Advances in Geophysics, Vol. 26

ADVANCES IN GEOPHYSICS VOLUME 26 Contributors to This Volume CHRISTOPHER R. LLOYD DAVID E. LOPER MIRLES. V. RAO MI...

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ADVANCES IN

GEOPHYSICS

VOLUME 26

Contributors to This Volume

CHRISTOPHER R. LLOYD DAVID E. LOPER MIRLES. V. RAO MICHAEL E. SCHLESINGER

Advances in

GEOPHYSICS VOLUME 26

Edited by

BARRY SALTZMAN Department of Geology and Geophysics Yale University New Haven, Connecticut

1984

ACADEMIC PRESS, INC. (Harcourt Brace Jovanovich, Publishers)

Orlando San Diego New York London Toronto Montreal Sydney Tokyo

COPYRIGHT @ 1984, BY ACADEMIC PRESS, INC. ALL RIGHTS RESERVED. NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR TRANSMITTED IN ANY FORM O R BY ANY MEANS, ELECTRONIC OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR A N Y INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT PERMISSION IN WRITING FROM THE PUBLISHER.

ACADEMIC PRESS, INC.

O r l a n d o , F l o r i d a 32887

United Kingdom Edition publislied by ACADEMIC PRESS, INC. (LONDON) LTD. 24/28 Oval Road, London N W l IDX

LIBRARY OF CONGRESS CATALOG CARDNUMBER: 52-12266

ISBN 0-12-018826-0 PRINTED IN THE UNITED STATES OF AMERICA 84 85 86 87

9 8 7 6 5 4 3 2 1

CONTENTS CONTRIBUTORS ................................................... ERRATUM ........................................................

vii ix

Structure of the Core and Lower Mantle

DAVIDE. LOPER 1. Introduction ................................................... 2 . Dynamo Energetics ............................................. 3 . Structure of the Outer Core ...................................... 4 . Structure of the Inner Core ....................................... 5 . Structure of D” ................................................. 6 . Structure of Deep-Mantle Plumes ................................. 7 . Thermal History of the Earth ..................................... 8. Summary...................................................... Appendix. Energy Available from Gravitational Separation ........... References.....................................................

i 4

6 12 15 19 21 24 25 27

Pre-Pleistocene Paleoclimates: The Geological and Paleontological Evidence; Modeling Strategies. Boundary Conditions. and Some Preliminary Results

CHRISTOPHER R . LLOYD 1. Introduction ................................................... 2. Paleoclimatic Indicators ......................................... 3 Pre-Pleistocene Paleoclimates and Paleoceanography................. 4 Forcing Mechanisms in Long-Term Climatic Change ................ 5 . Boundary Conditions for Paleoclimatic Modeling.................... 6. Paleoclimatic Modeling Strategies ................................. 7 . A Survey of Paleoclimatic Modeling Results ........................ 8 . Summary ...................................................... References. ....................................................

. .

36

39 52 74 80 101 108 120 124

Climate Model Simulations of CO. Induced Climatic Change

MICHAEL E. SCHLESINGER

1. Introduction .................................................. 2. Mathematical Climate Models ................................... 3. Comparison of Model Simulations of COJnduced Climatic Change . . 4. Discussion .................................................... 5 . Conclusions and Recommendations .............................. References.................................................... V

141 143 152 216 228 230

vi

CONTENTS

Retrieval of Worldwide Precipitation and Allied Parameters from Satellite Microwave Observations

MIRLES . V. RAO 1. Introduction .................................................. 2. The ESMR System............................................. 3. Conversion of Brightness Temperature to Rain Rate: A Theoretical Approach .................................................... 4 . Verification with Radar Data .................................... 5. Verification by a Specially Designed Experiment ................... 6. Generation of Oceanic Rainfall Maps ............................. 7. Intercomparison ............................................... 8. Analysis of Rainfall Maps ....................................... 9. New Features of Global Climatology Revealed by ESMR Rainfall Studies ....................................................... 10. Periodic Variations of Precipitation in the Tropical Atlantic Ocean .... 11. IceMapping .................................................. 12. Storm Structure Studies ........................................ I3. Qualitative Estimation of Rainfall Over Land Areas ................. 14. Retrieval of Other Geophysical Parameters ........................ 15. Conclusion ................................................... Appendix. Explanatory Notes ................................... References....................................................

INDEX

...........................................................

238 241 246 249 252 257 268 276 290 297 304 308 311 317 325 330 331 337

CONTRIBUTORS Numbers in parentheses indicate the pages on which the authors’ contributions begin.

CHRISTOPHER R. LLOYD,*Climatic Research Institute, Oregon State University, Cowallis, Oregon 97331 (35) DAVID E. LOPER,Geophysical Fluid Dynamics Institute, Florida State University, Tallahassee, Florida 32306 ( 1 ) MIRLES. V. RAO, 7223 North Olney Street, Indianapolis, Indiana 46240 (237) MICHAEL E. SCHLESINGER, Department of Atmospheric Sciences, and Climatic Research Institute, Oregon State University, Corvallis, Oregon 97331 (141)

* Present address: Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, New Jersey 08540. vii

This Page Intentionally Left Blank

Erratum Advances in Geophysics Volume 25 The following figures should appear on page 248:

O U T G O I N G LONGWAVE R A D I A T I O N

SUMMER ( 1 9 7 4

(Wrn-')

NOAA SR 0

0

NET R A D I A T I O N

SUMMER ( 1 9 7 4 - 1 9 7 7 )

(Wrn-')

N O A A SR 0

0

(4 FIG.3b and c. ix

-

1977)

This Page Intentionally Left Blank

STRUCTURE OF THE CORE AND LOWER MANTLE DAVIDE. LOPER Geophysical Fluid Dynamics Institute Florida State University Tallahassee, Florida 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Dynamo Energetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Structure of the Outer Core. . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Structure of the Inner Core . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Structure of D”. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Structure of Deep-Mantle Plumes . . . . . . . . . . . . . . . . . . . . . . . . . 7. Thermal History of the Earth . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. S u m m a r y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix. Energy Available from Gravitational Separation . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

4 6

12 15 19 21 24 25

27

1. INTRODUCTION

The earth’s deep interior is inaccessible and is likely to remain so. Therefore, our knowledge of it depends to a large degree on the interpretation of data available to us at or near the surface, from observations and measurements of phenomena containinginformation of the interior of the earth such as seismic waves and free oscillations,topography and gravity, magnetic and electric fields, heat flow and hydrothermal circulations, and chemical and isotopic variations in volcanic rocks. Accurate interpretation of these data requires the construction of models based upon the fundamental principles of physics, chemistry, and thermodynamics and tempered by laboratory experimentsat high pressure and temperature. This procedure is akin to the solution of an inverse problem in that the interpretation, and the model it is based upon, are not unique. Thus we are forced to choose between models on a somewhat subjective basis, discarding those that are impossible or appear implausible and retainingthose that are most elegant and in harmony with the data. This article is a review of recent progress in the construction of such models of the earth’s deep interior, particularly the inner and outer core and the lower mantle. The focal point of this review is the energy source for the geodynamo: What is the best explanation for the energy source and what are the implications for the structure and thermal evolution of the earth? The models provide a coherent, plausible picture of the core and lower mantle, although a number of questions remain unanswered. 1 ADVANCES IN GEOPHYSICS, VOLUME 26

Copyright D 1984 by Academic Press, Inc. All rights of reproductionin any form resewed.

.^_., - .^

^.^^^,

-

2

DAVID E. LOPER

For many years, one of the paradigms of geophysics was that the earth’s interior is in a thermal steady state, with the heat flux out at the surface equal to that produced by radioactivity within the interior. This model was based upon two observations. First, the radioactive heating of an earth of chondritic composition is very close to the heat flux observed at the surface (see Stacey, 1977a, pp. 183- 192). Second, the strong temperature dependence of viscosity regulates the speed of convective motion and hence the heat flux from the earth, thereby tending to stabilize the temperature (Tozer, 1972). Several studies (Verhoogen, 1961; Braginsky, 1963)of the power source for the geodynamo suggested that the earth may be cooling, but these were largely ignored. It apparently was not realized until recently that, because the strength ofradioactivitydiminisheswith time, the earth cannot remain at a constant temperature, but must evolve thermally. In the past few years, it has become abundantly clear that the model of an earth in thermal steady state is not correct (Schubert and Young, 1976; Sharpe and Peltier, 1978, 1979;Schubert et al., 1979a,b, 1980;Sleep, 1979;Daly, 1980a;Davies, 1980; McKenzie and Weiss, 1980; Stacey, 1980; Turcotte, 1980; Cook and Turcotte, 1981;McKenzie and Richter, 1981;Sleep and Langan, 1981;Spohn and Schubert, 1982) and that the earth is cooling at a rate of 10- 100 K/ 1O9 yr, with 50 K/ lo9 yr being a reasonable value. The development of a plausible model of the energy source for the geodynamo proceeded in parallel with the studies of thermal evolution; for a review of possible driving mechanisms, see Gubbins and Masters ( 1979). Precessional motion as an energy source (Malkus, 1963, 1968)appears implausible (Rochester et al., 1975;Loper, 1975;Stevenson, 1983). For along time, the most viable model was thermal convection, but there are serious problems concerning the efficiency of a thermally driven dynamo (see Stacey, 1977a, pp. 197-209; Verhoogen, 1980, pp. 75-83). However, in the past few years it has become clear that the best model for the energy source is gravitational energy released by the growth of the solid inner core, as first proposed by Braginsky (1963, 1964; Gubbins, 1976, 1977, 1978; Loper, 1978a,b;Gubbins et al., 1979;Gubbins and Masters, 1979;Loper and Roberts, 1983). It is of interest to note that this mechanism requires the earth to be cooling. The rate of cooling can be related to the growth of the inner core; the cooling rates thus obtained, 10 K/ 1O9 yr by Gubbins et al. ( 1979)and 23 K/ 1O9 yr by Loper and Roberts ( 1983),are compatible with those estimated for the entire earth by parameterized convection models. The existence of a solid inner core appears to play an important role in the magnetism of the terrestrial planets (Stevenson et al., 1983; Stevenson, 1983),and the growth of the solid inner core is caused by the cooling of the core from above. In such a situation, with the core coolest at the top,

STRUCTURE OF THE CORE AND LOWER MANTLE

3

freezing occurs first at the bottom provided the liquidus gradient with pressure, dTL/dP,is steeper than the adiabat, dTA/dP:

dTL/dP > dTA/dP The adiabat is given by

dT,/dP

= yT/K,

where

Y = aKslpC,

(1.3)

is the Griineisen parameter, a is the coefficient of thermal expansion, K, is the adiabatic incompressibility,p is the density, and C, is the specific heat. Assuming Lindemann’s law is valid (Stacey and Irvine, 1977),the liquidus gradient is given by

dTL/dP= 2(y - i)T/K,

(1.4)

It follows from Eqs. (1. l), (1.2),and (1.4)that if y > 3, the earth will grow a solid inner, as opposed to outer, core as it cools. A decade ago Higgins and Kennedy ( 1971) caused a great stir by asserting that Eq. (1.1)does not hold within the core, giving rise to the “core paradox” (Kennedy and Higgins, 1973). Current opinion (Irvine and Stacey, 1975;Jamieson et al., 1978; Stevenson, 1980) is that Eq. (1.1) is well satisfied within the core and that there is no core paradox, although there is some opinion to the contrary (Ullman and Walzer, 1980). In fact, from liquid-state theory, Stevenson (1980)prefers a value of y as high as 1.6 to 1.7. The idea that the power supply for the dynamo arises from the continual gravitationalseparation of the heavier and lighter constituentsof the core has gained rapid acceptance and has been extensively reviewed (Gubbins and Masters, 1979;Jacobs, 1980;Gubbins, 1981; Stevenson, 1981 ; Loper and Roberts, 1983). Therefore, the focus here will primarily be upon the implications of this idea for the structure and thermal evolution of the earth. Also, this article will not attempt to survey the seismologicalliterature; for a recent summary, see Bolt and Uhrhammer (1981) or Bolt (1 982). Following a brief review of dynamo energeticsin Section 2,the convective stability and morphological stability of the core are discussed in Sections 3 and 4. Next, the structure of D”, treated as a thermal boundary layer, is considered in Section 5, and the plumes that carry the core heat upward through the lower mantle are examined in Section 6. The thermal history ofthe earth is discussed briefly in Section 7. The current state of our knowledge of the structure of the core and lower mantle is summarized in Section 8.

4

DAVID E. LOPER

2. DYNAMO ENERGETICS It is very likely that the earth's core is composed principally of iron, with a small but significantpercentage of some light constituent. The nature and amount of this constituent is uncertain, with 5 -2OYo of sulfur or oxygen being quoted most often (Brett, 1976; Ringwood, 1977; Stevenson, 198 1; Brown and McQueen, 1982). However, this uncertainty does not affect the model of gravitational separation provided the light constituent in the core fractionates into the liquid as core material solidifies. Fractionation upon solidification is a universal property of alloys that are not at the eutectic composition (Chalmers, 1964). If the mass fraction of light material in the liquid is less than the eutectic value, the solid that forms contains less of the light material than does the liquid (see Fig. 1.4a of Chalmers, 1964), and a dense inner core grows. This leads to a simple model of the energy source for the geomagnetic field. Recently McCammon et al. (1983) revived the idea, first proposed by Braginsky (1 963), that the mass fraction of light material in the liquid exceeds the eutectic value. In this case, the structure of the inner core is complicated and the rate of growth of the inner core is severely constrained (Fearn and Loper, 1983). This possibility is considered to be implausible and will not be discussed further. The secular cooling of the core causes the growth of the inner core by freezing of iron-rich material from the outer core. This process leaves a residue of iron-poor material in the liquid above the inner-core boundary. This material is less dense than that above and consequently is convectively unstable. The details of this process are reviewed in Section 3, but for a rough estimate of the energy released, a simple calculation suffices. Consider a self-gravitating sphere of radius ro and mass Mo composed of two incompressible materials, one heavy and one light. Suppose the materials are uniformly distributed initially and, after some period of time, the heavy constituent collects into a central sphere of radius ri. In order to form this central sphere, heavy material must move downward and an equal volume of light material must move upward, releasing gravitational potential energy. The amount A E of energy released by this separation process is calculated in the Appendix and simplified for the case $ < po where $ is the density jump at r = ri due to change in composition and po is the mean density. The result is

AE = (2n/5)GM0~(r3r,,)( 1

- r:/r:)

where G = 6.67 X lo-'' N m2 kg-2 is the gravitational constant.

STRUCTURE OF THE CORE AND LOWER MANTLE

5

The power QG released by this process is QG

= d(AE)/dt

Assuming G, M,, r,, and6 constant, Eq. (2.1)may be used to write Eq. (2.2) as QG = 27cGMOj.?(3/5- r:/r2)(r:/r0)ti

where a dot denotes differentiation with respect to time. This may be expressed in terms of the rate of growth of the mass Mi of the inner sphere, noting that

hi= 4npi r: ii

(2.4)

where pi is the mass of the inner sphere. Now Eq. (2.3) is (2.5) QG = (GMo6/2rOpi)(3/5- rf/r2)Aki The parameters appearing in Eq. (2.5) are estimated in Table I. Using the preferred values we estimate the current QG = 2.5 X 10” W, sufficient to drive a geodynamowith a large toroidal field. Of the parametersused in this estimate, the most uncertain is the growth rate of the inner core. The value 6.8 X lo5kg sec-’ is obtained by dividing the current mass of the inner core, 1023kg, by the age of the earth, roughly 1.4 X 1O”sec. This assumes that the inner core has grown from zero size 4.5 X lo9yr ago at a constant mass rate of freezing. This gives an overestimate if the core were not completely molten followingits formation. On the other hand, it is an underestimate if the core remained molten for much of its history and the inner core is a recent feature. The age of the inner core is tied up with the question of the cooliiig of the core and mantle. We will return to this point in Section 7 and give an improved estimate for the power supply. In this simple calculation, the gravitationalenergy released due to compression has been ignored. This is a small fraction (7- 1OYo) of the total (Miiller and Hage, 1979) and goes primarily into adiabatic compression and hence is not available to drive motions in the core. Also, loss of gravitational potential energy due to diffusion of material has been neglected. It is difficult to estimate the size of the toroidal magnetic field sustained by a given power source because a realistic model of the geodynamo is not available. Using the parameterization of Loper and Roberts (1983) Q,= 1015 w T - 2 ( ~ a ) 2 (2.6) where Q, is the ohmic power loss and Ba is the averagetoroidal-fieldstrength in the core, we may equate Q, and QG to obtain an estimate for Ba of 1.58 X T (158 G).

6

DAVID E. LOPER

TABLEI. PREFERRED PARAMETER VALUES FOR THE CORE, INCLUDING SOURCE AND ESTIMATEDERROR ~~

Parameter

Magnitude

G rl(I r0 g,

Source

6.67 X lo-" N m2kg-' 1.22 X 106 m 3.48 X lo6 m 4.4 m s e c 2 10.68 m set+ 1.87 X loL3m2 1.52 X lOI4 m2 0.97 X kg 1.95 X kg 12.76 X lo3 kg m-3 9.9 X lo3 kg m-3 50 8X K-I 15.7 X K-' 670 J kg-I K-I 4168 K 3157 K 31 W m-I K-I 4.2 X m2 sec-l 8 X lo5J kg-' 0.0 12 500 kg m-3 6.8 X lo5kg sec-l 1.2 0.05 3X m2 sec-' 6.2 X lo7 J kg-l 1.2 X 10) kg sec-l 219

go

A, A0 MI M O PI

ps M ff, ff0

CP TI

TO

k K

L

!P.*

MI

f3 5

-D P. MCR

5M

Stacey (1977a) PREMb PREM PREM PREM A = 4nr2 A = 4nr2 Stacey (1977a) Stacey (1977a) PREM PREM Stacey (1977a) Stacey (19778) Stacey (1977b) Stacey (1977b) Stacey (1977b) Stacey (1977b) Stacey (1977b)

klPG Stacey (1977~) Stacey (1 977c) Masters (1 979) Text Loper ( 1978a) Loper and Roberts (1981) Loper and Roberts (1981) Eq. (3.10) Eq. (3.17)

Estimated error (%) 1 1

1 1 1 2 2 5 5

5 5 10 10 10 10 20 20 20 20 20 20 50 50 100 100 100 100 100 100

A subscript i denotes a value at the inner-core boundary; subscript o denotes the top of the core. PREM refers to Dziewonski and Anderson (1981).

3. STRUCTURE OF THE OUTERCORE The convective instability of a layer of fluid is governed by the vertical variation of density. If the fluid is modeled as a binary alloy, the density is a function of pressure, P; temperature, T; and mass fraction of the light constituent? However, the earth's interior is, to a very good approximation, in hydrostatic balance:

<,

dPldr = -pg

(3.1)

STRUCTURE OF THE CORE A N D LOWER MANTLE

7

where Y is the radial coordinate and g is the local acceleration of gravity. This may be used to replace Y by P and treat P as the (downward) vertical coordinate. That is, the static state of the fluid layer is determined by specification of T(P) and ((P). A static layer of fluid is convectively unstable if a parcel of fluid which is displaced downward is heavier than its surroundings;the condition for convective instability may be expressed as

<&I

dpl dP, dP,

(3.2)

where a subscript p denotes parcel, and s, surroundings. The equations of state for density and entropy are

dp = pp dP - pol dT - p2Bd( dS = -(a/p) dP

+ (Cp/T) dT + S d(

(3.3) (3.4)

where a is the coefficient of thermgl expansion, /3 is the isothermal compressibility, C, is the specific heat, 6 is the change of specific volume with composition, and Sis the entropy of mixing. Using Eq. (3.3), the stability condition may be expressed as

(3.5) If the parcel moves adiabatically such that

dS= d(= 0

(3.6)

we may use Eqs. (3.4) and (3.6) to write Eq. (3.5) as a2T PCP

-
%Ip

+pd-

-

fl,

(3.7)

This condition is not precise if the parcel can alter its density by a diffusive exchange of heat and material with its surroundings. Under normal circumstances, the precise condition is more severe than Eq. (3.7), but doublediffusiveinstabilities are possible in which it is less severe than Eq. (3.7). We will not consider double-diffusive instabilities here because, as we shall see, the core is prone to a direct convective instability. It has been shown by Loper and Roberts (1980) that within the core, the precise condition for convective instability is only marginally more severe than Eq. (3.7). Assuming the fluid layer to be static, the gradients of Tand (with Pare the result of diffusive fluxes of heat and material associated with the cooling and evolution ofthe core. It is reasonableto assume that the heat flux vector Q is

8

DAVID E. LOPER

related to the temperature gradient by Q=-kAVT

(3.8) where k is the thermal conductivity and A is the area, but the material flux vector I is related to both the compositional gradient and the pressure gradient (Landau and Lifshitz, 1959): I =-Dp[Vt

+ (;T/Si)VP]A

=

-t

(3.9) where D is the coefficient of material diffusion and ji is the gradient of the chemical potential with composition. This latter relation arises from the fact that I is proportional to the gradient of the chemical potential Vp; ji = d p / d c and, by a Maxwell relation, 3 = dp/dP [see Eq. (3.3)]. (Actuallyp is the relative chemical potential per unit mass: p = pl /MI - p 2 / M 2 where , pl is the chemical potential of constituent 1 having molecular weight MI, etc.) The coefficient of material diffusion for liquids near their freezing point is remarkably independent of the composition of the liquid (Frenkel, 1946; Majdic et al., 1969; Calderon et al., 1971): D = 3 X m2 sec-l. The coefficient 3 may be estimated from the densities of the heavy and light constituents of the system; Loper (1978a) has estimated p3 = 1.2 for Si or 0 in Fe and p3 = 2.4 for S in Fe. We have chosen the smaller value in what follows. The coefficient j i is rather difficult to quantify. The best way to estimate it is to assume that the alloy obeys ideal solution theory (Guggenheim, 1952), giving T/a&<M

(3.10)

a= +

where R = 8314 J kmol-' K-' is the gas constant, M2 (Ml - M2)Q is the mean molecular weight of the liquid, and tM is the maximum value of t. If the light constituent is a compound, such as FeO, FeS, or FeSi, then tM= 1, but if it is an element that forms a simple compound, then Ml/ ( M I M2). The choice of element or compound in estimatingp and S must be consistent. Using = 50, tM= 4 (for oxygen), and assuming ( = 0.05, we have j i = 6.2 X lo7 J kg-l. Combination of Eq. (3.1) and Eqs. (3.7)-(3.9) yields the condition

rM

cM

+

a

(3.11)

-

-

where I = I 3 and Q = Q 3. This is generalization of Eq. (14) of Loper and Roberts (1981), Eq. (8) of Fearn and Loper (1981), and Eq. (3.10) of Loper and Roberts (1983). The meaning of the terms in Eq. (3.1 1) has been discussed by Loper and Roberts (1 98 1, 1983).

STRUCTURE OF THE CORE AND LOWER MANTLE

9

At the inner-core boundary (ICB)

I = ( A ~ at r = r i (3.12) where i i s the differencebetween the mass fraction oflight constituent in the outer and inner core and ilk is the mass rate of freezing of the inner core (Loper-and Roberts, 1981). Using the integrated form of Eq. (3.3), we may relate < t o the compositionally induced density jump, p^, at the ICB:

t = p^/p28 (3.13) The values given in Table I for t,p^, and p2 are consistent with Eq. (3.13), considering the magnitudes of the estimated errors. If there is no exchange of material between the core and mantle, at r = r ,

Z=O

(3.14)

The heat flux is maximum at the top of the outer core

Q=Q,

at r = r ,

(3.15)

and decreases with depth. At the ICB, the heat flux is predominantly due to the latent heat released by freezing

Q = L A ~ at r = r i

(3.16)

although there is a small contribution due to secular cooling of the inner core. We wish to investigate the consequences of Eq. (3.1 1) at the bottom and the top of the outer core. Loper and Roberts (198 1) have shown that near r = ri the thermal terms are negligibly small compared with the compositional terms and Eq. (3.1 1) may be simplified to

< hk

(3.17)

= 4nr?gip28D/,Et

(3.18)

ilkCR

where McR

is the critical mass rate of freezing for the onset of convective motion near the inner core. With the data given in Table I, we see that ilkcR = 1.2 X lo3kg sec-I, and the inequality, Eq. (3.17), is satisfied by a factor of 580. This indicates that the outer core is likely to be in vigorous convective motion driven by compositional buoyancy generated at r = ri. This model of the core excludes the possibility of an F layer or region of anomalous composition (Alder and Trigueros, 1977; Liu, 1982)at the bottom of the outer core and is in agreement with the reinterpretation of precursors to PKP in terms of scattering near the core-mantle boundary (Cleary and Haddon, 1972;

10

DAVID E. LOPER

Haddon and Cleary, 1973). The convection occupies the entire outer core, with the possible exception of a thin layer at the top, which we will now investigate. Let us assume that the mass flux l i s distributed uniformly throughout the outer core. If we let r=r,-h (3.19) then near the top of the outer core

I = l&fh3ri/(r$

- r?)

(3.20)

The heat flux may be approximated by Q, and Eq. (3.1 1) becomes

Fearn and Loper (1981) have shown that the thermal terms in Eq. (3.21) are once again negligibly small and the inequality may be expressed as 47rgop2zD(r: - r?)/3Eiu < h

(3.22)

Using the data in Table I, the left-hand side of Eq. (3.22) is about 60 km, This indicates the possibility of a stable layer at the top of the outer core thickness of 60 km or less. Whaler (1980) has analyzed the velocity structure at the top of the core using geomagnetic secular-variation data and has concluded that there is a stably stratified layer there. Her analysis is statistical and is open to alternative interpretations. The information concerning the velocity structure at the top ofthe outer core availablefrom secular variation data is the subject of continuing investigation and controversy (Benton, 1979, 198 1 a,b; Muth, 1979; Backus, 1982; Gubbins, 1982; Whaler, 1982), and the existence of a stratified layer at the top of the outer core is not certain. A recent paper by Gubbins et al. ( 1 982) on this subject may be oftheoreticalinterest,but has no direct bearing on the actual state of the core for two reasons. First, they postulate an isothermal, noncooling mantle, which is at odds with all studies of the thermal history of the earth, and, second, they ignore compositional buoyancy, which has been shown to be the dominant factor in determining the convective stability of the outer core. A simple calculation equating the kinetic energy of a convecting fluid parcel to the potential energy gained as this parcel penetrates the stable layer at the top of the core shows that convection will penetrate only a few meters, provided the layer is already established. However, if the entire core were forcibly mixed by some cataclysmic process (such as core formation), the combined action of barodiffusion and the normal diffisive process would act to establish or reestablish the stable layer, but only very slowly. In fact, it

11

STRUCTURE OF THE CORE AND LOWER MANTLE

would take a length of time equal to the age of the earth to establish a layer several tens of kilometersthick by these processes. Furthermore, the growth of the layer would be severely inhibited by convectivemotions disruptingthe initially weak stabilizing compositional gradient. In view of this argument, it is unlikely that a stable layer at the top of the core could be the result of internal diffusive processes. A possible mechanism for the establishment of such a layer is a flux oflight material downward across the core-mantle boundary. That is, suppose the light constituent in the core is FeO, as proposed by Ringwood ( 1977),and that partial melting of the lower mantle yields an FeO-rich, dense liquid (Ohtani, 1983). Then any partial melting of the mantle subsequent to core formation may result in a flux of FeO downward into the core, which could easily generate a stable layer at the top of the outer core. Furthermore, it is conceivable that a breakdown of the stable layer, resulting in a sudden mixing of the previously static fluid at the top ofthe core, could explain the worldwide characterofthe 1969- 1970 impulsein the secular acceleration rate (Le Moue1et al., 1982). It should be emphasized'thatthe ideas in this paragraph are quite speculative and need to pass the test of careful quantitative analysis before they are accorded the same degree of veracity as those given previously. In the limit MCR<< M (3.23) the material flux is carried almost entirely by fluid motions. Convection driven by a source of buoyancy at a boundary is characterized by narrow regions of rapid flow away from the boundary (plumes) and a broad return flow; the area fractionfoccupied by plumes is small. The excess, of light material in a plume is related to Z by

e,

= I/&

WA

(3.24)

where Wis the upward speed within a plume. From dynamo theory (Busse, 1971; Frazer, 1973),typical horizontal velocities in the core are estimated to be'10-4m sec-l. Typical vertical velocities are believed to be much smaller than this. However, the speed of flow in a plume is somewhat larger m sec-l. than average; for want of a better estimate, let us take W = Assumingf= and using Eq. (3.12), we estimate that

r' = 1.2 x

10-6

(3.25)

This implies that to a very good approximation the bulk of the outer core is well mixed, with a uniform composition. With the growth of an iron-rich inner core, the mass fraction of light material in the outer core increases with time. Conservation of mass gives dQdt = (M/(M, - M i )

(3.26)

12

DAVID E. LOPER

where M,, - Mi is the mass of the outer core. Using Table I, we obtain

dQdt = 1.25 X

sec-’

(3.27)

At this rate the mass fraction would change by 0.0018 over the age of the

earth. The gravitationally powered dynamo will cease to function when the mass fraction at the bottom of the outer core reaches the eutectic value. Unless the core formed with a composition very nearly equal to the eutectic, the gravitationallypowered dynamo will continue to operate virtually indefinitely. 4. STRUCTURE OF THE INNER CORE The central feature of the gravitationallypowered dynamo is that the inner core is growing by freezing of outer-core material as the earth gradually cools. In the previous sections it was argued that the excess of light material released at the inner-core boundary by this process drives convection in the outer core, making it well mixed and adiabatic, except possibly for a thin, stable layer at the top. The common picture is of a completely solid inner core growing via freezing at a spherical ICB. In this section, we shall argue that this boundary is subject to a morphological instability and examine the implicationsfor the structure of the inner core. Before proceeding with this argument, we shall explain the physical nature of this morphological instability. The location and shape of the boundary separating two phases, such as liquid and solid, cannot be prescribed, but must be determined by solving a moving-boundary problem. Normally the energy of forming additional surface drives the system to adopt a phase boundary of minimum area, such as a plane or sphere. However, when the material is a mixture or alloy, the change of phase requires mass transport of one constituent relative to the other, and spatial gradients of composition can arise normal to the boundary. The melting/freezing temperature or liquidus of the system is a function of composition, and situations can arise in which the liquid adjacent to the surface is below the liquidus due to the strong gradient of composition. That is, the liquid becomes “constitutionally supercooled” when the mass rate of freezing per unit area is too large. The system avoids this paradoxical situation by increasing the area of the surface. This change of shape, which occurs spontaneously at a critical rate of freezing, is referred to as a morphological instability. Let us now quantify the discussion of the previous paragraph with the freezing of the inner core in mind. The liquidus temperature TLof a binary alloy is a function of pressure P, mass fraction of light constituent and, if

r,

STRUCTURE OF THE CORE A N D LOWER MANTLE

13

the surface between phases is highly convoluted, the surface tension. For a relatively flat interface, the temperature of the system must satisfy

r)

at the interface, and T 2

T = TLV, TL within the liquid. If

(4.1)

r)

T < TdPY (4.2) within the liquid, it is constitutionallysupercooled. Let us assume the ICB is spherical and expand Eq. (4.2) in a Taylor series in powers of P - Pi, where Pi is the pressure at the ICB. Keeping only linear terms and using Eq. (4. I), Eq. (4.2) becomes

+

dTL/dP (aT,/ar)(dr/dP) < dT/dP

(4.3)

where all terms are evaluated at the ICB. From Loper and Roberts (1980, Section 2.111), we have

aT,/aP

= TS/L

(4.4)

and

aTL/ar= - T ~ & L

(4.5) where Sis the change of specific volume upon melting. Using these relations plus Eqs. (3.1), (3.8), and (3.9), Eq. (4.3) becomes

Loper and Roberts ( 198 1) have shown that, as with the convective instability condition in Section 3, the thermal term in Eq. (4.6) is negligibly small and the condition may be written as

+

h&(l &/f2) < (4.7) is given by Eq, (3.18). Note the remarkable similarity between where the two instability conditions,Eqs. (3.17) and (4.7);the only difference is the extra term 1 S/@ in Eq. (4.7) which is about 1.3, using the data in Table I. Inequality (4.7) is the condition for the onset of the morphological instability of the ICB. Using the data in Table I, the condition is satisfied by a factor of 450. This indicates that the ICB is not spherical but is highly convoluted. The occurrence of this instability is common in the casting of metallic alloys (e.g., see Chalmers, 1964) and leadsto the formation of a zone of mixed phase, called a “mushy zone” by metallurgists, between the liquid and solid. It has been shown by Fearn et al. (1981) that the mushy zone within the inner core is likely to be very thick, possibly fillingthe entire inner core.

+

14

DAVID E. LOPER

There are two seismic observationsthat seriously constrain models of the inner core. First, the inner core is composed of a lossy material with a relatively low Q (Sacks, 197 1 ;Qamar and Eisenberg, 1974; Doornbos, 1974; Bolt, 1977; Cormier, 1981; Choy and Cormier, 1983). Second, the ICB is a sharp interface for phases PKiKP and PKIIKP at a small angle A (Bolt and O’Neill, 1965;Engdahl et al., 1970;Bolt and Qamar, 1970;Bolt, 1977). The relation between these observationsand the model of a mushy inner core has been investigated in two recent papers. Loper and Fearn (1 983) developed a seismic model of a partially molten inner core, with the bulk anelasticity being due to the effect of thermal and material diffusion associated with the melting and freezing of small liquid inclusions caused by the passage of the P wave. Their model provides a qualitative explanation of the anelasticity (Cornier, 198 1; Choy and Cormier, 1983) and S-wave-velocitygradient (Hage, 1983) believed to occur in the upper 200- 300 km of the inner core. They found it difficult to obtain quantitative agreement with the estimated anelasticity,partially because the parameter values for their theory, particularly the volume fraction of liquid, are poorly known in the inner core. The ability of a mushy inner core to reflect seismicwaves from its boundary has been investigated by Loper (1983). Specifically, he investigated the rate of growth of the mass fraction of solid with the depth below the ICB. An important feature of this study is the nature of the convective motions near this boundary. It is known from metallurgical experience (Copley et al., 1970; see also Roberts and Loper, 1983) that the convective motions near a cooling mushy zone are predominantly downward into the mush almost everywhereand upward strongly in isolated chimneysthat appear spontaneously within the mushy zone. The seismic properties of ICB are dominated by the regions of downward flow. A parcel of fluid descendingadiabatically toward the ICB warms due to adiabatic compression. However, since the liquidus is steeper than the adiabat, the parcel approaches the liquidus as it descends. That is, without exchanging heat or material with its surroundings, and despite the fact that it is actually becoming warmer, the parcel experiencesan effectivecooling as it descends, with the rate of cooling being proportional to the speed Wof descent. When the parcel reaches the ICB it has in effect been cooled to the liquidus, and further descent causes solid to form. This newly formed solid does not remain with the parcel, but instead coats existing dendrites below the ICB, causing the mass fraction of solid, 4, to increase with time at a given level. The rate of coating is proportional to W. At the same time, the slow secular cooling of the core causes the tips of the dendrites to advance at a speed U. The rate of growth of 4 with distance below the ICB is the result of the balance between the effect of convection, which causes the dendrites to thicken, and cooling, which tends to produce

STRUCTURE OF THE CORE A N D LOWER MANTLE

15

more tenuous dendrites. Since W >> U,the coating action of convection is quite strong in the core. Loper (1983) has estimated that

(d&drl= 1/300 m-l (4.8) That is, the mass fraction o f 4 becomes appreciable less than 1 km below the ICB. Thus the boundary looks sharp to a seismic wave with a length of 10 km and is able to produce reflections. The analysis of Loper ( 1983) can be used to predict the excess of light material in the upward plumes, with his parameter estimates = 3.3 x 10-7

(4.9)

in reasonable agreement with Eq. (3.25). The analysis of Loper ( 1983) is a Taylor expansion of the variables about the ICB, making use of the theory of Hills et al. (1983). The analysis is valid only if 4 << 1, so that the variation of 4 with depth beyond about 50 m below the ICB is not known accurately. The theory of seismic anelasticityof Loper and Fearn (1983) requires a significant fraction of liquid in the top 200 - 300 km of the inner core. It remains to determine whether this is possible. Recently Anderson (1980, 1983) has revived an old idea of Gutenberg (1957) that the inner core represents a glassy transition of the outer-core material with increasing pressure, in order to explain the discrepancy between free-oscillation and body-wave determinations of the radius of the inner core. If Gutenberg and Anderson are correct, then the nature of the energy source for the geodynamo once again becomes a serious problem. However, the seismic properties of a mushy inner core are poorly known. These properties should be studied carefully and compared with the seismic data before abandoning a model of the inner core that has successfullysolved the dynamo-energy problem. 5. STRUCTURE OF D” When D” was first labeled as a distinct layer at the base of the mantle (Bullen, 1949, 1950),it was believed to be the result of an increased density due to chemical inhomogeneity (e.g., see Bolt, 1972). However, the realization that the dynamo in the earth’score requires a significant heat flux across the core - mantle boundary (CMB) has led to a reinterpretation of D” as a thermal boundary layer (Stacey, 1975; Jones, 1977; Elsasser et al., 1979; Jeanloz and Richter, 1979; Karato, 1980). In this section we shall discuss the structure of D” necessary to accommodate the heat flux from the core and its relation to the seismic data.

16

DAVID E. LOPER

The D” layer has been modeled most commonly as a passive thermal boundary layer which is an integral part of lower-mantle convection (Turcotte and Oxburgh, 1967; Peltier, 1981; Jarvis and Peltier, 1982). However, the stability of such a layer against secondary convective motions has been questioned by Yuen and Peltier (1980). In particular, they found that if the viscosity within D” is smaller than that of the overlying mantle by a factor 1O4 or more, the layer is unstable against internal convective modes, primarily within the layer itself (mode B of their Fig. 1). Stacey and Loper (1 983) have estimated that the viscosity ratio exceeds lo4, so that the instabilities found by Yuen and Peltier (1980) are likely to occur in a passive D” layer. That is, the D” layer is not a passive thermal boundary layer, but is dynami-

cally active with a flow pattern distinct from lower-mantle convection. Yuen and Peltier (1980) proposed that D” would behave in the manner of a constant-viscosityfluid at high Rayleigh number, with heated blobs of fluid detaching chaotically from the layer and rising through the overlying medium. But the relatively cool mantle material must be heated and mobilized if it is to allow these D”-originated batholiths to pass through; that is, they must burn their way upward. Morris (1982) has shown that a single sphere will lose its excess heat after moving only one or two diameters vertically and thus is incapable of making its way upward through the overlying mantle. Only a succession of blobs rising from the same point or a persistent plume will be able to penetrate a significant vertical distance. This leads to the model of the D” layer proposed by Stacey and Loper (1 983). They treat D” as a dynamical feature of the lowermost mantle, which is horizontally uniform on a broad scale. The layer is stabilized against thermal-diffusive thickening and convective instability by a slow downward motion of the mantle toward the CMB at a speed of roughly 0.07 mm yr-l, with the upward return flow via narrow plumes. The thermal profile of D” is approximately a negative exponential characteristic of an ablating surface (Loveringet al., 1960)with an e-foldingdepth of 73 km and a temperature increment at the CMB of roughly 840 K. These thermal parameters were determined by comparing the effect of such a thermal profile on dK/dP and the delay of seismic waves with data published in a preliminary reference earth model (PREM) (Dziewonski and Anderson, 1981). Specifically the temperature increment AT was determined by the relation

where AP = 8.78 X lo9 Pa is the pressure change across D“ and K, is the adiabatic incompressibility. The values of ( d K , / d P ) ,and (dKJdP), were taken from PREM, while (dK,/dT), was determined using the thennody-

STRUCTURE OF THE CORE AND LOWER MANTLE

namic identity

(g$p

= YPCP[ Y

-

($)*I

+

17

($),

The scale height was determined by requiring that

[(T-

Ts) dy = mx,,

(5.3)

where Ts is the temperature of the lower mantle extrapolated adiabatically into D”, y is the height above the CMB, and H, = 75 km is the scale height of D“ according to PREM. As the mantle material descends into D”, it is warmed by thermal conduction of core heat upward from the CMB. The viscosity q of mantle material in subsolidus creep is a strong function of temperature: (5.4)

where vR is a reference value of viscosity, T M ( P ) is the melting (or, more specifically, the solidus) temperature, and P is a dimensionless constant taken to be 35 by Stacey and Loper (1983). The mantle material softens considerably as it is warmed; Stacey and Loper ( 1983) estimate the viscosity at the base of D” to be less than that of the overlying mantle by a factor more than 2 X lo4. The scale height for the variation of viscosity with height above the CMB is about 1 1 km. The descending material is arrested in this thin, low-viscosity region at the base of D” and flows horizontally toward the bases of a discrete number of persistent deep-mantle plumes. The vertical profile of the horizontal velocity, u, is independent of horizontal position: u = (W/4h)(x- X 2 / x ) (1

+ y/h) exp(- y/h)

(5.5)

where W is the downward speed of the mantle outside D”, h is the scale height of viscosity variation, xis the distance from a plume base, and Xis the radius of a plume catchment area. The horizontal flow is drawn to the bases of the plumes by a low pressure there that is generated by the draft of thermally buoyant material ascending the plume. The mass flux of material through the D” layer is proportional to the heat flux from the core, assuming the temperature increment to be constant. Using Eq. (4.27) of Loper and Roberts (1983) with no radioactive heating (Q,= 0) and & = 6.8 X l o 5 kg sec-’, the heat flux crossing the CMB is 1.6 X 10l2 W. The corresponding mass flux through D” is 1.9 X lo6 kg sec-I. This flux would accumulate over geological time to a geophysically significant volume, corresponding to a layer 150 km deep at the surface of

18

DAVID E. LOPER

the earth. The actual fate of this material is not known; some speculations are made in the following section. The DN-plumeflow pattern is ineffective in cooling the lower mantle; it would take 5.1 X 1O'O yr to cycle the lower mantle once at the present rate. This strongly suggests that the D"-plume flow pattern, which servesto cool the core, is independent ofthe lower-mantle convection necessary to remove radiogenic and primordial heat from the lower mantle itself. This observation has important consequences for models of the cooling earth (see Section 7). The thermal gradient, dTldy, at the base of D" is estimated by Stacey and Loper (1983) to be -9.6 K km-'. Considering the uncertainty in the parameter estimates, this is in good agreement with Jones' (1977) estimate of - 11.8 K km-I for this gradient. Stacey and Loper (1983) estimated the thermal conductivity k of D" using

k = QlA IdTldYl,

(5.6) where A = 1.52 X lOI4 m2 is the area of the CMB and Q = 1.6 X loL2W is the heat flux from the core. With these estimates, k = 1.2 W m-' K-', somewhat smaller than the estimate of 4 W m-' K-' made by Kieffer (1976). In view of the uncertainties in the parameter estimates and the accuracy of solid-state theory (Klemens, 1958), this discrepancy is tolerable. A larger value of Q would reduce the discrepancy, but it would then produce difficultiesfor the gravitationally powered dynamo. We will elaborate upon this point in Section 7. The precursors to PKIKP that were originally taken as evidence for the existence of the F layer are now explained in terms of scattering by a slightly rough CMB or by small-scale heterogeneities within D" (Cleary and Haddon, 1972; Doornbos, 1978). With the identification of D" as a thermal boundary layer with significantly reduced viscosity at its base and an organized flow structure, it is difficult to envisage a mechanism for producing irregularities on the CMB. The pressure difference needed to drive the horizontal flow in D" has been estimated (Stacey and Loper, 1983) to be only 8 bars, which can cause a large-scaleundulation of only 16 m on the boundary. The viscosity of D" is too low to allow small-scale undulations large enough to produce a seismic effect. One possibility is that the horizontal flow in D" is unstable to rolls aligned with the flow (analogous to Langmuir rolls). In this case the irregularitieswould be elongated features, as reported by Haddon (1982). Recently Lay and Helmberger (1983; see also Wright and Lyons, 1979, 1980) reported the identification of a shear-velocity discontinuity about 280 km above the CMB. However, their interpretation has been questioned (D. J. Doornbos, private communication; Cornier, 1983), and the existence of discontinuities in the lower mantle is an open question at present.

STRUCTURE OF THE CORE AND LOWER MANTLE

19

6. STRUCTURE OF DEEP-MANTLE PLUMES The concept of persistent deep-mantle plumes was first advanced by Morgan ( 197l,1972a,b) as an explanation for the origin of the material responsible for the linear chains of “hot-spot’’ volcanoes identified by Wilson (1963a,b,c, 1965). Subsequent recognition that the relative motion of plumes is slow (Molnar and Atwater, 1973; Minster et al., 1974), in spite of appearing at the surface as volcanoes on different lithospheric plates that are moving with respect to one another, appeared to confirm that they must be based in the deep mantle, where convective motion is presumably very slow. Furthermore, the distinct chemistry of hot-spot basalts (Hart et al., 1973;Schilling, 1975)compels the inference that their source is quite different from that of the midoceanridge basalts, which are believed to be derived by partial melting of convectively upwelling upper-mantle material. The observational evidence for plumes is compelling, but a convincing explanation of their cause has been elusive. Bodily cooling of the mantle cannot cause narrow plumes; they can arise only from a source of buoyancy at a boundary (Stacey, 1975), and the only plausible source is at the coremantle boundary; an origin in the midmantle as proposed by McKenzie and Weiss (1975) and by Anderson (1975) is deemed implausible. Attempts to fit plumes into a general scheme of mantle convection (e.g., Turcotte and Oxburgh, 1967; Anderson, 1975; Parmentier et al., 1975; Yuen and Schubert, 1976; Guan et al., 1979; Peltier, 1981; Olson and Yuen, 1982; Jarvis and Peltier, 1982)have only served to obscure their true nature by characterizing them as broad regions of upward motion with a narrow region of warm material imbedded within. While broad-scale flow undoubtedly exists in the mantle, it should not be identified with the plumes that Morgan proposed. A possible reason for the failure to model a narrow plume may be understood from the stability analysis of Yuen and Peltier (1980). Although their analysis pertains to the D” layer rather than to a plume, their conclusion is sufficiently fundamental to apply to either structure. They found that two types of convective instability are possible: either broad “mantle-filling” modes or narrow “boundary-layer”modes, labeled A and B, respectively, in their Fig. 1;a thermally induced viscosity contrast ofat least lo4is necessary to produce the sharply confined modes. Therefore, the failure to model narrow plumes can be attributed in part to a viscosity contrast that is too small, which in turn implies too small a temperature contrast between plume and adjacent mantle. A supply of hot material from the D” layer is necessary for the existence of a deep-mantle plume. In the previous section it was argued that the heat flux from the core requires a thermal boundary layer at the base of the mantle, and this bound-

20

DAVID E. LOPER

ary layer is a dynamical feature with hot material rising from it in the form of persistent deep-mantle plumes. Now we see that hot material from D” is necessary for the existence of a deep-mantle plume. Thus the D” as a thermal boundary layer and deep-mantle plumes that advect core heat upward are mutually dependent. A dynamical and thermal model of deep-mantle plumes is presented in Loper and Stacey (1983), in which the upward flow is constrained to a narrow axisymmetric “chimney” surrounded by a broader thermal halo. As in D”, it is possible to obtain a narrow, jetlike flow in spite of the very low Reynolds number because of the strong variation of viscosity with temperature, with the highest temperature and lowest viscosity occumng on the plume axis. The upward flow in the chimney is driven by the thermal buoyancy of the warm material from D”. Since the cooler mantle material surroundingthe chimney can flow horizontally into the chimney in response to a lower pressure, or “draft,” the pressure difference is very small and the vertical momentum balance is between buoyancy and viscous drag. This leads to an expression for the radius r, of the chimney in terms of viscosity qQ on the axis, the mass flux h, density p, gravity g, coefficient of thermal expansion a,and the temperature increment AZ

r: = 4q,,M/np2gaAT (6.1) This radius has been estimated to be about 12 km at the base of the mantle and 7.5 km at the top of the lower mantle. The material in the chimney is diluted slightly by the addition of cool mantle material through the sides, but this cooling is more than compensated by the heating due to viscous dissipation. As a result, the material in the chimney cools less than the corresponding adiabat as it rises through the lower mantle. In fact, the plumeaxis temperature reaches the melting temperature about halfway up the lower mantle, and the plume material is about 25% molten when it reaches the upper mantle. The mass rate of flow upward from D” has been estimated to be 1.9 X lo6 kg sec-’ (Stacey and Loper, 1983). Converted to a volumetric flow rate at upper-mantle density, this is 17 km3 yr-’. By comparison, the volumetric flow rate of Hawaii has been estimated to be from 0.05 km3 yr-’ (Moore, 1970) to 0.1 km3 yr-l (Swanson, 1972). Supposing that the total surface flow is equivalent to 20 Hawaiis, as an extreme example, the sum would be 1-2 km3 yr-l, far less than the total plume flow rate. Clearly the material extruded at hot-spot volcanoes is only a small fraction of what comes up from the D” layer. One obvious fractionation process is separation of the molten material from that which remains solid. With the molten fraction at least 25% of the total, the flow rate of molten material is at least 4 km3y r l . It would appear that a second fractionation process is necessary to reduce

STRUCTURE OF THE CORE AND LOWER MANTLE

21

this to the amount observed to reach the surface. The nature of this fractionation process and the fate of the material that does not reach the surface are not known at present. One possibility might be the partial recrystallization of molten plume material as it encounterscold lithospheric material. A resolution of the fate of the plume material will have an important bearing on the nature of mantle convection and whether the upper and lower mantle are distinct chemical reservoirs. The rheology of the mantle is most commonly assumed to be Newtonian for mathematical simplicity, but there is evidence (Weertman, 1970; Post and Griggs, 1973; Brennan, 1974; Cathles, 1975; Karato, 198 1; but see also Peltier et al., 198 1) that the proper rheology is a power-law creep of the form rl au,/ax/? = I~@/~,l"-'%/9

(6.2) for simple shear, where u, is a velocity component,xais a direction normal to u,, a@ is the deviatoric or shear stress, q is the apparent viscosity at stress level a,, and n is a real number 2 1. The variation of q with P and T is still given by Eq. (5.4). The Newtonian analyses of D"and plumes (Stacey and Loper, 1983; Loper and Stacey, 1983) described in this and the preceding sections have been generalized for a mantle rheology of the form Eq. (6.2) (Loper, 1984). The non-Newtonian effect causes a modest broadening of the lateral scales of the regions of rapid flow in D" and plumes, but these changes are structurallyunimportant and the essential physics of core cooling via convective flow in a thin D" layer and narrow plumes is remarkably independent of mantle rheology. The deep-mantle plumes are relatively narrow axisymmetric features which may be difficult to detect seismically. It remains to be determined whether the large-scale lateral heterogeneities in the lower mantle (Julian and Sengupta, 1973; Niazi, 1973; Powell, 1975; Sipkin and Jordan, 1975; Dziewonski et al., 1977) discerned from seismic data are due to plumes, ancient lithospheric slabs, or a combination of the two. 7. THERMAL HISTORY OF THE EARTH Until about a decade ago, thermal history calculations for the earth were based upon the assumption that heat is transported radially outward by conduction (e.g., see Lubimova, 1969). However, with the guidance of Tozer (1965, 1967, 1972, 1974, 1977), opinion shifted to the view that thermal convection is the dominant mechanism of heat transport in the interior of the earth. This would appear to make the heat-transportproblem more difficult to solve since the equations governingthermal convection are considerably more complicated than those governing conduction, and nu-

22

DAVID E. LOPER

merical solution would appear necessary. The numerical studies that have been attempted (e.g., Foster, 1969;Torrance and Turcotte, 1971; Hsui et al., 1972; Richter, 1973; McKenzie et al., 1974; Houston and DeBremaecker, 1975; Parmentier et al., 1976;DeBremaecker, 1977; Parmentier, 1978; Kopitze, 1979; Daly, 1980b; Cserepes, 1982; Parmentier and Morgan, 1982; Boss and Sacks, 1982) have proved to be unsatisfactory representations of mantle convection because present-day computers lack the storage capacity and computational speed to represent the flow accurately. However, if only the thermal history is desired and there is no interest in the details of the convective pattern, the parameterized convection models prove successful in obtaining quantitative results with a minimum of computational effort (see p. 2 for a list of papers using these models). Although Parameterized convection models are an efficient way to obtain a thermal history of the earth, a price is paid in not knowing whether the temperature and convectivemotions in the mantle have been parameterized accurately. Most of the models have been based upon Nusselt numberRayleigh number relations which are valid for constant-viscosityfluids. It is well known that the viscosity ofthe mantle is strongly dependent on temperature. Consequently, these parameterized convective models may not be quantitatively accurate (Daly, 1980b; Jarvis and Peltier, 1982; Spera et al., 1982). These difficulties were avoided in the thermal history calculation of Stacey (1980), who parameterized mantle convection by treatingthe mantle as a heat engine so that the rate of work is directly related to the heat flux:

I

a€ d V a Q

(7.1)

where c is the stress, C is the rate of strain, and Q is the mantle heat flux. A second relation Q o( $I2

(7.2)

is based upon the idea that the lithosphereis a conductiveboundary layer for mantle convection. These two relations, together with a power-law creep rheology of the form Eq. (6.2), allow the energy equation of the mantle to be solved for a representative “mantle temperature” as a function of time. Stacey (1980) ignored the cooling of the core and the effect of core heat fluxon mantle convection, arguing that the heat flux from the core would be carried to the surface via deep-mantle plumes that effectively decouple the problems of the cooling of the core and the mantle. However, the cooling of the core and the resulting growth rate of the inner core are of great interest to those studying the geomagnetic dynamo and its power supply. The question of core cooling has been studied by Schubert et al. (1979b)

STRUCTURE OF THE CORE AND LOWER MANTLE

23

using a Nusselt number - Rayleigh number parameterization of the thermal boundary layer at the base of the mantle (i.e., the D” layer). They found that the core cools quite rapidly by this mechanism and had difficulty constructing a model that did not result in a completely frozen core. This difficulty arises because the usual Nusselt number -Rayleigh number is not an accurate parameterization of the heat transfer properties of the thermal boundary layer at the base of the mantle. In the usual parameterization,based upon a constant-viscosityNewtonian model, the heat flux Q is related to the temperature difference AT across the layer according to Q 0: (7.3) e.g., see Schubert et al. (1979b). The analytic solutions for the D” layer obtained by Stacey and Loper (1983) and Loper (1984) can be used to construct a more accurate parameterization of the form

(7.4) for a Newtonian rheology, where T, is the temperature at the top of the core, and Qa

(AT)@n+l)/(Zn+3) ~ x P [ - P T M / T x ~+~311

(7.5)

for a power-law creep rheology. This new parameterization needs further refinement if ATis small, but it may be seen by comparison of Eqs. (7.3)and (7.4) that for AT small, the new parameterization yields a significantly smaller heat flux from the core than does the old. Thus the problem of rapid core cooling encountered by Schubert et al. (1979b) is greatly relieved. This new parameterization of the heat transfer properties of D /has been used by Stacey and Loper (1 984) to study the thermal history of the core and mantle. This study assumes an initially hot earth, with a completely molten core and the mantle at its solidus temperature, roughly 4.4 X lo9yr ago. If the mantle were partially molten prior to this, cooling of both core and mantle would have been via counterflow convection in the mantle (that is, upward flow of liquid and downward creep of solid). In this mode, there are no boundary layers. Hence, at the time the mantle became completely solid 4.4 X lo9 yr ago, there was no thermal boundary layer at the base of the mantle. According to Stacey and Loper (1 984), the subsequent thermal history of the mantle is similar to that found in many other parameterized studies: a period of rapid cooling lasting about 2 X los yr, followed by cooling at a gradually diminishingrate, with a present rate of cooling of 30 K/l O9 yr for a Newtonian mantle and roughly double this for a non-Newtonian mantle. However, they find the cooling history of the core quite different from previous studies (Schubert et al., 1979b, 1980; Stevenson et al., 1983)in that

24

DAVID E. LOPER

the core does not cool rapidly once the mantle reaches its solidus temperature. In fact, core cooling effectively ceases until the mantle cools via subsolidus convection sufficiently to build up a significant temperature difference across the D" layer. This model precludes the possibility of the dynamo being driven by thermal convection prior to the nucleation of the solid inner core as envisaged by Stevenson et al. (1 983). Stacey and Loper ( 1984)were able to construct models of the thermal history of the core that have the inner core nucleating prior to 3.5 X 1O9 yr ago, thereby providing a persistent source of energy for the dynamo via compositionally driven convection. They find that the power supply to the dynamo has been remarkably constant, at about 2 X 10" W, for the past 3 X lo9 yr. The corresponding heat flux from the core is also quite constant, with a current value of 1.6 X lo1*W. The fact that this heat flux is less than that conducted down the adiabat is not a barrier to the existence of a compositionallydriven dynamo, as it would be to a thermally driven dynamo (Loper, 1978b). They find that this low heat flux cannot be supplementedby radioactive heating in the core; their preferred model has no radioactive heat sources in the core. Stacey and Loper ( 1984) considered thermal histories of the mantle, assuming both a Newtonian rheology and a power-law creep rheology. The primary difference in the two results is that the mantle is cooling more rapidly at present if it is non-Newtonian. Their histories place the mantle temperature 3 X lo9yr ago about 160 K hotter than present for a Newtonian rheology and about 300 K for a power-law index n = 3 [see Eq. (6.2)]. The occurrence of komatiite lavas of age 3 X lo9 yr, with a melting point some 300 K hotter than contemporarylavas (Nisbetand Walker, 1982),is indirect evidence that the correct rheology for the mantle convection is a power-law creep with n = 3. 8. SUMMARY If the outer core is stirred by convective motions driven by compositionally buoyant fluid, as advocated in Section 3, then its structure must be very simple. The main bulk of the outer core is very nearly homogeneous in composition with an adiabatic temperature distribution. Since these motions are driven by a source of buoyancy at the inner-coreboundary, there is no possibility of an F layer. On the other hand, it is possible that a thin layer at the top of the outer core is stably stratified. This question may be resolved in the next few years, as the fluid motions at the top of the core are the subject of current investigation (Benton, 1979, 198la,b; Gubbins, 1982; Whaler, 1982). In contrast to the outer core, both seismic and theoretical studiesindicatea

STRUCTURE OF THE CORE A N D LOWER MANTLE

25

complicated structure for the inner core. First, the inner core is not completely solid, but is an intimate mixture of solid and liquid, with the mass fraction of solid increasing monotonically with depth. The initial growth rate of mass fraction of solid with depth is very rapid (Loper, 1983), but it is not known how this parameter vanes with depth and whether it can be the source of the seismic anelasticity in the top 200- 300 km of the inner core (Cormier, I98 1; Choy and Cormier, 1983; Loper and Fearn, 1983). The structure of the lower mantle appears to be more complicated and less well understood than that of the core, but this may only reflect the amount and quality of data available to us (the more we learn of any object, the more complicated it appears). It is virtually certain that the D” layer has a strong thermal component associated with the heat flux from the core. It is not clear whether all of the structure of D” discerned by seismic analysis can be explained in thermal terms or whether some chemical layering needs to be invoked. A key question concerns the variation of seismic speeds V, and V, and the adiabatic incompressibilityK, with depth at the base of the mantle. Seismic analyses (Cleary, 1974; Doornbos and Mondt, 1979) indicate that these wave speeds actually decrease with depth in D”, whereas the thermal analysis of Stacey and Loper ( 1 983) requires a positive but diminished gradient with depth. The resolution of this point could be pivotal in determining the structure of D”. The recent identification of the D-plume flow pattern as the mechanism for carrying the heat from the core upward through the lower mantle may prove beneficial to understanding the overall structure of the lower mantle and its thermal and dynamic state. That is, if the core heat is carried by a separate convective motion, the bulk of the lower mantle acts as if it had a thermally insulating lower boundary. This would make the task of modeling mantle convection numerically considerablyeasier since the thin bottom boundary layer could be ignored. In this case, convective motions must be driven by cooling from above. If this is so, the large-scale hererogeneities observed in the lower mantle may be ancient lithosphenc slabs. APPENDIX.

ENERGYAVAILABLE FROM GRAVITATIONAL SEPARATION

In this appendix we shall calculate the gravitational potential energy released as a self-gravitatingsphere of incompressiblematerials evolves from a uniform initial state to a differentiated final state. [For a more detailed analysis, see Muller and Hage (1979).] By conservation of mass

+

r:p, = (r: - r : ) k r:& = 3M0/4n (All where po is the density of the homogeneous sphere of radius r,, pH is the

26

DAVID E. LOPER

density of the heavier constituent that collects in a central sphere of radius ri , h. is the density of the lighter constituent that collects in the annular region between ri and r,, and M, is the total mass of the sphere. The gravitationalpotential energy AE released by this process is given by AE = ES- E, (A2) where E, and Efare the gravitationalpotential energy released upon assembly of the initial and final states, respectively, from dispersed matter. To calculate E, and E,, consider the addition of a small mass SMof densityp to a partially assembled sphere of mass M and radius r. When the centers of mass of these two objects are a distance r’ apart, the force of attraction is

F = GM(SM)/(r’)2 (A3) where G is the gravitational constant. The addition of SM to the partially assembled sphere will increase its radius by

+

Sr = [ r 3 3(6M)/4~cp]~/~ - r3 If SM << pr3, we have

Sr = (SM)/4npr2 644) The gravitationalpotential energy released by moving mass SMfrom r’ = m to r’ = r (ignoring Sr) is SE = GM(SM)/r (‘45) We may eliminate SMfrom Eq. (A5) using Eq. (A4) and write the expression as a differential dE = 4nGMpr dr For the homogeneous initial state

M = qnp0r3 and, in assembling a sphere of radius r,, an energy E, = ( 1 6z2/ 15)Gp:r:

(A61 (A71 (‘48)

is released. In assembling the inner sphere of the final state, an energy En = (16n2/15)Gp&rf

(‘49)

is released. The energy released in assembling the annular region may be calculated by integrating Eq. (A6) from r = ri to r = r, , with P=PL

(A 10)

STRUCTURE OF THE CORE AND LOWER MANTLE

27

and This contribution is The total energy released in assembling the final state is

+

E f = En Ef2 (A 13) The energy released in evolving from the initial to final state may be found from combination of Eqs. (A2), (A8), (A9), (A12), and (A13): AE = %n2G[3pL(pH -&)r;(r; - r:) p&r:- p;r:]

+

+ pt(r:-

r:) (A 14)

If we let

+ P^

(A 15) where: is the densityjump at ri due to the differencein composition,we may use Eqs. (Al) and (A15) to write Eq. (A14) as PH

= PL

A E = &n2Gj%;rq[p,( 1 - r f / r : ) j(r;/r:)( 1 - ~ ~ / r , ) ~ ri/ro)] (2 (A 16) If j? << po,the last group of terms in Eq. (A 16) is negligibly small. Using Eq. (Al) to eliminate po in favor of M,, we have

+

+

AE = (2n/5)GM0j(r:/r0)( 1

- r?/r;)

('417)

ACKNOWLEDGMENTS This work was supported by the Earth Sciences Section of the National Science Foundation under Grant EAR-8103342. This article is contribution No. 207 of the Geophysical Fluid Dynamics Institute, Florida State University, Tallahassee, Florida.

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Qamar, A., and Eisenberg, A. (1974). The damping ofcore waves. JGR, J. Geophys. Res. 79, 758-765. Richter, F. M. (1973). Convection and the large scale circulation of the mantle. J. Geophys. Res. 78,8735-8745. Ringwood, A. E. (1977). Composition of the core and implications for origin of the Earth. Geochem. J. 11, 1 1 1 - 135. Roberts, P. H., and Loper, D. E. (1983). Towards a theory of the structure and evolution of a dendrite layer. In “Stellar and Planetary Magnetism” (A. M. Soward, ed.), pp. 329-349. Gordon & Breach, New York. Rochester, M. G., Jacobs, J. A., Smylie, D. E., and Chong, K. F. (1975). Can precession power the geomagnetic dynamo? Geophys. J. R. Astron. SOC.43,66 1-678. Sacks, I. S. (197 1). Anelasticity of the inner core. Year Book Carnegie Inst. Washington, 69, 1969- 1970, pp. 416-418. Schilling, J.-G. (1975). Azores mantle blob Rare earth evidence. Earth Planet. Sci. Lett. 25, 103-115. Schubert, G., and Young, R. E. (1976). Cooling the Earth by whole mantle subsolidusconvection: A constraint on the viscosity of the lower mantle. Tectonophysics 35,201 -2 14. Schubert, G., Cassen, P., and Young, R. E. (1 979a). Subsolidusconvectivecooling histories of terrestrial planets. Icarus 38, 192- 2 1 1. Schubert, G., Cassen, P., and Young, R. E. (1979b). Core cooling by subsolidus mantle convection. Phys. Earth Planet. Inter. 20, 194-208. Schubert, G., Stevenson, D., and Cassen, P. (1980). Whole planet cooling and the radiogenic heat source contents of the Earth and moon. JGR. J. Geophys. Res. 85,353 1-2538. Sharpe, H. N., and Peltier, W. R. (1978). Parameterized mantle convection and the Earth’s thermal history. Geophys. Res. Lett. 5,737-744. Sharpe, H. N., and Peltier, W. R. (1979). A thermal history model for the Earth with parameI R. .Astron. SOC.59, 171-205. terized convection. Geophys. . Sipkin, S. A., and Jordan, T. H. (1975). Lateral heterogeneity of the mantle determined from the travel times of ScS. JGR, J. Geophys. Res. 80, 1474- 1484. Sleep, N. H. (1979). The thermal history and degassing of the Earth Some simple calculations. J. Geol. 87, 67 1-686. Sleep, N. H., and Langan, R. T. (198 1). Thermal evolution ofthe Earth: Some recent developments. Adv. Geophys. 23, 1-23. Spera, F. J., Yuen, D. A., and Kirschvink, S. J. (1982). Thermal boundary layer convection in silicic magma chambers: Effects of temperature-dependent rheology and implications for thermogravitational chemical fractionation. JGR, J. Geophys. Res. 87, 8755- 8767. Spohn, T., and Schubert,G. (1982). Modes ofmantle convection and the removal ofheat from the Earth’s interior. JGR, J. Geophys. Res. 87,4682-4692. Stacey, F. D. (1975). Thermal regime of the Earth’s interior. Nature (London) 255,44-45. Stacey, F. D. (1977a). “Physics of the Earth.” Wiley, New York. Stacey, F. D. (1977b). A thermal model ofthe Earth. Phys. Earth Planet. Inter. 15,341 -348. Stacey, F. D. (1977~). Applications of thermodynamics to fundamental earth physics. Geophys. Sun. 3, 175-204. Stacey, F. D. (1980). The cooling Earth: A reappraisal. Phys. Earth Planet. Inter. 22,89-96. Stacey, F. D., and Irvine, R. D. (1977). Theory of melting: Thermodynamic basis of Lindemann’s law. Aust. J. Phys. 30,631 -640. Stacey, F. D., and Loper, D. E. (1983). The thermal boundary layer interpretation of D’: and its role as a plume source. Phys. Earth Planet. Inter. 33,45 - 55. Stacey, F. D., and Loper, D. E. (1984). Thermal histories ofthe core and mantle. Phys. Earth Planet. Inter. (to be published).

