Kirchhofer (1974). The Kirchhofer metric is the score, S: N

S=

兺 (Z

ai

Zbi)2

i=1

where Zai is the normalized grid-point value at i and day a, Zbi the corresponding value for day b, N is the number of grid points, and: Zi =

冤x s x冥 i

the normalized grid point value where xi is the value of pressure (or height) at grid point i, x=

1 N

N

兺x

i

i=1

and s is the standard deviation of x over the grid. Toth (1991a) ﬁnds that root-mean square difference is signiﬁcantly better, as a measure of circulation similarity, than correlation for selecting analogs from daily 700 mb height ﬁelds. Nevertheless, this procedure is essentially equivalent to the correlation method developed by Lund (1963): S /N 2(1 r), where 0 ≤ S/N ≤ 4 (Willmott, 1987). Thus use of the Kirchhofer score with a threshold of 1.0 N for similarity gives identical results to the correlation method using a coefﬁcient of 0.5. El-Kadi and Smithson (1996) ﬁnd that this threshold for the total worked well for a study of the British Isles. Blair (1998) points out that the original Kirchhofer formulation contains an error which may bias the results. The grid-point values are normalized over the whole array, whereas the row and column values should be normalized separately. Less serious is the fact that the equivalence relationship with the correlation coefﬁcient should use the sample standard deviation rather than the population value, i.e. N should be replaced by N 1. In an evaluation of the consequence of these deﬁciencies, for 2,000 sample grids of ﬁve rows and seven columns, Blair ﬁnds that the corrected algorithm generates more key days and that more “corrected types” are needed to describe 80 percent of all the grids. Accordingly, published results based on the original algorithm may contain signiﬁcant biases if the separate row and column scores, as well as the total score, were used in the classiﬁcation. In some studies only the total score is actually used. It is also worth noting that a distance function that measures the difference between the gradient of pressure, or height, on a pair of maps appears to give better results than either root-mean square difference or correlation (Toth, 1991a). This function is deﬁned as:

Synoptic climatology and its applications 553 11 D=

1 N

N

兺关 a

x i, j

i=1

xai, l )2 (yai, j yai, l )兴

1/2

where xai,j the zonal gradient, and yai,l the meridional gradient. However, this metric does not yet appear to have been adopted in any classiﬁcations. Because the results of correlation-based classiﬁcations can be substantially inﬂuenced by decisions about the grid and sample sizes, and the threshold adopted for similarity, it is important to observe several practical points in order to obtain a valid synoptic classiﬁcation. Based on studies by Key and Crane (1986) and Yarnal and White (1987), the following recommendations are made:

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The types should be identiﬁed using a sample of at least 1,000 individual grids in order to obtain stable results. For example, Barry et al. (1981) used sixty months of daily grids for the western United States; for each calendar month, two months were selected with zonal circulations, two with strong meridional circulations, and one with conditions similar to the long-term mean for the region, as indicated by mean monthly ﬁelds. The threshold for similarity must be sufﬁciently stringent to avoid too much internal diversity within the types. For correlation coefﬁcients, a threshold of >0.7 is generally satisfactory for MSL pressure ﬁelds and >0.9 for 700 mb maps (Hartranft et al., 1970). For the Kirchhofer S score, the use of row and column thresholds of between 1.0 n and 1.4 n where n is the number of grid points in each row (or column) of the grid array seems to produce satisfactory results in terms of the percentage of the days classiﬁed in the population under study (Yarnal and White, 1987). However, this may also produce an inconveniently large number of types (~50). The “optimum” number of types used to describe the synoptic characteristics of an area depends on several considerations. One is the effectiveness of the categories in describing the area climatology. However, experience suggests that twenty to thirty types are an unmanageable number for interpretation and study. Rather than reducing the categories by lowering the threshold for similarity, it appears preferable to group the initial categories by objective clustering or by a subjective grouping appropriate to the problem under study (Yarnal, 1985).

A Monte Carlo technique for assessing the statistical signiﬁcance of a Kirchhofer classiﬁcation is illustrated by Kaufmann et al. (1999). A three-dimensional surface is generated from 5,796 observations of daily maximum temperature at sixteen grid points over the central United States for June–August 1931–93. One hundred experimental data sets are obtained by random drawings from a normal distribution (with zero mean and appropriate standard deviation) based on data at one grid point for Julian Day 200. Each 100 sets are analyzed by the Kirchhofer technique, using the same criteria as for the observational data, and their statistical signiﬁcance is then determined. For example, with a threshold for S of 0.50 and a minimum group size of sixty, ﬁfteen groups are distinguished from the observational data. However, ﬁfteen groups are at position 45 out of 100 values generated by the experimental data for the same criteria. This means that ﬁfteen or more groups will be identiﬁed 45 percent of the time from data where no meaningful patterns exist. It is apparent that further research is needed to assess the reliability of the critical values used in the Kirchhofer technique. Kaufmann et al. point out that the use of spatial averages and deviations in the Kirchhofer formula assumes spatial stationarity. They normalize the maximum temperature data as Zij scores with respect to a temporal mean and deviation. This avoids obtaining a simple south–north temperature gradient as pattern 1. Also, this normalization of location value addresses the problem identiﬁed by Blair (1998) with row and column scores. Kirchhofer’s approach has been widely adopted, especially in the United States. Daily

554 Synoptic and dynamic climatology 1

Figure 7.2 Six key MSL circulation patterns (mb) over western North America, obtained using the Kirchhofer typing procedure. (Barry et al., 1998)

Synoptic climatology and its applications 555 11

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0 Figure 7.3 The basic cyclone model and the associated synoptic types. (From Yarnal and Frakes, 1997)

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catalogues of sea-level pressure types have been produced for the Canadian Arctic (Bradley and England, 1979), the Arctic Ocean (Barry et al., 1987) Alaska (Moritz, 1979), and western North America (Barry et al., 1981); Yarnal (1984a, b, c, 1985) has used the available 500 mb data to classify upper air patterns in this last area. An illustration of the circulation patterns over western North America is shown in Figure 7.2. The issue of grid resolution for the Kirchhofer classiﬁcation applied to western Canada is considered by Saunders and Byrne (1999). More realistic precipitation patterns are simulated with a grid spanning western North America than with a regional grid. A recent application to the British Isles is discussed below. The Kirchhofer scheme has also been applied

556 Synoptic and dynamic climatology 1

to validate the representation of synoptic circulations patterns in GCM control runs with observational data (Crane and Barry, 1988; McKendry et al., 1995). A procedure combining some of the strengths of the manual and correlation-based approaches has been proposed by Frakes and Yarnal (1997b). They ﬁrst use a manual procedure to develop a classiﬁcation of daily surface pressure maps for a twelve-year period. This training set has ten types based on the Bergen cyclone model (see Figure 7.3) applied to daily weather maps. The authors also examine the preceding and following days in order to select the pattern that best represents each day’s weather. Composite mean pressure maps for each type are then used as “key” days in a correlation analysis of gridded pressure data for the region. An optimum threshold is determined by successively varying the threshold value by 0.01 and checking its effect on the percentage of days unclassiﬁed and on the standard deviation ﬁelds of the composites. There appeared to be no advantage in selecting r 0.60 with 80 percent of days classiﬁed over r 0.30 with 99 percent of days classiﬁed. Analysis of the manual and the hybrid daily classiﬁcation results for the twelve years showed only fair agreement with a 42 percent correspondence, although the total frequencies of the ten types agree better overall. A different procedure to modify a correlation-based classiﬁcation is illustrated by Brinkmann (1999a). She notes that within-type variability caused by small-scale circulation features can be reduced by incorporating subtypes, based on 700 mb geostrophic relative vorticity, into the classiﬁcation. Increasing the correlation threshold failed to provide any improvement. 7.3.2 Classiﬁcations based on data reduction methods The earliest studies in objective speciﬁcation of isobaric or contour patterns made use of Fischer–Tschebyschev orthogonal polynomial equations (e.g. Hare, 1958), but by the 1960s the advantages of the more ﬂexible principal component, or eigen vector, analysis was generally recognized. The principal components are the optimal set of mathematically determined functions that provide the most efﬁcient representation of variance in the data set. Each function is mutually uncorrelated (orthogonal) in space and the coefﬁcients of the functions are orthogonal in time. Because each component extracted is orthogonal with respect to all others the variances are additive. A researcher may therefore extract only the number of components that explain a signiﬁcant portion of the total variance of the original system (Kalkstein et al., 1987). The result is a considerable reduction in the volume of the data without loss of valuable information. The ﬁrst few principal components of pressure ﬁelds usually describe simple zonal and cellular patterns, as can be seen in Figure 7.4, which illustrates the ﬁrst three components of a daily 500 mb level analysis for western Europe (Kruizinga, 1979). An individual pressure or height map is represented by some combination of these components and a classiﬁcation based on principal components is constructed by deﬁning arbitrary ranges of the amplitude of each component. It must be emphasized that each component does not necessarily represent an actual circulation type. They are abstractions from reality by virtue of their orthogonality. It has been suggested by Richman (1981, 1986) that rotating some component scores with respect to others by using canonical variates can improve results. However, Joliffe (1987) and Richman (1987) have concluded that there are advantages and disadvantages with both rotated and unrotated solutions, and further work is needed to clarify which is more appropriate for particular applications. Vargas and Compagnucci (1983), analyzing pressure ﬁelds over southern South America, have drawn a useful distinction between two ways of resolving the input data matrix. These are: (1) correlating pairs of ﬁxed points over time – the S mode, (2) correlating pairs of time occurrences over ﬁxed points – the T mode. The S-mode (multiple station over time) correlation matrix is commonly used to develop a regionalization of climatic variables (White et al., 1991; Sumner et al., 1993; Comrie and Glenn, 1998).

Synoptic climatology and its applications 557 11

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Figure 7.4 The ﬁrst three components of daily 500 mb height data over western Europe. They account for 25 percent, 29 percent, and 13 percent of the variance respectively. (Kruizinga, 1979)

558 Synoptic and dynamic climatology 1

The T-mode (day-by-day) correlation matrix, followed by a cluster analysis, is particularly suitable for deﬁning circulation patterns (Romero et al., 1999a). S-mode analyses have been used by Crane and Barry (1988), Richman (1981), Cohen (1983), Bonnell and Sumner (1992), and Maheras and Kutiel (1999). However, Huth (1993) ﬁnds that S-mode analysis of 500 mb heights failed to detect the main circulation patterns over Europe, whereas T-mode (with oblique rotation of components) was successful. T-mode analysis is used by Romero et al. (1999b) to classify circulation types for days with significant rainfall in the Spanish Mediterranean area. Wilson et al. (1992) compared four methods of classiﬁcation – k-means clustering, fuzzy clustering, principal components, and principal components coupled with k means – and found that in terms of discriminating the circulation patterns responsible for precipitation events all methods performed approximately equally well. In a later paper (Huth 1996a) no unique solution is found to the circulation classiﬁcation problem, and no method was found to be best in all aspects. This result is substantiated by the work of Brinkmann (1999b) for eastern North America. Twenty-two 700 mb circulation types were obtained by using an S-mode PCA of both covariance and correlation matrices, with rotation of the ﬁrst ﬁve components, followed by k-means clustering of the PC scores. Four classiﬁcations were derived using the two matrices each with unrotated and rotated PCs. The within-type variability of temperature for each PC-based classiﬁcation was found to be comparable to, or in the case of the rotated covariance matrix worse than, that obtained through a correlationbased classiﬁcation (Brinkmann, 1998). Even when warm and cold circulation subtypes were identiﬁed, the best regression results for monthly temperature anomalies estimated from monthly circulation type frequencies were still inferior to those with the correlationbased approach. On the scale of a whole hemisphere, eigen vectors have been used to classify 500 mb ﬁelds in the northern hemisphere by Craddock and Flood (1969) and although the motivation behind this and subsequent work (Craddock and Flintoff, 1970) was primarily in connection with long-range forecasting, it throws into focus the primary patterns, ten of which contribute almost 80 percent of the total variance. In conclusion it should be noted that some disagreement remains as to whether objective approaches are always qualitatively an improvement on subjective approaches (Ladd and Driscoll, 1980). Many climatologists believe that the classiﬁcation of weather patterns should be as objective as possible but the subjective selection of both data and method preclude complete objectivity. In their review of the future challenge for climatic studies Yarnal et al. (1987) go so far as to say that “what is now being observed is a renewed conﬁdence in the more subjective pressure-pattern typing schemes.” Jones et al. (1993) ﬁnd little difference between an objective scheme for allocating daily weather types on the British Isles and the Lamb classiﬁcation, which is described next. 7.3.3 Weather types Multivariate statistical techniques have been applied to the categorization of weather types (“complex climatology”) since the 1970s, building on the pioneering study of Christensen and Bryson (1966). They used principal components analysis to identify ensembles of weather conditions from surface weather observations. Fifteen weather variables recorded twice daily over ﬁve years of observation at Madison, Wisconsin, were reduced by principal component analysis to nine new components accounting for about 80 percent of the original variance. They were then able to show that these weather types were synoptically reasonable, in this spatial organization. A different illustration is provided by Fechner (1977), who shows how empirical orthogonal functions can be used to provide a classiﬁcation of the vertical proﬁle for weather situations above a certain geographical point. Data on geopotential height, temperature, humidity, and wind measured by radiosonde ascents over the period 1948–65 at Ocean

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Figure 7.5 The ﬁrst EOF of radiosonde data, including the annual range 1948–65 at Ocean Weather Ship C (52.75°N–35.50°W), representing a cold air mass situation. (From Fechner, 1977)

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Weather Ship C provided the input data. The ﬁrst (most important) eigen vector, which represents a cold air mass situation, is shown in Figure 7.5. The multivariate technique of stepwise discriminant analysis of eight meteorological variables has been used to distinguish weather situations in southern California (McCutchan and Schroeder, 1973; McCutchan, 1978). The primary purpose of the technique is to set up the best combination of variables to differentiate classes. Cluster analysis to group cases automatically on the basis of minimum squared error was then employed. A variety of distance measures and measures of similarity can be used in weather typing, including string and tree-type representations (Tsui and Hay, 1979; Kalkstein et al., 1987). The recent approach to identifying synoptic weather complexes follows a series of steps (El-Kadi and Smithson, 1992; Yarnal, 1993): 1 2 3 4

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Principal component (eigen vector) analysis is used to reduce the input weather variables to a set of orthogonal components. Each day is described in terms of these components via a multiple regression analysis of the data. Using a clustering procedure, an objective grouping of the regression coefﬁcients is obtained. A threshold criterion is selected to derive the weather types.

An example of the clustering approach is provided by Kidson (1994a) for New Zealand. He identiﬁes thirteen circulation types by clustering daily sea-level pressure data for 1980–90. Standard weather elements (temperatures, precipitation, sunshine, and wind run) are further examined for eighty-two stations by ranking daily values into quintiles to remove seasonal effects. (Kidson, 1994b). The mean departures for the different classes are typically only in the second to the fourth quintiles, but they show dynamical

560 Synoptic and dynamic climatology 1

consistency. Kidson also performs an EOF analysis of the thirteen types and ﬁnds that the ﬁrst three rotated modes represent 60 percent of the variance in frequency. These EOF’s describe the strength of the westerlies, the north–south displacement of highpressure centers, and opposing variations in the frequency of northwesterly troughs and highs to the south. Further cluster analysis, based on the three-component scores for all months, led to the deﬁnition of eight sets of synoptic regime, for which weather anomalies are also calculated. A standardized set of eigenvector-based procedures has been developed, building on the single station analysis of twenty-eight weather variables at Greater Wilmington, Delaware, by Kalkstein and Corrigan (1986). Daily sea-level pressure and 500 mb charts are chosen to represent the synoptic circulation associated with each cluster. This procedure provides a “temporal synoptic index,” or TSI (Kalkstein et al., 1987). The approach was extended by Davis and Kalkstein (1990), to incorporate weather data for a network of US stations. However, the associated synoptic types had to be grouped subjectively. Moreover a fundamental and unresolved problem concerns the subjective selection of weather variables (Stone, 1989). Conceptually these should represent the radiatively, thermodynamically, and advectively determined components of local weather in a parsimonious manner. A methodology leading to a reduction of the minimum sample size that should be considered in order to obtain a reliable synoptic weather type classiﬁcation has been described by Lana and Fernandez Mills (1994). Most of the research studies following the “Kalkstein TSI approach” have been directed towards local environmental problems connected with air quality (Davis and Kalkstein, 1990), acid rain (Ezcurra et al., 1988), mortality statistics (Kalkstein, 1991), urban energy budgets (Todhunter, 1989), and climate change analysis (Kalkstein et al., 1990). Up to now, no major catalogs of circulation patterns developed through these procedures and spanning extended time intervals have been published. However, for the western United States, Davis and Walker (1992) developed a Spatial Synoptic Index to classify daily types of synoptic situation for 1979–88. Based on radiosonde data from 800 mb to 250 mb from twenty-one stations, thermal, moisture, and ﬂow parameters were used in a principal components analysis and clustering procedure to identify thirteen types. A new procedure to study weather types on a continental scale is proposed by Kalkstein et al. (1996). The ﬁrst step is to designate air mass types based on speciﬁed ranges of weather elements (afternoon air temperature, dew point, dew point depression, wind speed and direction, cloud cover, and diurnal temperature range) at a number of locations. A seed day group is chosen for each of six air mass types (polar, temperate, and tropical, each with dry and moist variants). Linear discriminant analysis is then used to obtain a daily categorization of air masses, and the process is repeated, with seed days for transitional synoptic events to identify transitional days. Maps of air mass frequency and their principal characteristics are prepared for the eastern United States for 1961–90. The goal of this “spatial synoptic classiﬁcation” is to facilitate continental-scale climatic impact analysis. 7.3.4 Artiﬁcial neural networks and self-organizing maps Weather systems display considerable non-linearity and an interconnectedness across scales. These characteristics are not well captured in correlation-based synoptic climatological methods, including PCA. Another limitation of these methods is their inability to learn in an iterative fashion, by converging on the “best” solution (transfer function) to the relationship between atmospheric predictor variables (e.g. 500 mb geopotential height, 850 mb moisture) and the local to regional-scale climate (dependent variables) at Earth’s surface (e.g. temperature, precipitation). These limitations are largely overcome with the use of artiﬁcial neural networks (ANNs) and self-organizing maps (SOMs). Artiﬁcial neural networks attempt to replicate the primary processes involved in human learning

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(e.g. pattern recognition, “trial and error”). The use of ANNs in synoptic climatology, and also satellite image classiﬁcation, exploits the increases in desktop and network computer power over the 1990s by enabling a large number of simulations to be undertaken efﬁciently (Key et al., 1989; Pankiewicz, 1995; McGinnis, 1997). The ANN “learns” the predictor–target relationship by successive iterations to produce a solution containing the smallest error (Hewitson and Crane, 1992a). Artiﬁcial neural networks are also proving to be a valuable tool in empirical “downscaling” (translation across scales) of the coarsegrid resolution output from GCMs to small (local) scales, and on time scales ranging from daily to centennial (Hewitson and Crane, 1992b, 1994; Crane and Hewitson, 1998; Frakes, 1998). Thus they are a valid alternative to more computer-intensive dynamical methods of downscaling which use regional GCMs or the “nesting” of grid models (Hewitson and Crane, 1996). An ANN consists of layers of interconnected nodes that each contain an activation function (Cavazos, 1997). In the feed-forward, or back-propagating, type of ANN, the “best” relationship describing the target variable (e.g. precipitation) and the input layer (e.g. 850 mb humidity) of the training data set, is achieved by successive iterations through one or more hidden layers. The output error is initially large, but enables adjustments to be made to the input layer through a series of weights (back-propagation). This leads to a reduction in the output–target error over successive iterations. A point is eventually reached where no further improvement in the output of the net occurs when the test data are used as input. Using an ANN to predict local-scale precipitation, for example, typically results in a high degree of predictability of the phase (timing) of events but a tendency to underestimate (overestimate) extremely high (low) events (Cavazos, 1999). These may be particularly expected during extremes in teleconnection modes, such as those associated with El Niño and La Niña. The use of ANN in ENSO prediction is discussed by Hsieh and Tang (1998). Self-organizing maps are another form of ANN that can be applied to synoptic climatological research, although such work is only just beginning. Self-organizing maps classify complex matrices having data elements that are related to each other non-linearly (Cavazos, 1998, chapter 3). They extract map patterns without being “trained” (i.e. they are unsupervised classiﬁers), relying upon the emergence of clustered structures from the bottom up (self-organization) (Kohenen, 1995). By reducing multidimensional processes to a two-dimensional problem, SOMs also permit the study of persistence of weather map patterns, as well as their day-to-day evolution (Cavazos, 1998, chapter 3, 1999). Selforganizing maps may provide an alternative to PCA (Cavazos, 1999). The map patterns so derived are meaningful physically, and readily permit the identiﬁcation of circulation features such as “split ﬂow” and blocking regimes.

7.4 Principal catalogs and their uses The principal catalogues that have been developed for different areas of the globe are listed in Table 7.2. 7.4.1 The Lamb classiﬁcation

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Lamb (1950, 1972), building in part on earlier work by Levick (1949), classiﬁed the airﬂow over the British Isles and the immediate surroundings for each day from 1881 to the present on the basis of eight directional types (Figure 7.6), each of which is subdivided into anticyclonic, cyclonic, and unspeciﬁed categories (Table 7.3). The Lamb weather type catalog constitutes the longest daily history of airﬂow patterns for any part of the world and provides an unrivalled perspective on the changing behavior of the atmospheric circulation around the British Isles (Perry and Mayes, 1998). A complete listing of the daily classiﬁcation from 1972 to 1995 can be found in Hulme and Barrow

Figure 7.6 The basic Lamb circulation types for the British Isles. (From O’Hare and Sweeney, 1993)

1

Synoptic climatology and its applications 563 11

Table 7.2 Principal synoptic catalogs Area

Catalog sources

Time period covered

Northern hemisphere Vangengeim (1960) Girs (1974) Dzerdzeevskii (1968) Savina (1987)

0

11

0

Europe Central Eastern Alps Hungary Switzerland Switzerland Italy Spain British Isles Regions of Britain Asia East

0

0

0 11

North America Contiguous US Eastern US Western US Western US Alaska

1891–present 1899–1985

Hess and Brezowsky (1977) Gerstengarbe and Werner (1993) Lauscher (1985) Peczely (1961) Schüepp (1968) Perret (1987) Urbani (1961) Goodess and Palutikof (1998) Lamb (1972) Hulme and Barrow (1997) Mayes (1991)

1861–present 1950–present

Yoshino (1968) Yoshino and Kai (1974)

1941–80

1881–1992 1946–83 1901–57 1955–present 1955–85 1945–60 1956–89

1969–78 1978–87 1899–1980 1979–88

Canadian Arctic Gulf Coast

Barchet and Davis (1984) Comrie and Yarnal (1992) Barry et al. (1982) Davis and Walker (1992) Putnins (1966) Moritz (1979) Bradley and England (1979) Muller and Wax (1977)

Australasia New Zealand – South Island

Sturman et al. (1984)

1961–80

1946–84 1946–74 1971–74

(1997), and updates are posted on the Climatic Research Unit website (http://www. cru.ac.uk). In addition three non-directional types are recognized, anticyclonic where high pressure dominates, cyclonic where a depression stagnates over or crosses the British Isles, and an unclassiﬁed type where the pattern is weak or chaotic. Figure 7.6 illustrates the basic types identiﬁed by Lamb. Among the advantages of the classiﬁcation is the relative ease with which individual days can be classiﬁed, and this allows updating to be carried out very readily. Less satisfactory is the fact that the size of area being classiﬁed often exceeds the size of the prevailing circulation features (O’Hare and Sweeney, 1993). Thus a day that is classiﬁed as cyclonic may have an easterly ﬂow in the north, over Scotland, and a westerly ﬂow over southern England. Mayes (1991) has addressed this problem with a simple airﬂow classiﬁcation for four regions of the British Isles. In fact the original airﬂow analysis scheme of Levick (1949, 1975) distinguished different types in ﬁve regions of the British Isles to take account of such regionally different patterns. Mayes and Wheeler (1997) illustrate the variable conditions over the British Isles during

564 Synoptic and dynamic climatology 1

Table 7.3 Mean seasonal and annual frequencies (days) of the Lamb “weather types” for the British Isles Type

Winter

Spring

Summer

Autumn

Anticyclone Directional AC (except AW) AW W CW NE, E, SE S, SW NW, N C Directional Cyclone (except CW) Unclassiﬁed

14.3 8.1 4.3 20.9 4.0 5.5 8.3 6.7 9.5 5.3 3.3

17.4 11.1 3.4 12.6 2.8 8.8 5.7 8.4 11.6 6.5 3.8

15.9 9.8 5.2 16.7 4.3 3.3 4.2 8.7 14.7 4.8 3.4

17.0 8.8 4.3 18.2 4.9 4.8 6.8 7.4 10.8 5.6 3.5

90

92

92

91

Annual 65.5 37.7 17.2 68.5 15.0 22.4 25.0 31.1 46.5 22.4 14.1 365

Source: from Briffa et al. (1990).

individual months with a predominant airﬂow pattern regime. Notwithstanding these difﬁculties, the Lamb classiﬁcation has been extensively used to produce regional synoptic temperature and precipitation climatologies both for large areas, e.g. England and Wales (Lawrence, 1971; Sweeney and O’Hare, 1992), Ireland (Sweeney, 1985) and for more local studies, e.g. southern England (Barry, 1963; Stone, 1983) and south Wales (Faulkner and Perry, 1974). Beaumont and Hawksworth (1997) calculate the areal mean daily precipitation associated with the Lamb types over Wales for 1982–91. The Cyclonic type, which is wettest, with 6.4 mm per day, occurs on forty-six days per year in the long term; the Anticyclonic type, which is driest (0.46 mm per day) occurs on sixty-ﬁve days annually. Precipitation trends during 1861–1995 are largely determined by these types; the Westerly type itself is not well correlated with precipitation in Wales, although strong zonal circulation and precipitation may occur with the Cyclonic pattern or hybrid types such as CW, CSW, CS, and CSE. Heavy rainfall events also occur predominantly with the Cyclonic type in many parts of the British Isles, excluding southwestern England and Ireland, where the Southerly type brings most such events (Perry and Mayes, 1998). The usefulness of the type categories as a “predictor” of local and regional annual precipitation amounts can be substantially improved, however, if the frequency of warm, cold, and occluded fronts is incorporated within the type categories (Wilby et al., 1995). With more than a century of daily data available, the Lamb classiﬁcation provides a useful tool for investigating the temporal context of seasonal weather (Figure 7.7). Similar regional airﬂow classiﬁcations based on the general trajectory of the ﬂow, subjectively assessed, have been developed for areas as geographically diverse as Labrador-Ungava (Barry, 1959, 1960), Poland (Litynski, 1970), southern South America (Sturman, 1979), and the South Island of New Zealand (Sturman et al., 1984). Various indices have been formulated in order to condense the vast amount of data in the Lamb catalog. Murray and Lewis (1966) devised a set of four indices to summarize the circulation over Britain for a given period of time, and these are known as P (Progression) – a measure of the frequency of westerly circulation types, S (Southerliness), C (Cyclonicity), and M (Meridionality). These indices have been related to monthly mean temperatures and summer rainfall totals in Britain (Murray, 1972; Hughes, 1980). They are also applied by Mayes (1994) to identify trends in airﬂow. The information content of the Lamb (1972) catalog of 27 daily “weather types” can be represented adequately by eight principal components, based on an analysis of annual type frequencies for 1861–1980 by Jones and Kelly (1982). Four combinations of the

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0 Figure 7.7 Annual totals and ten-year moving averages of the frequency (days) of the main Lamb types, 1861–1991. (From Lamb, 1994)

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major types are identiﬁed that account for about 70 percent of the total variance. Thus PC1 contrasts Westerly airﬂow and blocking Anticyclonic conditions; PC2 represents Anticyclonic and/or Cyclonic types versus Northwesterly and/or Northerly airﬂow; and PC4 contrasts Northerly and/or Northwesterly airﬂow with Anticyclonic Westerly, Southerly and/or Southwesterly ﬂows. Using the same approach, Briffa et al. (1990) show that the principal components can be used to fashion four generic indices of climatic variation over the British Isles. Jones et al. (1993) take a different approach by relating the Lamb types to an objective classiﬁcation of sea-level airﬂow and vorticity developed by Jenkinson and Collison (1977). The latter use grid-point pressure values over an array

566 Synoptic and dynamic climatology 1

from 45°N to 65°N, 10°E to 20°W to deﬁne westerly ﬂow (W), southerly ﬂow (S), resultant ﬂow F (S2 W2)1/2, westerly shear vorticity (ZW), southerly shear vorticity (ZS), and total shear vorticity (Z ZW ZS). Rules are devised to deﬁne ﬂow directions from 45° sectors (e.g. W is between 247.5° and 292.5°); if | Z| < F, the ﬂow is straight; if | Z | > 2F the ﬂow is strongly cyclonic (Z > 0) or anticyclonic (Z < 0); if F < |Z | < 2F a hybrid direction/curvature, type is indicated; and if F < 6 and |Z | < 6 the light, indeterminate ﬂow is unclassiﬁed. The method was tested for daily maps during December 1880–December 1989 against the Lamb catalog for seasonal and annual totals of Lamb’s seven basic types. Seasonal correlations are about 0.9 for anticyclonic and cyclonic types and 0.70–0.85 for directional types. Jones et al. attribute the differences primarily to Lamb’s attention to large-scale steering of weather systems rather than instantaneous surface winds. The good overall agreement suggests that an objective scheme is suitable for many applications, particularly the use of GCM outputs (Briffa, 1995). A number of studies suggest that low-level airﬂow may be a better predictor of precipitation than the Lamb types. Sumner (1996) reaches this conclusion from a cluster analysis of standardized patterns for more than 1,000 daily cases of signiﬁcant rainfall events in Wales. Mayes (1996) uses the W, S, and C indices determined from the Lamb types to examine trends in monthly precipitation over the British Isles. Similarly, Wilby (1997) argues that airﬂow indices of shear vorticity, cyclonicity, angular ﬂow direction, and ﬂow strength are preferable to discrete types in GCM downscaling because the indices are continuous. He also points out that weather type–precipitation relationships used in downscaling cannot be assumed to be stationary. The options are to ignore any non-stationarity or to incorporate it empirically or stochastically. The Kirchhofer method has recently been used to generate an MSL pressure pattern catalog for the British Isles, comparable with Lamb’s scheme (Figure 7.8). El-Kadi and Smithson (1996) identify ﬁfteen types for 1977–86. Seven types (westerly, northerly, southerly, easterly northwesterly, Netherlands high and British high) account for 78.5 percent of cases, and only 0.5 percent of days remained unclassiﬁed. Hybrid types are eliminated, and the smaller number of categories simpliﬁed applications of the catalog. Another virtue of the automated approach is the differentiation of three anticyclonic and three cyclonic types, according to the location of the system centers. 7.4.2 The Grosswetter classiﬁcation The concept of Grosswetter (literally, large-scale weather) has been developed in Germany and applied in a daily classiﬁcation by Hess and Brezowsky (1977). Baur (1947, 1951), the originator of the concept, deﬁned a Grosswetter as the mean pressure distribution (at sea level) over a time interval during which the position and the tracks of the major depressions and anticyclones remain essentially unchanged. Later, account was taken of the 500 mb mid-tropospheric circulation pattern also (Baur, 1963). The thirty Grosswetterlagen are described in Table 7.4 and the relative frequencies of each during the 1881–1970 period are shown. Median durations, frequencies of occurrence, and the probabilities of transition from one type to another have been calculated by van Dijk and Jonker (1985). The daily classiﬁcation extends back to 1881 and has been updated in the monthly publication Die Grosswetterlagen Mitteleuropas, published since 1947 by the Deutscher Wetterdienst (1994). Type averages and frequency values have been determined for various climatic parameters for regions and individual German cities (Bürger, 1958), as well as for other central European countries, e.g. Switzerland (Grutter, 1966), Hungary (Peczely, 1961). The frequencies of the main Grosswetter in relation to phases of the El Niño/Southern Oscillation have been studied by Fraedrich (1990). Schiesser et al. (1997) analyze the occurrence of severe winter storms in lowland Switzerland during 1864/65–1993/94 in relation to the Grosswetter; half the cases had cyclones located over the British Isles–North Sea.

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Figure 7.8 Objectively derived pressure patterns for the British Isles. (From El Kadi and Smithson, 1996)

568 Synoptic and dynamic climatology 1

Table 7.4 Description of the GWL and relative frequencies of the total number of Witterungen (Nt) that occurred during the periods 1881–1970 and 1949–1970 (%) No.

GWL

Description

1881–1970

1 2 3

Ws Wa Wz

4 5 6 7 8 9 10

BM HM SWa SWz NWa NWz HNa

11 12 13 14 15 16 17 18 19 20 21 22 23

HNz HB Na Nz TrM TM TB TrW Sa Sz SEa SEz HFa

24

HFz

25

HNFa

26

HNFz

27 28

NEa Ww

29 30

U NEz

Zonal circulation type, displaced southward Zonal circulation type, displaced northward Zonal circulation type (most frequently occurring GWL) Ridge of high pressure over Central Europe Anticyclone over Central Europe Southwesterly ﬂow, anticyclonic conditions Southwesterly ﬂow, cyclonic conditions Northwesterly ﬂow, anticyclonic conditions Northwesterly ﬂow, cyclonic conditions Anticyclonic over Norwegian Sea, anticyclonic conditions Anticyclonic over Norwegian Sea, cyclonic conditions Anticyclone over the British Isles Northerly ﬂow, anticyclonic conditions Northerly ﬂow, cyclonic conditions Trough over Central Europe Cyclone over Central Europe Cyclone over the British Isles Trough over Western Europe Southerly ﬂow, anticyclonic conditions Southerly ﬂow, cyclonic conditions Southeasterly ﬂow, anticyclonic conditions Southeasterly ﬂow, cyclonic conditions Anticyclonic over Scandinavia and/or Finland, anticyclonic conditions Anticyclonic over Scandinavia and/or Finland, cyclonic conditions Anticyclonic over the Norwegian Sea and Scandinavia, anticyclonic conditions Anticyclonic over the Norwegian Sea and Scandinavia, cyclonic conditions Northeasterly ﬂow, anticyclonic conditions Zonal circulation type, in Eastern Europe a southerly ﬂow, blocking anticyclone over Russia Not deﬁned, transitional situation Northeasterly ﬂow, cyclonic conditions

1949–70

2.55 5.62

2.34 5.10

11.58 5.90 9.89 2.26 1.79 4.87 4.45

10.11 7.12 8.50 3.03 4.14 1.79 5.74

3.69 1.60 3.17 1.41 3.19 4.52 2.79 2.17 3.30 2.06 0.94 2.17 1.48

2.21 2.02 3.13 0.97 2.71 5.79 2.44 2.53 5.28 1.75 1.10 1.98 1.06

3.59

2.67

1.06

1.61

1.16

1.15

1.54 3.04

2.16 2.44

2.86 2.21 3.12

3.54 2.80 2.80

Nt 8,554

2,176

Source: from van Dijk and Jonker (1985).

Although the classiﬁcation takes account of the circulation over a wide area of central and western Europe (30°W–45°E, 24°–70°N), it has been supplemented by modiﬁed schemes in some areas, particularly the Alps (Wanner et al., 1997, 1998), partly because it takes little account of Mediterranean inﬂuences, which become increasingly important towards the southern fringes of central Europe. Lauscher (1985), for example, has given details of a daily classiﬁcation for the period 1946–83 for the eastern Alps. The subtypes had a mean duration of four to eight days, with an overall mean of 6.4 days. Kahlig (1989) suggests that the categorization of circulation types can be assisted by the use of artiﬁcial intelligence techniques. He notes that an expert system has been applied to the classiﬁcation of Grosswetterlagen for the eastern Alps.

Synoptic climatology and its applications 569 11

0

11

0

0

0

0 11

Figure 7.9 Position of troughs and ridges in the mid-troposphere for the W, E and C types of hemispheric circulation pattern proposed by Vangengeim and Girs. (a) Winter. (b) Summer. (From Kozuchowski and Marciniak, 1988)

570 Synoptic and dynamic climatology 1

7.4.3 Hemispheric classiﬁcations Studies of hemispheric circulation patterns began in the Hydrometeorological Service of the Soviet Union in the 1930s and 1940s. Two “schools” developed under the leadership of B.L. Dzerdzeevski (1945) in the Institute of Geography, Moscow, and G. Y. Vangengeim (1935, 1946) in the Arctic and Antarctic Research Institute, St Petersburg. Interestingly, neither group references the other and there does not appear to have been detailed intercomparison of the two approaches. Elementary synoptic processes (Vangengeim–Girs) Vangengeim (1935, 1946) distinguishes three basic types of circulation in the zone 35°–80°N – westerly (W), easterly (E), and meridional (C). Each is characterized by a particular distribution of depressions and anticyclones at the surface and by an organization of the major long-wave pattern. The ridge and trough positions differ seasonally (Figure 7.9). W type refers to patterns with essentially zonal movement of small-amplitude waves; nine subtypes are distinguished on the basis of the latitude of the subtropical anticyclone cells. With C type (seven subtypes) there are large-amplitude, stationary waves. The subpolar lows are shallow, there is a well developed high, and the subtropical anticyclone cells are split and displaced northward. Pressure over Europe and western North America is low. E type (ten subtypes) is comparable with C, but the troughs are in different locations. The subpolar lows are well developed, the Siberian high is weaker and farther west than with C, the Azores and Paciﬁc anticyclone cells are also west of their normal position, and there are stationary highs over Europe and western North America. Subsequently Girs (1948, 1960, 1981) showed that in the North America–Paciﬁc Ocean sector two meridional patterns (M1 and M2) and one zonal type (Z) are the most signiﬁcant, and he indicated that these may combine with W, E, and C to give nine basic types. Table 7.5 indicates these and the annual frequencies for 1900–57. An algorithm based on a Euclidean distance measure was subsequently developed, and used, for twelve of the Vangengeim circulation types (Reitenbakh and Kozulin, 1982) to obtain a catalog for 1880–1973. The Vangengeim classiﬁcation has been widely used by Russian scientists in assessing seasonal departures from normal of temperature and precipitation for the former Soviet Union, or regions of the territory. Vorobieva (1967), for example, provides maps of seasonal departures of precipitation for the W, E, and C types. Correlations between the three major type categories and temperature and precipitation patterns over Europe are presented by Kozuchowski and Marciniak (1988). Elementary circulation mechanisms (Dzerdzeevski) Dzerdzeevski (1945, 1968) proposed that Elementary Circulation Mechanisms (ECMs) operate over a short time interval but govern the circulation pattern over an entire hemiTable 7.5 Frequency of Vangengeim’s types, 1900–57 (%) Annual frequency Type

Wz

WM1

WM2

Ez

EM1

EM2

Cz

CM1

CM2

Subtypes 9.9 Main types ←

7.5 26.5

9.1 →

14.3 ←

11.1 44.4

9.0 →

10.1 ←

8.9 29.1

10.1 →

Wz Zonal in the North Paciﬁc sector. M1 Surface anticyclone near the Aleutian Islands, lows to the north. M2 Ridge from the Paciﬁc high extending to western North America.