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Stevenson, D. J. (1980). Applications of liquid state physics to the Earth‘s core. Phys. Earth Planet. Inter. 22, 42- 52. Stevenson, D. J. (1981). Models ofthe Earth’s core. Science 214,611-619. Stevenson, D. J. (1983). Planetary magnetic fields. Rep. Prog. Phys. 46, 555-620. Stevenson,D. J., Spohn, T., and Schubert, G. (1983). Magnetism and thermal evolution ofthe terrestrial planets. Icarus 54,466-489. Swanson, D. A. ( 1972). Magma supply rate of Kilauea volcano 1952 - 197 1. Science 175, 169- 170. Torrance, K. E., and Turcotte, D. L. (1971). Structure of convective cells in the mantle. J. Geophys. Res. 76, 1154- 1161. Tozer, D. C. (1965). Heat transfer and convection currents. Philos. Trans. R . SOC.London, Ser. A 258,252-271. Tozer, D. C. (1967). Towards a theory of thermal convection in the mantle. In “The Earth’s Mantle” (T. F. Gaskell, ed.), pp. 325-353. Academic Press, New York. Tozer, D. C. (1972). The present thermal state of the terrestrial planets. Phys. Earth Planet. Inter. 6, 182- 197. Tozer, D. C. (1974). The internal evolution of planetary-sized objects. Moon 9, 167- 182. Tozer, D. C. (1 977). The thermal state and evolution ofthe Earth and terrestrial planets. Sci. Prog. (Oxford) 64, 1 -28. Turcotte, D. L. (1980). On the thermal evolution of the Earth. Earth Planet. Sci. Lett. 48, 53-58. Turcotte, D. L., and Oxburgh, E. R. (1 967). Finite amplitude convection cellsand continental drift. J. Fluid Mech. 28,29-42. Ullman, W., and Walzer, U. (1980). The core paradox reconsidered. Phys. Earth Planet. Inter. 22,204-210. Verhoogen, J. (1961). Heat balance of the Earth’s core. Geophys. J. R. Astron. SOC.4, 276-28 1. Verhoogen, J. (1980). “Energetics of the Earth.” Nat. Acad. Sci. Washington, D.C. Weertman, J. (1 970). The creep strength ofthe Earth‘s mantle. Rev. Geophys. SpacePhys. 8, 145- 168. Whaler, K. A. (1980). Does the whole of the Earth’s core convect? Nature (London) 287, 528-530. Whaler, K. A. (1982). Geomagnetic secular variation and fluid motion at the core surface. Philos. Trans. R . SOC. London, Ser. A 306,235-246. Wilson, J. T. (1963a). Evidence from islands on the spreading of Ocean floors. Nature (London) 197,536-538. Wilson, J. T. (1963b). Hypothesis of the Earth’s behavior. Nature (London) 198,925-929. Wilson, J. T. (1963~). A possible origin ofthe Hawaiian islands. Can. J. Phys. 41,863-870. Wilson, J. T. (1965). Convection currents and continental drift: Evidence from Ocean islands suggestingmovement in the Earth. Philos. Trans. R . SOC.London Ser. A 258, 145- 167. Wright, C., and Lyons, J. A. (1979). The identification of radial velocity anomalies in the lower mantle using an interference method. Phys. Earth Planet. Inter. 18, 27-33. Wright, C., and Lyons, J. A. (1 980). Further evidence for radial velocity anomalies in the lower mantle. Pure Appl. Geophys. 119, 137- 162. Yuen, D. A., and Peltier, W. R. (1980). Mantle plumes and the thermal stability of the D” layer. Geophys. Res. Lett. 7,625-628. Yuen, D. A., and Schubert, G. (1976). Mantle plumes: A boundary layer approach for Newtonian and non-Newtonian temperature-dependent rheologies. JGR, J. Geophys.Res. 81, 2499-2510.

PRE-PLEISTOCENE PALEOCLIMATES: THE GEOLOGICAL AND PALEONTOLOGICAL EVIDENCE; MODELING STRATEGIES. BOUNDARY CONDITIONS. AND SOME PRELIMINARY RESULTS CHRISTOPHER R . LLOYD* Climatic Research Institute Oregon State University Corvallis. Oregon 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Paleoclimatic Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Qualitative Paleoclimatic Evidence . . . . . . . . . . . . . . . . . . . . . . . 2.3. Quantitative PaleoclimaticEvidence . . . . . . . . . . . . . . . . . . . . . . 3 . Pre-Pleistocene Paleoclimates and Paleoceanography. . . . . . . . . . . . . . . . . . 3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Overview: Major Glacial and Nonglacial Epochs . . . . . . . . . . . . . . . . . 3.3. The Mid-Cretaceous Climate . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. The Late Cretaceous to Late Tertiary Cooling Trend . . . . . . . . . . . . . . . . 4. Forcing Mechanisms in Long-Tern Climatic Change . . . . . . . . . . . . . . . . . 4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Changes in Factors External to the Earth . . . . . . . . . . . . . . . . . . . . 4.3. Changes in Terrestrial Forcing External to the Ocean/Atmosphere/Biosphere System . . . 4.4. Factors Internal to the Ocean/Atmosphere/Biosphere System . . . . . . . . . . . . 4.5. Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 . Boundary Conditions for Paleoclimatic Modeling . . . . . . . . . . . . . . . . . . . 5.1, Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Paleogeography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3. Sea Surface Temperature and Ocean Circulation . . . . . . . . . . . . . . . . . 5.4. Other Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Paleoclimatic Modeling Strategies . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Summary of Boundary Conditions . . . . . . . . . . . . . . . . . . . . . . . 6.3. Statistical-Dynamical Models . . . . . . . . . . . . . . . . . . . . . . . . . 6.4. General Circulation Models . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5. Snapshot and Sensitivity Experiments. . . . . . . . . . . . . . . . . . . . . . 6.6. Comparison of Model Results with Paleoclimatic Evidence . . . . . . . . . . . . . 7 . A Survey of Palmclimatic Modeling Results . . . . . . . . . . . . . . . . . . . . . 7.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Results of Snapshot Simulations . . . . . . . . . . . . . . . . . . . . . . . . 7.3. Results of Sensitivity Experiments . . . . . . . . . . . . . . . . . . . . . . . 8. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 39 39 39 45 52 52 53 54 61 74 74 75 75 77 71 80 80 80 86 92 101 101 102

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* Present address: Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, New Jersey 08540. 35

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ADVANCES IN GEOPHYSICS. VOLUME 26

Copyright 0 1984 by Academic Press. Inc All rights of reproduction in any form reserved.

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1. INTRODUCTION

Climate modelers have in recent years become interested in a wide range of problems related to climatic change. These have mainly involved possible future changes due, for example, to increasing atmospheric C02 content. Studies of past climates have mainly been concerned with historical and glacial times. Interest in the latter has been stimulated by the general circulation model simulations of Gates (1976a,b) and Manabe and Hahn (1977). However, until very recently, climate modelers have not been in-

(a) ERA c

0 0 z 0

PERIOD

TIME (my)CLIMATES GLACIAL

Quaternary

N

Y

u s c - 8 W C h

z

-

C

Jurassic Triassic 250

?80

LL

z

Cretaceous

Permian

a

4

Tertiary

Carboniferous

320

0

I 0 N a 0 W

Devonian Silurian

1

a a

- z

Q m I U

0 W

U

Ordovician Cambrian

Proterozoic

I

2100 .:-:-?-::.? ..... ::... .. ;::...... 3;: .......... . . .;:: . .. .. .. . .\;. ...... .:.. 2700 .::.:. ..;.-L'i.;i

a

!8W

-.

1600

'

FIG.1. (a) Geological time scale. Dates of boundaries from Van Eysinga (1978). Glacial periods from Tarling (1978) and Crowell (1982). Scale is linear between 600 and 1.8 m.y. Note that significant Antarctic ice may have formed as early as 38 m.y. ago. (b) Cretaceous (from Van Hinte, 1976) and Cenozoic (from Berggren and Van Couvering, 1974) time scales. Scale is linear, except between 0 and 5 m.y.

37

PRE-PLEISTOCENE PALEOCLIMATES (t

EPOCH Holocene

Pleistocene PI iocene

r

AGE

I

1.8 5 10.5 14

Miocene

22

t

Oligocene

t lo1

_------------- 32 38 r3

Eocene

19

54 59 Maastrichtian

55

70

Companion

78 32 36 32 Cenornanian

100 08

Eorly

t-TkFHauterivian Valanginian Berriasion

115

121 126 131 135

FIG. Ib.

volved in studies of climates through the vast span of earth history preceding the glacial climates of late Tertiary and Quaternary times. This field has been the preserve of earth scientists, who have long recognized that rock types and fossil floras and faunas in ancient sedimentary sequences often show clear indications of climates very different from those occumng at present at the same locations. The most striking feature shown by the distribution of these indicators through space and time is that most of earth history has been characterized by warm, ice-free polar regions, while equatorial climates have always been similar to the present. (The major divisions of earth history are schematically represented in Fig. la.) Most attempts to interpret the significance of paleoclimatic data in terms of the global climate system have been ill-informed at best. Only with the recent advent of the plate tectonics hypothesis and the consequent availability of global paleocontinental reconstructions, at least for the past -230 million years (m.y.), has it been possible to study paleoclimates within a

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CHRISTOPHER R. LLOYD

realistic framework. Paleoclimatic indicators could at least be interpreted in the context of their paleolatitude and geographic situation. Despite this advance, the most important benefit from paleocontinental reconstructions has been in the field of paleoclimaticmodeling. For the first time it became possible to reconstruct boundary conditions for the ocean atmosphere system for a particular time and then model the climate consistent with these boundary conditions. In recent years this has been done qualitativelyby some workers who have postulated oceanicand atmospheric circulation patterns consistent with various land - ocean distributions (Lloyd, 1982;Parrish and Curtis, 1982). These modeled climates have been found to be consistentwith the available paleoclimatic evidence. However, numerical climate models offer the best opportunity for advances in our understanding of paleoclimates. Using these models, it is possible to simulate climates that are physically consistent with the boundary conditions, within the limitations of the model used. Also, the possible importance of changes in particular boundary conditions on the evolution of paleoclimates can be evaluated through sensitivity experiments. The two main classes of models available for these purposes are (1) statistical-dynamical models (SDMs), which can be zero to three dimensional and which include radiative - convective models and energy balance models, and (2) general circulation models (GCMs), which simulate atmospheric (or oceanic) processes more explicitly, including synoptic-scale features, and which predict, for example, both temperature and motion fields. Some energy balance model and GCM results have already been published for mid-Cretaceous time (- 100 m.y. ago) (Barron el al., 1981a; Barron and Washington, 1982a,b). These have given some interesting insights into the nature of global climate at that time. This article is written for earth and atmospheric scientists who may be interested in paleoclimatic modeling, as a survey both of the types of evidence available and the paleoclimaticrecord that it shows and of the modeling opportunities that now exist. Emphasis is on mid-Cretaceous to late Tertiary time (- 110 to 10 m.y. ago), the period that has seen a transition from fully nonglacial to glacial climates and which will be the subject of most pre-Pleistocene paleoclimaticmodeling in the foreseeablefuture. Figure 1b gives a more detailed time scale for the Cretaceous and Cenozoic. First, the most important paleoclimatic indicators will be described. Second, the major events of the earth’s climatic history will be briefly summarized, followed by an examination of the state of the atmosphere-ocean system during the last nonglacial maximum, the middle part of the Cretaceous period (1 10-80 m.y. ago). Then the progressive cooling that took place from late Cretaceousto late Tertiary times willbe described, along with a consideration of some of the mechanisms that may have brought this

PRE-PLEISTOCENE PALEOCLIMATES

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about. These reviews will be followed by a discussion of some of the forcing factors that are important in long-term climatic change. The remaining part of the article will be concerned with paleoclimatic modeling. The establishment of paleogeographicand other boundary conditions for any chosen period will be described. Then possible strategies for paleoclimatic modeling will be discussed, and the results of recent modeling experiments will be reviewed. 2. PALEOCLIMATIC INDICATORS

2. I . Introduction Before surveying the earth’s climatic history or discussing possible paleoclimatic modeling programs, it will be informative to briefly consider the main types of evidence that are available as indicators of pre-Pleistocene climates. These are the so-called proxy data that are preserved in marine and land-deposited sedimentary rocks. While some of these data can be used to give quantitative temperature estimates, most forms of evidence are qualitative, and these will be considered first.

2.2. Qualitative Paleoclimatic Evidence Paleoclimaticevidence from sedimentaryrocks may be derived both from the structure and composition of the rocks and from the fossil fauna and flora they contain. For recent reviews of paleoclimaticindicators see Frakes (1979) and Boucot and Gray (1982).

2.2.1. Data from the Composition and Structure of Sedimentary Rocks. 2.2.I . 1. Evidencefor land surface and coastal climates. Sedimentary indicators of land surface climates are functions of the evaporationprecipitation balance and/or temperature and their seasonal regimes. Because most sediments are deposited and preserved in low-lying regions of subsidence, land climate indicators are mainly representative of coastal plain or low-elevation inland regions. All pre-Pleistocene mountains have been eroded away and so their climates are not directly represented in the paleoclimatic record. This bias should be remembered when interpreting this type of paleoclimatic data. Coals are composed predominantly of the carbonized remains of vegetation that was deposited in environments in which precipitation exceeded evaporation and in which conditions allowed rapid burial of plant material

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CHRISTOPHER R. LLOYD

before oxidation could take place. Coals occur at both equatorial and midto high paleolatitudes (Krausel, 1964; Schopf, 1973). A plot of paleolatitudes of Phanerozoic coal depositsshows a minimum at around 25 (Briden and Irving, 1964). Coals are therefore not paleotemperature indicators, although fossil flora preserved in coal deposits(see later) may give an indication of the temperature regime. Conversely, evaporitedeposits form in lakes and restricted marine conditions in which evaporation exceeds precipitation. Modem evaporites show a bimodal distribution, with subtropical maxima and an equatorial minimum, and the locations of ancient marine evaporites, plotted against paleolatitudes, show a similar distribution (Gordon, 1975; Eugster, 1982). Note that the existence of a suitable restricted environment is essential. Modem and ancient desert (eolian) sandstones, not surprisingly, show a distribution similar to evaporites with respect to latitude or paleolatitude (Runcom, 1964;Drewry et al., 1974). In addition, the orientation of crossbedding in ancient desert dunes can give an indication of paleowind directions (e.g., Turner, 1980), which may, however, reflect directions of infrequent strong winds rather than the weaker prevailing winds (Cooper, 1967) or local topographic effects. Surface textures on quartz grains may provide evidence for the last wind velocity to which the grains were subject (Krinsley and Wellendorf, 1980). The use of paleosols (ancient soils) as paleoclimatic indicators has been reviewed by Van Houten (1982) and Retallack (1983). Laterites and bauxites, composed dominantly of femc and aluminum oxides, respectively, are products of intense in situ weathering of silicate rocks in hot (> 16°Cmean annual temperature), wet (year-round or seasonal) climates. Calcrete is a carbonate-rich red soil, characteristic of warm (subtropical) climates with light to moderate seasonal rainfall. Clay minerals in paleosols, formed by the decomposition of aluminosilicates, may also have some paleoclimatic significance. For example, in modern soils the kaolinite proportion increases with increasing precipitation, whereas smectite (montmorillonite) decreases. Also, kaolinite characteristicallyforms at tropical temperatures (Singer, 1980). Continental red sediments, or “red beds,” have traditionally been cited as evidence for arid or semiarid climates (e.g., Van Houten, 1961). However, red beds can form in wet as well as arid climates (Walker, 1974), and so they do not give a clear indication of an evaporation -precipitation regime. Red beds do signify warm conditions, however, showinga strong correlation with warm-climate indicators in the geological record (Boucot and Gray, 1982). The mineralogical composition of red beds and other detrital sediments can be used to draw climatic inferencesfor the source(not depositional)region of O

PRE-PLEISTOCENE PALEOCLIMATES

41

the sediments (Turner, 1980), although inferences made on this basis have been questioned by James et al. (1 98 1). Continental glacial conditions are characteristicallyindicated by diamictites, which comprise unsorted sediments with particles of all sizes from clay size up to large rocks, when these sediments contain faceted or striated rock fragments or are associated with other glacial features such as striated pavements. In these cases diamictitesare interpreted geneticallyas tillites,which are consolidated glacial moraines. Some diamictites, such as downslope slide deposits, have no glacial significance. Isolated large fragments in otherwise fine-grained, thinly bedded marine sediments are usually interpreted as ice-rafted debris, indicative of glaciation on nearby continents (Crowell, 1982). While mountain glaciers may have existed at any time in earth history, no direct evidence is availablebecause all of these regions have now been destroyed by erosion. 2.2.1.2. Marine sedimentary evidence. The principal components of marine sediment are carbonates,biogenic silica, quartz, feldspar,micas, clay minerals, zeolites, heavy minerals, and lithic (rock) fragments. Paleoenvironmental inferencescan be drawn from the nature and distribution of these components in both shallow-water marine sediments and deep-sea cores (McManus, 1970). Shallow marine carbonates have traditionally been regarded as a warmwater indicator, and both ancient and modern carbonates show a mainly low-latitudedistribution. Carbonatescan also form in cold water but cold/ cool-water sediments are dominated by detrital silicate minerals (Boucot, 1981). Some observations indicate that the aragonite to calcite ratio (both are polymorphs of calcium carbonate)and the Mg content of shell carbonate increase with increasing temperature (Leonard et al., 1981). Paleosalinity is not normally indicated by marine sediments. However, gypsum-rich horizons, dolomite, and certain clay minerals can indicate high salinity. Lack of these indicators,plus the presence of kaolinite, can indicate low salinity (Hallam and El Shaarawy, 1982). Wind-transported detritus is an important component of deep-ocean cores in regions remote from continents (Windom, 1975). Analyses ofmass accumulation rates and grain-size distributions of quartz can indicate changes in size or vegetation cover ofcontinental source areas and changes in wind speed, respectively, through time (Janecek and Rea, 1983). The clay mineralogy of these sediments can indicate the type of source: illite is characteristic of continental source areas, and smectite, of volcanic sources (Leinen and Heath, 1981; Rea and Janecek, 1981). Given a knowledge of continental positions and sufficientlywell-distributedcore locations for any time, inferenceson wind directions could, in theory, be made. For example,

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CHRISTOPHER R. LLOYD

using Pleistocene examples, Huang ( 1980) has shown that relative thicknesses of a single volcanic ash band in widely separated cores can indicate paleowind directions. As mentioned earlier, weathering under different land surface climatic regimes can produce different characteristic clay minerals. When these are eroded, transported by rivers, and deposited on continental shelves or margins or in the deep ocean, their presence in cores gives an indication of climate over nearby land areas, assuming that little postdepositional alteration occurs (Chamley, 1979). Biogenic opaline silica in deep-ocean sediments has been related to high productivity of plankton in surface waters resulting from open-ocean upwelling of the so-called biolimiting nutrient elements (P, N, Si). Drewry et al. ( 1974) found a maximum of Cretaceous siliceous sediments in the nearequatorial (paleolatitude) Pacific Ocean, which they related to upwelling driven by a trade wind pattern similar to the present. Similarly, Parrish and Curtis (1982) related Cretaceous and Tertiary organic-rich sediments to zones of high productivity resulting from near-coastal upwelling. Phosphate-rich rocks (phosphorites) are also associated with these locations (Frakes, 1979). High contents of organic carbon in some Cretaceous deep-ocean sediments are thought to result from deposition of organic matter in anoxic water, in which the organic matter could not be destroyed by oxidation or bacterial action. Widespread synchronous occurrences of such sediments were first related to so-called Cretaceousoceanic anoxic events by Schlanger and Jenkyns ( 1976), who suggested that they resulted from an expansion of an intermediate-depth oxygen-minimum layer in the oceans. Hypotheses for the oxygen minimum have included low oxygen solubility due to warm ocean waters, high plankton productivity, and/or input of terrigenous vegetation, giving high rates of organic carbon sedimentation; weak ocean circulation due to stable, salinity-controlled density stratification, with consequent slow replacement of oxygen-depleted deep water; or, conversely, vigorous circulation and upwelling causing a plentiful nutrient supply and high productivity. The field has been reviewed by Weissert (198 1) and will be further discussed in Section 3.3.2.

2.2.2. Paleontological Evidence. The methodology for drawing paleoclimatic inferences from fossil plants and animals is fundamentally different from that used for sedimentary data. While conclusionsfrom the latter are based on the uniformitarian principle of non-time-dependent physical and chemical processes, and so can be made directly on the basis of present sediment type/climate relationships, organisms have changed through time because of evolution. Therefore, while the climatic tolerances of modem

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43

species of plants and animals are mostly well known, direct comparison at the species level is only possible, in general, for recent geological times (the last few million years). Going progressively further back through time, the number of fossil species that are still alive today steadily decreases. However, comparisons at the generic or family level probably still give reliable climatic inferences. The earliest time for which this is possible depends on the evolutionary rate of the group of organisms concerned. Taking marine mollusks as an example, most Neogene species, Paleogene genera, and Cretaceous familiesare still extant (Boucot, 198 1). However, in Cretaceousand earlier sediments, some major groups of organisms that have no modern relatives are encountered. Bearing this situation in mind, it is possible to make useful climatic inferences using the following methods. 2.2.2.I . Paleobotanical evidence. Paleoclimatic inferences from fossil floras should be treated with care, because plants are influenced by temperature, precipitation, and evaporation, and not only by mean values, but also by seasonal ranges and occasional extremes, length of day as a function of latitude, and nonclimatic environmental factors (Frakes, 1979; Boucot and Gray, 1982). Most plant taxa of Tertiary age have close living relatives, and so useful climatic inferences can be drawn from a knowledge of climatic tolerances of these relatives, assuming that the physiology of these floras has remained constant. However, in some cases evolution may have changed these tolerancesin different directionsin different groups. Evidence for this may be given by changes through time of plant communities which originally had overlapping climatic tolerances. Also, problems with the identification of leaves, seeds, fruit, wood, and pollen can lead to uncertainties in assigning specimens to particular genera and families. Therefore, where possible, analysis of whole Tertiary assemblages is desirable in order to smooth out errors resulting from incorrect identification and changed climatic tolerances of individual species (Krausel, 196 1 ; Dorf, 1964; Wolfe, 1978). For pre-Tertiary floras, inferences based on comparisonswith close living relatives (same genera) become very limited (e.g., Krassilov, 1978). This is because late Cretaceous-earliest Tertiary time was a period of rapid evolution and extinction, and the angiosperms (flowering plants) were rapidly becoming adapted to a wider range of environments. Fortunately, there is an additional source of information, in that some morphologicalcharacteristicsof plants, regardlessof m a , seem to be climate related. This is of much use paleoclimatically,particularly for Cretaceous and earlier floras, assuming that in fossil plants the relations between morphology and environment were the same. For example, tough leaves with thick, prominent veins and sunken stomates are characteristic of arid climates; proportion of woody plants in an assemblage, proportion of entire-

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CHRISTOPHER R. LLOYD

margined leaves (smooth rather than serrated), and leaf size all increase at present from cool to equatorial climates (Krausel, 196 1;Dorf, 1964; Wolfe, 1978; Davis and Taylor, 1980; Boucot and Gray, 1982). However, Dolph and Dilcher ( 1979) reported that the leaf morphology-climate relationship may not always hold, and Dolph and Dilcher (1980) argued that leaf morphology is a complex function of environmentalconditions, including temperature, precipitation, and potential evapotranspiration. Apparently morphology cannot be simply and quantitatively related to a specific variable such as mean annual temperature, as claimed by Wolfe (1978). The presence of tree growth rings may indicate seasonality of either temperature or precipitation. Other morphological features such as plant size and root structure can give additional paleoenvironmental information (Boucot and Gray, 1982). Floral assemblages can also be identified from fossil spores and pollen (palynomorphs) in continental, shallow marine, and deep-sea sediments. The latter are useful if they can be related to a nearby land source area (McLachlan and Pieterse, 1978). 2.2.2.2. Paleozoological evidence. Faunal evidence for pre-Pleistocene land climates is limited and based mainly on reptiles and lacustrine animals. Temperature tolerances of dinosaurs are uncertain (Colbert, 1964), and the widely scattered and sparse occurrence of fossils makes sampling unrepresentative (Charig, 1973). However, some relatives of early Tertiary crocodiles, turtles, and lizards still exist at the genus level, and in these cases climatic tolerances can be inferred with confidence (e.g., Estes and Hutchison, 1980). Insects also have potential as paleoclimatic indicators because their evolutionary rate is slow-a significantproportion of close relatives at the genus level exist back into the Cretaceous. However, they are only preserved at restricted locations (A. J. Boucot, personal communication, 1983).

Marine faunas are generally more useful, and analyses of their distributions (paleobiogeography)can give important paleoclimaticand paleoceanographic information. (Biogeographic distributions can also be used in some cases to constrain paleogeographic reconstructions.) Some groups such as reef corals can, by comparison with temperature tolerances of living relatives, be useful indicators of near-surface water temperatures. However, the most useful information does not depend on a knowledge of climatic preferences of individual taxa. Stehli (1973) showed that, for most groups of marine organisms, present-day taxonomic diversity, i.e., number of species, is greatest at the heat equator and decreases poleward. Similar latitudinal gradients are found for fossil organisms. Therefore, diversity gradients apparently correlate with temperature gradients, although Valentine (1 982) has suggestedthat the poleward decrease of diversity is a function

PRE-PLEISTOCENE PALEOCLIMATES

45

of increasing seasonality rather than of decreasing mean annual temperature, possibly acting through the seasonality of nutrient supply. Nevertheless, diversity changes through time at a given location probably indicate temperature or other environmental changes (Berger and Roth, 1975). Another method involves the recognition of regions characterizedby lowand high-latitude faunas at any given time, which are generally assumed to indicate the existence of a poleward temperature gradient. Analyses of planktonic (near-surface) microfossil assemblages from deep-ocean cores have shown, for example, latitudinal fluctuations through time of the cold/ warm assemblage boundaries during early Tertiary times. These changes have been related to major cooling and warming phases, which correlate with the oxygen-isotope paleotemperature record (Haq et al., 1977; Haq, 1982). Conversely, for any given time, longitudinal variation of the boundary between warm and cold assemblagescan indicate temperature anomalies associated with ocean circulation. As with plants, morphological characteristics may be dependent on environment. For example, tropical organisms tend to have thicker shells than do comparable cold-water species. The direction of coiling of the shells of planktonic foraminifera (zooplankton) appears to be correlated with summer temperature (e.g., Schopf, 1980), and the ratio of planktonic to nonplanktonic larvae of shelf benthonic marine invertebrates (bivalves, gastropods, and echinoderms, for example) decreases poleward (Boucot, 1981). Marine biota may give indications of paleosalinity. For example, low diversity but high population density of marine invertebrates, characterized by small, thin-shelled individuals, can indicate abnormal salinity (Hallam and El Shaarawy, 1982). Echinoderms, brachiopods, and corals are typically absent in hypersaline environments,while some brackish-waterassemblages of foraminifera, for example, can be recognized (Boucot, 1981). As a cautionary note, it should be emphasized that, whereas distributions of marine organisms are functions of temperature and salinity, other factors such as nutrient supply, depth and nature of the sea floor, and barriers to migration strongly influence distributions (Hallam, 1973; Hughes, 1973).

2.3. Quantitative Paleoclimatic Evidence 2.3.1. Introduction. Quantitative paleoclimatic evidence for pre-Pleistocene times can be gained from the oxygen-isotope technique, and carbonisotope analyses can also give some paleoenvironmentalinformation. The factor analysis-transfer function technique has found considerable use for the late Pleistocene, but so far has no significant pre-Pleistoceneapplication and will not be considered here.

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CHRISTOPHER R. LLOYD

2.3.2. The Oxygen-Isotope Paleotemperature Method. 2.3.2.1. Introduction. Oxygen-isotope analyses of unaltered carbonate shells of marine organisms provide both quantitative determinations of Cretaceous and preglacial Tertiary marine paleotemperatures and a detailed record of late Tertiary-Quaternary ice volume fluctuations. The first application will be dealt with here. 2.3.2.2. Basic theory. The oxygen-isotopemethod was suggestedby Urey (1 947) and first applied to paleotemperature determinations by Urey et al. (1951). The method has been reviewed by, among others, Craig ( 1 965), Bowen (1966), Hecht (1976), Savin (1977, 1982), Van Donk (1977), Duplessy (1978),Rye and Sommer (1980),and Shackleton (1 982), upon which the following account is largely based. The isotopic equilibrium reaction for the deposition of CaCO, in equilibrium with environmental water is

+

* jCaC1803+ H2I60

jCaC1603 H2180

(2.1) The equilibrium constant for this reaction is not unity and varies with temperature. In other words, there is a temperature-dependent chemical fractionation of the isotopes I8Oand l6Owhen calcium carbonate is precipitated from water. As temperature increases, I6O is preferentially incorporated in the carbonate, i.e., the difference between leg/l6O of the environvaries mental water and l 8 0 / l6Oof the calcium carbonate, expressed as SL80, as a function of water temperature. 6180is conventionally expressed as

In practice, the carbonate samples used are the calcite and, occasionally, aragonite shells of marine organisms, particularly planktonic foraminifera (unicellular zooplankton), benthonic (bottom-dwelling) foraminifera, and mollusks. The standard is calcite from a belemnite (an extinct marine cephalopod mollusk) guard (internal shell) called PDB (because the belemnite came from the Upper CretaceousPeedee Formation in the southeastern / ratios are experimentally determined by mass United States). The l80I6O spectrometric analysis of C02obtained by reacting sample calcite with acid. Analytical techniques are described in detail by Van Donk (1977). Given a knowledge of 6l8O for a sample, the next step is to relate it to a paleotemperature ( T ) . For this purpose, Epstein et al. (1953)grew different species of marine mollusks at constant temperatures under laboratory conditions. From analyses of these shells they established an empirical relationship between 6l80 and temperature (Fig. 2), which Emiliani (1954a) found is also applicable to living benthonic foraminifera. Because Epstein et al. (1953) did not allow for systematic errors in their mass spectrometer

PRE-PLEISTOCENE PALEOCLIMATES

-

0

-3 -2 -1

0

1

2

47

3

6 '*o(per mil) FIG.2. Growth temperatures of mollusk shells plotted against l8O/I6O ratios of shells, expressed as P O relative to the PDB standard. [After Epstein ef al. (1953).]

measurements, Craig (1965)applied correction factorsto their data, deriving a new equation which, in this or slightly modified form (e.g., Shackletonand Kennett, 1975), has been the basis for most subsequent paleotemperature calculations: T ("C) = 16.9 - 4.2(Sc- 8 ),

+ O.13(Sc - SW)'

(2.3)

where S, is P O for C02extracted from the sample, and

where the standard is for standard mean ocean water (SMOW). (Inconsistencies in paleotemperature determinationsthrough the use of slightly different equations are not sufficiently large to significantly affect intercomparisons of results, given the other sources of error, which will be discussed later.) The construction of a temperature scale has been reviewed by Rye and Sommer (1980) and Shackleton (198 1). Given an accuracy of approximately+:0.1 per mil in P O determinations, it should be possible, in theory, to determine Tto +OS"C. However, there are several problems which must be carefully considered before meaningful paleotemperatures can be derived. These are mean isotopic composition of paleo-ocean water, variation of isotopic composition within the oceans, nonequilibrium deposition of shell calcite, postdepositional alteration, and ecological factors. 2.3.2.3. Mean isotopic composition of ancient ocean water. It is clear from Eqs. (2.3) and (2.4) that l8O/l6Ofor the ocean water in which a calcite sample was precipitated must be estimated in order to calculate a paleotemperature. This is difficult because no water samples are available from the geological past. H2160is more volatile than H2180,so polar ice formation

48

CHRISTOPHER R. LLOYD

has preferentially removed l6Ofrom the oceans relative to the ice-free Cretaceous and early Tertiary, and hence the present ocean is enriched in l80 relative to the Cretaceous and early Tertiary preglacial ocean. [In fact, post-mid-Mioceneglobal ice volume fluctuationshave produced an imprint on 6l80values of shell calcitethat is comparableto or greater than that due to the associated temperature variations (Savin and Yeh, 1981). For the preglacial Tertiary and Cretaceous, fluctuationswith time should be principally a function of temperature.] Given a knowledge of the volume and mean isotopic composition of present global ice, the mean isotopic composition of preglacial ocean water can be estimated. A value of - 1.O per mil relative to SMOW (- 1.2 relative to PDB), suggested by Shackleton and Kennett (1975), is now generally accepted. It is important to make this correction in all preglacial determinations, since it is equivalent to a paleotemperature difference of approximately -4 to - 5 "C. [Matthews and Poore (1 980) dissent from the assumption of no significant pre-mid-Tertiary ice. They argue for constancy of equatorial sea surface temperature (SST) and note that correction for no significant global ice gives apparently low (20- 24 "C) early Tertiary equatorial near-surface paleotemperatures.] 2.3.2.4. Variations in isotopic composition within the oceans. Because l60 is more volatile than l8O, l80/l 6 0 of seawater is affected by evaporation, precipitation, and freshwater runoff. Salinity variations throughout the oceans are therefore reflected by lSO/l60variations, and observations show a linear Sl8O-salinity relationship (Epstein and Mayeda, 1953; Craig and Gordon, 1965). At present, surface salinity in the open oceans reflects the evaporation-precipitation balance, showing an equatorial minimum, a subtropical maximum, then a poleward decrease, and is within the range variation of 2.0 per mil. 33.0-38.0 per mil with a corresponding 180/160 Salinity variation of this magnitude could give apparent isotopic temperature anomalies of up to k8 "C. Since some of the present salinity variation is associated with the formation and melting of polar ice, the preglacial variation in the open oceans was probably smaller. Therefore, if analyses are confined to open-ocean organisms, salinity variation should not be a major problem. Conversely, data from shallow water or near-shore environments may be subject to either freshwater dilution or excess evaporation and should be treated with caution. These data include most of the early determinations on belemnites and other fauna from shelf sediments. Note that the deep ocean was possibly more saline during the Cretaceous if significant sinking of subtropical surface water to the deep ocean was occurring, as suggested by Lloyd (1977) and Brass et al. (1982). This would give lower apparent paleotemperatures and should be considered when interpreting results from ocean-bottom organisms. 2.3.2.5. Nonequilibrium deposition of shell calcite. Most modem mol-

PRE-PLEISTOCENE PALEOCLIMATES

49

lusks and foraminifera secrete their shells in isotopic equilibrium with sea water (Savin, 1977). However, some species of planktonic foraminifera do values have been found not (the so-called vital effect), and different l 8 0 / l60 in different growth stages of the same species (Vergnaud-Grazzini, 1976; Berger, 1981). In some cases these effects may be due to individuals occupying different depths at different stages of their life cycles or occupying constant-density surfaces and so being subject to varying temperature/salinity conditions. Hence, apparent species dependence of l 8 0 / l60from planktonic organisms may simply be a function ofdifferent depth habitats of these organisms (Curry and Matthews, 1981). Nonequilibrium fractionation has also been found in different species of benthonic foraminifera. While this disequilibrium is speciesdependent, it is constant for each species (Vinot-Bertouille and Duplessy, 1973; Woodruff et al., 1980). In general, these effects are small (51 per mil) and not significant for paleotemperature studies (Savin, 1977). Calcareous nannofossils, which are carbonate-secreting marine phytoplankton, may also be useful for paleotemperature determinations, especially because they inhabit the near-surface photic zone and thus should be more representative of surface temperatures than are most planktonic foraminifera. While these organisms show a temperature-dependentdisequilibrium fractionation (Douglasand Savin, 1975;Savin, 1977),this departure is relatively constant (Margolis et al., 1975), and temperatures could be inferred given an appropriate calibration. 2.3.2.6. Efects of postdepositional alteration. Even if calcite has been deposited in isotopic equilibrium in water of known composition,postdepositional (diagenetic) changes can alter isotopic composition after burial because recrystallized calcite will have an l 8 0 / l60ratio in equilibrium with pore-water temperature and salinity. Therefore, any evidence for recrystallization, chemical alteration, or encrustation makes a sample useless for analysis (Savin, 1977). Postdepositional alteration is more common in shallow marine sediments, which may have been uplifted and subjected to groundwater percolation, than in relatively undisturbed deep-ocean sediments (Hallam, 198la). For example, belemnites, the principal organism used for determinations from shallow marine sediments, although relatively massive, are often partially or completely recrystallized (Spaeth et al., 1971). Inoceramus, a bivalve mollusk, is also used. Stevens and Clayton (1971) found greater alteration in Inoceramus shells compared to those of other organisms, but Buchardt (1978) and Saltzman and Barron (1982) made apparently successful paleotemperature determinations on Inoceramus from some Cretaceous and Tertiary shallow marine and deep-ocean sediments, respectively. Because postdepositional alteration shifts 6l80toward more negative

50

CHRISTOPHER R. LLOYD

values and apparently higher isotopic temperatures, the lowest temperatures determined from an assemblage analysis should be used if alteration is a possibility (Stevens and Clayton, 1971). There is also evidence that the shallowest-livingindividuals of a given species of planktonic foraminifera may be selectively dissolved when they sink to the ocean floor. This selective solution biases isotopic temperatures to lower values (in planktonic analyses only), possibly by 2 - 3 "C. However, with smaller preglacial vertical temperature gradients the effect would have been less important (Savin, 1977).

Generally, calcite in deep-ocean sedimentsis less subject to alteration, but with increasing depth ofburial and hence age, calcite shellsare more likely to have undergone recrystallization. This is one of the reasons that suitable material for analysis becomes progressively less abundant back through geological time. Killingly (1983) gave evidence for recrystallization based on an analysis of Sr/Ca ratios in calcite that indicated that calcite in most early Tertiary sediments may be completely recrystallized. However, only carefully selected unaltered material is used for analyses, and Van Donk ( 1977) concluded that unaltered foraminifera are available back into the Cretaceous. 2.3.2.7. Ecological factors. An isotopic paleotemperature is of little use unless the depth habitat of the organism analyzed is considered. Different species of planktonic foraminifera occupy different water depths, or may migrate vertically seasonally (they seem to remain in water of constant density) or at different growth stages. Isotopic temperatures from plankton do not therefore indicate surface temperatures, but rather temperatures over a wide range of subsurface depths. Depth ranges of extinct species inferred from their shell morphologiescorrelate with their relative depths as indicated by isotopic paleotemperatures (Savin, 1977). Savin et al. (1975) used this information and a knowledge of relations between modern surface temperatures and planktonic isotopic temperatures to postulate Tertiary tropical surface temperatures 2 - 3 "C above planktonic temperatures at low latitudes, and Douglas and Savin (1978) used isotopic results to determine depth habitats of Tertiary and late Cretaceous planktonic foraminifera. These inferences were, however, based on assumptions about the vertical temperature gradient in the preglacial oceans. When interpreting isotopic temperatures from benthonic (by definition, bottom-dwelling) organisms, the paleodepth should be considered. In all oceans there is a depth at which the rate of dissolution of carbonate rapidly increases (the lysocline). Below this is the calcite compensation depth (CCD), at which the rate of dissolution of calcite equals the rate of supply. Below the CCD, carbonate sediments do not accumulate. The maximum depth at which benthonic foraminifera with carbonate shells can live is

PRE-PLEISTOCENE PALEOCLIMATES

51

restricted by these depths (Douglas and Woodruff, 1981). The CCD has varied through time and location but has mostly been above the deep-ocean floor (Van Andel, 197 5). Therefore, paleotemperature determinations on benthonic foraminifera are done on material originally deposited at intermediate depths on midocean ridges, seamounts, or submarine plateaus. This is an important fact to remember when benthonic results are equated with “deep-ocean” temperatures. Paleodepths can be determined in most cases from a knowledge of present depth, age ofdeposition of the sample,and age of the oceanic basement at that location by using an ocean floor agedepth curve such as that of Sclater et al. (1977). [Resultsfrom the Deep-sea Drilling Project (DSDP) have shown that the mean depth of the ocean floor increases with age, and theoretical models (Parsons and Sclater, 1977)show that this is a function of cooling and density increase of the oceanic lithosphere as it moves in opposite directions away from each side of the midocean ridge at which it was formed. In other words, the midocean ridges are the topographic expressions of the axes of formation of new (hot) oceanic lithosphere. As the lithosphere moves away from each side of a ridge, it cools and sinks. Obviously, there are anomalies such as seamounts and submarine ridges, but it is generally accepted (Sclater et al., 1977)that these maintain the same elevation relative to the surrounding ocean basement through time; paleobathymetric determinations may therefore be made for these also.] Most of the early paleotemperature determinations (summarized by Stevens and Clayton, 1971) were done on Jurassic and Cretaceous belemnites from the sediments of shelf seas. Since belemnites have no living relatives, their depth of habitat is unknown, although Naydin et al. ( 1966)argued from sedimentary associationsthat belemnitesinhabited the deeper parts of these seas. If this were the case, belemnite temperatures could be significantly below the SST. One other consideration should be mentioned here. If carbonate is only precipitated in one season of the year, the resulting isotopic temperature will reflect a seasonal temperature- this, of course, only applies to near-surface organisms. Most modern planktonic foraminifera show a bias toward summer temperatures (Vincent and Berger, 1981). 2.3.2.8. Analyses on materials other than calcite. Some attempts at paleotemperature determinations have been made on materials other than calcite, most notably by Longinelli and Nuti (1973) on fish teeth and bones, and by Kolodny and Epstein (1 976) on silica of deep-ocean cherts. These materials may yield useful results in the future, particularly where carbonate is not available. The silica method may provide a coarse signal useful for periods before unaltered carbonates are available. 2.3.2.9. Conclusion. If the factors discussed above are allowed for and

52

CHRISTOPHER R. LLOYD

results are carefully interpreted with respect to organisms’ habitats, meaningful paleotemperatures, accurate to possibly 3 -4°C (Savin, 1977), can be obtained. A large number of results from late Cretaceous and Tertiary planktonic and benthonic foraminifera and Jurassic and Cretaceousbelemnites are available and will be summarized in Sections 3.3.2 and 3.4.1,

*

2.3.3. Carbon-Isotope Analysis. Biological activity preferentially extracts 12Cfrom the ocean, so the 13C/12Cratio of the oceanic reservoir should be a function of this activity. Ratios of I3C/I2Care determined by the analysis of calcite shells using much the same method as that described for oxygen isotopes. The Cretaceous- Tertiaryrecord shows several major 613C excursions, the significance of which is not fully understood. The dL3C changes in marine carbonates may reflect changes in the oceanic carbon reservoir through time, due to changes in the balance between organic carbon and carbonate deposition. Positive 613Cexcursions indicate increased burial of organic matter and negative excursions indicate increased carbonate deposition. These events reflect changes in biological productivity, the global carbon cycle, paleocirculation, water mass stratification, and the rate of storage of organic matter in sediments. More precise paleoceanographic deductions will have to wait for further refinement of the method (Arthur, 1979; Margolis and Kroopnick, 1980; Berger, 1981).

3. PRE-PLEISTOCENE PALEOCLIMATES AND PALEOCEANOGRAPHY 3.1. Introduction

Paleoclimaticstudies have traditionally been mainly descriptive,i.e., concerned with deducing the nature of ancient climates from evidence in the geological record. Because of this, there is a considerableamount of documented paleoclimatic evidence available. Several summaries of evidence for the evolution of global paleoclimatesexist, notably National Academy of Sciences (1975), Frakes (1979),Habicht (1979), and Crowley (1982). Here we shall not try to emulate these surveys, but rather to look more closely at the nature of the atmospheric and oceanic environments during the last nonglacial maximum [the middle part of the Cretaceous period, about 1 10 to 80 million years (m.y.) ago], and the subsequent cooling which culminated in the late Tertiary -Quaternary glaciation. First, however, the preCretaceous history of global climates will be briefly summarized.

PRE-PLEISTOCENE PALEOCLIMATES

53

3.2. Overview:Major Glacial and Nonglacial Epochs The glacial regime of the late Tertiary and Quaternary has been atypical of most of earth history: the paleoclimaticevidence indicates that major glaciations have occurred through only 5 - 10%of geological time (Crowley, 1982), during the periods schematicallyrepresented in Fig. 1a. Widespread glaciation, indicated by tillites, occurred in central North America and possibly southern Africa and western Australia at various times between 2700 and 2 100 m.y. ago. This episode was followed by approximately 1300 m.y. for which no clear evidence for glaciation is known. There is evidence for intermittent late Precambrian (950-560 m.y. ago) glaciations in all the continents except Antarctica (where the evidence may be obscured by present ice), with glacial maxima occumng approximately 940,770, and 6 15 m.y. ago. Between 450 and 430 m.y. ago (lateOrdovician and early Silurian time), widespread glaciation occurred initially in the western Sahara, which was in a south polar position at the time (Smith et al., 1981), and later in South America. There is also questionable evidence in the Amazon Basin for a mid-Devonian (- 365 m.y. ago) glaciation. The last pre-Tertiary glaciation took place during the late Carboniferous and early Permian (320250 m.y. ago) and affected all the Gondwanaland (southern) continents (South America, Africa, Antarctica, Australia, and India, which formed one supercontinent). During that time Antarctica was centered over the South Pole, and the southern parts of South America, Africa, and Australia were in high southern latitudes (Tarling, 1978: Crowell, 1982). Cyclic sedimentation in distant continents, presumably related to sea level fluctuations,indicates that global ice volume fluctuated significantly through this glacial episode (Crowell, 1982). From 240 (the mid-Permian)until 14 m.y. ago (the mid-Miocene),there is no direct evidence for significant continental ice sheets, although Matthews and Poore (1980) argue that continental ice volume may have been significant from the late Eocene onward. Also, isolated occurrences of ice-rafted material in marine sediments may indicate the existence of winter sea ice in high paleolatitudes during the Cretaceous and early Tertiary (Hambrey and Harland, 1981). Generally, however, the record shows that most ofgeological time has been characterizedby equable climateswithwarm, ice-freepolar regions and high sea levels (Frakes, 1979). The late Tertiary- Quaternary glacial climate is the culmination of a cooling trend that began during the late Cretaceous, around 80 m.y. ago (Savin, 1977). The middle part of the Cretaceous(1 10- 80 m.y. ago) is therefore the most recent and best documented maximum of the warm climates that characterized most of earth history. In the following section, some of the available evidence will be used to build up a picture of the marine and

54

CHRISTOPHER R. LLOYD

terrestrial environments around that time. Then the subsequent changes until the initiation of Antarctic glaciation will be examined.

3.3. The Mid-Cretaceous Climate Evidence for the climate of the mid-Cretaceous was extensively surveyed by Lloyd (1977) and subsequently summarized by Frakes (1979), Barron and Washington ( 1982a), and Lloyd (1 982). Much of the following discussion will be based on these accounts. Some of the most important evidence is plotted in Fig. 3.

3.3.1. Land Climates. Evidence for mid-Cretaceous land climates is qualitative and is based mainly on distributions of climate-sensitive sediments and floras, principally from near-coastal and lowland environments. Despite these limitations it is possible to make significant inferences on evaporation- precipitation balances and on temperature and precipitation gradients. The distribution of mid-Cretaceous coals and evaporites (Nalivkin, 1960; Briden and Irving, 1964; Lotze, 1964; Meyerhoff, 1970; Bohor et al., 1976; Frakes, 1979; Habicht, 1979; Parrish et al., 1982) shows evaporites mainly between 10 and 30" latitude, indicating arid subtropics, and coals poleward of 30°, indicating humid midlatitudes (Fig. 3). Note that the extensive occurrences of Cretaceous evaporites documented, for example, by Frakes (1 979) and Habicht (1 979), are almost all of early Cretaceous age (Meyerhoff, 1970). Many evaporites on the southern continents, particularly on the margins of the newly opened South Atlantic Ocean (similar in width to the present Red Sea), were deposited in restricted marine conditions and essentially continental interior climates. By the mid-Cretaceous, the South Atlantic had widened, and with the establishment of freer marine conditions evaporite deposition there ceased. [Changes in floral communities as indicated by an analysis of fossil spores and pollen from a deep-sea core off the southwest tip of Africa indicate a change from arid conditions in the early Cretaceous to a wetter late Cretaceous climate on the surrounding continents of South America, Africa, and Antarctica, according to McLachlan and Pieterse ( 1978).] Interestingly,Gordon (1975) plotted histogramsofthe percentage area of evaporite in each 10" latitude belt for the present and Cretaceous, and found the Cretaceous median latitude to be 15- 20' (and the minimum at 0- lo's), compared to the present 25-30". Thisindicates that the subtropical anticycloneswere no farther poleward than their present latitudes and were possibly equatorward,contradictingthe classical scenario (e.g., Flohn, 1 964) of poleward-expandedcirculationbelts during the ice-free periods of earth history.

FIG.3. Distributions of Cretaceous paleoclimatic indicators plotted on the paleogeographic map of Lloyd ( 1982). C and E, Mid-Cretaceous (Albian and Cenomanian) coals and evaporites, respectively. L, Early Cretaceous laterites and bauxites. Numbers are paleotemperature determinations ("C) on Albian and Cenomanian belemnites. See text for sources of above. Numbers enclosed in square in the Pacific (longitude is indeterminate) are paleotemperature results on planktonic and benthonic foraminifera of Douglas and Savin (1975). R, Early and mid-Cretaceous warm-water faunas, including rudist bivalves, hermatypic corals, Orbitolina, and keeled globotruncanid foraminifera (from Bergquist, 1971; Dilley, 1971 ;Coates, 1973; Gordon, 1973). S, Early Cretaceous deepocean organic siliceoussediments (Drewry et al., 1974). (O), Cenomanian sediments rich in nontemgenous organic carbon (Pamsh and Curtis, 1982). Dashed line in Eurasia is Albian boundary between the Siberian and Indo-European paleofloristicregionsof Vakhrameev (1964). [From Lloyd (1982), with additions and modifications.]

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CHRISTOPHER R. LLOYD

While Cretaceousdesert sandstones do occur in southeast South America, southwest Africa, and Thailand (Krausel, 196 1; Meyerhoff, 1970; Bigarella, 1972; Stauffer, 1974), those from the former two areas are of early Cretaceous age and thus are products of the same continental interior climates mentioned above. None are known to be of mid-Cretaceous age. Early to mid-Cretaceous bauxite and laterite occurrences are mainly in southeastern North America, southern Europe to central Asia, and north Africa (Berlin et al., 1966; Singer, 1975; Nicolas and Bildgen, 1979; Van Houten, 1982), indicating warm and at least seasonallywet climatesin those regions. There is no clear evidence for an Intertropical Convergence Zone (1TCZ)-associatedequatorial rain belt over Africa: near-equatorial evaporites occur in north Africa and there are no low-latitude coals. However, laterites in northeast Africa indicate at least seasonal precipitation, possibly occurring during equinoctal migration of the ITCZ across the region. Mid-Cretaceousfloras reflect a poleward temperature decrease over North America and Eurasia (Vakhrameev, 1964; Krassilov, 1975, 198 1). Vakhrameev, for example, delineated a boundary between “Siberian” and “IndoEuropean” paleofloristic regions characterized by distinct floral communities, which he concluded was temperature-controlled and which moved northward to the Albian position shown in Fig. 3. In central Asia, floral localities occur north of the zone in which arid lithologiesare found (Barron and Washington, 1982a), indicating a northward transition to a more humid climate. In east Asia, floras indicate a change from relatively humid conditions near the coast to a more and climate inland (Krassilov, 1973). Given that the east Asian margin was mountainous (Lloyd, 1982), this change was probably related to orographic influences. Interestingly, coals, evaporites, and intermontane basin deposits are closely associated in this region (Kobayashi and Shikama, 196 l), indicating that precipitation varied greatly over small distances, presumably due to orographic influences. A notable feature of mid-Cretaceous floras is the occurrence of “cool temperate” to “subtropical” assemblages (based on leaf morphology and proportions of warm and cool taxa) in northern Alaska and the Soviet Arctic at 70- 80” paleolatitude (Smiley, 1967, 1970; Krassilov, 1975). These assemblages are important because of the lack of high-latitude isotopic paleotemperature determinationsfor this time. In the Southern Hemisphere, an early to mid-Cretaceous warm flora occurs on Alexander Island, off the southwest Antarctic Peninsula (Taylor, 1972).

3.3.2. Marine Environments. 3.3.2. I. General. Marine faunal distributions clearly show the existence of both latitudinal and longitudinal temperature gradients in the near-surface waters of the mid-Cretaceous oceans.

PRE-PLEISTOCENE PALEOCLIMATES

57

A low-latitude “Tethyan Realm” is characterized by reef-building hermatypic corals and rudist bivalves and by Orbitolina, a large foraminifer (Fig. 3). It is generally accepted that these organisms indicate a tropical marine environment (e.g., Coates, 1973; Matsumoto, 1973). The Tethyan Realm was separated by an approximately east -west boundary to the north from the “Boreal Realm,” characterized by distinctly different assemblages, lacking the warm-water organisms and generally assumed to indicate temperate (but not cold) near-surface marine temperatures (Middlemiss, 1973). Most groups of organisms, such as calcareous nannofossils (marine phytoplankton), bivalves, gastropods,ammonites, and foraminifera show characteristic high- and low-latitude assemblages,and diversity decreases poleward (Bergquist, 1971; Sohl, 1971; Kauffman, 1973; Matsumoto, 1973; Berger and Roth, 1975; Roth and Bowdler, 1981). Cretaceous reef corals on Queen Charlotte Island (Sohl, 1971), at an apparent paleolatitude of approximately 60”N, occur in a displaced terrane that has probably accreted to North America and moved northward relative to the stable continent since the mid-Cretaceous. [See Saleeby (1983) for an account of the tectonic evolution of this margin.] In the Southern Hemisphere, an “Austral Realm,” analogous to the Boreal Realm, has been recognized (Stevens, 1971;Scheibnerovh, 1973),but even in West Antarctica foraminifera indicate moderate water temperature (Webb and Neall, 1972). Mid-Cretaceous calcareous nannofossil assemblagesindicate weak latitudinal gradients in surface water properties, but there are Boreal and Austral assemblages poleward of 40” latitude (Roth and Bowdler, 1981). Faunal distributions also indicate east -west temperature gradients in near-surface waters, which were presumably related to surface currents and upwelling (Gordon, 1973). For example, the occurrence in Japan of early and mid-Cretaceous rudists and Orbitolina, which are absent at similar latitudes in the northeast Pacific, indicates anomalous warmth, presumably due to a northward-flowing ocean current (Fig. 3). Pacific ammonoid distributions give a similar picture. Similarly, along the east coast of Africa, Tethyan ammonoids occur in Madagascar and southern Africa, and rudists and Orbitolina occur in Aptian- Albian sediments as far south as southern Tanzania, whereas they are absent at similar paleolatitudes in the southeast Pacific, indicating a warm, presumably southward-flowingcurrent along the east African coast through the mid- and late Cretaceous. On the South Atlantic margins, absence of warm-water faunas on the African side and the occurrence of warm-water keeled globotruncanid foraminifera as far south as Argentina on the west side (Bergquist, 1971) indicate a counterclockwise gyre. The occurrence of Orbitolina in upper Lower Cretaceous sediments from Flemish Cap (at the edge of the North American continental shelf 700 km east of Newfoundland), of Cenomanian tropical planktonic foraminif-

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CHRISTOPHER R. LLOYD

era at nearby Orphan Knoll, and of Orbitolina in the Cenomanian of Ulster indicates the development of a “proto-Gulf Stream” by the mid-Cretaceous, advectingwarm water northward and eastward alongthe continental margin of eastern North America (Berggren and Hollister, 1974). The occurrence of Tethyan faunas in the Caribbean-Gulf Coast region indicates migration from the Tethyan region (the ancestral Mediterranean and Indian Ocean), and hence east to west surface flow across the low-latitude North Atlantic (Berggren and Hollister, 1974). Generally, the homogeneity of the Tethyan fauna is indicative of an unrestricted circumglobal equatorial circulation (Gordon, 1973). The locations of open-ocean upwelling can also be recognized from the geological record. Drewry et al. (1974) showed that deep-ocean Lower Cretaceous siliceous sediments(formed from the shells of planktonic radiolaria) were deposited in a belt corresponding with the paleoequator (Fig. 3). They related these sediments to nutrient-rich surface waters, resulting from organized upwellingin an equatorial current system induced by steady trade winds. This indicates that the subtropical anticyclonescould not have been significantlymore poleward than their present positions. If they had been, a consideration of the global surface angular momentum balance indicates that weak, variable equatorial winds would have resulted, giving patchy upwelling that is inconsistent with the evidence. Near-coastal upwelling can also be recognized. Parrish and Curtis (1 982) related organic-rich shallow marine sedimentsto upwelling-related zones of high nutrient supply and phytoplankton productivity for several intervalsin the Mesozoic and Tertiary. For the Cenomanian they plotted sediment distributions (Fig. 3) along with hypothetical atmospheric circulation and wind-induced zones of coastal upwelling on a paleogeographic reconstruction. They found a statisticallysignificant correlation between the sediment distribution and predicted upwelling areas. High abundances of calcareous nannofossils in mid-Cretaceous sediments of the eastern (relative to the western) North and South Atlantic indicate high productivity and probable upwelling at those locations (Roth and Bowdler, 198 1). Evidence from deep-sea sediments has also provided direct indications of atmosphericcirculation. Analysesof the eolian component from a deep-sea core in the Pacific, at approximately 10’S in late Albian time [asindicated by the 100-m.y. paleoceanic map of Firstbrook et al. (1979)], showed a maximum in the mass accumulation rate at that time. This could be due to a maximum land source area (sea level was relatively low) in South America and Africa, on the assumption that winds were uniformly sluggish through the Cretaceous (Rea and Janecek, 198 1). However, vegetation changes in the source area, or the northward drift of the Pacific plate through the Cretaceous, could also have influenced this result.

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PRE-PLEISTOCENE PALEOCLIMATES

3.3.2.2. Mid-Cretaceous oxygen-isotope paleot ernperature results. Shelf temperatures are indicated by isotopic data on mid-Cretaceous belemnites, which have been reported by numerous investigators; Urey et al. (1951) provided the first report, and subsequent summaries were given by Bowen (1966), Naydin et al. (1966), Stevens and Clayton (1971), Savin (1977), Barron and Washington (1982a), and Lloyd (1982). These data are plotted in Fig. 3. As noted in Section 2.3.2, depth of habitat and season of shell formation are unknown, so care should be exercised when relating results to SST. Two problems are that different workers used different equations to calculate temperature from P O , and also most did not correct P O to allow for lack of polar ice. The former problem does not introduce a significant inconsistency into the results, given the other sources of uncertainty. However, the now-accepted - 1.00 per mil adjustment for preglacial ocean water relative to SMOW does mean that the early determinationswere about 4 - 5 "Ctoo high. Hence the results shown in Fig. 3 are 4°Clower than those originally reported. The lowest temperatures from each series of analyses are plotted, since these represent the samples that have been least subject to postdepositional alteration and freshwater dilution. Unfortunately, most data are concentrated in limited geographic regions, particularly western Europe, southern Russia, eastern India, Australia, and New Zealand, which were almost all at 30-45 paleolatitude except for New Zealand at 55 60"s. They do show general consistency, being mostly in the 15-20°C region (Fig. 3). The only open-ocean isotopic paleotemperature data for the mid-Cretaceous are those of Douglas and Savin (1975) from the Shatsky Rise in the northwest Pacific, which 100 m.y. ago was at approximately 5 " s according to Firstbrook et al. (1979). Paleotemperatures were calculated using the equation of Craig (1965) and recalculated by Savin (1977)to correct for the lack of polar ice by assuming that P O of ocean water was - 1.00 per mil relative to SMOW (see Section 2.3.2 for an account of the theory). Results were 25-26°C for planktonic foraminifera and 15.5"C for benthonic foraminifera. The planktonic data probably represent a surface temperature 2 - 3 "Chigher, dependent on the depth of habitat and the near-surface vertical temperature gradient at the time. The significance of the benthonic results depends on the mid-Cretaceous paleodepth at this location. The benthonic samples came from DSDP sites 47, 50, and 305, at present depths of 4700 - 2900 m. The oceanic basement in the Shatsky Rise region is 130- 140 m.y. old, hence it was 30-40 m.y. old during the mid-Cretaceous. Using this information and the ocean floor age-depth curve of Sclater et al. (1977), we can deduce that the sample sites were at approximately 3500-2000 m during the mid-Cretaceous, and they therefore represent intermediate-depth ocean temperatures, probably close to deep-ocean O

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CHRISTOPHER R. LLOYD

temperatures. Savin (1977) related the benthonic temperature to that of polar surface waters, assuming that these sank to form deep water. However, if a component of deep water was derived from sinking saline subtropical water, polar temperatures could have been lower (Lloyd, 1982). Therefore the benthonic result cannot be unequivocally equated with polar SST. 3.3.2.3. Cretaceous oceanic anoxic events. The middle part of the Cretaceous was characterized by three episodes (1 17- 100, 93-90, and 86-77 m.y. ago) of organic carbon deposition in the deep ocean (the last episode was minor), reflecting a lack of dissolved oxygen in depositional environments at those times (Schlanger and Jenkyns, 1976; Weissert, 198 1). These oceanic anoxic events are recognized from deep-ocean drilling data, mainly in the North and South Atlantic oceans, but also in the Indian Ocean, on Pacific seamounts (Jenkyns, 1980), and in the Mariana Basin of the west Pacific (Moberlyet a/., 1983). These events clearly have paleoceanographic, and hence paleoclimatic, implicationsand may have some relation to atmospheric COz excursions (Berger, 1977). Some workers (Schlanger and Jenkyns, 1976; Fischer and Arthur, 1977; Thiede and Van Andel, 1977) have related anoxic events to expansion of the intermediate-depth oxygen-minimum layer due to high temperatures (low oxygen solubility) and sluggish circulation (the result of the low equator pole temperature gradient). This hypothesisis supportedby the observation that the organic- carbon-rich sediments in the Pacific were mainly deposited at less than 2 km paleodepth on submarine rises (Thierstein, 1979; Weissert, 198 1). Others have postulated the stagnation of entire ocean basins (Ryan and Cita, 1977; Arthur and Natland, 1979), which could have been induced by salinity stratification (Ryan and Cita, 1977; Thierstein, 1979) and/or a low equator-pole temperature gradient (Degens and Stoffers, 1976). According to this hypothesis, reduced overturn would have resulted in inadequate renewal of oxygen-depleted deep water. It seems that anoxia in the South Atlantic (which was similar in width and bathymetry to the present Red Sea) can be explained by stagnation due to salinity stratification. Hypersalinity in the Angola -Brazil and CapeArgentine basins of the South Atlantic during Aptian - Albian time is indicated by carbonate- mudstone rhythms, dolomite chemistry, and clay mineralogy. The Angola-Brazil Basin of the northern South Atlantic was tectonically isolated by barriers to the north and south until late Albian time, when shallow marine connections opened. Also, the Cape -Argentine Basin, south of the Walvis Ridge, was restricted to the south by the Falkland Plateau (Arthur and Natland, 1979). The evaporitic Angola - Brazil Basin may have periodically supplied dense saline water to the Cape- Argentine Basin and possibly to the North Atlantic and Pacific, causing intermittent stable stratification and anoxia in those oceans. It is possible that these

PRE-PLEISTOCENE PALEOCLIMATES

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overflows may have been related to climatic changes around the South Atlantic margins (Thierstein and Berger, 1978). As the interbasin sills subsided through the late Cretaceous,barriers to deep circulationwere removed, and anoxia became less frequent as deep circulation was established (Tissot et al., 1979). Late Cretaceous stagnation in the Caribbean but not the Pacific ended in the Santonian, possibly through removal of a “Panama” sill which had prevented deep interchange with the Pacific (Saunders et al., 1973). Hay et al. (1982) pointed out that high organic carbon input, as well as stagnation and temperature effects, is important in the development of anoxia. High carbon input can be due to increased planktonic productivity in surface waters, reflecting nutrient supply through upwelling, or to high input of temgenous carbon (vegetation debris) to the ocean from rivers. Organic geochemical analysis shows that carbon came predominantly from terrigenous sources in the western North Atlantic and from marine sources in the eastern North Atlantic (Roth and Bowdler, 1981). These models and interpretations of oceanic anoxic events have recently been challenged. Southam et al. (1 982) questioned conventional interpretations of anoxic events with the aid of results from a one-dimensional advective-diffusive ocean model, including C, 0, and P concentrations. They suggested that anoxic events may be associated with vigorous deep circulation giving high surface productivity, possibly at times when sea level rise gives an increase in the flux of warm saline bottom water derived from subtropical shallow seas to the deep ocean. Johnson (1982) criticized the simplistic deep circulation models with their concepts of nonmixing water masses and without consideration of abyssal teleconnections that have been proposed by geologists in recent years to explain anoxic events and other features in the deep-sea record. He pointed out that the present deep circulation, even though poorly understood,is far more complexthan any of these models, and the circulation was probably similarly complex during the Cretaceous. 3.4. The Late Cretaceous to Late Tertiary Cooling Trend

Having reviewed evidence for the state of the mid-Cretaceousatmosphere and oceans, we can now follow the transition from the equable, ice-free Cretaceous to the glacial late Tertiary and Quaternary. 3.4.1. The Marine Isotopic TemperatureRecord, 3.4.1,l. The late Cretaceous. Shelf temperature trends are indicated by the belemnite paleotemperature data compiled by Stevens and Clayton

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CHRISTOPHER R. LLOYD

(1971) mainly using minimum values from multiple determinations. It should be remembered that these data may partially reflect high or low salinities in shelf seas, and also that the sites have moved latitudinally through time. The data (Fig. 4)for Europe, the USSR, Australia, and New Zealand all show a rise to a Coniacian - Santonian maximum, followed by a decrease to the end of the Cretaceous. Note that temperatures are reduced by 4°Cfrom those originally reported to allow for a 6'*0of preglacial ocean water of - 1 per mil relative to SMOW. A compilation by Savin ( 1977) shows similar trends. The oceanic paleotemperature record gives a much clearer picture of global trends (Fig. 5). Data from planktonic and benthonic foraminifera from the Shatsky Rise in the (Cretaceous)equatorial Pacific show an apparently smooth temperature decrease (which may, however, be an artifact of inadequate sampling) to an end-Cretaceous minimum, and a nearly constant 6 - 7 "C difference between bottom and near-surface temperatures. The total decrease was 8 - 10°C, with end-Cretaceous near-surface and bottom temperatures of 18-20 and 10- 12"C, respectively (Savin, 1977; Douglas and Woodruff, 198 1). Boersma and Shackleton (1 98 l), using samples from the same area, found Turonian -Coniacian and late Campanian near-surface temperature maxima, decreasing to an early Maastrichtian minimum. They found a similar bottom temperature decrease from 16 to

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f

M

I Comp I S icon1 Tur I

Cen

I

I

70

~

I

80

l

90

I I

I-

'.

I

Alb

Apt

I

100 110 Million years

I

I Bor

IHoutl VoI leer

I

120

~

I

I

I

130

FIG.4. Cretaceousisotopic paleotemperature trends for shelf seas from oxygenisotope analyses of belemnites. Modified from Stevens and Clayton (1 97 1). Curves are for minimum temperatures of each group of analyses, except for (f) and (g), which are averages. (a) New Zealand, from Stevens and Clayton (1971); (b) Australia-New Guinea, from Lowenstam and Epstein (1954), Dorman and Gill (1959), Bowen (1961a), and Dorman (1968); (c) western Europe, from Lowenstam and Epstein (1954); (d) southern France, from Bowen and Fontes (1963); (e) USSR, from Naydin et al. (1966), Teis et al., (1957), and Berlin et al. (1966); (f) Europe (average), from Bowen (1 96 1b) and Dorman ( 1 968); (g) USSR (average), from Naydin et al. (1966). Time scale from Van Hinte (1976).

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-Subantarctic ---'Tropical Pacific

-31

._..

+4 1 LblE

L I E LlMlELIE M I C I S I C I T I C I A I A I B

P Mio 1

ED

Olig 1

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1

Pal 1

Cretaceous 1

1

1

1

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1

FIG.5 . Cretaceous- Tertiary oceanic paleotemperature trends from oxygen-isotopeanalyses of planktonic nannofossils and foraminifera and benthonic foraminifera from deep-ocean cores. Subantarctic (southwest Pacific) results from Shackleton and Kennett (1975); tropical Pacific from Douglas and Savin (1973, Savin et al. (1975), Savin (1977), and Douglas and Woodruff (1981). Benthonic curves run through averages of results, and planktonic curves through warmest (nearest surface) results. Tropical Pacific planktonic curve probably represents temperatures 2 - 3 "Cbelow SST. Paleotemperature scaleis from Shackletonand Kennett (1975) and is inapplicable to post-mid-Miocenetime (14 m.y. ago) because of ice sheet formation. Note that the tropical Pacific locations moved northward through the time span shown, from 10"s to -30"N (e.g., Firstbrook ef al., 1979), and so some of the signal reflects this movement. Time scale from Van Hinte (1976) and Berggren and Van Cowering (1974).