Synoptic climatology and its applications 571 11

sphere. He recognized that the identiﬁcation of circulation types for a limited sector is hindered by the different synoptic histories of the airﬂows entering the region. The “inﬂuence ﬁeld” of the atmosphere for a twenty-four-hour forecast period at a location in mid-latitudes extends over about a quarter of the earth’s circumference and from the tropics to the polar circle. Discrete categories of atmospheric process can be distinguished because: 1

0 2 11

The hemispheric circulation is determined by a ﬁnite number of characteristic circulation mechanisms. The number of those mechanisms is small over short time periods when the incoming solar radiation and the properties of the earth’s surface are constant, but their characteristics differ greatly with season. The features of each circulation mechanism (including its spatial organization) persist longer than the time scale of synoptic processes. Thus the hemispheric circulation is a real macroprocess, not a chance combination of independent synoptic processes. Individual disturbances and fronts are regarded as “noise”.

The approach takes account of the degree of organization of the hemispheric ﬂow (Dzerdzeevski et al., 1946). Cyclone and anticyclone tracks at the 700 mb or 500 mb level are used as an indicator of the main mid-tropospheric steering currents (Dzerdzeevski and Monin, 1954). It is emphasized that charts averaged over several days provide the best view of the various types. Special attention is given to polar intrusions and associated blocking in the westerlies in six hemispheric sectors of 50°–60°. The four major patterns are shown in Figure 7.10. They are:

0

1 2 3

0

4

0

0 11

A zonal ring of cyclone tracks in high latitudes; two or three breakthroughs of midlatitude cyclones (two types, ﬁve subtypes). Interruption of zonality with a single polar intrusion; one to three breakthroughs of mid-latitude cyclones (ﬁve types according to the sector of the intrusion, thirteen subtypes). Northerly meridional motion with two, three, or four polar intrusions (ﬁve types according to their location, twenty-one subtypes). Southerly meridional; no polar intrusions and poleward movement into the Arctic in two to four locations (one type, two subtypes).

The patterns for the forty-one subtypes are illustrated in Dzerdzeevski (1970), and their seasonal tendencies are summarized by Savina (1987), together with a catalog of their daily occurrence, 1899–1985. She also uses them to characterize six “natural seasons” and tabulates the durations for each year. A similar approach is evident in Russian studies of the southern hemisphere (Davidova, 1967). The term “synoptic process” is used to refer to the movement of pressure systems over a two to three-day period. The classiﬁcation refers to the three southern oceans from 20°S to the coast of Antarctica. The types, between ﬁve and seven for each ocean, refer primarily to the zonal or meridional character of the circulation at MSL, based on six years of data. Davidova shows that meridional patterns of circulation are dominant in all three oceans; in the South Paciﬁc meridional forms have an 81 percent annual frequency, with 94.8 percent in the winter months. Figure 7.11 shows the most common patterns in the south Indian Ocean. The earliest hemispheric-scale categorizations of circulation regime in Western literature were based on the concept of high and low index described in section 4.3. A classiﬁcation for the northern hemisphere 500 mb circulation was developed by Wada and Kitahara (1971), based on 500 mb zonal index anomalies in four quadrants (0°–80°E, 90°–170°E, 100°–180°W, and 10°–90°W). Based on the degree of zonality and meridionality, ten basic types and six subtypes were distinguished for ﬁve-day mean and monthly circulations for

572 Synoptic and dynamic climatology 1

Figure 7.10 The four basic types of elementary circulation mechanism of Dzerdzeevski (1962). Solid arrows: cyclone tracks; open arrows: anticyclone tracks.

1946–70. This approach was extended by Kletter (1962), based on the identiﬁcation of progressive, stationary, and retrogressive planetary wave motion, using the Rossby wave formula (section 4.3.2) applied to time–latitude proﬁles of the 850 mb zonal wind component in the northern hemisphere. For 1955–58 the type frequencies shown in Table 7.6 were observed. The subtypes had a mean duration of four to eight days, with an overall mean of 6.4 days. The typical sequence of these types shows that a zonal pattern generally evolves progressively towards the omega or cellular pattern through ampliﬁcation of the waves. However, the cellular and blocked patterns may revert directly to zonal ﬂow (see Figure 4.31). The approach is inadequate for regional applications, however, because the geographical aspects of the patterns are insufﬁciently speciﬁed. A hemispheric classiﬁcation for monthly sea-level pressure patterns has been developed by Bartzokas and Metaxas (1996). Monthly pressure grids for January, February, July, and August 1890–1989 and 1,000–500 mb thickness values for 1945–89 were used poleward of 20°N. Patterns were obtained by T-mode eigen vector analysis with varimax

Synoptic climatology and its applications 573 11

0

11

0

Figure 7.11 The most frequent large-scale circulation patterns over the south Indian Ocean. (a) Type 1. Zonal, with mid-latitude traveling depressions, 42 percent frequency. Above (1) MSL isobars, (2) cyclone tracks. Below (1) Streamlines, (2) isotachs (m s1). (b) Type 2. Meridional, with break-up of the subtropical high southeast of Madagascar, 26 percent frequency. (Davidova, 1967)

0

Table 7.6 Hemispheric circulation types Circulation type 1

0

0

Zonal a Zonal ﬂow 40°–65°N; main low enters north of 60°N b Zonal ﬂow (subdivided according to latitude of maximum westerlies and degree of uniformity over different sectors) c North or southward trend of the wind maximum over the North American or European–North Atlantic sectors

2 27 20

2

Planetary waves (at least three waves)

23

3

Cellular a Blocking anticyclone with split westerlies b Omega block linked to subtropical high pressure c Weak cellular circulations

11 9 8

Source: from Kletter (1962).

11

Frequency (%)

574 Synoptic and dynamic climatology 1

rotation. The February and August analyses are used to check the likely validity of the patterns obtained in January and July, respectively. Pattern 1 in January (pattern 2 in February) account for 30.6 percent (27.4 percent) of the variance and show a strong extensive negative pressure anomaly over the north Siberian coast, with a positive anomaly southwest of the British Isles, giving a zonal circulation over the northeastern Atlantic. Pattern 2 in January (28 percent) and pattern 1 in February (21.3 percent) feature a strong positive anomaly in the Denmark Strait and negative anomalies over the eastern subtropical Atlantic and the central North Paciﬁc (and for February also over north-central Siberia). In July (August) pattern 1 (pattern 2) accounts for 45.3 percent (41.6 percent) of the variance. The closely similar patterns show a strong negative anomaly over the Arctic Ocean and a weak positive anomaly over Europe. The July pattern 2 (August pattern 1), with 43.5 percent (42 percent) explained variance, is the opposite of the previous patterns, with a strong positive anomaly over the Arctic Ocean. Since about 1900 the positive (negative) pressure anomaly over the Arctic Ocean in summer has been decreasing (increasing) in frequency.

7.5 Regional applications The original purpose of most synoptic climatological studies was extended, or long-range, weather forecasting. This is apparent in the description of the World War II developments by Jacobs (1947); airﬂow patterns and related weather conditions over Japan were analyzed as a basis for prediction. Modern developments along these lines are discussed below. More generally, it was assumed that airﬂow patterns would discriminate between the weather conditions in a particular region. Hence the use of the term “weather type” as applied by Lamb (1972), among others. Many regional studies sought to describe the typical conditions experienced during each airﬂow, or circulation type, according to season. This has been done for the Alps (Lauscher, 1985; Schüepp, 1968; Kerschner, 1989; Kirchhofer, 1976), Germany (Flohn and Huttary, 1950; Bürger, 1958), the British Isles (Barry, 1963; Sowden and Parker, 1981; Storey, 1982; Stone, 1983), Alaska (Moritz, 1979), the Canadian High Arctic (Bradley and England, 1979), New Zealand (Kidson, 1994b) and many other individual regions. Few synoptic classiﬁcations, apart from the Hess–Brezowsky Grosswetter, have addressed the problem of linking surface and mid-tropospheric circulation characteristics, although several schemes consider surface and upper-air patterns independently (Mosino, 1964, for Mexico; Schüepp, 1957, for the Alps). In a study of precipitation in Italy, Gazzola (1969) identiﬁed twenty-two patterns separately at the surface and at the 500 mb level. Contingency tables of their joint occurrence on a seasonal basis showed that, out of 484 possible combinations, 44 percent never occurred and a further 19 percent occurred three times or less over an eleven-year period. Sixty-nine percent of days were acccounted for by 13.4 percent (or sixty-ﬁve) of the possible types. Thus the concern that a multiplicity of types may result when the vertical structure of the circulation is considered may not represent an insuperable problem. Synoptic systems and upper-level waves possess considerable vertical coherence, as discussed in Chapter 6. The use of a uniﬁed system in categorizing surface and upper air ﬁelds is illustrated by the second classiﬁcation developed for Switzerland by Schüepp (1959). He identiﬁes thirty-three weather situations that may last several days (Witterungslagen), although the number is increased to forty patterns in a subsequent analysis of related station weather conditions (Schüepp, 1979). The scheme is based on principles set out by Lauscher (1958, 1972) for low-level airﬂow and upper-level circulation patterns over the eastern Alps but modiﬁed to take account of airﬂow curvature and vertical motion. The six basic classes and the convective, advective, and mixed types are summarized in Table 7.7. Convective situations are 46 percent of the total, advective situations 46 percent, and mixed patterns 8 percent of days during 1951–70. The types for the four cardinal directions and low

Synoptic climatology and its applications 575 11

Table 7.7 Witterungslagen scheme for the Alps Partial collective

500 mb pattern

Subtypes

I

1 High pressure 2 Weak (average) pressure 3 Low pressure

Each has ﬁve subtypes: weak surface winds with W, N, E, S, or weak winds at upper levels

4 5 6 7

Each has ﬁve subtypes: upper jet, upper/lower ﬂows almost parallel with above or belowaverage pressure aloft, wind turns height with above or below average pressure aloft

Convective patterns

II

0

11

III

Mixed patterns

Westerly ﬂow Northerly ﬂow Easterly ﬂow Southerly ﬂow

8Z Wave (strong surface winds) 8B Upper jetstream 8C Surface ﬂow (weak aloft)

B and C have two subtypes: pressure aloft and above or below average

Source: Schüepp (1979).

0

0

0

0 11

Figure 7.12 Climate of Zürich in winter and summer for the Schüepp (1979) Witterungs-lagen classiﬁcation. (Above) mean 13.00 temperature (circle) and ± range (vertical lines); (center) sunshine duration (columns, hr), + (dots), and days < 2 hr d–1 shown by open circles plotted downward (left scale, percent); (below) mean daily precipitation plotted downward (solid columns, mm), + (vertical lines) and percent days 1 mm (solid circles), for each of the forty types. The horizontal lines show annual mean values of the three elements. H anticyclonic, F average pressure, L low pressure; W, N, E, and S are the advective types; X mixed types. (From Schüepp, 1979)

576 Synoptic and dynamic climatology 1

pressure are illustrated for a representative day in the climate atlas of Switzerland (Kirchhofer, 1995) by a satellite image, ﬁelds of 500 mb height and sea-level pressure, and a map of signiﬁcant weather over Switzerland; a synoptic climatological summary at twelve Swiss stations for each of the forty types is provided by Schüepp (1979). Figure 7.12 shows the winter and summer conditions associated with the types at Zürich. A further classiﬁcation for Switzerland by Perret (1987) adopts elements of the Grosswetterlagen over Europe and the northeastern Atlantic. Perret recognizes directional patterns of the upper circulation and associated steering of synoptic systems, upper anticyclonic patterns, and upper cyclonic or trough patterns, giving nine basic types with subtypes (thirty-one patterns in all). The Swiss Meteorological Institute (1985) updates thirty-four synoptic parameters on a daily basis, including the Schüepp Wetterlagen type, three parameters from Perret (1987), and two from Fliri and Schüepp (1984), and maintains a database of this information. For the Alpine region spanning Switzerland, the Tirol and northern Italy, a comprehensive precipitation synoptic climatology based on the Schüepp classiﬁcation has been developed by Fliri and Schüepp (1984). For each type in each season a map of mean sea-level pressure and 500 mb contours is provided, together with maps of the mean daily precipitation and the probability of daily amounts of at least 1 mm. There are also a description of the weather conditions over the region and tables for selected stations of temperature averages, Föhn and thunderstorm frequency with each type. Figure 7.13 illustrates the conditions associated with Northerly and Southerly types in winter 1946–79. Stochastic models of daily precipitation have recently been developed using the conditional probability of speciﬁed precipitation characteristics for selected atmospheric circulation patterns. In a study of daily precipitation in Nebraska, for example, Bogardi et al. (1993) use a ﬁrst-order autoregressive model of precipitation days with nine types of 500 mb circulation pattern, obtained by PCA and k-means clustering for the area 25°–60°N, 80°–125°W. For three stations in Washington state Wilson et al. (1992) examine wet/dry days and precipitation amount on rain days in relation to four circulation classes, deﬁned by tropospheric height patterns and wind components over western North America, 35°–65°N, 100°–145°W. Precipitation conditions at the three stations are linked through a hierarchical precipitation event model. In these approaches, geographical factors that may inﬂuence precipitation are not considered. In contrast, Kilsby et al. (1998) use regression models to estimate points precipitation statistics (mean, proportion of dry days) at any station in England and Wales, taking account of MSL airﬂow indices and factors of geographical location (distance from sea, latitude, and longitude). Recently the value of synoptic catalogues for validating the simulations of modern climate via general circulation models (GCMs) has been recognized (Hay et al., 1992; McHendry et al., 1995). Most GCM validation studies use mean ﬁelds and their standard deviation, although diagnostic parameters, such as eddy kinetic energy as a measure of synoptic eddies, have also been examined. Schubert (1994) has noted that empirical (Wigley et al., 1990), semi-empirical (Giorgi and Mearns, 1991), and dynamic methodologies have been developed as downscaling techniques. Hulme et al. (1993) employ an objectively derived version of Lamb’s classiﬁcation for the British Isles to analyze the annual course of the principal airﬂow pattern types in versions of the UK Meteorological Ofﬁce and Max Planck Institute GCMs, in comparison with the observed values. Jones et al. (1993) show how objectively deﬁned weather types derived from surface pressure maps can achieve strong correlation with the original Lamb types. This means that objectively deﬁned Lamb weather types could be derived from model-derived pressure patterns, either to express future scenarios in a meaningful form or to validate control runs of general circulation models against the actual Lamb types. For Canada, McHendry et al. (1995) analyze the Canadian Climate Model, using types derived by the Kirchhofer

(b)

0

11

0

0

0

0

11

Figure 7.13 The circulation pattern and mean daily precipitation over the Alpine region during (a) Northerly and (b) Southerly type of the Schüepp classiﬁcation. Upper Sea-level pressure (mb) (solid lines) and 500 mb contours (Dm) (dashed). Lower Precipitation (mm). n number of days. (From Fliri and Schüepp, 1984)

(a)

11

578 Synoptic and dynamic climatology 1

method. For the New Zealand region Kidson (1995) also uses the Kirchhofer approach with the CSIRO model daily sea-level pressure ﬁelds for 1980–88 and examines the changes in type frequency with the results of a doubled carbon dioxide simulation.

7.6 Analogs The concept that atmospheric ﬂow patterns tend to recur, i.e. that analogs of any pattern exist, has a long history. This argument served as one approach to long-range forecasting beginning in the 1920s with the work of F. Baur (1951, 1963) in Germany. More recently it has been utilized in seasonal forecasts by Bergen and Harnack (1982), but with only marginal skill. Good analogs on a hemispheric scale appear to be uncommon (Lorenz, 1969). A study of 500 mb circulation patterns over the northern hemisphere north of 20°N, using twicedaily analyses for 1956–79, shows that only about 3 million out of 135 million pairs (2 percent) are relatively good analogs (Ruosteenoja, 1988). These analogs are most common in late winter and least frequent in summer and autumn. Good analogs are more likely, and their lifetime is longer, when the seasonal difference between the patterns is short (three weeks). However, only 842 good analogs were found, and most of these were not independent (separated by at least ten days). Additional difﬁculties arise because the surface and upper-level ﬁelds may not match equally well. Even in rare cases where they do match well the patterns both two days earlier and two days later differ considerably from one another, as illustrated in Figure 7.14. By using EOFs to describe the hemispheric 500 mb ﬁeld Ruosteenoja was able to determine the contributions of planetary, large-synoptic (3,000–4,500 km) and small-scale synoptic (1,000–3,000 km) waves to the analog index.1 Analog similarity is determined mostly by the planetary waves; the similarity of short-wave patterns at 500 mb, for example, rapidly diminishes (Gutzler and Shukla, 1984). At the hemispheric scale, Toth (1991b) concludes that analogs of daily circulation and time-derivative analogs are no more useful than traditional analogs in forecasting beyond a few days. However, they yield useful information on the gross structure and phase space of circulation patterns. Toth (1991c) ﬁnds that the best analog to a circulation pattern is likely to be closer to the climatic mean than to the base case and that analog predictability does not depend on the distance, in phase space, of the initial ﬂow from the mean. Another interesting suggestion from his analysis is that persistence of ﬂow increases with increasing proximity to the climatic mean. The use of limited regions in which to deﬁne analogs is re-examined by van den Dool (1989). For ﬁfteen years of data, the ﬁrst ﬁve analogs, weighted according to their quality, were found to provide a sufﬁcient basis for twelve-hour forecasting within a limited area (a circle of 900 km radius), which avoids rapid advection of changes. A further test was made for a 2,000 2,000 km area in the eastern United States using 500 mb height forecast maps. The overall conclusion is that analog forecasting, in these situations, has more skill than previous work suggests. The possibility of using other parameters to deﬁne analogs for different locations and situations is also proposed. For example, in a further regional study of eastern North America van den Dool (1991) ﬁnds that negative or antianalogs are almost as common as analogs, except for cases of deep low-pressure systems. Such “antilogs” show a comparable skill level to analogs for twelve-hour forecasts. This is a result of the linear component of the absolute vorticity advection (represented in the linearized barotropic model).

7.7 Seasonal structure Synoptic climatological methods have found wide application in descriptions of the temporal characteristics of climate. These include both the structure of the annual climatic

0

11

0

0

0

0

11

Figure 7.14 Illustration of the temporal evolution of an exceedingly good 500 mb analog pattern (b) for March 12 1956 and March 6 1976, and the corresponding patterns (a) two days earlier and (c) two days later. (Ruosteenoja, 1988)

11

580 Synoptic and dynamic climatology 1

cycle, in terms of “natural seasons,” weather spells and singularities, and the contribution of synoptic variability to long-term climatic trends (discussed in the subsequent section). The idea that certain types of weather tend to recur around a speciﬁc calendar date has a long history in traditional weather lore (Pilgram, 1788; Zimmer, 1941; Inwards, 1950; Yarham, 1966). Nineteenth-century European meteorologists were particularly interested in cold spells occurring in winter and spring (Brandes, 1820, 1826; Dove, 1857; Talman, 1919). Dove wrote a memoir on the frosty weather spell, referred to as the “Ice Saints” after the corresponding saints’ days, that was thought to be frequent in central Europe during May 11–15. He concluded that associated outbreaks of cold polar air were too irregular in their occurrence to be connected reliably with the speciﬁed dates. The recurrence tendency of certain weather characteristics around a speciﬁc time of year is referred to as a singularity. Schmauss (1928) used the term in the mathematical sense of a single point on a time plot of some weather element. However, three approaches have been used to identify singularities: 1 2 3

Examination of mean daily values of a climatic parameter over a long period to identify irregularities in the seasonal trends. Harmonic analysis is one tool used to ﬁlter an annual time series for this purpose (Craddock, 1956). Analysis of synoptic catalogs to identify time intervals when particular types are unusually prevalent (Brooks, 1946). Study of the synoptic and spatial characteristics of selected singularities (Bryson and Lowry, 1955; Duquet, 1963; Carleton, 1986; Carleton et al., 1990).

Many of the weather spells that were recognized in early studies were not substantiated when tests of statistical signiﬁcance were applied (Marvin, 1919). Also, it was found that false singularities could be generated in a random series by the introduction of persistence (Bartels, 1948; Baur, 1948). In a statistical analysis of the mean daily central England temperature series Battye (1980) found no evidence of consistent year-to-year variations of one to ten-day duration that cannot be ascribed to chance or seasonal trends. Flohn and Hess (1949) note that the Ice Saints occurred with 77 percent frequency from 1881 to 1910 but that this decreased to 58 percent during 1911–47. Bissolli and Schoenwiese (1990) show that the period May 6–18 had become a warm anomaly for 1949–85 in six regions of Germany. Likewise, the Old Wives’ Summer (September 27–October 1) occurred on September 17–20 during 1916–50 and September 25–6 during 1951–85 compared with 1881–1915. Such differences probably reﬂect changes in the large-scale circulation. Table 7.8 Singularities in Central Europe, 1881–1947 Period

Circulation type

Characteristics

Frequency (%)

January 15–26 May 22–June 2

Continental anticylcones Northern and Central European highs Northwesterly

Dry, night frosts Dry

78

June 9–18 July 21–30 August 1–20 September 3–12 September 21–October 2 December1–10

Westerly Westerly Central European highs Central and South-eastern European highs Westerly

Source: from Flohn and Hess (1949).

80 Summer monsoon, thundery rains, cool Summer monsoon Thundery rains Dry “Old wives’ summer,” dry Mild

89 89 84 79 76 81

Synoptic climatology and its applications 581 11

Table 7.9 Singularities in the British Isles, 1873–1961 Period

Circulation type

Characteristics

Type frequency (%) and signiﬁcance level

Period

January 20–3

AC, S, and E together

50

D

March 12–23

AC, N, and E together

35 (1% level)

D

May 12–18

N type

30

A, B, C

May 21–June 10

AC type

5% level

A, B, C

June 18–22

W, NW, and AC together

Generally dry and sunny in central and southern England. Year’s lowest frequency of C type (10–12%) January 24–6 Notable rainfall minimum in central and southern England. March 12–14 peak of AC Annual maximum about these dates; May 14–20 is sunniest week of the year in Ireland Annual maximum frequency, 40% or more on some days during most of this period; driest weeks of the year in Scotland, Ireland; more yearto-year variations in southern half of England Generally dry and sunny in southern England; cloudy and wet in Scotland and Ireland W type frequency 52% on 20 June Sharp peak (replaced by twin maxima around July 20 and mid-August) Wet in most areas

70

D

1% level

D

35% (5% level)

B, A, C

70

D

0

11

0

0

July 31– August 4

C type

August 17– September 2

W and NW together C type AC, N, and NW together

September 6–19

0

October 5–7

AC type

October 24–31

C, E, and N types

November 17–20 AC type December 3–11

W and NW together

December 17–21 AC type

0 11

Peaks August 19 and 28 Dry, especially east and central England C type frequency, <20% between September 6 and 12 Slight check to seasonal cooling Great decline to year’s minimum frequency of AC type (10%) about October 28–31 Stormy, wet weather Dry, foggy period in central and southern England Wet and stormy in most areas with December 3–9 generally wettest week of year on average Generally dry, foggy weather

Source: from Lamb (1964). Note Period A 1873–98, B 1898–1937, C 1938–61, D 1890–1950 ± about ten years.

30 (5% level) D 80 A, B, C 5% level

A, B, C

40 (5% level) D 1% level

A, B, C

5% level 30 (1% level) A, B, C 70

A, B, C

25

A, B, C

582 Synoptic and dynamic climatology 1

The ﬁrst calendar of singularities was developed by Schmauss (1938, 1941), based on investigations of temperature, pressure, precipitation frequency, and wind data for Germany. Later, research in Germany, Britain, the United States and Japan focused on circulation characteristics. Using the Hess–Brezowsky Grosswetter catalog for 1881–1947, Flohn and Hess (1949) identify periods when a particular Grosswetterlage occurred on three or more consecutive days in at least two-thirds of the sixty-seven-year record. The deﬁnition allows a variation of ± 5 days about the middle date for each singularity. Table 7.8 summarizes periods having a frequency of 75 percent or more. The high frequency of the summer events is particularly striking. Singularities in the winter half-year, that are not shown, have frequencies of around 70 percent and are mainly anticyclonic in character. For the British Isles, Lamb (1964) provides a similar calendar covering 1873–1961. Events with at least a 25 percent frequency are shown in Table 7.9. It is apparent that several of the intervals correspond closely to those in central Europe. Synoptic studies of well known singularities or weather spells began in the early twentieth century. Lehmann (1911) investigated the autumnal warm spells in Europe known as the Altweibersommer (“Old Wives’ Summer”). In the 1950s such regional phenomena began to be explored in a global context. Wahl (1953) differentiated between primary singularities affecting the general circulation and the secondary regional manifestations of the former. Bayer (1959) deﬁnes a primary singularity as a period with more intensive meridional air mass exchange in particular sectors of the hemisphere. It is associated with enhanced long waves in the 500 mb circulation. A secondary singularity represents the increased tendency for particular Wetterlage to occur in a speciﬁed period and region. Examples of regional events linked with the larger-scale circulation are the January thaw in New England, associated with Gulf Coast cyclones (Wahl, 1952; Guttman, 1991) and the “midsummer high jump” in the southwestern United States (Bryson and Lowry, 1955). The latter involves the northward shift of the subtropical high over the eastern North Paciﬁc and the incursion of southwesterly airﬂow advecting moisture into Arizona. From a harmonic analysis of the 700 mb heights for 30°–90°N, 160°E to 0° longitude, Lanzante (1983) identiﬁes some temporal anomalies of regional signiﬁcance. In Alaska height rises in late December to early January are terminated by a January thaw. A rapid height rise in the eastern Paciﬁc in February may account for a minimum in Hawaiian rainfall totals. An Indian Summer pattern, associated with low heights in high latitudes and weakly positive anomalies over most of North America, occurs generally in late September–early October. The second harmonic of the heights suggests that a semi-annual east–west oscillation in the North Paciﬁc may explain these singularities. In addition to pronounced peaks or troughs in the frequency of types of weather regime about a particular date, it is apparent that there may be times of the year when there is a tendency to the abrupt onset or decline of a certain regime. These short-period events are superimposed on broad maxima and minima of individual circulation regimes during the course of the year. Synoptic climatologists recognized that the overall frequency of circulation types could be used to characterize seasons, and that abrupt transitions often mark their beginning or termination. The idea that calendar intervals deﬁned by particular types of weather regime can be distinguished was ﬁrst developed by Lamb (1950). Based on the frequency of time intervals with twenty-ﬁve or more days of a particular weather pattern (a weather spell), he proposed a calendar of natural seasons for the British Isles. The frequency and timing of such long spells, disregarding brief interruptions up to three days in length, are illustrated in Figure 7.15. During 1898–1947 158 long spells occurred, seventy-nine of which involved Westerly type and ﬁfty-six involved Anticyclonic and a hybrid type. Long spells are most frequent in October and midsummer, with a minimum in April–June. A similar seasonal calendar was prepared for central Europe by Baur (1958), and the results are compared in Table 7.10. For Japan the duration of six seasons – winter monsoon, spring, the Bai-u rains, midsummer, the Shurin rains and late autumn – has been determined from curves of

Synoptic climatology and its applications 583 11

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Figure 7.15 Percentage frequency of spells of more than twenty-ﬁve days of Lamb weather types and ﬁve natural seasons, 1898–1947. (After Lamb, 1950)

Table 7.10 Natural seasons in the British Isles and Central Europe

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Season

Calendar period in Central Europe

Calendar period in the British Isles

High winter Fore-spring Spring Fore or early summer High summer Late summer or early autumn Autumn Fore-winter

January 1–February 14 February 15–March 31 April 1–May 16 May 17–June 30 July 1–August 15 August 16–September 30 October 1–November 15 November 16–December 31

January 20–March 31 January 20–March 31 April 1–June 17 April 1–June 17 June 18–September 9 September 10–November 19 November 20–January 19

Source: after Lamb (1950) and Baur (1958).

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ﬁve-day and daily means of sunshine duration, cloudiness, and precipitation; temperature and vapor pressure are secondary criteria for the winter monsoon season (Maejima, 1967). For East Asia, Yoshino (1968) uses the pentad frequency of the seven major types of pressure pattern for 1946–65 to develop a seasonal calendar. The concept of a “monsoon year” has been revived by Yasunari (1991), based on anomalies of precipitation and SSTs in the eastern and western Paciﬁc. He emphasizes the high autocorrelation of the variables during May–December and the low persistence in January–April. For the Paciﬁc Northwest of the United States a more conventional single variable approach is adopted by Alsop (1989). He applies cluster analysis to weekly maximum,

584 Synoptic and dynamic climatology 1

minimum, and mean temperatures to deﬁne four seasons. Wos (1980) also uses a taxonomic clustering method to group geometric distances between every pair of pentads in a year at thirty-nine stations in northwest Poland for 1951–65. Forty weather states are deﬁned, based on mean temperature (grouped according to maximum and minimum values above or below freezing), cloudiness (above/below 50 percent), and above/below-average precipitation. Nine types of seasonal structure, with four to six seasons, are found within northwest Poland. Temporal principal component analysis of temperature, vector wind, and precipitation is used to determine monthly seasons in southern California by Green et al. (1993). For the Atlantic and Gulf coasts of the United States, Pielke et al. (1987) suggest using an air mass–frontal approach, deﬁning seasons according to the frequency with which the polar front is south of a given location. They distinguish ﬁve synoptic categories: 1 2 3 4 5

mT air in the warm sector of an extratropical cyclone. mT/cP or mP/cA air ahead of a warm front in a region of cyclonic curvature. cP or cA air behind a cold front in a region of cyclonic curvature. cP or cA air in a polar anticyclone. mT air near a subtropical ridge.

Winter (summer) is deﬁned as having the highest frequency of category 2, 3, and 4 (1 and 5) air occurrences, with spring and autumn being transitional. Quantitative criteria, based on cumulative changes in categories 2, 3, and 4, were developed in order to provide threshold values for the seasonal transitions. They show that, over most of these south and east coastal regions of the United States, winter lasts from late October or early November to late March or early April and summer from late May or early June to late August or late September. Moreover the frontal movement is characterized by rapid shifts rather than steady displacement. They suggest that this approach can have hemispherewide application in middle latitudes and could be reﬁned using such parameters as 700 mb thermal advection and 500 mb vorticity advection in relation to location within a frontal cyclone. Shifts in the mid-latitude long-wave structure have been investigated as a cause of abrupt seasonal changes. For example, the 500 mb trough over eastern Europe is farther east in summer than in winter, and a subsidiary trough may develop upstream (De la Mothe, 1968). Since the wave number is an integer, such changes in the tropospheric long waves can greatly modify the regional circulation. In an attempt to deﬁne primary singularities and the seasonal structure of the year, Bryson and Lahey (1958) assembled a synthesis of various hemispheric circulation indices and regional criteria for North America (Figure 7.16). Considerable coherence is apparent in late March, mid-August, and the beginning of October, although this last date is not identiﬁed as a seasonal break. Kalnicky (1987) used EOFs of the daily Dzerdzeevski types for 1899–1969 to examine seasonal transitions in terms of the frequencies of hemispheric patterns. The ﬁrst four eigen vectors of thirteen major types accounted for 74 percent of the variance in the frequency of daily circulation patterns. Twenty-ﬁve days of maximum discontinuity were identiﬁed in annual plots of the day-to-day variation of the coefﬁcients of these four eigen vectors. The four seasons he deﬁned were winter: October 23–March 8; spring: March 9–June 17; summer: June 18–August 31; and autumn: September 1–October 22. The summer–autumn transition had the largest discontinuity and occurred within ± four days of the speciﬁed date in 51 percent of years. However, the length and character of the seasons were found to be variable. The period 1899–1919 had the longest winters, while 1920–52 had the longest summers and the most frequent Indian Summer episodes. During the 1953–69 cool episode the summers tended to have circulation features typical of spring and autumn. As a result, plots of frequency distributions of the start dates of northern hemisphere seasons for 1899–1965 show a variation about the mean date of around plusor-minus twelve days (Bradka, 1966).

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Figure 7.16 A summary of the criteria used to establish a natural calendar for the northern hemisphere. (From Bryson and Lahey, 1958). Abbreviations: Di2 40° – Storminess index. MSL meridional circulation at 40°N. Anticyc. – Difference between mid-latitude westerlies and subtropical easterlies. Baro. – Difference between MSL and 700 mb zonal indices; SP = subpolar; ML = mid-latitude; ST = subtropical. Index var. – Standard deviation of circulation indices. Ortho Poly. – Orthogonal polynomials for the western hemi– sphere at 500 mb. G.A. and M. – Occurrence of Aleutian low in Gulf of Alaska and Manchuria. 45/155P5 – Normal ﬁve-day mean – pressure at 45°N, 155°W. Ariz. P5 – Same as above over Arizona. N.W. U.S. High – Anticyclones in northwestern USA; migr. = migratory; basin = Great Basin. Alberta – Dominant synoptic pattern in southern Alberta. GWT. – Grosswetter type; = 3 change, x = 2 change. Medit. Low–High – Cyclonic/anticyclonic regime in the Mediterranean area.

11

586 Synoptic and dynamic climatology 1

Conceptual efforts have been made to relate natural seasons to gross features of the energy budget and its latitudinal gradient. Bradka (1966) argued that the latitudinal energy budget gradient determines the zonal circulation in middle latitudes, while the differential effects of solar radiation over land and ocean foster meridional components. Additionally, radiative cooling in high latitudes intensiﬁes the meridional temperature gradient and favors blocking situations. James (1970) applied the (essentially linear) quasibarotropic relationship between air mass temperature and mean pressure: T ≈ T0

∂T (p p0) ∂p

where T0 the air mass temperature at a conventional pressure level p0 (say, 1,012 mb). Plots of mean daily maximum temperature against mean pressure for each month are used as seasonal indicator diagrams for Australian stations to show when the surface air performs work against the ambient environment (decreasing p and increasing T) or vice versa. A number of factors are involved in the seasonal transitions and what Lamb (1964) termed master seasonal trends. Undoubtedly, too, there are complex interactions between them. Following Lamb and Bradka (1966), seven factors can be recognized as playing a role in the seasonal development of the large-scale circulation in the northern hemisphere: 1 2

3 4 5

6

7

The midwinter maximum in the latitudinal gradient of the energy balance and the associated maximum of the zonal index. The late winter maximum extent of snow cover and sea ice, linked with the expansion of the tropospheric polar vortex and the southernmost position of extratropical cyclone tracks about February–March. Meridional circulation patterns are common, with enhanced north–south interchange of air masses. Persistent negative net radiation values over the Beaufort Sea–Canadian Arctic Archipelago and the related development of the major polar anticyclone in this region in March–April. The minimum meridional energy balance gradient and related minimum intensity of the circulation in the Atlantic sector in May; the circulation over the North Paciﬁc remains predominantly zonal. The summer contrast in heating between the snow-free land in northern Eurasia and North America and the cold Arctic Ocean and its sea ice cover. This probably controls the northward displacement of cyclone tracks over western North America in July and August. The “cold pole” shifts towards the Atlantic–European sector and this is accompanied by a southward displacement of cyclone tracks. Early autumn cooling, assisted by the cold seas off eastern Canada (Barry, 1993), helps to strengthen the upper trough over eastern North America in September. The Icelandic low deepens and the Atlantic circulation intensiﬁes. Depressions now move northeastward into the Barents Sea. High-latitude cooling and the expansion of sea ice and snow cover lead to expansion and intensiﬁcation of the circumpolar vortex and the southward displacement of cyclone tracks, particularly from October onward (Lamb, 1955).

It is apparent that the jumps which often accentuate the seasonal transitions can in some cases be attributed to speciﬁc factors. These include the rapid change in surface albedo, and the associated relative magnitudes of the surface energy budget terms, with the disappearance of snow cover. In the Arctic tundra this transition typically lasts only ten days (Weller and Holmgren, 1974). A delay is imposed on the seasonal poleward retreat of the subtropical jetstream by the inﬂuence of the Tibetan Plateau–Himalayan ranges on the circulation; the eventual disappearance of snow cover over the Tibetan

Synoptic climatology and its applications 587 11

Plateau in June contributes to the circulation shift over South Asia in June. Nevertheless, changes in the external forcing may not be required. Chapter 4 discussed the role of synoptic eddies in transitions between zonal and blocking modes, and the ordered chaos of the “almost intransitive” atmospheric circulation has also been noted.