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- 10°Cin the later Cretaceous. Late Cretaceous near-surface paleotemperatures of approximately 14°C from high southern latitudes were similar to bottom temperatures elsewhere (Margoliset al., 1977), indicating a high-latitude source for bottom water. However, in the Angola-Brazil Basin of the South Atlantic during Campanian - Maastrichtian time, the highest paleotemperature results from the bivalve Inocerarnus are those from the deepest parts of the basin (Saltzman and Barron, 1982),indicating that there, at least, warm saline surface water was sinking to the deep ocean. 3.4.1.2. The Cretaceous- Tertiary boundary event. The CretaceousTertiary (K-T) boundary was a critical time in earth history, when over half of the species of organisms inhabiting the planet became extinct in the most severe event of its kind during the last 250 m.y. At the boundary (65 m.y. ago), several major faunal groups that had dominated Mesozoic communities became extinct, including the dinosaurs, many shallow marine organisms, particularly the ammonites and belemnites, and the rudists and inoceramids (bivalves). Most calcareous phytoplankton and planktonic

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CHRISTOPHER R. LLOYD

foraminifera became extinct over a period of a few thousand years, as recognized in continuous sections of marine sediments through the boundary, and the diversity of almost all fossil communities decreased (Russell, 1979; Thierstein, 1982). However, benthonic foraminifera showed no evolutionary change across the boundary (Douglasand Woodruff, 198 1). Land plant communities in western interior North America showed only gradual change (Hickey, 198 I), but in east Asia many characteristicMesozoic genera of ferns and gymnospermsbecame extinct (Krassilov, 198 1). In summary, swimming marine organisms in the tropics and subtropics (fish and marine reptiles) were most affected, bottom-dwelling (deep and shallow)and floating marine organisms less affected, and terrestrial and freshwater organisms least affected. The marine extinctions are thought to have been synchronous within the limits of stratigraphic resolution, but it is difficult to correlate these with the land extinctions (Russell, 1979). Also, there is some evidence from a continuous sedimentary sequence in Montana that the main land plant extinctions occurred 80,000 years after the dinosaur extinctions (Archibald and Clemens, 1982). Many hypotheses have been proposed to account for the K-T event, including (1) a sudden release of low-salinity water into the oceans from a previously isolated Arctic ocean, directly causing the near-surface marine extinctionsand possibly causing land extinctionsthrough associated climate changes (Gartner and Keany, 1978; Thierstein and Berger, 1978); (2) an atmospheric C02increase resulting from massive extinction of marine phytoplankton (due to a rise in the CCD), which caused warming and the land extinctions (McLean, 1978); (3) a nearby supernova or unusual solar flare (Russell, 1979); or (4) an impact of a 10-km-diameter comet or asteroid (which would account for anomalously high iridium contents in K-T boundary clays worldwide), which could have caused the extinctions through a prevention of photosynthesis (Alvarez et al., 1980) or through cooling due to stratospheric dust (OKeefe and Ahrens, 1982), poisoning of the oceans by cometary debris (Hsii, 1980), or a temperature rise due to release of impact energy and injection of water vapor into the atmosphere (Emiliani et al., 1981). In northern New Mexico, the iridium anomaly occurs at the stratigraphic level at which several species of Cretaceous pollen disappear, indicating synchroneity with the land plant extinctions (Orth et al., 1981). Officer and Drake (1983) and Rampino and Reynolds (1983) have questioned the impact hypothesis, because the clay mineralogy of the high-Ir boundary clays indicates that they contain locally derived components and altered volcanic ash, which may account for the Ir content. Officer and Drake also found high Ir concentrations at levels above and below the boundary and no support for the shallowing of the CCD. The extinctions followed a long period of warm, equable climates, high

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biotic diversity, and high sea level (Arthur, 1979). Could they have been related to climatic change? The isotopic paleotemperature data of Saito and Van Donk ( 1974) for the South Atlantic and of Douglas and Savin ( I 975) for the north equatorial (paleolatitude) Pacific show early to middle Maastrichtian temperature falls of a few degrees centigrade, followed by a late Maastrichtian to early Paleocene warming, except for a South Atlantic near-surface decrease through the boundary. However, their sampling resolutions were not sufficientlygood to detect any short temperature departure at the K-T boundary (Savin, 1977). Boersma et al. (1979), using closer (- 1 m.y.) sampling intervals in North and South Atlantic cores, found a rapid surface and deep temperature rise across the K- T boundary, with the greatest rise at the bottom and at high latitudes, to an earliest Paleocene maximum. Also, the oxygen-isotope data of Utolle (1979) on bulk carbonate samples composed mainly of planktonic nannofossils from the North Atlantic off Portugal show a negative S1*0“spike” (i.e., higher temperature pulse) just before the K-T boundary. Isotopic evidence from shelf seas also indicates late Maastrichtian warming (Stevens and Clayton, 197 1). Conversely, changes in floras of east Asia (Krassilov, 1978), Wyoming- Montana (Hickey, 198 l), and elsewhere (Axelrod, 1981) indicate cooling across the boundary. In Saskatchewan, Maastrichtian tropical - subtropical animal-pollinated floras were replaced by Paleocene wind-pollinated plants characteristic of a more seasonal climate (Gartner and McGuirk, 1979). Therefore, the evidence for marine and land surface temperature trends is somewhat contradictory. There is no reason why the isotopicpaleotemperature evidence, at least, should not represent a real temperature signal. However, the data lack the time resolution necessary to show a brief temperature excursion that could have been associated with the extinctions-a brief but large unresolved climatic change is therefore a possibility. However, the long-term change that is resolved has a smaller magnitude than do the cooling events at the Eocene-Oligocene boundary and in the middle Miocene (see later). Whatever the cause and duration of the K-T boundary event, it is important to note that a brief but rapid global sea level fall of 100 m occurred in latest Maastrichtian time (Vail et al., 1977). This fall would clearly have reduced the area of shelf seas and thus increased the global albedo -a direct climatic effect. Also, ocean circulation would have been affected through shallowing of shelf seas and a change in source areas for deep-ocean water. These primary effects may have been accentuated through feedback mechanisms, both in the ocean -atmosphere system and through the biosphere. Climate-modelingstudies could contribute to an understanding of events at this time. 3.4.1.3. Tertiary. During Tertiary times, sampling intervals are 1 m.y.

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CHRISTOPHER R. LLOYD

or better. This factor, along with a greater proportion of unaltered samples, has given a much better paleotemperature record than for the Cretaceous. The now-classic results of Emiliani (1954b) first showed the decline in Tertiary ocean bottom temperatures (from 10 to 2°C on his temperature scale) from analyses of Oligocene, Miocene, and Pliocene benthonic foraminifera from a deep-ocean core in the eastern equatorial Pacific. Emiliani equated these results with high-latitude surface temperatures, assuming that bottom water was formed by sinking of polar surface water as occurs at present. Subsequent results have mainly served to improve spatial and temporal resolution of the temperature record. The Tertiary record is noteworthy for the progressive divergence of tropical planktonic and benthonic curves, which reflects increasing vertical temperature stratificationand also showsthat surface cooling was a high-latitude phenomenon (Fig. 5). The curve shows a series of bottom water coolings and warmings (Douglas and Savin, 1975; Shackleton and Kennett, 1975; Boersma and Shackleton, 1977; Keigwin, 1980; Douglas and Woodruff, 1981). The Eocene was the last period with fully nonglacial, Cretaceous-like climates. Shackleton and Boersma ( 1981) plotted all their Eocene data on a 50-m.y. paleoceanic reconstruction to show a small equator- pole surface temperature gradient and east -west temperature anomalies in the North and South Atlantic consistent with expected current directions. Bottom temperatures showed no significant latitudinal variation, being in the range 9- 12°C. This uniformity and closeness to subantarctic SST indicates that bottom water formed at a limited number of high-latitude locations and spread throughout the deep oceans (Savin and Yeh, 1981). The overall trend was for coolingafter the middle Eocene. The first major cooling event occurred at the Eocene-Oligocene boundary (- 38 m.y. ago) (Kennett and Shackleton, 1976). It is recognized in surface and bottom (intermediate depth) subantarctic waters (Shackleton and Kennett, 1975; Keigwin, 1980), tropical Pacific (Douglas and Savin, 1975; Keigwin, 1980) waters, and in deep water of the North Atlantic west of Portugal (Utolle, 1979; Vergnaud-Grazzini el al., 1979) and the Bay of Biscay (Miller and Curry, 1982). Near-surface temperatures declined from 16- 20°C in the late Paleocene to 8°C during the Eocene in the Bay of Biscay (VergnaudGrazzini et al., 1978), and isotopic data on shallow-water benthonic mollusks from around the southern North Sea show a similar trend, with a rapid fall from - 25 to 10°C following a middle Eocene maximum (Buchardt, 1978). [Matthews and Poore (1980) suggest that the increase in isotopic ratios represents the withdrawal of I6Ofrom the ocean by the formation of an East Antarctic ice sheet. They base their argument on an assumed constancy of tropical near-surfacetemperatures through the Cenozoic, whereas the tropi-

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67

cal Pacific planktonic curve of Douglas and Savin (1 975) shows an apparent temperature decrease. However, the data of Keigwin (1 980) for a site in the West Philippine Sea show only a slight change in planktonic P O while benthonic 6l80increases in the earliest Oligocene, indicating that a significant change in mean oceanic isotopic composition did not occur and that tropical deep water cooled more than surface water. Also, an increase in abundance of ice-rafted material in mid- to late Oligocene deep-sea sediments around Antarctica (Margolis et al., 1977)indicates that glaciers did not reach the coast until this later time, and there is no clear evidence for a large ice sheet until mid-Miocene time (Hambrey and Harland, 1981). However, a minor terminal Eocene sea level fall (Vail and Hardenbol, 1979) may reflect some ice development. There is also the possibility that the change to heavier oxygen-isotope ratios resulted from increased salinity of the deep ocean, perhaps through a reduction in precipitation over the waters immediately adjacent to Antarctica (Berger et al., 1981). If this were the case, it is difficult to see why the subantarctic planktonic and benthonic isotopic results (Fig. 5 ) should vary in unison. If bottom water salinity increased, then the benthonic ratios should increase more than the planktonic ratios.] A rapid mid-Miocene (- 14 m.y. ago) P O increase in bottom waters and subantarcticsurface waters is generallyrelated to Antarctic ice cap formation (Douglas and Woodruff,1981). It is interesting that tropical Pacific nearsurface temperatures, which had fluctuated in parallel with bottom temperatures since the mid-Cretaceous, showed a rise duringthe Miocene, indicating a strengthening of the thermocline (Savin, 1982). In high southern latitudes, however, surface temperatures continued to fall in parallel with lowand high-latitude bottom temperatures (Shackleton and Kennett, 1975). These results indicate a marked increase in the equator-pole surface temperature gradient (which had only increased gradually from late Cretaceous to mid-Miocene time) and hence a major change in global heat transport mechanisms. Late Tertiary and Quaternary glacial climates have been reviewed extensively elsewhere (National Academy of Sciences, 1975; Frakes, 1979; Crowley, 1982) and will not be covered here. However, it is important to remember that after the mid-Miocene formation of extensive Antarctic ice, fluctuations in global ice volume caused a stronger isotopic signal than did the associated temperature changes (Savin and Yeh, 1981). 3.4.2. Marine Biogeography. During the late Cretaceous the tropical Tethyan Realm at first increased in width, possibly reflecting sea level rise rather than temperature increase, but toward the end of the Cretaceous, faunal distributionsreflect an increase in latitudinal gradients. The pattern

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CHRISTOPHER R. LLOYD

showed much greater change in the Tertiary, with a narrowing of the tropical belt (Berger and Roth, 1975; Berggren and Hollister, 1977; Berger, 1981). The biogeography of nannofossils and planktonic foraminifera in the early Tertiary North Atlantic shows a series of expansions and contractions of the tropical and extratropical assemblages, which were related by Haq et al. (1977) and Haq (1982) to warming and cooling cycles that, they claim, correlate with temperature changes indicated by floras and oxygen-isotope data. In the Caribbean, intermittent incursions of higher latitude radiolarian faunas during Paleocene -Oligocene times may be associated with climatic fluctuations(Maurasse, 1979). The North Atlantic record of marine nannoplankton shows that the most poleward (50- 55 "N) excursions of low-latitude assemblages(now found at 5 25 -28 ON), reflectingthe warmest near-surface waters, occurred during early Eocene time. Another warm interval in the late Eocene was followed by Oligocene cooling. A sharp boundary migration presumably reflectinga temperature decrease occurred in the late middle Miocene (the early Miocene record is unclear). These trends are similar to those shown by North American land floras (Wolfe and Poore, 1982). In New Zealand, numerous studies of marine faunas, including nannoplankton and coral distributions,coiling directionsof planktonic foraminifera, and diversity of mollusks, have been used to derive Tertiary paleotemperature curves that, despite some differences, show general agreement with the isotopic data, indicating major late Eocene to early Oligocene and Miocene cooling episodes (Savin, 1977). Marine fauna from the west coast of North America, particularly corals, show a relatively steady Eocene to middle Pliocene temperature decrease. Only some of the data show the late Oligocene-early Miocene warming seen in the isotopic data, but this may be due to poor sampling (Savin, 1977). It should also be remembered that fragments of westernmost North America have moved northward relative to the stable continent during the Tertiary (Saleeby, 1983). 3.4.3. Marine Sediments. Rea and Janecek (198 1) found a high accumulation rate of eolian dust through the late Cretaceous in a central Pacific core (Cretaceous equatorial paleolatitude) with a high Maastrichtian volcanic component (smectite). They related the high accumulation rate to land surface aridity, and changes in rate to source area changes resulting from sea level changes. [Note that Chamley (1979) did not recognize smectite as a volcanic indicator, but rather as a hot-climate soil mineral.] In central North Pacific Tertiary sediments, Janecek and Rea (1983) found a Paleocene- Eocene boundary peak in accumulation rate that was possibly due to volcanism, but a decrease in grain size, indicating decreased wind speeds. Accumulation rates were low and grain size small through the

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Eocene. Grain size was larger, indicating greater wind speeds, during the periods 37 - 30 m.y. (early Oligocene), 20- 10 m.y. (early and middle Miocene), and since 5 m.y. Davies et al. (1977) correlated a mid-Eocene peak in oceanic detrital sedimentation with increased precipitation and runoff on the continents at that time. Similarly, an increase in continent-derived detrital sedimentation rate in the North Pacific and Atlantic oceans over the past 15 m.y. has been related by Donnelly (1982) to increasing precipitation irregularity and decreased surface vegetation cover during this period. In deep-ocean sediments off the North Atlantic margins, clay mineral assemblages(assumed to be derived from soils on nearby land) changed from the late Cretaceous into the Tertiary from warm-climate smectite-rich assemblages to cool-climate assemblages with illite, chlorite, and the primary minerals quartz and feldspar. Some of this change may also reflect increased deep-water transport from high latitudes (Chamley, 1979). 3.4.4. Paleofloras. Changes in floras indicate that land surface temperatures changed in a way similar to marine temperatures through the late Cretaceous and Tertiary (Fig. 6). Note that the absolute temperature values shown in the figure are very approximate and that floras respond to other climatic and environmental variables in addition to temperature.

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CHRISTOPHER R. LLOYD

Taxonomic and morphological characteristics of floras of north Alaska, west Greenland, the eastern United States (Smiley, 1967),the USSR (Krassilov, 1975, 1981), and east Asia (Kobayashi and Shikama, 1961) indicate progressive mid- to end-Cretaceous cooling and subsequent warming to a mid-Eocene peak. Tertiary floras of the Northern Hemisphere have been reviewed by Wolfe ( 1978) and Wolfe and Poore ( 1982). They are notable in that Paleocene early Eocene floras at 70- 80"N paleolatitude in Spitzbergen (Schweitzer, 1980) and in west Greenland, Alaska, and northern Siberia (Wolfe, 1980) indicate high annual-mean temperatures (- 15"C). Similarly, in Ellesmere Island, early Eocene mammals (McKenna, 1980), and reptiles similar to those of the present southeast United States and China (Estes and Hutchison, 1980),probably indicate warm climates and a rarity of freezing temperatures. The plant assemblages also indicate low annual temperature ranges and heavy precipitation. Floral change shows that Arctic cooling began in the late Eocene. Wolfe and Poore (1982)compared the records of southeast North American floraswith the Atlantic marine isotopic and faunal record, finding agood correlation of major temperature trends, with warm periods in the early Paleocene, end-Paleocene-early Eocene, late middle Eocene, and latest Eocene. These floras indicate increasing dryness through this period. Wolfe and Poore suggested that mean annual temperatures of 25°C extended to at least 36"N paleolatitude in the early to middle Eocene of southeast North America. Floras elsewhere in North America clearly reflect the Eocene- Oligocene boundary cooling event which is so clear in the marine isotopic record (Wolfe, 1978). Tropical floras at 40 - 45 on the west coast and subtropical floras in southeast Alaska (Wolfe, 1978)are, again, probably at those apparent paleolatitudes because of poleward tectonic transport. Oligocene floras at high latitudes in western North America indicate cooler temperatures and higher annual temperature ranges (Wolfe, 1978), with some warming by the latest Oligocene, reaching a middle Miocene high followed by late Miocene cooling. These floras also indicate an Oligoceneto Miocene decrease in summer temperatures and a change from summer to winter maximum precipitation (Wolfe and Poore, 1982). Savin (1977) has shown that previously published Tertiary temperature curves for Japan and western Europe are similar generally, but not in detail, to those for North America, showing late Paleocene (Europe) or late Eocene (Japan) maxima followed by progressive cooling. Both curves indicate a middle Paleocene minimum (Fig. 6). O

N

3.4.5. Tertiary Global Cooling and the Initiation of Antarctic Glaciation. The paleoceanographic and paleoclimaticevents leading up to Antarc-

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tic and later Arctic glaciation appear to have been related to the opening or closing of several oceanic gateways, particularly the separation of Australia from East Antarctica, and the Antarctic Peninsula from the southern Andes. Other important changes were the disruption of the circumequatorial current by (1) northward movement of India across the equator (Paleocene- Eocene), (2) northward movement of Australia toward Southeast Asia, (3) northward movement of Africa -Arabia into southern Eurasia, and (4) restriction and eventual closing of the Atlantic- Pacific passage through the Caribbean (Kennett, 1977; Berger et al., 1981). During Eocene time the Southern Ocean and the tropical deep ocean were relatively warm, with isotopic paleotemperatures in the range 9 - 15 C (Douglas and Savin, 1975; Shackletonand Kennett, 1975). Antarctica was probably not glaciated, except perhaps locally, and there was cool temperate vegetation around its margins (Kemp, 1978). A change to heavier oxygenisotope ratios at the Eocene-Oligoceneboundary indicates a major subantarctic bottom and near-surface temperature decrease from 9 to 5 "C, the first formation of cold bottom water [the so-called psychrosphere of Kennett and Shackleton(1976)l. As mentioned earlier, this event is also observed in benthonic oxygen-isotopedata from the tropical Pacific and the Atlantic off Portugal. There were contemporaneouschanges in marine biotas (Benson, 1975), including benthonic foraminifera (Douglas and Woodruff,198 1). A terminal Eocene change in ocean chemistry is shown by a depression in the calcite compensation depth of up to 1500 m (Van Andel, 1975), which may reflect the development of younger bottom waters with a lower COz content due to initiation of a more vigorous deep circulation (Kennett, 1977). Finally, there is direct evidencethat ocean circulation underwent a reorganization at the Eocene - Oligocene boundary. Widespread early Oligocene hiatuses in Southern Ocean deep-sea sedimentary sequences (Barker et al., 1977) have been interpreted as evidence for an increase in bottom-water circulation, particularly along the western margins of the Tasman and Coral seas (Kennett et al., 1975) and in the southwesternSouth Atlantic (Barker et al., 1977). [In the latter basin the late Eocene initiation of biogenic silica deposition parallels the decrease in equatorial silica deposition, reflecting a change in upwelling patterns (McCoy and Zimmerman, 1977).] This was the most extensive episode of bottom erosion in the Cenozoic, and Kennett et al. (1975) hypothesize that it was due to the first production of bottom water during the early stages of glaciation and that this early Antarctic bottom water (AABW) could possibly move northward more easily at this time than at later times when the deep circum-Antarctic current had developed. Several hypotheses have been proposed to account for the cooling, but O

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most have not been generallyaccepted. These include an episode of intense global volcanism, for which there is evidence in North America, Peru, and east Greenland (Axelrod, 1981);the impact of an extraterrestrialbody somewhere on earth (Alvarez et al., 1982); or the temporary formation of a Saturn-like ring around the earth that could have reduced solar radiation receipt at the surface (OKeefe, 1980). Other proposed mechanisms involve paleogeographic changes that could have influenced ocean circulation. Thierstein and Berger ( 1978)suggestedthat brackish water from a previously isolated Arctic basin could have flowed out to form a "lid" over the world ocean due to the opening of the Greenland Sea passage to shallow-water exchange. By reducing vertical heat exchange in the ocean, this event could have resulted in a higher tropical sea surface temperature and a greater high-latitude SST range that, they argue, would have increased snow accumulation on Antarctica and resulted in further cooling through positive albedo feedback, and hence an increase in cold bottom-water formation. However, the Iceland- Faroe Ridge probably prevented significant exchange of water between the Arctic and North Atlantic until the late Oligocene (Tucholke and Vogt, 1979). Another hypothesisis that the breaching of the Rio Grande Rise about 38 m.y. ago would have allowed intermediate or deepwater exchange between the northern and southern parts of the South Atlantic and hence between northern and southern high latitudes. The appearance of psychrospheric faunas and evidence of cooling in the North Atlantic would appear to support this hypothesis (Berggren, 1982). However, passages from the Indian Ocean to the North Atlantic between southern Eurasia and northern Africa-Arabia and from the Pacific to the Atlantic between North and South America were still open at this time (Keller and Barron, 1983), and cold bottom water originating from the Antarctic margins could have reached the Atlantic by these routes. Also, the distributionof lithofacies in the South Atlantic indicatesthat the Rio Grande Rise-Walvis Ridge was still a barrier (the northern limit of siliceous sedimentation) to deep circulation at the end of the Eocene (McCoy and Zimmerman, 1977). The seaway between Indonesia and northern Australia began to be restricted at this time by the northward movement of Australia (Kennett et al., 1975). Perhaps the most widely accepted hypothesis (Kennett, 1978,1980,1982; Schnitker, 1980; Kvasov and Verbitsky, 1981; Keller, 1983) is that the progressive separation of Australia from Antarctica and the deepeningof the seaway over the South Tasman Rise (a long southward extension of Australian continental crust that obstructed the otherwise wide and deep late Eocene seaway between Australia and Antarctica) allowed development of a significant flow of cool water from the southern Indian Ocean into the southern Pacificby the end of the Eocene, although deep circulation was still

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blocked at this time. A period of nondeposition across the EoceneOligocene boundary at DSDP site 281 on the southern part of the rise indicates increased current velocity at this time (Kennett et al., 1975). By the end of the Eocene this cool flow became sufficient to prevent warmer water that flowed from the north along the east coast of Australia from reaching the Antarctic margin. As a result, the water entering the extensive shallow seas that probably existed in the Ross embayment and between West and East Antarctica (Webb, 198 1) became cooler. Even if this initial cooling was small, a resulting increase in Antarctic snow cover or seasonal sea ice formation could have led to progressive cooling through the albedotemperature feedback mechanism, increasing the production of cold water which sank to the deep ocean. It is generally thought that the Drake Passage was closed at this time by a continuous barrier linking the southern Andes and the Antarctic Peninsula (Norton and Sclater, 1979). Initiation of circulation over the South Tasman Rise was the first event in the development of the circum-Antarctic current, which Kennett (1 977) suggests led to a progressive reduction in oceanic heat transport to high southern latitudes. During the Oligocene (38-22 m.y. ago), sea level was low (Vail et al., 1977). The first direct evidence for the development of Antarctic sea ice is the occurrence of ice-rafted debris and sand-size quartz with glacial surface features in Oligocene deep-ocean sediments (Margolis et al., 1977). It is thought that Antarctica was partially glaciated during the Oligocene,but that an ice cap did not exist. Erosion of deep-ocean sediments in the western parts of the oceans indicates vigorous deep circulation at this time. Mid- to late Oligocene initiation of deep flow south of the South Tasman Rise is indicated by a reorganization of deep sediment patterns (Kennett, 1977). Marine magnetic anomalies indicate that the Drake Passage between South America and the Antarctic Peninsula opened initially around 30 m.y. ago (Barker and Burrell, 1977) and widened and deepened sufficiently to allow development of a deep circum-Antarcticcurrent by latest Oligoceneto early Miocene time (25 -22 m.y. ago) (Kellerand Barron, 1983), because siliceous sedimentation in the Southern Ocean, which has been related to upwelling south ofthe Antarcticconvergence, began in early Miocenetime (Barkerand Burrell, 1982). This event was marked by a major change in planktonic biogeography, extinctions of Paleogene faunas and evolution of Neogene faunas (Keller and Barron, 1983). During early to mid-Miocene time, sea level rose (Vail et al., 1977) and subantarctic isotopic paleotemperatures were relatively high (Shackleton and Kennett, 1975). Global changes in oceanic sedimentation patterns at the early - middle Miocene boundary probably reflect the disruption of the circumequatorial seaway due to the collison of Africa with Europe and of Australia with Indonesia. Also, around this time the Atlantic- Pacific pas-

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sage through the Caribbean became restricted and the Iceland- Faroe Ridge subsided below sea level (Keller and Barron, 1983). At 14 m.y. ago, soon after these events, P O in high-latitude planktonic and high- and low-iatitude benthonic foraminifera increased sharply, probably reflecting formation of the East Antarctic ice cap and a major oceanic temperature fall (Douglas and Woodruff, 1981). Whatever the relative importance of the it is unlikely that one event temperature and ice volume effects on 6**0, occurred without the other (Savin, 1977). Warmingof tropical near-surface waters at this time (Douglas and Savin, 1975)implies that ice cap formation was associated with an increase in the equator-pole sea surface temperature gradient. Schnitker (1980) suggested that subsidence of the Iceland Ridge allowed North Atlantic deep water to flow south and upwell south of the Antarctic convergence, warming the sea surface adjacent to Antarctica, causing an increase in snow accumulation and ice sheet formation. He proposed that although Antarctica had been cold enough for this to occur since the Oligocene development of the deep circum-Antarctic current, snowfall was insufficient to lead to ice sheet development until this later event. It is also possible that major mid-Miocene island-arc volcanism and a progressive post-late Miocene increase in volcanism may have helped to initiate and accentuate major cooling. A widespread hiatus in end-Miocene deep-ocean sediments may have been caused by more vigorous Antarctic bottom-water circulation (Arthur, 1979). The remaining period of earth history is notable for the so-called endMiocene ( 5 m.y. ago) Messinian salinity event, first recognized by Hsu et al. (1973), in which the Mediterranean was periodically isolated from the world ocean. This resulted in massive evaporite formation, reduced salinity (- 6%) of the open ocean, and 10-m sea level changes (e.g., Arthur, 1979). The Antarctic ice sheet has probably existed continuously from its midMiocene formation (Kennett, 1977). At 3.0 m.y. ago (mid-Pliocene time), Arctic glaciation started [althoughClark (1982) reported evidence for Arctic sea ice as early as Miocene time]. This may have been related to the closing of the Panama Strait around this time (Keigwin, 1978), resulting in greater northward transport by the Gulf Stream and increased available moisture in the North Atlantic (e.g., Berger et al., 1981).

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4. FORCING MECHANISMS IN LONG-TERM CLIMATIC CHANGE 4.1. Introduction

What are the forcing mechanisms that have given rise to the very-longterm climatic changes documented in the preceding sections? At present,

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the exact way in which changes in boundary conditions and changes in the internal feedback mechanisms in the atmosphere/ocean/biosphere system have interacted to produce the observed record is unknown. However, some major boundary conditions are known to have changed. These are briefly described below. 4.2. Changes in Factors External to the Earth 4.2.1. Solar Luminosity. Most models of solar evolution predict that solar luminosity has increased through the history of the solar system (e.g., Newkirk, 1979). However, in cosmologies with a variable universal constant of gravitation, the solar luminosity could actually have decreased through time (Hoyle and Narlikar, 1972). Whatever the long-term change, solar luminosity could also have fluctuated about the mean due to intermittent mixing in the solar core (Dilke and Gough, 1972) or could have been periodically affected by passage of the solar system through interstellar hydrogen clouds during its rotation around the galactic center (Pollack, 1982). 4.2.2. Changes in Earth-Orbital Parameters. It is well known that there are periodicitiesin the obliquity of the earth’srotation axis and the eccentricity of its orbit and that the rotation axis precesses about the normal to the orbital plane (Pollack, 1982). While eccentricity variations have probably been constant through geological time, the precession frequency has probably decreased due to asymmetric tidal torque (Pollack, 1982) and mean obliquity has probably changed (Hunt, 1979). These changes would have affected the seasonal and latitudinal distribution of solar radiation over the earth’s surface. 4.2.3. Rotation Rate of the Earth. Lunar and solar tidal torque, acting mainly through tidal friction in shallow seas, causes a slow deceleration of the earth’s rotation rate, the rate of which varies with the extent of shallow seas (Broscheand Sundermann, 1979). This deceleration changes the magnitude of the Coriolis parameter through time and hence the nature of atmospheric and oceanic circulation systems. 4.3. Changes in Terrestrial Forcing External to the OceanlAtmospherel Biosphere System Circulation cells in the upper part of the earth’s mantle give rise to movements in the earth’s outer shell or lithosphere, which are to a large extent described by the plate tectonics hypothesis, according to which rigid fragments of lithosphere, often comprising both oceanic and continental crust,

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move across the earth's surface relative to the rotation axis. Oceanic lithosphere is created at midocean ridges, moves away from the ridges, and is destroyed in subduction zones whose surface expressions are deep-ocean trenches, with the implications: 1. Continent- ocean positions change constantly through time, both with respect to each other and relative to the rotation axis. This has major climatic implications through constraint of ocean currents and heat transport, and redistribution of surface albedo and thermal forcing of the atmosphere. 2. Orogeny (mountain building) and lesser vertical movements of more stable regions affect the atmosphere through, for example, changed orographic forcing, surface energy balance, and cloud characteristics. 3. The bathymetry of the ocean basins changes: midocean ridges, island arcs, and other barriers to deep circulation can form, change position, and disappear, with consequent effects on deep-ocean circulation. 4. Global sea level changes through time as a function of many factors, but principally due to changes in oceanic spreadingridge volume, which is a function ofglobal-mean spreadingrate and total ridge length (Hallam, 1977; Pitman, 1978). As the volume of these ridges changes, so does the volume of ocean water displaced onto the continents. Sea level on individual continents or parts of continents can, of course, change through local vertical movements. However, probably only global (eustatic)sea level changes are important for global climatic change through changes in surface albedo, ocean heat transport, continentality,surface evaporation, location, and area

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FIG. 7. Cretaceous-Tertiary sea level curve. Relative changes are well documented but absolute scale is tentative, based on change of midocean ridge volume through time calculated by Pitman (1978). Time scale from Van Hinte (1976) and Berggren and Van Cowering (1974). [After Vail ef al. 1977).]

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of source regions for bottom water, and, indirectly, cloudiness, marine productivity, and atmospheric C02content. Cretaceous and Tertiary sea level changes can be recognized from the sedimentary record on land (e.g., Hancock and Kauffman, 1979) and on the continental shelves through core samples and seismic stratigraphy (Vail et al., 1977). Sea level was high during the Cretaceous and fell in a series of steps through late Cretaceousand Tertiary time (Fig. 7). Note that sea level change is a function of processes external to the ocean-atmosphere system, except when a significant ice volume exists, such as during late Tertiary and Quaternary times. Therefore, sea level during nonglacial times is an independent forcing factor for climate. 5 . Volcanic activity, which is probably a function of global-mean ocean floor spreading rate, affects the amount of stratosphericaerosol and the rate of outgassing of C02 into the atmosphere. Changes in these parameters could be climatically significant.

4.4. Factors Internal to the OceanlAtmospherelBiosphere System

4.4.1.Atmospheric CO, Content. Apart from volcanic outgassing mentioned above, atmosphericC02content is a complex function of removal by surface weathering, removal by photosynthesis, production by oxidation of organic carbon (hence net removal by burial of organic carbon), removal through dissolution of carbonate sediments, and other processes (Berner et al., 1983). 4.4.2. Other Coupling Mechanisms. These include all the feedback mechanisms in the climate system that may act to enhance, reduce, or otherwise change the nature of primary responses to changed external boundary conditions, for example, ice/snow albedo -temperature, water vapor - temperature, sea level -ice volume - climate, cloudiness-temperature, radiative-dynamical, surface temperature - lapse rate, evaporation/ precipitation- soil wetness/vegetation - surface albedo, and other coupling mechanisms. 4.5. Comments

Figure 8 schematically shows the major forcings for long-term climatic change and some of the major internal coupling mechanisms on a nonglacial earth. If major polar ice were present, there would be additional coupling mechanisms. Is it possible to synthesize our knowledge of these mechanisms into a

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CHRISTOPHER R. LLOYD

car bonote sedimentat ion

Marine biologic productivity

FIG.8. Schematic representation of major changes in boundary conditions external to the ocean/atmosphere/biosphere system that are capable of forcing long-term climatic change during nonglacial times. Also shown are the principal couplingmechanismswithin the system, which act to modify the primary climatic effects of the changed boundary conditions. Coupling mechanismswithin the atmosphere are omitted for simplicity.

unified theory of paleoclimates? Two principal regimes may be recognized in the ocean/atmosphere/biospheresystem which seem to change with an approximately 30-m.y. period (Fischer and Arthur, 1977; Fischer, 1982). 1. Episodes characterizedby high, uniform SST, high sea level, maximum biotic diversity,large predator size, widespqead marine anaerobism (oceanic

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anoxic events), continuous deep-ocean sedimentation, shallow calcite compensation depth, and heavier carbon-isotope values in marine calcareous organisms and organic matter (polytaxic episodes), such as the mid- to late Cretaceous, Eocene, and early Miocene. 2. Episodes characterized by lower SST and lower sea level, stronger latitudinal and vertical temperature gradients, interruptions of marine sedimentation (due to stronger currents),a lack of anoxic sediments, deep calcite compensation depth, lighter carbon-isotope values, lower biotic diversity, and loss of predators (oligotaxic episodes), such as the Maastrichtian,Oligocene, and present. These episodes correlate with similar changes in land climates. The present glacial period is an oligotaxic episode that became cold enough for major polar ice formation and further cooling through ice albedotemperature, sea level -temperature, and other feedback mechanisms. It is clear that any improvement in understanding of nonglacial climates must relate these observed associations through consistent cause -effect relationships, particularly the relation of high sea level with the warmest climates. High sea level in nonglacial times is a function of processes external to the ocean/atmosphere/biosphere system, and in particular of the midocean ridge volume. (During glacial periods, it is also a function of climate through global ice volume. Therefore, if cooling is a function of falling sea level, then formation of polar ice acts as a positive feedback on global temperature, quite separately from the albedo -temperature coupling.) Given that high sea level is characteristic of the warmest nonglacial periods, and that it is not a result of those wann climates, then either high sea level causes warm global climates through, for example, lowered surface albedo, increased water vapor content of the atmosphere, increased poleward oceanic heat transport, and decreased continentality, or the mechanism that causes high sea level also has another effect on the climate system. For example, large midocean ridge volume should be correlated with high global volcanicity, and hence high C02input into the atmosphere. High rates of global volcanism could therefore give high atmospheric C02 levels and be associated with high sea level. However, it should be borne in mind that atmospheric C02 content is a function of many complex interacting physical, chemical, and biological mechanisms that tend to buffer major C02excursions (Berger, 1977). Sea level change and atmospheric COzcontent are only two of the more obvious mechanisms that force very-long-term climaticchange. Variations in other factors such as continental positions, orography, and solar luminosity must also be major influences. In all cases, the system’s reaction to changes in these parameterswill be complicated by internal feedback mechanisms.

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How can we proceed to a better understanding of the problem of prePleistocene climates? Climate models will have to be the major tool. By reconstructingthe boundary conditionssuch as paleogeography for a chosen period, model simulations should show some of the effects of these changed forcings on global climates, and, depending on the complexity of the model used, how internal feedback mechanisms would affect the climatic response. Sensitivity experiments using the appropriate paleoboundary conditions and changing one forcing factor at a time should increase our understanding of the causes of paleoclimatic changes. These and other aspects of paleoclimatic modeling will be discussed in the following sections.

5, BOUNDARY CONDITIONS FOR PALEOCLIMATIC MODELING 5.1. Introduction The main characteristicdistinguishing paleoclimaticmodeling from modeling of the present climate is the specification of boundary conditions for some earlier period in earth history. Principal among these boundary conditions are: 1. Paleogeography, including continent - ocean positions and paleolatitudes, coastlines on the continents, land surface topography, and, if a deepocean model is to be used, paleobathymetry. 2. Sea surface temperature (SST) distribution for an atmosphericgeneral circulation model (GCM), if it is not coupled to an ocean model. 3. Land surface albedo distribution. 4. Values of critical physical parameters, especially rotation rate of the earth, the solar constant, earth-orbital parameters, and atmospheric composition, particularly C02 content.

In the following sections, we will consider how these boundary conditions can be derived and examine some of the major problems and uncertainties involved. 5.2. Paleogeography 5.2.I . Paleocontinental Reconstructions. The repositioning of the major continentsrelative to each other and with respect to paleolatitude is the most important step in the establishment of a realistic paleogeography for any period of earth history. This has only been possible since the late 1960s, when the advent ofthe plate tectonics hypothesis provided a unifying frame-

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work for an understanding of past continental motions (Heirtzler et al., 1968; Isacks et al., 1968; Le Pichon, 1968; Morgan, 1968). Applying this hypothesis, rigid lithosphericplates can be repositioned relative to each other from a knowledge of the spreading histories of the oceans, derived from dated marine magnetic anomalies. Given a knowledge of the ocean floor spreadingdata,the history of motion between a plate pair can be represented by a set of instantaneous rotation vectors, each specified by a tectonic rotation pole and angular rotation rate. Reassembliescan be made by applying a series of rotations with specified rotation angles and poles to presentday continents (as parts of plates) or pieces of these continents. By this method, continents separated by accreting margins, such as the Atlanticbordering continents, may be repositioned relative to each other if the ocean floor spreadinghistory is known. Prebreakup positions of continentscan be determined from “best fits” of continental margins, sometimes constrained by matching of geological features across the margins (e.g., South America and Africa). Measurements of declinations and inclinations of remanent magnetism in rocks of a given age on each continent give apparent paleomagnetic poles for those continents. Paleolatitudes may then be determined by subjecting these paleomagnetic poles to the same finite rotations used to move the respective continents (Smith et al., 198 1). Because oceanic lithosphere is continuously destroyed in subduction zones, there is no known ocean floor (except that which has been emplaced on the continents in orogenic belts) older than Jurassic in age. Therefore, while individual continents can be positioned with respect to latitude, they cannot be positioned relatively, from geophysical evidence alone, for preMesozoic times (Smith el al., 198 1). However, biogeographic, paleoclimatic, and other geological information can be used as additional constraints on longitudinal relations of the continents in Paleozoic reconstructions as done by Ziegler et al. (1979) to a limited extent. Paleozoic reconstructions can be better constrained by detailed biogeographic data combined with considerations of likely ocean circulation patterns (Boucot and Gray, 1983). At present, however, paleoclimatic modeling studies requiring realistic paleogeographies(GCM simulations) are not possible for pre-Mesozoic earth history, although modeling requiring only a knowledge of land- sea proportions in latitude belts is possible. Several global paleocontinental maps are available for intervals in the Mesozoic and Tertiary, in particular those of Dietz and Holden (1970), Irving (1977), Zonenshayn and Gorodnitsky (1977), Barron et al. ( 198 lb), Smith et al. (198 l), and Ziegler et al. (1983). Those of Barron et al. and Ziegler et al. have been modified by the addition of coastlines, and Ziegler et al. made adjustments to compensate for microplate movements (see later). It should be noted that, while the positions of the Atlantic-bordering

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continents are well known through Mesozoic and Cenozoic time, there are uncertainties and hence differences in the reconstructions, particularly in the positions of Madagascar and West Antarctica, and the late Mesozoic evolution of northeast Siberia is poorly understood. 5.2.2. Modifications to PaleocontinentalMaps. In order to obtain a realistic paleogeography, a paleocontinental map must be modified for the following reasons. 1. Deformation of the margins of some of the continents in orogenic regions, and also movements of “microplates” relative to the stable continents, have changed the shapes of these continents. These movements are not compensated for in most paleocontinental reconstructions. For example, the northern margin of India and the southern margin of Asia may have both been shortened by about 750 km in the north - south direction during the Cenozoic Himalayan orogeny, as deduced from the thickness of the continental crust in the Himalayas and Tibet and from observed deformation (Powell and Conaghan, 1973; Klootwijk, 1981); pre-Tertiary reconstructions should compensate for this. Baja California and California west of the San Andreas Fault have moved northward as part of the Pacific plate by several hundred kilometers since the mid-Tertiary (Dickinson, 198 1); in pre-late Tertiary reconstructions, this block should be moved southward to its original position. Other examples are the complex Central AmericanCaribbean region, New Zealand, and the Tethyan (ancestralMediterranean) microplates. In northeast Siberia it is likely that at least one continental fragment sutured with Asia during the Cretaceous. Also, the northeasternmost tip of Siberia has probably been part of the North American plate, continuous with northern Alaska, through Mesozoic- Cenozoic time (Churkin, 1972; Fujita and Newberry, 1982). This is therefore another region that needs modifying. There is much geological and geophysical evidence that West Antarctica and the southern Andes formed a continuous orogenic belt in the Cretaceous and early Cenozoic (Dott et al., 1982). In addition, floral and faunal similaritiesbetween South America and Australia show that free migration occurred between these continents, possibly through Antarctica, during the Cretaceousand early Cenozoic until the early Eocene (- 50 m.y. ago) separation of Australia from Antarctica (Hallam, 198lb). Also, some investigators have suggested that West Antarctica, which may include several microcontinents, has moved relative to East Antarctica (e.g., De Wit, 1977). Evidence for movements such as these should be carefully considered, and the necessary modifications should be made to paleocontinental maps.

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2. Coastline positions on the continents have changed continuously through geological time, due to global (eustatic) sea level fluctuations and local vertical movements of the continents. For any time, shoreline positions can be mapped from the nature and distributions of contemporary marine and nonmarine sediments, or, in exceptional cases, from recognition of actual shoreline sediments. Many workers have synthesized these data to prepare shoreline maps for the individual continents for most geological periods. Coastlinesin currently ice-covered regions of Greenland and Antarctica cannot be determined by this method. Lloyd (1 982) mapped them for the mid-Cretaceous from the isostatically corrected sub-ice zero-elevation contour, having compensated for the higher mid-Cretaceous sea level. 3. The surface elevation of major land surface topography has changed over geological time and should be estimated for the period of interest. Active orogenic regions at a particular time can be recognized from the eroded roots of mountain ranges, which are characterized, in particular, by evidence for intermediate igneous activity, tectonism, and deposition of thick syn- or postorogenic sediments around that time. Approximate estimates of the elevation of a mountain range can be made by comparisonwith elevations in analogous present-day tectonic environments, supported by evidence for the volume and nature of syn- and postorogenic sedimentsand the time interval during which they were deposited. For example, a situation with active subduction along a continental margin (indicated principally by intermediate igneous activity and tectonism) and significant synorogenic sediments is comparable to the present-day central Andes, indicating a mean axial elevation of 3 - 4 km, with a steeper gradient trenchward. Mean elevations should be derived with a specific modeling requirement in mind. If, in the model to be used, mountains have been smoothed by an envelope over the highest peaks, or, alternatively, through intermediate levels somewhere between peaks and valley bottoms such as pass elevations, paleotopography should be mapped in the same way. Surface elevationsin nonorogenic regions can also be considerable,as, for example, in present-day east Africa, which has been uplifted during late Cenozoic times (Smith, 1982). Major nonorogenic uplifts for any period could be estimated from a knowledge of sea level at the time, present elevation, estimates of present and ancient thermal uplift, and evidence for associated volcanism and sediments. The mid-Cretaceous (100 m.y. ago) paleocontinental map of Smith and Briden (1977) was modified by Lloyd (1 982) using the methods described above to produce a realistic paleogeography suitable for paleoclimatic modeling purposes (Fig. 9).

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5.2.3. Ocean Gateways. While some errors in paleogeography may not be critical for atmospheric modeling given the typical 4 - 5 grid spacing in GCMs, for example, the location, width, and depth of certain ocean gateways can be important for surface and deep-ocean circulation (Berggren, 1982). Careful estimation of the width and depth of these ocean gateways is important if a dynamical ocean model is used. For example, in the midCretaceous reconstruction of Fig. 9, there are significant passages between South and North America and between Iberia and north Africa. If these gaps were narrow as shown by other reconstructions for the same time (and as occurred later in the Cretaceous and Cenozoic), then ocean circulation would be significantly affected. It is clear that the opening or closing (and deepening/shallowing) of such gaps can be critical for the global ocean circulation and hence for the climate (Berggren, 1982), as is shown by the relationship between Southern Ocean evolution and the development of Cenozoic glaciation described in Section 3.4.5. Therefore, paleocontinental reconstructions should be critically examined in these areas, and data on sediment types and their distribution, differencesin ocean chemistry across the gateway, and the biogeography of organisms from different depths should be used to determine whether free circulation existed through a doubtful gateway at a particular time. 5.2.4. Paleobathymetry. If an oceanic GCM with deegocean circulation were to be used, it would be necessary to specify major elements in the paleobathymetry, particularly ocean floor depth, midocean ridge configuration, and any other major barriers to deep circulation. As mentioned in Section 2.3.2, it is possible to plot the mean paleodepth of the ocean floor in some parts of the oceans. The evolution of North and South Atlantic paleobathymetry is well known from the Jurassic to the present (Sclater et al., 1977). While the paleodepth ofany part ofthe ocean floor can in theory be determined, there is a problem with locations in the Pacific Ocean in that through the Mesozoic and Cenozoic this region has been occupied by several oceanic plates moving independently of the surrounding continents. The Pacific plate is the main survivor of these. From paleomagnetic data it is possible to map latitudes of points on the Pacific plate through Cretaceous and Cenozoic time, but longitudinal control is poor (Lancelotand Larson, 1975;Firstbrook et al., 1979). Most ofthe other oceanic plates that occupied the Pacific Basin during the Cretaceous have now been destroyed by subduction, including much of the Pacific plate itself. The same has happened to the oceanic lithosphere which occupied much of the “Tethys” ocean south of Eurasia (of which the Mediterranean is the principal remaining fragment). Therefore, we have no direct evidence for the age or topographyof these vast areasof ocean floor for Cretaceousand

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most of Cenozoic time, although age patterns and midocean ridge positions for parts of the Pacific Basin plates can be tentatively mapped from a knowledge of Pacific plate spreading history (Kanasewich et al., 1978), but longitudinal control is again poor (Firstbrook et al., 1979). There are other problems. First, Cretaceous midplate volcanism in the Pacific plate would have given large areas of the ocean floor shallower than indicated by the standard age-depth relationship (Schlanger et al., 1981). Second, barriers to deep circulation for which we have no evidence may have existed at times in the past. For example, Cretaceous island arcs in the Caribbean region (Malfait and Dinkelman, 1972) could have been an effective barrier to deep circulation between the Atlantic and Pacific oceans. To determine whether such a barrier existed it may be necessary to look for other evidence, such as similarities or differences in deep-ocean chemistry between the Atlantic and Pacific, as reflected by sediments. For example, mid-Cretaceous organic-carbon-rich sediments in the Caribbean indicate stagnation until 80 m.y. ago, while at the same time in the Pacific these sediments were confined to seamounts, indicating freer circulation and perhaps the existence of a barrier to deep circulation (Thierstein, 1979). In conclusion, paleobathymetry can be determined for all of the Atlantic and some of the Southernand Indian oceans through the Cenozoic,and even into the Cretaceous for the Atlantic. It can also be reconstructed for the Pacific plate, although paleolongitudes of points on this plate are not precisely known. However, for large areas of the Pacific Basin and for the ancestral Mediterranean, there is no direct evidence for age and hence for depth or for other bottom topographic features, although for parts of the now-subductedPacific Basin plates, approximate positions and age patterns can be estimated from a knowledge of Pacific plate spreading history. Before realistic paleosimulations with a deep-oceanic GCM can be undertaken, much further work will clearly have to be done on paleobathymetry. 5.3. Sea Suvface Temperature and Ocean Circulation 5.3.1. Introduction. For atmospheric GCM simulations, the sea surface temperature must be specified as a boundary condition if a coupled ocean model is not used. In order to specifyglobal SST distribution,we need to ( 1) determine referencepoints for ocean temperatureand (2)postulate a relative temperature distribution based on a best estimate of ocean circulation patterns and upwelling locations and calibrated by the reference points. 5.3.2. Establishment of Reference Points for SST. In Sections 2.3.2 and 3.4.1, the oxygen-isotopepaleotemperature method and some results for the

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Cretaceous and Tertiary were reviewed. The following main points should be remembered: 1 . Results from unaltered planktonic and benthonic foraminifera from deep-ocean sediments are more reliable than those from belemnites and other organismsin shallow marine sedimentsbecause of the relative freedom of the former from salinity variations and postdepositional alteration. 2. For increasingly old determinations, results become less certain because of the increased possibility of postdepositional alteration. 3. Going back in time, especially into the Cretaceous, the number of data decreases and their geographic distribution becomes more limited. For early and mid-Cretaceous(1 30 - 80 m.y. ago) times, the only results are from the central Pacific (Douglas and Savin, 1975). By the late Cretaceous the results of Saito and Van Donk (1974), Margolis et al. (1977), Boersma and Shackleton ( 198 l), and Saltzman and Barron (1 982) for the Pacific, South Atlantic, and Indian oceans are available. Paleocene and later results are much more numerous, and, in addition to the above regions, include the subantarctic southwest Pacific (Shackleton and Kennett, 1975; Keigwin, 1980) and the North Atlantic (Vergnaud-Grazzini et al., 1978). However, mid-Miocene (- 14 m.y. ago) and later results are dominated by the effect of ice volume changes on the oceanic isotopic composition (Savin and Yeh, 198 1) and so cannot be used directly for paleotemperaturedeterminations. This effect may even be significant as far back as late Eocene times (Matthews and Poore, 1980).

Therefore, the availability of oxygen-isotope results places an early to mid-Cretaceous limit on the earliest possible time for noncoupled atmospheric GCM simulations. However, given that the Cretaceous is so poorly represented, only Paleocene to mid-Miocene simulations can reasonably be considered. We now consider how SST distribution can be derived from a limited number of isotopic paleotemperature determinations. 5.3.2.1. Interpretation of planktonic paleotemperatu.rm. Results from planktonic foraminifera and nannofossils can represent temperatures slightly below the SST. Savin et al. (1 975) showed that this difference is probably in the range of 2 - 3 C for preglacial times at low latitudes, assuming that vertical temperature gradients were less than at present; this correction should therefore be added to low-latitude results to derive SST. At high latitudes, the near-surface vertical temperature gradient was small as shown by the subantarctic results of Shackleton and Kennett (1975), and so a correction is unnecessary. 5.3.2.2. Interpretation of benthonic paleotemperatures. Benthonic paO

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leotemperatures have to be interpreted carefully because their significance depends on the original bottom depth and on assumptionsabout the nature of oceanic deep circulation. Most workers (e.g., Emiliani, 1954b; Savin, 1977)have assumed that Cretaceous-early Tertiary deep water was formed by the sinking of cold high-latitude surface waters, as occurs at present. In this case, bottom temperatures would be close to those of polar surface waters, and so results from benthonic foraminifera could be interpreted as such. However, it is possible, as first suggested by Chamberlin (1906) and later by Lloyd (1977) and Brass et al. (1982), that with no polar ice and large areas of subtropical shallow seas, warm saline water from these seas had sufficientlygreat density and volume to sink to the deep ocean and hence to form a significant component of deep water. If this were the case, then the deep-watertemperature would be similarto that of subtropical surface water or be intermediate between subtropical and polar surface waters, depending on the proportion of mixing from these sources. Note also that subtropical sinking would occur mainly in winter when the water is most densebottom-water temperature would then reflect winter subtropical SST. [The warm saline bottom-water (WSBW) hypothesis of course has climatic implications. Formation of large volumes of subtropical saline water requires a large ocean-to-atmosphere water vapor flux and hence a large poleward latent energy flux. Precipitation in higher latitudes would have reduced surface salinity there. Also, the nature of the circulation and heat transport in the deep ocean would have been very different. Polar upwelling of warm saline water could have been an important mechanism in preventing polar ice formation and maintaining the warm equable climates indicated by the Cretaceous- early Tertiary flora and fauna of the Arctic Ocean margins (see Section 3.4.4). Given the climaticconsequencesof such a deep circulation,it is interestingto note that, accordingto the hypothesis,sea level would have been an important control of WSBW formation.] This hypothesis could be tested by (1) determination of high-latitude near-surfacetemperatures or (2) recognition of evidence for high paleosalinity in deep-ocean sediments or fauna. For the mid-Cretaceous and earlier times there is apparently no clear evidence. However, for the late Cretaceous we have the subantarctic (- 60"s paleolatitude) isotopic data of Margolis et al. ( 1977)and for the Paleocene onward the results of Shackleton and Kennett ( 1975), which can be compared with the tropical temperatures of Douglas and Woodruff (1981) (Fig. 5). These results show that early Cenozoic high-latitude SSTs were only 2 - 3 "C higher than bottom temperatures, indicating that bottom water was derived from still higher latitudes because if bottom water was derived from subtropical water, it would have been warmer than surface water at subantarctic latitudes. (However, if subantarctic surface waters had low salinity, the apparent planktonic temperatures

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would be anomalously high, i.e., SST could have been lower than bottom temperature.) Hence, during late Cretaceous and Paleocene - Eocene times it seems that high-latitude sinking of cold surface water was predominant. The situation may, however, have been different during mid- and early late Cretaceoustime when the extent of shallow seas was at its maximum (Vail et al., 1977). Therefore, pre-Campanian SST distributions are dependent on speculations on the nature of deep circulation. In the absence of such evidence, Lloyd (1982) arbitrarily assumed that mid-Cretaceous deep water formed from a mixture of 75% polar surface water and 25% subtropical surface water. Then, using the benthonic and planktonic paleotemperature determinations of 17 and 29 “C, respectively, of Douglas and Savin (1975) from the mid-Cretaceous equatorial Pacific, and assuming subtropical SST was 25”C, a polar winter SST of 14°Cwas derived. If bottom water at that time was formed from a higher proportion of subtropical surface water, then polar surface temperatures would have been lower. Paleodepths should also be considered when interpreting benthonic results. These depths can be approximately determined from present depth, age of sediment, age of oceanic basement, and an ocean floor age-depth curve, Clearly,benthonic results from paleodepths of the order of 1 km may not be representative of deep-ocean temperature.

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5.3.3. Reconstruction ofSurface Circulation. Various methods have been used to gain insight into Cretaceous and Tertiary ocean circulation. Luyendyck et al. (1972) used a rotating, water-filled plane tank, with mid-Cretaceous Northern Hemisphere paleocontinental positions outlined by present coastlines, and a simulated zonal wind stress supplied by air jets. The experiment was run using “glacial” (present observed) and “nonglacial” mean meridional wind profiles, the latter with the westerlies farther north but of equal strength. There seems to be no justification for the nonglacial profile, which is apparently based on ideas such as those of Flohn (1964), who argued that with reduced meridional temperature gradient, the atmospheric circulation belts would move poleward. This is not consistent with paleoclimaticevidence such as paleolatitudes of evaporite deposits (Gordon, 1975) and would not satisfy global surface angular momentum balance without a readjustment in relative speeds of surface easterly and westerly winds. The circulation forced by the present observed surface wind profile showed a circumequatorial east-to-west flow and clockwise gyres in the North Pacific and North Atlantic. Gordon ( 1973)reconstructed surface currents for early, middle, and latest Cretaceous times by analogy with present surface circulation, but constrained by biogeographic evidence for regions of relatively cold and warm

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water and for current directions. Berggren and Hollister (1974) postulated surface currents for the North and South Atlantic from foraminifera1biogeography, and Berggren and Hollister (1977) described biogeographic and sedimentary evidence for the evolution of ocean circulation through the Mesozoic and Cenozoic. Pamsh and Curtis (1982) postulated zones of near-coastal upwelling for periods in the Mesozoic and Cenozoic, based on patterns of wind forcing deduced from the hypothetical seasonal positions of the quasi-permanent surface circulation cells at those times. Lloyd ( 1982) postulated mid-Cretaceous ocean surface circulation (Fig. 10) based in the Northern Hemisphere on the experimental results of Luyendyck et al. ( 1972), but modified to allow for differences in paleogeographies. Also, the results of Stommel's (1957) model for circulation in a homogeneous, idealized ocean basin were used as a guide to circulation in the near-symmetric mid-CretaceousPacific. A symmetricwind profile was assumed, with surface easterlies equatorward of 30"latitude and westerlies poleward. This circulation is consistent with the biogeographicevidence of Gordon (1973) and Berggren and Hollister (1974).

5.3.4. Reconstruction of SST Distribution, Given SST Reference Points and Surface Circulation. Given scattered reference points for low- and high-latitude SST and a postulated ocean surface circulation, how can an SST distributionbe mapped? Ocean circulation mainly determines the SST distribution by horizontal heat transport and through open-ocean and coastal upwelling. Therefore, for the mid-Cretaceous situation, for example, it can be assumed that upwelling zones occupied equatorial and eastern boundary positions analogous to those at present, i.e., off the coasts of western North and South America. Note that exact locations of nearcoastal upwelling will depend on the paleobathymetry of shelf seas, because the water has to be sufficiently deep to permit a separation of incoming and outgoing flows. If polar easterly winds existed, upwelling would also be expected at the boundary between these winds and the midlatitude westerlies, particularly at the locations of quasi-permanent, mid- to high-latitude low-pressure cells (such as the Aleutian low). It is possible that upwelling regions may be recognized from local occurrences of relatively cold-water fauna. Generally, over the oceans the SST pattern can be deduced by analogy with present-day observed relationships between surface currents, upwelling, and SST. Postulated negative temperature anomalies in regions of upwelling should be less than at present, given the lesser vertical temperature gradient of the preglacial oceans. Seasonal values in mid- and high latitudes may be estimated from observed seasonal ranges in the present-day open oceans at these latitudes. Using this method, and reference points of

FIG.10. Suggested mid-Cretaceous Ocean surface current directions. [From Lloyd (1982).]

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14 C for polar winter SST and 30 C for equatorialPacific SST, derived from Douglas and Savin (1979, Lloyd (1982) produced speculativemaps of midCretaceous “January” and “July” SST distributions (Fig. 1 1). For an atmosphericGCM simulation, the method described here can give the SST distribution that is necessary as a boundary condition. It is somewhat speculative but does at least give a seemingly realistic distribution. Because of the greater number and accuracy and better geographic distribution of oxygen-isotope paleotemperature reference points for SST, Paleocene to mid-Miocene SST distributions would be more realistically constrained than those for the Cretaceous. O

O

5.4. Other Boundary Conditions 5.4.1. Surface Albedo. Surface albedo (a,)is a critical parameter in the computation of the surface energy balance in climate models. Water surface albedo is routinely calculated as a function of solar zenith angle. Sea and land ice extents have to be specified if they are not explicitly modeled. Assuming that a, is not a model-generatedparameter, its distribution has to be specified for all land surfaces, depending on the surface type (unless a mean value is specified for all land). In some models an effective a, is parameterized in terms of a base a,,solar zenith angle, and snow depth. Values of a, for vegetation-covered land surfaces depend on height (a, decreases with taller plants) and type of vegetation, fraction of cover, seasonal foliage variation, leaf type, and other factors and are in the range 0.05 -0.20. The albedo of bare soils varies as a function of, in particular, water content, surface roughness, and parent rock type. Typical desert values are 0.25-0.30. Snow albedo varies according to age, depth, and nature of the surface covered. Typical values range from 0.4 to 0.8. In climate models it can be parameterized in terms of snow depth and the albedo of the underlying surface (Posey and Clapp, 1964; Sellers, 1965; Budyko, 1974; Thompson and Barron, 1981). For a recent and well-documented period such as the last glacial maximum, several land surface/vegetation categories can be identified from the geologic record and appropriate a, values assigned (CLIMAP Project Members, 1981). For more distant times, flora and sedimentary evidence for a given period can be used to identify vegetation-covered and arid land areas. For such times a, could be divided into the categories vegetation or desert, each with mean values for each type. Note that the earliest known vascular (higher) land plants are of middle Ordovician age (Gray et al., 1982),although it is possible that primitive plants such as lichens and mosses covered significant land areas long before this time.

FIG.1 1 . Hypothetical mid-CretaceousSST distributionfor July. Based on equatorial Pacific near-surfaceand bottom oxygen-isotope paleotemperatures and the surface circulation of Fig. 10. [From Lloyd (1982).]

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5.4.2. Rotation Rate ofthe Earth. The rotation rate of the earth (a) determines the magnitude of the Coriolis “acceleration.” Therefore, for dynamicalmodeling of paleoclimatesit is important to determine the appropriate value of R. The rotation rate of the earth decreasesthrough time due to tidal friction, which produces a torque through which the earth loses rotational energy. At the same time, the angular momentum of the moon’s orbital motion increases due to an increase in the radius of the lunar orbit (although lunar orbital angular velocity actually decreases) (Hipkin, 1970; Brosche and Siindermann, 1979). The present rate of decrease of R can be calculated from telescopic observations of the change in lunar orbital angular velocity (Momson, 1978) and from records of ancient eclipses (Muller and Stephenson, 1975). The deceleration of R thus determined can be extrapolatedback through geological time. However, (1) theory and observations indicate that L? fluctuates over short time scales, mainly due to motions in the earth’s core and mantle (McCarthy and Pilkington, 1979), and (2) because a large proportion of tidal torque is due to energy dissipation in shallow seas (Brosche and Sundermann, 1979),their changing extent and location with respect to latitude must have given varying deceleration rates. Fortunately, independent estimates of days per year in the geological past have been made from counts of daily growth increments per annual growth band in corals and other marine organisms (for reviews see Runcom, 1975; Lambeck, 1978), although uncertainties with this method do exist (Lambeck, 1980). Because counts on modem marine organisms show that numbers of growth lines are always equal to or less than the number of days elapsed, maximum values from counts of fossils should be used (Clark, 1968). Extrapolation of eclipse deceleration data (Muller and Stephenson, 1975) and interpolation of paleontological days-per-year results (Wells, 1963, 1970; McGugan, 1967; Berry and Barker, 1968; Mazzullo, 1971; Pannella, 1972; Scrutton, 1978)can be plotted to give a days-per-year curve through Phanerozoic time (Fig. 12). For paleoclimatic modeling purposes, the curve can be used to estimate R for any time in the last 500 m.y. 5.4.3. Solar Luminosity. According to most models of solar evolution, solar luminosity ( L )has increased by -25% (Newman and Rood, 1977) to 40%(Sagen and Mullen, 1972;Newkirk, 1979)since the origin of the solar system approximately 4600 m.y. ago. However, these estimates should not be used to infer the value of L for an arbitrary geological time for the following reasons.

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1. If the universal constant of gravitation (G) has decreased through time (as the universe expanded), as first suggested by Dirac (1938), there could have been a threefold decrease in L since the origin of the solar system,

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-3

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420 410-

X

L

0

400 -

/

.

-

/p

-

xx

/'

L

a 0

/:

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In

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0" 390-

n

Cen

0

Cret

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Tr

I

I

I00

200

Perm Corb

Dev

Sil

Ord

Comb

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300 Million years

400

500

570

FIG.12. Days per year through Phanerozoic time. Dashed curve is based on extrapolationof ancient eclipse data by Muller and Stephenson (1975). Plotted points are from maximum (0) growth-band counts of fossil corals, bivalves, and other invertebrates. Mean counts (X) are plotted where maxima (0)are not available. See text for sources of above. Solid curve is subjective best fit through the plotted points. Time scale from Van Eysinga (1978).

which, coupled with an increase in the radius of the earth's orbit, would have given a fivefold decrease in mean solar constant (Hoyle and Narlikar, 1972). Conversely, in cosmologiesthat include variable G and the creation of matter through time, L increases through time even more than in the standard model (Pollack, 1982). 2. Whether luminosity has, on average, increased or decreased over earth history, it may also have fluctuated about some mean curve. For example, L may be periodically affected by passage of the solar system through interstellar hydrogen clouds during its rotation around the galactic center (Pollack, 1982). Also, the observed flux of neutrinos from the sun is smaller than is theoretically predicted, indicating that present processes in its core are in disequilibrium with observed L (Newman and Rood, 1977). Dilke and Gough (1972) were the first to propose that this is due to intermittent mixing of 3Heinto the core, which could result in a 5% L decrease over a - 10-m.y.period, followedby a longer period of enhanced luminosity. This cycle could have a 300-m.y. period. It is also possible that shorter period fluctuations in luminosity may result from changes in the efficiency of convection (Newkirk, 1979). While this hypothesis is still controversial (Pollack, 1982), its implication is that even if a mean curve for L were known, specificationof L for any time in earth history would have to include a wide margin of error. In a single-run paleoclimate modeling experiment,

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it would therefore be necessary to specify L at its present value. It is desirable, of course, to investigate effects of possible higher or lower values by conducting sensitivity experiments. 5.4.4. Changes in Earth-Orbital Parameters. It is well known that the seasonal distribution of solar radiation over the earth varies with time due to quasi-periodic variations of the obliquity of the earth’s rotation axis relative to the orbital plane and of the eccentricity of its orbit and due to the precession of the rotation axis about the normal to the orbital plane (Pollack, 1982). These periodicities show a significant correlation with Pleistocene glacial/interglacial cycles, as recognized by fluctuations of oxygen-isotope ratios in deep-sea cores (Hays et al., 1976). It is probable that similar periodicities have operated throughout earth history, although during icefree periods their climatic effects would have been less due to lack of the ice albedo-temperature feedback mechanism. Interestingly, evidence for cyclic sedimentation patterns with periods of tens of thousands of years, and presumably related to climatic fluctuations, have been found in late Cretaceous and older sediments (Ken, 1983). Eccentricity variations are regular through time. However, the precession frequency, which also influencesthe obliquity variations, is a function of the slightly asymmetrical lunar and solar torque on the earth’s tidal bulge. With greater torque during past epochs when the moon was closer to the earth, the precession frequency would have been greater and the obliquity variations smaller (Pollack, 1982). It is unlikely that these parameters would have changed significantlyover Mesozoic- Cenozoic time. It is also possible that mean obliquity has changed through time: core - mantle interactions within the earth should tend to cause a decrease (Pollack, 1982), whereas asymmetrical lunar torque on the equatorial tidal bulge should increase obliquity by -3.5”/109 yr (MacDonald, 1964). Hunt (1979) concluded that there has been an increase in obliquity. Whatever the net change, it seems likely to be insignificant for the purposes of modeling Cretaceous and Tertiary climates. Because of orbital periodicities, specification of the solar radiation distribution has to be carefully considered in paleoclimatic modeling, even assuming a constant value of L through time.

1. For a simplifiedbut time-dependent model that is to be run over tens of thousands of years, the calculated seasonal and latitudinal variation of solar radiation should be specified as a function of time. 2. For “snapshot” simulationsof, e.g., 18,000yr BP (- 2000-yr averaging period), the magnitude and distribution of solar radiation given by the state of the orbital parameters at that time should be used.