7.8 Climatic trends

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The existence of a number of long catalogs of airﬂow types and circulation patterns (see Table 7.2) has stimulated studies of the role of changes in regional or hemispheric circulation in climatic ﬂuctuations and trends. Dzerdzeevski (1963, p. 293) stated, “all changes in temperature and precipitation are connected with changes in frequency and duration of large-scale circulation patterns.” Many studies in the 1950s and 1960s focused on the synoptic climatology of extreme conditions (cold summers, snowy winters) that might favor a glacial climatic regime. For example, Rex (1950) concluded that reduced precipitation over Europe associated with Scandinavian blocking implied that such patterns would inhibit glacial onset. Namias (1957) showed that extreme continentality, with warm summers and interdependent cold winters in Sweden, occurs during weakened zonal circulation which would therefore interdict a glacial regime. Regional investigations of airﬂow patterns and associated weather conditions in Labrador-Ungava (Barry, 1960, 1966) and Keewatin, Canada (Brinkmann and Barry, 1971), and in Tasmania (Derbyshire, 1971) attempted to identify patterns favoring winter snowfall and cool summers that would be important for glacierization. Synoptic weather typing is now ﬁnding increasing application in historical climate reconstruction for Europe from the seventeenth to the nineteenth centuries (Glaser and Walsh, 1991). A recent attempt to characterize circulation patterns during the late Maunder Minimum cool episode (1675–1704) uses standardized monthly temperature and precipitation indices for winter and spring in Iceland, England, and Switzerland, together with documentary information on wind direction and weather events, to infer air masses and monthly mean surface pressure maps over Europe. Each month of 1675–1704 was also classiﬁed according to the Lamb scheme (Wanner et al., 1994) and this was repeated for comparison for 1961–90. Finally, the P and S indices (see section 7.4.1) were determined. The results show a clear difference between the two thirtyyear periods. The late Maunder Minimum experienced frequent blocking and northerly ﬂow, with outbreaks of cold continental air, especially in the 1690s. The extreme NAO (“pressure reversal”) mode of January 1963 (Moses et al., 1987) is proposed as a candidate analog. Changes in the frequency of circulation types during the twentieth century have received increased study as a result of the accumulating evidence for global warming. Lamb (1965) drew attention to the long-term ﬂuctuations in the frequency of the Westerly circulation type over the British Isles (Figure 7.7); the available record shows an average frequency of eighty-ﬁve days per year in the 1880s and 1890s, a peak of about 110 days per year around 1920, followed by a decline to the early 1940s and, after a brief recovery, a decrease to a minimum of only about sixty days per year in the early 1980s. Recent years show an irregular recovery. Trends in circulation have similarly been identiﬁed and related to climate changes over Europe, using the Grosswetter catalogue for 1881–1989 (Bardossy and Caspary, 1991; Klaus, 1993). In winter, zonal types prevailed from 1890 to 1920, followed by more meridional conditions until about 1965, when zonal patterns again increased considerably. This is thought to have contributed to the winter warming, especially after 1980. In summer, zonal ﬂows peaked around 1900, 1920, and 1945, with meridional patterns increasing in the 1960s; the frequency and persistence of southerly ﬂows increased signiﬁcantly, for example, while northerly ﬂows decreased. Earlier, van Dijk and Jonker (1985) analyzed the duration of Grosswetterlagen (GWL) and Grosswettertypen (GWT) for 1899–1970. The durations are found to be log-normally distributed. For the GWL, the median duration is about three and a half days, except for Wa and

588 Synoptic and dynamic climatology 1

Table 7.11 Circulation epochs based on the Vangengeim–Girs large-scale patterns of circulation WC W E C EC E

1891–99 1900–28 1929–39 1940–48 1949–71 1972–93 Source: Dmitriev (1994).

Wz, where it is four days. The median duration of the GWT is about half a day larger in each case. For 1949–70 the frequency of occurrence of GWL and GWT differed significantly from the overall values. However, the type durations for 1949–79 indicated that no changes in circulation could be demonstrated statistically for this period. For the British Isles, Perry (1970) concluded that a decrease in frequency of Westerly types in winter between 1910–30 and 1948–68 was accompanied by a pronounced reduction in the mean duration of a Westerly spell, whereas Easterly types increased in frequency and run length. For the northern hemisphere the Russian catalogues of Vangengeim–Girs and Dzerdzeevski have been used by several workers to identify “circulation epochs.” Table 7.11 shows those recognized by Dmitriev (1994) with the former scheme; however, this differs in some details for the 1930s from one put forward earlier by Kozuchowski and Marciniak (1988) for 1891–1983. The annual ratio of zonal to meridional patterns in the Dzerdzeevski catalogue is low in the early twentieth century (around 0.5 in 1915), rises to 1.1 from the 1930s to the 1940s, declines to 0.75 around 1960 and recovers to 0.9–1.0 in the 1970s (Savina, 1987). The major trends seem to be broadly consistent with changes in other indices, although more thorough intercomparisons are needed. On a regional scale, the frequencies of the three general classes of synoptic type for the Alps, according to Schüepp (1979) – namely convective, advective, and mixed – show pronounced trends during 1945–94 (Stefanicki et al., 1998). The convective types show a linear increase of thirteen days per decade, while advective types decrease by eleven days per decade (both signiﬁcant at the 0.1 percent level) and the mixed types decline slightly. Most of the increase in convective types is due to high-pressure situations in winter and autumn which are shown to be strongly correlated with the positive NAO mode. Wanner et al. (1997) also show an increase in Anticyclonic GWL, especially in July, and of West–maritime and SW GWL patterns in autumn during the 1970s and 1980s. The possibility that changes may also occur in the weather (or air mass) characteristics of the individual circulation types was pointed out by Barry and Perry (1969, 1970). For example, the change in mean daily temperature for a given month can be regarded as comprising a component due to changes in the frequencies of the circulation types and another component due to changes in the characteristics of individual types. The total change in mean temperature between two time periods can be expressed (Perry and Barry 1973):

兺冤 K

T =

i=1

fi (Ti Ti ) fi Ti n n

冥

where fi frequency of type i during the ﬁrst time period, Ti mean temperature of type i during the ﬁrst time period, n total number of days in the ﬁrst time period, f i f frequency of type i during the second period, and Ti Ti mean temperature of type i during the second time period. The last term on the right-hand side of the

Synoptic climatology and its applications 589 11

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equation, which is independent of any change in frequency, represents the component due to within-type temperature changes. The ﬁrst term on the right side represents the effect on T of a change in type frequency when a change occurs in the temperature of type i in the second period. For some purposes it may be of interest to separate the two elements making up the ﬁrst term on the right-hand side. For four stations in the British Isles for 1925–35 and 1957–67, the changes in mean daily maximum and minimum temperatures were associated with changes in frequency of the Lamb types in January; in April, July, and October, however, there were signiﬁcant “within-type” temperature changes affecting Anticyclonic, Westerly, Northerly, and Easterly types. Although it is unlikely that the types were inhomogeneous between the two periods, owing to Lamb’s procedure of classifying the years in random order, there could be subtle differences in wind speed or trajectory within a single type grouping between the two periods (Lawrence, 1970). For large samples, however, it would be reasonable to assume a comparable range of wind speeds and trajectories within each type. This technique has also been applied to temperature and precipitation associated with circulation patterns over the western United States (Barry et al., 1981). The most common type, a zonal pattern which is most frequent in winter, is warmer than average in the mean in Colorado but becomes colder than normal in cold years. Type 2 with a high over the northern Rocky Mountains tends to remain cold even in months with a warm anomaly, and changes in its frequency exert strong control on temperatures in all seasons in Colorado. Other studies of climate changes employing a synoptic climatological approach for stations in Yukon–Alaska (Kalkstein et al., 1990) and the contiguous United States (Kalkstein et al., 1998) examine changes in air mass frequencies and types. In Alaska the frequency of warm (cold) air masses has increased (decreased) since about 1948–88. Additionally, the coldest air masses have warmed by 1°– 4°C. However, they do not quantify the separate effects of between and within-type changes. The second study ﬁnds a winter decline in the frequency of air mass transitions, related to more persistent meridional circulation patterns. There are increased frequencies of dry polar and dry moderate air masses during winter 1948–93. In summer there are increases in the frequency of moist tropical air. This gave rise to higher summer temperatures and increased cloud cover.

7.9 Environmental applications

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Applications of synoptic climatology use information about airﬂow movement close to the earth’s surface to evaluate a variety of environmental problems. Muller (1977) was among the ﬁrst to recognize that a climatology of synoptic weather types could form an important baseline for studying the link between atmospheric conditions and environmental phenomena. Synoptic approaches have unique appeal because they permit evaluation of the synergistic impacts of an entire suite of weather elements, and, as a consequence, other physical scientists are incorporating them within their research design. Synoptic climatology provides a convenient framework for interpreting variations in atmospheric chemistry because changes in transport, diffusion, and stability accompany different patterns of airﬂow and weather type. Hence there have been numerous synoptic studies of spatiotemporal variations in the concentrations of atmospheric pollutants. For example, Kalkstein and Corrigan (1986) analyzed sulphur dioxide concentrations in Wilmington, Pennsylvania, using air mass categories. The typical synoptic circulations associated with each category were also illustrated. The Lamb weather types have been used to evaluate ozone pollution in the United Kingdom and Ireland (O’Hare and Wilby, 1995), establishing the dominant role of stable Anticyclonic and Easterly types in producing peak ozone episodes. The Lamb types are also used to study synoptic inﬂuences on air pollution in Birmingham, UK (McGregor and Bamzelis, 1995). In the arid southwestern United States air quality is shown to be related to synoptic-scale variations

590 Synoptic and dynamic climatology 1

in middle and upper tropospheric wind direction, temperature, and humidity. Categories are speciﬁed in a four-dimensional classiﬁcation of circulation types developed through a two-stage hierarchical clustering approach (Davis and Kalkstein, 1990; Davis and Gay, 1993). Similar work has been carried out by Comrie and Yarnal (1992) in developing a synoptic-sequence climatology of surface ozone concentrations for Pittsburgh. Comrie (1992) demonstrates a procedure to distinguish within-type (non-synoptic) components from between-type (synoptic) components in a time series of visibility data at Pittsburgh. This allows removal of the synoptic climate signal from environmental data. Precipitation composition has been interpreted in relation to synoptic types in the United Kingdom (Davies et al., 1990, 1991). Cyclonic, Westerly, and Anticyclonic types play the dominant roles in affecting precipitation chemistry. Dorling et al. (1992, 1995) combine the pattern recognition capabilities of cluster analysis with isobaric trajectory data to study source effects on precipitation ion concentration. They show that, for the United Kingdom, trajectory clusters, combined with precipitation amount, explain much of the daily variability in precipitation chemistry. In Norway, however, topographic effects confound the other signals. In an interesting application to wind-energy potential, Palutikof et al. (1987) investigated the relationship between Lamb’s types and wind speed. Todhunter (1989) uses weather type clusters to stratify the energy budgets of urban surfaces for Boston, Massachusetts. Since the pioneering work by Hoinkes (1968), relating glacier variations in Switzerland to frequencies of particular circulation types, there have been a number of similar studies. Glacier mass balance in British Columbia was related to synoptic-scale atmospheric circulation by Yarnal (1984a). Trends in summer energy balance on the West Gulkana glacier, Alaska, are analyzed in terms of a Temporal Synoptic Index (TSI) and changes in type frequency by Brazel et al. (1992). In an extension of Hoinkes’s work, Fitzharris (1998) has looked at four glaciers in the European Alps that have direct mass balance measurements extending back more than thirty-ﬁve years and analyzed the frequency of Grossswetterlagen during the accumulation and ablation seasons for high and low mass balance glacier years. High mass balance years have higher frequencies of westerlies, troughs, and depressions in summer and winter than low mass balance years. The associations between weather types and circulation pattern can be quantiﬁed into formulae that allow estimates of Alpine glacier mass balance from a weather index based on Grosswetterlagen frequency. There have been several synoptic climatology studies of snow melt and snow accumulation. Recent examples are the analysis of synoptic controls of melt conditions on the Greenland ice sheet (Mote, 1998) and of snowpack melt conditions in the Southern Alps of New Zealand (Hay and Fitzharris, 1988; Neale and Fitzharris, 1997). Snowfall variability in relation to synoptic type has been examined for New England and the Great Lakes (Ellis and Leathers, 1996). Fitzharris and Bakkehøi (1986) relate winters with a high risk of avalanches in Norway to synoptic type. In addition to weather much colder than normal, northerly and easterly airﬂow patterns tend to predominate in major avalanche winters. Many investigations have linked atmospheric circulation and basin hydrology, and especially runoff over seasons (Keables, 1988), basin-scale discharge and water quality (Yarnal and Draves, 1993; Yarnal, 1997), and ﬂood events (Maddox et al., 1980). Hay et al. (1991) used six conceptual daily weather types to simulate average and extreme precipitation in the Delaware River basin. The variability in type frequency and precipitation characteristics among types was determined for a GCM control case and one with doubled carbon dioxide concentration. Hughes and Guttrop (1994) devised a statistical model to relate circulation ﬁelds, rainfall, and hydrologic processes in Washington state (1993, 1997), while Wilby (1993) used the Lamb weather types to look at river water quantity and quality in the United Kingdom.

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Weather types have been used as a concise surrogate measure of weather conditions in a number of ecological studies. Masterman et al. (1996) have examined the autumn migration of the bird cherry aphid, while Boyd and Bell (personal communication) are investigating the role of wind direction in affecting the migration of geese between Iceland and Scotland. The literature in agricultural meteorology using synoptic techniques is surprisingly sparse, although Dilley (1992) studies corn (maize) yields in southwestern Pennsylvania, making use of composite maps of surface pressure. Pepin (1995), investigating the potential for ecological change in the northern Pennines of England, shows that scenarios of warming for high-altitude sites depend critically on the frequency of airﬂow types. The beneﬁcial effects of any future regional warming would be reduced for this area if the frequency of westerly winds were to increase, since the lapse rates in such airstreams are generally much steeper than those of continental airstreams reaching northern England. In bioclimatology, attention is turning to the identiﬁcation of weather types associated with human health and mortality. Kalkstein (1991) found that summer days with the highest mortality rate in St Louis, Missouri, were associated with cases of tropical air that were oppressively hot at night, although such cases were infrequent. Unlike many other cities, high pollution levels did not appear to have a signiﬁcant effect on mortality in St. Louis. Using the air mass-based Temporal Synoptic Index (TSI), Jamason et al. (1997) ﬁnd that air mass-related pollution levels in spring and summer strongly inﬂuence New York city hospital admissions of asthma sufferers. The TSI has also been used with twenty-four and forty-eight-hour local forecasts for Philadelphia, Pennsylvania, to develop a hot weather health watch/warning system (Kalkstein et al., 1996). The relationship between air pollution and mortality has been studied in Utah, using the TSI (Pope and Kalkstein, 1996). More recently the continent-wide Spatial Synoptic Classiﬁcation (SSC) of air masses has been used to determine high-risk summer and winter air masses for both total and elderly mortality rates in forty-four US cities (Kalkstein and Greene, 1997). In the eastern and Midwest United States two air masses are associated with sharp increases in mortality. A very warm maritime air mass is important in the eastern United States and a hot dry air mass is important in many cities. Cities in the south and southwest have a weaker climate–mortality relationship and, in winter, air mass-related mortality increases are small. Work on extreme mortality levels has been initiated in the United Kingdom for Birmingham by McGregor (1998), and wider application of synoptic climatological techniques in environmental health can be anticipated. The number and diversity of applied synoptic climatological studies continues to proliferate, which in itself is a tribute to the usefulness and robustness of the method. As Yarnal (1993) asserts, “relating the atmospheric circulation to the complex surface environment adds another major dimension to the demands on the synoptic climatologist.”

Note 1

The analog index (Y) used by Ruosteenoja (1988) is the mean-square difference between two 500 mb ﬁelds: 100

Y=

兺 [C (t ) C (t )]

2

n a

n b

n=2

where C(t) = the time-dependent coefﬁcient of the respective EOF, n = the EOF component, n = 2 to 100, and ta , tb = the two dates. The ﬁrst EOF is omitted, since it largely represents the annual variation of the 500 mb height pattern. The Y value is further normalized, to remove the greater variability of winter 500 mb heights, using the function:

0 11

N() = 1 0.5 cos where is an angle describing the time of year; N varies sinusoidally from 1.5 on January 31 to 0.5 on July 31. The normalized index is:

592 Synoptic and dynamic climatology 1

YN =

Y [N(a) · N(b)]1/2

where a and b are the seasonal angles of the two dates. The expected value of YN ~ 20,000 m2. A relatively good analog has YN < 8,000 and a good one YN < 6,000.

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Retrospect and prospect

The foundations of dynamic and synoptic climatology were laid in the nineteenth century with the advent of weather maps, the study of cyclones, and theories of the general circulation. The great strides that have been made in these areas over the last thirty years can be attributed to several factors. First, the importance of the increased availability of gridded hemispheric and global meteorological ﬁelds and satellite data cannot be overemphasized. Until at least the 1970s the absence of such readily accessible data was hampering climatological research, as noted by Barry and Perry (1973, p. 448). The recent advent of the global reanalysis products and the Pathﬁnder satellite products is providing a continuing stimulus. Second is the increasing use of GCM simulations to diagnose large-scale processes and to treat their statistics as a surrogate for observational data. Third, there has been recognition of the importance of climatic processes on intraseasonal, interannual, and decadal-centennial time scales, fostered by the World Climate Research Program (WCRP). The current programmatic elements of the WCRP are: the Global Water and Energy Experiment (GEWEX), the Arctic Climate System (ACSYS), Climate Variability (CLIVAR), and Stratospheric Processes and their Role in Climate (SPARC). There is also a new project for Climate and Cryosphere (CliC). Information about these programs can be obtained via http://www.wmo.ch. Fourth, the establishment of several new climate journals in the 1970s and 1980s also contributed to the renaissance of dynamic and synoptic climatology by creating a wider visibility for publications about climate and for the related scientiﬁc community. Observational records made possible the detailed investigation of mesoscale and synoptic weather systems worldwide, and of large-scale recurring circulation anomalies and teleconnections. Crucial to these advances were the existence of more than a century of basic meteorological observations and surface weather maps for at least a sector of the northern hemisphere, near-global surface and upper data since 1945, and global satellite data available digitally from the 1980s. Longer time series are needed, however, to analyze the characteristics and mechanisms of multidecadal phenomena. Until such records do become available, coupled global model simulations of these features, using integrations performed for several model centuries, are likely to receive increased attention. Areas where progress may be more limited include the southern hemisphere, especially the Southern Ocean, owing to the unavoidable sparsity of the surface-based observational network, and the Arctic Ocean since the discontinuance of the Soviet North Pole Drifting Stations in 1991. The coverage available for the period 1950–90 in both the Arctic and across the former Soviet Union, in terms of hydrometeorological data, is unlikely to be repeated in the foreseeable future. Degradation of surface hydrometeorological networks is also occurring in other areas, including Canada and parts of Africa. Expanded satellite capabilities will enhance the data assimilated into operational products and future reanalysis products, although the continuance of some important long time series will necessitate data blending and careful homogenization of records collected by different sensors.

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The development of new models of mesoscale and synoptic systems has been facilitated by special ﬁeld campaigns involving aircraft, ocean buoys, and augmented surface networks, as described in Chapter 6. Additionally, the renewed interest in analyses of isentropic surfaces using potential vorticity, as well as new diagnostic tools such as Q vectors and Eliassen-Palm ﬂux, has served to sharpen the insight into dynamical processes. In studies of planetary to global-scale systems, many recent advances have relied on the use of advanced statistical techniques to identify patterns that, it is assumed, may have an underlying physical basis. EOF analyses and their variants are particularly common. The separation of stationary and transient circulation features has been another important topic, and applications of the new technique of wavelet analysis are becoming common. Empirical analyses frequently go hand in hand with diagnostic analysis of model output. The four-dimensional assimilation procedures incorporated into operational models make their ﬁelds, in principle, self-consistent and therefore of especial value in data-sparse regions. Nevertheless, considerable care is needed due to the effects of the truncation of the spectral resolution on smaller-scale features, as a result of Gibbs oscillations, and the particular errors that arise in areas of mountainous terrain. Coupled GCMs are beginning to be widely used and there are continuing investigations of the effects of the parameterizations used in the various subcomponent models, especially for the land surface. The ﬁeld of synoptic climatology has witnessed a renaissance in the 1980s, with wide application of its methods to environmental problems, including human health and the analysis of climatic change, as well as to the diagnosis of GCM performance. Classiﬁcation procedures, which evolved from manual to automated methods based on correlation or EOFs and clustering routines, are now being further modiﬁed to combine the strengths of both approaches. The pace of research in dynamic and synoptic climatology shows no sign of slackening, so that any text can inevitably provide only a snapshot of the state of knowledge. Nevertheless, it is hoped that the present work represents the main strands of our understanding of the global climate and its main components at the turn of the century and the millennium.

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Further reading

Inevitably, important new literature has appeared since the main text was completed. Here we provide references to some of these works and their context in the book.

Chapter 2 Climate data and their analysis 2.1 Synoptic meteorological data Strangeways, I. 2000. Measuring the Natural Environment. Cambridge University Press, Cambridge, 365 pp.

2.3 Climate variables and their statistical description von Storch, H. and Zwiers, F.W. 1999. Statistical Analysis in Climate Research. Cambridge University Press, Cambridge, 484 pp.

2.4 Analytical tools for spatial data Persson, A. 2000. Back to basics: Coriolis. 1. What is the Coriolis force? 2. The Coriolis force according to Coriolis. 3. The Coriolis force on the physical earth. Weather, 55: 155–70, 182–7, 234–9. Phillips, N. 2000. An explication of the Coriolis effect. Bull. Amer. Met. Soc., 81 (2): 299–303.

2.5 Time series Mann, M.E. and Park, J. 1999. Oscillatory spatiotemporal signal detection in climate studies: a multiple-taper spectral domain approach. Adv. Geophys., 41: 1–131.

Chapter 3 Global climate and the general circulation 3.1 Planetary controls Gittelman, A.I., Risbey, S.J., Kass, R.E., and Rosen, R.D. 1999. Sensitivity of a meridional temperature gradient index to latitudinal domain. J. Geophys. Res., 104 (D14): 16709–17. Hoinka, K.P. 1999. Temperature, humidity, and wind at the global tropopause. Mon. Wea. Rev., 127 (10): 2248–65.

3.2 Basic controls of the atmospheric circulation Barry, R.G. and Serreze, M.C. 2000. Atmospheric components of the Arctic Ocean freshwater balance and their interannual variability. In: E.L. Lewis, ed., The Freshwater Budget of the Arctic Ocean, Kluwer, Dordrecht, pp. 45–56.

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3.3 Circulation cells Wunsch, C. 2000. Moon, tides and climate, Nature, 405 (5788): 743–4.

3.6 General circulation models Mote, P. and O’Neill, A. (eds). 2000. Numerical Modeling of the Global Atmosphere in the Climate System. Kluwer, Dordrecht, 517 pp.

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Hastenrath, S. 1999. Dynamics of the equatorial Paciﬁc dry zone. Met. Atmos. Phys., 71: 243–54. Hastenrath, S. 2000. Zonal circulations over the equatorial Indian Ocean. J. Climate, 13 (15): 2746–56. Robertson, A.W. and Mechoso, C.R. 2000. Interannual and interdecadal variability of the South Atlantic Convergence Zone. Mon. Wea. Rev., 128 (8): 2947–57.

3.7.3 Monsoons Lau, K.M., Kim, K.M., and Yang, S. 2000. Dynamical and boundary forcing characteristics of regional components of the Asian summer monsoon. J. Climate, 13 (14): 2461–82. Qian, W. and Lee, D.K. 2000. Seasonal march of Asian summer monsoon. Intl. J. Climatol., 20 (11): 1371–86.

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3.8 Centers of action Burnett, A.W. and McNicoll, A.R. 2000. Interannual variations in the southern hemisphere winter circumpolar vortex: relationships with the semi-annual oscillation. J. Climate, 13 (5): 991–9. Cohen, J., Saito, K., and Entekhabi, D. 2001. The role of the Siberian High in northern hemisphere climate variability. Geophys. Res. Lett., 28 (2): 299–302.

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3.9 Global climatic features Raymond, D.J. 2000. Thermodynamic control of tropical rainfall. Quart. J. Roy. Met. Soc., 126: 889–98. Yang, D.-Q. 2001. Compatibility evaluation of national precipitation gage measurements. J. Geophys. Res., 106 (D2): 425–22.

Chapter 4 Large-scale circulation and climatic characteristics 4.2 Jet streams

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Inatsu, M., Mukougawa, H., and Xie, S.-P. 2000. Formation of a subtropical westerly jet core in an idealized GCM without mountains. Geophys. Res. Lett., 27 (4): 529–32.

4.3 Planetary waves DeWeaver, E. and Nigam, S. 2000. Do stationary planetary waves drive the zonal-mean jet anomalies of the northern winter. J. Climate, 13 (13): 2160–76.

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Feldstein, S.B. 2000. Is interannual zonal–mean ﬂow variability simply climate noise? J. Climate, 13 (13): 2356–62. Robinson, W.A. 2000. A baroclinic mechanism for the eddy feedback on the zonal index. J. Atmos. Sci., 57 (3): 415–22.

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4.7 Low-frequency circulation variability and persistence Bongioannini Cerlini, P., Corti, S., and Tibaldi, S. 1999. An intercomparison between low-frequency variability indices. Tellus, 51 (5): 773–89.

Chapter 5 Global teleconnections 5.1 Pressure oscillations and teleconnection patterns Robertson, A.W. and Ghil, M. 2000. Large-scale weather regimes and local climate over the western United States. J. Climate, 12 (6): 1796–813.

5.2 The Southern Oscillation and El Niño An, S.I. and Wang, B. 2000. Interdecadal change of the structure of the ENSO mode and its impact on the ENSO frequency. J. Climate, 13 (11): 2044–55. Burgers, G. and Stephenson, D.B. 1999. The “normality” of El Niño. Geophys. Res. Lett., 26 (8): 1027–30. Federov, A.V. and Philander, G.S. 2000. Is El Niño changing? Science, 288 (5473): 1997–2002. Trenberth, K.E. 1996. El Niño–Southern Oscillation. In: T.W. Giambelluca and A. HendersonSellers, eds, Climate Change: Developing Southern Hemisphere Perspectives, Wiley, New York, pp. 145–73. White, W.B. and Cayan, D.R. 2000. A global El Niño–Southern Oscillation wave in surface temperature and pressure and its interdecadal modulation from 1900 to 1997. J. Geophys. Res., 105 (C5): 11223–42.

5.3 ENSO mechanisms Chan, J.C.L. and Xu, J. 2000. Physical mechanisms responsible for the transition from a warm to a cold state of the El Niño–Southern Oscillation. J. Climate, 13 (12): 2056–71. Matthews, A.J. 2001. Propagation mechanisms for the Madden–Julian Oscillation. Quart. J. Roy. Met. Soc., 126: 2637–51. Neelin, J.D., Jin, F.-F., and Syu, H.-H. 2000. Variations in ENSO phase locking. J. Climate, 13 (14): 2570–90. Saravanan, R. and Chang, P. 2000. Interaction between tropical Atlantic variability and El Niño– Southern Oscillation. J. Climate, 13 (13): 2177–94. Vecchi, G.A. and Harrison, D.E. 2000. Tropical Paciﬁc sea surface temperature anomalies, El Niño, and equatorial westerly wind events. J. Climate, 13 (11): 1814–30. Webster, P.J. and Fasullo, J. 2000. Atmospheric and surface variations during westerly windbursts in the tropical western Paciﬁc. Quart. J. Roy. Met. Soc., 126: 899–924.

5.4 Teleconnections with ENSO Hoskins, B.J. and Young, G.Y. 2000. The equatorial response to higher-altitude forcing. J. Atmos. Sci., 57 (9): 1197–1213. Kirtman, B.P. and Shukla, J. 2000. Inﬂuence of the Indian summer monsoon on ENSO. Quart. J. Roy. Met. Soc., 126: 213–39. Krishnamurty, V. and Goswani, B.N. 2000. Indian monsoon–ENSO relationship on interdecadal timescale. J. Climate, 13 (3): 579–95. Slingo, J.M. and Annamalai, H. 2000. The El Niño of the century and the response of the Indian monsoon. Mon. Wea. Rev., 128 (6): 1778–96. Torrence, C. and Webster, P.J. 2000. Comments on “The connection between the boreal spring Southern Oscillation persistence barrier and biennial variability.” J. Climate, 3 (3): 665–7. (A.J. Clarke and S. Van Gordes, Reply. Ibid., pp. 668–71.)

5.6 North Atlantic Oscillation Dickson, R.R. et al. 2000. The Arctic Ocean response to the North Atlantic Oscillation. J. Climate, 13 (15): 2671–96.

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Garcia, R., Ribera, P., Gimenoo, L., and Hernandez, E. 2000. Are the NAO and SO related in any time scale? Annal. Geophyicae, 18: 247–51. Joyce, T.M., Deser, C., and Spall, M.A. 2000. The relationship between decadal variability of subtropical mode water and the North Atlantic Oscillation. J. Climate, 13 (14): 2250–69. Kodera, K., Koide, H., and Yoshimura, H. 1999. Northern hemisphere winter circulation associated with the North Atlantic Oscillation and stratospheric polar night jet. Geophys. Res. Lett., 26 (4): 443–6. Kwok, R. 2000. Recent changes in Arctic Ocean sea ice motion associated with the North Atlantic Oscillation. Geophys. Res. Lett., 27 (6): 775–8. Morison, J., Aagard, K., and Steele, M. 2000. Recent environmental changes in the Arctic: a review. Artic, 53 (4): 359–71.

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Sheng, J. 1999. Correlation between the Paciﬁc/North America pattern and the eastward propagation of sea surface temperature anomalies in the North Paciﬁc. J. Geophys. Res., 104 (D24): 30885–95.

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Baldwin, M.P. and Dunkerton, T.J. 1999. Propagation of the Arctic Oscillation from the stratosphere to the troposphere. J. Geophys. Res., 104 (D24): 30937–46. Deser, C. 2000. On the teleconnectivity of the “Arctic Oscillation.” Geophys. Res. Lett., 27 (6): 779–82. Perlewitz, J., Graf, H.F., and Voss, R. 2000. The leading variability mode of the coupled troposphere–stratosphere winter circulation in different climatic regimes. J. Geophys. Res., 105 (D5): 6915–26. Wallace, J.M. 2000. North Atlantic Oscillation/annular mode: two paradigms for the same phenomenon. Quart. J. Roy. Met. Soc., 126: 791–805.

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5.10 Tropical–extratropical teleconnections Mo, R.P., Fyfe, J., and Derome, J. 1998. Phase-locked and asymmetric correlations of the wintertime atmospheric patterns with the ENSO. Atmos.-Ocean, 36: 213–40.

5.12 Time-scale aspects of teleconnections Pozo-Vasquez, D., Esteban-Parra, M.J., Rodrigo, F.S., and Castro-Diez, Y. 2000. An analysis of the variability of the North Atlantic Oscillation in the time and frequency domains. Intl. J. Climatol., 20 (14): 1675–92.

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5.13 Interannual to interdecadal oscillations Dunkerton, T.J. 2000. Inferences about QBO dynamics from the atmospheric “tape recorder” effect. J. Atmos. Sci., 57 (2): 230–46. Salby, M. and Callaghan, P. 2000. Connection between the solar cycle and the QBO: the missing link. J. Climate, 13 (14): 2652–62. Haarsma, R.J., Selten, F.M., and Opsteegh, J.D. 2000. On the mechanism of the Antarctic Circumpolar Wave. J. Climate, 13 (5): 1461–80.

Chapter 6 Synoptic systems 0

6.1 Early studies of extratropical systems 11

Grahame, N. 2000. The development of meteorology over the last 150 years as illustrated by historical weather charts. Weather, 55 (4): 108–17.

610 Further reading 1

6.2 Climatology of cyclones and anticyclones Sickmoller, M.A., Blender, R., and Fraedrich, K. 2000. Observed winter cyclone tracks in the northern hemisphere in reanalyzed ECMWF data. Quart. J. Roy. Met. Soc., 126: 591–620. Simmonds, I. and Keay, K. 2000. Variability of southern hemisphere extratropical cyclone behavior, 1958–97. J. Climate, 13 (3): 550–61.

6.3 Development of cyclones Joly, A. et al. 1999. Overview of the ﬁeld phase of the Fronts and Atlantic Storm Track Experiment (FASTEX) project. Quart. J. Roy. Met. Soc., 125: 3131–63. Parker, D.J. 2000. Frontal theory. Weather 55 (4): 120–34.

6.5 Satellite-based climatologies of synoptic features Hewson, T.D., Craig, G.C., and Claud, C. 2000. Evolution and mesoscale structure of a polar low outbreak. Quart. J. Roy. Met. Soc., 126: 1031–63. Laing, A.G. and Fritsch, J.M. 2000. The large-scale environment of the global populations of mesoscale convective complexes. Mon. Wea. Rev., 128 (8): 2756–76.

6.6 Synoptic-scale systems in the tropics Elsner, J.B., Liu, K.-B., and Kocher, B. 2000. Spatial variation in major US hurricane activity: statistics and a physical mechanism. J. Climate, 13 (13): 2293–305. Emmanuel, K. 2000. A statistical analysis of tropical cyclone intensity. Mon. Wea. Rev., 128 (4): 1139–51.

Chapter 7 Synoptic climatology and its applications 7.3 Objective typing procedures Brinkmann, W.A.R. 2000. Modiﬁcation of a correlation-based circulation pattern classiﬁcation to reduce within-type variability of temperature and precipitation. Intl. J. Climatol., 20 (8): 839–52. Cavazos, T. 2000. Using self-organizing maps to investigate extreme climate events: an application to wintertime precipitation in the Balkans. J. Climate, 13 (10): 1718–32. El-Kadi, A.K.A. and Smithson, P.A. 2000. Probability assessment of pressure patterns over the British Isles. Intl. J. Climatol., 20 (11): 1351–69.

7.5 Regional applications Kidson, J.W. 2000. An analysis of New Zealand synoptic types and their use in deﬁning weather regimes. Intl. J. Climatol., 20 (3): 299–316.

7.7 Seasonal structure Keller, L.M. and Houghton, D.D. 2000. Characteristics of the large-scale circulation changes during the sudden onset of the fall transition season. Intl. J. Climatol., 20 (4): 397–415.

7.8 Climatic trends Werner, P.C., Gerstengrabe, F., Fraedrich, K., and Oesterle, H. 2000. Recent climate change in the North Atlantic/European sector. Intl. J. Climatol., 20 (5): 463–71.

11

Index

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absorption spectra 25 adiabatic method of computing vertical velocity 51 adiabatic lapse rate 111, 140, 242 adiabatic motion 55 advection: thickness 525; vorticity 185, 276–7, 320, 459, 525 aerosols 111, 116, 160 Africa 499, 501, 507, 514, 605; East 188–90; West 274, 511–12 African waves 512–16 airﬂow: regional types 563–6 air mass 235, 550, 559, 584; Arctic 237-8, 504, characteristics 237–40, 588, 589, 591; classiﬁcation 237–8, 560, 584; deﬁnition 235; depth changes 238, 240; modiﬁcation 238–40; polar 237–8; relation to general circulation 238–9; temperature 586; tracers 236–7; trajectories 57, 238–40; tropical 237–8 air pollution 238, 589, 591 air–sea interaction (see also atmosphere–ocean interaction) 490 Alaska 57, 555, 574, 582, 589 albedo: cloud 116, 159, 478; feedback 157–9; planetary 110–1; sea ice 9; snow 9, 157; surface 159 aliasing 23, 71 Aleutian low 211, 216, 385, 404, 411, 424, 495 Alps 548, 568, 574, 576–7, 588, 590 alternation index of cyclones and anticyclones 442 altimetry 381 Amazon Basin 160 AMIP (Atmospheric Model Intercomparison Program) 166 Amundsen Sea low 486 analog 578–9 Andes 282, 294 angular momentum 124–6, 223, 275–6, 520–1; transport 125–9, 135, 149, 241, 306 angular velocity 124, 126, 286 ANN (artiﬁcial neural network) 560–1 anomaly: ﬁeld 424–5; height 61, 311, 399, 406, 468–9, 470; pattern 388; potential vorticity 461, 464, 513; precipitation 385–8; pressure

61, 372, 399, 400–1; sea surface temperature 367, 368–70, 376, 383–4, 388–9, 395, 417, 420 422–3; temperature 385–8, 406; wind 382, 389 Antarctic Circumpolar Current 230 Antarctic Circumpolar Trough 486 Antarctic Circumpolar Wave 421–3 Antarctic ice-sheet 9 Antarctic Peninsula 223, 308, 410, 494 Antarctica 40, 209, 219, 281–2, 409, 484, 493, 571 anticyclogenesis 215, 449–50 anticyclone 441; blocking 309–11, 313, 317, 319–20, 22, 573; frequency 442–3; polar 209, 238, 449–50, 586; Siberian 204–5, 238, 570; subtropical 166–71, 215, 216, 218, 238, 370; tracks, 469, 571; warm 309, 311 aphelion 112 Arctic 57, 238, 526, 555, 574, 604; front 208, 240; high 216–17; low 490 Arctic Ocean 209, 224, 446, 448–9, 555, 574, 586, 604 ARMA (autoregressive moving averages) 64, 376 Asia: East 57, 205, 271, 583 Asian monsoon 113, 145, 169–71, 192–7, 204–5, 275, 236, 391–2, 421 ASII (air–sea interaction instability) 490 assimilation 22, 166, 478, 605 Assmann, R. 16 astronomical periodicities 112–13, 115 asynoptic (satellite) data 22 Atlantic Ocean 370–1 ATEX (Atlantic Tropical Experiment) 230, 506 atmosphere: barotropic 235, 271, 281; Boussinesque 147; gases 25, 26, 110–11; mass 128; tides in 288–91; vertical structure 120–3, 230, 235–6 atmosphere–ocean interaction 6–9, 231–3, 364, 377–8, 524 atmospheric bridge 395 atmospheric circulation 113–14, 139, 166–209; and hydrology 590 atmospheric energy 114–18, 129–43 atmospheric GCMs 161–3 (see also general circulation models (GCMs)