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3. For snapshot simulations of, e.g., 50 or 100 m.y. ago, the paleogeography and SST boundary conditions and the paleoclimatic data used for comparison with the simulated results are averaged over a 1- to 5-m.y. period. Therefore, the distribution of solar radiation with latitude and season should be an average calculated over many cycles of orbital periodicities. 5.4.5. AtmosphericAerosol Content. It is well known from both observations and climate modeling (Toon and Pollack, 1980;Turco et al., 1982)that atmospheric aerosols can affect climate. This is because they scatter and absorb shortwave radiation and absorb and emit infrared radiation (IR) (Prosper0 et al., 1983). Observations of surface temperature anomalies following major volcanic eruptions and the results of climate modeling indicate that stratospheric aerosols increase the temperature in the lower stratosphere and reduce surface temperature. This is because the effect due to the increase in planetary albedo is greater than that due to the absorption and emission of IR (Pollack et al., 1976). Stratospheric aerosol content is closely related to one source, explosive volcanic eruptions that periodically inject silicate dust particles and sulfur gases (H,S, SOz) into the stratosphere. The sulfur gases are converted .through photochemical reactions to sulfuric acid particles. After major eruptions, stratospheric aerosols decline to background levels after 1-2 yr (Castleman et al., 1974). While the mean global atmospheric optical depth (7)due to aerosol is in the range 0.05 to 0.2 (Deirmendjian, 197I; Pollack et al., 1976), and major eruptions can increase this to 0.4-0.5 for several months, the mean volcanic contribution over the last few centuries to total atmospheric aerosol z has been - 0.02, constituting about 80% of the total stratospheric aerosol z (Pollack et al., 1976). Therefore, periods of enhanced volcanic activity in the geological past could have had climaticeffects through increased z. Approximate estimates of the variation of stratospheric aerosol content can be made, given a knowledge of changes in the global rate ofvolcanism through time. This could be achieved by the following methods. 1. Major volcanic episodes (lasting maybe a few million years) can be recognized in the geologic record from widespread and thick ash horizons (Axelrod, 1981). However, there is insufficient data to document changes in the global rate of volcanism with any confidence. 2. The global average ocean floor spreading rate slowly varies over tens of millions of years. This must affect mean rates of volcanism at midocean ridges .andin island arcs and Andean mountain belts along destructive plate margins. Hays and Pitman ( 1973)argued that the mean ocean floor spreading rate between 1 10 and 85 m.y. ago was double the present value [although

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Berggren et al. (1975) claimed that this rate resulted from the use of an incorrect timescale]. It seems likely that a combination of data on both mean spreading rate and total midocean ridge length would give a more accurate picture. There have also been major episodes of widespread midplate volcanism, as in the mid- and late Cretaceous Pacific (Schlanger et al., 1981). Because midocean ridge spreading rate and length and midplate volcanism are all associated with thermal uplift of the ocean floor, which is the main control of global sea level through geological time (Pitman, 1978), then a smoothed sea level curve (after removing ice volume effects) should reflect the mean rates of volcanism over 1 0 ' 9 periods, and hence the mean volcanic contribution to aerosol z. It should be remembered that the present observed rate of volcanism may be greater or less than the mean rate characteristic of the present ocean floor spreading rate.

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Long-term mean levels of stratospheric aerosol z could have reached approximately twice the present level. Energy balance model results indicate that this could have decreased global-mean surface temperature by 0.3"C, in the absence of water vapor, ice albedo-temperature, cloudiness, or other feedback mechanisms (Pollack et al., 1976). This is not very significant. However, it is possible that short-term increases in the rate of volcanism above the mean [such as that recognized by Axelrod (1981) for the latest cretaceous] could have caused major cooling. Tropospheric aerosols have a less clearly defined climatic influence because, in addition to the direct radiative effects that can influence environmental lapse rates, they act as ice or condensation nuclei and so may affect global cloud amount and distribution (Junge, 1971). Tropospheric aerosol loadings must have varied through time due to variations in sea surface area (sea salt), land surface area, and vegetation characteristics(silicate particles) and precipitation characteristicsand surface wind speeds (salt and silicate). Because it is a function of these different physical processes, it is difficult to make quantitative estimates of changes in tropospheric aerosol loading through time. However, evidence for variations in the silicate component is shown by changes in accumulation rates and grain size of the eolian component in Cretaceous and Cenozoic deep-sea sediments (Janecek and Rea, 1983).

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5.4.6. Atmospheric C02 Content. Assuming that solar luminosity has increased through earth history, it is necessary to postulate an enhanced greenhouse effect for earliest times, given the lack of evidence for continuous glaciation during the first part of earth history (Fig. la), the occurrence of water-deposited sediments 3800 m.y. old, and the possible existence of life on earth from 3500 m.y. ago. Sedimentary evidence indicates that little

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oxygen was present before 2100 m.y. ago. This is not surprising, because volcanic gases are reducing. Atmospheric oxygen is a product of photosynthesis, and so its build-up reflected the origin and developmentof life, Most workers agree that atmosphericoxygen and ozone approached levels similar to the present by about 600 m.y. ago. By this time, the high early CO, content would have been depleted through photosynthesis, and any carbon monoxide, ammonia, or methane of the early atmosphere would have long since disappeared (Hunt, 1979; Pollack, 1982). Therefore, during the past 600 m.y., CO,has been the only naturally occurring atmosphericgas whose variations could have significantly affected the atmospheric energy balance [other than water vapor, the content of which is largely determined by SST (and which acts as a positive feedback to any warming or cooling due to CO, changes), and stratospheric ozone, variations of which have little effect on climate (Reck, 1976)l. The only direct evidence for paleo-CO, levels comes from gaseous inclusions in polar ice which show that the partial was lower during the last glacial maxipressure of atmospheric COz (Pco,) mum (Neftel et al., 1982). No direct samples are available for earlier periods. Controls of atmosphericCO, and the possible recognition of excursionsin the geological record have been reviewed by Berger (1977), Holland (1978), Arthur (1982), and Berner et al. (1983). PCo2is a function of complex physicochemical processes in the oceans, biosphere, atmosphere and lithosphere. It is important to note that the quantity of carbon in the oceanic reservoiris much greater than that in the atmosphere, whereas the amount in the biosphere is intermediate. Also, the amount in carbonate and organic carbon in sedimentsis far greater than in any ofthese (Holland, 1978). The following processes are some of the important mechanisms affecting P,,, (Berger, 1977; Holland, 1978; Arthur, 1982; Berner el al., 1983). 1. Primary CO, is introduced to the atmosphere by volcanic outgassing, the rate of which is related to ocean floor spreading rates. 2. Surface weathering reactions such as the decomposition ofsilicatesand carbonates by acid soil, groundwater, and C0,-rich surface runoff are a major sink for atmosphericCO, , The rate of surface weathering vanes as a function of many factors, including total land area, depending on sea level, climate, vegetation cover, and also orogenic uplift. 3. Photosynthesis of land plants and marine phytoplankton removes CO, from the atmosphere. Therefore, burial of organic carbon in sediments represents a loss of CO, from the atmosphere. The burial rate of organic matter is a function of ocean surface productivity (and hence nutrient supply and deep-ocean circulation) and also depends on the supply of terrigenous carbon to the ocean.

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4. Oxidation of old organic carbon releases C02to the ocean and atmosphere. This can be inhibited by anoxia, which is a function of ocean circulation. 5 . High C02levels in the atmosphere and ocean could be buffered by the dissolution of deep marine carbonates. This situation would be represented in the geological record by dissolution horizons in carbonate sequences and rises in the calcite compensationdepth. At such times, widespread shallowwater carbonate deposition can occur, which also reduces Pa, (Moore, 1983). These processes are functions of ocean circulation and productivity and may be reflected by 613Cchanges in marine carbonates. 6 . Evaporite (CaSO,) deposition influences ocean chemistry through transfer of Ca2+from the carbonate to the evaporite reservoir, causing decreased carbonate deposition and a gain of C02 by the atmosphere. It is therefore possible that rapid early Cretaceous deposition of evaporites around the newly opened Atlantic caused a Pcoz increase. At this time, the CCD rose (Thierstein, 1979)and 613Cbecame more positive. The ensuing high burial rate of organic carbon (which could have been related to the COz increase) would have reduced Pcoz . 7. Solubilityof C02in water decreasesas temperature increases, so Pco2in equilibrium with sea water increases as SST increases (Broecker, 1974). Despite the complexity of the system, the followingsignals in the geologic record, when occurring synchronouslywith climaticwarming, may indicate increased Pco2 (Berger, 1977): (1) increased dissolution of deep-sea carbonate, (2) changes in the isotope ratio (13C/lZC)of total carbon, (3) a change in rate of organic matter accumulation in marine or nonmarine sediments, and (4)no evidence for a major change in ocean circulation that could have caused any of the above. These changes have been observed in early Paleocene sediments, indicating a Pco, increase at that time. Interestingly, late Paleocene and early to middle Miocene warming coincide with evidence for peaks in global volcanism, which may have increased Pa, (Arthur, 1982). Given the obviouscomplexity of the relationshipbetween Pco, and ocean chemistry, the biosphere, and other factors, it is surprising that, on the basis of an analysis of rates of carbon deposition in sedimentary rocks on land only and the volumes of volcanic rocks, Budyko and Ronov (1980) published a curve of Pco2through Phanerozoictime showing levels up to approximately 10 times the present. It is difficult to see how they can justify this result on the basis of such evidence. Berner et al. (1983), using a model principally based on removal of C02by surface weathering and on addition by volcanic outgassing, showed that these mechanisms work together; high outgassing corresponds with high sea level (due to increased midocean ridge volume), with both increasing C02. Their curve for Cretaceous to present C02levels

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shows mid-Cretaceous and Eocene maxima. They do not include the organic carbon cycle, however, so their results should be treated with caution. In summary, Pcs is one variable in a complex global carbon cycle. It is a function of many factors, including ocean floor spreadingrate as a control on volcanic outgassing and rate of removal by surface weathering (through control of sea level), and also land and marine biological productivity and sedimentation, which in turn are functions of, at least, land surface climate, sea level, and ocean temperature and circulation. In conclusion, COzlevels cannot be specified with confidencefor pre-Pleistocenetimes. As with solar luminosity, it is best to run sensitivityexperiments to determinethe reaction of the system to changes in specified CO,levels.

6. PALEOCLIMATIC MODELING STRATEGIES 6.1. Introduction

In this section we shall describe the types of numerical climate model that are available for paleoclimatic studies and also consider which models or model combinationsare most appropriate for different paleoclimatic experiments. Atmospheric general circulation models (GCMs), which can be coupled with ocean models of varying complexity, and statistical-dynamical models (SDMs)are available. [For reviews of climatemodels, see Schneider and Dickinson ( 1974), World Meteorological Organization ( 1975), Ramanathan and Coakley (1978), Saltzman (1978), and North et al. (1981). An extensive documentation of GCM and SDM intercomparisons is given in World Meteorological Organization (1979).] The main features of GCMs and SDMs, with special reference to their potential use in pre-Pleistocene paleoclimatic modeling, have been reviewed by Gates (1982) and will not be described here. However, some specific paleoclimatic modeling strategies and problems will be examined. We shall also discuss the two basic approaches to modeling: “snapshot” experiments, in which boundary conditions are specified as completely as possible in order to conduct a simulation of the atmosphericand/or oceanic climate for a particular period of interest; and sensitivity experiments, in which individual forcing factors such as land- sea distribution or orography are varied in order to determine the effect of these changes on the modelsimulated climate and, consequently, to identify which of these changes in forcing factors were important in causing the evolution of paleoclimatesthat is shown by the paleoclimatic record.

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6.2. Summary of Boundary Conditions

The methods and problems associated with the reconstruction of boundary conditions for Cretaceous and early Tertiary paleoclimatic modeling have been discussed in Section 5. To summarize, the boundary conditions are paleogeography, including coastlines, land surface topography, and paleobathymetry; SST distribution; rotation rate of the earth; surface albedo; atmospheric COz and aerosol contents; and solar luminosity. Of these, paleogeography,SST, rotation rate, and surface albedo can be specified with varying degrees of confidence. Carbon dioxide leaves a complex signal in the geologic record and is difficult to specify for any time, given the present level of understanding. Guesses could be made of aerosol and solar luminosity values, but would be difficult to justify. To investigate possible effects of changes in the latter three parameters, sensitivity experiments would be necessary. The accuracy of some boundary condition reconstructions is time dependent. This is an important constraint on the time range within which paleoclimatic modeling can be undertaken. Paleobathymetrycan be specified with some confidence for the Atlantic back to 120 m.y. ago (early Cretaceous), but is very uncertain for the other oceans. The SST distribution could be specified back to the mid-Cretaceous, but only on the basis of very limited oxygen-isotope paleotemperature data. More numerous and reliable data are available for Cenozoic times. Paleocontinental positions are known with some certainty back into the Triassic, although the degree of accuracy is better for more recent times. For pre-Triassic times, before the continents sutured together to form the supercontinent Pangea, it is only possible to position continents latitudinally, not longitudinally, from geophysical data alone, although their relative positions could be constrained from a knowledge of biogeography and possible ocean circulation patterns. Therefore, at present, the possibility of paleoclimaticsimulationsusing realistic paleogeography for Paleozoic time can be disregarded. However, modeling that requires only specification of land-sea proportions in zonal belts would be possible.

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6.3. Statistical-Dynamical Models

Results from statistical-dynamicalmodels are equilibrium statistics: synoptic developments are not explicitly simulated-their effects are parameterized. Statistical-dynamical models may be zero to three dimensional (Saltzman, 1978). Commonly used forms are radiative-convective models, which determine the equilibrium vertical temperature profile of an

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atmospheric column (Ramanathan and Coakley, 1978), and energy balance models (North et al., 198 l), which are built around the principle ofan energy balance at the earth’s surface. Energy balance models have been used to simulate meridional surfacetemperature profiles (Lloyd, 1977; Barron et al., 198 la) and surface temperature distributions (Donn and Shaw, 1977) for some preglacial times. Dynamical models can also be used. Saltzman and Vernekar ( 1975) used a one-dimensional (zonally averaged) dynamical model to simulate, for example, meridional profiles of mean zonal wind speed for present and last glacial maximum conditions. There are problems, however. Some SDMs can give different equilibrium solutions for identical boundary conditions. Also, the close correspondence of some model results with observations in present-day simulations results from the tuning of, in particular, radiative and heat transport formulations on the basis of present climate statistics. For ancient times, with different cloud characteristics and oceanic heat transport, for example, these formulations may no longer be valid (Schneider and Thompson, 1980). With these reservations in mind, paleoclimatic modeling with SDMs could be useful in the following cases. 1 . For Paleozoic times, relative paleocontinentalpositions are unknown, but latitudes of individual continents and land-sea distributions on those continents can be approximately determined. Therefore, land- sea proportions in zonal belts can be used as a boundary condition in, for example, one-dimensional energy balance model simulations of meridional surface temperature profiles. Zonally averaged dynamical models may also be useful for modeling effects of rotation rate changes, for example. 2. Since SDMs require much less computer time than GCMs, large numbers of sensitivity experiments can be run in which parameters such as land - sea proportions, the atmosphericC02content, solar luminosity,cloud characteristics, stratospheric aerosol content, or surface albedo are varied.

6.4. General Circulation Models

6.4.1.Introduction. General circulation models predict temperature, pressure, and motion fields and their interactions and explicitly simulate synoptic-scale motions. Climate statistics are extracted by averaging over a sufficiently long period after the model has reached equilibrium. General circulation model results of particular interest to paleoclimatologistsinclude the surfacetemperature, pressure, wind field, evaporation,precipitation, soil moisture, and snow depth and, of course, their seasonal variations. For paleoclimatic simulations, the land - sea distribution, land surface

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topography, SST and surface albedo distributions, and the rotation rate of the earth are all boundary conditionsdifferent from the present which can be specified. Other boundary conditions are either the same as at present (e.g., radius of the earth, atmospheric mass) or may have uncertain paleovalues (e.g., atmospheric CO, content, solar constant). If an ocean model is coupled to an atmospheric GCM, the SST is model generated and so does not have to be specified as a boundary condition.

6.4.2. Possible GCM Modeling Strategies. 6.4.2.1. Atmospheric GCM with speciJied SST. An atmospheric GCM may be used for a snapshot simulation of a chosen period. If SST is specified, the availability of oxygen-isotope paleotemperature data limits the choice of period to the late Paleocene (- 60 m.y. ago) onward. Remember also that isotopic data cannot be used to determine paleotemperatures from 14 m.y. ago onward (or possibly even 38 m.y. ago) when they are dominated by the ice volume signal. For the early Tertiary, we also have the most accurate paleogeographic reconstructions and the most abundant and reliable paleoclimatic evidence for comparison with model results for any period of preglacial time. Note that specification of SST effectivelysidestepsthe problem of oceanic circulation and heat transport and also partially accounts for the possible different values of CO, and the solar constant. 6.4.2.2. Atmospheric GCM with coupled ocean model. If an atmospheric GCM were coupled with an ocean model, SST would be model generated. Therefore, the need to specify SST from oxygen-isotopedata and the consequent restriction of modeling to Tertiary time would be avoided. The time constraint would then be the earliest reliable paleocontinental reconstructions, i.e., for 230 m.y. ago. 6.4.2.2.1. Atmospheric GCM with coupled oceanic GCM. Coupled atmosphere- ocean GCM simulations of the present system have been run at the Geophysical Fluid Dynamics Laboratory (Manabe et al., 1979),National Center for Atmospheric Research (NCAR) (Washington et al., 1980), and at Oregon State University (OSU)(Y.-J. Han, personal communication, 1983). Since oceanic GCMs are multilayer models that treat the deep ocean explicitly, results for a Cretaceoussimulation would be particularly interesting in the light of present uncertainty over the nature of Cretaceous deepocean circulation (polar or subtropical deep-water source). They should also generate realistic oceanic poleward heat transport. However, these models still have problems simulating various features of the present system. Also, coupled runs require more computer time than an atmospheric GCM run. For a paleosimulation, this would be a particular problem, since the deep-ocean circulation would have to be “spun-up” from rest over sev-

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era1 hundred model years, although asynchronous coupling techniques can reduce total computer time. Also, there are problems with the specification of paleobathymetry, as explained in Section 5.2.4. While the Atlantic bottom topography can be reconstructed with some confidence for Cretaceous and Tertiary times, elsewhere (particularly in the Pacific and Tethys) the positions of the midocean ridges and major submarine rises can at best be approximately located (Kanasewichet al., 1978). Failure to recognize other major bamers to deep circulation would have a major effect on oceanic GCM results. A possible solution is to use a GCM with a specified flat bottom. While less complete than a full GCM, it would include the deep ocean and thus be more realistic than an upper-ocean model. 6.4.2.2.2.Atmospheric GCM coupled with simpler ocean models. Given the computer time and boundary condition requirementsof oceanic GCMs, would a simpler ocean model be adequate? In the simplest case, the ocean can be modeled as a “swamp,” i.e., treated as a wet surface with no heat capacity and with the temperature determined by surface radiative energy balance and vertical sensible and latent heat fluxes. More realistic than the swamp model is the fixed-depth mixed layer (slab), which includes the heat capacity of near-surface waters but which lacks horizontal heat transport and fluxes through its base (Manabe and Stouffer, 1980; Pollard et al., 1980). The fixed-depth mixed-layer model of Pollard et al. (1980) coupled to the OSU atmospheric GCM showed simulated SSTs markedly different from observationsin some parts ofthe oceans, and simulated sea ice was less than observed. This was probably due to noninclusion of upwelling and horizontal heat transport and to errors in the atmospheric GCM-simulated fields (Schlesingerand Gates, 1981). Also, as will be documented later, Barron and Washington (1 982a,b) described a mid-Cretaceous simulation with a version of the NCAR GCM coupled with a fixed-depth mixed-layer ocean model with sea ice formation. Simulated SSTs were far lower than indicated by paleoclimatic evidence, possibly because oceanic heat transport was not included. This result, and those of some Cretaceous SDM simulations (described in Section 7.3), which imply that a Cretaceous-likeequator-pole temperature profile can only be maintained by more efficient poleward heat transport, indicate that the use of an ocean model without horizontal transport is unrealistic for paleosimulations. Some mixed-layer models can include horizontal transport. In these models SST is a function of horizontal advection, as well as surface energy balance and vertical mixing. Pollard ( 1982)describeda two-layer, variabledepth model with sea ice formation in which horizontal advection is predicted in both layers by the primitive momentum equations. When coupled with the OSU atmospheric GCM, this model showed errors of up to 6°C in

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SST in some parts of the oceans, similar to those found in coupled ocean atmosphere GCMs; these errors appear to be mainly due to poor resolution of western boundary currents by the model’s coarse grid. 6.4.2.3. Coupled ocean or specifiedSST? The formulation of a paleoclimatic modeling strategy using GCMs, in particular the decision whether to use an atmospheric GCM with specified SST or to couple with an ocean model, therefore depends on several considerations,in addition to the obvious constraint of available computer time. 6.4.2.3.I. Period of interest. Sea surface temperature cannot be specified with confidence before the late Paleocene (- 60 m.y. ago). Also, because of the effect of polar ice formation on the mean isotopic composition of the ocean, isotopic paleotemperatures can only be determined for the period before 14 m.y. ago. Pre-Cenozoic or post-mid-Miocene simulations therefore have to include a coupled ocean model. 6.4.2.3.2. Importance of deep circulation. Mixed-layer ocean models with horizontal advection coupled with an atmospheric GCM have simulated SST with acceptable accuracy and do not have the excessive computational requirement of deep-ocean spin-up. Therefore they appear to be suitable for paleoclimatic modeling. However, given the importance of oceanic heat transport in maintaining ice-free poles during warm geological periods, any ocean model that does not include the deep ocean will only be of limited benefit in improving our understanding of poleward heat transport and the maintenance of these conditions. Despite this limitation of mixedlayer models, it is not possible at present to contemplate paleosimulations including an oceanic GCM with specified bottom topography for pre-late Cenozoic times because paleobathymetry cannot be determined everywhere, particularly in parts of the Pacific and Tethys. However, use of a flat-bottom oceanic GCM is a possibility. 6.5. Snapshot and Sensitivity Experiments

6.5.1. Snapshot Experiments. In some modeling situationsthe principal interest may lie in simulating a “realistic” atmospheric and/or oceanic climate for a given period. Model-simulated circulation patterns and upwelling locations may provide a useful framework for resource exploration, for example. For this kind of result, we clearly need to use an atmospheric GCM, preferably coupled with a dynamical ocean model. However, it must be recognized that there are significant uncertainties in the specification of the different elements of paleogeography, particularly orography, and of other boundary conditions, as is made clear in Section 5 . Also, we know that even GCMs are very imperfect approximations to the real atmosphere

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and oceans. Notwithstanding these problems, and remembering that paleoclimatic evidence has given us only a very poor picture of past atmospheric circulations, snapshot simulations with GCMs of periods in the Cretaceous or Tertiary should be of considerable interest to geologists and paleontologists concerned with environmental conditions at these times. 6.5.2. Sensitivity Experiments. While snapshot experiments may give an interesting picture of climate for a chosen period, they do not significantly advance our understanding of why climate at that time was so different from the present climate, and of why climates subsequentlyevolved to the present state. Answers to these questions may be sought through sensitivity experiments, which may use any of the hierarchy of models described above. By running a series of experiments in each of which a single forcing factor is changed, we can identify at least the model-simulated effect of that change on climate. For example, energy balance models can show the (modelsimulated) effect of the known evolution of land- sea distribution (acting through surface albedo changes) on meridional temperature profiles. However, even though large numbers of runs may be made with such a model, many important atmospheric processes are parameterized rather than treated explicitly, and many important feedback mechanisms are not included. Therefore results have to be interpreted with great caution. If a GCM were used, the effects of specified changes in continent-ocean positions, sea level, and orography on atmospheric temperature,circulation, and precipitation fields, for example, could be identified. With these models, however, available computer time limits the number of runs that can be made. If a series of sensitivity experiments indicate that known boundary condition changes can account for the observed evolution of paleoclimates, as indicated by the record, we may have tentatively identified the forcing factors responsible for the observed changes. If, however, we find no correspondence between model-simulated and observed climatic changes, we may conclude that some other forcing factor, such as changing atmospheric C02 content or solar luminosity, was responsible. Further sensitivity experiments could then be run to identify climatic effectsofpossible changes in these other forcing factors. 6.6. Comparison of Model Results with Paleoclimatic Evidence

Having obtained the results of a snapshot climate model simulation, available paleoclimatic evidence for the appropriate period should be assembled and compared with the simulated fields. This exercise should show whether

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the model has simulated temperature, precipitation, and circulation fields consistent with the paleoclimatic evidence. Also, in the case of some sensitivity experiments such as the modeled effect of a particular paleogeographic change, it will be desirable to compare the model-simulated climatic effects of the prescribed change with the evidence for climatic change over the same period. The main groups of evidence available include land floras, faunas, and sediment types as indictors of surface evaporation- precipitation balance, temperature, and wind direction, and the seasonality of these parameters; evidence from distributions of marine organisms for temperature and circulation patterns; and oxygen-isotope paleotemperature estimates for nearsurface and bottom marine temperatures. Most of these data are qualitative and so can only be used to show consistencywith the model-simulated fields in regions where indicators of the appropriate age are preserved in sediments. Also, the data will represent an average of climates over a period at least as long as the limit of stratigraphic resolution at that time (rarely less than 1 m.y.). An important restriction concerns paleoclimatic evidence that has been used to establish boundary conditions for a particular model run. These data should not be used for comparisonwith model resultsbecause they have partially determined these results, assuming that the model has simulated a climate physically consistent with the boundary conditions. For example, if oxygen-isotope paleotemperature data and marine biogeographic evidence are used to determine sea surface temperature distribution, and evidence for vegetated and desert regions is used to specify land surface albedo distribution, these data should not be used for verification. However, if a uniform mean land surface albedo were specified, then all land surface paleoclimatic evidence would be available. This would be particularly important with an atmospheric GCM simulation with specified SST for which comparison would have to be mainly confined to land climates. If an ocean model were coupled with an atmosphericGCM, then all marine evidence could be used.

7. A SURVEY OF PALEOCLIMATIC MODELING RESULTS

7.1. Introduction In the preceding sectionswe have discussed our knowledge of the paleoclimatic record, the principal forcing factors for long-term climatic change, and, in more detail, how paleogeographic and other boundary conditions can be reconstructed for periods in the Cretaceous and Tertiary and the

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types of models available. Given this knowledge, climate modelers have tried to answer two questions: 1 . What were the main characteristics of the oceanic and atmospheric circulations at specific intervals in the past? 2. Why was the climate maintained in these states, which were very different from the present climate, particularly with regard to warm polar regions, and what particular changes in forcing mechanisms caused the changes in global climates that are shown by the paleoclimatic evidence?

The first question has been addressed using the snapshot method, in which qualitative and numerical climate models have been employed to simulate climates for various times, once the appropriate paleogeographicand other boundary conditions have been specified. The second question has been approached by using numerical climate models to conduct sensitivity experiments in which the responses of the model-simulated climates to specified changes in particular forcing factors have been examined. Results of these two categories of modeling experiments will now be examined.

7.2. Results of Snapshot Simulations 7.2.1. Results of Qualitative Models. After realistic paleocontinental reconstructions first became available around 1970, it was natural that attempts would be made, for the first time, to consider paleoclimates within realistic paleogeographic frameworks. Frakes and Kemp ( 1972) sketched oceanic and atmospheric circulation patterns for both late Eocene and early Oligocene times and identified probable regions of high precipitation, mainly by analogy with the present. Robinson (1 973) postulated a distribution of precipitation regimes on the Permian and Triassic continents,assuming present-day distributions of precipitation belts with respect to latitude and using the pattern of Koppen’s climatic zones on an idealized continent. This was an interesting exercise, but it took no account of oceanic or atmospheric circulation characteristics that may have been forced by the very different paleogeographies of those times -it simply assumed present-day climatic patterns on the ancient continents. More realistically, Parrish and Curtis (1982), by analogy with present-day zonal circulation belts and the relationships of the major circulation cells to continental distributions, postulated atmospheric surface pressure and circulation patterns for several periods in the Mesozoic and Cenozoic on the paleogeographic reconstructions of Ziegler et al. (1983). From these patterns they predicted locations of wind-forced coastal upwelling around the

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continental margins. These showed a good correlation with observed occurrences of organic-rich rocks. Using the same atmospheric circulation patterns Pamsh ef al. (1982) also postulated precipitation patterns on the continents for the same periods, by analogy with present patterns and circulation/geographic/precipitationrelationships. They found that their postulated rainfall patterns were more successful in predicting the locations of ancient coal and evaporite depositsthan was a simple zonal rainfall distribution. Marsaglia and Devries Klein (1983) plotted likely hurricane tracks on paleogeographicmaps for several periods in the Paleozoic and Mesozoic and found a very approximate correlation with the locations of sediments thought to be characteristic of storm deposition. Lloyd (1977, 1982) reconstructed mid-Cretaceous (100 m.y. ago) paleogeograpy and postulated ocean surface circulation, SST, and atmospheric circulation characteristics and compared these with the available paleoclimatic evidence. The 100-m.y. paleocontinental map of Smith and Briden ( 1977) was modified to include coastlines and land surface topography and to compensate for microplate movements using the methods described in Section 5.2.2 (Fig. 9). Given this paleogeography,ocean surface circulation was postulated (Fig. 10). This circulation was used, along with equatorial and polar reference points for SST, to map speculative global seasonal patterns of SST on the basis of present-day observed relationshipsbetween SST and circulation (Fig. 1 I), as described in Section 5.3. Having established paleogeographic and SST boundary conditions, the main features of atmospheric circulation were reconstructed as follows (Fig. 13 shows the postulated July surface wind patterns):

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1 . With mean equatorial and polar SSTs of 30 and 15 “C,respectively, the thermal wind relation gives a mean tropospheric zonal wind speed of about 30%of the present value. 2. The rotating dishpan and annulus experiments of Fultz et al. ( 1 959) in which the “equator -pole” temperature difference was varied were interpreted to indicate that, with a reduction in the equator-pole temperature difference to 30%of the present value, the subtropical anticyclones would have shifted a few degrees equatorward of the present position near 30”. A 3% higher i2 would have contributed another - 1 ’ to this shift. The maintenance of atmospheric angular momentum balance would then have required that the tropical easterlies be stronger relative to the midlatitude westerlies than at present. 3. In winter over the warm, ice-free polar oceans, anticyclones probably did not form. Rather, they would have been located over the cold, high-latitude continents. Because the summer Arctic Ocean was colder than the

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PIG.13. Hypothetical midCretaceous monthly mean surface wind directions for July. Dashed arrows, directions tentative. Thick band in the tropics is the Intertropical Convergence Zone. Shading denotes land surface topography over 2 km. [From Lloyd (1982).]

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surrounding continents, a high could have existed then with polar easterly winds. 4. Mean wavenumber in the westerlies would have been 6 - 7, compared with the present 5, accordingto the results of Fultz et al. ( 1959). Results of other experimental and theoretical models indicate that in the weaker zonal flow the orographic forcing of standing waves would have been less important. Conversely, thermal forcing due to land- ocean temperature contrasts would have been more important, because seasonal land - sea temperature contrasts would have been greater due to weaker advection of temperaturemoderating air from the oceans. In other words, continentalitywould have been more severe. Despite high SSTs and extensive shallow seas, surface temperatures in the winter continental interiors could have been well below freezing. Therefore, east -west surfacepressure anomalies would have been greater than at present, with the strongest (surface)pressure gradients across the coasts. This would have given more meridional (seasonally reversing) flows at midlatitudes. [Interestingly,the GCM runs of Barron and Washington (1 982a,b) (see later) simulated a similar result.] Over the mid- to high-latitude continents, low pressure would have dominated in summer and high pressure in winter, with most interior precipitation occumng in summer and with winter precipitation associated with baroclinicity around the coastlines. 5. With a small meridional temperature gradient, midlatitude cyclones and fronts were probably weak and over the Pacific would have shown little seasonal variation. However, given the continental margin baroclinicity mentioned above and higher atmosphericwater vapor content, precipitation due to cyclonic activity could have been important in some situations, for example on the margins of southern North America-Europe and east Asia in winter. 6. Summer monsoonalconditions probably prevailed in locationssimilar to present-day southern Asia: for example, the margin of southern North America - Europe -Asia. 7. By analogy with present-daytropical east- west “Walker” circulations, year-round ascent and heavy precipitation probably occurred over the high SST region of the west Pacific and over Southeast Asia, whereas descent and aridity would have prevailed over the cool upwelling region of the east Pacific. With higher SST and hence greater latent heat release, these circulations and the Hadley circulations associated with them could have been more vigorous than at present. 8. A comparison of the results with available mid-Cretaceous paleoclimatic evidence (see Section 3.3) showed good support for the postulated ocean circulation/SST patterns and consistencywith the atmosphericcirculation.

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7.2.2. Results of General Circulation Models. Atmospheric GCM simulations of the last glacial maximum have been described by Williams et a/. ( 1974),and also by Gates ( 1976a,b)and Manabe and Hahn (1977), who used the August 18,000-yr BP data on SST, ice sheet topography, sea level, and surface albedo assembled by CLIMAP Project Members (1976) as boundary conditions. The first published pre-Pleistocene GCM simulation was that of Barron and Washington (1982a,b),using a version of the National Center for Atmospheric Research GCM coupled with a fixed-depth (5-m) slab ocean, and mid-Cretaceous land - sea distribution, but no land surface topography. The atmosphericGCM has an explicit hydrologic cycle and predictive equations for soil moisture and snow cover, while cloudiness is diagnostically determined from relative humidity. In the ocean model, temperature at each grid point is calculated from surface heat balance, and sea ice forms when the SST falls to -2°C. The solar constant, the atmospheric C02 content, and the rotation rate of the earth were specified at their present levels. The model was run for perpetual March, January, and July conditions, and results were obtained from 30-day averages. Apart from the present-day control simulation, for each month two Cretaceous cases were run: case 1 with SST determined everywhere by the slab ocean model, and case 2 with SST constrained to be 2 10°C. The principal results were as follows: 1. Computed high-latitude SSTs for case 1 were much below those indicated by mid-Cretaceous paleoclimatic evidence. Sea ice formed at high latitudes, despite the lack of evidence for Cretaceous sea or land ice. 2. Even for case 2 with warmer oceans, the mid- and high-latitude continental interiors were cold in winter (- -20"C), resulting in strong temperature gradients around the continental margins. These gradients were associated with a less zonal pressure distribution, adjacent highs and lows at continental margins, and zonally averaged wind speeds greater than at present in some latitudes. Jet stream speeds in both cases were similar to, or greater than, those at present. 3. Polar highs developed in both cases, being strongest in January. 4. The Northern Hemisphere subtropical high-pressure belt was slightly equatorwardof its present latitude, and the Southern Hemisphere belt was at a latitude similar to the present. 5 . A well-developed equatorial rainbelt was simulated, shifting seasonally, along with greater than present midlatitude rainfall, mainly over the continents. 6. Large variations in surface moisture balance were simulated across some continental margins.

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7. While the surface equator-pole temperature gradient was less, the average tropospheric equator-pole temperaturedifference showed a smaller decrease because greater latent heat release in the equatorial troposphere amplified the small equatorial SST increasein the upper troposphere. Also, polar warming eliminated the near-surface temperature inversion without significantly affecting tropospheric temperature. 8. Cretaceous case 1 shows a 3°C planetary warming relative to the present, greater than in the simpler models described later. This indicates that the more realistic feedback mechanisms in the GCM act to enhance the surface albedo-related temperature increase. 9. Similaritiesbetween the two Cretaceouscases indicate the importance of paleogeographyin determining angular momentum and energy balance.

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Barron and Washington ( 1982a) compared the warm-ocean-simulated results with some Cretaceous paleoclimatic evidence, finding either reasonable agreement or consistency except for a subtropical flora in Mongolia, where the model predicted a January mean surface temperature of - 10 C. Some comments may be made on these experiments. 1. Barron and Washington made no mention of what land surfacealbedo distribution was used- whether it was constant everywhere on snow-free land, or variable, depending on geologic evidence for surface vegetation distribution. This is important, because if they did specify surface albedo from floral and sedimentary data, the model-simulated results would, to some extent, be predetermined over land to be consistent with the observed distributions of paleoclimatic evidence for arid and wet regions. In other words, the same data should not be used both to establish boundary conditions and to verify the simulated climate. Barron and Washington did not consider this issue in their papers. 2. The fact that the slab ocean generated high-latitude SSTs that were so much lower than those given by the paleoclimaticevidence shows the desirability of using an ocean model that explicitly includes horizontal heat transport and upwelling. 3. Barron and Washington describe these runs as sensitivity experiments. This is true in the sense that model sensitivity to the change from present to mid-Cretaceous geography and from model-generated to partially specified SST is shown. However, they apparently regard Cretaceouscase 2 (SST 5 10°C)as a “realistic” snapshot, since they go to some length to show that its model-simulated climate is consistent with most of the available paleoclimatic evidence. 4. The results of Barron and Washington (1982a,b) should not be interpreted as a realistic simulation of climate at that time. The main reasons for this are (1) the model-simulated climate of Cretaceous case 2 is strongly

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dependent on an arbitrarily specified SST distribution,and (2)they specified a flat land surface without justification. There is, in fact, considerable geological evidence that major orogenic belts existed in the mid-Cretaceous (Spencer, 1974), and subsequent sensitivity experiments by Barron and Washington (1984) have shown the importance of orography as a paleoclimatic forcing factor. Therefore, if they had included the orography that existed at that time, the simulated climate would have been considerably different.

7.3. Results of Sensitivity Experiments 7.3.I . Results of Energy Balance Models. The most fundamental difference between preglacial climatesand the glacial climatesof the late Cenozoic has been the much smaller equator- pole temperaturedifference,with equatorial SST near, or even lower than, present values and polar SST much warmer, in the region of 5 - 15°C. What mechanism kept the poles sufficiently warm to prevent the formation of polar ice (and subsequent cooling through ice albedo- temperature feedback) during the nonglacial periods that have characterized most of earth history? Atmosphericenergy balance and temperature are dependent on the values of many physical parameters which were different in the Cretaceousand early Tertiary. These include the distribution of land and sea with respect to latitude and the extent of shallow seas on the continents (hence surface albedo distribution), the shapes of the ocean basins, solar luminosity, aerosol and cloud characteristics, atmospheric C02 content, and SST distribution (which is itself a function of the above-mentioned parameters). Could any reasonable paleovalues of surface albedo distribution, the solar constant, CO,, cloudiness, or other less important parameters either separately or in combination have maintained polar temperatures at their “observed” Cretaceous-early Tertiary levels? The results of some sensitivity experiments with energy balance models can help identify the important mechanisms. Donn and Shaw (1977) used a two-dimensional surface energy balance model of the Northern Hemisphere and paleocontinental positions to compute mean annual surface temperaturedistributionsfor some intervalsin the Mesozoic and Cenozoic. Results showed temperatures of 0-5°C in the Mesozoic Arctic, with progressive cooling as the continents moved northward. However, this model was unrealistic due to the use of present coastlines, lack of feedback mechanisms, and an unrealistic horizontal heat transport formulation. Also, by prescribing snow cover at present latitudes they were largely predetermining high-latitude cooling as the continents moved northward into those latitudes. Barron et al. (1 980) used paleogeographicreconstructions to estimate the

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fraction of land area in each 10" latitude belt from 180 m.y. ago to the present at 20-m.y. intervals. Making certain assumptions about the latitudinal distribution of desert, snow, and sea ice, and calculating the areaweighted mean albedo (aT)for each latitude belt, they calculated absorbed solar radiation at the surface, Q(1 - a,), where Q is the solar radiation received over the total area of the latitude belt. Results showed an approximately 1% decrease in global absorbed radiation from 120 m.y. ago to the present. This decrease occurred at low and high (northern) latitudes, with the greatest absolute decrease in low latitudes because of the greater insolation receipt there. The decrease in global absorbed radiation showed a close inverse correlation with sea level, indicating that over this time period, sea level fall acting through decreased a, (rather than latitudinal change in continental positions) has been the most significant factor in changing the surface energy balance. Specification of desert albedo for all land in the zone 10- 30"gave a much greater subtropical decreasein absorbed radiation through this period. In summary, these results show the importance to global energy balance of low-latitude land area, sea level, and desert. Similarly Burrett (1982) calculated Q(1 - aT)for intervals through Paleozoic time based on his own paleogeographicreconstructions and assumingdesert albedo for all land surfaces in pre-Devonian time. Results showed no correlation between the global Q(1 - a,) curve and Paleozoic glaciations. [Higher land plants probably appeared in middle Ordovician time (Gray et al., 1982);land surfaces before then would have had desert/wet soil albedos unless primitive plants covered a significant portion of the land area.] Cogley (1979) also computed Q(1 - a,) for latitude belts from 240 m.y. ago to the present at 20-m.y. intervals, using evolving continental positions. However, the results are unrealistic because he used present coastlines. Nevertheless, Cogley did point out that because water surface albedo increases more rapidly poleward than does land surface albedo, the landocean albedo contrast for snow-freeland is greatest in low latitudes. Therefore, a change in land-sea proportion in a low-latitude belt has a greater effect on a, in that belt than does the same proportional change in a high-latitude belt. This has enhanced the effect of the increase in low-latitude land over the last 80 m.y. on global Q( 1 - aT), relative to the increase in high-latitude land (in the absence of snow albedo-temperature feedback). Thompson and Barron ( I 98 1) used a planetary albedo model to examine the influence on absorbed radiation of changes in land- sea distribution, temperature, sea ice, vegetation, and cloud cover, which were specified for 100 m.y. ago and the present. Results showed a 2.39/0 increase in global absorbed solar radiation for the Cretaceous case relative to the present, of which - 40% was due to paleogeographicand land surface albedo differences in low latitudes, and 60%due to differences in specified mean annual surface

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temperature (T,) (hence snow cover) and sea ice. Interestingly, the Cretaceous simulation was insensitive to changes in tropical land surface albedo because of the small proportion of land in low latitudes, indicating that land area is important in determining the sensitivity of planetary albedo to changes in land surface albedo. Cloud characteristics (the same in both cases) are of course critical; any change would have a large effect on absorbed radiation. Most recently, Barron et al. ( 198la) used a version of the zonally averaged energy balance climate model of Thompson and Schneider (1979), including the planetary albedo formulation of Thompson and Barron ( 198l), to model Cretaceous and present climates. In this model, outgoing IR is a linear function of T, and also of cloud cover. The temperature of a latitudinal strip is a function of land- sea heat capacity, radiation balance, and the divergence of poleward heat transport. Poleward energy transport is in two components: sensible heat potential energy, proportional to the equator pole surface temperature difference, and latent energy, proportional to the meridional gradient of atmospheric water vapor concentration. Oceanic heat transport is specifiedto be the same as at present. The model simulated the present annual temperature cycle well. Using mid-Cretaceous( 100m.y. ago) paleogeography to determine mean zonal albedos, and with no polar ice, results showed a 1.5 “Cglobal T, increase (relative to the present), with the greatest increase (- 5 -6°C) in high northern latitudes. While this is a more “Cretaceous-like” meridional temperature profile, it is still 10 C colder in mid- and high latitudes than even the coldest possible “observed” mid-Cretaceous temperatures (- 0°C polar T,, assuming that the paleoclimatic evidence is correct). Increased ground wetness (acting through decreased surface albedo) and decreased cloud cover each gave slightlywarmer meridional T, profiles, the latter because the effect of decreased albedo was greater than the increased outgoingIR. [Some GCM results (e.g., Schneider et al., 1978) indicate that increases in prescribed temperature cause cloud cover to increase. However, this is a complex relationship, which is at least latitudinally and seasonally variable. It also may be model dependent (Roads, 1978).] Also, increasing the difference between cloud-top and surface temperatures increased T, because outgoing IR was reduced. For the Cretaceous simulations, enhanced latent energy transport due to increased T, was not sufficient to compensate for decreased sensible heat potential energy transport. Barron et al. found that in order to generate a Cretaceouslike T, profile, absorbed energy would have to increase by 0.5 -2.5%, depending on cloud cover and vertical temperature assumptions, and also a large, nondiffusive heat transport would be necessary, i.e., it would be necessary to maintain poleward heat transport at a level close to the present level, despite a decreased meridional temperature gradient. This would be possi-

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CHRISTOPHER R. LLOYD

ble through (1) greater poleward latent energy transport, (2) a different pattern of thermally or orographically forced quasi-stationaryatmospheric waves, or (3) greater poleward oceanic heat transport due to an ocean basin configuration allowing poleward penetration of western boundary currents and deep-water circulation to high latitudes. Lloyd (1977) also investigated the effect of mid-Cretaceous paleogeography relative to present global geography on the meridional annual mean T, profile by using a version of the energy balance model of Budyko ( 1974)with no ice/snow albedo- temperature feedback and with land -sea proportions in 10" latitude belts derived from Fig. 9. The albedo of land surfaces was constant at all latitudes (except for a small land surface albedo increase poleward of 50" in both cases to account for winter snow cover), and present cloudiness was specified so that results were dependent on paleogeographic changes only. The model simulates the present meridional T, profile well (Fig. 14, P). The Cretaceous profile (Fig. 14, C), with no sea or land ice, showed much warmer poles, as expected. However, this profile was only marginally warmer than that for the present geography with no ice (Fig. 14, PNI), with global mean T, only 0.4"C higher and polar T, 2 "C higher, and so with little change in the equator-pole temperature difference relative to the Cretaceous case. These results indicate that surface albedo differences

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FIG. 14. Mid-Cretaceousand present annual-mean meridional surface temperature profiles calculated with a version of the energy balance model of Budyko (1 974). P, Present geography and observed land/snow ice albedo; PNI, present geography with no ice; C, Cretaceouspaleogeography with no ice; C2H, same as for (C), but with doubled linear diffusion coefficient. All cases include present cloudiness. Plotted from results of Lloyd (1977).

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resulting from paleogeographic changes alone have made only a small contribution to cooling from the mid-Cretaceous to present in the absence of snow/ice albedo feedback effects. In other words, most of the difference between the present (with ice) and Cretaceous profiles is due to the presence and absence, respectively, of polar ice. The important question is, therefore, why did the poles remain ice free during the Cretaceous and at least until the latest Eocene, and why did temperatures fall sufficiently to allow ice formation during later Tertiary time? Did this occur because total poleward oceanic plus atmospheric heat transport decreased up to the time of first major ice formation? A doubling of the model's linear diffusion coefficient (Fig. 14, C2H) does, in fact, give a more Cretaceous-like meridional temperature profile, with polar T, 3 "C and equatorial T, 24°C. This result has the implicationthat more efficient poleward heat transport and possibly also an increase in absorbed radiation are necessary in order to maintain polar temperatures high enough to prevent ice formation and equatorial temperatures no higher than the present. Looking at mid-Cretaceous paleogeography (Fig. 9), it seems likely that western boundary currents could have penetrated to higher latitudes than at present. More meridional surface winds, as suggested by Lloyd (1982), could have contributed to such a pattern. Free oceanic conditionsextended to higher latitudes, sea level was high, and there was no circum-Antarctic current contributing to the thermal isolation of high southern latitudes. Another factor may have been important: the high sea level and hence greater area of sea surface could have given a higher mean atmosphericwater vapor content and greater poleward latent heat transport. These mechanisms, in addition to upwelling ofwarm bottom water, could have prevented the formation of polar ice and the ensuing further cooling of high latitudes through ice/snow albedo- temperature feedback. Since the mid-Cretaceous, enclosure of the Arctic, sea level fall, and establishment of a circumAntarctic current have probably reduced oceanic heat transport to high latitudes, while increased land areas in low and high latitudes have resulted in decreased surface energy absorption and poleward latent heat transport. These changes would have allowed temperaturesto fall until land and sea ice formed. Increased understanding of the rates and relative importance of these mechanisms will have to await a series of sensitivityexperiments using coupled ocean -atmosphere models.

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7.3.2. Results of a Spectral General Circulation Model. Barron and Washington (1984), using a version of the NCAR Community Climate Model, a spectral GCM with a swamp ocean (a wet surface with no heat capacity) and annual mean insolation, reported results of sensitivity experiments in which continental positions, sea level, and orography were trans-

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formed one step at a time from present to mid-Cretaceous conditions. Results showed the effect of each change on the simulated atmospheric climate. However, the results of these experiments do not show any of the effects on ocean circulation and heat transport of changed paleogeography.

7.3.3. Other Useful Sensitivity Experiments. The results of some climate model sensitivity experiments can be used to give insights into paleoclimates. For example, Manabe and Wetherald (1980) described results of simulations using the Geophysical Fluid Dynamics Laboratory (GFDL) GCM with a swamp ocean in which atmospheric C02content was doubled and quadrupled. Global warming resulted, and the direction in which various atmospheric characteristicschanged as a result of this warming can qualitatively indicate the nature of nonglacial climates with higher atmosphericC02. For example,they found that increased global-mean temperatures resulted in greater poleward latent energy transport, which, along with decreased high-latitude surface albedo, reduced the equator -pole temperature difference. As would be expected, eddy kinetic energy decreased. Changes also occurred in precipitation amount and distribution, with enhanced high-latitude precipitation. There were also changes in cloudiness. Interestingly, increasingthe solar constant, although affecting radiative processes differently, had very similar climatic effects because it resulted in a similar warming. This kind of GCM experiment, and also results of SDM sensitivity experiments for such cases as Arctic ice removal, increased C02,changed solar constant and changed cloudiness, aerosol characteristics, obliquity, and static stability, can qualitatively indicate certain characteristicsof preglacial climates, or at least the influence any of these changed parameters may have had in maintaining those climates. These results do not, of course, include all the feedback effects that may enhance or reduce the primary changes nor the specific paleoboundary conditions. 8. SUMMARY 1. Paleoclimates can be recognized from a number of different qualitative indicatorspreserved in sedimentary rocks deposited on land and in the sea. These indicators include climate-sensitive sediment types, such as coal, evaporite, and desert sandstone, and fossil plants and animals thought to be indicative of particular climatic regimes. These indicators give information on parameters such as land surface evaporation -precipitation balance and temperature and also on marine temperature gradients.

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2. Quantitative determinations of marine paleotemperatures in preglacia1 times can be made from oxygen-isotope analyses of the carbonate shells of marine fossils. Given careful interpretation, values for oceanic near-surface and bottom temperaturescan be derived with reasonableaccuracy as far back as mid-Cretaceous time (- 100 m.y. ago). 3. Interpretations of paleoclimatic evidence show that most of earth history has been characterized by “nonglacial” climates with ice-free poles and a small equator-pole temperature difference (Fig. la). The most recent nonglacial maximum, the middle part of the Cretaceous Period ( 1 10- 80 m.y. ago), was characterized by (Fig. 3) extensive shallow seas on the continents; polar and equatorial SST probably in the ranges 10- 15°C and 2830’ C, respectively; temperate conditions on Arctic-bordering coasts; a circumequatorial westward-flowing ocean surface current; equatorial and coastal upwelling; and intermittent anoxia in some of the deep oceans, possibly due to stagnation. 4. Paleoclimatic evidence shows a gradual and intermittent transition from the warm mid-Cretaceous to the glacial climates of late Tertiary and Quaternary times (Fig. 5). Cooling of the oceans began late in the Cretaceous (- 80 m.y. ago). After a relatively cool period during end-Cretaceous and Paleocene times (- 70-60 m.y. ago), there was a partial return to Cretaceous-like conditions during the early Eocene (- 50 m.y. ago). Major cooling events affecting high-latitude surface water and bottom water throughout the ocean occurred during end-Eocene and mid-Miocenetimes (38 and 14 m.y. ago), the latter event probably being related to major Antarctic ice sheet formation. This progressive cooling probably resulted from the breakup of the southern continents, particularly the separation of Antarctica from Australia and South America, which allowed the establishment of the Antarctic Circumpolar Current and consequent reduction of oceanic heat transport to the highest southern latitudes. Major Arctic glaciation began 3 may.ago, about the time the Panama Strait was closed. 5 . Climate changes over these very long time scalesare probably related to changes in several major boundary conditions external to the ocean/atmosphere/biosphere system. These are as follows:

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a. Changes due to factors external to the earth: solar luminosity, mean obliquity of the earth’s axis, and rotation rate of the earth. b. Changes due to earth-interior processes: continental drift and orogeny, affecting, for example, surface albedo distribution, ocean circulation, cloud characteristics,and locationsof thermal and orographic forcing; bathymetry of the ocean basins, particularly barriers to deep circulation; sea level (primarily a function of midocean ridge volume), affectingsurface albedo distri-

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bution in particular; and volcanic activity, affecting stratospheric aerosol content and C02outgassing.

6 . The primary climatic effects resulting from the changing boundary conditions must have been modified by complex interactions within the ocean/atmosphere/biosphere system, acting as positive or negative feedbacks on the primary effects. Notable among these mechanisms is atmospheric C02content, which is a complex function of marine and land plant photosynthesis, organic carbon and carbonate sedimentation in the oceans, and, hence, ocean circulation. 7. Methods for the establishment of boundary conditions for preglacial paleoclimatic modeling have been described. The boundary conditionsare as follows: a. Paleogeography: continent - ocean positions, shorelines, land surface topography, and bathymetry. b. SST distribution: from near-surface and bottom oxygen-isotopepaleotemperature determinations,biogeographicdata, and a qualitative model of ocean circulation. c. Other boundary conditions: rotation rate of the earth, land surface albedo distribution, solar luminosity, earth-orbital parameters, and atmospheric aerosol and CO, contents. Paleovalues can only be specified for rotation rate and surface albedo. While the other parameters have probably varied, there is no clear evidence for their paleovalues. 8. Some of these boundary conditions place time limits on the periods that can be modeled a. Paleocontinental positions can only be reconstructed with acceptable accuracy as far back as Triassic time (- 230 m.y. ago). Before then, paleolatitudes of individual continents can be determined, but their relative positions are largely unknown. Therefore, at present, the possibility of paleoclimatic simulations using realistic paleogeography for Paleozoic time can be disregarded. b. Oxygen-isotopepaleotemperature results are only sufficiently well distributed and reliable to constrain a reconstruction of SST distribution for late Paleocene time onward. Any pre-Cenozoic GCM modeling must therefore include a coupled ocean model, so that SST will be model generated. Also, from mid-Miocene time (- 14 m.y. ago) or possibly latest Eocene time (38 m.y. ago) onward, major Antarctic ice formation resulted in the ice volume signal dominating the temperature signal in oxygen-isotope data.

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Therefore, these data cannot be used to determine post-14-m.y. (or possibly post-38-m.y.) paleotemperatures.

9. Bearingin mind boundary condition time constraints, different modeling techniques are available as follows: a. Statisticaldynamical model simulations back to at least early Paleozoic time. This is possible because land-sea proportions in zonal belts can be determined. Possible experiments are sensitivity studies of the effects of changes in land- sea distribution, solar luminosity, atmospheric C02 content, and surface albedo. b. Atmospheric GCM simulations with specified SST for the early Cenozoic. c. Atmospheric GCM with coupled ocean model simulations for any post-Paleozoic time, bearing in mind that paleogeographic reconstructions become progressively less certain back through this period. Oceanic GCMs cannot at present be used for pre-late Cenozoic time unless a flat bottom is specified because the paleobathymetry of large parts of the Pacific, in particular, is unknown. This limits ocean models to the swamp or mixed-layer varieties, the latter preferably including horizontal advection. d. Whatever type of model is used, experiments may be divided into two categories: Snapshot simulations, in which realistic boundary conditions for a given period are specified for a single model run, and sensitivity experiments, in each of which a single forcing factor is changed in order to identify the model-simulated effects of these changes on the climate system. 10. Results of model simulationsshould be evaluated by comparisonwith available paleoclimatic evidence for the appropriate period. Climate changes simulated in sensitivity experiments can be compared with actual changes shown by the evidence. However, those data used to establish boundary conditions have partially determined the simulated climate; they should therefore not be used for verification of model results. 1 1. Energy balance climate models have been used by some researchers in sensitivity experiments to investigate the effects of evolving Cretaceous to present paleogeography on meridional profiles of solar energy absorption and surface temperature. Results have indicated that changing continent ocean positions and falling sea level can account for only a small part of the cooling observed through this period. Paleogeographicreconstructions have been used as the basis for snapshot qualitative models of oceanic and atmospheric circulations, and a mid-Cretaceous GCM simulation has been reported. Notable among the results of the latter were the generation by a coupled slab ocean model of high-latitude

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SSTsthat were far colder than those shown by paleoclimaticevidence,and of mean zonal wind speedsgreater than would be expected,given the reduction in equator -pole surface temperature difference. ACKNOWLEDGMENTS I would like to thank Drs. W. L. Gates, A. J. Boucot, and J. Gray for their careful reviews, Dr. Y.-J. Han for useful discussions, Naomi Zielinski for typing the manuscript, and Linda Haygarth for drafting some of the figures. This work was supported by the National Science Foundation under Grant ATM82-05992.

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Teis, R. V., Chupakhin, M. S., and Naydin, D. P. (1957). Determination ofpaleotemperatures from the isotopic composition of oxygen in calcite of certain Cretaceous fossil shells from Crimea. Geochem. Int. 9,323 - 329. Thiede, J., and Van Andel, T. H. (1977). The paleoenvironment ofanaerobic sediments in the late Mesozoic South Atlantic Ocean. Earth Planet. Sci. Lett. 33,30 1- 309. Thierstein, H. R. (1979). Paleoceanographic implications of organic carbon and carbonate distribution in Mesozoic deepsea sediments. In “Deep Drilling Results in the Atlantic Ocean: Continental Margins and Paleoenvironment” (M. Talwani. W. Hay, and W. B. F. Ryan, eds.), Maurice Ewing Ser. No. 3, pp. 249-274. Am. Geophys. Union, Washington, D.C. Thierstein, H. R. (1982). The terminal Cretaceous extinction event and climatic stability. In “Climate in Earth History,” pp. 90-96. Natl. Res. Counc., Natl. Acad. Sci., Washington, D.C. Thierstein, H. R., and Berger, W. H. (1978). Injection events in ocean history. Nature (London) 276,461 -466. Thompson, S. L., and Barron, E. J. (1981). Comparison of Cretaceous and present Earth albedos: Implications for the causes of paleoclimates. J. Geol. 89, 143- 167. Thompson, S. L., and Schneider, S. H. (1974). A seasonal zonal energy balance climate model with an interactive lower layer. JGR, J. Geophys. Res. 84,2401 -2404. Tissot, B., Deroo, G., and Herbin, J. P. ( 1979). Organic matter in Cretaceous sediments of the North Atlantic: Contribution to sedimentology and paleogeography. In “Deep Drilling Results in the Atlantic Ocean: Continental Margins and Paleoenvironment” (M. Talwani, W. Hay, and W. B. F. Ryan, eds.), Maurice Ewing Ser. No. 3, pp. 362 - 374. Am. Geophys. Union, Washington, D.C. Toon, 0. B., and Pollack, J. B. (1980). Atmospheric aerosols and climate. Am. Sci. 68, 268 -278. Tucholke, B. E., and Vogt, P. R. (1979). Western North Atlantic: Sedimentary evolution and aspects of tectonic history. Init. Rep. Deep Sea Drill. Proj. 43, 791 -825. Turco, R. P., Whitten, R. C., and Toon, 0.B. (1982). Stratosphericaerosols: Observation and theory. Rev. Geophys. Space Phys. 20,233-279. Turner, P. (1980). “Continental Red Beds,” Dev. Sedimentol., No. 29. Elsevier, Amsterdam. Urey, H. C. (1947). The thermodynamic properties of isotopic substances. J. Chem. Soc., London pp. 562-581. Urey, H. C., Lowenstam, H. A., Epstein, S., and McKinney, C. R. (1951). Measurement of paleotemperatures and temperatures of the Upper Cretaceous of England, Denmark, and Am. Bull. 62,399-416. the southeastern United States. Geol. SOC. Vail, P. R., and Hardenbol, J. (1979). Sea-level changes during the Tertiary. Oceanus 22, 71-79. Vail, P. R., Mitchum, R. M., Jr., and Thompson, S., I11 (1977). Seismicstratigraphyand global changes of sea level. Part 4. Global cycles of relative changes ofsea level. Mem. -Am. Assoc. Pet. Geol. 26,83-97. Vakhrameev, V. A, (1 964). Jurassic and Early Cretaceous floras of Eurasia and paleofloristic provinces of this period. Trans. USSR Acad. Sci., Geol. Inst. 102, 1-26 1. Valentine, J. W. (1982). Seasonality and the structure of the biosphere. In “Climate in Earth History,” pp. 183- 188. Natl. Res. Counc., Natl. Acad. Sci., Washington, D.C. Van Andel, T. H. (1975). Mesozoic/Cenozoic calcite compensation depth and the global distribution of calcareous sediments. Earth Planet. Sci. Lett. 26, 187- 194. Van Donk, J. (1977). 0I8as a tool for micropalaeontologists. In “Oceanic Micropalaeontology” (A. T. S. Ramsay, ed.), Vol. 2, pp. 1345- 1370. Academic Press, London. Van Eysinga, F. W. B. (1978). “Geological Time Table,” 3rd ed. Elsevier, Amsterdam.

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CLIMATE MODEL SIMULATIONS OF COZ-INDUCED CLIMATIC CHANGE MICHAEL E. SCHLESINGER Department of Atmospheric Sciences and Climatic Research Institute Oregon State University Corvallis. Oregon 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Mathematical Climate Models. . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Energy Balance Models (EBMs) . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Radiative-Convective Models (RCMs) . . . . . . . . . . . . . . . . . . . . . 2.3. General Circulation Models (GCMs) . . . . . . . . . . . . . . . . . . . . . . 3. ComparisonofModelSimulationsofCO,-InducedClimaticChange . . . . . . . . . . 3.1. Description of GCMs and C02 Simulations . . . . . . . . . . . . . . . . . . 3.2. Simulated Temperature Changes. . . . . . . . . . . . . . . . . . . . . . . . 3.3. Simulated Precipitation Changes. . . . . . . . . . . . . . . . . . . . . . . . 3.4. Simulated Soil Moisture Changes . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Model-Dependent Results . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Time Required to Reach Equilibrium. . . . . . . . . . . . . . . . . . . . . . 4.3. Statistical Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 . Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 , INTRODUCTION

Measurements taken since 1958 at Mauna Loa, Hawaii, and at other locations show that although carbon dioxide (CO,) makes up only about 0.03% by volume of the earth's atmosphere, its concentration has been increasing (Bolin and BischoE, 1970; Keeling et al., 1976a,b; Keeling and Bacastow, 1977). A study by Rotty (1982) indicated that the C02concentration increased from 1860to 1973due to a nearly constant 4.6%/yr growth in the consumption of fossil fuels (gas, oil, coal), and is continuing to increase due to the diminished 2.3%/yr growth in fossil fuel consumption since 1973. Projections of the future usage of fossil fuels predict that the CO, concentration may reach double the 1860 value of about 295 parts per million by volume (ppmv) sometime in the next century (Baes et al., 1976; Keeling and Bacastow, 1977; Rotty and Marland, 1980), and could eventually peak at 8 to 10 times the preindustrial level early in the twenty-second century (Council on Environmental Quality, 1981). As the CO, level increases, less of the temperature-dependent infrared 141 ADVANCES IN GEOPHYSICS, VOLUME 26

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radiation emitted by the earth can escape through the atmosphere to space, but the amount of solar radiation absorbed by the earth remains almost unchanged. It is to be expected that this so-called greenhouse effect will result in a warming of the average temperature of the earth such that a balance between the solar heating and infrared cooling will be restored. However, it is also to be expected that the temperature change at any location may be higher, lower, or even of opposite sign from the average temperature change, and that there may be concomitant changes in other climatic elements such as precipitation and soil moisture. The question of the possible climatic effects of increased atmospheric CO, has in recent years received more attention than any other global anthropogenic effect (National Academy of Sciences, 1976, 1977, 1979, 1982;Council on Environmental Quality, 1981). Much of this interest stems from the potential impacts that a significant climate change could have on agricultural production and energy-use practice, and hence on the patterns of global economics. One approach to estimate what the climate of a future warmer earth might be like is the analog merhod. In the analog method the seasonaland regional patterns of past warm climates are used to construct scenarios for a future warm climate (Kelloggand Schware, 1981). An advantage of this method is that the scenarios represent “surprise-free”projections (Kahn et al., 1976)in the sense that they are based on warm climatesthat have actually existed. A disadvantage of the method is that the quality of the reconstructionsof past climates, which are based on proxy data such as tree rings and ice cores, decreases with age before the present. Also, it is not possible to reconstruct all of the elements of climate that may be of interest. More importantly, the causes of most of the earth’s past warm climates are not known, and it is likely that not all were the result of elevated levels of atmospheric CO,. Consequently, a future C0,-induced warmer climate may differ substantially from the “surprise-free” scenarios based on past warm climates. Another approach to estimate what a C0,-induced climate change might be like is the physical method. In the physical method the behavior of the components of the earth’s climate system, namely, the atmosphere, oceans, snow and ice, vegetation (biomass),and land surface (Fig. I), is determined on a physical basis from the fundamental laws of nature such as the conservation of energy. These physical laws are expressed mathematically to form a mathematical climate model. The advantage of such a model is that it can be used to simulate, in a physically consistent manner, not only the present climate, but also how that climate would change in response to a change in some “external” forcing such as in the energy received from the sun or in the composition of the atmosphere. A disadvantage of the physical method is the inherent inability to construct a model that has perfect similitude to the actual climate system.

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I

6

SPACE

Terrestrial Radiation

+

ATMOSPHERE

a Clouds

He01 N , S O , , C O , , O , ,

O~C.

Aerosols A i r h e Coupling

Precipitation. Evaporation

Air/biomass/ land Coupling

AtmospherdOcean Couph

FIG. 1. Schematic illustration of the atmosphere/ocean/ice/land/biomassclimatic system, with some examplesof physical processes responsible for climate and climatic change. [From Gates (1979).]

The object of this article is to formulate and describe the current issues attendingthe physical method in the study of possible C0,-induced climatic change. To this end an elaboration of mathematical climate models is given in the next section. This is followed in Section 3 by a comparison of the results of these models for the climatic change resulting from increased levels of atmospheric CO, . In Section 4 the issues that are raised by that comparison are described, and Section 5 contains recommendations for resolving those issues. 2. MATHEMATICAL CLIMATE MODELS Several types of mathematical climate model have been developed that differ in the comprehensiveness of their treatment of the climate system components. Individual models of the climate model hierarchy can be classifiedbroadly as either thermodynamic or hydrodynamic models. Thermodynamic climate models explicitly predict temperature but either ignore the motion field and its influence on the temperature, or incorporate that influence in a highly simplified and approximate way. Hydrodynamic cli-

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mate models explicitly predict both the temperature and the motion fields and their mutual interactions. Hydrodynamic climate models therefore allow conversion between the two forms of energy- the total potential energy, which is proportional to the temperature, and the kinetic energy, which is proportional to the square of the velocity-while thermodynamic climate models do not. In the following discussions we describe the characteristics of two thermodynamic climate models, the energy balance and radiative- convective models (EBMs and RCMs), and one hydrodynamic model, the general circulation model (GCM).

2.1. Energy Balance Models (EBMs) In their simplest "zero-dimensional" formulation EBMs determine the effective radiating temperature of the planet, Tp,from the radiative equilibrium condition that the infrared radiation emitted by the earth to space, ep~T;47ra2,equals the solar radiation absorbed by the earth, S( 1 - ol,)na2. Here aT: is the infrared radiation per unit area that would be emitted by the planet if it were a blackbody radiator, with Q the Stefan-Boltzmann constant, 4naZ the surface area of the planet with radius a, eP the effective emissivity of the planet, S the solar radiation per unit area at the top of the atmosphere (insolation), .a2 the cross-sectional area of the planet, and (1 or,) the planetary absorptivity, with crp the albedo (reflectivity). If the earth emitted as a blackbody, ePwould be unity and Tp= - 18.6"C, which is considerably colder than the observed global-mean surface air temperature of 14.2"C. This warmer temperature is the result of the effects of the infrared-absorbing gases of the atmosphere (and clouds), i.e., the greenhouse effect, which yields eP 0.6. The value of ePthus depends on the composition of the atmosphere and must be determined by EBMs. The planetary albedo is frequently made a function of the size of the polar icecap through a prescribed dependence of a, on Tp. In one-dimensional EBMs the north south (meridional)distribution of temperature is determined by including a simplified, semiempiricalformulation for the effective meridional transport of heat by the motion fields of the atmosphere and ocean; however, as noted earlier, neither of these motion fields is determined by EBMs. [For further information, see the review article by North et al. ( 198I).] 5

2.2. Radiative- Convective Models (RCMs) Radiative- convective models determine the equilibrium vertical temperature distribution for an atmospheric column and its underlying surface for given insolation and prescribed atmospheric composition and surface al-

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bedo. An RCM includes submodels for the transfer of solar and terrestrial (infrared)radiation, the turbulent heat transfer between the earth's surface and atmosphere,the vertical redistributionof heat within the atmosphereby dry or moist convection, and the atmospheric water vapor content and clouds. The radiative transfer models used in RCMs are frequently identical to those used in GCMs. The surface heat exchange is treated either as an equivalent radiative exchange, or is parameterized] as a Newtonian exchange with a prescribed transfer coefficient. The vertical heat redistribution by convective atmospheric motions is modeled as an adjustment whereby the temperature lapse rate of the atmosphere is prevented from exceeding some given value. The amount of water vapor is determined in RCMs either by prescribing the absolute humidity or the relative humidity; in the latter case the amount of water vapor increases (decreases) with increasing (decreasing) temperature. Finally, the fractional cloudiness and the temperature or altitude of the cloud tops are prescribed [see the review article by Ramanathan and Coakley ( 1 978)] or predicted (Wang et al., 198 1 ; Hummel and Kuhn, 1981; Charlock, 1982).

2.3. General Circulation Models (GCMs) The principal prognostic variables2of an atmospheric GCM are the temperature, horizontal velocity, and surface pressure, which are governed, respectively, by the thermodynamic energy equation, the horizontal momentum equation, and the surface pressure tendency equation. With the mass continuity equation and the hydrostatic approximation and appropriate boundary conditions, these equations form a closed system for an adiabatic and frictionlessatmosphere. But the general circulation of the atmosphere is the large-scale, thermally driven field of motion in which there are interactions between the heating and motion fields. Therefore, several additional prognostic variables, with corresponding governing equations and appropriate boundary conditions, must be added to simulate the heating. Of these, the most important is the water vapor, which is governed by the water vapor continuity equation. Because the atmosphere is largely heated by the underlying surface through the exchange of sensible and latent heat, and because snow lying on the ground can have a large influence on the surface albedo, the ground temperature, soil moisture, and mass of snow on the ground are prognostic variables, governed by energy, water, and snow The treatment of physical processeswhose characteristicsize is smallerthan the smallest size resolved by a model. Variables whose time rates of change of magnitude are determined by the governing equations.

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TABLE I. THEPRINCIPAL PROGNOSTIC AND DIAGNOSTIC VARIABLES IN ATMOSPHERIC GENERAL CIRCULATION MODELS Prognostic variables

Diagnostic variables

Surface pressure Temperature Horizontal velocity Water vapor concentration Soil temperature Soil moisture Snow mass

Vertical velocity Geopotential height Density Cloudsb Surface albedob

a In spectral models (see text) the vertical component of vorticity and the horizontal divergence replace the horizontal velocity as the prognostic variables, and the latter are determined diagnostically from the former. These quantities are prescribed in some models.

budget equations for the ground. In addition to the prognostic variables, GCMs have many diagnostic variablesY3 among which clouds may be one of the most important. A summary of the prognostic and diagnosticvariables in GCMs is given in Table I. The governing equations of GCMs are nonlinear, partial differential equations whose solution cannot be obtained except by numerical mathematical methods on the fastest computers. These numerical methods subdivide the atmosphere vertically into discretelayerswherein the variables are “carried” and computed (Fig. 2). For each layer the horizontal variations of the predicted quantitiesare determined either at discretegrid points over the earth, as in the grid point (’nite diference) models (Fig. 3), or by a finite number of prescribed mathematical functions, as in the spectral models. The values of the predicted variables for each layer (including the surface) and grid point (or mathematical function) are determined from the governing equations by “marching” (integrating) forward in time in discrete steps starting from some given initial conditions(Fig. 4). To prevent the solution from becoming numerically unstable the time step must be made smaller than a value that depends on the speed of the fastest moving disturbance (wave), the size of the grid (or smallest scale mathematical function),and the integration method. The spatial resolution of GCMs is constrained for practical reasons by the speed and memory capacity of the computer used to perform the numerical Variables whose magnitudes are determined by the governing equations.

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FIG.2. The vertical structure and principal variables of the Oregon State University (OSU) two-level atmospheric general circulation model. Here u = (p - pT)/(p,- pT), where p is pressure, pTthe (constant) pressure at the model top, andp, the (variable) pressure at the earth’s surface; u and v are the eastward and northward velocity components, Tthe temperature, CD the geopotential, q the water vapor mixing ratio, Sand R the solar and terrestrial radiation at the top of the model atmosphere (subscript 0)or at the earth’s surface (subscript s), apand cu, the planetary and surface albedos, Q and Fthe diabatic heating and friction, H, the surface sensible heat flux,Pthe precipitation rate, E, the surface evaporation rate, and GWthe ground wetness. CL,-CL, denote the model’s cloud types. [From Schlesinger and Gates (1979).]

integrations. Increasing the resolution not only increases the memory required (linearly for vertical resolution and quadratically for horizontal resolution), but also generally requires a reduction in the integration time step. Consequently, the computer time required increases rapidly (nonlinearly) with increasing resolution. Contemporary GCMs have from two to about nine vertical layers, a horizontal resolution of a few hundred kilometers, and a time step ranging from 10 to 40 min. These models require from one-half

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to a few minutes to simulate 1 day on a fifth-generation computer such as the Cray 1 and CYBER 205. Due to their limited spatial resolution GCMs do not resolve several physical processes of importance to climate. However, the effects of these subgrid-scale processes on the scales resolved by the GCM are incorporated in MATSUNO

1

L E A P F R O G ___)

FIG. 4. Sequence of time steps in the time integration of equations of the form ay/at = D ( y ) S(y)in the OSU atmospheric GCM. Here y is any prognostic variable; dy/at the time rate of change of' y at a fixed location; S(y) the source term, e.g., the heating term in the thermodynamic energy equation; and D(y) all other terms, including the transport by the motion field. The circumflex (-) refers to the predictor estimate of the Matsuno integration scheme, the single prime (') refers to the corrector estimate of the Matsuno scheme and to the leapfrog scheme estimate, and the tilde (-) refers to the estimate before the source terms are added. The time step At,, = 6A2,and At = 10 min. [After Ghan el al. (1982).]

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TABLE 11. SUBGRID-SCALE PROCESSES THATARE PARAMETERIZED IN ATMOSPHERIC GENERAL CIRCULATION MODELS Turbulent transfer of heat, moisture, and momentum between the earth’s surface and the atmosphere Turbulent transfer of heat, moisture, and momentum within the atmosphere by dry and moist (cumulus) convection Condensation of water vapor Transfer of solar and terrestrial radiation Formation of clouds and their radiative interaction Formation and dissipation of snow Soil heat and moisture physics

the model by relating them to the resolved-scale variables themselves. Such a relation is called a parameterization, and is based on both observational and theoretical studies. The subgrid-scaleprocesses that are parameterized are shown in Table 11. To simulate climate and climate change with an atmospheric GCM it is necessary to prescribe certain parameters and boundary conditions as shown in Table 111. For the earth it is also necessary to include the ocean and ice components of the climate system (Fig. 1). How this is done depends on whether the purpose of the simulation is to test (validate) the atmospheric GCM or to simulate a climate change. To validate an atmospheric GCM it is possible to treat the sea surface temperature (SST) and sea ice thickness as given boundary conditions rather than as prognostic variables of the climate system. Then, since it is the ability of the GCMs to simulate climate change that is of interest, and since the seasons are the best-documented climate changes, the seasonal performance of the models can be evaluated from a simulation in which the SST and sea ice distributions are taken as equal to their observed values. This has been done most frequentlyby simulatingsingle winter and summer months, TABLE 111. THEPRESCRIBED PARAMETERS AND BOUNDARYCONDITIONS IN ATMOSPHERIC GENERAL CIRCULATION MODELS Radius, surface gravity, and rotation speed of the planet Solar constant and orbital parameters of the planet Total atmospheric mass and composition Thermodynamic and radiation constants of the atmospheric gases and clouds Surface albedo Surface elevation

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usually January and July, and comparing the simulated atmospheric variables with their observed counterparts. [See, for example, Global Atmospheric Research Programme (1979), Schlesinger and Gates (1979), Shukla et al. (1 98 I), and Pitcher et al. (1983).] The seasonal performance of several models has also been determined from extendedintegrations over more than one annual cycle wherein the SST and sea ice distributions are prescribed to repeat their observed annual cycles (Manabe and Hahn, 1981; Schlesinger and Gates, 1981b; Hansen et al., 1983). The ability ofan atmospheric GCM to simulate a climate different from that of the present has also been evaluated by prescribing the SST and sea ice distributions equal to those that have been reconstructed for the most recent ice age (the Wisconsin glacial period 18,000yr ago), along with the different geography of land, ocean, and continental ice sheets, and comparing the simulated surface air temperature with the temperature field reconstructed from fossil pollen and other periglacial evidence (Gates, 1976a,b). These validation studies show that atmospheric GCMs are capable of simulating many of the characteristic differences between the present summer and winter climates, and between the present interglacial climate and its glacial antecedent. To simulate a climate change such as that which may be induced by elevated CO, levels, however, it is not desirable to treat the sea surface temperature and sea ice distributions as given boundary conditions. To do so would, as is illustrated later, proscribe the response of the climate system by preventing the interaction and feedback among the atmosphere, ocean, and sea ice components. Consequently, atmospheric GCMs have been coupled with different ocean and sea ice models, and these models also form a model hierarchy wherein the individual models may be classified as either thermodynamic or hydrodynamic models. The simplest thermodynamic ocean/sea ice model is one in which both the heat storage and the heat transport ofthe ocean are ignored and the SST is diagnostically determined such that the net energy exchange at the air - sea interface is zero (Manabe, 1969a; Manabe and Wetherald, 1975). This model is called a swamp model because of its similarity to perpetually wet land. In a swamp model the existence of sea ice is predicted whenever the SST is below the temperature at which sea water normally freezes. It is not desirable to include either the diurnal or the annual solar cycles in a simulation with a coupled atmospheric GCM/swamp ocean model because the absence of oceanic heat storage would result in the freezing of the ocean in the nighttime hemisphere and in the region of the polar night. To permit simulations of climate and climate change with the annual and diurnal cycles included, atmospheric GCMs have been coupled to slab models of the uppermost layer of the ocean, the oceanic mixed layer, wherein the temperature is relatively uniform with depth. In slab models a fixed

MODELS OF COyINDUCED CLIMATIC CHANGE

151

depth of the mixed layer is prescribed such that the simulatedannual cycle of SST, and therefore of the heat storage, is in close agreement with the observed annual cycle (Manabe and Stouffer, 1980). In this type of thermodynamic model the sea ice thickness is predicted based on a thermodynamic energy budget that includes the accumulation of snowfall, sea water freezing, and ice melting and sublimation. Because the fixed depth of the slab model is chosen to reproduce a periodic climate change (the annual cycle), while it is likely that a climate change induced by rising COz (and other forcing mechanisms) will be secular, it is desirable to have models of the mixed layer in which the depth is a prognostic variable. Such a variable-depth mixed-layer model has been coupled to an atmospheric GCM and tested in a 16-month simulation (Pollard, 1982a). In this model the horizontal heat transport as well as the heat storage is included through the hydrodynamic prediction of the mixed-layer currents, while the sea ice thickness is determined thermodynamically as described above. An extension of this hydrodynamic ocean model has been made in which the depth, temperature, and currents of the seasonal thermocline4are predicted, as well as the corresponding quantities for the mixed layer (Pollard, 1982a). Although the hydrodynamical models of the upper ocean include the storage and horizontal transport of heat, they do not include the vertical transport of heat associated with the large-scale upwelling and downwelling of water. However, this vertical heat transport is of particular importance in the heat budgets of the equatorial and polar seas. For this reason models of the general circulation of the ocean have been developed that are the dynamical counterparts to the atmospheric GCMs. In oceanic GCMs the prognostic variables are the temperature, horizontal currents, and salinity, and the diagnostic variables include density, pressure, and the vertical velocity. There are subgrid-scale processes that must be parameterized in ocean GCMs, including the turbulent transfers of heat, momentum, and salt in both vertical and horizontal directions, and parameters and boundary conditions similar to those in Table I1 that must be prescribed. The solution of the governing equations is obtained numerically in a manner similar to that used for atmosphericGCMs. The ocean is subdividedvertically into layers and horizontally into grid boxes, and the predicted quantities are determined as a function of time by numerical integration (Fig. 4). Oceanic GCMs have been tested by prescribing either the air - sea fluxes of heat, momentum, and water, or the state of the atmosphere (World Meteorological Organization, 1977). Oceanic GCMs have also been coupled with atmospheric GCMs, The layer immediately beneath the mixed layer wherein the temperature decreases with depth.

152

MICHAEL E. SCHLESINGER

and simulations have been performed to validate these coupled (or joint) GCMs (Manabe, 1969b; Bryan, 1969;Wetherald and Manabe, 1972; Manabe et al., 1975; Bryan et al., 1975;Manabe et al., 1979; Washington et al., 1980). In all but one of these joint GCMs (Washington et al., 1980) the transport of sea ice by the upper ocean currents was included in the thermodynamic sea ice model, but the life cycle of ice leads5and their effects were uniformly ignored. These have been predicted in a dynamic sea ice model (Hibler, 1979), but not yet in association with a coupled GCM. To validate a coupled atmosphere/ocean/sea ice GCM it is possible to treat the remaining components of the climate system- the biomass, land ice, and land (Fig. 1)-as given boundary conditions in a manner similar to that used in the validation of atmospheric GCMs alone. Whether any one of these climate system components can also be regarded as known in the simulation of climate change with the coupled GCMs depends on its characteristic time scale relative to that for the climate change. Only in the case when the time scale for the climate change is much less than that for the climate system component can that component be regarded as constant. For example, it is reasonable to assumethat the geography of the land, ocean, and land ice will not change sufficiently in the next 50 yr due to increased CO, to warrant its inclusion in coupled GCMs as a predicted quantity. That is, it is not likely that the West Antarctic ice sheet will surge in the next 50 yr, which would result in a rise in sea level and flooding of low-lying coastal regions. However, a similar assumption regarding the biomass component seems unjustified. Ironically, several models have been developed for land ice (for example, Sergin, 1980; Pollard, 1982b), albeit none has yet been included in a coupled GCM, but very little attention has been devoted toward the development of a completelyinteractive biomass model wherein, for example, old forests perish and new ones grow in response to a changing climate.

3. COMPARISON OF MODELSIMULATIONS OF C 0 2 - I ~ ~ ~ ~ ~ CLIMATIC CHANGE The climate change induced by increased atmospheric carbon dioxide has been simulated by models of each of the types described earlier. Reviews of these simulationshave been given by Schneider (1973, the National Academy of Sciences (1979, 1982), Watts (1980), Gates (1980a,b), and Kellogg and Schware ( 1981). The principal simulationsare summarized in Table IV in terms of the models’ basic characteristics. As described below, two types Open water lanes within the sea ice pack.

TABLE IV. (Continued) Type of simulation

-

u, P

Equilibrium quadrupling (4x1

Equilibrium decupling

Energy balance models (EBMs) Idso (1980) [23] (S,N,N), (I,N,N), (A,T,A), <0.3"C Kande1(1981)[27] (S,N,N), (N,N,N), (G,E,A), 0.7 to 8.8'C SchneiderandThompson (1981) (P,G,N), O,N,D), (RE,A); lag time varies with latitude with minimum in tropics Raswl andSchneider(l97I) [4] (P,N,N),(N,N,N),(B,H,A), 1.1 to 1.3'C Weare and SneU (1974) [7] (P,N,N), (N,N,N), (R,P,A), 1.1 "C Tempkin and Snell(1976) [I I] (PB,N), (I,N,N), (R,P.A), 2.6"C

Radiative-convective models (RCMs)

General circulation models (GCMs)

Augustrson and Ramanathan (1977) 1121 W,NM, ("9(R,H.A), 4.4'C

Rasool and Schneider(l971) [4] (P,N,N), (N,N,N), (R,H,A), 2.5-C

(lox) Nonequilibrium

Robock (1978) (P,GA". (I,N,F), (R,E,S) Thompson and Schneider ( I 979) (P,G,N), (I,N,D), (E,E,A); lag time of 5-20 yr depends on depth of ocean's response and surface-deep water mixing rate

Bryan ef al. (1982) W,G,S), (I,N,O), (P,H,A); 10- to 25-yr lag time, a function of latitude for firsI 10 yr with more rapid adjustment in tropics

Hoffert d 01. ( 1980) (P,N,N), (I,N,D), (R,H,A); lag time of 10-20 yr depends on prescribed ratio of temperature changes in polar sea to surface mixed layer Cess and Goldenberg(1 98 1) (P.N,N),(I,N,D),(E,E,A), lO-to2O-vlastime Schneider and Thompson (198 1) (P,G,N). (I,N,D), (E,E,A); lag time depends on latitude Michael ef a/. (1981) (P,N,N), (I,N,D),(R,H,A); lag time about 4Oyr

The models’ characteristics are shown by three sets of three symbolseach. The I hset shows the domain (vertical,latitude, longitude). The second set shows (land/ocean distribution, topopphy. ocean temperature model). The third set show (humidity model, cloud model, insolation). The symbols for each of these nine characteristicsare defined below. The simulated surface air temperatw change averaged over each model’s horizontal domain (unless otherwise noted) and/or the Simulated lag time (time raluired for temperature change to reach the equilibrium temperature change) are alsn given. The numbers [ I ] through 1.341 following the citation are shown in Fa 20 for reference. Vertical: S, surface energy balance; P,planetary energy balance; T,troposphere; U, troposphere and upper atmosphere.

2

latitude: N, none; H, equator to pole; G, pole to pole. Longitude: N, none; Z, zonally averaged; S, 120” sector, G, 360”. Land/disuibution: N, none; I, idealized, R, realistic (for given resolution). Topography: N, none; 1, idealized, R, realistic (for given resolution). Ocean tempeXaNre model: N, none; G,prescribed sea surface temperature; S, swamp; SO, swamp with 6xed sea ice; F,mixed layer with @kd depth; D,F with deeper owan; P,mixed layer with predicted depth; 0,oceanic general circulation model. Humidity model:A, hxed absolute humidity; R, 6xed relative humidity; B,both 6xed absolute and relative humidiv, E,empiricallybuilt into longwave parameterization;G, humidity change prescribed; P, predicted. Cloud model: N, no cloud; F, fixed cloud; 8, both 6xed and no cloud; H, fixed cloud “height”; T, 6xed cloud temperature; C, both fmed cloud “height” and temperature; E,empiricallybuilt into longwave radiation parametetization; P, predicted. YHeight” is b e d only with altitude as the vertical coordinate. With pressure ( p ) or normalized pressure (a)as the vertical c ~ o ~ d i ~the t eactual , altitude for 6xed p or u varies with temperature followingthe hydrostaticequation.] Insolation: A, annual mean; S, seasonally varying;J, January.

156

MICHAEL E. SCHLESINGER

of study have been performed with these models, an equilibriumstudy and a nonequilibrium study. In an equilibrium study a control simulation is made with a fixed CO, concentration (typicallyabout 300 ppmv), and an experiment simulation is made with another fixed CO, concentration. Both the control and the experiment are run sufficiently long to achieve their respective equilibrium climates as illustrated in Fig. 5. The object of most equilibrium studiesis to determine what the change in the climate would be if the CO, concentration increased to some constant value and the climate system reached a new equilibrium for that higher CO, level; however, the time required to attain that new equilibrium has not been ascertained from the equilibrium simulations. As shown in Table IV, equilibrium simulationshave been performed for elevated experiment COzconcentrationsthat are double (2X), quadruple (4X), and 10 times (IOX) their control values. The 2X simulations have been made because, as noted in Section 1, it is projected that the CO, level will reach twice the preindustrial value sometime in the next century. The 4X and 1OX simulationswith GCMs have been made not because such large increases are foreseen, but rather to increase the statistical significanceof the results. In GCM simulations,as in nature, there is an inherent variability for any I

I

I

I

I

1

I

a:

; W

-

0.-

I-

-5

-

-10

-

MASS-AVERAGED TEMPERATURE

--------.---------

21c02 IXCOp

Averaging Period

-

MODELS OF CO2-INDUCED CLIMATIC CHANGE

157

time-averaged quantity, even though the climate is unchanged. This natural variability constitutes noise against which the signal of the difference between the experiment and control must be contrasted. If the ratio of the signal to the noise is sufficientlylarge, then it can be said that the experiment climate is not just another realization (sample) of the control climate, but is in fact a climate different from that of the control. To increase the signal-tonoise ratio the noise can be reduced by averaging over longer time periodsthis requires extending the length of the simulations. Alternatively, the magnitude of the signal call be increased by increasing the magnitude of the forcing, e.g., by quadrupling the C02 level. This approach, which may be called the superanomaly method, has been used previously to study the influence of sea surface temperature anomalies on climate (e.g., Chervin, 1979). Its validity depends on whether there is a known transferfunction from the superanomaly response to the anomaly response. In the case of increased levels of CO,, Augustsson and Ramanathan ( 1977)have shown by an RCM calculation that the contribution to the surface air temperature warming by the CO, 15-pm absorptanceband increaseslogarithmicallywith increasing CO, , while that due to the weaker C02bands increases linearly. Their results show that the combined effect of the 15-pm and weak bands is such that the warming due to quadruplingis about 2.4 times greater than the warming due to doubling. Based on this it has been assumed that the thermal response for a CO, doubling could be taken as approximately half that obtained from a superanomaly CO, quadruplingexperiment. We shall reconsider this point subsequently. In a nonequilibrium study the C02concentration in the experiment either is changed at the initial time and held constant or is made to increase with time. (See Fig. 6 for an example of the latter.) The response of the climate system in the experiment at any time is compared with the equilibrium response for the C02 concentration at the same time, the equilibrium response having been determined from a control equilibrium simulation. The object of the nonequilibrium study is to determine the lag in the response of the climate system, that is, the difference between the times when the nonequilibrium and equilibrium responses reach any specified value. Knowledge of the lag time is required to estimate when a C0,-induced climate change may become detectable. As shown in Fig. 6, the lag time depends critically on the rate of heat exchange between the upper ocean (mixed layer) and the intermediate ocean (thermocline), with the lag time increasing with the rate of heat exchange. Because the value of the heat exchange rate is poorly known from observations, the time of first detectability is uncertain by about 10 yr, as shown in Fig. 6 (Schlesinger, 1983a). In this article attention is focused on comparing the simulations from the three-dimensional GCMs because it is the geographical distribution of a

*

158

MICHAEL E. SCHLESINGER

Ea!

c

e a2

E

a2 +

1.5 -

c .0)

c CI, .0 c V

E

.-0 e A

c-

c z

gg 801

0 V

0.5

-

3zzzzzlo I860

1900

I950

2000

2050

250 I860

I900

1950

2000

2050

2100

Year FIG.6. The change in surface air temperature (top) induced by the change in C02concentration (bottom). A,, is the coefficient of heat exchange between the mixed layer and deep ocean (W m-* K-I). [From Schlesinger (1983a).]

possible C0,-induced climatic change that is of importance to humanity. Moreover, since only one nonequilibrium study has been performed with a GCM (see Table IV), the comparison can be made only for the equilibrium GCM studies. However, the global-mean surface temperature changes from these GCM studies will be compared with those from the EBMs and RCMs. Before presenting the comparisons, a brief overview of the characteristics of the GCMs and CO, simulations is given.

3.1, Description of GCMs and CO,Simulations Simulations of the climate changes induced by elevated CO, levels have been carried out with GCMs at the Geophysical Fluid Dynamics Laboratory/NOAA, Princeton University, New Jersey (hereafter abbreviated as GFDL);the NASA Goddard Space Flight Center Institute for Space Studies, New York, New York (GISS);the Lawrence Livermore National Laboratory, Livermore, California (LLNL); the National Center for Atmospheric Research, Boulder, Colorado (NCAR); the Climatic Research Institute, Or-

MODELS OF C0,-INDUCED CLIMATIC CHANGE

159

egon State University, Corvallis, Oregon (OSU); the United Kingdom Meteorological Office, Bracknell, Berkshire, United Kingdom (UKMO); and the Computing Center of the USSR Academy of Sciences, Moscow (USSR). These simulations are referenced in Table IV by author(s) and are listed in chronological order. Not all of the simulations listed in Table IV (and shown in Fig. 2 1) are included in the present comparison: the LLNL simulations (Potter, 1978, 1980) are not included because the model is only two dimensional (latitude and height); the GISS simulations [J. Hansen, 1978, 1979, as reported by the National Academy of Sciences (1979)l and the USSR simulations (Aleksandrov et al., 1983) because a description of the model and/or results were not available; and the UKMO simulations (Mitchell, 1983) either because of the similarity of their prescribed-SST results to those of the OSU simulation (Gates et al., 198 1) or because of the ad hoc 2°C increase in SST that was prescribed in the second UKMO doubled-CO, simulation. The simulationsthat are included in the present comparison are listed in Table V along with some characteristicsthat will be discussed subsequently. Some of the characteristicsof the GCMs used to perform the CO, simulations are presented in Table VI. The principal differences among these GCMs are in their domain, land/ocean geography, orography, ocean/sea ice model, clouds, and the albedo of snow and sea ice. 3.I . 1. Domain, LandlOcean Geography, Orography. The first GCM simulation of C0,-induced climate change was performed at GFDL by Manabe and Wetherald (1975). In that simulation the horizontal domain extended over only 120" of longitude and from the equator to 81.7"N,as shown in Fig. 7. In this sector model cyclical boundary conditions were imposed at 0 and 120" longitude, and an idealized land/ocean geography was prescribed along with no topography, that is, the surface elevation was uniform everywhere. As shown in Fig. 7, slightly different sector models were employed by Manabe and Wetherald (1980) and Wetherald and Manabe ( 1 98 1 ). In all other simulationslisted in Table VI the horizontal domain was global,that is, 360" of longitudeand extendingfrom pole to pole, and the landlocean geography and orography were realistic within the models' horizontal resolution. (SeeFigs. 10, 1 1, and 18 for the OSU, NCAR, and GFDL models, respectively.) All the simulationsexcept those carried out at OSU were performed with nine-layer models that included both the troposphere and the stratosphere. The OSU simulationsemployed a two-layer model of only the troposphere (see Fig. 2).

3.1.2. OceanlSeaIceModel. In the simulationsperformed with the OSU model by Gates et al. (1981) both the sea surface temperature and sea ice

160

MICHAEL E. SCHLESINGER

TABLEV. EQUILIBRIUM GCM SIMULATIONS OF CLIMATIC CHANGEINDUCED BY C02 DOUBLING AND QUADRUPLING Model GFDL Manabe and Wetherald (1975) Manabe and Wetherald (1980) Manabe and Stouffer ( 1980); Manabe et al. (1981) Wetherald and Manabe (1981) NCAR Washington and Meehl (1983b)

2 X C02

4 X C02

Annual cycle

Length of simulation (yr)

Averaging period of results (yr)

Yes

No

No

2.2

0.3

Yes

Yes

No

3.3

1.4

No

Yes

Yes

No

Yes

Yes

4

No

7 (1.1) 5’ 8 ( 1.6) 6’ 8(1.2)1lU 9(1.7) 1 1 26(1.1) 1 . 1 1.6, 1.8b

1

Yes

Yes

No

Yes Yes

Yes No

Yes No

3

0.5

osu

Gates et al. ( 198 1) Schlesinger (1983b)

1.3 2.0

1 0.5‘

~~

In these simulations the annual cycle of the atmospheric GCM is first accelerated with respect to the annual cycle of the ocean model. The ocean model is integrated through the number of annual cycles shown by the first number in the row. The atmospheric model is integrated through the same number of annual cycles; however, since these annual cycles are accelerated, the amount of time simulated is less, as shown by the second number in the row (in parentheses). The accelerationofthe atmospheric annual cycle is gradually reduced until there is no acceleration. The models are then synchronouslyrun for the number of years shown by the third number in the row. The first row is for the control, the second row for the experiment when different from the control. For the simulations with fixed clouds and computed clouds, respectively. Unless otherwise noted.

distributionswere prescribed to vary according to their observed present-day annual cycles. In these simulations, then, neither the SST nor the sea ice could respond and feed back on the changed atmospheric climate. The only other simulations that included the annual cycle of solar insolation were those reported by Manabe and Stouffer (1 980) and Wetherald and Manabe ( 1981) [see Table V under heading “Annual cycle”; note that the papers by Manabe and Stouffer (1980) and Manabe et al. (198 1) report different aspects of the same simulation]. These simulations were performed with the GFDL atmospheric GCM coupled with a 68-m slab model ofthe oceanic mixed layer and with the thermodynamic sea ice model noted in Section 2.3. All the remaining simulations with the models listed in Table VI employed the swamp ocean/sea ice model and, therefore, were made with only annually averaged solar insolation (Table V).

MODELS OF CO2-INDUCED CLIMATIC CHANGE

161

-00

90-N

FIG.7. Horizontal domain and land/ocean distribution of the sector models of Manabe and Wetherald (1975) (top), Manabe and Wetherald (1980) (middle), and Wetherald and Manabe (1981) (bottom).

3.1.3. Clouds. As already noted in Table I, clouds are diagnostic variables in some atmospheric GCMs and are prescribed in others. In three of the four GFDL models shown in Table VI clouds are prescribed and, therefore, cannot respond and feed back on the C0,-induced climate change. Clouds are predicted variables in the simulations with both the OSU and NCAR

TABLE VI.

CHARACTERISTICS OF GENERAL CrRCULATlON

MODELS USED TO SIMULATE C02-INDUCED

CLIMATIC CHANCES

~

~

Domam

Model

vatlcal (z)

Surface treatment

Longitude(A)

Landlocean distribution

F.quator to 81.7' (grid point, A@ = variable)

120" sector (grid point, M = 6')

Idealized land/azan

Equator to pole (grid point,

120' sector (grid point, M = 5 ' )

Latitude (CP)

Topography

ocean'

sea Ice.

Sail moisture and snow

GFDL

e

N

Manabe and Wetherald (1975) Manah and Wetherald (1980) Manabe and stouffcr (1980); Manabe er al. (1981) Wetherald and Manabe (1981)

NCAR Washington and Meehl (1983b)

Surface t o p = 0 (9 layen)

A@

@ P e c w 15

Surface t o p = 200 mbar (2 layen)

Pole to pole (grid point,

Surface to p = 200 mbar (2 layen)

Pole to pole (gnd point,

A@ Schlesinger (1983b)

-port)

Idealired land/-

None

A@

Swamp (no heat cawcity or

transport)

360' (spectral, 15 waves and 2 I waves)

Realistic

120' sector (spccrml, waves 3,6.9, 12, 15)

Idealized Land/azan

None

360" (spectral, I5 waves)

Realistic

Realistic

360' (grid point,

Realistic

Reatistic

waves)

osu

Gates er a/. (1981)

Pole to pole

Swamp (no heat capacity or

= 4.5')

Pole to pole (spectral, 15 waves and 2 1 Wave)

Surface to p = 0 (9 layen)

None

Realistic

M=5')

Oaxurence predicted when T,---Z'C

Predicted (Manah, 1969a)

occumrra

Predicted

predicted when To= -2°C

(Manabc, 19696

Rediacd

68-m mixed layer (no heat -sport)

ThiChesS

69-m mixed layer (no heat -sport)

ThiChCSS

Swamp (no heat opacity or transport)

Occurrence predicted when To = 1.8"C

Predicted

Toannual cycle

Sea ice annual

Predicted (Ghan el al.. 1982)

PmiM

predicted (Bryan.1969); TD--2'C when sea ice exists

predid (Bryan, 1969); T,=-2'C when sea ice exists

-

cycle pmribed

(Manah, 1969a)

Predicted ( M a ~ hI969a) ,

Washington and Williamson, 1977)

= 4')

= 4")

360' (grid point, M =5')

Realistic

Realistic

Swamp (no heat capacity or transport)

Occurrence predicted when To= - 1.6-C

Predicted (Ghan eral.. 1982)

Radiation Solar radiation GFDL Manabe and Wetherald (1975)

Annual mean; H,O,CO, , 0,. clouds, Rayleigh

scatming(Manaheand Manabe and Wetherald (1980)

Manate and Stouffer (1980) Manate ef a/. (1981) Wetherald and Manabe (1981)

(1983h)

Wetherald, 1967) Annual mean; H,O, CO,, 0,. clouds,Raylei& scattering (Manah? and Wetherald,1967) Annual cycle, no diurnal cycle; H,O, CQ,, 03, clouds, Rayleigh scattering (Lacis and Hansen, 1974) Annual mean and seasonal Variation runs,H,O, CQ, 0 3 , clouds, Rayleigtl scattering (Lacis and Hansen, 1974)

Terrestrial radiation

H,O,CO,, O,, clouds (Manabe and Wetherald, 1967)

H,O,

m,

O,, clouds (StoneandManah?, 1968)

HIO,CO,,O,, clouds (StoneandManah?, 1968)

Schlesinger( I 983b)

.

RMibed annual mean

fnct (z,0)

Land and ocean premibed(Manate,1969a);snow and sea ice = 0.70 (T, <-25"C), -0.45 and 0.35 (T,>-25'C)

Predicted when condensation

Land and ocean prescribed (Manabe, 1969a); snow and sea ice = 0.70 (T, < - IO'C), -0.45 and 0.35 (T,2 - 10°C)

Rescribedannual mean fnct (z, 0 )

Land and ocean prescribed (Poseyand Clapp, 1964); snow s 0.80, hct (a,snow mass, underlying surface); sea ice S 0.70, fnct (0,ice thickness, melting)

-

(Manate,1969a);snow and sea ice 0.70 Land and ocean &bed (T, < IO"C),-0.60 (T, 2 IO'C), -0.45 for melting sea ice

-

Annual mean; H20, CO,, O,, 01,clouds, Rayleigh scattering (Ramanathan ef al. 1983)

H,O, CQ, 0,.clouds

Annual and diurnal cycle; H,O,O,, Rayleigh scattering (Ghan efa/., 1982) Annual mean; H,O,O,, Ftayleigb scattering (Ghan ef a/.. 1982)

H,O, CO,, clouds (Ghan ef al.. 1982)

(Ramanathana a/. 1983)

Rediaed when

RH > 80%

-

),0.55 (0.9-4pm); Land = 0.13; desert = 0.25; snow = 0.80 (0-0.9~ sea ice = 0.70

(convective when re< 0, nonconvective when r, a 0);also run with prnrribed clouds

osu Gatesetal. (1981)

surface albedocd

cloud@

Predictedwhen cumulus convection or

-

Land and ocean premibed (Posey and Clapp, 1964); snow s 0.80, fnct (snow mass, underlying surface); sea ice 0.45

RHa 100% HIO,CO,, clouds (Ghanef a/., 1982)

Predictedwhen cumulus

convection or

Land and ocean &ted (Posey and Clapp, 1964); snow s 0.70, fnct (snow mass, underlying surface); sea ice = 0.35

RHa 100%

T,, Ocean temperature. RH, Relative humidity; re,vertical gradient of equivalent potential temperature. Tv Temperature of earth'ssurface. The surface albedos for sea ice are for the condition of zero snow cover. The surface albedos for snowcovered sea ice are as shown for snow.

164

MICHAEL E. SCHLESINGER

models; however, doubled- and quadrupled-CO, experiments with fixed clouds as well as computed clouds were conducted with the NCAR model to assess the importance of clouds in the simulated climate change. 3.1.4. Albedo of Snow and Sea Ice. The albedo of snow and sea ice in GCMs may be of particular importance in the simulation of C0,-induced warming in high latitudes. As shown in Table VI, the albedo of snow is a discontinuous function of the surface temperature T, in the simulations reported by Manabe and Wetherald (1975), Manabe and Wetherald (1 980), and Wetherald and Manabe ( 198l), with a value of 0.70 for T, < -25, - 10, and - 10°C,respectively, and 0.45,0.45, and 0.60 for T, 2 -25, - 10, and - 10°C,respectively. On the other hand, in the simulationsby Manabe and Stouffer (1 980), Gates et al. (1 98 l), Schlesinger (1983b), and Washington and Meehl(1983b) the albedo of snow is independent of T,. In the latter simulation the albedo is simply taken as equal to a constant value of 0.80, while in the former three simulations it is a function of the nature of the underlying surface, for example, forest or tundra, and depends on the snow depth (water equivalent) up to some value. The albedo of snow-free sea ice is also a discontinuous function of Tgin the simulations of Manabe and Wetherald (1979, Manabe and Wetherald (1980), and Wetherald and Manabe (1981), with a value of 0.70 for T, < -25, - 10, and - 10°C, respectively, and 0.35, 0.35, and 0.60 for T, 2 -25, - 10, and - 10°C, respectively; in the simulation of Wetherald and Manabe (1 98 1) the albedo was further reduced to 0.45 during the melting of sea ice. In contrast, the albedo of snow-freesea ice was independent of T, in the simulationsby Washington and Meehl (1983b), Gates et al. (198l), and Schlesinger (1983b), and was taken as equal to the constant value of 0.70, 0.45, and 0.35, respectively. 3.1.5. Length of Simulation and Averaging Period. The length of the different COz simulations is shown in Table V along with the length of the averaging period for the results that follow. Of the simulationswithout the annual cycle of solar insolation, all but the results reported by Washington and Meehl(1983b) are for at least 2 yr; the averaging period for the results varies from 0.3 yr (100 days) to 1.4 yr (500 days). Of the simulations with the annual solarcycle, that by Gates eta). (198 1)with prescribed SST and sea ice was for only 1.3 yr ( 16 months), while those performed at GFDL with the slab mixed-layer model were for at least 12 yr (given by the sum of the first and third numbers in the appropriate row); the simulationby Wetherald and Manabe (198 1) with the slab model but without the annual cycle extended over 27 yr. The number of realizations (samples) in the averaging of the results in the simulationswith the annual cycle ranges from 1 (for example, 1 January, 1 winter, or 1 yr), as reported by Gates et al. (1981), to 4 (for

MODELS OF C02-INDUCED CLIMATIC CHANGE

165

example, the averageof 4 Januaries, 4winters, or 4 yr), as given by Wetherald and Manabe (1 98 1). However, Wetherald and Manabe (1 98 1) averaged the results from the two hemispheres shown in Fig. 7 to effectively double the averaging period to 8 yr. In the followingsectionswe present and compare results for the changes in temperature, precipitationrate, and soil moisture simulated as the result of a CO, doubling and a CO, quadrupling.

3.2. Simulated Temperature Changes 3.2.1. CO,Doubling. 3.2.1.I . Latitude- height cross sections. Latitude - height cross sections of the change in zonal-mean6air temperature simulated for a CO, doubling are presented in Fig. 8. [In this and other figures for CO, doubling, hatching (or dense shading) indicates warming greater than 4"C, sparse stipple a warming between 2 and 4 "C,and dense stipple a cooling. A similar scheme is used for the CO, quadrupling but with these temperatures doubled.] The two panels on the left side of Fig. 8 are from the earliest simulationsthat were performed at GFDL with sector models by Manabe and Wetherald (1 975, 1980) (see Tables V and VI); the two panels on the right are from the recent simulations performed at NCAR with a global model by Washington and Meehl(l983b). In the top panels, for both the GFDL and NCAR results, the clouds were fixed, whereasthe clouds were computed for the results shown in the bottom panels for each model. Each of the four simulations shows that stratospheric temperatures decrease and tropospheric temperatures increase in response to the doubled CO, . The magnitudes of the stratosphericcooling simulated by the GFDL and NCAR models are nearly equal, particularly for the simulations with fixedclouds. However, the tropospheric warmings simulatedby the models are quite different,with the warming ofthe NCAR model being considerably less than that of the GFDL model. The stratosphericcooling increases with increasing altitude in the GFDL and NCAR simulations. In the Manabe and Wetherald (1975) simulation with fixed clouds, the cooling at any altitude in the stratosphere is a maximum in the tropics and approachesa smaller constant value in the poleward direction. In the Manabe and Wetherald (1980) simulation with computed clouds, the C0,-induced stratospheric temperature decrease also becomes smaller from the tropics toward the poles above about 26 km. Below this altitude, however, the cooling decreases with latitude only to the subtropics The average over the longitudinal domain of a model.

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and then increases toward the pole. This results in a minimum cooling in the subtropical stratosphere. In both of the NCAR simulationsthe cooling is relatively independent of latitude near 18 km except near the poles, where the cooling extends downwardto lower altitudes. In the upper model stratosphere in both simulations the cooling is minimum in the tropics and increases toward the middle latitudes in both hemispheres. In the simulation with fixed clouds the cooling continues to increase to the South Pole, while there is a cooling minimum near 70"s latitude in the simulation with computed clouds. The tropospheric warming increases from the surface upward to a maximum value at about 10 km in tropical and subtropical latitudes in the Manabe and Wetherald (1975) simulation. This upward amplification of the tropospheric warming was attributed to the maintenance of the moist adiabatic temperature lapse rate by moist (cumulus) convection, a parameterized subgrid-scale process (see Table 11),and to the fact that this lapse rate decreases with increasing temperature. The upward amplification is also found in the Manabe and Wetherald (1980) simulation, although it is somewhat more confined to low latitudes. A similar upward amplification was obtained at almost all latitudesin the global model simulationby Schlesinger (1983b) (not shown in Fig. 8). In contrast, an upward amplification of the tropospheric temperature increase is not evident in either simulation with the NCAR model. The GFDL simulations also display a poleward amplification of the warming in the lower half of the troposphere, with maximum temperature increases at the surface near 80" latitude that are five to six times the minimum increases in the tropics. This poleward amplificationwas attributed to the ice albedo feedback mechanism, whereby an initial warming in high latitudes is amplified by melting snow and/or sea ice that results in a large decrease in surface albedo and an increase in the absorbed solar radiation, and to the vertical confinement of this surface warming by the low-level temperature inversion (Manabe and Wetherald, 1975). Both of the NCAR simulations show a weak poleward amplification in the Southern Hemisphere but not in the Northern Hemisphere. Although the GFDL models used by Manabe and Wetherald in 1975 and 1980 differ in more than their treatment of clouds (in particular, there is a difference in the parameterization of longwave radiative transfer; see Table FIG.8. The zonal-mean temperature differences ("C) for doubled C 0 2 simulated by the GFDL models of Manabe and Wetherald (1975, 1980) (a and b) and the NCAR model of Washington and Meehl (1983b) (c and d; fixed and computed clouds, respectively). Dense stipple indicates a temperature decrease, light stipple an increase between 2 and 4 ° C and hatching an increase larger than 4°C. P is pressure in millibars, P* is surface pressure.

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VI), a comparisonof the GFDL simulationswith fixed and computed clouds suggests that the influence of clouds on C0,-induced tropospheric temperature change is of secondary importance. A similar comparison of the NCAR simulationswith fixed and computed clouds also suggeststhat clouds may not be of primary importance, at least insofar as the zonal-mean temperatures are concerned. 3.2.1.2. Geographical distribution of surface air temperature change. The geographical distributions of the surface air temperature change simulated by the sector models of Manabe and Wetherald (1975, 1980) are presented in Fig. 9. The averageswith respect to longitudeof the temperature changes shown in this figure are those shown at the 990-mbar level in the left-hand panels of Fig. 8. Thus the surface air temperature in these and the other GFDL models shown in Tables V and VI represents the temperature at about 200 m above the earth's surface. Figure 9 clearly shows the poleward amplification of the warming of the surface air, with a maximum of 12°C at 82" latitude in the Manabe and Wetherald (1975) simulation and 8°C at the same latitude in the Manabe and Wetherald (1980) simulation. It may be that the 4" smaller maximum warming in the 1980 simulation is the result ofevaporative cooling over the high-latitude ocean that did not exist poleward of 66.5' latitude in the 1975 simulation. In both simulations land/ocean contrasts in the warming are found equatorward of about 45 latitude. In particular, in both simulations there is a secondary warming maximum near 40" latitude over about the western half of the continent without an oceanic counterpart. As can be seen from Figs. 22 and 3 1, these continental warming maxima occur where there was a decrease in the precipitation rate and a large decrease in the soil moisture. Another region of maximum continental warming is found over the southeastern coast in the 1975 simulation, but this region exhibits a minimum warming in the 1980 simulation. The latter result is co-located with an increase in precipitation rate (Fig. 22) and soil moisture (Fig. 3 1), while the former result occurs in associationwith a decrease in the precipitation rate (Fig. 22) and soil moisture (Fig. 3 1). The negative correlation that is found between the changes in surface air temperature and soil moisture occurs presumably through the change in surface evaporation, with increased (decreased)soil moisture resulting in increased (decreased)evaporative cooling and smaller (larger)warming of the earth's surface and surface air. The geographical distribution of the surface air temperature change simulated by the OSU global model of Schlesinger (1983b) is presented in Fig. 10. In this model, as in the model employed by Gates et al. (1981), the surface air temperature is determined by a constant-flux layer approximation (see Ghan et al., 1982)and thereforerepresentsthe temperatureat about O

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10 m above the surface. Figure 10 shows a warming of the surface air temperature over most of the earth. The warming increasesfrom the tropics to the subtropics of both hemispheres, as was also found from the 1980 GFDL sector model (Fig. 9), but then decreases in the middle latitudes before again increasing toward both poles. As a result of the decrease in warming in middle latitudes, the poleward amplification is not monotonic and the polar warming is only three times the tropical warming. This is in contrast to the amplificationof six and five obtained with the 1975and 1980 GFDL sector models, respectively. Figure 10also shows a significant longitudinal variation of the surface air temperature change at virtually all latitudes, some of which is clearly due to contrast between land and ocean. (Recall that the OSU model has realistic orography as well as land/sea contrastswhereas the GFDL sector models do not.) In the Northern Hemisphere, regions of warming in excess of 4°C are located over western and northern Greenland, the Arctic Ocean north of Spitsbergen, the Kara Sea, northeast of the Caspian Sea, and the Sahara, Arabian, and Gobi deserts. Similar but smaller warming regions are found in the Southern Hemisphere in South Africa, southwesternAustralia, the Ross ice shelf, and over Antarctica near 30"E. A region of large cooling is located in central east Africa. Figure 32 shows a large increase in soil moisture in this region, and Fig. 23 reveals a large increase in the precipitation rate there. Further analysis indicates that in general there is a negative correlation over land between the

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changes in surface air temperature and soil moisture, as was evident in the GFDL sector models' results. The changes in the surface air temperature simulated by the NCAR global model with computed and fixed clouds are presented in Fig. 1 I. This figure shows that although most of the earth's surface is warmed in response to the

FIG.1 1. NCAR model simulationsof the change in surface airtemperature ("C)for doubled C02with computedclouds(top) and fixed clouds(bottom). Heavy stipple indicatesa temperature decrease, light stipple an increase between 2 and 4"C,and hatching an increase larger than 4°C. [From Washington and Meehl(1983b); unpublished results.]

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COz doubling, there are regions of cooling simulated by the NCAR model, particularly with computed clouds. Ignoring these cooling regions for the moment, it can be seen that the warming tends to be small in the tropics and middle latitudes and large in the subtropics and polar latitudes. In the simulation with computed clouds, warming in excess of 4°C occurs over Antarctica inland from the Ross ice shelf. Regions of warming in excess of 3 "C are found extending from Greenland over Europe toward the Arabian Sea, inland from the Gulf of Mexico over the United States, and inland west of the Sea of Okhotsk. Regions of smaller warming are seen over the Sahara Desert, eastern Brazil, South Africa, and Australia. The NCAR simulation with fixed clouds shows some features that are nearly identical to those with computed clouds, such as the regions of maximum warming located between the Black and Caspian seas. However, there are fewer similarities than dissimilarities between these model simulations. This suggests that clouds may be important in the geographical distribution of C0,-induced temperature changes. 3.2.I .3. Zonal-mean surface air temperature change. A comparison of the zonal-mean surface air temperature changes simulated by the models of the preceding section is shown in Fig. 12 along with the change simulated by the OSU model with prescribed SST and sea ice (Gates et al., 1981). The zonal means for the GFDL sector models of Manabe and Wetherald (1975, 1980)are the longitudinally averaged data of Fig. 9, which are plotted symmetrically about the equator in Fig. 12. These curves both show a minimum warming of about 1.5"Cin the tropics followed by an increase toward the subtropics. The rise is more rapid in the 1975 simulation and reaches a maximum value of2.5 "C near 15 " latitude. The warming then decreases to about 2°C between 20 and 30" latitude and increases virtually monotonically toward a maximum value of nearly 11"C at the highest model latitude. On the other hand, the 1980 simulation does not display a maximum value in the tropics, but rather increases monotonically to a maximum value of about 7.5 "Cat 83 latitude and then decreasesto about 5.5 "C near the pole. The zonal means of the OSU global simulation by Schlesinger (1983b), that is, the longitudinally averaged data of Fig. 10, are quite similar to those of the 1980 GFDL simulation between 30"s and 25"N, with a minimum warming of about 1.25"C in the tropics and an increase to about 3 "C in the subtropics. However, in marked contrast to the almost 1 "C increase in warming that occurs between 34 and 38 latitude in the 1980GFDL simulation, there is a decrease in warming in the OSU simulation to about 1.5 "Cat 5 5 " s and 40"N. This is similar to the result ofthe 1975 GFDL simulation, albeit there is about a 10" latitude difference in positions of the resultant low-latitude warming maxima. Poleward of these latitudes the warming in the OSU simulation again increases with latitude to a value that is larger in O

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FIG.12. The change in zonal-mean surface air temperature (AT,) simulated by seven GCMs for doubled CO,. The data of Manabe and Wetherald (1975, 1980) (curves a and b, respectively, for 1975and 1980) are plotted symmetricallyabout the equator. Other curves: c, Gates et al. (1981); d, Schlesinger (1983b); Washington and Meehl(1983b); e, predicted clouds; f, prescribed clouds. (All data are unpublished results.)

the Arctic than in the Antarctic, namely, 3.9"C at 86"N and 3.0"C at 86"s. If the OSU curve is shifted upward such that the midlatitude minimum warming in each hemisphere intersects the 1980GFDL curve, it is seen that the poleward amplification in the OSU simulation is approximately half that of the 1980 GFDL simulation. The zonal means of the NCAR simulation with computed clouds, that is, the longitudinally averaged data of the top panel in Fig. 1 1, show a minimum warming of about 0.8"C in the tropics and a relatively uniform warming of about 1.3"Cin the midlatitudes of the Southern Hemisphere, with a poleward amplification there of about two. In the Northern Hemisphere the

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warming is a maximum of 2°C near 40"Nlatitude and decreasestoward the pole. Similar results are obtained in the fixed-cloud simulation, again indicating that the influence of clouds may not be important for the zonal-mean temperature changes. The zonal-mean warming in both NCAR simulationsis smaller than that obtained by the GFDL and OSU simulationsalmost everywhere. Figure 12 also shows that the zonal-mean surface air temperature change simulated by the OSU model with prescribed, noninteractiveSST and sea ice (Gateset al., 1981) is about an order of magnitude smaller than the warming obtained by the OSU model with an interactive swamp ocean (Schlesinger, 1983b).

3.2.2. CO, Quadrupling. 3.2.2.I. Latitude- height cross sections. Figure 13 showslatitude- height cross sections of the change in zonal-mean air temperature simulated for a CO, quadrupling. The top left panel is from the sector model of Manabe and Wetherald (1980)and is the counterpart to the cross section for the CO, doubling shown in the lower left panel of Fig. 8. The bottom left panel of Fig. 13 is the annually averaged temperature change from the global model of Manabe and Stouffer (1980),in which the ocean was treated as a 68-m slab mixed layer, the solar insolation was varied over its annual cycle, and the clouds were fixed. The two panels on the right are from the global model of Washington and Meehl(1983b) with the swamp ocean model and annually averaged insolation. The simulations with fixed and computed clouds are shown in the top and bottom panels, respectively, as in Fig. 8 for the CO, doubling. Each of the cross sections in Fig. 13 shows a warming of the troposphere and a cooling of the stratosphere in response to the CO, quadrupling, with the stratosphericcooling increasingwith altitude. This is the same response as that obtained for the CO, doubling (Fig. 8). Comparingthe top left panel of Fig. 13with the lower left panel of Fig. 8, and the right-hand panels of Fig. 13 with the right-hand panels of Fig. 8, it is seen that the response of the GFDL model for CO, quadrupling is everywhere very nearly double the response for CO, doubling, while a similar linear behavior is clearly evident only in the stratosphere in the NCAR simulations. Although there are tropospheric regions in the NCAR simulations where the warming for CO, quadrupling is twice as large as the warming for doubling, it is difficult to FIG.13. The zonal-meantemperaturedifferences("C)for quadrupledC 0 2simulated by the GFDL models of Manabe and Wetherald (1980) (a) and Manabe and Stouffer (1980)(b), and the NCAR model of Washington and Meehl (1983b) with prescribed (c) and predicted (d) clouds. Heavy stipple indicatesa temperature decrease, light stipple an increase between 4 and 8"C, and hatching an increase larger than 8°C.

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discern such a relation elsewhere owing to the coarse contour interval shown for the doubling results (Fig. 8). Comparing the left-hand panels in Fig. 13 shows that the annually averaged response of the tropospherein the Manabe and Stouffer (1980) simulation with the annual insolation cycle is smaller than the tropospheric response of the Manabe and Wetherald (1 980) simulation with annually

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averaged insolation. In particular, the maximum high-latitude surface warming decreases from 15 to 9 "C and occurs in the polar cap instead of at 83' latitude. Furthermore, the inclusion ofthe Southern Hemisphere in the Manabe and Stouffer (1980) model reveals hemispheric differences that could not be shown by the sector models of Manabe and Wetherald (1975, 1980). In particular, the surface warming in the Arctic is larger than that in the Antarctic. A similar hemispheric asymmetry was found in the doubling simulation with the OSU model by Schlesinger(1983b)(Fig. 12). However, the NCAR quadrupling simulations display nearly equal warmings in the Arctic and Antarctic. Finally, it should be noted that the stratospheric cooling of the Manabe and Stouffer (1 980) simulation with the annual solar cycle is considerably larger than that obtained by Manabe and Wetherald (1980) without the annual solar cycles, particularly in high latitudes. A more comprehensive and rigorous assessment of the effects of the annual insolation cycle on C02-induced climatic change was conducted by Wetherald and Manabe (1 98 1) by performing two simulationswith the same (sector)model, one with the annual solar cycle (hereafter called the seasonal model) and the other with annual-mean insolation (the annual model). In this model the ocean was treated as a 68-m slab mixed layer (see Tables V and VI). The zonal-mean temperature differences of the annual and seasonal models in response to quadrupled C02are presented in Fig. 14. Comparing these annual-mean temperature differencesshowsthat the principal effect of the annual insolation cycle is to reduce the warming of the zonal-mean surface air temperature at all latitudes, with the reduction increasing with latitude from about 0.5"C in the tropics to 4°C poleward of 70" latitude, 3.2.2.2. Geographical distribution of surface air temperature change. The geographical distribution of the surface air temperature change simulated by the sector model of Manabe and Wetherald (1980) is shown in Fig. 15. This figure for quadrupled CO, is the counterpart of the lower panel of Fig. 9 for doubled COz. To aid in discerning whether the quadrupling-induced warming is equal to twice the doubling-induced warming, these two figures have dense shading for warming greater than 8 "C for the quadrupling (dense stipple, Fig. 15) and 4°C for the doubling (hatching, Fig. 9) and light shading for warming between 4 and 8' C for the quadrupling (light stipple, Fig. 15) and between 2 and 4°C for the doubling (stipple, Fig. 9). Comparing Fig. 15 with Fig. 9 shows that the response for the CO, quadrupling is qualitatively quite similar to that for the doubling, with a poleward amplification of the warming, maximum warming near 83" latitude over land and ocean, land/ocean contrasts only equatorward of about 45 latitude, a secondary warming maximum near 40" latitude over the western part of the continent, and minimum warming over the southeasterncoast. A comparison of the magnitude of the two C0,-induced temperature increases shows O

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LONGITUDE

FIG.15. GFDL model simulation of the change in surface air temperature ("C)for quadrupled C02. Light stipple indicates a temperature increase between 4 and 8"C,dense stipple an increase larger than 8°C. [From Manabe and Wetherald (1980).]

that the warming for the quadrupled CO, level is slightly less than twice the warming for doubled CO, over most of the sector domain. Comparing Fig. 15 with Fig. 35 shows again the negative correlation between changes in the surface air temperature and soil moisture; that is, the warming is large where the soil is desiccated and small where there is moistening. Figure 16 shows the geographical distribution of the surface air temperature change simulated by the NCAR model of Washington and Meehl (1983b). Comparing this figure with its counterpart for the C02doubling, Fig. 1 1, shows that the warming patterns have several similarities. In particular, for the simulations with computed clouds there are regions of maximum warming extending from Greenland over Europe toward the Arabian Sea; inland from the Gulf ofMexico over the United States;over the Atlantic Ocean, the Sahara Desert, the regions east of the Caspian Sea and west of the Sea of Okhotsk; and over eastern Brazil, South Africa, and Australia. Fur-

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thermore, for both simulations with computed and fixed clouds most of the regions of cooling for the doubling (Fig. 1 I ) are eliminated for the C02 quadrupling and are replaced by regions of minimum warming. This result is what would be expected if the cooling regions in Fig. 1 1 for the doubling are manifestations of noise. The superposition of “large” noise on a “small”

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FIG.17. GFDL model simulationsof the change in surface air temperature ("C)fox.quadm pled C02. (a) Annual model; (b) seasonal model. Stipple indicates a temperature increase between 4 and 8°C and hatching an increase larger than 8°C. [From Wetherald and Manabe (1 98 1); unpublished results.]

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doubling signal could produce negative temperature changes, but the superposition of such noise on the enhanced quadrupling signal would result in both fewer and weaker cooling regions. It is in fact the purpose of a superanomaly experiment such as a CO, quadrupling to increase the signal-to-noiseratio by increasingthe signal. As noted earlier, the validity of this approach lies in the assumed known relationship between the responses for quadrupled and doubled CO, . A comparison of the regions of maximum warming in Figs. 1 1 and 16 indicates that the warming for quadrupling is somewhat less than twice that for the doubling, as is also found for the GFDL sector model results. However, it is not clear whether such quasi-linearity exists in those regions where the cooling for the CO, doubling has changed to warming for the CO, quadrupling. The only way to verify this would be to extend the doubling simulation sufficiently long that the time-averaged signal significantly exceeds the timeaveraged noise so that the cooling regions disappear. If this were done, however, the need to perform the superanomaly quadrupling experiment would de facto be eliminated. Nevertheless, the occurrence of extensive cooling regions for the doubling lowers its global-mean warming and tends to make the ratio of the global-meanwarmings for quadruplingand doubling larger than the linear value of two (see Table VII). The geographical distributions of the change in surface air temperature simulated by the annual and seasonal models of Wetherald and Manabe ( 1 98 1) are shown in Fig. 17. A comparisonofthe annual-mean temperature differences reveals that the longitudinal variation of the response of the seasonal model is smaller than that of the annual model, particularly equatorward of 45 ' latitude. Furthermore, the seasonalmodel is less sensitiveto the CO, quadrupling than is the annual model, especially in high latitudes where the maximum warming is 4°Csmaller.' This reduced sensitivity of the seasonal model is attributed to the absence of the ice albedo feedback mechanism in summer when there is no snow cover or sea ice in both the seasonal model experiment and the control, whereas ice albedo feedback apparently exists perpetually in the annual model (Wetherald and Manabe, 198 1). This comparison indicates that the influence of the seasons on the annual-mean climate change is not negligible. The geographical distribution of the change in the annual-mean surface air temperature simulated by the global GFDL seasonal model of Manabe and Stouffer (1980) is shown in Fig. 18 along with the corresponding maps for December, January, and February and June, July, and August. The Preliminaryresults from a version of the NCAR model with a 50-m slab mixed-layerocean and the annual solar cycle indicate a greater sensitivity of this seasonal model compared to the annual model of Washington and Meehl(1983b) (Washington and Meehl, 1983a).

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annual-mean temperature change shows warming everywhere, with minimum values in the tropics and maximum values in the polar latitudesin both hemispheres. These features are in general agreement with the changes simulated by the NCAR model for quadrupled-CO, concentration (Fig. 16). The GFDL model simulation shows that the warming in the Arctic is about 4°C larger than that in the Antarctic, in contrast with the more nearly equal warming obtained by the NCAR model. Furthermore, less longitudinal variation in the warming is simulated by the seasonal GFDL model than is simulated by the annual NCAR model. This is what was also found for the seasonal and annual GFDL model simulations of Wetherald and Manabe (198 1). Figure 18 also shows that there is a large seasonal variation of the C02-induced warming in the middle and high latitudes of both hemispheres, with large warming in winter and small warming in summer. There is even a small region of cooling located over the Ellsworth Highland in austral summer, as well as over northern Australia and central South America. This nonuniformity of the C0,-induced temperature change throughout the year is even more strikingly revealed by Fig. 19, in which the annual cycle of the change in the zonal-mean surface air temperature is shown. It can be seen here that the zonal-mean warming is maximum in winter and minimum in summer over both the oceans and the continents in the high latitudes of the Northern Hemisphere. This indicates a large reduction of the amplitude of the annual temperature cycle in these latitudes and is attributed to the change of the thermal insulation effect of sea ice (Manabe and Stouffer, 1980). A similar seasonal asymmetry in the warming is found over the high-latitude oceans of the Southern Hemisphere, but not over Antarctica. Furthermore, there is little variation of the warming throughout the year in the tropics. 3.2.2.3. Zonal-mean surface air temperature change. A comparison of the zonal-mean surface air temperature change simulated by the models of the preceding section is shown in Fig. 20 along with the change simulated by the OSU model with prescribed SST and sea ice (Gates et al., 1981). The results for the GFDL sector models of Manabe and Wetherald (1 980) and Wetherald and Manabe (I 98 1) are shown plotted symmetrically about the equator, and the curves for the seasonal models of Manabe and Stouffer (1980), Wetherald and Manabe (1 98 I), and Gates et al. (198 1) represent the

FIG. 18. GFDL model simulation of the change in surface air temperature for quadrupled CO,. (a) Annual mean; (b) December/January/February; (c) June/July/August. Sparse shading indicates a temperature increase between 5 and 7.5"C, dense shading an increase greater than 7.5"C. [From Manabe and Stouffer (1980).]

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changes in the annual-mean surface air temperature. The simulations by the GFDL models all show a minimum warming in the tropics and a poleward amplification. The latter is smaller in the seasonal models of Manabe and Stouffer (1980) and Wetherald and Manabe (1 98 1) than in the annual models of Manabe and Wetherald ( 1980) and Wetherald and Manabe (198 1). There is also an asymmetry in the poleward amplification in the global model of Manabe and Stouffer (1980), with greater warming in the Arctic than in the Antarctic. In contrast, the NCAR model simulations of

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Washington and Meehl(1983b) show a smaller poleward amplification and little difference in the Arctic and Antarctic warnings. The zonal-mean warming in the NCAR simulationsis almost everywheresmaller than that in any of the GFDL simulations. However, the warming obtained by the NCAR model is everywhere considerably larger than that obtained with the OSU model when the sea surface temperature and seaice were not allowed to respond and feed back on the C0,-induced climatic change (Gates et al. 1981).

3.2.3. Comparison of Global-Mean Warmings Simulated for Doubled and Quadrupled CO,. 3.2.3.1. GCMsimulations. A summary of the temperature changes simulated by eight GCMs for CO, doubling and quadrupling is shown in Table VII in terms of the area-averaged surface air temperature. For the four models with the annual solar cycle, that is, the seasonal models of Manabe and Stouffer (1980), Wetherald and Manabe (1981), Mitchell (1983), and Gates et al. ( 1 98 I), the annual-mean change is presented in Table VII. For I

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FIG.20. The change in zonal-mean surface air temperature (AT,) simulated by seven GCMs for quadrupled COz. The data of Manabe and Wetherald (1980) (a) and Wetherald and Manabe ( 1981;curves c and d: seasonal and annual insolation, respectively) are plotted symmetrically about the equator. Other curves: b, Manabe and Stouffer (1980); c, Gates et al. (1981); Washington and Meehl(1983b): f, predicted clouds; g, prescribed clouds. (All data are unpublished results.)

186

MICHAEL E. SCHLESINGER

TABLEVII. AREA-MEAN ANNUAL-MEAN SURFACE AIRTEMPERATURE AND COz-INDUCED TEMPERATURE CHANGES ("C) I X C02-OBS'

2 X COZ- 1 X CO2

4 X COZ- 1 X C02

Mode1

1 X C02

Manabe and Wetherald (1975) [81b(300, 600, --y Manabe and Wetherald (1 980) [24] (300,600,1200) Manabe and Stouffer (1980) I201 (300, -, 1200) Wetherald and Manabe (1981)d (301 (300, -, 1200) Washington and Meehl(1983b3)'[33] (330,660, 1320) Mitchell (1983) [28] (320.7, 641.4, -) Gates et 01. (1 98 1) [25](326,644, 1289) Schlesinger (1983b) (321 (326,644, -)

20.9

6.7

2.9

21.3

6.9

3.0

14.8

0.6

4.1

16.0 16.8

1.8 2.6

6.0 4.8

11.6 11.6

- 2.6 - 2.6

1.3 1.3

3.4 2.7

12.3

-0.9

0.2

0.4

14.8

0.6

0.2

17.9

3.7

2.0

5.9

Observed value of 14.2"C based on data of Crutcher and Meserve (1970) and Taljaard et al. (1 969) as given by Jenne (1975). These numbers correspond to those in Tables IV and IX and Figs. 21,29, and 30. C 0 2concentrationsin ppm for 1 X CO,, 2 X C02,and 4 X CO,. First row is for annual-mean insolation, second row is for seasonal insolation. First row is for computed clouds, second row is for prescribed clouds.

both the doubling and the quadrupling each simulation shows an increase in the global-mean surface air temperature. The smallest warming for each increased C02 level is obtained by the UKMO and OSU models with prescribed SST and sea ice (Mitchell, 1983; Gates et af.,198 1). However, the OSU model with an interactive (swamp) ocean and sea ice (Schlesinger, 1983b)gives a 10-fold increase in the warming, at least for the C02doubling. A similar amplification was first obtained by the author from a pair of RCM sensitivity calculations, one in which the surface temperature in the experiment was fixed and the other in which it was predicted. In both the RCM and GCM calculations with prescribed surface temperatures (the latter only for the ocean), the surface (ocean) acts as an infinite heat sink. That is, the increased downward infrared radiation

MODELS OF C02-INDUCED CLIMATIC CHANGE

187

from the atmosphere to the earth’s surface, which is a direct result of the increased opacity of the C0,-enriched atmosphere, simply passes into the surface (ocean) and is lost. As pointed out by Watts (1982), in this case the models’ climate system is not energetically closed. When the surface (ocean)temperature is free to respond, however, it warms and returns energy to the atmosphere through the fluxes of latent, sensible, and radiative (infrared) heat. This in turn warms the atmosphere and increases the downward infrared (IR) radiation to the surface and the upward IR radiation to space. This feedback process continues to warm the surface and the atmosphere until the increasein the IR radiation to space due to the warmer temperature compensates the decrease that initially occurred from the increased C02 opacity. When this compensation occurs, the balance between the solar heating and IR cooling of the climate system is restored. It is not restored when the surface (ocean) temperature is fixed and energy is lost through the lower surface. The response of the NCAR model with both fixed and computed clouds (Washington and Meehl, 1983b) is smaller than that of the OSU model (Schlesinger, 1983b)that has computed clouds, while the warming obtained by the GFDL sector model with computed clouds (Manabe and Wetherald, 1980) is larger than that of the OSU model. For the quadrupled-CO, concentration, the global-mean warming of the seasonal model of Wetherald and Manabe (1 98 1) is smaller than that of their annual model, and a similar reduced sensitivity is found for the seasonal model of Manabe and Stouffer ( 1980)in comparison with the annual model of Manabe and Wetherald (1980), albeit these two models also differ in several other ways (see Table VI). The warming ofthe annual NCAR model of Washington and Meehl(1983b) is not only smaller than the warming of the annual GFDL models, it is also smaller than that of the seasonal GFDL models. In fact, the global-mean warming of the NCAR model simulations for quadrupled COzis comparable to the warming of the GFDL models for doubled CO, (see Figs. 2 1 and 29). As Table VII shows, four models have been used to simulate both a CO, doubling and a quadrupling. The OSU model (Gates et al., 1981), the GFDL model (Manabeand Weatherald, 1980),and the NCAR model simulation with fixed clouds (Washington and Meehl, 1983b) show that the warming for quadrupling is virtually equal to twice the warming for doubling, while the NCAR model simulation with computed clouds gives a ratio closer to three. Table VII also shows the global-mean surface air temperature of the control (1 X CO,) simulation for each model, the observed value, and their difference. This reveals that of all the models’ simulations of the present climate, only those of the UKMO and NCAR models are colder than the

188

MICHAEL E. SCHLESINGER

TABLEVIII. RCM STUDYOF THE DEPENDENCE OF THE SURFACE AIR TEMPERATURE WARMING INDUCED BY DOUBLED CO, ON THE TEMPERATURE OF THE CONTROL SIMULATION

10.6

5.3

3.3 1 3.27

13.9 8.5

3.10 3.23

observed temperature. It is interesting therefore that, excluding the simulations by Mitchell (1983) and Gates et al. (198 1) with prescribed SST and sea ice, the warming simulated for both doubled and quadrupled C 0 2 concentrations isa minimum for the NCAR model. Moreover, as shown by the top panel of Fig. 29, there is an increasing relation between the C0,-induced surface air temperature warming and the surface air temperature of the control. That is, in the sense of intermodel comparison, the warmer the control the larger the C0,-induced warming.8 This relation could be due to the combined effect of the nonlinear increase of evaporation with surface temperature as suggested by Newel1 and Dopplick (1979), and the resultant increase in the atmosphere's water vapor content and its contribution to the greenhouse effect. I have made a preliminary test of this hypothesis with an RCM in which the relative humidity was fixed; there were no clouds; the surface heat exchange was parameterized as a latent heat flux C,(q,*- q,), with C, a prescribed transfer coefficient in units of langleys per day, @ the saturation mixing ratio at the temperature ofthe ground and surface pressure, and qsthe mixing ratio of the surface air that changes with surface air temperature T, due to the fixed relative humidity. In this test the temperature of the control was made to increase by decreasing the prescribed surface albedo from 0.1 to 0.05. Table VIII shows that increased warming does occur with increased temperature of the control for the smaller value of C,,but the opposite occurs for the larger value. This suggests that the sensitivity of the warming to the control temperature may depend on the dynamics. In any event, the changes shown in Table VIII are small in comparison with the differences between the models' warnings shown in Table VII and Fig. 29. This indicates that some explanation other than the nonlinear increase ofevaporation with surface temperature may have to be sought to explain these differences.