612 Index 1

AMIP (Atmospheric Model Intercomparison Program) 166 atmospheric moisture 139, 474; transport 136–40 atmospheric motion 11–13; laws of horizontal motion 161–2; vertical 49–53, 212–13, 223 atmospheric window 26, 116 Australia 275, 494, 502, 505, 552; summer monsoon 206–7, 336, 421 Australian Bureau of Meteorology 480 autocorrelation 64, 66, 83, 360, 365 avalanches 590 averages 126–7 AVHRR (Advanced Very High Resolution Radiometer) 20, 21, 23, 26, 31 axial path wobble 112 Azores 313, 397; high pressure 215, 313 Baiyu rains 197, 582 band-pass ﬁlter 322–3 baroclinic adjustment 122, 123 baroclinic instability 148, 186, 450–1, 472, 514 baroclinic leaf 463–4, 483 baroclinic wave 470, 473–5, 481 baroclinic zone 208, 235, 270, 452, 456–9, 476 baroclinicity 490 barotropic: atmosphere 271; ﬂuid 235, 346; instability 331 barotropic mode 324 Bay of Bengal 199, 201, 515 Bergen school 441, 450, 476 Bernoulli probability 37 beta distribution 39–40 beta effect 11, 188 biennial cycle 392 biennial oscillation 420–1 (see also Quasibiennial oscillation) bimodality 325–8, 331, 332 binomial distribution 37 bioclimatology 591 biosphere 9–10; feedback 160–1, 169 black body radiation 25, 27–8, 241 blocking 305, 307, 309–22, 324; anticyclone 309–11, 313, 316–17, 319–20, 322, 573; in southern hemisphere 311–12, 409, 494; mechanisms 313–22; pattern 284, 308–13, 328–9, 571, 587 “Bomb” (cyclone) 460, 483 BOMEX (Barbados Oceanographic and Meteorological Experiment) 506 boreal forest 240 boundary conditions 164 boundary layer 227–31, 462, 501, 513 Bowen ratio 117 break: in monsoon 197, 207; in time series 66–7, 325 brightness temperature 28, 527 British Isles 552, 555, 561–7, 574, 581, 582, 587, 589; airﬂow types 561–6

Brunt–Väisälä frequency 11, 122, 472 buoyancy 229, 232–3, 242 calendar: synoptic 580–7, 583, 584–5 California 559, 584 Canada 57, 555, 576, 586, 587, 604 canonical correlation 42, 183 CAPE (convective available potential energy) 232, 234, 242, 523 carbon dioxide 116, 160 Caribbean 508, 510, 511 Carnot cycle 524 catalog of weather types (see synoptic) CAV (constant absolute vorticity) 275–6 center of action 209–20 China: summer monsoon 198; winter cold waves 204–5, 211 circulation: atmospheric 113–14, 139; cells 143–53, 373; epochs 588; hemispheric 414, 569–74, 578, 587; types 547–66, 576–7, 580–1 circumpolar trough (Antarctica) 218–19, 443 circumpolar vortex 263–6, 305–6, 339, 586; eccentricity 279 CISK (Conditional Instability of the Second Kind) 183, 336, 490, 523 classiﬁcation: air mass 237–8, 560; circulation 572–8; cloud 477–8; evaluation of synoptic classiﬁcations 549; Grosswetterlagen 566–8, 576; objective 558; pressure ﬁeld 549, 551–8, 566–8; weather map 547–51; weather types 550, 558–60, 574 classiﬁcatory methods 81–4 Clausius–Clapeyron relationship 156 climate: deﬁnitions 13, 31; global 223–35; regimes 233–4; scales of variation 5, 10–13; system 1–2; variables 31–2 climate change: causes of 112–15, 119–20, 587; glacial period 157, 587; historical period 587; recent 587–9 climate models 153–6, 161 climatic forcing 3–5 climatic regime 81, 233, 575 climatic regions 81, 223–8, 238–9 cloud band: 484, 485, 517, 520; classiﬁcation 477–8; cluster 177, 336–8, 486–7, 508, 521–2, 523; ﬁelds 478–85;forcing 159–60; system 224, 484, 505; top temperature 21–2, 498, 499–500; types 224, 226, 231 cloud cover 23, 27, 116, 223–26; feedback 159–60 cloud vortices 485–6, 502; tropical 486–7, 506, 508–9 clustering methods 80–2, 360–1, 558, 559, 584 clusters: of atmospheric states 332–3; of cyclone tracks 467; of trajectories 551, 590 coherence: spatial 61–3 cold fronts 452, 455–8, 501, 521 cold lows 449, 493–4

Index 11

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cold surge 204–5, 395 cold tongue: equatorial 183, 376 comma cloud 463–4, 488–92 complex climatology 551, 558 composite differences 386–7 composite pressure ﬁelds 412 conditional instability 462 conﬂuence 44–5, 176–7; zone 274 conservation of angular momentum 126, 275–6 continental location: effect on climate 153–5, 192–3 contingency analysis 34 continuity equation 50, 162, 425 contour chart 40, 547 convection 233–4, 378, 390, 396, 478, 500, 501, 513, 515, 520, 521, 524; slantwise 224, 453, 481, 505 convective systems 514–15; mesoscale 21, 207, 235, 487, 595–7, 508 convergence 451, 480–1, 501 convergence zone (see ITCZ and SPCZ) conveyor belt 453–4; oceanic Coriolis force 47 Coriolis, G. 144 Coriolis parameter 11, 47, 186, 188, 223, 286, 507, 519 correlation: canonical 42, 183; decay 58–9; ﬁeld 61, 359–60; lag-correlation maps 470; matrix 78, 82; patterns 359–60, 362, 414–16; serial 63–8; spatial 58–9, 101–3 correlation velocity patterns 474 correlogram 71 coupled models 163–4, 166, 192–3, 383, 417, 422, 424 COWL (cold ocean, warm land) 418 Cressman interpolation 57 cryosphere 9 cumulonimbus 145, 177, 181, 226, 230–2, 521 cumulus convection 173, 228–33, 500, 523, 524 curl 48, 87 cut-off low 309, 320 cycle: index 305–6; solar 420 (see also periodicity) CYCLES (cyclonic Extratropical Storms) project 476 cyclogenesis 216, 318, 321, 324, 442–3, 459–65, 486, 493; secondary 464–5; tropical 515, 519–23; types 452–5, 460–4, 488 cyclone 441; development 450–9; frequency 410, 442–7; models 441–2, 452–5, 485–6, 555–6; stage 136, 485–6; tracks 217, 218, 219, 442, 465–76 cyclonic vortices: classiﬁcation 484–6 cyclosis 216

0 11

data: quality 41–2; sources 41–2 De Bort, Teisserenc 16, 120–21, 209 deforestation 160–61

613

deformation 45 degrees of freedom 63, 66, 78 delayed oscillator 383–4 depression tracks (see cyclone tracks) depressions: monsoon 198–200; tropical 186–87 desertiﬁcation 160–1, 169 deserts 201–3, 392 development 525; of cyclones 451–2, 459–65, 483; historical ideas on 450–2 Devil’s staircase 383 diabatic heating 130–1, 275, 294, 298 difﬂuence 44 Dines compensation 480 discriminant analysis 83–4, 559–60 dissipation 142–3 distance measures 80, 552–3, 570 distribution (see frequency distributions) diurnal dancing 336 diurnal pressure wave divergence 44–6, 49, 50, 87, 188, 238, 450, 451, 459, 480–1, 501; coastal 47 DMSP (Defense Meteorological Satellite Program) 20, 29, 31, 488–9, 494, 502 doldrums 176, downscaling 166, 561, 566, 576 drift pattern 188–9 dry adiabatic lapse rate (DALR) 111, 140 duration of circulation types 587–8 dynamic climatology 13–14, 604–5 dynamic instablility 275, 329 Dzerdzeevski classiﬁcation 414, 548, 570–1, 584, 588 Earth dimensions 6, 110 Earth–atmosphere system 223 easterly tropical jet stream 195 easterlies: subpolar 209; tropical 171–4, 228, 268, 509–12 easterly wave 508–12, 514 eccentricity of Earth’s orbit 112, 115 ECM (Elementary Circulation Mechanism) 570–1 ECMWF (European Centre for Medium Range Weather Forecasts) 41, 166, 511, 547 ecological studies using weather types 591 eddies 126–7, 413, 470; horizontal 128; stationary 126, 132; transient 126, 132, 268, 277, 319, 323, 414, 469 eddy transport 126–8, 132–5 eddy vorticity 314 eigen vectors (see empirical orthogonal functions (EOFs)) Ekman pumping 232 El Niño 136, 145, 275, 364, 367, 377, 382, 385, 486 Eliassen-Palm ﬂux 320, 341–4, 346, 472–3, 606 emissivity 27–8 EMS (electromagnetic spectrum) 18

614 Index 1

energy 7; balance 114–17; conservation 27; conversion 140–3, 512; kinetic 10, 13, 117, 141, 143, 277, 293–4, 413, 451, 464, 512, 526; latent 117; potential 117, 129, 526; static 133; total 130, 132–4; transport 129–36; vertical transfer 114–15 energy budgets 274, 590; atmospheric 114–18; of heat low 203 energy transfers: in the atmosphere 120, 121, 129–36, 180–1; in the ocean 130, 132 enhanced convection 483 ENSO (El Niño Southern Oscillation) 136, 145, 204, 364, 367–76, 384–96, 404, 409–10, 414, 422, 496, 494, 502, 505, 516, 561; El Niño 136, 145, 275, 364, 367, 377, 382, 385, 404, 410, 486, 494, 495, 502, 514, 561, 566; events 367–70, 374–6, 377, 385–8, 495; indices 367; mechanisms 376–84; Southern Oscillation 358–9, 361–75, 391; teleconnections 384–96, 409–10 ENSO/PNA circulation modes 411 enstrophy 319 enthalpy 117, 132 EOFs (empirical orthogonal functions) 78–81, 330, 332–3, 334, 340, 360–1, 417, 556, 558, 559–60, 572–3, 578, 586, 605 equatorial duct 188–9, 507 equatorial trough 176–82 equatorial westerlies 175, 195 equivalent temperature ERICA experiment 462, 476 Ertel potential vorticity 122, 241, 319, 321–2 ESMR (Electrically Scanning Microwave Radiometer) 20, 29 Etesian winds 171 Ethiopian Highlands 511 Eulerian method 53, 84–7 Eurasia: teleconnection pattern 397–8 Europe 403, 449, 556–7, 566, 568, 570, 580, 587 evaporation 137, 139, 462 exploratory data analysis 33 eye 517–18 factor analysis (see empirical orthogonal functions) fast Fourier transform 69 feedback: mechanisms 155–61 feedback studies (see air–sea interaction) Ferrel cell 127, 141, 144 Ferrel, W. 144 Ferrel westerlies 207 ﬁeld: intercomparison 61–3; signiﬁcance 62–3 ﬁeld of view 23 ﬁltering: time series 65–6, 74, 323–4, 468, 469 ﬁnite differences 85 ﬂow patterns 566; equatorial 188–9 föhn 576

forecasting: long range 574, 578; numerical 22, 161, 166; seasonal 578 Fourier analysis 71–3, 278, 325; time series 71–3 frequency distributions 32–3; beta 39–40; binomial 37; gamma 38–9; normal 35–6; Poisson 37–8 frequency domain 71–7, 474 friction 125 front: anafront 453; Arctic 208, 240; cross-front circulation 526; katafront 453; Polar 584 frontal analysis 453–9; objective 456–8; threefront model 453 frontal contours 453 frontal cyclone; life cycle 455, 460–1 frontal zones 270 frontogenesis 448, 452, 526; instant (see occlusion, instant) frontolysis 526 FRONTS 92 experiment 464 gamma distribution 38–9 GATE (GARP Atlantic Tropical Experiment) 230–2, 506, 511–12 general circulation 109 general circulation models (GCMs) 109, 116, 118, 119, 148–51, 160–6, 183–4, 300–1, 561, 576, 590, 605; AMIP (Atmospheric Model Intercomparison Program) 166 geopotential 40; height ﬁelds 263–5 Geosat 30, 484, 487 geostrophic wind; resultant 238 Germany 566, 574, 580, 582 Gibbs oscillation 163, 165, 605 glacial anticyclone 217 glacial period 155, 157, 587 glacier variations 590 global analyses 41–2 GOES (Geostationary Operational Environmental Satellites) 18, 20, 478–9, 496, 497, 504, 505, 518 GPCP (Global Precipitation Climatology Project) 29, 224 GPI (GOES Precipitation Index) 28, 29 gradient operator 45, 87 gravity wave 285, 420 Great Salinity Anomaly 423, 424 greenhouse gases 116, 156 Greenland 40, 57, 217, 296, 400–1, 590 Grosswetter 566, 574, 587 Grosswetterlagen 331, 548, 566–8, 576, 582, 587–8, 590 GTS (Global Telecommunications System) 17 Gulf Stream 400, 418, 462, 473 Hadley cell 119, 127, 137, 141, 144, 148–53, 169, 186, 234, 268 Hadley circulation 144–5, 148–9, 374, 378, 385, 391, 395

Index 11

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Hadley, G. 144, 191 Halley, E. 191 harmonic analysis 68–70; spherical 296, 298, 340–1 Hawaii 57, 200 HCMM (Heat Capacity Mapping Mission) 23 heat; latent 117, 132, 145, 299–300, 508, 519; sensible (enthalpy) 117, 132, 145, 298, 472, 486, 519; sources 129–30, 470; transport 130, 132–6, 486; turbulent ﬂuxes 117, 299 heat low 201–4, 215, 274 hemispheric circulation 414, 569–74, 578; Fourier analysis 278–9; indices of 587; types 569–74 Himalayas 272, 296, 586 hot towers 145 Hovmöller diagram 200, 282–3, 487 HRCs (highly reﬂective clouds) 27, 177–9, 233, 378, 476 human health and weather types 591 hurricanes: development 515; intensity 516, 522, 524 Hurst phenomenon 65 hydrological cycle 136 hydrosphere 5–9 hydrostatic equation 14 hydrostatic equilibrium 162 ice (see sea ice) Ice Saints 580 ice sheets 7, 9 ice storm 504, 505 Iceland 397, 587 Icelandic low 211, 215–16, 399, 404, 423, 446 IGY (International Geophysical Year) 507, 547–8 index cycle 305–6 India 77, 499, 507; summer monsoon 195–8; relationship to ENSO 391–6 Indian Ocean 385, 390–6, 571, 573 Indonesia 339 inertial circle 188 inﬂuence ﬁeld 571 infrared radiation 24, 26–7, 112, 115, 121 INSAT (Indian National Satellite System) 20 instability: baroclinic 148, 186, 451; CharneyStern criterion 45; frontal 464; hydrodynamic 275–6 intransitive system 329 inverse distance weighting 58 inversion 501, 519; trade wind 172–4, 182–4, 229–31; of satellite sounding radiances 24 ISCCP (International Satellite Cloud Climatology Project) 224, 233 isentropic analysis 53–5 isentropic surface 451, 605 ISO (intra-seasonal oscillation) 332–40 isobaric analysis 55 isocorrelate 61 isogon 42

615

isotach 42 Italy 574 ITCZ (Intertropical Convergence Zone) 152, 176–80, 186–7, 191, 224, 232, 370, 478, 495, 497, 514, 519 January thaw 582 jet streak 459–60, 525 jet streams 270–8, 408, 413, 469, 472, 481, 483, 513; African easterly 274, 511, 513; Australian 275, 385; discovery 271; low-level 190, 196, 455, 458–9, 497, 501; polar front 208, 270, 410, 494; polar night 405; related to precipitation 26; split 311, 319; subtropical 145, 147, 186, 195, 204, 207–8, 270, 274–5, 385, 416, 494, 501, 586; tropical easterly 195, 272–4, 460; velocity maxima 271, 276–7 Joseph effect 65 Kelvin wave 234, 293, 334, 346, 379–84, 508 Kernlose winter 222–5 kinematic analysis 42–5, 55, 551 kinetic energy 10, 13, 117, 141, 143, 277, 293–4, 413, 451, 464, 512, 526; spectrum 10 kinetic temperature 27 Kirchhofer method 552–6, 566, 576, 578 Kirchhoff’s Law 26–7 Kona storm 200 Köppen climate classiﬁcation 155, 165 kriging 58–60 Kuroshio current 424, 473 kurtosis 35–6 La Niña 136, 145, 275, 364, 404, 410, 486, 494, 514, 561 Labrador-Ungava 564 Lagrangian method 53–7, 84–7, 465, 467 Lamb catalog 561–6, 587, 589 Lamb types 42, 562, 564–5, 576, 589, 590 Landsat 23 Laplace tidal functions 288, 291 Laplacian operator 49, 87, 458, 524 lapse rate 119, 121 latent heat 5, 117, 508, 519 laws of thermodynamics 161 Legendre functions 290–1, 340–1 LIE (Line Islands Experiment) 506 lightning 500 limit cycle 330–1 linkage 80 long waves (see planetary waves) low frequency variablility 322–32, 411, 414–16 low-latitude circulation 171–90 maritime continent 145, 198, 370, 391 Markov chain (process) 64 mass ﬂux 152 master seasonal trends 586