* However, in the sense of inlramodelcornpurrson. as revealed by the solar constant sensitivity studiesofwetheraidand Manabe( 1975,1980), the warmerthecontrol thesmallerthe temperature sensitivity.

MODELS OF C02-INDUCED CLIMATIC CHANGE

189

3.2.3.2. EBM and RCM simulations. Figure 21 shows the change in surface temperature simulated by EBMs, RCMs, and GCMs for COz concentrations that are halved, doubled, quadrupled, and decupled. The characteristics of the individual models used to perform these simulations are summarized in Table IV. Focusing attention on the C02 doubling we see that both the maximum and minimum values of 9.6 and 0.1 "C have been obtained by the EBMs, namely, by Moller (1963) (model) [ 13 and Sellers (1973) [5], respectively. Similar large temperature changes have been obtained by Kandel (1981) [27], and small changes by Rasool and Schneider (1971) [4], Newell and Dopplick (1979) [22], Idso (1980) [23], and Kandel(l981) [27]. Manabe and Wetherald ( 1967), Schneider ( 1979, and Manabe ( I 983) have shown that the wide range of warming obtained by Moller (1963) [ 11 was due to his

Ratio of C02 concentration to preindustrial value 1. Moller 11963) 2. Manabe and Wetherald 119671 3. Manabe 11971) 4. Rosool and Schneider 11971) 5. Sellers (19731 6. Sellers 119741 7. Weore and Snell 119741 8. Manabe and Wetherald (19751

9. Romonothon 119751 10. Schneider (19751 I I. Rmkin and SnelI 119761 12. Augustssan and Romonothan 119771 13. Patter 11978, 19801 14. Hansen 119781 15. Rowntreeand Walker 11978) 16. HOnSen (19791 17. Humrnel and Reek 119791

18. Hunt and Wells 119791 19. MacDonold et Of. (1979) 20. Monabe and Stouffer (1979, 19801 21. RamonathanefaL 11979) 22. Newell and Oopplick 119791 23. Idso (19801 24. Monabe and Wetherald 119801 25. Gat%se/of. 119811

26. 27. 28. 29. 30. 31. 32. 33. 34.

Honsen ef a/. 11981I Kandel (1981 I Mitchell (1983) Ramanathan 11981I Wetherald and Monabe 119811 Hall ct a/. 11982) Schlesinger 11983b) Washington and Meehl I1983 bl Alrkrondrov ctol. 119831

FIG.2 1. The change in surface temperature induced by halving (fx), doubling(2X), quadrupling (4X), and decupling (1OX) the preindustrial C02concentration as simulated by energy balance models (EBMs), radiative-convective models (RCMs), and general circulation models (GCMs). [After Schlesinger (1983a).]

190

MICHAEL E. SCHLESINGER

use of a model that required an energy balance for the earth's surface rather than for the entire earth - atmosphere climate system as described in Section 2.1. Kandel ( 1981) found a similar explanation for the small values obtained by Newell and Dopplick (1 979) [22] and Idso (1980) [23], but argued that the surface energy balance model can be reconciled with the planetary energy balance model. However, uncertainty in the response of the atmospheric water vapor gives the wide range shown for Kandel's model [27], in agreement with that obtained by Moller (1963) [l]. In a study by Watts (1 982) it was shown that the temperature changes predicted by surface energy balance models are highly sensitiveto the values of their parameters, so much so that Watts concluded that such models are not useful and should be replaced with tropospheric heat balance models. Watts (1982) also showed that the small warming obtained by Newell and Dopplick (1979) [22] is the result of not having iterated their surface energy balance model to its equilibrium solution. Interestingly, Sellers ( 1974)also discounted his earlier small warming (Sellers, 1973 [S]) as due to not having run his model sufficiently long to reach equilibrium. Schneider (1975) has discussed the reasons for the small warming of his earlier study (Rasool and Schneider, 1971 [4])and for that of Weare and Snell(l974) [7]. If we exclude all of the EBM simulations discussed above from consideration, the range of the warming simulated by EBMs for a C02 doubling is 1.3-3.3"C. The range ofwarming simulatedby RCMs for doubled C02is 1.3 - 3.2 "C, in remarkable agreement with that given by EBMs. Augustsson and Ramanathan (1977) have shown that this range of RCM warmings can be explained by the models' different treatments of the water vapor and clouds. It was first shown by Manabe and Wetherald (1 967) [2] that prescribing the relative humidity instead of the specific humidity approximately doubles the RCM's surface warming, from 1.3 to 2.4"C with prescribed average cloudiness and from 1.4 to 2.9"C for a cloud-free atmosphere. This enhancement, or positive feedback, occurs because the atmospheric water vapor must increase with increasing temperature to maintain constant relative humidity, and because of the strong greenhouse effect of water vapor. Augustsson and Ramanathan (1977) showed that when the relative humidity itself was made an increasing function of the surface temperature, the C02-induced warming increased (for the same reasons as described above). They also showed an enhancement of the warming when the temperature of the cloud tops was fixed rather than when the cloud top altitude was fixed. This finding was confirmed by the study of Hansen et al. (1981). The range of surface warming simulated by the GCMs when the prescribed-SST/sea ice simulations of Gates et al. (1981) and Mitchell (1983) are excluded from consideration is somewhat larger than that of the purely thermodynamical models, namely, 1.3- 3.9"C.

MODELS OF C02-INDUCED CLIMATIC CHANGE

191

3.3. Simulated Precipitation Changes

3.3.1. CO, Doubling. The geographical distributions of the change in precipitation rate simulated by the sector models of Manabe and Wetherald (1975, 1980) for a CO, doubling are presented in Fig. 22. (In this and subsequent precipitation figures for both the doubling and quadrupling, dense shadingindicates a decrease, sparse shadingan increase between 2 and 5 mm/day, and hatching an increase greater than 5 mm/day.) This figure shows that the precipitation rate in both simulationsincreased almost everywhere poleward of about 45" latitude, albeit by less than about 1 mm/day. Equatorward of this latitude there are large regions where the precipitation rates decreased as well as increased, and the magnitude of the changes is larger than that in the high latitudes. Accompanying this in both simulations there is a much larger longitudinal variation of the changes in low and middle latitudes than in high latitudes. These features are reflected in the changes in the zonal-mean precipitation rates that are shown in Fig. 24 (upper left panel). The longitudinal variation equatorward of about 45 latitude causes cancellation in the zonal means there such that the increased precipitation rate of about 0.3 mm/day in high latitudes is comparable to the maximum decrease in the zonal mean near 40" latitude and the maximum increase near 25 latitude. As shown by Fig. 22 the decrease in the midlatitude zonal-mean precipitation rate is the result of the extensive region of decreased precipitation that extends from the center of the ocean to the center of the continent in both simulations. [Recall that cyclical boundary conditionsare imposed in the sector models. Thus there is ocean to the left (west in the Northern Hemisphere) of the continent equatorward of 66.5" latitude in the 1975 simulation and at all latitudes in the 1980 simulation.] This latter region is the location of a maximum warming in both simulations, as already shown in Fig. 9. Figure 22 shows that the increased zonalmean precipitation rate near 25"latitude is the result of the increased precipitation rate over the continent and ocean at this latitude in the 1980 simulation,and of a similar continentalincreasecombined with a minimum maritime decrease in the 1975 simulation. Figure 24 reveals that the largest differencebetween the two simulations occursin the equatorial region where the 1975 simulation gives an increase of 0.8 mm/day and the 1980 simulation gives a decrease of 0.35 mm/day. Figure 22 shows that these equatorial differences occur predominantly over the ocean where increased precipitation occurs in the 1975 simulation and decreased precipitation occurs in the 1980 simulation. While clouds were predicted in the 1980 simulation and were fixed in the 1975simulation, it is premature to ascribe the differences in equatorial maritime precipitation to cloud feedback (R. T. Wetherald, personal communication, 1982). O

192

MICHAEL E. SCHLESINGER

80

0

60

I20

Longitude

FIG.22. GFDL model simulations of the change in precipitation rate (millimeters/day) for doubled CO,. Top, data from 1975; bottom, 1980. Dense shading indicates a rate decrease, sparse shading an increase larger than 2 mm/day. [From Manabe and Wetherald (1975, 1980); unpublished results.]

193

MODELS OF CO2-INDUCED CLIMATIC CHANGE 9 0N

70N 50N 3 0N 10N 10s

30 S 50s

70s 90s 180

150W

120W

90W

60W

30W

0

30E

60E

90E

120E

150E

FIG.23. OSU model simulation of the change in precipitation rate (millimeters/day) for doubledCOz. Dense shading indicates a rate decrease, no shading an increase between 0 and 2 mm/day, sparse shading an increase between 2 and 4 mmlday, and hatching an increase in excess of 4 mm/day. [From Schlesinger (1983b).]

Figure 23 shows the geographical distribution of the change in precipitation rate simulated by the OSU model (Schlesinger, 1983b) for C02 doubling. It can be seen that unlike the surface air temperature (Fig. lo), the precipitation rate decreased as well as increased over a large fraction of the earth's surface. In general the largest changes in precipitation occurred between 30"s and 30"N latitudes. Increases in the precipitation rate by more than 2 mm/day are located predominantly on or near the equator over Indonesia, northeast of New Guinea, the central Pacific Ocean, Equador, and central east Africa. The latter region coincides with the decrease in surface air temperature shown in Fig. 10. Decreases in the precipitation rate by more than 2 mm/day are located over the Arabian Sea- Indian Ocean, the Coral Sea, north of the Gilbert' Islands, the Atlantic Ocean, and north Africa. The regions of increased precipitation dominate, however, so that there is an increase in the zonal-mean precipitation rate around the equator, as shown by Fig. 24 (upper right panel). This is in agreement with the increase simulated by the GFDL sector model of Manabe and Wetherald (1975). However, there is disagreement between the changes in these models' zonal means in the Northern Hemisphere, where the OSU model simulates a decreased precipitation rate in the subtropics and an increased rate in midlatitudesand both the 1975and 1980GFDL models simulate the reverse. Interestingly, there is better agreement in the Southern Hemi-

180

194

MICHAEL E. SCHLESINGER

sphere, not only in the subtropicsand midlatitudes,but also in high latitudes, where all three models simulate an increase. (However, recall that there is only one hemisphere in the sector models.) In the high latitudes of the Northern Hemisphere all three models simulate an increase in the precipitation rate, with that of the OSU model being smaller than that of the GFDL models. This occurs, at least in part, because there are regions of decreased precipitation rate in the high latitudes of both hemispheres in the OSU simulation (Fig. 23), while such negative regions are virtually absent from high latitudes in the GFDL simulations (Fig. 22).

-

v

r. -0.4

0 0

90N

I

70

l

50

I

30

l

ION

I

10s

1' U-&*t 50

0 j 3

70 90s-0.490N

30 1

50 1

70 1

ION I

90N

30 I

50 I

70 1

1

O 0.6' I

-0.41

IOS 1

I

70

I

50

I

30

I

ION

8

IOS

I

30

I

50

I

I

70 90s

Latitude

FIG. 24. The change in zonal-mean precipitation rate (AP) simulated by six GCMs for doubled CO,. The data of (A) Manabe and Wetherald (1975, curve a; 1980, curve b) are plotted symmetrically about the equator. (B) Data from Gates et af. (1981, curve a) and Schlesinger (1983b, curve b). (C) Data from Washington and Meehl (198313): curve a, predicted clouds; curve b, prescribed clouds. (All data are unpublished results.)

1

90s

MODELS OF C02-INDUCED CLIMATIC CHANGE

195

The changes in the zonal-mean precipitation rate for the doubled-CO, simulations by the NCAR model (Washington and Meehl, 1983b) with computed and fixed clouds are shown in the lower panel of Fig. 24. (The corresponding geographical distributions were not available.) Both of the NCAR simulations show an increase in the precipitation rate in the higher latitudes of both hemispheres, in agreement with the GFDL and OSU (Schlesinger, 1983b) simulations. Both NCAR simulations also show a band of decreased precipitation near 30"N latitude, as do the GFDL simulations and, somewhat more equatorward, the OSU simulation. The NCAR results show disagreement between each other in the tropics, where the model with computed clouds simulatesa decrease (increase) in the Northern (Southern) Hemisphere and the model with fixed clouds simulates the reverse. This result is similar to that found for the GFDL models with fixed clouds (Manabe and Wetherald, 1975) and computed clouds (Manabe and Wetherald, 1980). Figure 24 (upper right panel) also showsthe changes simulatedby the OSU model with prescribed sea surface temperature and sea ice. It is evident that these changes are not only generally considerably smaller than those of the other models, but that they are negative almost everywhere.

3.3.2. CO,Quadrupling. The geographical distribution of the change in precipitation rate simulated by the sector model of Manabe and Wetherald (1980) for a C02quadruplingis presented in Fig. 25. A comparison of this figure with the lower panel of Fig. 22 for the CO, doubling with this model showsthat there are many qualitative similarities. In particular, the precipitation rate increases almost everywhere poleward of about 45 latitude,there are decreases as well as increases equatorward of this latitude, the magnitude of the changes in low latitudes is much larger than that in high latitudes, and the longitudinal variations are larger in low latitudes than in middle and high latitudes. These similaritiesin the geographicaldistributions of the changes in precipitation rate for the quadrupling and doubling simulations of Manabe and Wetherald (1 980) lead to the similarity of the respective changes in the zonal-mean precipitation rates that are evident from a comparison of Figs. 28 and 24 (upper left panels). Both figures show a decrease near the equator that is the result of the decreased precipitation rate over the ocean shown by Figs. 25 and 22. The zonal means for the quadrupling and doubling show increased precipitation rates in the subtropics and high latitudes and a small region of decreased precipitation in the middle latitudes. This latter feature is the result of the decreasein precipitation rate that occursover the western half of the continent and eastern half of the ocean (if the sector is taken to be the Northern Hemisphere) at 40" latitude for both the quadrupling and the doubling. There is also agreement in the locations of the O

196

MICHAEL E. SCHLESINGER

80 70

60 50 3 c_ t

5

40

30 20 10

0 0

60

I20

Longitude

FIG. 25. GFDL model simulation of the change in precipitation rate (millimeters/day) for quadrupled C 0 2 . Dense shading indicatesa rate decrease, sparse shading an increase between 2 and 5 mm/day, and hatching an increase larger than 5 mm/day. [From Manabe and Wetherald ( 1 980); unpublishedresults.]

regions of increased and decreased precipitation over the southeastern section of the continent. However, the increased precipitation that occurred over the southwestern coast in the doubling is replaced with a weak decrease in the quadrupling. It is evident that there are other differences between the two simulations, and that the changes shown for the quadrupling are not simply twice those forthe doubling. There is more similarity for the changes in the zonal means for the quadrupling and doubling, but, again, the former is not simply twice the latter. Figure 26 shows the geographical distributions of the change in the annual-mean precipitation rates simulated by the annual and seasonal models of Wetherald and Manabe ( 1981) for the COz quadrupling. A comparison of these results reveals several interesting similarities and differences. First, there are only increases in the precipitation rate poleward of about 45" latitude in both simulations, and there are both decreases and increases equatorward of this latitude. Second, both the annual and seasonal simulations display reduced precipitation rates in midlatitudes over the eastern ocean and western continent, as well as over the western part of the ocean in

197

MODELS OF C02-INDUCED CLIMATIC CHANGE

0

60

I20

Longitude

FIG.26. GFDL model simulations of the change in precipitation rate (millimeters/day)for quadrupled C02. Top, annual model;bottom, seasonal model. Shadingas in Fig. 25. [From Wetherald and Manabe (1 98 I); unpublished results.]

198

MICHAEL E. SCHLESINGER

low latitudes. However, the seasonal model simulates increased precipitation rates everywhere over the continent in low latitudes, as well as over the eastern ocean, while the annual model does not. These similarities and differences are reflected in the changes in the zonal-mean precipitation rate shown in Fig. 28 (upper right panel). In the equatorial region there is an increase in the zonal mean in the seasonal model simulation, in qualitative agreement with the larger increase in the simulation with the annual model. Also, both model simulationsshow increased zonal-mean precipitation rates in the subtropicsand high latitudes,while only the annual model simulation has a decrease in midlatitudes. Undoubtedly, some of the differencesin the zonal-mean precipitation rate reflect the considerably smaller changes seen in the simulation with the seasonal model compared with those for the annual model simulation (Fig. 26). This does not mean that the changes in the seasonal simulation are small throughout the year; rather, it indicates that there is cancellation due to the seasonal variation of the changes. (This is evidenced by the latitude-time distributions shown by Wetherald and Manabe, 1981 .) A similar effect was seen in the seasonal model simulation of surface air temperature in comparisonwith that for the annual model (Fig. 17). The results for the precipitation rate also indicate that there is a nonnegligible influence of the seasons on the annual-mean climate. The geographicaldistribution of the change in the annual-mean precipitation rate simulated by the seasonal model of Manabe and Stouffer ( 1980)is shown in Fig. 27. [The hydrological aspects of this simulation are also referred to in the following discussion as those of Manabe et al. (1981).]

60E

120E

I80

120w

60W

0

FIG. 27. GFDL model simulation of the change in the annual-mean precipitation rate (miIlimeters/day) for quadrupled C02. Shading indicates a rate decrease. [From Manabe and Stouffer ( 1 980); Manabe et a/. (1981); unpublished results.]

MODELS OF CO2-INDUCED CLIMATIC CHANGE

199

This figureshows the predominance of precipitation rate increases poleward of about 45 latitude in both hemispheres. This agrees with the results of the GFDL sector models (Figs. 22, 25, and 26), but not with that of the OSU model (Fig. 23), and is also apparent in the increased zonal-mean precipitation rates in high northern and southern latitudes shown in Fig. 28 (lower right panel). Figure 27 also shows regions of both decreased and increased precipitation rates between about 45"N and 45"S, with the largest changes between 30"N and 30"slatitudes, in agreement with the OSU global model and the GFDL sector models. The magnitude of the largest changes in Fig. 27 for the CO, quadrupling are smaller than those of the OSU model for doubling (Fig. 23). As suggested by the discussion in the preceding paragraph, this probably occurs because the GFDL simulation is seasonal, while the OSU simulation is not. However, it is interesting that the location and sign of several of the extreme precipitation rate changes agree in the two simulations. For example, both models simulate increased precipitation over central east Africa and decreased precipitation extending southeastward from the equator at 60 'E longitude. There are also many differences between the two simulations;for example, over most of the United Statesthe OSU model simulates an increase, and the GFDL model a decrease, in the precipitation rate. Figure 28 (lower right panel) shows that the changes in the zonal-mean precipitation rate simulated by the global GFDL model (Manabe et al., 1981) are positive almost everywhere and, in particular, there is again no large decrease near the equator as found by Manabe and Wetherald (1980). Similar changes are seen for the NCAR model simulations with both computed and fixed clouds (lowerleft panel). (The geographicaldistribution of the change in precipitation rate was not available for the NCAR model.) In contrast, the change in zonal-mean precipitation rate simulated by the OSU model with prescribed SST and sea ice (Gates et al., 1981;Fig. 28, upper right panel) is again negative almost everywhere. O

3.3.3. Comparison of Global-Mean Precipitation Rate Changesfor Doubled and Quadrupled CO,. A summary of the precipitation rate changes simulated by eight GCMs for C02 doubling and quadrupling is shown in Table IX in terms of the area-averaged precipitation rate. As in Table VII, the annual-mean change is presented in Table IX for the seasonal models of Manabe and Stouffer (1980), Wetherald and Manabe (198 l), Mitchell (1983), and Gates et al. (198 1). The changesin precipitation rate simulated by the models are also shown in Table IX as a percentage of the corresponding control precipitation rate. The latter is also shown in the table, along with its difference from the observed global-mean precipitation rate.

TABLEIX. AREA-MEAN ANNUAL-MEAN PRECIPITATION RATEAND C 0 2 - I PRECIPITATION ~ ~ ~ ~ RATE ~ ~ CHANGE

1

xco*

1XC0,-0BS"

2 x c 0 , - 1 XCO, (mm/day)

100

2 x c 0 , - I XCO, 1 x CO,

4 X C 0 2 - 1 XCO2

4 x c 0 , - 1 XCO, 1 XCO,

Model

(mm/day)

(mm/day)

Manabe and Wetherald

2.55

-0.10

0.20

7.8

-

2.58

-0.07

0.18

7.0

0.30

11.6

0.18

6.7

0.30 0.24

12.8 10.0

(%)

(mm/day)

I00

(%)

(1975) [8Jb (300, 600, N

8

-Y Manabe and Wetherald (1 980) [24] (300, 600,

1200) Manabe and Stouffer

2.69

0.04

( 1980) [20]

(300, -, 1200)

Wetherald and Manabe (1981y 1301 (300. -, 1200)

2.35 2.40

-0.30 -0.25

N

2

Washington and Meehl (1983b3)’ [331(330, 660, 1320) Mitchell (1983) [28] (320.7, 64 1.4, -) Gates et al. (1981) [25] (326, 644, 1289) Schlesinger (1983b) [321(326, 644,-)

0.12 0.10

6.0 6.5

1.01 1.02

2.83

0.18

-0.07

-2.5

-

-

2.69

0.04

-0.04

- 1.5

-0.09

- 3.3

2.13

0.08

5. I

-

0.14

Observed value of 2.65 mm/day from Jaeger (1976). These numbers correspond to those in Tables IV and VII and Figs. 29 and 30. CO, concentrations in ppm for 1 X CO,, 2 X C 0 2 ,and 4 X CO, . First row is for annual-mean insolation, second row is for seasonal insolation. First row is for predicted clouds, second row is for prescribed clouds.

3.3 2.7

0.22 0.24

3.66 3.67

-

202

MICHAEL E. SCHLESINGER

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203

MODELS OF CO2-INDUCED CLIMATIC CHANGE

The precipitation rate Pfor each control simulation is shown plotted in the lower panel of Fig. 29 versus its corresponding surface air temperature T,. The observed values of precipitation rate and surface air temperature are denoted by "+OBS." It is evident that the seasonal simulations with the global GFDL model of Manabe and Stouffer( 1980)[20] and the OSU model of Gates et al. (1981) [25] are identical and closest to the observed climate, albeit the OSU model used prescribed SST and sea ice whereas the GFDL model predicted the SST from a 68-m mixed-layer wean model and the sea

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MICHAEL E. SCHLESINGER

ice from a thermodynamic model. The seasonal and annual simulationsof Wetherald and Manabe (198 1) [30] employed the same ocean and sea ice models as did the simulations of Manabe and Stouffer [20], but Wetherald and Manabe’s simulated T,and Pare warmer and smaller, respectively,than the observed values. This increased discrepancy of the model [30] simulation compared with that of 1201 is likely, in part, the result of [30] using a sector model in contrast to the global model of [20]. When the mixed-layer Ocean and thermodynamic sea ice models are replaced with a swamp ocean in the sector model, as in [24] (Manabe and Wetherald, 1980),the temperature error increases considerably while the precipitation error decreases. A more accurate portrayal of the present climate with a swamp model, at least insofar as temperature, is given by the OSU model [32] (Schlesinger, 1983b). However, it is seen that the NCAR simulations with a swamp model for both computed and fixed clouds [33] have relatively larger temperature and precipitation errors. The results shown in the bottom panel of Fig. 29 suggest a decreasing relation between the precipitation and surfaceair temperature in the control simulations; that is, in the sense of intermodel comparison, the warmer the control, the smaller the control precipitation rate. Table IX shows that, with the exceptions of the UKMO and OSU models with prescribed SST and sea ice (Mitchell, 1983;Gates et al., 198l), all of the models simulate an increase in the global-mean precipitation rate for both doubled and quadrupled C 0 2 . The decreased precipitation rates simulated by the models in which the SST is prevented from warmingin response to the enhanced downward IR radiation from the increased CO, may occur because the surface evaporation is thereby inhibited from increasing while the atmosphere warms slightly (see Table VII). The joint effect would be to reduce the relative humidity of the atmosphere and thereby reduce at least the nonconvective precipitation. Table IX shows for doubled CO, that the NCAR model simulation with prescribed clouds (Washington and Meehl, 1983b) gives the smallest increase in the global mean precipitation rate. The increase is slightly larger when clouds are predicted in the NCAR model. The largest increase in the global-mean precipitation rate is simulated by the GFDL model of Manabe and Wetherald (1975), while the OSU model of Schlesinger (1983b) simulates an increase somewhat larger than that of the NCAR model with predicted clouds. If the simulated precipitation rate increases A P for the doubling and quadrupling are plotted versus their corresponding surface air temperature increases ATs, as shown in the top panel of Fig. 30, it can be seen that there is an increasing relation. That is, in the sense of intermodel comparison, the larger the simulated warming, the largerthe increase in the simulated precip-

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itation rate. Because the global-mean surface evaporation rate must equal the global-mean precipitation rate for an equilibrium simulation,the above relation impIies an identical relatioc between the surface evaporation increase and the surface air temperature warming. Both relations are likely the result of the increase of the surface saturation mixing ratio with temperature (due to the Clausius-Clapeyron relation between saturation vapor pressure and temperature) and, presumably, a smaller increase in the water

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MICHAEL E. SCHLESINGER

vapor mixing ratio of the surface air. The latter needs to be verified by further analysis of the models' results. If the preceding relation between A P and AT, for any particular model were linear, then the ratio of A P for quadrupling to A P for doubling would equal the ratio of AT, for quadrupling to AT, for doubling. (This assumes that A P = 0 for AT, = 0.) The results for the Manabe and Wetherald (1980) doubling and quadrupling simulations, aswell as those for the NCAR model, show that this is not the case. This indicates, therefore, that the APand AT, relation is nonlinear, at least for these models. Since A P increases with AT,, and AT, increaseswith T, of the control, AP must increase with T,. In other words, again in the sense of intermodel comparison, the warmer the control, the larger the C02-induced precipitation rate increase. For this reason the precipitation rate increase simulated by the seasonal model of Manabe and Stouffer (1980) [20] is smaller than that of the annual model of Manabe and Wetherald (1980) [24], and similarly for the seasonaland annual models of Wetherald and Manabe ( 1981) [301. As already discussed, the bottom panel of Fig. 29 shows that P of the control simulation tends to decreasewith increasing T, of the control. Since A P increaseswith T, ,there is a tendency for A P to decrease with increasingP of the control. This is substantiatedby the bottom panel of Fig. 30. (There must be separate relations for the doubling and quadrupling.) As a result, in the sense of intermodel cornparison, there is a tendency toward the relation that the warmer the control, the larger the percentage increase in the precipitation rate. 3.4. Simulated Soil Moisture Changes

3.4.1. CO,Doubling. The geographical distributions of the soil moisture change simulated by the sector models of Manabe and Wetherald (1975, 1980) are presented in Fig. 3 1. This figure shows that in both simulations the soil moisture decreased almost everywhere poleward of 35 latitude and increased over most of the continent equatorward of this l a t i t ~ d e . The ~ maximum drying of the soil occurs in a band that stretches from coast to coast centered near 35" and 40" latitude in the 1975 and 1980 simulations, respectively. This drying band in each simulation is the most prominent feature of the zonal-mean soil moisture change shown in Fig. 33 (from data over the continent only). This latter figure also reveals the weak drying in high latitudes and the larger moistening in the subtropics and tropics. The changes of soil moisturein high latitudesin the annual-meansectormodelsmay not be a meaningful indicator of hydrologic change there because this area is covered by snow (R. T. Wetherald, personal communication, 1982).

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FiG. 31. GFDL model simulation of the change in soil moisture (centimeters)for doubled CO,. Sparse shading indicates a decrease smaller than 0.5 cm, dense shading a decrease larger than 0.5 em. Left, data from 1975; right, 1980. (From Manabe and Wetherald [ 1975 (unpublished results), 19801.)

Figure 3 1 shows a soil moisture decrease over the southeastern coast in the 1975 simulation, but a soil moisture increase in the 1980 simulation. A comparison of Fig. 31 with Fig. 9 shows that there is a tendency for the tropical and midlatitude regions of minimum and maximum warming to occur where the soil is moistened and dried, respectively. Similarly,there is a tendency for the tropical and midlatitude regions of increased and decreased soil moisture to occur where the precipitation rate increased and decreased, respectively, as can be seen by comparing Figs. 3 1 and 22. Although the above relation between the changes in precipitationrate and soil moisture appears to be intuitively correct, that it must hold it is not entirely evident from the governing soil moisture balance equation

awiat = P,+ s, - E, - R

(3.1) Here W is the soil moisture, which changes in time (awlat)due to (1) the addition of water by the precipitation that fallsas rain (P,) and by the melting

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MICHAEL E. SCHLESINGER

of snow (S,) and (2) the subtraction of water by the surface evaporation (E,) and runoff (R).In equilibrium, the time-averaged (denoted by the overbar) soil moisture (@) is constant @%/at = 0), and the time-averaged soil moisture sources and sinks must balance, that is

F, +s,= E, + R When the CO, level is changed there is a change (denoted by A) in the equilibrium climate. The difference between the equilibrium climates, insofar as the soil moisture balance is concerned, is given by Eq. (3.2) as or

AF, + A3m = AE, + A R

(3.3) In other words, the changes in the soil moisture sources and sinks must balance. However, Eq. (3.3) does not give any information about the change in the equilibrium soil moisture A E That information can be obtained only as a result of the integrated effect of the soil moisture sources and sinks over time as the climate changes from equilibrium state 1 to equilibrium state 2 as a result of increased CO,. That is, from Eq. (3.1)

Consequently,it is interesting that there appears to be a positive correlation between A@and AF,. What may be occumng is the following relation. Suppose in equilibrium state 1 the snowmelt and runoff El are both zero so that by Eq. (3.2) Es,l= and @ is constant. Consider now that the CO, concentration is changed and in response P, changes to PI, A& but Es remains essentially equal to Esl. Then by Eq. (3.1) aW/dt = Pr,! AP, - gA, = AP,, and the soil moisture must change in the same direction as AP,. As this occurs, E, must change in the same direction toward F,,I AP, so that a new equilibrium can be reached in which Es,2= and g2is constant. Thus, if AF, S 0, then A @ 2 0 and AE, S 0. Since evaporation acts to cool the surface, these changes should result in ATg S 0, where Tgis the ground temperature. Figure 32 shows the geographical distribution of the change in soil moisture simulated by the OSU model of Schlesinger (1983b). This figure shows a small moistening of the soil over most of the contiguous United States, western Europe, central Asia, and northern Australia, and a larger increase in the soil moisture over the Plateau of Tibet and in central east Africa. A large drying of the soil is simulated in Canada, north Africa, and to the east of the Himalaya Mountains, and smaller drying in Mexico, central Europe to

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MODELS OF C02-INDUCED CLIMATIC CHANGE 90 N 70 N 50 N 30 N ION

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western Asia, and southern Australia. By comparing Fig. 32 with Fig. 10 it can be seen that the changes in surface air temperature tend to be negatively correlated with the changes in soil moisture; that is, regions of maximum warming often occur where the soil moisture is decreased, particularly over the deserts, while regions of reduced warming or even cooling occur where the soil is moistened. The latter is particularly evident in central east Africa, where the surface air temperature actually decreased as a consequenceofthe enhanced evaporation (not shown) over the moistened soil. A comparison ofFig. 32 with Fig. 23 also showsthe tendency for the change in soil moisture to be positively correlated with the change in precipitation rate. For example, both the precipitation rate and soil moisture decreased over north Africa, and both increased substantially over central east Africa. These correlations between ATs and A Wand between A Wand AFr are the same as evidenced by the sector model of Manabe and Wetherald (1980). The changes in the zonal-mean soil moisture over land simulated by the GFDL and OSU models are presented in Fig. 33. A comparison of the results of the 1975and 1980sector model simulationsof Manabe and Wetherald shows qualitative agreement in the tropics and subtropics, where the soil moisture increased in response to the doubled CO,, and quantitative agreement in the position and intensity of the midlatitude desiccation region. However, differences in intensity and sign are evident in high latitudes. These may reflect the different land/ocean geometries poleward of 66.5" latitude in these sector models. For the OSU model the soil moisture increasein east Africa and, to a lesser extent, in northern Australialeadsto an

2 10

MICHAEL E. SCHLESINGER 2.4

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FIG.33. The change in zonal-meansoil moisture (over land only) simulatedby three GCMs for doubledCO, . The data of Manabe and Wetherald ( 1975, curve a; 1980,curve b) are plotted symmetricallyabout the equator. Curve c, Schlesinger (1 983b). (All data are unpublished results.)

increase in the zonal-mean soil moisture in the tropics. This generally agrees with the result of the GFDL models. The zonal-mean soil moisture in the OSU simulation decreased near 30"Ndue to the desiccation in the Sahara and decreased near 40"s due to the drying in South Africa and southern Australia. There is an increase in soil moisture at 50"s that is solely due to the change in the single grid box in South America at this latitude, and an increase near 35"Npredominantelydue to the increase over the United States and Himalayas. This increase is in marked contrast to the midlatitudedrying simulated by the GFDL model. . However, both the OSU and the GFDL model simulate drying over most of the high latitudes of the Northern Hemisphere.

FIG.34. Simulation of the change in soil moisture for doubled C02given by the NCAR model with fixed clouds. Crosshatchingindicates regions where there was a decrease in soil moisture in the experiment compared to the control during all seven 30day segmentsof a 2 loday period near the end of the integration. Hatching indicates regions where six of seven segments were drier, and stippling, five of seven segments. Dashed-line contours indicate regions of increased soil moisture during at least five of the seven 30-day segments. [From Washington and Meehl(1983b).]

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The change in soil moisture simulated by the NCAR model with fixed clouds (Washington and Meehl, 1983b) is shown in Fig. 34. This figure shows the regions of persistent drying and moistening rather than the actual values of the soil moisture changes, as in Figs. 3 1 and 32. Figure 34 shows that persistent soil moisture increases (areas within dashed contours) occurred over north central Siberia and Canada, northwestern Mexico, and southwestern United States, as well as the Gulf states. Persistent soil moisture decreases (crosshatching,hatching, and stipple)are located over Australia, the Amazon River Basin, the central eastern United States, central Europe, most of Asia, and Ethiopia. A comparison of Fig. 34 with Fig. 32 indicates that the changes in soil moisture simulated by the NCAR model (with fixed clouds) and the OSU model (with computed clouds) are of the same sign in some regions (southern Australia, the Amazon River Basin, central eastern United States, eastern Canada, Sahara, central Europe,

LONGITUDE

FIG.35. GFDL model simulation ofthe change in soil moisture(centimeters)for quadrupled CO,. Sparse shading indicatesa decrease smaller than 0.5 cm, dense shading a decrease larger than 0.5 cm. [From Manabe and Wetherald (1980).]

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northeast of the Caspian Sea, and Southeast Asia), and of opposite sign in others (northern Australia, Peru, and southwestern United States).

3.4.2. CO, Quadrupling. Figure 35 shows the change in soil moisture simulated by the sector model of Manabe and Wetherald (1980)for quadrupled CO, . Comparing this figure with Fig. 3 1 for the doubling shows that there are many similarities: in particular, the general decrease of soil moisture poleward of about 35 latitude, the band of strong drying centered near 40"latitude stretching across the continent, and the general increase in soil moisture equatorward of 35 latitude, particularly along the east coast. The principal differences between the soil moisture changes induced by quadrupled and doubled CO, are found in the polar region, where there is an O

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FIG.36. GFDL model simulations of the change in soil moisture (centimeters)for quadrupled C02. (a) Annual model; (b) seasonal model. Stipple indicates a decrease. [From Wetherald and Manabe (198 1); unpublished results.]

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MICHAEL E. SCHLESINGER

increase for the quadrupling but not for the doubling (see footnote on p. 206) and along the west coast in the tropics and subtropics, where there is a drying for the quadrupling but generally not for the doubling. The latter differenceis also seen for the precipitation rate, particularly near the equator where there was an increase for the doubling (Fig. 22) but a decrease for the quadrupling(Fig. 25). Finally, it is evident that the changes in soil moisture for the quadruplingare in magnitude quite similar to those for doubling, and are certainly not twice as large. The changes in the annual-mean soil moisture simulated by the annual and seasonal models of Wetherald and Manabe (198 I) are shown in Fig. 36. Comparison of these results reveals several notable differences. First, the seasonal model simulatesa very large moistening in high latitudesin contrast to the small drying simulated by the annual model. Second, the intense midlatitude drying belt in the annual model simulation is broader and much stronger than that in the seasonal model simulation. Finally, the extremes of drying and moistening simulated by the seasonal model in the tropics, particularly over the east coast, are weaker than those simulated by the annual model. The smoothing and weakening of the changes in soil moisture in low and middle latitudes that are simulatedby the seasonal model in

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FIG.38. GFDL model simulation of the change in soil moisture (centimeters) for quadrupled CO,. The upper and lower panels (a and b) are simulations with 15 and 2 1 waves, respectively (for both longitude and latitude; see Table VI), and the left and right panels are for northern hemisphere spring (March, April, May) and summer (June,July, August). [From Manabe et ul. (198 I).]

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comparison with the annual model were also found for the surface air temperature (Fig. 17)and the precipitation rate (Fig. 26). What was not shown in those temperature and precipitation comparisons, however, is the reversal of sign and amplification exhibited by the soil moisture changes in high latitudes. As shown in the bottom panel of Fig. 37, this is the result of the large asymmetry in the seasonal variation of the soil moisture changes in high latitudes, with the large increases from October through April dominating the small decreasesfrom May through September. This summer drying is also simulated by the global seasonal model of Manabe and Stouffer (1 980) over both middle and high latitudes of Asia and North America, as shown in Fig. 38.

4. DISCUSSION

In the preceding section we compared the changesin temperature, precipitation rate, and soil moisture induced by doubled and quadrupled CO, concentrations as simulated by atmospheric GCMs coupled to two different models of the ocean. For the first, or swamp ocean model, the simulations were performed only for annual-mean insolation; for the second, or slab mixed-layer ocean model, the simulations employed the annual cycle of insolation. The comparisons that have been made between these seasonal and annual models clearly show that the annual-mean climate change simulated by a model is dependent upon whether or not the annual cycle of solar forcing is included. For the temperature and precipitation rates there is a general smoothing of the change in the annual mean due to the geographical shift of the changes with season, while this leads to a sign reversal and an intensification for the annual-mean soil moisture change in high latitudes in the sector model simulations of Wetherald and Manabe ( 1981). Moreover, it is the seasonal cycle of the C0,-induced climatic change that is likely to be of importance with respect to the impact on humanity, notjust the change in annual-mean climate. Consequently, future simulations of possible C0,induced climatic changes should be performed with seasonal models. This will require an ocean model other than the swamp model and, therefore, extended integration periods to reach equilibrium. Furthermore, such simulations should be performed with hydrodynamic ocean models so that the oceanic heat transport is included. This may be of particular importance in the calculation of the change in sea ice resulting from increased CO, and, in turn, the poleward amplification of the C0,-induced warming. The above notwithstanding, the simulations of CO,-induced climate change that have been made with atmospheric GCMs coupled with swamp

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ocean models have been useful to identify whether the projected increasing level of CO, could cause a climatic change of nontrivial proportion. The utility of these models is that they allow the ocean to participate and feed back on the climate change of the atmosphere, unlike the case when the sea surface temperature and sea ice are prescribed, and they reach their equilibrium climatic state in a relatively short time due to the absence of oceanic heat storage (see later). Furthermore, because such simulations have been carried out with different atmospheric GCMs, it is now possible to identify the model dependenceof the simulated C0,-induced climate changes. This will also be possible to do for atmospheric GCMs coupled with more realistic models of the ocean, but, as noted previously, this will require considerably longer numerical integrations. Consequently, it behooves us to make the maximum possible use of the extant simulationswith swamp ocean models, and to continue to use these models as necessary to understand further the causes for the model dependencies. In the following section we summarize the model-dependent results that are apparent from the preceding comparison, and discuss two possible causes of such model dependence. 4.1, Model-Dependent Results

The comparisons of Section 3 have shown that the simulated changes in the global-mean surface air temperature and precipitation rate induced by doubled and quadrupled CO, are model-dependent results (Table VII, Fig. 2 1, and Table IX). However, when these results are plotted as in Figs. 29 and 30, we see that in the sense of intermodelcomparison (1) the warmer the control, the larger the C0,-induced warming; (2) the warmer the control, the larger the C0,-induced precipitation rate increase; (3) the warmer the control, the greater tendency toward a larger percentage increase in the precipitation rate; and (4) the warmer the control, the smaller the control precipitation rate. These findings suggest that the differences among the models’ sensitivitiesto increased CO, levels, at least insofar as the changes in globalmean temperature and precipitation are concerned, are linked to the differences among the models’ simulations of the control climate. If this is correct, then the models’ sensitivitiesshould convergeas their simulated control climates converge, and it appears that this convergence should happen simultaneously for precipitation and temperature. Figure 29 shows that the controls for models [20] and [25]are quite close to the observed present-day climate. What is remarkable about this is not that the simulated temperatures are identical and closeto the observed, because the sea surfacetemperature was prescribed in model [25] and some tuning was performed in model

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[20] (as evidenced by the choice of mixed-layer depth), but rather that the simulated precipitation rates for both models are also identical and close to the observed value. This suggeststhat if the control temperature of (annual) model [33], for example, were somehow made closer to that of (seasonal) model [20], the control precipitation rate of model [331 would become closer to that of model [20]. Then not only should ATs of model [33] approach that of model [20], but A P of model [33] should also approach that of model [20]. If this conjecture is valid, then the differences in the models’ sensitivities lies in the differences in the models’ control simulations, not only for temperature, but for precipitation and perhaps other quantities as well. In this case it is of paramount importance to understand the reasons for the differences in the models’ control simulations. Modeldependent results are also exhibited by the zonal-mean climatic changes resulting from CO, concentrations that are doubled and quadrupled. Of particular prominence is the difference in the poleward amplification of the surface temperature warming: the GFDL models give polar warmings about five times greater than those simulated in the tropics, the OSU model about three times, and the NCAR models about three times in the Southern Hemisphere but only about one time in the Northern Hemisphere for doubled CO, . While the lower value of poleward amplification simulated by the OSU model compared with that of the GFDL model could be the result of a weaker or missing inversion due to the OSU model’s coarse vertical resolution, this cannot be the cause of the small poleward amplification of the NCAR model because it has the same vertical resolution as the GFDL model. Alternatively, the comparatively large warming of at least the GFDL sector models may be due to the temperature dependence of the surface albedo for snow and ice, which was not in the NCAR or OSU models. As shown in Table VI and discussed in Section 3.1.4, the albedo in the GFDL sector models decreased discontinuously at some critical temperature below freezing. Consequently, there should be an ice albedo feedback (temperature increase causes a reduction in ice/snow and thus a decrease in the surface albedo, which causes an increase in absorbed solar radiation and thereby a further temperature rise) at the subfreezingcritical temperature in addition to the “normal” ice albedo feedback at 0°C. It should be possible to use an RCM to test whether this gives increased warming compared with the case in which there is no temperature dependence of the ice/snow albedo. However, it is likely that this will not explain all of the poleward amplification since such amplification was also obtained by the global GFDL model (Manabe and Stouffer, 1980), which did not have a temperature-dependent ice/snow albedo (see Table VI). Although comparisons have been made for the global and zonal means without regard to the differences between the geography and orography of

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the sector and global models (see Table VI), it is not really possible to comparethe geographicaldistributions of the C02-inducedclimatic changes simulated by such widely different models. Moreover, the global model simulationperformed at GFDL includes the annual solar cycle, and we have seen that there are appreciable differences between the simulations of the seasonal and annual models with the same or similar geography and orography. Consequently,it is not desirable to compare the geographicaldistributions of the OSU and NCAR annual models with the annual-mean distribution of the GFDL seasonal model. Therefore, the only intermodel comparisons that can be made for the geographical distributions of C02-induced climate change are between the OSU and NCAR models for temperature and soil moisture; the latter can be compared only in a qualitative way because of the different presentations for these models. In the light of the differences between the global- and zonal-mean climate changes simulated by the models, we should not expect good agreement between the models’ geographical distributions of climate change. However, before endeavoring to understand the physical significance of these differences,it is essential to establish that they represent differences between the models’ equilibrium climates and that they are statistically significant. Since these issues are of fundamental importance to the interpretation of all GCM results, they are discussed further in the following section.

4.2. Time Required to Reach Equilibrium Most of the simulationspresented in the preceding discussions were performed with an atmospheric GCM coupled to a swamp ocean model for which there is zero heat capacity. Therefore,the Ocean (and land) surface is in thermal equilibrium at each time step of the numerical integration, and the time required to reach equilibrium depends only on the atmosphere. The conventional wisdom is that this time is not more than about 60 -90 days. To show that this is not the case, Fig. 39 presentsthe time evolution of the differencebetween the experiment and control simulations of the globalmean surface air and mass-averaged temperatures for the OSU model (Schlesinger, 1983b). It is the latter quantity that is of predominant importance in the assessment of whether a simulation with an atmospheric GCM/ swamp ocean model has reached equilibrium. Figure 39 clearly shows that the OSU model simulations did not reach equilibrium by day 100, and are just reaching equilibrium by day 200. The figure suggests that a conservative estimate of the time required for the global-mean temperatures to reach equilibrium is about 300 days. While 300 days appears to be sufficiently long for the global-mean temper-

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MICHAEL E. SCHLESINGER 3.0I

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FIG.39. Time evolution of the difference between experiment (2 X C 0 2 )and control ( 1 X CO,) simulationsof the global-meansurface air and mass-averagedtemperatures for the OSU atmospheric GCM/swamp Ocean model (Schlesinger,1983b). The average temperatures over the last 180 days are shown by horizontal lines.

atures in an atmosphericGCM/swamp ocean model to reach equilibrium, is this sufficiently long for the geographical distribution to reach equilibrium? To address this question, Fig. 40 shows the time evolution over days 320720 of the difference between the experiment and control surface air temperatures at four geographical locations from the OSU doubling study. These locations correspond to several of the surface air temperature extrema as can be seen by reference to Fig. 10. Although the first 320 days are not shown in Fig. 40,the last 400 days of this simulation pair (experiment minus control) do not indicate any long-term trend at these four pointsas was evident forthe global-mean temperatures during the first 200 days (Fig. 39). This suggests that 300 days is sufficient for the geographical distribution of the C0,-induced temperature changes to reach equilibrium. However, it is clear from Fig. 40, as it was from Fig. 39, that there are both low-frequency and high-frequency oscillationsin some of these temperature records. Consequently, in order to obtain a statisticallysignificant measure of the C0,-induced temperature changes once equilibrium has been reached requires averaging over long periods to reduce the noise. We will focus on this issue in the following section. So far we have considered the time required to reach equilibrium only for the atmosphericGCM/swamp ocean models. When an atmospheric GCM is instead coupled to a slab mixed-layer model, as was done, for example, by Manabe and Stouffer (1980), it is to be expected that the heat capacity of the

22 1

MODELS OF C02-INDUCED CLIMATIC CHANGE

mixed layer will require many years of integration for the ocean to achieve equilibrium. This is of course the reason for the long integrations that have been made with this type of ocean model, as shown in Table V. While it appearsthat these simulationshave been of sufficientlength to reach equilibrium [in particular, see Figs. 4 - 6 of Manabe and Stouffer ( 1980)], the length of their averaging period may not have been lohg enough to establish statistically significant changes for some variables, as is discussed below.

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222

MICHAEL E. SCHLESINGER

4.3. Statistical Significance

The statistical significance of the changes in surface air temperature, precipitation rate, and soil moisture simulated by the OSU model for doubled CO, has been determined for the global and zonal means and for the geographical distributions. For the surface air temperature and soil moisture changes, the statistical significance has been determined by the parametric time-series modeling approach developed by Katz ( 1980, 1982b) for nonindependent samples of climatic quantities that are continuous in time. In this method the variance V(1XCO,) of the population meanp( 1XCO,) of the control and the variance V(2XC0,) of the population mean p(2XCO2) of the experiment are estimated from the sample variance for each time series by fitting an autoregressive process (AR) of order p to each time series, with 0 I.p 5 5 chosen to minimize a certain statistic (the Bayesian information criterion). The estimated variances for the lXC0, and 2XC02 simulations are then used to generate the signal-to-noiseratio statistic

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where $1 XCO, ) and x(2XC0,) are the Nday sample means of the 1XC02 and 2XC0, simulations, respectively, and the subscript sim is a reminder that the value of Z is calculated from the data of the two simulations. The signal-to-noise ratio statistic 2,under the null hypothesis that the difference p(2XC0,) - p( IXCO,) is zero, has a Gaussian distribution with zero mean and unit variance in the limit as the number of points Nin the time series is indefinitely increased. The significance level Pis defined as the probability that I ZI can be larger than any particular value IZ-1 of the simulationspurely by chance, and can be computed from the Gaussian distribution. For example, for IZ,, I = 1 and 3,l ZI > IZ-1 can occur by chance with a probability P = 3 1.74 and 0.26%, respectively. The confidence interval for the change in the population means can also be calculated. A [loo( 1 - a)]% confidence interval for p(2XC02) - p( 1XCO, ) is (4.2) where Z,/, is determined such that the probability that Z > Z,,, = a/2. For example, a 95% confidence interval gives a = 0.05 and, using the Gaussian distribution, Z,: = 1.96. For the precipitation rate the statistical significance has been determined by the parametric time-seriesmodeling approach developed by Katz ( 1982a, 1983) for climatic quantities whose occurrence is discontinuous in time. The mean and variance for both the control and the experiment are esti-

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MODELS OF C02-INDUCED CLIMATIC CHANGE

mated from their individual time seriesby estimatingthe mean and variance of the amount of precipitation only on those days with precipitation (wet days), and the mean and variance of the total number of wet days. Because there are no days with negative precipitation, the probability distribution of precipitation amounts on wet days is not Gaussian. However, the probability distribution of the logarithm of precipitation amounts is approximately Gaussian. Therefore the above estimates are obtained for logarithmically transformed precipitation data rather than for the precipitation data themselves. Then the statistical significance test proceeds as given by Eq. (4.1) and the preceding description. However, the corresponding confidence interval for the transformed data that could be obtained from Eq. (4.2) would correspond to the difference of the medians, not the means, of the control and experiment precipitation rates. For this reason it is not dealt with in the following discussion. The statistical significance parameters obtained by the preceding methods for the changes in global-mean surface air temperature, precipitation rate, and soil moisture are presented in Table X. The 2.00"C change in the global-mean surface air temperature induced by doubled COz (the signal) is 32 times larger than the noise (see Figs. 5 and 39), hence the probability that this difference is due to chance is virtually zero. The corresponding 95% confidence interval is 0.12"C, that is, the probability that the simulated global-mean warming is smaller than 1.88"C or larger than 2.12"C is only 5%. Similarly, the change in the logarithmically transformed global-mean precipitation rate is significant at virtually the 0% level. However, the 0.02-cm decrease in the global-mean soil moisture is less than the noise, consequently this change could occur by chance with a probability of 72%. The 95% confidence interval of 0.09 means that the probability that the TABLEX. STATISTICAL SIGNIFICANCE PARAMETERS OF THE CHANGE IN GLOBAL MEAN QUANTITIES SIMULATED BY THE osu MODELFOR DOUBLED COz4

Surface air temperature ("C)

Experiment Control Difference Signal/noise Significance 95% confidence interval

precipitation [lo&(mm/da~)l

Soil moisture (cm)

19.87 (2.5 X 10-2)b 1.05 (3.5 X 10-3)b 3.41 (3.0 X 10-2)b 17.88 (5.7 X 10-2)b 1.00 (3.6 X 3.43 (3.5 X 10-2)b 2.00 0.05 -0.02 32.1 9.6 -0.4 0.0% 0.0% 7 I .9% 0.12 No estimate 0.09

a From Schlesinger (1983b). The results are for the last 180 days of the 72Oday simulation. The first and second numbers are the mean and estimated standard deviationof the mean.

224

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change is smaller than -0.1 1 or larger than +0.07 is 5%. Because this interval includes zero, that is, no change, the simulated change in globalmean soil moisture is not statistically significant. We now consider the statistical significance of the changes in the zonalmean surface air temperature, precipitation rate, and soil moisture. The top panel of Fig. 4 1 shows the absolute value of the signal-to-noise ratio for the zonal-mean surface air temperature. Because the values are everywhere greater than three except at the South Pole, the corresponding changes are significant at better than the 0.1% level. The bottom panel of Fig. 4 1 shows directly the significance levels for the changes in zonal-mean precipitation rate and soil moisture. This shows that the precipitation rate changes are not everywhere significant at the 10%level or less, and the soil moisture changes are significant at the 10% level almost nowhere. In other words,

MODELS OF C02-INDUCED CLIMATIC CHANGE

225

there is a degradation in the statistical significance of the changesin the zonal means compared to that of the global means, at least for precipitation rate. The statisticalsignificanceof the simulated changes is further degraded for the geographical distributions, as is evident from Fig. 42. In this figure the regions where the significance level P I 1% are unshaded, the regions where 1Yo IP I10%are lightly shaded, and the regions where P > 10%are heavily shaded. The top panel is the significancelevel of the surface air temperature change shown in Fig. 10. It can be seen that most of the simulated temperature changes are significant at below the 1% level over most of the ocean, while many of the simulated changesover land are above the 109'0 level. It is particularly noteworthy that the cooling over central east Africa is not statistically significant. The data for Tanzania in Fig. 40c show that this is the case because of the large noise in the 180-dayaverage caused by the low-frequency variations in the time series. Returning to Fig. 42, the middle panel shows the significancelevel of the (transformed) precipitation rate change shown in Fig. 23. Here it can be seen that the significancelevel is higher than 10%almost everywhere, indicating that the changes are not statistically significant. It is interesting, however, that the large increase in precipitation rate simulated over central east Africa is significant at below the lYo level. This is also true for the increased soil moisture in this region (Fig. 32), as well as for the drying in north Africa. The changes in soil moisture almost everywhereelse over the continents are not statistically significant. The point that must be taken from Fig. 42 is that it is senselessto compare the climate changes simulated by different models without first establishing the statistical significance of those simulated climate changes. Otherwise, we may simply be comparing the models' noise levels, a not very fruitful exercise. But, having performed the analysis of statistical significance as illustrated in Fig. 42, what should be done about the nonsignificant changes such as for surface air temperature in central east Africa? It may be that although the simulated change is not statistically significant, it is too small based on some other criterion to be of interest even if its statistical significance were established somehow. In this case, we can simply not continue the analysis. On the other hand, if the simulated change is of such a magnitude as to be of interest, then its statistical significance can be established by reducing the noise through extending the averaging period. How long an averaging period is required to reduce the noise, say, by a factor of two? The conventional wisdom would suggest a fourfold increase in the averagingperiod, at least for a reasonably well-behaved quantity such as temperature. To illustrate this noise reduction by increased averaging period, Fig. 43 shows the significancelevel of a 400-day period in compari-

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son with that of a 180-dayperiod for surface air temperature. As is evident, there is a reduction in the significancelevel over the oceans and most of the continents. But it is noteworthy that more than doubling the averaging period does little to improve the statistical significanceover several regions, FIG.42. The significance level (percentage) of the change in surface air temperature (top), precipitation rate (transformed) (middle), and soil moisture (bottom) simulated by the OSU atmospheric GCM/swamp Ocean model for doubled C02. The averagingperiod is the last 180 days ofthe 72O-day simulation. Dense stipple indicates a level greater than 10%; sparsestipple, between 1 and 10%;and no stipple, less than 1%.

228

MICHAEL E. SCHLESINGER

both where the signal is small and large, as, for example, over the United States and central east Africa, respectively. And, while it might be argued that this occurs for surface air temperature because of the absence of thermal inertia for the earth’s surface by the assignment of zero heat capacity everywhere, there is also little improvement for the soil moisture that has hydrologic inertia and for the precipitation rate (not shown). In summary, it may require very long integrationsto establish the statistical significance of the climatic change simulated for increased CO, levels. Even the 8-yr analysis period of the Wetherald and Manabe (1981) simulation may not be sufficientlylong to establish the statistical significanceof the geographical distributionsof many ofthe climaticquantities of interest. For example, if the statistical significanceof the simulated changes for a particular month is desired, then there are at most 248 data points in an 8-yr record. This is less than the 400 data points used to create the top panel of Fig. 43. The number of data points can of course be increased by extending the period from a month to a season or longer. But then the seasonal cycle must be removed lest it increase the variance. The analysis of the statistical significance of multiyear simulations deserves increased attention. 5 . CONCLUSIONS AND RECOMMENDATIONS

As stated in Section 1, the object of this article is to formulate and describe the current issues concerning the study of possible C0,-induced climatic change by the physical method, that is, by the use of mathematical climate models. In this article we have focused on the general circulation models and their simulations of C0,-induced climatic change because it is the geographical distribution of that change that is of importance to humanity, and because only the GCMs simulate that geographical distribution. The equilibrium simulations of eight GCMs for both doubled and quadrupled concentrations of CO, have been considered and the geographical distributions, zonal means, and global means of the C0,-induced changes in surface air temperature, precipitation rate, and soil moisture have been compared. While these comparisons reveal similarities and differences among the models’ simulations, it may be premature to draw firm conclusions at this time. The reasons for this are as follows. First, the differences between the models’ geography/orography (sector with idealized land/sea geography at zero elevation versus realistic land/sea geography and realistic orography), ocean treatment (swamp ocean versus slab mixed-layer ocean), solar forcing (annually averaged insolation versus the annual cycle), and vertical resolution (number of levels in the vertical) reduce the comparisons that can be rigorously made between the models’ simulations. Second, it may be that

MODELS OF CO2-INDUCED CLIMATIC CHANGE

229

some of the simulations were not run sufficiently long for equilibrium to have been reached by the time at which the averaging of the results was begun. Finally, it is likely that not all of the simulated climatic changes are statistically significant. The following recommendations are made to reduce these problems in comparing the GCM simulations of C0,-induced climate change: 1. The existing simulations should be extended as required to ensure that they reach equilibrium. 2. The statistical significanceof the C0,-induced climate changes should be determined for the existing or extended simulations. 3. The simulations should be extended further as required to obtain statistically significant results or to decide that the nonstatistically significant changes are too small to be of interest, even if they were subsequentlyshown to be statistically significant. 4. The comparison of this article should be expanded to include other climatic quantities, in particular the cryospheric quantities of sea ice and snow. 5. EBMs and RCMs should be used where possible, and GCMs where necessary, to perform studies to understand the causes for the differences among the existing (or extended) simulations. 6. Seasonal model simulationsof C0,-induced climatic change should be performed with models other than the GFDL model, and the comparison of this article and recommendations (1) through ( 5 ) should be repeated as necessary. 7. Seasonal model simulationsof C0,-induced climatic change should be performed with coupled atmosphere/ocean GCMs to incorporate the oceanic horizontal and vertical heat transports, and such simulationsshould be compared with each other and with the simulations made with the simpler ocean models.

ACKNOWLEDGMENTS I would like to thank Syukuro Manabe and Richard T. Wetherald of the Geophysical Fluid Dynamics Laboratory and Warren M. Washington and Gerald A. Meehl of the National Center for Atmospheric Research for making their results available to me, and for their discussions of those results. I especiallywant to thank R. T. Wetherald and G. A. Meehl for providingme with the unpublished results that appear in this article. I also want to thank Michael R. Riches ofthe Carbon Dioxide Research Division, Office of Energy Research, Department of Energy, for inviting me to prepare this article for presentation at the DOE C02 Research Conference “Carbon Dioxide, Science and Consensus,”which was held at the Coolfont Conference Center, Berkeley Springs, West Virginia, September 19-23, 1982.

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I express my gratitude to W. L.Gates for reviewing a preliminary version of the manuscript, to R. L. Mobley, D. S. Christopherson, and C. S. Mitchell for assistingwith the computations and graphics, to C. Beck, L. Riley, and N. Zielinski for typing the manuscript, and to J. Stark for drafting the figures. This research was supported by the National Science Foundation and the U.S. Department of Energy under Grants ATM 80-01702 and ATM 82-05992. REFERENCES

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Washington, W. M., and Williamson, D. L. (1977). A description of the NCARglobal circulation models. Methods Comput. Phys., 17, 1 1 1 - 172. Washington, W. M., Semtner, A. J., Jr., Meehl, G. A., Knight, D. J., and Mayer, T. A. (1980). A general circulation experiment with a coupled atmosphere, ocean and sea ice model. J. Phys. Oceanogr. 10, 1887- 1908. Watts, R. G. (1980). Climate models and C0,-induced climatic changes. Clim. Change 2, 387 - 408. Watts, R. G. (1982). Further discussion of “Questions concerning the possible influence of anthropogenic CO, on atmospheric temperature.” J. Appf. Meteorol. 21,243- 247. Weare, B. C., and Snell, F. M. (1 974). A diffuse thin cloud structure as a feedback mechanism in global climatic modeling. J. Atmos. Sci. 31, 1725- 1734. Wetherald, R. T., and Manabe, S. (1972). Response of the joint ocean-atmosphere model to the seasonal variation of the solar radiation. Mon. Weather Rev.100,42-59. Wetherald, R. T., and Manabe, S. (1975). The effects of changing the solar constant on the climate of a general circulation model. J. Atmos. Sci. 32,2044-2059. Wetherald, R. T., and Manabe, S. (1980). Cloud cover and climate sensitivity. J. Atmos. Sci. 37, 1485-1510.

Wetherald, R. T., and Manabe, S. (198 1). Influence of seasonal variation upon the sensitivity of a model climate. JGR, J. Geophys. Res. 86, 1194 - 1204. World Meteorological Organization (1977). “Report of the JOC/SCOR Joint Study Conference on General Circulation Models of the Ocean and their Relation to Climate (Helsinki, 23-27 May 1977),” Vols. I and 11. World Meteorological Organization, Geneva.

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RETRIEVAL OF WORLDWIDE PRECIPITATION AND ALLIED PARAMETERS FROM SATELLITE MICROWAVE OBSERVATIONS MIRLES. V. RAO 7223 North OIney Street Indianapolis. Indiana

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. The ESMR System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Nimbus-5 ESMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Nimbus6 ESMR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. What the Instrument Measures . . . . . . . . . . . . . . . . . . . . . . . 2.4. Factors Contributing to the Observed Brightness Temperature . . . . . . . . . . . 2.5. The Suitability of ESMR for Rainfall Estimation . . . . . . . . . . . . . . . . 3. Conversion of Brightness Temperature to Rain Rate: A Theoretical Approach . . . . . . . 4. Verification with Radar Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Verification by a Specially Designed Experiment . . . . . . . . . . . . . . . . . . . 6. Generation of Oceanic Rainfall Maps . . . . . . . . . . . . . . . . . . . . . . . 6.1. Sources of Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Steps in Writing the Program . . . . . . . . . . . . . . . . . . . . . . . . 7. Intercomparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Analysis of Rainfall Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. New Features of Global Climatology Revealed by ESMR Rainfall Studies . . . . . . . . . 9.1. Characteristicsof the ITCZ in the Pacific . . . . . . . . . . . . . . . . . . . 9.2. Previously Unrecognized Rain Area in the South Atlantic . . . . . . . . . . . . . 9.3. Bimodal Behavior and Other Features of Rainbelts in the Indian Ocean . . . . . . . 9.4. Interannual Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5. Low Southern Hemispheric Rain . . . . . . . . . . . . . . . . . . . . . . 9.6. Periodical Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10. Periodic Variations of Precipitation in the Tropical Atlantic Ocean . . . . . . . . . . . 10.1. Mainstudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2. Comparison with Other Results . . . . . . . . . . . . . . . . . . . . . . . 10.3. Models and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4. Another InterestingOscillation . . . . . . . . . . . . . . . . . . . . . . . 10.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11. Ice Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2. Bulk-Emitting Media- Effective Physical Temperature. . . . . . . . . . . . . . 11.3. Satellite Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I 1.4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12. Storm Structure Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

238 241 241 241 242 243 245 246 249 252 257 257 262 262 268 276 290 290 293 294 295 296 297 297 297 302 303 303 304 304 304 304 306 307 308 308

237 ADVANCES IN GEOPHYSICS.VOLUME 26

Copyright 0 1984 by Academic Press. Inc. All nghts ofreproduction in any form reSNed . rcnhin-t?-ntQQ?r: n

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12.2. Case Studies . . . . . . . . . . . . . . . . . . . . . 12.3. Study of Western Pacific Storms . . . . . . . . . . . . . 13. Qualitative Estimation of Rainfall Over Land Areas . . . . . . . 13.1.General. . . . . . . . . . . . . . . . . . . . . . . 13.2. Intensity and Polarization of Radiation Received at the Satellite 13.3. A Statistical Technique for Detecting Rainfall Over Land . . . 13.4. Error Analysis . . . . . . . . . . . . . . . . . . . . 13.5. Verification . . . . . . . . . . . . . . . . . . . . . 13.6. Summary and Conclusion . . . . . . . . . . . . . . . 14. Retrieval of Other Geophysical Parameters . . . . . . . . . . . 14.1. Ovewiew . . . . . . . . . . . . . . . . . . . . . . 14.2. TheSMMR. . . . . . . . . . . . . . . . . . . . . 14.3. General Principles . . . . . . . . . . . . . . . . . . 14.4. Retrieval Technique. . . . . . . . . . . . . . . . . . IS. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . 15.1. Suggestions for Further Work . . . . . . . . . . . . . . 15.2. Long-Term Goals. . . . . . . . . . . . . . . . . . . 15.3. Summary. . . . . . . . . . . . . . . . . . . . . . Appendix. Explanatory Notes . . . . . . . . . . . . . . . . A. 1. General Notation . . . . . . . . . . . . . . . . . . . A.2. Grid Cell Legend . . . . . . . . . . . . . . . . . . . A.3. Method of Averaging . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . .

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1. INTRODUCTION

Two of the major parameters in atmospheric and hydrospheric investigations are rainfall and sea ice. Satellite-borne microwave radiometers provide a unique method of estimating these variables on a global scale. In particular, the Electrically Scanning Microwave Radiometer (ESMR) has proved of value in the quantitative determination of oceanic rainfall from satellite data. The objective of this article is to describe how significant meteorological, hydrological, glaciological,and oceanographic information has been extracted from satellite microwave observations (especially those from ESMR) and to discuss the limitations, as well as the vast capabilities,of such observations in future investigations. Since oceans cover more than two-thirds' of the earth's surface, a better estimation of oceanic precipitationand sea ice will enhance substantially our knowledge of global climatology. Some of the following contributions may be expected: 1. Indications of the annual variability of precipitation and of possible long-term changes in climate.

' According to Rand McNally (1982), the total area of the earth is 196,940,400 square miles, whereas the total land area (includinginland water and Antarctica) is 57,280,000 square miles. The remaining sea area works out to be 139,660,400 square miles, which is 70.9% of the earth's surface.

SATELLITE-DERIVED PRECIPITATION PARAMETERS

239

2. A better understanding of the shorter time scale motion of rainbelts such as the Intertropical Convergence Zone (ITCZ) and the monsoonal type of precipitating systems. 3. An understanding of the large amount of latent heat released over oceans; consequently, an improvement in the description of the energy balance not only over the oceans, but over all areas influenced by the global general circulation. Progress in the field was very slow until the mid 197Os, even with the availability of visible and infrared satellite imagery. In the presatellite era (i.e., before 1960), the dependence was mainly upon island reports and relatively infrequent ship observations. Island reports are not truly representative of surrounding oceans because of the orographic modification of airflow and the radiative heating effect. Additionally, there is a sampling problem, because it is not uncommon to find vast oceanic areasthat have too few islands in them. Precipitation measurements from ships are unsatisfactory due to platform instability and sea-spray problems. Therefore, most ships do not even carry precipitation gauges. Radar did, to a certain degree, provide some insight into the complex nature of rainfall structureand variability. But serious problems with attenuation and calibration limit the accuracy of radar for determination of precipitation, and its fixed location and limited range make it unsuitable for global-scale measurements over the oceans. This position improved to some extent in the early years of satellite observations (the 1960s) due to better coverage. However, improvement was not significant because the derivational methods were indirect and generally qualitative. Many ingenious schemes for indirect estimation of rainfall have been devised in the recent past. They fall generally into two categories: those based on the relationship between rainfall and cloud observations (extent, type, etc.) and those based on indirect statisticalrelationships. A few examples may be cited here. Barrett (1970) worked out a rainfall coefficient based on cloud cover and cloud type. Follansbee (1973) modified Barrett’s aerial statisticstechnique, concentrating upon rain-producing clouds (Cb, Ns, and Cu congestus) to the exclusion of others. Another approach that has been developed relies upon the relationship of reflected solar brightness of satellite pictures and rainfall rates. Martin and Suomi (1972) found that brightness regions correlate well with large radar echoes. Similarly, Woodley et al. (1972) concluded that the relationship of brightness area and precipitation depends upon whether the cloud system is young and vigorous or old and decaying. (It is also worthwhile to remember that the brightness enhancement technique suffersfrom the dependence of sun angle and viewinggeometry as well as from signal saturation and signal degradation.) Griffith et al.

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(1976, 1978) adapted this technique to estimate rainfall from geosynchronous visible and infrared imagery. Scherer and Hudlow ( 197 1) utilized cloud height and area derived from High-Resolution Infrared Radiometer (HRIR) data to estimate precipitation,cirrus contamination being ignored. One more noteworthy life history scheme is that of Scofield and Oliver (1977), which attempts to monitor storm rainfall utilizing enhanced infrared satellite pictures. Yet another indirect method adapted by Tucker ( 1 96 1) and followed by Reed and Elliott (1973) to estimate precipitation in the northern Pacific involves developing quantitative relationships between current weather (ww) and rainfall amounts (RR) in the present weather reports from land stations and extending these relations to infer rainfall from ship weather reports. A good review of most of these efforts is contained in Barrett and Martin (1981). The ESMR provides the first direct approach to the problem. The main advantage of the system is its selective response to liquid water in the atmosphere. Furthermore, because the emissivity of water tzW in the vicinity of 19 GHz is low (approximately0.4)and inversely proportional to the thermodynamic temperature (T, ), whereas the brightness temperature as observed by ESMR is proportional to the product E , T,, the oceans provide a convenient uniform background to the satellite-borne radiometer. Although admittedly there are certain limitations, ESMR seems to be a better system than any other available at present for estimation of rainfall over oceans. Over land areas, the problem becomes complicated. The emissivity of land is generally high and greatly variable, depending upon various factors such as soil type, moisture content, vegetation cover, temperature, frost conditions, snow cover, etc. However, upwelling radiation at 37 GHz emerging from hydrometeors is essentially unpolarized, whereas emission from a land surface, when viewed obliquely, is polarized when the dielectric constant is increased (e.g., by adding moisture). Therefore, with the use of two polarizations, it is possible to obtain some qualitative information over dry land. Such limited information would be useful over areas where conventional methods fail to provide adequate data. The microwave radiometer is a useful tool in detecting sea ice through clouds and in the polar night. This capability results from the high emissivity of sea ice. With the use of multiple wavelengths (emissivity being a function of wavelength) and dual polarization, there is a potential for unfolding certain ice surface parameters. Attempts are currently in progress to retrieve other geophysical parameters (such as surface wind over oceans, sea surface temperature, atmospheric water vapor, and liquid water content) from satellite microwave data. The Scanning Multichannel Microwave Radiometer is a major system in this context. The degree of success attained to date using this and other systems is discussed in Section 14.

SATELLITE-DERIVED PRECIPITATION PARAMETERS

24 1

2. THEESMR SYSTEM Treatment is given in this section only to those aspects of the ESMR system that are pertinent to understandingand utilizing intelligentlythe data received from spacecraft. The instruments carried on Nimbus-5 and Nimbus-6 satellites are briefly described. An introduction to microwave radiometry follows, stressing aids to the interpretation of ESMR data. A discussion of to what extent ESMR is suitable for estimation of precipitation is also included.

2.1. Nimbus-5 ESMR

The ESMR system aboard Nimbus-5 had a microwave receiver sensitive to radiation from 19.225 to 19.475 GHz (except for a 10-MHz gap in the center of the band). The antenna beam scans perpendicular to the satellite velocity vector beginning 50" to the left of nadir in 78 steps to 50" to the right, every 4 sec. The half-power beam width of the antenna is 1.4" at nadir. At the 50" scan extreme, the beam width remains 1.4"downtrackbut degrades in the crosstrack direction to 2.2". This corresponds (for a nominal orbit of 1 100 km) to a resolution of a 25-km circle near nadir, degrading to an oval 45 km downtrack X 160 km crosstrack at the ends of the scan. The polarization is linear, parallel to the satellite velocity vector. Since the orbit-to-orbitcoverageoverlapsat the equator, completeglobal coveragecan be obtained every 12 hr. (It may, however, be mentioned here that at large scan angles, data are subject to slight error; consequently, if only high-quality data are desired, there would be a small gap between orbits.) Onboard calibration is achieved by using warm (instrument ambient) and cold (cosmic background) inputs to the radiometer.

2.2. Nimbus-4 ESMR The main differences between the ESMR systems on Nimbus-5 and Nimbus-6 are in wavelength, polarization, and scanning geometry. The underlying physical principles are similar. The Nimbus-6 ESMR system operates on a center frequency of 37.0 GHz (bandwidth 250 MHz). The antenna beam scans ahead of the satellite with a constant earth incidence angle of 50" along a conical surface every 5.3 sec. In azimuth, the beam positions are from 35 to the right in 7 1 steps to 35 to the left. The angular resolution in elevation varies from 0.84" at the extremes of scan to 1.Oo at the midscan position 36 (when the beam is viewing O

O

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straight ahead). In azimuth, the resolution varies from 1.17 at the scan extremes to 0.95” at beam position 36. Expressed in terms of distance on earth, the resolution is approximately 45 km downtrack and 20 km crosstrack. Two separate radiometer channels are used to receive both the horizontally and vertically polarized components of microwave radiation. At 37 GHz the resolution is good and the sensitivity is high, but there is a saturation problem. Furthermore, due to the small width of the image area (compared to Nimbus-5 ESMR) there are substantial coverage gaps in the tropics. the gaps decreasing farther away from the equator. O

2.3. What the Instrument Measures The ESMR measuresthe intensity of microwave radiation it receives. It is convenient to put forth the data in terms of brightnesstemperatures, because at microwave frequenciesand at temperaturesprevailing in the earth’s atmosphere, the intensity or radiance becomes simply proportional to the equivalent blackbody temperature, as shown in the following relations. Planck‘s function for the intensity of radiation (energy flux per unit wavelength per solid angle) emitted by a blackbody is

W,(T)= (2hc2/A5)/(ek’lclT - 1)

(2.1)

with the usual notation. At wavelengths of the order of a centimeter and at temperatures in the range 200-300 K (hc/kAT -c l), the Rayleigh-Jeans approximation becomes applicable and so the intensity reduces to W J T ) = (2ck/a4)~ which (holding wavelength constant) may be written simply as

(2.2)

(2.3) W ( T )= aT a = 2ck/A4 being constant when A is held constant. When the emitting source (at thermodynamic temperature T) is not black, the radiance diminishes to W’(T ) = a T,

(2.4) where TBis the equivalentblackbody temperature, which in this case may be called the “brightness temperature.” Furthermore, by definition, the emissivity of the nonblackbody at physical temperature T is E=

W’(T ) /W(T )

Therefore

E = T ~ T or T , = E T

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It must be mentioned that the preceding arguments are not valid when it is not appropriate to apply the Rayleigh- Jeans approximation,in which case, reverting to Planck's formula, we may write

W' = ( 2 h ~ ~ / A ~ ) / (1)e ~ ~ / ~ ~ e

(2.7) where T, is the generalized equivalent blackbody temperature. Solving for T, we obtain T, = For large I

T,

-

hc/kA ln(2hcZ/A5 W'

+ 1)

(A4/2ck)W' = W'/a = TB

(2.9)

where TB is as previously defined.

2.4. Factors Contributing to the Observed Brightness Temperature From simple considerations of microwave radiative transfer it is obvious that the brightness temperature TB(H)recorded by ESMR aboard a satellite at height H above the earth's surface may be expressed as

+ z(1 - ~ , ) ] e -+?

TB(H)= [T,E,

r

ThF(h)dh

(2.10)

where T, is surface thermodynamic temperature and surface emissivity/ absorptivity, so that brightness temperature of the surface is TB( S )= T,E, . q i s the temperaturecharacteristicofthe radiation incident on the surface, of which a fraction Ges is absorbed and the remainder Ti(1 - E,) reradiated. The term within the brackets corresponds to the total radiance at height h = 0. e-? is the transmittance of the atmosphere from surface to satellite altitude. Evidently, 7 = J fy sec 8 dh, where y is the absorption coefficient and 8 the viewing angle measured from vertical. In Eq.(2.lo), the entire term [TSes q(1- ~,)]e-'corresponds to the total radiation from the surface attenuated by the atmosphere up to satellite height H. This the physical temperature of the atmosphere at height h. F(h) is a weighting function such that the product ThF(h)Ah= AT,, the fractional contribution to the brightness temperature registered by ESMR due to the net radiance from the atmospheric layer between heights h and h Ah. F(h)involves ( 1) E h , the emissivity at height h, which in turn is a function of yh, the local absorption coefficient, as well as (2) e-$ (where z'= J f y sec 8 dh), the transmissivity of the portion of the atmosphere above height h up to satellite level, or, in other words, the distribution of y all the

+

+

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way between levels h and H . The entire integrable term of Eq. (2.10) is the total contribution to the satellite-observed TB from all the atmosphere intervening between the surface and the radiometer. The main variables affecting TB as measured by ESMR are the surface emissivity and associated temperature, as well as the profile of the atmospheric absorption coefficient (7)and the profile of temperature. Ground emissivity may vary from 0 to 1, but the factor by which the absolutetemperature of the surface vanes is much smaller. Similarly, the fluctuation in atmospheric temperature profile is usually less significant than the fluctuation in the absorption coefficient. At microwave frequencies, the main constituents responsible for absorption are oxygen, water vapor, and liquid water. Ice crystals are essentially transparent. The spectrum of atmospheric oxygen (Meeks and Lilley, 1963; Lenoir, 1968) has no prominent peaks in the vicinity of 19 or 37 GHz. There is certainly a component in this frequency range, due to the pressure broadening of oxygen resonances at other frequencies (principally 50- 70 GHz). However, since the mixing ratio of oxygen is substantially constant, and the absorption coefficient is only weakly dependent on temperature, the effect is no more than a constant offset to the observed values, So, when consideringvariations in TB,oxygen is not a problem. This leaves only water vapor and liquid water. The latter may be subdivided into nonraining clouds and rain. (Although the difference is only in drop size, there is important variation in resultant attenuation.) Thus, for purposes of further discussion, it is possible to express the radiometric brightness temperature in the following form: (2.1 I ) TB = A ( E , ) +.L(V) +h(LS) +h(LL) where E, is surface emissivity, I/ is water vapor in a vertical column of unit cross section, L, is liquid water in small nonraining droplets in a unit vertical column, and LL is liquid water in large drops (or rain) in a unit vertical column. A consideration of the relative magnitude of terms and their variations reveals that the dominant contributors to TB and (more importantly) its fluctuations are surface emissivity and rain. Let us first consider terms involving V,L,, and LL. The attenuation due to water vapor (Staelin, 1966) at 19 GHz is of the order 0.0 1 dB/km. The attenuation due to nonraining cloud droplets (approximately 0.2 g/m3) is of the order of 1.0 dB/km. Obviously, of these three terms, the final one involving LL is the most vital. Turning our attention to E ~ the , value of emissivity over land is large (typically 0.9) and highly variable. This can cause fluctuations in the observed TB of the order of 100 K, virtually masking all the other factors, thereby rendering the simple (single-frequency and polarization) ESMR unsuitable for rain estimation.

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245

Over oceans, the situation is totally different. The emissivityew ofwater is low and nearly constant (approximately 0.4)In the vicinity of 19 GHz, E , 0: l/T,,,, where T, is the thermodynamic temperature of water. This is fortunate because the brightness temperature observed by the satellite depends on the product of E, and T,. Therefore, the sea provides a uniform background against which the raining atmosphere can be viewed. Salinity variationshave a negligible effect on the resultant TB. The only factorworth considerationis surface wind, which, when strong, may produce foam coverage (Nordberg et al., 197 1) affecting the radiometric brightness temperature. The brightness temperature observed over oceans may therefore be written TB= A B(FF) C V + DL, E(L,) (2.12) Here, A , a constant, is 125 K (Wilheit, 1972). B(FF) is a function of the surface wind speed FF. This will be discussed later in more detail. C is a constant of normal value 7 K/g per cm2(Wilheit, 1972). D is a constant of normal value 300 K/g per cm2(Wilheit, 1972). E(L,) is a function of liquid water of large drop size or rain. The order of magnitude of B(FF)can be judged from the followingconsideration (Nordberg et al., 1971). The change in sea surface brightness temperature is zero for wind speeds less than 7.5 m/sec, and for higher wind speeds may be expressed approximately as2 B,(FF) = 1.27(FF- 7.5) (2.13)

+

+

+

Thus, a value of FF = 12 m/sec (i.e., 25 mph) elevates the surface (unattenuated) brightness temperature by only about 5 K. The magnitude of the next term of Eq. (2.12)for a typical value of Vof 3 g/cm2is 2 1 K. The succeeding term, for a value of LL= 0.02 g/cm2,attains a magnitude of 6 K. The final term, for a rain rate of 10 mm/hr (0.4 inches/hr), has a magnitudeof 100 K. Thus, the last term is by far the largest of the variable terms. When we consider the range of variations (which really concerns us far more than the absolute magnitude), the differences are even more pronounced. The last, or rain, term becomes at least one order of magnitude higher than the other terms in the expression for brightness temperature.

2.5. The Suitability of ESMR for Rainfall Estimation From the foregoing discussion it is apparent that the use of ESMR for the estimation of rainfall over land is extremely difficult, mainly because of the large and highly variable value of surface emissivity. However, further Foam generation begins at wind speed 7-7.5 m/sec and affects emissivity significantly at higher speeds.

246

MIRLE S. V. RAO

research with dual-polarization and multiple-frequency microwave radiometers may be expected to lead to improved qualitative precipitation estimates. The problem is less complex over oceans. Undoubtedly there are limitations, some of which, such as atmospheric water vapor and nonraining clouds, have already been discussed; others, such as anomalous mode, saturation, field of view, freezing-level height, etc., will be dealt with at appropriate places in the succeeding sections. These problems are not insurmountable, and with good experience and proper interpretive skills valuable quantitative results can be obtained. A critical and comprehensive survey of presently available methods of estimation of oceanic precipitation, many of which were mentioned in Section I, reveals that every method without exception is fraught with problems. It also becomes clear that ESMR remains, as of today, the best method of estimating rainfall over the oceanic areas of the world.

3. CONVERSION OF BRIGHTNESS TEMPERATURE TO RAINRATE:A THEORETICAL APPROACH In order to interpret the ESMR readingsquantitativelyin terms ofrainfall, it is important to obtain a calibration curve. A good first approach would be a theoretical model, the results of which are experimentally verified as far as practicable. But, as any meteorologist of experience knows too well, because of the idealization and parameterization that are involved even in the best of models, in actual complex atmosphericsituations skilled adjustments will be necessary before applying model results. Wilheit et af.(1977) proposed a model for calculating microwave radiative transfer in raining atmospheres, taking into account both absorption and scattering. Their reasoning, in simplified logical terms so that it may be grasped by the average atmospheric scientist, is outlined below. The reader who desires to contend with a more rigorous treatment is referred to the original paper. The model assumes essentially a Marshall - Palmer dropsize distribution with slight modification. The modifications to this distribution and the other main assumptionsinvolved in the model are as follows: 1. An additional 0.5 km of cloud water droplets (with a concentration of 25 mg cm-2) just beneath the freezing level. 2. Considering fall velocity, and modifying the rain rate in accordance with Waldteufel ( 1973)and Foote and du Toit (1969). Ifp represents drop radius, V(p) the fall velocity, and N( p) dp the number density of drops with

SATELLITE-DERIVED PRECIPITATION PARAMETERS

radius between p and p

247

+ dp, the computed (modified) rain rate is

R' = /(4n/3)Plv(P)N(P) dP

(3.1)

the value of V(p)at sea level being Vo(p)= 965 - 1030e-*2p(Waldteufel, 1973) and the value at any other level (height h) being given by the Foote and du Toit relationship Atmospheric density at sea level Atmospheric density at height h 3. A lapse rate of 6.5"/km (the surface temperature being adjusted as a parameter of the calculation for five different freezing levels, i.e., 1, 2, 3, 4, and 5 km). 4. A vertical profile of humidity, with the relative humidity value at 80% near surface, increasing linearly to 100% near freezing level. The relative humidity above the freezing level is assumed to be that given by the 1962 United States Standard Atmosphere. 5. The reflectivity at the surface of the ocean according to Fresnel relations (Jackson 1962). For purposes of determiningthe angulardistribution, the ocean surface is assumed to be infinitely rough or Lambertian (Born and Wolf, 1975). The rationale for amving at a relationship between rain intensity and brightness temperature may be expressed succinctly as follows: 1 . For a given rain rate R,the Marshall-Palmer relationship

N( p) dp = Noe-&'p

dp

(3.3)

gives a particular drop-size distribution, i.e., a number density depending on the radius p. 2. The interaction of a plane electromagnetic wave with a dielectric sphere was first treated by Mie (1908) and was discussed in the context of cloud and rain droplets by Gunn and East (1954), yielding the equations m

o,,,

=

-(A2/2n)Re C(2n + l)(a,+

b,)

(3.4)

lb,12)

(3.5)

1

m

oca= (A2/2n)X ( 2 n 1

+ l)(ja,12 +

These enable the extinction and scattering cross sections of a liquid droplet, pCxtand pe, to be evaluated from the magnetic and electric 2" pole coeffi-

248

MIRLE S. V. RAO

cients a,, and b, . The radiation is obtained by summing the radiation from the magnetic and electric 2" poles. 3. Combining these cross-sectional values with the Marshall - Palmer number density N( p), the absorption and scattering coefficients Yabs

= N ( P ) % s ( P dP )

(3.6)

,Y

= N P ) % a ( P ) dP

(3.7)

and and angular distribution can be readily obtained. 4. These coefficients may now be substituted in the general equation of radiative transfer (Chandrasekhar 1960): dTB(e)/ah

= Yabs T(h) - Yext

TB(8) + ?sea

I"

TB(es)F(8,

8s)

sin es

des

(3.8)

Here, the change in radiance in thedirection 8, i.e., aTB(8)/dh,is expressed as the algebraic sum of three terms, viz. (1) the emission of the medium of thermodynamic temperature r h ) , (2) the change due to extinction, i.e., both absorption and scattering away from the specified angle, and (3) the increase in radiance in the 8 direction due to scattering from other angles [F(8,8,) is the angular distribution of scattering integrated azimuthally and normalized such that F(8, 8,) sin 8, dos= I]. 5. The equation of radiative transfer [Eq. (3.8)] is now solved iteratively for TB, first ignoring scattering so that intermediate values of T, could be computed for a number ofangles independently, and then using these values for the scattering term and recomputing the final values of TB iteratively until satisfactory convergence (to better than 0.1 K) is attained. Brightness temperaturescorresponding to any rain rate value are obtained as described above, separately for the five freezing levels 1,2,3,4, and 5 km. The results for 19S GHz are presented in graphical form in Fig. 1. In the graphical representation,the brightness temperature (TB)is plotted on a linear scale, whereas the rain rate (R)is plotted on a logarithmic scale. Notice that the brightness temperature versus rainfall relationship is highly nonlinear. TB increases very slowly from 0 to 1 mm/hr and then rapidly to a maximum (saturation) between 20 and 50 mm/hr depending upon the freezing level. At higher rain rates TB decreases slightly due to strong backscattering. It may also be noticed that for a given brightness temperature, rain rate increases greatly as the freezing level decreases. At a typical value of TB = 225 K, for example, the magnitude of R decreases by 100%from 5 to 4 km, and a further 75% from 4 to 3 km, 40%from 3 to 2 km, and 15% from 2 to 1

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249

RAINFALL RATE Imm/hr]

FIG.1. Calculatedbrightness temperature (at 19.5 GHz) as a functionof rain rate for freezing levels of 1 - 5 km.

km (an overall 800%from 5 to 1 km). This dependence on freezing level is unrealistically excessive. The method of parameterization by adjusting the surface temperature (model assumption 3) may be largely responsible. In any event, this is one of the major weaknesses of the model, as will be discussed later. 4. VERIFICATION WITH RADAR DATA

One of the means by which the calibration curve deduced from the model may be checked is radar. Radar measurements of precipitation are subject to a number of uncertainties. 1. The estimation depends upon an empirical 2 and R relationship of the form 2 = uRb, where 2 is the radar reflectivity factor defined by Z N O D , No being the number of hydrometeors of diameter D per unit volume (Rayleigh scatteringbeing assumed). The values of u and b have been found to be within a wide range, from 200 to 600 and 1.5 to 2.0, respectively, depending upon the type of precipitation involved. 2. There are possible differencesin the precipitation sampled by radar and that which reaches the surface. 3. For intercomparison purposes, there are additional drawbacks. Because of the high temporal and spatial variability of rainfall, simultaneous radar observations with satellite overpasses are needed, but these are rare. 4. Most radars are ground based and have a limited range. Thus, over

250

MIRLE S. V. RAO

oceans they are capable only of detecting coastal rainfall. It is precisely in this region, i.e., close to the coast, that satellite-borneESMR observationsare subject to a serious error due to an earth-location problem, as will be discussed later. Nevertheless, a few of the measurements by the WSR-57 meteorological radar at Miami, Florida (a system that has been calibrated by gauge measurements) were found to be within 5 min of certain Nimbus-5 overpasses and could be used for intercomparison. A detailed description of the radar and the interpretation of its readings are given by Wiggert and Ostlund (1975). Briefly, WSR-57 has the followingcharacteristics:operating frequency, 2.96 GHz (10.3 cm); range, 200 km; and resolution (1) in azimuth, 2" and (2) in range, 1.2 km. The return signal in each range-azimuth bin is converted into rain rate (expressed in tenths of millimeters/hour) by means of a statisticallyderived relationship. Four cases were found in which data taken by the Miami meteorological radar observations and Nimbus-5 overpasses were near simultaneous (to within 5 min). The dates and times are given in Table I. In each of these cases, in order to compare the data, the WSR-57data were first plotted on a map base. The ESMR data were then overlaid using the sharp change of brightness temperature in these data at the coastline of Florida as well as at the Lake Okeechobee coast to ensure proper alignment. An example is shown in Fig. 2, in which the crosshatchedregion is a sample ofthe radar data and the crosses indicate the location of the beam centers for the ESMR data. The two ovals surrounding one of these crosses in the figure show the approximate 3-dB (half-power) and 1S-dB antenna gain contours for a typical ESMR beam position. While averaging the radar data around an ESMR beam center (in the effort to match the two types of data), full weightage was given to radar data within the 1S-dB gain contour, whereas half-weightage was given to the data between the 1.5- and 3.0-dB contours. Because ESMR data at high scan angles as well as those close to land are subject to error, only those beam positions with scan angles less than 40" and beam positions with centers more than 50 km from the coast were considered. On all four dates TABLE I. DATESAND TIMES OF RADAR OBSERVATIONS

Date

Time (GMT)

June 20, 1973 July 07, 1973 June 24, 1974 June 25, 1974

1632 1610 1605 1705

25 1

SATELLITE-DERIVED PRECIPITATION PARAMETERS

\ 80"W

82"W 27"N

-

+

+ 26"N

+

-+

-26"N

+

+ +

+

+

+ +

+

+

+ 25'N-

+

+ I 82"W

+

i

81"W

r250N

80"W

FIG.2. A portion of the WSR-57 radar data for the June 25,1973, case. The crosshatching shows the range and azimuth resolution of the radar. Isopleths of 0,5, 10, and 50 mm/hr rain rate are indicated. The crosses (+) indicate the locations of the beam centersofthe corresponding Nimbus-5 ESMR data. The 1 S-dB (inner oval) and 3-dB (outer oval) contours are shown for a typical ESMR beam position.

listed in Table I, a tropical maritime atmosphere was assumed over the Florida coast, with the freezing level at 4 km. When all the available data were processed in the above manner and tabulated prior to plotting, it was found that most of the points (more than could be plotted retaining clarity) fell within the rain intensity range of less than 1.5 mm/hr, and very few (all ofwhich were plotted) fell within the range of higher rain rates. The number available in the range 1.5 mm/hr and higher was so few that it is questionable whether the comparison is really meaningful. Figure 3 includes the final results. The solid line in the figure is the theoretical calibration curve for the 4-km freezing level. The solid points are the radar rain rates versus ESMR brightness temperature. The two dashed lines represent departures of a factor of 2 in rain rate, or 1 mm/hr, whichever is greater. It has been argued that the departure of the radar observations from the theoretical curve is mainly to the right of the curve and below it, supposedly because within the ESMR field of view local intense rain would contribute

252

MIRLE S. V. RAO

150

-

I

I

I

0.1

-

I

1

I 10 RAINFALL R A T E (mmlhr)

I

U

100

1000

+,

FIG.3. Intercomparison of brightness temperature curves: 0, radar data; trailer experiment data; solid line, theoretical curve; dashed lines, departures of 1 mm/hr or a factor of 2 in rain rate, whichever is greater.

(because of ESMR saturation) a smaller value of brightness temperature than is due, toward the average brightness temperature of the entire field of view. Although the reasoning is plausible, the quality of the observations is not good enough to prove it. With the same reasoning, the bias (in departures to the right and lower side) should be expected to increase at higher rain intensities, but there is little evidence of this in the figure. In reality, radar has many shortcomings as a tool for rain estimation (as pointed out earlier in this section), and the scatter of radar observations is too large to draw very definite conclusions. The utmost that could be stated with genuine confidence is that there is broad agreement between the radar observations and the theoretically based ESMR calibration curve.

5. VERIFICATION BY A SPECIALLY DESIGNED EXPERIMENT A second method of verification, which helped greatly in making necessary modification to the calibration curve derived from the theoretical model, is described in this section. This was a speciallydesigned experimental arrangement set up at NASA/Goddard Space Flight Center (GSFC) in Greenbelt, Maryland.

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253

Two microwave radiometer receivers were installed in a trailer, each being connected to a rectangular pyramid-shaped (standard gain) antenna horn mounted on the top of the trailer. The main parameters ofthe two receivers are listed in Table 11. The axes of the pyramidal horns were pointed upward at an angle of 45 '. The antennas were mounted with the electric-fieldvector horizontal so that the E-plane antenna gain pattern caused a minimum variation in elevation throughout the field of view. The horns were shielded from direct rain by a wooden (dog-kennel-type) housing open on one side. They were protected against wetting from blowing rain or against otherwise reaching saturation with moisture by using a plastic wrapping across their apertures and a blower device that directed a stream of dry air across the plastic wrap. The receivers were connected in turn for a period of 15 sec each ( 1 ) to their antennas, (2) to a reference cold load, and (3) to a reference warm load. The output from this radiometer system was fed to a small computer that worked out both of the mean brightness temperatures for the 15 sec when the radiometerswere sensingthe radiation from outside through the antennas (separately at 19.35 and 37.0 GHz) and printed out the results at intervals of 0.8 min. Two rain gages of different types were used to measure the rainfall intensities simultaneously with the radiometer observations. The first was a conventional tipping-bucket rain gage located on the top of the trailer adjacent to the antenna housing. The number of times the bucket tipped was registered by a counter and recorded on the computer alongside the radiometer readings. The second was the recently developed (Raymond and Wilson, 1974) electronic rain intensity gage with a fast (1-sec) response time, located at a horizontal distance of 77 feet (23.5 m) from the radiometers, in the direction of the antenna beam. In this type of rain gage, measurement is made of the ratio of the resistance of rainwater flowing in a trough between two electrodes spaced along the trough (R,) to the resistance of the same rainwater in a chamber of fixed geometry (Rz). Since R, varies as the resistivity divided by the cross-sectional area of the flowing water, while R, varies only as the resistivity, the ratio RJR, is independent of resistivity and varies directly as the cross-sectional area, i.e., it is proportional only to the TABLE11. RADIOMETER RECEIVER PARAMETERS

Frequency Wavelength Bandwidth E-Plane beam width H-Plane beam width

Radiometer I

Radiometer I1

19.35 GHz 1.55 cm 400 MHz 6.5" 9.0"

37.0 GHz 0.81 cm 400 MHz 6.5"

9.0"

254

MIRLE S. V. RAO

rate of flow. An alternating current is used in the measurement to avoid electrolyticaction. The frequency of the current is varied as the conductivity of the water (by means of a multivibrator) because the frequencyrequired to prevent electrolysis increases with the conductivity of the electrolyte. Furthermore, the capacitative coupling that disturbs the measurements of resistance is significant when the conductivity is low, and so the reduction in frequency at low conductivity becomes an important compensating factor. Prior to the installation of the rain gage, it is essential to calibrate it, and this was done in the laboratory using a varistalic pump (Monostat Corporation), with controlled rates of flow up to 900 ml/min. The flow generated by the motor was increased in steps from 0 to 820 ml/min (i.e., rain rate 0 to 75 mm/hr for a funnel aperture of 36 inches) and then decreased in steps back to 0. The gage voltage was sampled for a minute at each step and was plotted against the flow rate to arrive at the rain gage calibration curve. The rain gage was then placed inside a barrel and a funnel 36 inches (91 cm) in diameter was supported over it. The output terminals were connected to a recorder (Brush Recorder Mark 280) in which two pens with different sensitivities traced records of the output voltages on moving chart paper. The recorder system accuracy was 0.5%. For purposes of synchronization between the temperature records on the computer printout and the rain rate records on the chart, at frequent intervals simultaneous time marks were made on the two charts. With this experimental arrangement, data were coIlected at NASA/GSFC during the period June through September, 1974. On all occasions on which data were collected, the freezing levels, as interpolated from the National Weather Service freezing-level charts, for the trailer location (i.e., 39.0°N,76.8"W, and for the times ofthe experiment)were within 0.5 km of 4 km. With the aid of the laboratory calibration curve, the voltage records on chart paper were translated to rainfall rates. The brightness temperatures of the 19- and 37-GHz radiometer systems were tabulated against rainfall inten~ities.~ For this purpose, only those occasions when rain rate and temperatures were sensibly steady for 2 min or more were considered (in order to avoid excessive scatter in the data). The observations were then grouped under 18 categories according to rainfall rate intervals (10 categories at 1 mm/hr intervals from 0 to 10 mm/hr, 5 categories at 2 mm/hr intervals from 10to 20 mm/hr, 2 categories In actual practice, it was found that the sensitivity of the tipping-bucket rain gage (with a 36-inch funnel) is better at low rainfall rates, but at moderate and high rainfall rates the performance of the electronic rain gage was superior both with respect to sensitivity and to response time. Accordingly, weightage was given more to the tipping-bucket readings in the low-intensity range, and more to the electronic rain gage in the other intensity ranges.

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255

RAINFALL RATE (mm/hr)

FIG.4. Trailer experiment results( 19-GHzsensor looking up). At each point, the height and width of the error bars represent two standard deviations in the corresponding dimensions. The line is the theoretically calculated curve.

at 10 mm/hr intervals from 20 to 40 mm/hr, and 1 category greater than 40 mm/hr). In each category, the mean and standard deviations were calculated separately with respect to brightness temperature (TBand oTB) and rainfall (Rand oR). The diagramsin Figs. 4 and 5 show the results for 19and 37 GHz, respectively. In both diagrams, in the error bars, the vertical lines are equal to two standard deviations in brightness temperature (20,)~ and the horizontal lines to two standard deviations in rainfall (20,).

-L

12 14 16

i a 20

A

A _L--L

22 24 30 32 34 36 38 40 42 44 4 48 50 RAINFALL RATE (mrn/hr)

FIG. 5. Trailer experiment results (37-GHz sensor looking up). Explanation same as in Fig. 4.

256

MIRLE S. V. RAO

It is possible to convert the brightness temperature as observed from the trailer (sensor looking up at 45") to the brightness temperature as observed by the satellite (sensor looking down at or near nadir) from theoretical considerations of radiative transfer. A simplified first approach would be as follows. Consider the atmosphere as a rectangular block of transmissivity e-7scceand assume that we are looking at a region of emission at 273 K (freezinglevel) from the ground upward at 45" elevation. Since the absorptivity (= emissivity) = I - crJZ, the brightness temperature as observed on the ground would be

TB= ( 1

- e-'&)273

(5.1)

Therefore t = -(

l/&) In( 1 - Td273)

Looking vertically downward from the satellite, if it is assumed that the thermodynamic temperature of the ocean background is T, = 293 K and that the emissivity of the background water surface for I9 GHz is E, = 0.4, i.e., the product cwTw= 12 1 K, then the brightnesstemperature, as observed by the radiometer in the spacecraft, is

TB= ( 1 - e-')273

+ I2le-'+

[(I - e-7JZ)273(1- 0.4)]e-'

(5.3)

The first term on the right-hand side of Eq. (5.3)represents the emission from the atmosphere (rain level), the second term the ocean surface emission

160 -

SATELLITE-DERIVED PRECIPITATION PARAMETERS

257

(attenuated by the atmosphere), and the third term the ocean surface reradiation (also attenuated by the atmosphere). So, from the temperature observed from the trailer, the values of z can be worked out from Eq. (5.2); substituting these in Eq. (5.3) the corresponding values of TBfor Nimbus geometry could be evaluated. A table of equivalent temperatureswas thus constructed for all 19-GHztrailer temperaturesfrom 10 to 272.75 K in steps of 0.25 K. Individually observed trailer temperatures were all then converted to equivalent satellite brightnesstemperatures, and with these the statisticalanalysis previously described was repeated. By this means, another curve for downward-looking brightness temperature versus rainfall rate was obtained (Fig. 6). Here again the error bars are two standard deviations, i.e., 2a, (vertical) and 2aR(horizontal). It will be seen from this curve that the threshold temperature for the detection of rainfall by 19-GHzsatellite-borneESMR seems to be 172 K. It is also apparent that the curve becomes sensibly parallel to the x (rainfall) axis at approximately 22 mm/hr, i.e., saturation is reached at about that intensity.

6. GENERATION OF OCEANIC RAINFALL MAPS Rao et al. (1976) attempted to map oceanic rainfall on aglobal scale, using the calibration curves derived theoretically from the model as a guide, adjusted suitably to fit in with the more dependable experimental curve discussed in the Section 5. In actual practice, several problems4developed and various sources of error became apparent, necessitating many corrections and modifications. Final adjustment had also to be made by generating preliminary global maps and comparing them with available global-scale rainfall data. 6.1. Sources of Error The possible sources of error that came to light are categorized below and their relative importance pointed out. 6.1.I . Problems Associated with the Model. (1) The model assumes a Marshall - Palmer distribution, which works best for rain from stratiform These problems were faced and solved as best as could be done within certain logistic (financial and time) constraints, before the first Global Oceanic Rainfall Atlas was published. The remedial measures that were taken, as well as those that could be adopted with better logistic support, are outlined in this section.

258

MIRLE S. V. RAO

clouds. The deviations of the actual atmospheric conditions from this assumption led to error (though this may not be serious). (2) Above the freezing level, there is supercooled water, which is ignored in the model. This calls for investigation and appropriate modification of the model. (3) Melting snow causes a similar problem not taken care of in the model, calling for parallel action. (4) It was realized in actual practice that the model is grossly oversensitive to 0" isotherm height. When preliminary maps were generated, it was found that if rainfall in the middle latitudes was normalized,the tropics (high freezing-level zone) would go dry; conversely, if tropical rainfall was normalized, the middle and high latitudes (lower freezinglevel zones) would be extensively under deluge. (The degree of dependence of the calibration curves on freezing level was discussed in Section 3.) Part of the reason for this could be supercooled water above the freezing level, apart from model assumption 3 mentioned in Section 3. The error is serious, and a thorough investigation of its causesand of the necessary modifications to the model would be well worth the effort. Empirical correctionshad to be applied prior to the production of the Global Oceanic Rainfall Atlas in order to achieve a realisticdistribution of rainfall. It was found most expedient (instead of again adjusting the calibration curves for this purpose) to make an equivalent change by feeding the computer a modified freezinglevel pattern over the globe, with 0°C isotherm height at 4 km (instead of 5 km) over the equator, sloping to 2 km (instead of 0 or 1 km) at high latitudes. ( 5 ) Water vapor in the atmosphere is ignored in the model. This results in a minor error, which was discussed in Section 3. Simplified correction was effected by lumping it together with similar errors under low-level noises [see (18) in Section 6.1.41. (6) Nonraining clouds not being considered adequately cause again another minor error (treated in Section 3). This was also dealt with for purposes of correction within logistic limits under low-level noises [see (18) in Section 6.1.41. (7) Variation of sea surfacetemperature and sea surfaceemissivity is a nearly negligible source of error (see Section 2), which again is taken care of lumped under low-level noises [see (1 8) in Section 6.1.41. 6.1.2. Problems Inherent in the ESMR System. Mainly because of the characteristic inadequate isolation of a femte switch in the ESMR system and also because of the effects of antenna side lobes, the calibration temperatures depend upon scan number. In the system recording design, corrections for these sources of error have been applied (Wilheit, 1972), but the result is far from perfect. Probably there has been some overcorrection. Preliminary study by Kidder ( 1976), as well as the experience of the author, indicates that when the average of day and night observations is considered, the brightness temperature for a given rain rate increases slightly for beam

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259

positions away from nadir. For oblique view close to extremes of scan, the error is not small. Furthermore, there is a day- night variation possibly due to the thermal effect of the solar cycle on the orbiting ESMR system. These problems form a subset, giving rise to minor errors, and they were treated as follows. (8) A small correction was applied to remedy the linkage of brightness temperature to scanning angle by approximatingthe dependence to a linear variation. Satellite brightness temperatureswere decreased to the extent of 7% of the nadir angle in degrees (i.e., 0-2.1 K linearly from nadir to 30"). (9) Owing to the unreliability of data near extremes of scan, beam positions with oblique view greater than 30"were ignored (i.e., only data from scan positions 14-64 were taken into account for rainfall computation). (10) The day- night variation in calibration leads to offsets in brightness temperature in the range 2 - 4 K and may therefore be regarded as a minor-to-average error, which is worth investigation. However, when generatingmaps of precipitation temporally averaged for day and night over several days, the error becomes negligible, and was therefore ignored. There are other problems (some serious) associated with the ESMR system. These are now considered. ( I 1) With the passage of years, there is probably a change in calibration due to deterioration of the ESMR instrument. Periodical recalibration (at least once a year) becomes necessary when the instrument gets old. As the Global Oceanic Rainfall Atlas was produced with data of only the first 2 yr of ESMR, this was not done. (12) The field of view of Nimbus-5 ESMR is relatively large (resolution of 25 X 25 km at nadir, changing to 45 X 160 km at scan extremes). The radiometer registersthe average brightness temperature over the field of view. This can be translated through the calibration curve to the rain rate similarly averaged over the footprint area, without any error as long as the relationship between TBand R is linear. It is only when aZT&3R2 is appreciably different from zero that error arises. (For discussion in this context, Fig. 1 is unsuitable, not only because the rainfall is plotted on a logarithmic scale, but also because of the limitations of the model referred to in Section 6.1.1. Figure 6 is really more satisfactory.) For the 19-GHz ESMR, the curvature of the TB-R curve becomes appreciable only above rain rates of 12 mm/hr, and even here the curvature is too small to cause large error. Furthermore, in areas of synoptic-scaleand mesoscale rainfall, the beam partial-filling problem rarely arises, and it is only in the case of convective rainfall that the footprint problem needs to be considered at all. Everything considered, the problem is so trivial for large-scale study that it was ignored while producing the atlas maps. (1 3) Nimbus-5 ESMR reaches saturation (see Fig. 6) at about 22 mm/hr, causing a problem similar to the preceding one. Even when the beam is fully filled, if there is a heavy

260

MIRLE S. V. RAO

rain (exceeding 22 mm/hr), partially or wholly in the field of view, the registered brightness temperature will be somewhat lower than it should be without the problem. This was remedied by normalizing the calibration with the aid of preliminary maps and corresponding ground truth on a large spatial scale. However, this saturation problem, of average significance, is worth some attention. In this connection, a possible overestimation of rainfall in the low precipitation range due to a curvature in the opposite direction of the calibration curve may also be examined. (14) During certain periods the ESMR system gets into anomalous mode due to an intermittent problem with a few instrumental parts. This gives rise to a very seriouserror in the mapping process. Improvement of the ESMR system to eliminate this defect is advisable. The alternative of removing abnormal brightness temperatures through computer programming is by far less satisfactory. During the production of the atlas, Nimbus-5 ESMR pictorial displays were individually examined, and all data at times when the system was in anomalous mode were carefully removed. (15 ) Uncertainties in the instrumental count and other factors leading to the measurement of TB (brightness temperature) need to be looked into. 6.1.3. Data Collection Errors. (16) The geographical referencing process is imperfect and gives rise to an earth-location error. This error is not systematic and has no directional bias, but is totally random. When a preliminary ESMR rainfall map of a huge island such as Australia was generated, spurious high rainfall was observed uniformly along the entire coast, extending to about 50 miles away from land (due to smearing of the high brightness temprature of land over the adjoining sea). In order to eliminate this problem, all data within 1 (- 110 km) of coastlines and all significant islands were disregarded in preparing maps for the atlas. This is a minor-to-averagesource of error that could be amelioratedby improving the geographical referencing. ( 1 7) Ephemeris errors occur at times, the conespondence between the time registered and the geographical location becoming disrupted. This does not happen often, but is a minor-to-average source of error worth investigationand elimination. One type of ephemeris error that was detected and corrected after the publication of the atlas arose during orbits being executed at midnight (Universal Time). The change of the clock from 2359 to 0000 brought about an offset, which threw out ofgear the readings for the remainder ofthe particular orbit (until the orbit number changed).

6.1.4. MiscellaneousProblems. ( 18) Low-level noises attributable to factors such as surface wind, water vapor, and nonraining clouds give rise to a minor source of error. Surface wind, when strong, produces foam coverage

SATELLITE-DERIVED PRECIPITATION PARAMETERS

26 1

over oceans, the effect of which was dealt with in Section 2. Water vapor, nonraining clouds, and sea surface temperature [see (4), (5), and (6) in Section 6.1.11 were also discussed in Section 2. The estimated research effort to eliminate/minimize these errors is of average magnitude. While producing the maps for the atlas, a low-level cutoff was imposed, ranging from 1 mm/hr in the tropics to 2 mm/hr in high latitudes. (The main reason for adopting a greater cutoff at higher latitudes is the higher frequency of storms.) (19) Sampling only twice a day (near local noon and near local midnight) is inadequate and causes error. A second orbiting satellite would enable four observationsto be made per day (at 6-hr intervals). Preliminary statistical study shows that this would reduce the samplingerror a great deal, TABLE111. SOURCES OF ERROR

Category'

Source of error

Problems associated with the model 1 Deviation from Marshall-Palmer distribution Supercooled water above freezing level 2 3 Melting snow Oversensitivity to 0°C isotherm height 4 5 Water vapor 6 Nonraining clouds 7 Variation of surface temperature and emissivity Problems inherent in the ESMR system 8 Scanning angle variation Invalidity at oblique view greater than 30" 9 10 Day-night variation in calibration Change in calibration due to instrument deterioration 11 12 Partial beam filling 13 Saturation 14 Anomalous mode 15 Uncertainties in T, measurement Data collection errors 16 Earth location Ephemeriserrors (includingGMT midnight problem) 17 Miscellaneous 18 Low-level noises 19 Sampling 20 Defects in software 21 Unidentified

Estimated effect on datab

Estimated effort to minimize ero+'

2 2

2 2

2 3 1-5 1-5

3 5 3 1-5

Numbers refer to discussion in Section 6.1. Number scale for estimates ofeffect as well as effort: 1, insignificant;2, minor; 3, average; 4, major; 5, serious. a

262

MIRLE S. V. R A O

bringing it within acceptable limits for most purposes. While generatingthe atlas maps, this deficiency was ignored, relying upon large spatial and temporal averagesto keep the error within limits. (20)In the development ofthe complex computer program to generate precipitation maps, errors creep in from time to time. These defects in software need to be avoided with vigilance, but when they do creep in, require detection, by careful testing at every stage, and removal. (21) It is not presumed here that all possible sources of error have been discovered and discussed. Other as yet unidentified errors may come to light and will have to be dealt with as is appropriate. It is, however, hoped (in view of degree of agreement, on a large spatial and temporal scale, of the results with ground truth) that no seriouserrorsremain other than those discussed above. Table 111 contains a list of the souces of error along with an estimate of the seriousnessof the effect on the data set and also an indication of the degree of effort needed to eliminate/minimize each of the errors. In this table, both effect and effortare gauged on a scale from 1 to 5 , l being least (or minor) and 5 most (or major).

6.2. Error Analysis Estimation of the overall error may be accomplished in different ways. It is conventional to attempt to parameterize a certain number of the souces of error and compute error rates according to well-known formulas. A second approach (which may be combined with the first or adopted by itself) is to make case studies. A fairly good example of error analysis (combined method) is given in Section 13. However, experience indicates that efforts on these lines, although impressive, prove in general of little value. Most often the procedures are seen in the last analysis to be subjective, both with respect to the number of parameters chosen and to the mode of parameterization. Furthermore, in the field of meteorology, case studies that produce striking results in one or two cases fail too often when applied to other cases. A third method of assessing errors by making comparative studies based on large spatial- and temporal-scale data is preferable in many situations. This approach was deemed best in the present instance. The procedure and the results obtained are presented at length in Section 7.

6.3. Steps in Writing the Program The main steps in writing a computer program for rain mapping are briefly as follows:

SATELLITE-DERIVED PRECIPITATION PARAMETERS

263

1. Read in TB-R relationships at suitable intervals in appropriate matrices separately for five freezing levels. 2. Read in the freezing levels for grid blocks over the globe, for four different seasons in appropriate matrices. 3. Read in a matrix showing land or water state (say, in a 1 latitude X 1 ’ longitude grid) over the entire globe. 4. Read in the observationtimes for the period to be depicted on the map. 5. Bypass the observations (during the map time) made over land. 6. For each observation during the mapping period, read out TB. 7. Apply scan angle correction to each observation. 8. From the time of observation, check season of the year. 9. Check the freezing level from geographical position and season. 10. Convert TBto rain rate. 11. Cut off at lower limit. Adjust at upper limit. 12. Skip data close to land (say, within 1 of coast or island). 13. Arrange precipitation data in a 1 latitude X 1 longitude grid over the entire seaflake area. 14. Average the data over 4” latitude X 5 ” longitude grid blocks. 15. Print (on world map outline). O

O

O

Proceeding in the manner outlined above, global oceanic rainfall maps were generated for the period December 11, 1972 (ie., the day of Nimbus-5 launch), through the end of February, 1975, on a weekly, monthly, and seasonal basis. Annual averages for 1973 and 1974 were also worked out and ~ h a r t e d .A~ specimen of each of the time period averages (weekly, monthly, seasonal, and annual) appears in Figs. 7,8,9, and 10,respectively. As these maps are intended for the study of oceanicrainfall, care was taken to preserve the major oceans unsplit by choosing the vertical edges of the map to cut the African continent in half. The entire set of maps was published as National Aeronautics and Space Administration Special Publication-410, under the title “Satellite-Derived Global Oceanic Rainfall Atlas (1973 and 1974)” (Rao et al., 1976). The Appendix at the end of this article contains explanatory notes needed to interpret the numbers on the maps reproduced from the atlas. Of the maps presented in the atlas, only the annual and monthly maps and just a few of the weekly maps could be analyzed within the time frame for publication. Some examples of the maps so analyzed appeared in color in the first few pages. However, all the maps generated prior to publication were printed without isopleths or analysis in the appendices to the preliminary edition of the atlas. Data for later years have been collected and partially processed; these will be included in later editions of the atlas.

264

2 Q 0

.c

265

266

3 'R * 0;

d G

268

MlRLE S. V. RAO

7. INTERCOMPARISON There is a scarcity of precipitation data acquired over oceans that can be considered reliable enough to make intercomparison. Two means of intercomparison exist, but each has limitations. First, large-scale precipitation maps may be used, such as those put forth by the Weather Service of the Federal Republic of Germany. These maps, produced on a monthly basis, are available for periods concurrent with ESMR maps. [Under the same category we may consider global climatologicalannual precipitation charts such as the one produced by Dr. Rudolph Geiger, of which an updated version has been published by the German Weather Service in Bericht No. 139( Jaeger, 1976).] Second, localized radar data [of which a good example is Global Atmosphere Research Project (GARP) Atlantic Tropical Experiment (GATE)data] may be used, but are limited both in spatial extent and in times concurrent with ESMR observations. The first means of intercomparison is imperfect because it is based on ship observations (which suffer from platform instability and sea-spray problems) and island reports (which do not correctly represent the surrounding ocean because orographic and radiative heating effects modify air flow). The second means (radar) is unsatisfactory because of its dependence on the Z-R relationship, with its indeterminate coefficients, its limited range, and other shortcomings as discussed in Section 4. Of the two means, the author has a personal preference for the first because it enables large spatial- and temporal-scale comparisons, i.e., the scale on which ESMR data yield valuable results. Figure 1 1 depicts the January, 1973,map produced from ESMR data; Fig. 12 is a map for the same month, produced by the Weather Service of the Federal Republic of Germany. The rain regions are marked by letters A, B, C, and D on the maps. It may be seen that there is a broad agreement between the two maps. Both maps indicate the principal areas of rainfall to be the southwest Indian Ocean (A), the mid-Pacific Ocean (B and C),and the North Atlantic Ocean (D). When the magnitudes are examined, these are also found to be comparable. In the southwestIndian Ocean, the rain rate in the maximum rain area (A) is 0.4 mm/hr in the ESMR map, compared to a monthly total of 450 mm in the German map. When converted to commensurate units, a mean rain rate of 1 mm/hr corresponds to a normal (30 days) monthly total of 720 mm. [The total value changes a little, proportionately with the number of days in months (28, 29, or 31 days); thus, in January, an average intensity of 1 mm/hr would match a monthly total of 744 mm.] In the mid-Pacific, the rain rate figures in the rainy area are (B) 0.8 and (C) 0.6 mm/hr (ESMR); the corresponding German map monthly total values are (B) 700 and (C)470 mm. In the North Atlantic in the region marked (D), ESMR gives a rain rate of 0.5 mm/hr while the German map shows a monthly total of 200 mm.

FIG.1 1. Analyzed satellite-derived rainfall map for January, 1973. Explanation same as in Fig. 7. Rain regions: A, southwest Indian Ocean; Band C, mid-Pacific; D, North Atlantic.

FIG. 12. Monthly precipitation map for January, 1973 (Weather Service of the Federal Republic of Germany). Rain regions denoted as in Fig. 1 1.

SATELLITE-DERIVED PRECIPITATION PARAMETERS

27 1

Figures 13and 14show the ESMR July, 1973, and the German July, 1973, monthly maps, respectively. In this case, the comparable areas are Bay of Bengal (A), the China Sea (B), and the South Pacific (C). In these areas, the ESMR rain rates of 0.6,0.5, and 0.4 mm/hr roughly conform to the equivalent German monthly totals of 250,300, and 300 mm. (See Table IV for a little more clarification.) Figure 15 is the annual ESMR map for 1973. No parallel map from conventional data is available for comparison. However, comparison was made with the climatological annual precipitation chart produced by Dr. Rudolph Geiger (Fig. 16). There is a striking similaritybetween the regions of intense rainfall in both maps. The coincidencewith respect to regions of very low rainfall is even better. The regions of maxima are (a) eastern Indian Ocean and Bay of Bengal (magnitudes 0.2-0.3 mm/hr compared6 to an annual total of 2000- 3000 mm), (b) China Sea (0.4 mm/hr compared to a total of 2000 mm), (c) equatorial Pacific and Atlantic (i.e., the Intertropical ConvergenceZone; magnitude 0.2 mm/hr compared to a total of 2000 mm), and (d) Gulf of Alaska (0.2 mm/hr as against a total of 2000 mm). Furthermore, we have the following extraordinarily similar regions of minima: (1) the northwest Arabian Sea and the west coasts of (2) California, (3) north Africa, (4) Australia, ( 5 ) South America, and (6) South Africa. The ESMR rain rates in these regions were < 100 mm, often as low as 50 mm. Table IV summarizes the intercomparison. It must be conceded that the differencesare not small, although there is general agreement. However, it would be precipitatejudgment to conclude that ESMR results are unreliable, based on the comparison. It is not beyond possibility that the fault lies to a greater extent in the conventional observations. The effort to improve the quality of ESMR-derived data should, nevertheless, continue. Prior to the production of the atlas, no reliable radar data to enable largescale comparison could be found. For this reason, and also because of the unsatisfactory nature of radar in general for estimation of rainfall as discussed earlier, no intercomparison with radar data was attempted, apart from the attempt to verify the calibration curves with the aid of Miami, Florida, coastal radar (described in Section 4). Subsequent to the publication of the atlas, however, radar data over a limited region of the Atlantic Ocean became available during the period of the GATE experiment? In a comparative study, Austin and Geotis (1978) concluded that relative to radar, ESMR underestimates rainfall by nearly 40%, and that the variability of the difference between radar and ESMR estimates is large. The merits An average rain rate of 1 mm/hr corresponds to a normal annual total of 8760 mm (8784 mm in a leap year). In the course of GATE, rainfall measurements were made by radar as well as by ships’rain gages; large differences are found among these data.

N

4

N

FIG.13. Analyzed satellite-derivedrainfall map for July, 1973. Explanation same as in Fig. 7. Rain regions;A, Bay of Bengal; B, China Sea;C, South Pacific.

h, -4 W

FIG.14. Monthly precipitation map for July, 1973 (Weather Service of the Federal Republic of Germany). Rain regions denoted as in Fig. 13.

214

N 4

P

FIG.15. Analyzed satellite-derivedrainfall map for 1973. Regions of maxima and minima (letters and numbers, respectively)are discussed in text.

FIG. 16. Mean annual precipitation map (from Prof. Rudolph Geiger). Regions of maxima and minima as in Fig. 15. See text for details.

276

MlRLE S. V. RAO

TABLE IV. INTERCOMPARISONS Total Rain Region

Rain rate ESMR (mm/hr)

ESMR (mm)

Comparison maps (mm)

Month January, 1973

German A

B C D

0.4 0.8 0.6 0.5

298 595 446 372

450 700 470 200

Month July, 1973 A

B

0.6 0.5

C

0.4

446 372 298

250 300 300

Year January - December, 1973

Climatological a b C

1-6

0.2-0.3 0.4 0.2 <0.5

1752-2668 3504 I752 <438

2000- 3000 2000 2000

< 100

and limitations of this study will become clearer after a perusal of the related subject matter in Section 10.

8. ANALYSISOF RAINFALL MAPS Many types of analytical studies are possible with the maps. For example, the weekly maps are suitable for investigation of rainbelts such as the Indian and Southeast Asian monsoons. The monthly maps aid in understanding the changes in the position and intensity of the ITCZ from month to month. The seasonal maps are valuable for examining the changes in the general circulation of the Northern and Southern Hemispheres. The Southern Hemispheric region deserves special attention because there is a lack of conventionaldata. The annual maps are appropriate for the study of interannual variability. A detailed analysis of the rainbelts was attempted separately in each of the three major oceanic areas of the world, namely, the Pacific, Atlantic, and Indian oceans. It is in order to facilitate this type of study that the vertical

SATELLITE-DERIVED PRECIPITATION PARAMETERS

277

edges of the maps were chosen to cut the African continent in half, preserving the major oceans unsplit. The analysis followed two main lines of approach. The first was through the computation of zonal averages. The mode of analysis and general results are described briefly in this section. Specific results of interest are discussed in the next section. Figure 17 represents the meridional profile of zonally averaged rainfall rate in the Pacific Ocean. This was amved at from the monthly, seasonal, in the and annual maps by the simple process of adding up the rain rates (R) grid cells in the Pacific Ocean lying alongthe same latitude (i.e., horizontally) and dividing by the total number (n)of the oceanic grid cells; l/n 27 R is then plotted against the mean latitude of the horizontal row of grid cells. Proceeding thus for all the latitudinal rows of grid cells in the Pacific, the meridional profile of zonal rainfall is obtained, separately for each month, season,and year. From Fig. 17 it may be seen that the ITCZ showsa definite displacement to the north of the equator in nearly all months of the year. Figure 18 portrays the meridional profiles of zonally averaged rainfall in the Atlantic Ocean. The ITCZ is perhaps more sharply defined in the Atlantic than in the Pacific. Furthermore, the seasonal movement of the ITCZ is more pronounced in the Atlantic. In April, the ITCZ lies a little to the south of the equator but moves north in later months, assuming its most northerly position (about 6"N of the equator) around October, thereafter returning southward. The polar front near 40"N is seen to be active from September through November, judging by the prominent peaks. The complementary polar front in the Southern Hemisphere is noticeably weaker; however, it manifests its greatest activity from March through May. It may also be observed that the precipitation in the Northern Hemisphere is on the whole much larger than in the Southern Hemisphere. (This may be partially due to the effect of the larger land masses in the Northern Hemisphere affecting the air flow.) Figure 19 represents the zonally averaged rain rates in the Indian Ocean. Two main crests may be distinguished in the tropics between latitudes 20"N and 20"s. (These two are apart from a third smaller crest in the extratropical region around 40"s.) It is worthy of note that the crest at northern latitudes grows at the expense of the crest in the southern low latitudesas the monsoon advances, and vice versa as it retreats. This will be discussed at greater length in the next section. Figure 20 represents the global integrated picture, i.e., the meridional profile of zonally averaged rainfall over all the oceanic areas of the world. Even on this large spatial scale we perceive that the Northern Hemisphere receives more rainfall than the Southern Hemisphere, that the Northern Hemisphere polar front is more active than its Southern Hemispheric coun-

JAN

OEC

FEB

(1972)

RAINFALL RATE (MM/HR)

SEASON

MC74FEE 75

FIG. 17. Zonally averaged rain rate (satellite derived) versus latitude-Pacific Ocean. Dashed lines represent curves based on inadequate data.

600N 400N 20ON

0

20°S 40°S

1973

609 FEE

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

JAN

FEE

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

JAN

FEE

JAN

#

3

2 a

600N 40'N mON

0 200s

N

4 \o

400s

197$,,,0s

RAINFALL RATE (MM/HR)

DEC

SEASON EASON SEASON ?€ASON YEAR oEC73- MAR-MAY JUN-MIG SEP-NOV JAN-MC

SWON Mc74FEE 75

FIG. 18. Zonally averaged rain rate (satellite derived) versus latitude- Atlantic Ocean. Dashed lines represent curves based on inadequate data.

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

DCT

NOV

DEC

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

JAN

FEB

SEASON S E W YEAR SEWN S E W DEC72- MAA-MAV JUN-NJG SEP-NOV JAN-MC 1973 1973 1973 FEE73 1973

SEWN SEASON SEWN SEWN YEAR M C 7 3 - MAR-MAV JUN-AUG SEP-NOV JAN-OEC FE874 1974 1974 1974 1974

DEC (1972)

RAINFALL RATE (MM/HR)

SEWN FEE 75 M C 74

FIG. 19. Zonally averaged rain rate (satellite derived) versus latitude-Indian Ocean. Dashed lines represent curves based on inadequate data.

60" 40ON 20°N 0

200s

40%

1973m

JAN

FEE

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

SEASON SEASON SEASON SEASON YEAR D E C ~ Z -MAR-MAY JUN-AUG SEP-NOV JAN-DEC FEE73 1973 1973 1973 1973

197ToS -- JAN

FEE

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

NOV

DEC

~SEASON ~ 7 MAR-MAY 3SEASON JUN-AUG SEASON SEP-NOV SEASON JAN YEAR -DEC

600N

40'"

k I-

MON 0

20°S c

M O S

FEE 74

JAN

FEE

RAINFALL RATE ( M A R )

DEC

SEASON

(Ig7*)

DEC74FEE 75

1974

1974

1974

1974

FIG.20. Zonally averaged rain rate (satellitederived)versus latitude-all oceansover the globe. Dashed lines representcurves based on inadequate data.

282

MIRLE S. V. RAO

terpan, and that the ITCZ has a seasonal displacement, attaining its northernmost position around October. The second method of analysis consisted in examining phenomena of particular interest with the aid of maps with better resolution in space and/or time. Two instances of this type of study will be briefly outlined here, leaving more detailed discussion to a later stage. 1. The southwest monsoon affects the lives of millions of people in India. The weekly maps for May 29 through June 14, 1973 (Fig. 21a-c), portray the onset of the monsoon; the two weekly maps for October 8 through October 2 1, 1973 (Fig. 22a and b), show the retreat of the monsoon. According to the Indian Official Weather Summary, in 1973the date ofonset of the monsoon to peninsular India was June 10, and the date of withdrawal from the main part of that region was October 16. This closely corresponds to what may be seen on the ESMR-derived maps. Thus, this preliminary analysis suggests the desirability of producing maps with better resolution (daily maps with grid cells of 1 ' X 1 ") to monitor the advance of the monsoon from the Indian Ocean toward the Southeast Asian land mass and to predict its onset and later development. 2. In the Pacific Ocean, interannual comparisonsbetween two successive austral summers, the first from December, 1972, through February, 1973, and the second from December, 1973, through February, 1974, reveal that they are meteorologically very different seasons. In January, 1973, intense rainfall occurred over a wide region along the equator and to the south of it in the mid-Pacific (Fig. 23a), whereas in January, 1974, the same region was relatively very dry (Fig. 23b). The extensive and heavy rain in the first of these years must be associated with pronounced convection and a large-scale rising current. Considering this region of precipitation at the same time as the dry region of subsidence off the coast of South America leads to the concept of a possible important local variation in the Hadley cell circulation. This could very likely be linked with the relaxation in upwelling, commonly known as the El Niiio phenomenon, which has a disastrous effect on the plankton and fish in the waters of the Pacific off the west coast of South America. To understand this phenomenon and its serious economic consequences,it would be of help to investigate the precipitation in sections of the Pacific with better resolution maps ( 1 X 1 on a weekly basis). Two first-attempt maps (monthly maps for January, 1973, and January, 1974) generated with ESMR data are reproduced in Figs. 24a and 24b. These are very preliminary and need improvement in several respects. Nevertheless, they demonstrate the feasibility of really useful investigation with proper utilization of ESMR data. Suggestions for further work are advanced in Section 15. (Mention may be made here that it is feasible to produce a 1 X 1 map of the entire globe, O

O

O

O

FIG.21. (a-c) Satellitederived Indian Ocean rainfall maps for three successive periods during May 29 through June 14,1973, showing the onset of the Indian monsoon.

N 00

P

FIG.22. (a and b) Satellite-denvedIndian Ocean rainfall maps for two successiveperiodsduringOctober8 through 2 1,1973, showing retreat of the monsoon.

P

Q

a

b

FIG.23. ESMR-derived Pacific Ocean rainfall maps for (a) January, 1973 (El Niiio year), and (b) January, 1974 (non-El Nifio year).

286

287 FIG.24. (b) Pacific Ocean rainfall map for January, 1974 (non-El Niiio year).

288

h)

00

00

f

Q'

5

c-

e,

0

E

O

ma

d

3W

d

v;

N

r;:

FIG.25. (a) Quadrant 1 of an ESMR 1 X 1 rainfall map of the entire globe in four quadrants for June 1, 1974.

289 FIG.25. (b) Quadrant 4 of the ESMR 1' X 1" global rainfall map for June 1, 1974.

290

MIRLE S. V. RAO

which should aid some types of research. It may be of interest to add that maps of this class, in four global quadrants, were actually produced. Two sample maps of this variety are reproduced in Figs. 25a and 25b.) 9. NEWFEATURES OF GLOBAL CLIMATOLOGY REVEALED BY ESMR RAINFALL STUDIES Several interesting aspects of global climatology are disclosed even from a preliminary study ofthe ESMR-derived global oceanic maps. The objective of this section is to describe some outstanding features.8 9.1. Characteristics of the ITCZ in the Pacific

Although it has been known for some time that there is a dry zone near the Gilbert Islands in the Pacific (mean position 1"S, 174"E) flanked by wet regions to the north and south (Seelye, 1950), the precise structure of the ITCZ has remained obscure. Extensive observations of tropical cloudiness, such as those compiled by the US.Department of Commerce and U S . Air Force (197 1 ) and by Sadler et af. (1976), provide useful information on the subject to the extent to which reliance can be placed on the relationship between cloudiness and rainfall. Furthermore, the mean monthly rainfall maps based on island observations drawn up by Taylor (1973) indicate a maximum along both hemispheres, with a minimum along the equator. It may be repeated here that island reports are not necessarily representative of the surrounding ocean because (1) there are vast oceanic areas where there are few islands and (2) even in regions with a fair number of islands, convectional heating and orographic effects modify airflow. Rainfall values derived from the ESMR system throw further light on the rain pattern, not only because of the more complete coverage provided by the satellite, but also because of the direct approach in estimation. From the typical ESMR-derived oceanic rainfall map shown in Fig. 26, it is seen that as we move eastward along the equator in the Pacific, the rainbelt of the ITCZ bifurcates in the neighborhood of longitude 170"E; the upper branch proceeds eastward, maintaining itself slightly north of the equator, whereas the lower branch runs eastward or southeastward and merges with the South Pacific Convergence Zone (path of storms) in the vicinity of longitude 160"W. This feature is also discernible in the annual average rainfall map for 1974 presented in Fig. 27.

* Four of the major features were discussed on the basis of results only up to February, 1975. in an article in the American Meteorological Society bulletin (Rao and Theon. 1977).