616 Index 1

Maunder Minimum 587 maximum entropy method 73 MCC (Mesoscale Convective Complex) 477, 495–501; criteria 498 MCS (Mesoscale Convective System) 21, 207, 235, 487, 495–6, 497, 508 mean sea level pressure 40; reconstruction 41–2 meander index 305, 309 median 23 Mediterranean 558 Meiyu 197–8 meridional circulation 130, 144–53 merry-go-round formation 493 mesocyclone (see mesoscale cyclone) mesoscale cyclone 4, 10, 477, 486, 488–505 mesosphere 121 methane 160–1 Mexico 574 microwave radiation 27, 28–31 MJO (Madden – Julian Oscillation) 197, 270, 334–6, 339 models: air–sea interaction; baroclinic 296; barotropic 296–7, 300, 322, 331; cloud and circulation 454–5, 462–4; 480–3; coupled 63–4, 166, 192–3, 383, 417, 422, 424, 605; cyclone 441–2, 450–5, 462–4, 476, 480–3, 485–6; GCMs 161–6, 561, 576, 590; hemispheric circulation 144; numerical prediction 22, 161, 166; parameterization 163, 183–4; primitive equation 511–12; probability 34–5; regional circulation 166; resolution 163; stochastic 576; synoptic 441–2, 455–60, 476, 480, 555–6 modon 298, 318–19 moist convective adjustment 183 moisture: recycling ratio 140; transport 136–40, 410 moisture burst 395, 503–4 Molniya satellite 23 momentum: angular 124–5; equation 161–2; ﬂux 268, 270, 474 MONEX (Monsoon Experiment) 191, 203 monsoon trough 176, 192, 200 514, 519 monsoons 190–8; Asian 113, 145, 169–71, 204–5, 275, 336, 391, 421; Australian 206–7, 336, 421; depression 198–200, 508; East Asian winter 204–5, 583; Indian 195–8, 336; North American 496, 497; West African 113; Western Paciﬁc 198 Monte Carlo simulation 63, 80, 553 Montgomery potential 55 Morning Glory 207 mountain barriers 501; airﬂow over 287, 295–7 moving average 65 multiple ﬂow equilibria 318, 330–1 NAO (North Atlantic Oscillation) 358, 361, 396–403, 413, 416, 418, 424, 495, 587, 588; index 397, 399–400

natural season 571, 580, 582–6 NCEP (National Centers For Environmental Prediction) 41, 166, 446, 547 NDVI (Normalized Difference Vegetation Index) 9 negative viscosity 13, 140 nephanalysis 20 neural network 560–1 New Zealand 308, 371, 409, 443, 494, 559, 564, 574 Noah effect 65 non-linear dynamics 330–1, 384, 523 normal (Gaussian) distribution 35–6 normal modes 291–2, 342, 345, 514 North America 385–7, 479, 499, 502, 554–5, 558, 576, 578, 586 North Atlantic 215–16, 417, 423, 452, 472, 495, 502, 511, 515, 519; seesaw mode 328, 398, 400–2; subtropical anticyclone 215 North Atlantic Current 418, 423 North Paciﬁc 216, 417, 472, 494–5, 502, 515, 519–20, 552; oscillation 358, 361, 403–4 Norwegian Sea 490, 493 nowcasting 22 numerical weather prediction 22, 161, 166 Nyquist frequency 71–2 objective analysis 58 objective typing 551–61, 565–7; artiﬁcial neural networks 560–1; correlation-based 551–6; data reduction 556–60; multivariate techniques 558–60 obliquity 112–13 observing stations: surface 16–17, 604; upper air 16–17 occlusion 452, 460; bent-back 455, 460; instant 21, 463–4 ocean-atmosphere interaction 182, 383, 392, 420–1 ocean currents 8 oceans 6–8; heat transport 130, 132; thermocline 182, 376, 377, 379, 381, 383–4, 417 Old Wives’ Summer 580–1 OLR (outgoing longwave radiation) 26, 28, 180, 233–4, 334, 336, 339, 392, 395 omega equation 51, 52, 448 operators 87 orbital forcing 112–13 orographic effects: on climate 155, 171, 193–5, 226, 282, 294–8, 300–1 orthogonal polynomial surfaces 556 oscillation: Antarctic 405–8; Arctic 404–7, 418; interdecadal 418; interannual 418; Madden–Julian 197, 270, 334–6, 339; North Atlantic 358, 361, 396–403, 413, 416, 418, 424, 495, 587, 588; Paciﬁc-South American 409, 422; Pan-Atlantic 424; quasi-biennial 374–5, 507; quasi-decadal 423–4; semi-annual

Index 11

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220–3; semi-diurnal 289, 291; Southern 358–9, 361–75, 391; Tropic-wide 396; zonally symmetric 404–8 ozone 116, 121, 123, 589, 590; hole 222 Paciﬁc Ocean 172–4, 176–80 palaeoclimate 119, 153–5, 157, 165 parameterization 163, 183–4, 605 partial collectives 32, 34, 575 partial derivatives 85 partial frequency 237 partitioning 424–5 passive microwave radiation 29, 527; imagery 483 Pathﬁnder Program 21, 604 PBL (planetary boundary layer) 378 perihelion 112, 113 periodicity 68–9, 112–13, 115 persistence 64–6, 72, 325–32, 392, 417, 561, 578 Peru 367, 375, 377 phase changes 5 photosynthesis 9, 160 PILPS (Program to Intercompare Land Process Schemes) 166 pixel 23 Planck’s Law 24 planetary climate 109–13 planetary waves 278–302, 325, 572–3, 578; stationary PNA (Paciﬁc–North American) pattern 266, 331–2, 360, 361,396, 398, 410–14, 416, 418, 420 Poisson distribution 38 Poland 564 polar front 208, 274, 462, 584; jet stream 208, 270; theory 450 polar low 452, 483, 490, 493 polar orbiter 18, 20, 23 polar vortex 217, 263–6, 268, 305–6, 339, 405, 409 pollution 238, 589, 591 POP (Principal Oscillation Pattern) 371–2 potential energy 117, 129, 277, 526; available 140–2, 513 potential temperature 123, 212–13, 455–6; equivalent 232, 234, 242–3, 452, 456; wet bulb 237, 458 potential vorticity 122, 143, 186, 188, 235–6, 240–1, 458, 464, 606; conservation of 286–7, 509; Ertel 122, 241, 319, 321–2 precession of the equinox 112–13 precipitable water 136–9 precipitation 61, 137, 139, 224–7, 231, 235, 298, 527, 576, 577; anomaly 370; recycling 140; seasonal regime 223–4 (see also rainfall) precipitation estimation: from satellite data 28–9 predictability barrier 392, 394

617

pressure: mean surface 42, 128, 168 pressure ﬁeldanomaly 61, 129, 372, 399,400–1; classiﬁcation 549, 551–8, 566–8; decay of correlation in 58–9; mean 168, 566; sea level 41–2, 168, 218, 483, 547 pressure pattern types (see circulation types) pressure tendency equation 459 pressure torque 125 primitive equations 162–3, 307 principal component 78–80, 82, 360–1, 391, 398, 404; analysis 551, 556, 558–9, 565 probability: distribution 326; models 34–5 PSA (Paciﬁc–South American) pattern 409, 422 PSCM index 564, 566 pseudo-adiabatic chart 53 Q vector 524–6, 605 QBO (quasi-biennial oscillation) 374–5, 418–21, 516 quasi-barotropic relationship 586 quasi-geostrophic ﬂow 346 radar 31 radar altimetry 30 radiation: balance 118; budget 114–15, 159–60; net 117–18 radiation laws: Beer 27; Kirchoff 26–7; Planck 24; Schwarzchild 27; Stefan-Boltzmann 24, 241; Wien 24, 116, 241 radiative convective model 121 radiosonde 16; data 558–9, 560 rain band 198, 452, 454 rain rates 28–9 rainfall 77, 139, 564; in East Africa 189; indices 367; related to ENSO events; types 233–4 rawinsonde 413–14 ray path 300–3, 388–90, 413–14 reanalysis 41, 166, 446, 547 recent climatic ﬂuctuations 578–9 recurrence 324 red noise 66, 72 regimes: of circulation 322–32; of climate 233–4; of precipitation 333–4; quasi-stationary 324–5 regional climate 561–9, 574–8 remote sensing 18–21; data 17–23; history 18–21; infrared 18, 20 21, 23, 26, 27; multispectral 18; passive microwave 20–21, 23, 26, 27; radar 31; visible 18–20, 21, 23 resonance 286, 289, 314 resultant vector 36 Reynolds, Osborne 126 Richardson, L.F. 140 Richardson number 464, 526 Rocky Mountains 294, 296, 452 Ross Sea 492, 493, 494 Rossby, C.-G. 271 Rossby number 147, 286 Rossby radius of deformation 379, 426, 507

618 Index 1

Rossby wave 122, 171, 285–6, 288, 291, 314, 388, 390, 508; dispersion 316; equation 286, 572; in ocean 379–81, 383 rotating annulus 306 rotation rate 110, 118, 125, 145, 147 rotational velocity 125 running mean 65–6 Sahara 171 SAR (synthetic aperture radar) 31 SASS (Seasat-A Satellite Scatterometer) 18 satellite imagery 18–21; cloud classiﬁcation 21–2, 28 satellites 18–23 scalar 85 scale interactions 523–4 scale of weather systems 11–12 Scandinavia: teleconnection pattern 397 scanning radiometer 20 scattering 116 scatterometer 16, 30 Schwarzschild’s Law 27 sea ice 9, 30–1, 402, 409, 586; Arctic Ocean 31, 83; albedo feedback 159; motion 31; Southern Ocean 219, 220, 221, 282, 308, 410 season 578–80, 582–4; natural (see natural season) seasonal trends 586 seclusion 455, 461 secondary cyclones 464–5 seesaw pattern 398, 400–2, 418 self-organizing map 560–1 semi-annual oscillation 220–3 sequences of circulation 305, 572 shadow band 481, 483 Shurin rains 583 Siberian high pressure 40, 204–5, 211–15, 570 signiﬁcance tests 61–3, 66–7, 68, 84, 359–60 similarity measures 551–3 simulation 153–6, 164–5 Singular Spectrum Analysis 73, 340, 374, 425 singularities 580–2; primary 582 skewness 35 SMMR (Scanning Multichannel Microwave Radiometer) 20, 31, 527 snow 226, 590 snow cover 31, 194, 393, 418, 586 SOI (Southern Oscillation index) 73, 74, 136, 361, 364–66, 392, 395, 397, 399, 425, 486, 502 soil moisture 9, 155 solar constant 116 solar inﬂuences on climate 375, 420 solar irradiance 111–12, 118; at surface solar radiation 114–17, 299 Somali jet stream 274 sounder (see TOVS, VAS) South Africa 502

South America 234, 308, 377, 385, 388, 499, 501, 519, 556, 564 South Atlantic Ocean 417, 423 South China Sea 197, 200, 204, 514 South Paciﬁc Ocean 371–2, 519, 502, 571, 604 southern hemisphere 208, 210, 214, 217–22, 281, 268, 281, 371–2, 407–10, 418–19, 462–4, 467–9, 492–4, 502, 547–8, 571, 604 Southern Ocean 219, 223, 224, 282, 308, 407–10, 418–19, 421–2, 442–44, 484, 493–4, 604 Southern Oscillation 358–9, 361–75, 397, 420, 502; index 73, 74, 136, 361, 364–6, 392, 395, 397, 399, 425, 486, 502 Soviet Union 446, 551, 570, 604 spatial coherence 61 spatial data 40–63 spatial interpolation 56, 57–61 SPCB (South Paciﬁc Cloud Band) 502 SPCZ (South Paciﬁc Convergence Zone) 179–80, 371, 373–4, 382 spectral gap 10, 488 spectrum analysis 71–3, 506; singular 73, 340 spell: statistical properties 64 spells of weather 580, 582–3 spherical harmonic analysis 296, 298, 340–1 spline methods 60 split window method 26 SPOT (Système probatoire pour l’observation de terre) satellite 23 squall lines 495, 508 SSM/I (Special Sensor Microwave/Imager) 21, 29, 31, 483–4, 487, 492, 500, 527 SST (sea surface temperature) 16, 18, 20, 220, 368–70, 377–8, 399, 417, 418; anomalies 367, 368–70, 376, 383–4, 388, 389, 395, 417, 420, 422, 423 standard deviation 33, 35, 548 standing wave 136 state diagram 110–11 static energy 133 static stability 142, 147, 526 stationarity 66 stationary wave 127, 287–88, 294–96, 314, 474–5 statistical tests 61, 63, 84; for shifts of mean 66–7; of trend 68 steering 267, 566, 571 storm tracks 322, 400, 403, 414, 465–76 strange attractor 230 stratiform cloud 227–8, 477–8, 495, 496 stratocumulus 177, 228–9 stratopause 121 stratosphere 121, 405, 409; quasi-biennial oscillation 420 stream function 45, 48, 49, 130, 241, 470; isentropic 55 streamlines 42–4, 196, 459, 504, 512; analysis 48, 53–4 Student’s t 84

Index 11

0

11

0

subpolar easterlies 209 subpolar lows 210 subsidence 160–70, 215, 226–7, 322, 478, 519 subtropical cyclone 200–2, 508, 517 subtropical high pressure 166–71, 218, 442 subtropical jet stream 145, 147, 186, 195, 208, 270, 274–5 summer monsoon 233–4; Asian 169–71, 194–97; Australian 206–7; East Asian 197–8; Indian 195–8, 372 sunglint 476 supercluster 487 surface/upper-air relationships: and the formation of depressions 459–64 SVATS (Surface Vegetation–Atmosphere Schemes) 160 SVD (singular value decomposition) 81 Switzerland 64, 566, 574, 576, 587 (see also Alps) symmetry about the equator 424–5 synoptic: catalogs 561–6; data 16–17; indices 560, 564, 565–6, 578, 591–2; models 450–5, 476; systems 441–2, 476–87, 506–24; weather map 40–1, 549–50, 604 synoptic classiﬁcation: objective 551–61; subjective 549–51 synoptic climatology 487, 494, 497, 547–91, 604–5; analysis 547–9; deﬁnitions 13–14; environmental applications 560, 589–91; historical development of 548–9; regional applications 574–8; scales 549–50 synoptic weather maps 40–1, 549–50

0

0

0 11

Tasmania 587 TECB (Tropical-Extratropical Cloud Bands) 21, 477, 501–5 teleconnection 83, 358, 384–96, 415–16; forcing 413; indices 329, 426; patterns 358, 425; tropical-extra tropical 410–13 temperature; annual cycle 220; annual wave 209; anomaly 385–8, 406; change components 588–9; correlation with 700 mb height 61–2; effective 111–12; frequency distribution 33, 236; latitude–height cross-sections 212–13, 281; latitudinal gradient 119–20, 149, 269; mean for Earth 110, 112, 118, 119; mean at standard pressure levels 269; potential 123, 212–13; range 207, 478; sea surface 16, 20, 118–19, 220, 368–70, 377–8, 417, 418, 422, 423, 487, 519; vertical structure 120–23, 194–95; virtual 242; wet bulb potential 458 tephigram 53 terrain-induced vertical velocity 53, 294–5 tesselation methods 60 thermal advection 452, 460 thermal forcing of planetary waves 294, 298–300 thermal Rossby number 147 thermal wind 266–7, 271, 456, 458, 498, 525

619

thermocline 182, 376, 377, 379, 381, 383–4, 417 thermodynamic equation 162 thermohaline circulation 423 thickness 208, 267–8, 456; advection 52, 525 Thiessen method 60–1 three-cell circulation model 144 three-front model 238 thunderstorm 478, 495, 497, 501, 576 Tibetan Plateau 155, 194, 208, 272–4, 294, 300, 586 TICA (tropical intraseasonal convention anomalies) 336 time series 63–7, 506; deterministic elements 64; Fourier analysis 71–3; periodic components 68–71; persistence 64–5; random elements 64 TIROS (Television and Infrared Orbiting Satellite) 18–20, 476 TOGA (Tropical Ocean Global Atmosphere) 231, 233, 506 TOMS (Total Ozone Mapping Spectrometer) 477 TOPEX-Poseidon 30, 487 topographic effects 155, 171, 193–5, 287, 300–1, 452, 501, 590 tornado 496 TOVS (TIROS Operational Vertical Sounder) 18, 20, 21, 477, 483, 487, 492 trade wind cumulus 229–31 trade winds 171–4 trajectory 53–7, 188; air mass 57, 238–40; isentropic 55–6; isobaric 55–6, 590 Trans-Polar Index 308, 408–9 transformation of data 39 transition of circulation types 325, 332, 566 (see also sequences of circulation) transport mechanisms in the general circulation 126–39, 143–53 trends: in cyclone frequency 446 tropical cyclones 186–87, 385, 487, 496, 515–24; frequency 516; intensity 519–20, 52; structure 516–19 tropical easterly jet 195, 272–4, 460 tropical/northern hemisphere teleconnection pattern 397–8, 410–11, 420 tropical plume 503–4 tropical waves 506–16 tropopause 120–3, 235–7, 406; deﬁnition 122; discovery 120–1 troposphere 121, 130, 237 tropospheric rivers 137–8 trowal 453 TRMM (Tropical Rainfall Measuring Mission) 23 truncation 341, 605 turbulence 143 TUTT (tropical upper-tropospheric trough) 519–20 typhoons 515, 522

620 Index 1

United States 61, 62, 64, 70, 365, 385, 411, 495–6, 499, 501, 502, 505, 519, 552–3, 560, 582–4, 589, 591 upper air: charts 41; cyclones 200–2, 446; data 16–17 upwelling 217, 377, 381, 383 US Weather Bureau analyses 41, 217 vacillation 306 Vangengeim-Girs catalogue 548, 569–70, 588 vapor ﬂux (see moisture transport) vapor pressure 156, 158 variance 35, 64; analysis of 84; spectrum analysis 71–3 variogram 60 VAS 18 vector notation 45 vegetation 9, 62, 160–1 velocity potential 69, 197, 390–1 Venezuela 511 Venus 110–11, 147 vertical velocity 49–53, 212–13, 227, 389, 448, 459, 480; computation methods 50–3, 524–5; relative magnitudes 50; terrain-induced 53, 294–5 VIHMEX (Venezuela International Meteorological and Hydrologic Experiment) 506 virtual temperature 242 VIS/IR (visible/infrared) imagery 476, 478, 480, 483, 486, 487 vortex stripping 123 vorticity: absolute 47, 52, 123, 175–6, 184–5, 208, 275–6; advection 52, 276–7, 459–60, 470, 525; anomaly 513; conservation 319, 460; cyclonic 451, 459; equation 47, 51, 276, 460; ﬂux 474–5; geostrophic 458, 556; potential 122, 143, 186, 188, 513; relative 45–7, 49, 87, 286–7, 442, 458, 459–60, 512, 556; shear 566; tangential 520–2 Walker circulation 145, 361, 374, 391–3, 396, 507 Walker, Sir Gilbert 358, 359 WAMEX (West African Monsoon Experiment) 191, 506 warm front 455, 460, 462 warm pool: in mesocyclones/polar lows 490; in western Paciﬁc 145, 179, 232, 233, 365–6, 392, 501, 506 water vapor 26, 29, 116, 121, 136, 138, 139, 484, 492; temperature feedback 156–7

water vapor ﬂux 136–8 Wave-CISK 336, 514 wave disturbance 441, 508–15 wave guide 407–8, 413 wave train 391, 413, 414, 417, 418; propagation 300–2 waves: African 512–16; easterly 508–11; global modes 288–93; gravity 285, 420; inertiogravity 507–8; Kelvin 234, 293, 334, 346, 379–84, 508; numbers 143, 279, 282, 288, 300; properties 282–8; planetary 278–302, 314; Rossby 285–6, 288, 291, 298, 508; Rossbygravity 508, 514; unstable 288–9; tropical 506–15 wavelet analysis 73–7, 393, 397, 605 WCRP (World Climate Research Programme) 604 weather: elements 551; satellites 18–23; types 550, 558–60, 574, 591 Weibull distribution 39 wet-bulb potential temperature 458 West Africa 274; monsoon 113 westerly ducts 389 westerly winds 207–9, 263, 303; bursts 336, 379–81; equatorial 175, 195, 379; polar night 271 white noise 64, 472 Wien’s Law 24, 116, 241 wind: belts 126, 144, 207; components 42; constancy 223; ﬁeld 42; geostrophic 267, 271, 302; irrotational 241–2; katabatic 493; nondivergent 49, 241–2; resultant 238; shear (vertical) 271; streamline 42–4, 196, 459, 504, 512; thermal 266–7, 271, 456, 458, 498, 525; velocity 42, 267; zonal 210, 212–13, 272, 275–6, 288, 326, 328 WISHE (wind-induced surface heat exchange) 234, 524 Witterungslagen 574–5 World Weather Watch 16 Zebiak–Cane model 383 zonal ﬂow 303, 308–9, 568, 573, 587 zonal index 302–8, 403, 571, 586; cycle 305–6; high 303–4, 313, 327, 328, 571; low 303–4, 307, 313–14, 328, 571 zonal pattern 284, 313–14, 328, 568, 570, 573 zonal wind (see wind) zonally-varying teleconnection pattern 284, 313–14, 328, 408