FIG.26. Previously unrecognized rain area in the South Atlantic as seen in a monthly map (June, 1973).

FIG.27. Previously unrecognized rain area in the South Atlantic as seen in an annual map (1974).

SATELLITE-DERIVED PRECIPITATION PARAMETERS

293

It was explained in the previous section how, from the quantitative ESMR observations of rainfall over the oceans, zonally averaged rainfall rates were computed separately for each of the three major oceanic areas of the world, namely, the Pacific, Atlantic, and Indian oceans. Figure 17 shows the plot of the zonal averages of rainfall in the Pacific Ocean, month by month in different years. This analysis bears out that the rainbelt of the ITCZ splits in two, not just seasonally, but during nearly all months of the year. However, it should be pointed out that the southern branch and the contiguous cyclone path in the westerliesof the southern Pacific (which extends ultimately to the southern polar front) attain their maximum development during the austral summer season (i.e., December through February). 9.2. Previously Unrecognized Rain Area in the South Atlantic

In the Atlantic to the southeast of South America, an extensive area of rainfall becomes apparent in the ESMR-derived maps, extending approximately between latitudes 25 and 50”sand longitudes 50 and 25”W,as may be seen in Figs. 26 and 27. This rainy region was not known before; it does not appear on any existing map of global rainfall (e.g., Haurwitz and Austin, 1944;Geiger, 1971). The main reasons why such a wide region of precipitation remained unidentified are probably that (1) few ships traverse the area and (2)there are no islandsg reporting precipitation in that part of the Atlantic. Visible and infrared cloud observations from satellites (e.g., “Global Atlas of Relative Cloud Cover,” U.S. Department of Commerce and U.S. Air Force, 1971) often indicate gross precipitation features, but they do not readily show this precipitation maximum. However, after identification of the specific area from ESMR quantitative rainfall charts, reference to cloud cover maps confirm that this portion of the Atlantic is among the areas of very great cloudiness. The truth is similar with respect to the percentage frequency of precipitation charts drawn up in this region from more or less sporadic ship observations. The mean rain intensity within this area 25 longitude X 25 latitude as revealed by ESMR-derived quantitative rainfall maps turns out to be 0.1 mm/hr. This corresponds to an annual rainfall of 900 mm. Subsequent to the disclosureofthe rain area by ESMR and the report on it (Rao and Theon, 1977), Riehl(l978) pointed out that this substantiated a theoretical concept held by him. According to Riehl, after development of Rossby’s theorem of conservation of potential vorticity, a relation was thought to have been established between flow over the Rockies and the semipermanent winter trough aloft in the eastern United States. If so, a O

9

The Falkland Islands are outside the region under discussion.

O

294

MIRLE S. V. RAO

similar trough was called for in the Southern Hemisphere, east of the Andes. Indeed, Boffi (1949), from Argentina, a former student of Riehl, investigated this phenomenon and found upper southwest winds well over the ocean. Thus, a trough position could be postulated at a distance from the Andes that was equal to that of the North American east coast trough from the Rocky Mountains. The northern trough would be near or west of the coastline. Cyclonesshould travel southeastward over the western South Atlantic, a counterpart to the storms moving northeast over and near the North American east coast. There are other aspects of the large rain area disclosed by ESMR-derived maps. This area is possibly an extension of the southern Pacific rain zone mentioned in Section 9.2. In other words, rainfall in the southern Pacific rain zone as well as in the Atlantic area under discussion could be produced by the same dynamical circulation pattern, the flow being modified and the rain pattern interrupted by the Andes and the land protrusion of the South American continent. The interruption in the rain pattern would (as in North America) extend to some extent to the immediate west of the land. Another aspect worth mentioning is that this rain area detected by ESMR is in conformitywith the general global pattern of relatively dry regions close to the west coast of continents and wet regions close to the east coast, where greater baroclinity due to increased temperature contrast may be expected. 9.3. Bimodal Behavior and Other Features ofRainbelts in the Indian Ocean Several interesting features relative to the Indian monsoon come to light from the analysis of ESMR maps and the statistical and graphical study of ESMR-derived quantitative precipitation data, These are briefly as follows. 1. The rainbelt in the Indian Ocean is most pronounced in the annual mean at latitude 6"S, as may be seen, for example, in Fig. 27. (This is referred to below as the first, or southern, tropical maximum belt; see also Fig. 19.) 2. In the south Indian Ocean, rainfall is greater in the eastern half than in the western half (Fig. 26). 3. Meridional profiles of zonally averaged rainfall rates in the Indian Ocean (displayed in Fig. 19) manifest a minimum along the equator. 4. North of the equator, the precipitation rate in the tropical Indian Ocean reaches a second maximum in the vicinity oflatitude 16"N. (This is referred to hereafter as the second, or northern, tropical maximum belt; see Fig. 19.) 5 . As the monsoon advances, the second maximum (in the Northern

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295

Hemisphere) grows at the expense of the first maximum (in the Southern Hemisphere). This becomes clear from scrutiny of Fig. 19. In the middle of the monsoon season (August), the northern maximum attains an amplitude five times that of the southern maximum. During the retreat of the monsoon, the reverse development occurs, and in January the southern maximum grows to an amplitude five times that of the northern maximum. These features suggest modifications to current theories of the monsoon. One school of thought in tropical meteorology holds that the atmospheric monsoon current involves a progressive advance over the Indian Ocean from the Southern Hemisphere across the equator to the Southeast Asian land mass. Another school of thought maintains that monsoon rainfall is due to moisture picked up entirely in the Northern Hemisphere, mainly in the Arabian Sea. In view of the features discussed herein, it seems necessary to postulate a circulation mechanism that does not necessarily demand a regular progression of the entire monsoon from the Southern to the Northern Hemisphere, but one that accomodates the coordinated way in which both hemispheres are affected so as to sustain the bimodal changing wave pattern (the fifth feature noted herein). This question is important (considering that the monsoon affects the lives of hundreds of millions of people) and merits further investigation.

9.4. Interannual Variation

The extent of variation of rainfall over the oceans from year to year has been largely a matter of conjecture until the present time. The weekly, monthly, seasonally, and annually averaged maps generated from ESMR data provide a new insight into this problem. A typical example is the rainfall over the Pacific in the month of January of the years 1973 and 1974 (Figs. 23 and 24a and b). In January, 1973, heavy rainfall occurred over a wide region all along the equator (between 0 and 8 ON and 175 to 100"W) and also to the south ofthe equator (between 0 and 24"s and 170"E to 155OW). This was at the time of the El Niiio phenomenon. The El Niiio (so called because it generally developsjust after Christmas) is a warm oceanic current attended with relaxation of upwelling along the coasts of Ecuador and Peru. This hinders the nutrients being brought up to the surface, thus producing a disastrous effect on fish population and bird life along the west coast of South America. In the corresponding month of 1974 (non-El Niiio year), the region was relatively dry. The ratio of rainfall in the equatorial Pacific in the period December, 1973, through February, 1974, to rainfall in the period

296

MIRLE S. V. RAO

December, 1972, through February, 1973 (see Fig. 17), is 1 :6. Whether such variation is true for other El Niiio and non-El Niiio periods can be determined only after satellite rainfall data for many more years are processed. Such extensive and heavy precipitation as occurred during the El Niiio year has to be associated with pronounced convection and large-scale rising current. Considering this region of precipitation in juxtaposition with the dry region of subsidence off the coast of South America leads to the concept of a possible important local variation in the Hadley cell circulation. The rainfall center and the center of the dry region bear a northwest - southeast orientation as demanded by the Coriolis effect in the Southern Hemisphere. A slightly displaced and considerably intensified Southern Hemisphere Hadley cell suggests itself as an explanation of the intense precipitation close to the equator. (It may be pointed out that in the descending limb of the Hadley cell, or the dry region of subsidence, intensification of the circulation will not produce any measurable change in rainfall in a region that is already dry.) The above postulate may or may not hold up when data of more years accumulate and are analyzed. In any event, the rainfall pattern disclosed by ESMR is a distinctiveexampleof rainfall anomaly. Investigationsof similar anomalies and their correlations are certainly valuable in weather as well as in climatic studies. 9.5. Low Southern Hemispheric Rain Conventional meteorological observations over the Southern Hemisphere, which is largely covered by water, are understandably sparse. Consequently, although meteorological phenomena occumng over that vast region are of importance to the global general circulation, they are not well understood. Hence arises the importance of ESMR data in providing useful information with respect to precipitation in the Southern Hemisphere. An examination of the global oceanic annual precipitation maps for 1973 and 1974 (Figs. 15 and 27) reveals that the rainfall in the Northern Hemisphere is heavier than that in the Southern Hemisphere. This is especially true in the middle latitudes of the two hemispheres. Figure 18 represents the zonal averages of rainfall in the Atlantic Ocean. From a scrutiny of this diagram, the following feature is noticed. The polar front in the Southern Hemisphere (the normal position of which is in the neighborhoodof latitude 40"s) is much weaker than its Northern Hemisphere counterpart. The same feature may be observed to a less marked extent in the Pacific Ocean (Fig. 17). The contrast between the two hemispheres is also evident in the

297

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overall zonally averaged rainfall rate diagram for all the oceans of the world (Fig. 20). Considering the Southern Hemisphere in its entirety, the average rain rate is only about 50% of the corresponding Northern Hemisphere rain rate. The first possible explanation is the larger land masses in the Northern Hemisphere. But it is mainly in coastal areas that the effect of land should be dominant. (It may be remembered that in generatingthe maps of the first Global Oceanic Rainfall Atlas, regions within 1 of coastlines have been ignored.) The differencein rain rate revealed by ESMR is much too large to be explained by this factor (i.e., the differences in land masses) alone. Other possible explanations, such as the presence of warm currents (the Gulf Stream, Kuroshio, etc.), fall short of accountingfor the great difference observed. Further investigation is necessary to establish the cause, or combination of causes, giving rise to the observed contrast between the two hemispheres. O

9.6. Periodical Variation From ESMR data, it is possible to obtain a good insight into the diurnal and other periodical variations of precipitation. A limited study for this purpose was done for the tropical Atlantic region, with interesting results. Because this study was undertaken with specially prepared maps (not generated for the atlas, but produced with a higher resolution of 1 " X 1 and also separated for day and night), it is deemed desirable to treat this subject separately and in a rather detailed manner. Therefore, this subject is dealt with in the next section. O

10. PERIODIC VARIATIONS OF PRECIPITATION IN THE TROPICAL ATLANTIC OCEAN 10.1. Main Study With the objective ofinvestigatingthe local characteristicsof precipitation in a limited area such as the tropical Atlantic, a special set of high-resolution maps were generated (Rao and Theon, 1979). The region chosen for the study is the rectangle bounded by latitudes 25"N and 10"sand longitudes 95"W and 15"E. The choice of this particular area was guided by the consideration that it coincides almost exactly with the GATE area, so that ESMR results could (1) add to the information collected by GATE and (2) be intercomparedwith similar data from other sourcesin the same geographical and time frame.

298

MlRLE S. V. RAO

The rain rate data were averaged over 1 latitude X 1 longitude gnd cells. Precipitation charts were produced separately for daytime and nighttime observation for each of the days of the GATE period, namely, June 15 through September 30,1974. (It may be recalled here that Nimbus-5 ESMR took observations only near local noon and near local midnight, so that the day - night variation is the nearest approximation to diurnal variation that can be attained.) Samples of the charts produced are presented in Figs. 28 and 29. Figure 28 reproduces the map of daytime precipitation for the first day of the GATE period (i.e., June 15, 1974) and Fig. 29 represents the nighttime precipitation for the same day. Statistical analysis of these rainfall observations for the entire GATE period was made. As a first step, the following procedure was followed: O

O

1 . Calculate the total number of observations NT = C. N, where N is the number of ESMR spot observations in each 1 ' X 1 grid cell, the summation being extended over all the grid cells in the rectangular area bounded by latitudes 25"N and 10"sand longitudes 95"Wand 15"E. 2. The product total Z ( N X r), where ris the average rain rate in each grid cell, is then computed. 3. The average rain intensity for the entire area l/NTX(NX r) is then obtained. 4. The total number of observationsNTRwhen it was rainingat the time of data collection (R > 0), i.e., N T R = ENR(where NRis the number of nonzero observations in an individual grid cell), is calculated next. 5. The ratio of the number of observations N&NR is then calculated.

The above process is repeated for each individual daytime map and nighttime map of the region chosen, for all the 107 days for which these special maps were generated. The results are easiest to understand when they are presented graphically. Figure 30 presents the average rainfall intensity in the entire area as a function of time, separately for daytime and nighttime precipitation. It is easy to see that rainfall near local noon is generally more intense than near local midnight. Computation from the statistical data yields a figure for the overall ratio ofthe day-to-night intensity (for the whole region and for the entire GATE period) of 1.7 : 1 .O. Similar daytime and nighttime curves of the frequency of rainfall occurrenceare presented in Fig. 3 1 . The curves are actually plots of Nm against time in days. (Probably it would be more appropriate to display a plot of the ratio NTR/NT, but since NT over the entire area is large and does not deviate too much from 12,000,the difference is not significant.) The frequency of rainfall occurrence is also greater near local noon than at local midnight. Computation reveals that the overall ratio of day-to-night frequency is 1.4: I .O.

FIG.28. GATE area rainfall map for the first day of the GATE period-daytime, June 15, 1974.

FIG.29. GATE area rainfall map for the first night of the GATE period-nighttime, June 15, 1974.

30 1

SATELLITE-DERIVED PRECIPITATION PARAMETERS 20 .19 .18 .l?

I I

1

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DATE

FIG.30. Rain intensity over GATE area as a function of time (June 15 through September 30, 1974).

Because of the crude statistical method adopted in this preliminary approach to the problem of diurnal variation, absolute confidence cannot be placed in the figures arrived at. Nevertheless, it has to be conceded that the magnitude of the day-night variation in the precipitation over the GATE area is large. Dynamical considerations do not favor such large variation being uniformly valid over all oceanic areas in general. This is because precipitation is indicative of vertical motion, and ascending motion over large areas needs to be compensated by descending motion more or less simultaneously in other areas. (It is realized that in restricted areas, there may be local ascending motion with rain compensated by adjacent descending motion, giving rise to precipitation, but with no net ascent. However, in general, the statistical correlation between rainfall and vertical motion is high.) It is unlikely that there is a maximum of ascending motion at the same time of day over the entire area of a major ocean. From this reasoning it is concluded that the presently observed diurnal variation is a location-dependent characteristic of the tropical Atlantic Ocean. It is possible (even likely) that outside the tropical region the diurnal variation is opposite in phase. It would be interesting to extend this study to other oceanic areas. It would also be profitable to subdivide the GATE area examined here into sectors and repeat the investigation.

/ I,

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302

MIRLE S. V. RAO

2400 2300 2200 2100

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FIG.3 1. Rain frequency over GATE area as a function of time (June 15 through September 30, 1974).

10.2. Comparison with Other Results

Several previous investigations have been made of the diurnal variation of cloud and precipitation over oceanic areas, with conflicting results. The hypothesesthat have been advanced are also varied and confusing. Some of these studies are summarized very briefly herein. Riehl ( 1947), from a study of weather ship data over the North Atlantic, concluded that low-cloud maximum occurs near 0600 local time (LT), with a second maximum near 1800 LT. The minimum was found to occur close to 2200 LT. Based upon a numer of years of observations on the West Pacific Atolls, Lavoie ( 1963)found a precipitation maximum near 0300 LT, 40% larger than a minimum near 1000 LT. He also observed a secondary maximum near 1300 LT and a secondary minimum near 1700 LT. Weickman et al. (1977) observed that infrared satellite photographs reveal a maximum frequency near midnight in the time of initial development of rapidly growing cumulonimbus clouds. Brier and Simpson (1 969) examined hourly observations for 12 yr at Wake Island and inferred a rainfall frequency maximum near 0500 LT, almost twice as frequent as a minimum near 1500 LT. Ruprecht and Gray ( 1974) made a study of tropical oceanic cloud clusters and reported a maximum of rainfall cloud clusters from 0700 to 1200 LT, twice as much as in the late evening, from 1900 to 2400 LT.

%

~

SATELLITE-DERIVED PRECIPITATION PARAMETERS

303

From harmonic analysis of data collected during GATE, McGarry and Reed (1978) showed that over the ship array area, rainfall maximum occurs in early afternoon, at about 1400 LT. Furthermore, they state that satellite results also yielded an afternoon maximum in convection cloudcover. This last investigation pertains most closely to the area and period of the ESMR study reported earlier in this section. There is certainly good agreement between the results of the two entirely independent investigations. 10.3. Models and Hypotheses As stated earlier, diverse explanations have been put forward to account for diurnal variation of oceanic precipitation. One explanation (Lavoie, 1963; Krauss, 1963) presupposes enhanced nocturnal infrared cooling at the top of clouds and consequent increase in lapse rate and instability. The main objections to the acceptance of these arguments are that (1) increase of low-level instability normally leads to development of shallow cumulus, not precipitating, clouds of large vertical development and (2) observations in many oceanic areas do not reveal significant differences between day and night lapse rates. A second hypothesis (Brier and Simpson, 1969) seeks to relate the semidiurnal solar atmospheric tide (pressure oscillationassociated with the S, component of the solar tide) with the early morning and late afternoon maxima of convection. But the main difficulty in accepting this hypothesis is that the second (afternoon)peak in convection is not observed over oceanic surface except in the vicinity of large islands. A third proposal (Gray, 1976) attempts to explain the observed diurnal variation as due to radiational differences between cloud clusters and surrounding cloud-free areas causing, at night, a greater outward pressure gradient at cirrus levels and a greater inward pressure gradient to the cluster at middle and lower tropospheric levels. This is an involved explanation that needs further substantiation. All in all, a really satisfactory theory on the subject has yet to be developed.

10.4. Another Interesting Oscillation The graphs in Figs. 30 and 3 1 display another striking feature. Even a cursory naked-eye examination reveals an oscillation of a short period of about 3 days. Analysis of the curves leads to a fluctuation of periodicity of 3.3 days in rainfall occurrence, as well as in intensity. It is well known that easterly waves over Africa have a similar periodicity. Thus, the time scale of the oscillation observed in this analysis is consistent with easterly waves traveling from the African continent over the tropical Atlantic belt under study.

304

MIRLE S. V. RAO

10.5. Conclusion In summary, although ESMR studies indicate higher daytime rainfall over the tropical Atlantic ocean, it is necessary to investigatefurther the subject of diurnal variation. The investigation may be carried out profitably over many different localities in the oceanic areas of the globe. Satellite microwave data will be of help in such investigation. An attempt at a satisfactory theoretical explanation may be made after the type of the variation in many local areas has been studied.

11. ICE MAPPING 11.I . General

One of the important applications of ESMR is mapping sea ice. The microwave radiometer is a valuable tool for sensing sea ice through clouds and in the polar night. This capability arises from the high emissivity (typically 0.8 -0.95) of sea ice, which may be contrasted with the low emissivity (- 0.4) of water. ESMR gray-scale displays of orbital swaths are utilized by the U.S. Navy Fleet Weather Facility in the preparation of their weekly sea ice charts that help guide ship operations. (In January, 1975, ESMR data revealed a band of reduced sea ice concentration around 75 "S and 156 W, which helped to route an icebreaker, with considerable savings in ship transit time.) Since the emissivity is a function of wavelength, there is also a potential for unfolding certain ice parameters by utilizing multiple microwave frequencies. Considerable work on observing ice and snow has been done by many workers, among whom mention may be made of Gloersen e? al. (1973) and Zwally (1977). 11.2. Bulk-Emitting Media- Effective Physical Temperature

In the case of most solids or liquids, microwave radiation emanates from a thin, nearly isothermal surface layer. Thus the parameters in the equation TB= ET (see Section 2) are well defined. However, if the radiation emanates from a thick layer that is not isothermal and in addition has inhomogeneities that cause internal reflection and scattering, the situation becomes complicated. It becomes necessary to consider how well the medium transfers emitted radiation from each depth of the surface. In general, the

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305

contribution to the surface emission from a given depth below the surface tends to decrease approximately exponentially with depth. The optical (or skin) depth may be defined as the thickness of the top layer from which approximately 63% of the external radiation emanates. It is interesting to compare the optical depth for different materials. This parameter is of course wavelength dependent, and we may consider its values at 19.5 GHz. For water, soil, and first-year sea ice, the optical depth is of the order of millimeters; for multiyear sea ice, of the order of centimeters; and for dry snow, of the order of meters [see Zwally (1977)l. It is almost obvious that TB

=

[

e(z)T(z)f(z) dz

(11.1)

where e(z) and T(z) are the local emittanceand local physical temperatureat depth z in the medium andf(z) is a weighting function derived from the radiative transfer from a given point at depth z. An effective physical temperature ( T ) may be defined as the weighted average of the actual physical temperature over depth within the medium (Zwally et al., 1976). That is

(1 1.2) It then becomes possible to write

TB= €( T )

(11.3)

which is similar to Eq. (2.6) in Section 2. It should, however, be pointed out that the preceding treatment is somewhat simplistic. One optical depth in the case of typical firn is as large as 5 m. Quantitative evaluations are complicated by a number of uncertain factors, such as crystal size profiles, initial size and growth rate of crystals, and imperfections in measurements of the complex index of refraction as a function of frequency and ice temperature. Other complicatingfactors also arise in actual practice. For example, the brightness temperature registered by ESMR is really a combination of the brightness temperature of water and ice in proportion to the relative fractions of each in the ESMR field of view. One of the most serious complications arises from the effect of the atmosphere intervening between the sea surface and the satellite. Fortunately, in dry polar atmospheresthis effect is ordinarily minimal, although this is not true of regions and periods affected by storms.

306

MIRLE S. V. R A O

11.3. Satellite Study

Zwally and Gloersen ( 1977) created pseudocolor images from digital maps of ESMR brightness temperature data, grouped in 3-day periods. First, a special data matrix was originated to cover the area of a polar stereographic map inside the 50” latitude circle. This matrix was expanded by linear interpolation to create nine pixels for each matrix cell. Each pixel was assigned a color intensity according to its brightnesstemperature value. The color scale was designed to show warmer colors for higher brightnesstemperature values. In deriving sea ice concentration, the following assumptions are made. When ice concentration is zero, the brightness temperature is 135 K ( 120 K contributed by sea water and 15 K contributed by atmospheric emission and cosmic radiation). At 100%sea ice concentration, the brightness temperature is €To= 245 K (hereit is assumed that E, the emissivity of the ice surface, is 0.95, which is true for the first-year ice, and that To, the ice surface temperature, is 258 K, ignoring seasonal and other variations). On the above basis, the additional contribution to TB from 100% sea ice would be 245 - 135 = 1 10K. Assuming a linear relationshipbetween TB and sea ice concentration C, it is possible to write

TB= 135

+ llOC

(11.4)

or

C = (TB- 135)/110

(1 1.5)

When the emissivity and ice temperature are appreciably different from 0.95 and 258 K,respectively,the determination of Cbecomes less simple, for

C = (TB - I 35)/(€T0- 135)

(1 1.6)

One of the problems involved in determining ice concentration is the estimation of “ice temperature,” To. Taljaard et al. (1 969) have suggested that ice temperature is to be taken as colder than the sea surface by threefourths of the difference between the sea surface freezing point and the climatologic air temperature, for air temperature below freezing. As was pointed out earlier, the emissivity of ice is by no means constant. First-year ice has high emissivity. But as time advances, snow or firn with alternate melting and freezing enhances grain sizes. Radiation from within is scattered on its way to surface. The larger the grain size, the greater the internal scattering and the lower the surface emissivity. However, when the snow or firn becomes wet, emissivity increases sharply, because the internal scattering is reduced by the film of water on the snow grains (Gloersen and Salomonson, 1975; Chang and Gloersen, 1975).

SATELLITE-DERIVED PRECIPITATION PARAMETERS

307

Despite the uncertainties outlined above, ESMR is providing useful information on sea ice in all seasons. It is certainly possible to detect the edge of ice packs because of the high contrast between sea ice and water. To a certain degree, information of ice type within the pack can also be gleaned from the dependence of emissivity on ice type [see Wilheit et a!. (1972); Gloersen et al. (1973)l. Furthermore, it is possible to gain some knowledge on ice dynamics, because decreasing ice concentration is indicative of ice divergence, assuming that the disappearance of ice due to melting is small (Gloersen et al., 1973). A limited idea of interannual variability can also be obtained from ESMR data that have been gathered for only about 3 yr. 11.4. Results

Campbell et al. (1974) noted the variable ice structure and dynamic activity of Arctic sea ice in ESMR imagery. Studies by Zwally and Gloersen (1977) show that in the Arctic the extent of sea ice is maximum around January. Melting appears to be most rapid in July. The minimum extent of sea ice occurs generally around September. Some of the areas exhibiting interannual variability in winter sea ice cover are the Sea of Okhotsk (55ON, 150"E),theGreen1andSea(75"N,OoE/W),the WhiteSea(66"N, 37"E),and the Gulf of Bothnia (64"N, 22"E). Antarctic sea ice variations in extent and concentration based upon Nimbus-5 ESMR observation for the 3-yr period 1973,1974,and 1975have been described by Zwally and co-workers (Zwally et al., 1979). In the Southern Ocean as a whole they find that the ice cover is at its maximum in September and its minimum in February. Melting appearsto be most rapid during the period mid-November to midJanuary. This large decrease in total ice extent actually occurs about a month later than the maximum decrease in the area of highly concentrated ice. As for interannual variability there was perhaps a smalltrend toward decreasing ice extent from 1973to 1975. Furthermore,in 1973,themaximumarealextentoficeoccurred2-4 weeks later than in 1974 and 1975. Turning attention to individual sectors, Zwally et al. (1979) observed that the Weddel Sea contains the largest extent of sea ice (approximately onethird of the ice in the entire SouthernOcean); the Pacific Ocean sector has the smallest sea ice extent. The Weddel Sea also has the largest proportion of concentratedice. Most of the interannual variability in total ice extent is to be perceived in the Weddel Sea. Another interesting feature of this sector is the major polynya (open water in the middle of ice) that formed in 1974and 1975. This polynya lies long the Antarctic divergence. Zwally et al. (1979) believe in the following explanation: On the southern side, the prevailing

308

MIRLE S. V. RAO

easterly winds are strengthened by inversion winds from the ice sheet. On the northern side, the winds are westerly. Thus, there is divergence of ocean surface currents and consequent upwelling of warm water in the polynya, and this offsets the radiative cooling of surface water and the transfer of heat to the cold winter atmosphere. The months of maximum and minimum ice extent are not the same in all sectors; for example, in the Indian Ocean sea ice extent peaks in October and diminishes most in February. As may be expected, interannual variability in individual sectors is higher than in the Antarctic Sea as a whole. The Ross Sea and the Bellingshausen- Amundsen seas show a more pronounced variable cycle af sea ice than does the entire Southern Ocean. Evidently, meteorological and oceanographic processes are different in the different sectors. Further study with many more years data is certainly needed to reach a clear understanding of these processes. 12. STORMSTRUCTURE STUDIES

12.I . General Adler and Rodgers (1977a,b) attempted to study tropical cyclone rainfall characteristics such as the distribution of rainfall rate and the dependent latent heat release utilizing Nimbus-5 ESMR data. Nearly 100 satellite passes over Pacific storms (in various stages of development) were examined. The storms chosen reached typhoon intensity in all cases. (Their study includes a comparison between western Pacific typhoons and eastern Pacific hurricanes.) 12.2. Case Studies First, a case study was made of Nora, a tropical cyclone that formed toward the end of September, 1973, and developed into an intense typhoon. In this investigation, for converting ESMR brightness temperature to rainfall rate, the relationship derived by Wilheit et al. (1977) was found to be less satisfactory than was the earlier version as published in Wilheit et al. (1975). A constant freezing level of 5 km was assumed (ignoring spatial variation associated with horizontal temperature gradientsin the storm field and temporal variation that may be expected as the storm intensifies). Brightness temperatures above 256 K were assigned a fixed rain rate of 10 mm/hr (which is less than what would be expected in the heavy rain of tropical storms). Consideration was given to some of the sources of error

309

SATELLITE-DERIVED PRECIPITATION PARAMETERS

described in Section 6. Correction was applied mainly to one of these, the scan angle error, placing a great deal of faith in the analysis (mentioned earlier) by Kidder (1976). The center of storm circulation was determined based on microwave imagery and digital information as well as infrared imagery and information from the 1973 Annual Typhoon Report. In the case of mature systems, the center is fairly well defined in microwave data, but in the disturbance and depression stages, the center was assumed to be the point of maximum rainfall (or brightness temperature), although this may not be perfectly justifiable. However, calculations based on this semiobjective center location and on the objective location based on maximum TBdo not differ greatly. A circular area of 4" latitude radius around the center was considered,and the integrated rainfall and consequent latent heat release in this area were calculated. If R denotes rain rate (in centimeters/ second) and dA an element of area (in square centimeters), the quantity of rain MR(in grams) that falls per second over the circular areaA (of 4 latitude radius) is O

MR=

1

RdA

(12.1)

assuming the density of rain water to be 1 g/cm3. The latent heat released LHR is simply a constant multiple of the rainfall: LHR = L,MR

(12.1)

where L, is the constant of latent heat of vaporization, which is equal to 597.3 cal/g. The study of Nora was based mainly on ESMR observations made six times from September 29 through October 6. (Some observations were lost due to missing orbits and some others because the storm center was between data areas.) The main conclusionsofthis investigation were that (1) in the initial stages of the storm (September 29-October 1) the rain area expanded while the intensity of rainfall did not increase notably and (2) from the depression to typhoon stage (October 1-5) both the area and intensity of rainfall increased. It naturally follows that the integrated rainfall increases relatively slowly during the early stages of the storm and rapidly as the storm system intensifies; the consequent latent heat release behaves likewise. The mean rainfall rate rose in magnitude from 0.6 mm/hr in the disturbance stage to 2.0 mm/hr in the typhoon stage. Correspondingly the rate of latent heat release increased from 2.7 X 1014W at the time of first observation to 8.8 X lOI4W at the time of the last observation. As may be expected, higher rainfall rates were found to make a larger contribution to the total rainfall as the storm matured. (The contribution of rainfall rates of 6 mm/hr or more rose from 8%in the disturbance stage to

310

MIRLE S. V. RAO

33% in the typhoon stage.) Furthermore, a study of the distribution of rainfall rate as a function of radial distance from the center confirmedthat as the cyclone deepens, rainfall concentrates toward the center of the storm. In a subsequent study (Adler and Rodgers, 1977a) the characteristics of two more western Pacific typhoons, Irma and Gilda, both of which occurred in 1974, were investigated. This time a new relationship was chosen between brightness temperature and rain rate by offsetting the 4-km curve by 17 K based on the differences in theoretical calculationsfor freezing levels of 4 and 5 km by Wilheit et al. (1977). This revision resulted in approximately 20% smaller values of rainfall than the values produced by the relationship chosen in the Nora study. Furthermore, the demarcation between light and heavy rainfall was lowered from 6 to 5 mm/hr, and a precipitation intensity parameter (PIP) was defined simply as the fraction of precipitation contributed by rainfall rates greater than 5 mm/hr. In the case study of Irma, in the early stage between November 17 and November 21, 1974, the average rainfall in the 4" circular area was low (lessthan 0.5 mm/hr). The fractional contribution of heavy rain (PIP) was also low (-0.15). At the next available observation (November 23), the storm was about to reach typhoon intensity. As the winds strengthenedand the central pressure fell, the average rain intensity reached a maximum of 1.8 mm/hr (or latent heat release of 7.9 X lOI4W) (November 25) and the PIP increased to 0.55. Also, the rainfall inside the 2' radius circular area was found to increase more rapidly. No reliance can be placed on observations once the outer perimeter of the 4" circle approaches the land areas [because of error ( 16) discussed in Section 61. Thus, the intensification of Irma, like that of Nora, was accompaniedby increasing rainfall rate, concentration of rainfall toward the center, and higher fractional contribution of heavier rain rates to the total rainfall. The results of the case study of Typhoon Gilda are generally similar. The system intensified from June 29, 1974, onward. The maximum precipitation observed was on July 4 (rain rate magnitude 1.5 mm/hr, with a corresponding LHR of 6.6 X 1014W and a low fractional heavy rainfall PIP value of0.29). Adler and Rodgers speculate on a connection between the rainfall parameters and lack of further intensification of Gilda, but both the real cause of these figures and their predictive value in actual practice remain unclear. 12.3. Study of Western Pacific Storms A study was made of a subcategory of western Pacific Ocean storms. A number of storms in the Pacific between latitudes 0 to 25"N and east of 125"E were chosen for the study, thus avoiding storms that crossed over the land mass of the Philippines or those that recurved. A scatter diagram shows

SATELLITE-DERIVEDPRECIPITATION PARAMETERS

31 1

an understandable positive correlation between storm intensity (as judged by reported surface winds) and average rainfall rate over a circular area of radius 4”(and the latent heat release in that area). There were seven observations when the storm was intense (surface wind > 40 m/sec) and in these cases the mean rainfall rate in the circular area was 1.6 mm/hr, yielding a median LHR value of 7.2 X lOI4 W. Another scatter diagram indicates that the rain in weak and developing stormsis primarily light and that there is a positive correlation between storm strength and the fractional heavy precipitation (the PIP). A third scatter diagram of storm intensity versus fraction of storm precipitation and LHR within 2“ of the center shows again a weak positive correlation, generally indicating the concentration of precipitation toward the storm center as the storm matures. An examination of the azimuthal distribution of rainfall of tropical cyclones indicated a slight preference for higher rainfall rates to the right of the storm motion axis. This may be compared with the results of Frank (1977) showing maximum rainfall in the right rear quadrant. It may, however, be pointed out that in the case of cyclones in the Indian Ocean, Bay of Bengal, and Arabian Sea, it has been observed over many years that precipitation tends to be heaviest in the left quadrant [see Rao (1976)l. A comparison between the tropical storms1° in the western Pacific and those in the eastern Pacific was also attempted, assuming a freezing-level height of 5 km in both cases (although the freezing level is possibly lower in the eastern Pacific). The results indicate that eastern Pacific hurricanes have less average rainfall within the 4”radius circle (and, consequently, less LHR), less intense rainfall rates, and rainfall more compact or concentrated toward the center. These tentative results need confirmation by further study taking into consideration, among other factors, a more realistic distribution of freezing-level height.

ESTIMATION OF RAINFALL OVERLANDAREAS 13. QUALITATIVE 13.I . General

Estimation of rainfall over land from satelliteobservationsis undoubtedly important. Attempting to do this utilizing passive microwave data is fraught with difficulties, however, as was indicated in Section 1. The main complicating factor is the high and greatly variable emissivity of the land surface. The emissivity depends upon various factors such as soil type, lo Tropical storms of more than moderate intensity are termed as follows: “cyclone” in the Indian Ocean, Bay of Bengal, and Arabian Sea; “typhoon” in the western Pacific; and “humcane” in most other tropical latitudes.

312

MIRLE S . V. RAO

temperature, moisture content, vegetative cover, frost conditions, snow cover, and so on. Compared to the effect of these variations on the upwelling microwave radiation, the effect of rain in the atmosphere is relatively feeble. However, it follows from elementary electromagnetic theory that when moisture is added to the surface, thereby increasing its dielectric constant, the emission will be polarized when viewed obliquely. Also, microwave radiation at a frequency such as 37 GHz emerging from atmospheric precipitation is essentially unpolarized. It is therefore possible to extract limited rain information from 37-GHz observations,utilizing two polarizations (as in Nimbus-6 ESMR). The use of Nimbus-5 ESMR data for delineation of rain over land was demonstrated to be impracticable by Meneely (1975). Schmugge et al. ( 1977) showed that there is an inverse relationship between microwave brightness temperature at 1.55 cm and soil moisture levels as indicated by antecedent rainfall in regions where vegetation cover is sparse, such as agricultural fields before the planting of crops. Based upon the correlation between a simple antecedent precipitation index and emissivity and consequent brightness temperature, McFarland and Blanchard ( 1977) concluded that a potential exists for remote sensing of soil moisture at the 1.55-cm wavelength of ESMR. Savageand Weinman ( 1975)and Savageet al. ( 1976) concluded that at the Nimbus-6 ESMR wavelength, the scattering from hydrometeors is strong enough to render rainclouds distinguishable over land.

13.2. Intensity and Polarization of Radiation Received at the Satellite It may be well at this stage to recapitulate some basic ideas about the characteristics of the microwave radiation upwelling from the earth atmosphere system to the satellite-borne sensor. The intensity of radiation received by ESMR and the consequent brightness temperature registered by the system were dealt with at length in Section 2, and it is not proposed to repeat those aspects here. Only the effects of polarization will be discussed herein. We may consider the fractional energy contained in two planes mutually perpendicular to each other and to the direction of propagation (see Fig. 32). One of these planes may be chosen to contain the normal to the earth’s surface, i.e., the plane ofthe paper in the diagram. This is called the “vertical plane,” in which the plane of polarization [i.e., the plane containing the line of propagation and the electric vector (E vector)] is vertical, but in which the E vector is not vertical. The mutually perpendicular plane passing through the direction of propagation is termed the “horizontal plane”; in this plane the E vector is horizontal but the plane of polarization is not horizontal.

SATELLITE-DERIVED PRECIPITATION PARAMETERS

313

SATELLITE

FIG.32. Polarization diagram. Solid lines are in the xz (or "vertical")plane. Dashed lines are supposed to be in the xy (or "horizontal") plane or parallel to it, i.e., perpendicularto the plane of the paper. E, Electric vector; M, magnetic vector.

First we may consider the ocean surface. Water is a polar molecule with a high dielectric constant (at microwave frequencies) and consequent large reflectivity. In that part of the radiation from the surface that is due to reflection, the horizontally polarized component will be predominant. High reflectivity implies low emissivity. The emission from the ocean surface, which is low, will be predominantly vertically polarized when viewed obliquely. At nadir view, the distinction of polarization disappears. The microwave radiation emitted and scattered from the near-spherical raindrops in the atmosphere is essentially unpolarized. We may next consider the land surface. The emission from dry ground is generally high and is not considerably polarized. When the surface is flooded, surface reflectivityis increased, emissivityis lowered, and the polarization distinction (at oblique view) is enhanced. The emitted radiation is predominantly vertically polarized. Thus the distinction between dry ground and wet soil should be observed at satellite level, when there is no rain in the intervening atmosphere, by a decrease in brightness temperature but an increase in polarization difference. When there is rain in the atmosphere, the attenuation by the spherical raindrops reduces the polarization difference (and also intensity of radiation, as the rain becomes heavy due to backscatter of surface emission by large raindrops). Thus, rain over wet

3 14

MIRLE S. V. RAO

ground should be distinguishable from wet ground beneath a clear atmosphere, principally by reduced polarization difference and secondarily, as rain becomes heavier, by slightly reduced brightness temperature.

13.3. A Statistical Techniquefor Detecting Rainfall Over Land Rodgers et al. (1978, 1979) utilized the aforementioned characteristics of microwave emission and transfer to develop an empirical method to delineate synoptic-scale rainfall over land utilizing Nimbus-6 ESMR measurements. In eight cases of rainfall on a synoptic scale over the southeastern United States, they collected rain data, coinciding in time not exactly but as nearly as possible with Nimbus-6 overpasses, from WSR-57 radar data and also from stations reporting hourly rainfall amounts. In the statistical analysis, rain areas were taken to be regions with rain rate 2 2.5 mm/hr as indicated by WSR-57 or hourly reporting stations. Dry land surfaces were taken to be areas where rain had not fallen in the 24-hr period prior to Nimbus-6 pass, and wet land surfaces were sampled upwind and adjacent to the rain cells observed on the WSR-57 radar. The superposition of ESMR horizontally polarized brightness temperature data and ground rain data confirmed that brightness temperatures from rain areas over land are generally less than those from dry land surface areaswhen the surfacetemperature was not less than 5°C. Scatter diagrams were prepared testing the vertically polarized brightness temperature versus the horizontally polarized brightness temperature. Fisher linear discriminant lines were drawn, separating two-by-two the three populations representing rain, wet land surface, and dry land surface. For each population, a frequency concentration ellipse encompassing 68%(one standard deviation) of the data within the population was drawn. The three ellipses so drawn show a slight overlap between dry and wet land, but more overlap between wet land and rain. It could be confirmed from this analysis that the difference between horizontally and vertically polarized brightness temperature is generally small over areas representing rain compared to that over wet land surface. Assuming that the populations were statistically distinguishable and that they satisfied the Gaussian distribution, Rodgers et al. (1978) proceeded to develop a classification algorithm based on the Bayesian technique. (The Bayesian classifier is a Gaussian parametric maximum likelihood quadratic classifier.) They arrived at the following classification algorithm. If TH and T, represent horizontally polarized and vertical polarized brightness temperatures, the corresponding pixel belongs to the rainfall area, dry ground, or wet soil, depending upon which of the three probability

SATELLITE-DERIVED PRECIPITATION PARAMETERS

315

functions PR (rainfall), PD (dry ground), or Pw (wet ground) is largest, their values being given by

PR= -0.027Tk - o.038THTv- 0.042T$

+ 3.826TH+ 12.25013,- 2094.097

PD = -0.030Th

(13.1)

0.020T~ Tv - 0.022TC

+ 10.720TH+ 6.81lTv - 2412.165 Pw = -0.034Th + 0.070THTv- 0.053T$ - 1.678TH+ 10.84613, - 1261.721

( 1 3.2)

(13.3)

13.4. Error Analysis An error estimate was made, assuming that the populations satisfy the Gaussian distribution and have different means and the same covariance matrices and applying the asymptotic formulas given by Okamoto (1963). The analysis revealed that the chance of misclassifying wet land surface or dry land surface as rain over land is nearly 23%. Error matrices constructed separatelyfor data over land areas where the surface temperaturewas greater than or less than 15 "C indicated that the average accuracy was 76.1 and 36.7%,respectively. It is apparent that the classificationsare not definitive when the surface thermodynamic temperature falls below 15 "C. 13.5. VeriJication

In order to verify the performance of the Bayesian classification, a case previously not used (synoptic-scale rain pattern over the southeast United States on September 14,1976)was utilized. The radars for comparison were located in Georgia and South and North Carolina. The reporting times of hourly precipitation amounts were 1500, 1600, and 1700 GMT. Surfacestation data taken at 1800 GMT were also utilized. The agreement was generally good. However, discrepancies were found between the rainfall indicated by ESMR and ground truth over North Carolina and southwest Georgia. It was surmised that the classified area of rainfall over North Carolina (contrary to ground observations) is due to suspended liquid water in the clouds and that the discrepancy over southwestern Georgia could be due to wet land surfacesproduced by rain that fell a few hours prior to the Nimbus-6 pass. When the Bayesian classification was applied to a nighttime satellite pass over the same geographical area, unsatisfactory results occurred. The Nimbus-6 pass was at 0525 GMT on September 13,1976, at a period when

316

MIRLE S. V. RAO

the surface thermodynamic temperatures were greater than 15 "Cand there was no synoptic-scale rainfall. However, nearly all the pixels were classified by the algorithm as rain over land. This contradictory result was attributed to the presence of dew over the surface. Thus, the classification algorithm trained by data sampled from daytime satellite passes can be employed only when the land is bereft of dew. 13.6. Summar)i and Conclusion

The problem of estimation of rainfall over land from satellite microwave observations is beset with difficulties. Statistical analyses performed on Nimbus-6 passive microwave data revealed that under certain conditions, namely ( 1 ) ground temperature certainly greater than 5 "C(i.e., >4 1O F ) and

\

I

m

I'

239

I

I 251

I

I

I

m

m3

Horizontal temperature

1

I 287

(K)

FIG.33. Horizontally polarized versus vertically polanzed Nimbus-6 ESMR T, for each sampled category [rain over land (*), and wet (+) and dry (X) land surfaces]. C, The mean points ofthe populations; the ellipses encompass 68% (one standard deviation) of the data from the respective categories. The three concurrent lines are the Fisher linear discriminant lines, which separate two-by-two the three populations representing rain over land, and wet and dry land surfaces. [From Rodgers ef al. (1978, 1979).]

317

SATELLITE-DERIVED PRECIPITATION PARAMETERS

I_ _

m

I

239

I 2+8

1

I 257

I

RAIN

c

I

I

m

I

I 276

I

286

(K) FIG.34. Same as Fig. 33 except for surfaces whose thermodynamictemperatures are greater than 15°C. [From Rodgers et al. (1978, 1979).] Horizontal temperature

preferably greater than 15"C(i.e., > 59"F),(2)during daytime, (3) no dew on the surface, and (4) dry ground in the general area prior to the time of observation, it is possible to delineate synoptic-scale rainfall over land (see Figs. 33 - 35). It is worth noting that even when the first three of the specified conditions are satisfied, ambiguity arises between rainfall areas and wet land surface (adjacent or otherwise). 14. RETRIEVAL OF OTHER GEOPHYSICAL PARAMETERS 14.1. Overview

In this section the possibility is examined of retrieving certain parameters such as sea surface temperature, near-surface wind over oceans, atmospheric content of water vapor, and liquid water (clouds) on a global scale from microwave data. The advantages of the satellite microwave approach are that information can be obtained ( 1 ) over inaccessible areas from larger

3 18

MIRLE S. V. RAO

*. .

*

+x

m-

&

-

y

+

*

? m3+-

I

x -

E -

+

+

.E 257-+* c

&

>

-

=-

x x

247

'

*

+

-

*

+

a

+ I

I

I

I

I

I

I

I

I

samples than is possible by conventional methods and (2) under cloudy weather conditions when visible or even infrared approaches fail. The disadvantage, however, is that the accuracy of the measurement attained (up to the present time, at any rate) is not good enough to be of any practical use. The foremost among the systems conceived to extract the above-mentioned parameters is the Scanning Multichannel Microwave Radiometer (SMMR). Although systems such as the Nimbus-E Microwave Spectrometer (NEMS) on Nimbus-5 and the Scanning Microwave Spectrometer (SCAMS)on Nimbus-6 made significantatmospheric observations, it is not proposed to discuss them within the limited scope of this article. Also omitted from discussion is the attempt to measure wave height using radar pulses. Interested readers are referred to Chelton et a/. ( I 98 I). 14.2. The SMMR

The Nimbus-7 and Seasat satellites,both of which were launched in 1978, carried SMMR. A detailed description of the instrument is contained in

SATELLITE-DERIVED PRECIPITATION PARAMETERS

319

Gloersen and Hardis (1978), and an algorithm for retrieval of geophysical parameters from its observations is outlined in Wilheit and Chang ( 1 979). The radiometer delivers orthogonallypolarized antenna temperaturedata at five microwave frequencies (6.6,10.7,18.0,21.O, and 37.0 GHz). An 80-cm parabolic reflector focuses the received power into a simple feedhorn covering the entire range of operating frequencies. The scan of the radiometer is such that the antenna beam sweeps a conical arc of 50°,with a cone angle of 42 at the satellite (the incidence angle at the earth's surface being approximately 49"). The physics of microwave radiative transfer insofar as it relates to rainfall measurement was dealt with fully in Sections 2 and 3, and the extent of benefit that may be derived from dual polarization observations was explained at length in Section 13. It will suffice to recall now a few aspects relevant to measurements by SMMR and to make additional observations wherever needed as we proceed. In SMMR, all frequencies share a common aperture. Therefore, the spatial resolution at the earth's surface is proportional to the wavelength. At 37 GHz, the resolution is good (30 km), but it will be remembered from earlier discussions that although the sensitivity is high, there is a serious saturation problem. SMMR 37-GHz brightness temperatures are found to saturate at a rain rate as low as 4 mm/hr. Probably the best SMMR frequency for rain measurement is 18 GHz, but the resolution is coarse (60 km as against the 25 km of Nimbus-5 ESMR). At lower frequencies, the problem worsens, the resolution becoming 150 km at 6.6 GHz. Previous discussion has also demonstrated that sensitivity of brightness temperature to rain far outweighs the sensitivity to the parameters now proposed to be measured. Indeed, Wilheit and Chang (1979) concluded that the retrieval errors induced by rain become comparable to the retrieval error due to all other causes (and thus unacceptably large) at rain rates even in a range as low as 0.5 - 1 .O mm/hr. O

14.3. General Principles

The actual retrieval is made through regression equations, bearing in mind certain basic principles; this physical background may first be examined. 14.3.1. Suvface Wind. When wind blows across the surface of the ocean, it generates roughness and foam. As was pointed out in Section 2, Nordberg et al. (197 1) showed that for nadir viewing at 19 GHz there is no effect on brightness temperature for wind speeds less than 7 m/sec, and an increase occurs of about 1.27 K per m/sec for higher wind speeds. Webster et al. (1 976) examined a frequency range from 1.4to 37 GHz (both polarizations)

320

MIRLE S . V. RAO

NADIR

+ VERTICAL (36)

+ HORIZONTAL (38') (0)

-1.01' 2

'

6

10

I

14

I

18

INFERRED

'

22

I

26

I

30

I

34

38

FREQUENCY (GHz)

FIG.36. Spectrum of increase in brightness temperature caused by wind at the ocean surface. [Webster ei a!. (1976).]

and a view angle of 38 Figure 36 represents their results. It may be seen that brightness temperature is only weakly frequency dependent, and in horizontal and vertical polarization TBenhances and diminishes, respectively, relative to nadir viewing. Wilheit (1978a) came up with a model in which the roughness of the surface is partially obscured by foam at wind speeds greater than 7 m/sec. The question may well be asked, "What precisely is near-surface wind?' Wilheit ( 1978b) suggests the following definition, which is as good as any other. First obtain the friction velocity ( U * )using actual air and sea temperatures. following the Cordone (1 969) model. Then, assuming the air/sea temperatures to be equal (neutral stability), compute the wind speed at an altitude of 20 m. O .

14.3.2. Water Vapor. Water vapor has a weak resonance at 22 GHz. It has strong resonances at and above 183 GHz. The wings ofthese contribute significantly to the absorption coefficient at the frequencies SMMR is concerned with (although mainly above 10 GHz). Owing to pressure broadening, the absorption is undoubtedly a function of height. However, it may reasonably be assumed that the bulk of water vapor is to be found in the lowest few kilometers of the atmosphere, where the variation in pressure broadening is not large. This assumption reduces the number of degrees of freedom and enables estimation of this and other parameters.

14.3.3. Liquid Water in Cloud Form. In Section 3 the interaction was alluded to of a plane electromagnetic wave with a dielectric sphere. This interaction was discussed in the context of clouds by Gunn and East (1954) using the Rayleigh approximation and the dielectric data of Lane and Saxton (1 952). Wilheit and Chang (1979) examined the absorption coefficientfor a

SATELLITE-DERIVED PRECIPITATION PARAMETERS

32 1

' I

ABSORPTION COEFFICIENT .ol k m-'

1 1

///.;. b

10

40

FREQUENCY (GHzI FIG.37. Microwave absorption coefficient for 1 g/m3 concentration of cloud water droplets. [After Wilheit and Chang (1979).]

cloud with a liquid water content of 1 g/m3, at three different temperatures (- 20,0, and 20°C). Figure 37 shows the results, from which the conclusion is drawn that the absorption coefficientis almost precisely quadratic in frequency and varies by about a factor of three with temperature, over the range considered. This spectral characteristic may be borne in mind in attempts at correcting for clouds or estimating cloud liquid water.

+

14.3.4. Sea Surface Temperature. For most meteorological purposes (long-range weather forecasting, general circulation studies, etc.) the degree of accuracy needed in sea surface temperature values is of the order of 0.1 "C. This degree of accuracy is presently unattainable from satellite microwave observation. The radiometer brightness temperature (see Section 2) is proportional to the thermodynamic temperature (TB= ET),and so it should be possible to estimate sea surface temperature (SST). This advantage is largely offset by the variation of emissivity with SST, however. (Indeed, in the vicinity of 19 GHz, E varies almost inversely with T.) However, Wilheit and Chang (1979) developed a regression equation to retrieve SST, using as input the 10 SMMR temperatures and the earth incidence angle. In the equation, weightage is given principally to the two coarse-resolution channels 6.6 and 10.7 GHz. The nonlinearitiesinherent in the problem are removed by following two techniques in the regression process, as will be described later.

322

MIRLE S. V. RAO

14.4. Retrieval Technique

The brightness temperature observed at the satellite depends upon a multitude of meteorological parameters, some of which (e.g., water vapor, liquid water content) are functions ofaltitude. The problem has infinitedegrees of freedom; a solution from a finite set of brightness temperatures (dual polarization at five frequencies) is possible only by resorting to many gross approximations. Another problem of much smaller magnitude arises from the variation of SMMR spatial resolution inversely with the frequency. In order to use all five frequencies in determining any parameter, some common basis has to be found such that all measurementsapply to the same area. This is strictly possible by accepting the resolution of the lowest frequency, i.e., 150 km. Since this is unsatisfactory, the following scheme is resorted to. SMMR outputs are reduced to four grids (see Njoku, 1979). Grid 1 has a resolution of 150 km and uses all the frequencies. This grid is considered suitable for retrieving sea surface temperature. Grid 2 has a resolution of 90 km and leaves out 6.6 GHz while retaining all the other frequencies and is used for near-surface wind speed estimation. Grid 3 has a resolution of 60 km, leaves out 6.6 and 10.7 GHz, and uses the remaining three frequencies. It is deemed suitable for estimation of cloud liquid water.'' Grid 4 has a resolution of 30 km and only 37-GHz information. Its use is limited merely to add structural detail to rain rate retrieval that depends primarily on the I8-GHz output. Wilheit and Chang (1979) modified a statistical technique originally applied by Waters ef al. (1975) to derive atmospheric temperature from satellite microwave observations. In the modified scheme, an artificial data set covering the approximate expected range of all the concerned geophysical parameters is generated. The database assumes 10 wind speeds, 9 sea surface temperatures, 9 cloud models, and 9 atmospherictemperature profiles. Apart from the above meteorological parameters, another variable is also taken into account. Because of small variations in spacecraft attitude (pitch, roll, and yaw) and scanning geometry, the angle at which the earth's surface is viewed is dependent on time and scan position. Therefore, the angle of incidence (6& is treated as one more observable variable in the process of regression and is included in two steps, 48 and 50 . Each combination of the parameters represents a member of the data set ensemble. Expected correlations are, in general, left out (e.g., even arctic winter atmospheric profile with an SST of 299 K is included). A weak correlation is, however, introduced between water vapor and cloud liquid water. O

It

37-GHz information is not used in the regression equation for water vapor.

SATELLITE-DERIVED PRECIPITATION PARAMETERS

323

For each member of the ensemble, 10 brightness temperatures (five frequencies with dual polarization) are computed, as well as the parameters of interest in the final form (e.g., water vapor in g/cm2). An attempt is made to reduce the effect of nonlinearities ignored by the (essentiallylinear) regression technique in the following manner. First, on the basis of arguments that produce results, although difficult to justify physically (Wilheit et al., 1977),use is made of a certain function of brightness temperature in the place of actual brightness temperature. For the channels 18, 2 1, and 37 GHz (which are affected most by the atmospheric constituents) the function employed is F(TB) = ln(280 - TB)

(14.1)

A further step in the direction of compensatingfor nonlinearity is taken in limited cases. The expression of Nordberg et al. (1971) for brightness temperature (see Section 2) is

B,(FF) = 0

for FF 5 7.5

(14.2)

and

B,(FF) = 1.27(FF- 7.5)

for FF> 7.5

(14.3)

The abrupt change in slope at 7 - 7.5 m/sec causes a nonlinearity. Wilheit and Chang ( 1979) attempted to mitigate this problem by resorting to iteration. The general principle is to interpret the data using the retrieval based on the entire ensemble and then utilizing the approximate values of the geophysical parameter(s)to select the matrix derived from the most appropriate restricted ensemble. This principle is applied only to sea surface temperature and wind speed, the solution being iterated (in both cases) once to decide whether the wind speed is above or below 7 m/sec. The regression equations of Wilheit and Chang (1979) for the retrieval of the various parameters are as follows:

Wind Speed Retrieval- Wind Speed Unknown

ws

+

+

(m/SeC) = -465.3 O.62l6TBlO,, 0.28737~10.7~ 168.7 ln(280 - TB18v) - 86.31 ln(280 - TB18H) 15.84 ln(280 - TBzIv) - 37.18 ln(280 - TBZIH) 2.357e,, (14.4)

+ +

+

Wind Speed < 7 m/sec

ws

+

(m/SeC) = -523.9 - O.2229TB10.7v 0.6056T~lo.7~ 130.3 ln(280 - TB18v) - 39.19 ln(280 - TBI8H) 10.24 ln(280 - TB21v) - 32.75 ln(280 - TB21H) 2.999emC (14.5)

+ + +

324

MIRLE S. V. RAO

Wind Speed > 7 m f sec

+

+

WS (m/sec) = - 338.4 0.31 1 5TB10.7v 0.4509TB,0,7H 151.8 ln(280 - TBl8V) - 9 1.12 ln(280 - TB18H) - 26.66 ln(280 - TBzIv) f 12.89 ln(280 - TB2IH) 1.4326mc (14.6)

+ +

Sea Siivface Temperature (Wind Speed > 7 m/sec)

+

SST (K)= 188.9 3.040T~6.6~-1.188T~6.6~ - o.709TB10,7v 0.2405TB10,7H - 6.1 14 ln(280 - TBl8V) 20.37 ln(280 - TB18H) - 4.003 ln(280 - T,,,,) 0.986 ln(280 - TB21H) - 4.735emC (14.8)

+

+

+

Cloud Liquid Water

+

CLW (mg/cm2)= 246.1 - 5 1.72 ln(280 - TB18V) 134.4 ln(280 - TBI8H) 46.14 ln(280 - T B I I V ) 24.95 ln(280 - TB21H) - 155.5 ln(280 - TB37V) - 36.63 ln(280 - TB'37I.3) - 3.39 16,Nc (14.9)

+

+

Wilheit and Chang (1979) claimed the following accuracy. For wind speeds greater than 7 m/sec, the wind speed retrieval precision is about 1 mfsec and SST retrieval precision is about 1.5"C. For lower wind speeds, the accuracy of the wind speed retrieval degrades to 1.6 m/sec, while that of the SST retrieval improves to less than 1"C. Regardless of wind speed, the accuracy in water vapor retrieval is about 0.15 gfcm', and in liquid water content, about 4 mg/cm2.

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325

15. CONCLUSION Enough was said earlier to indicate the potentialities as well as the limitations of satellite-bornemicrowave radiometer systems in deriving geophysical parameters on a global scale. The conclusions that may be drawn from the previous discussions are briefly as follows. It is safe to conclude that in spite of various drawbacks, the microwave radiometer is at present indeed the best available means for estimating oceanic rainfall on a worldwide scale. With the use of improved radiometers operating at appropriate frequencies (preferably in the range 1820 GHz) it should be possible to get really accurate quantitative estimates of oceanic precipitation.'* It would then be possible to evaluate the enormous energy released as latent heat over oceans, a parameter vital for a deep insight into the general circulation of the atmosphere. Incidentally, a better understanding of storm structure may be expected from studies of precipitation over oceans. Over land the problem bristles with difficulties. As explained in Section 13, it is barely possible to glean some qualitative information, and even that under certain favorable conditions (e.g., during daytime when the ground is dry and not cold). It is similarly difficult to obtain an idea of soil moisture (based on the inverse relationship between microwave brightness temperature and moisture levels as indicated by antecedent rainfall) in regions where vegetation cover is sparse. The microwave radiometer is capable of sensing sea ice through clouds and in the polar night. Although quantitative evaluation is complicated by size and growth rate of ice crystals, storms in the intervening atmosphere, and other factors outlined in Section 1 1, the current microwave technique is certainly applicable to mapping sea ice. It is difficult to be equally sanguine right at present about evaluatingother geophysical parameters such as sea surface temperature, surface wind over oceans, atmospheric water vapor, and liquid water. The accuracy attained is indeed limited. This is not to say that future developmentscannot change the situation.

15.1. Suggestions for Further Work The satellite-deriveddata of oceanic rainfall could be improved with just a little effort, at a relatively low cost. Used in conjunction with other data l2 The Commission for Marine Meteorology of the World Meteorological Organization agreed at their seventh session (November-December, 1976) that continuous study ofprecipitation over oceans is essential.

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(based on radar or other observations, or derived from satellite by means other than microwave), satellitederived data provide a base for investigation that can hardly fail to produce interesting and significant new insights into precipitation climatology. The following lines of research are promising. 15.1.1. Energy Releasedfrom Latent Heat. One important aspect of precipitation is the accompanying energy release from latent heat. Over any area A , the amount of energy released may be evaluated simply from the expression

E=

1

RLdxdy

(15.1)

A

where R is the precipitation and L the latent heat of evaporation. The energy thus released over the oceansof the world is enormous. Just over a 1 latitude X 1 longitude cell, even when it is raining at a modest rate of 3 mm/hr, lo9 kJ are generated every second. In the atmosphere, latent heat amounts to as much as one-third of the net solar input. This is bound to have a serious impact on the energy budget of the earth-atmosphere system. A study of the spatial and temporal distribution of latent heat release, with its far-reaching consequences, is rendered possible by satellite-derived precipitation data. O

O

15.1.2. General Circulation Models. Oceanic precipitation is a good index of vertical motion in the absence of orography. How well general circulation models (GCMs) reproduce this feature is a test of the models. Here we have an opportunity to verify GCMs. The convective and largescale precipitation predicted by modelsL3such as the Smagorinsky - Manabe (GFDL) model, the Arakawa- Mintz (UCLA) model, the Kuo - Schneider (NCAR) model, and other models may be compared with the quantitative seasonal and regional distribution of oceanic precipitation from microwave data. The possibility also arises of defining an initial state of a new dynamical model with vertical motion (obtained through the inversion of the w equation) and latent heat as inputs. 15.1.3. Rainfall Patterns in the Major OceanicAreas and ClimaticAnomalies. Analysis of data in the three major oceanic areas of the world (in continuation of the preliminary work reported in Sections 8 and 9) is of surpassing importance. For the first time ever, a significant amount of data l 3 GFDL, General Fluid Dynamics Laboratory; UCLA, University of California, Los Angeles; NCAR, National Center for Atmospheric Research.

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on precipitation over the oceanic areas of the world has become available from Nimbus-5 and -6 satellites (which camed ESMR) as well as Nimbus-7 and Seasat satellites (both of which were launched in 1978 and carried SMMR). Preliminary to analysis, it would be profitable to collect all the above data in combination with data from surface sourcesand aircraft. This would greatly aid the investigation of patterns of rainfall in the major oceans of the world (the Pacific, Atlantic and Indian oceans), of characteristicsof the Intertropical Convergence Zone, of the progress of other rainbelts, and of possible interactions with weather phenomena over continental areas. 15.I .4. Histograms. Histograms representing frequency distribution of precipitation intensity regionally and in different months may be prepared. This will assist the studies indicated above and those to be suggested hereafter. 15.1.5. Interannual Variability. The variability of rainfall from year to year is important from an economic point of view. This ought to be studied on a global hemispheric scale as well as on smaller regional scales.

15.I .6. Periodic Variations.Diurnal variation. The preliminary study in the tropical Atlantic reported in Section 10 indicated a large diurnal variation in rain frequency as well as in intensity. Dynamical considerations do not favor such large variation being uniformly valid everywhere over the oceans. The phase difference between the diurnal variation in different regions is worth exploration. Monthly and seasonal variation. The movement of rain patterns in the major oceanic areas of the world can lead to new insights into monsoons of the world. This again is a problem of considerableeconomic consequence. Other periodic variations. Graphical analysis of GATE area ESMR rainfall observations indicates an oscillation of periodicity of 3.3 days, which is consistent with easterly waves traveling from the African continent over the GATE oceanic belt. This deserves further investigation. Oscillations of different time periods are certainly to be expected regionally-a matter that should be looked into. 15.I . 7. Diagnostic Studies. Climatic anomalies may be expected to stand out from the scrutiny of histograms and other studies referred to above. Regional and global diagnostic studies could be conducted, attempting to explain the underlying mechanism wherever possible.

15.1.8. Signijicance of the Southern Hemisphere to the Global General Circulation. Very little is known about the precipitation characteristics of

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the Southern Hemisphere, which is largely a water hemisphere. Meteorological phenomena occurring over the vast region affect markedly the global general circulation. Satellite microwave radiometry is a valuable means of filling this serious data gap. 15.1.9. Teleconnections. If the atmosphere is considered as a conservative system, marked deviations from normal over one region are likely to be compensated for elsewhere in the system. The physical linkage usually takes the form of a pressure oscillation. The following three global teleconnections are well recognized: 1. In the Atlantic, an oscillation involving the Icelandic low and the Azores high. 2. In the Pacific, an oscillation involving the Aleutian low and the North Pacific high. 3. The southern oscillation involving the South Pacific Ocean and the equatorial Indian Ocean.

All these teleconnections were discovered by studies such as those of Walker (1923, 1924) in the presatellite era, when observations over oceans were relatively sparse. There must be other linkages and teleconnections over the earth, and there is a good chance that the extensive data from satellites will reveal some new and possibly valuable ones. Two major phenomena that have been discussed before deserve further attention. A detailed investigation of the El Niiio phenomenon in relation to precipitation in the equatorial Pacific would be useful. It would be interesting to correlate the precipitation over the United States and its annual variation to the precipitation in the preceding years over the Pacific and other regions. Possible interrelationshipsthrough mechanisms such as the southern oscillation and Walker circulation may be investigated. Similar investigation may be carried out with respect to the Indian and Southeast Asian monsoons. The data acquired in the Indian Ocean and the China Sea may profitably be scrutinized for intercorrelations that will enable longrange monsoon forecasting. 15.1.10. Extended Forecasts. Two main parameters in extended forecasts are temperature and precipitation. It is recognized that precipitation forecasting is more difficult than temperature forecasting. Working with empirical orthogonal functions, Gilman (1957) found that three such functions could reproduce the pattern of mean monthly temperature anomaly over the United States so that 81% of the variance was accounted for. Twenty functions accounted for practically all the variance. On the other

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hand, precipitationrequired 20 functionsto account for an 80%reduction in variance. It is hoped that investigation with the aid of satellite-derived oceanic precipitation data will improve the situation.

15.2. Long-Term Goals The ultimate aim of this research is to utilize precipitation as well as sea surface temperature data as inputs into a suitable general circulation model to derive extended-rangeforecasts. But first, primary physical mechanisms must be understood. It may be advisable to start with a spatial and temporal matrix of precipitation data in grid cells covering the globe in weekly periods. At the beginning, time-lag correlations could be worked out and physical reasoning for high cross-correlations sought. From diagnostic studies we may initially be able to explain gross characteristics before detailed answers can be provided. In this connection,Namias (1 968) holds the view that “these details may require even more exact knowledge of such elusive physical processes as release of latent heat, momentum and water vapor exchanges, internal turbulent exchanges, radiation transfers, and in fact the entire gamut of meteorological processes . . . there is no guarantee that a completely physical solution will be found.” This is a view based upon vast experience. Therefore, during the course of this investigation, in addition to the search for physical mechanisms, it is preferable that a semiempirical approach also proceed. 15.3. Summary In summary, the research effort may be three-pronged, in the following manner: 1. All validated data may be put into a three-dimensional grid (say, 2” latitude X 2” longitude X 1 week). Cross-correlations could be worked out. Physical reasons for high correlationsshould be sought and at the same time regression functions established wherever appropriate. 2. Simultaneously, the analysis of oceanic precipitation data might be progressed further with a view to examining seasonalvariations, interannual fluctuations, and movement of rain patterns. Regional diagnostic studies could be conducted, looking for linkages through Hadley-type and Walkertype circulations. 3. Sea surface temperature, latent heat, and other related data may be examined. Every effort should be made to look into energy budget problems, particularly with the objective of sensing new teleconnections or offer-

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ing a thermodynamic explanation for those teleconnectionssuspected from the two preceding approaches. Analysis of the substantial amount of new satellite-deriveddata along these lines is very likely to lead to valuable new insights into ocean/land interactions and to improvements in extended forecasts, particularly in midlatitudes. The scheme of research outlined herein intentionally avoids being overspecific in the description both of the method of approach and of the expected findings. Flexibility rather than rigidity ought to be the keynote of the investigation. It is considered prudent to poke and peer among the data within prescribed guidelines, in the best tradition of basic experimental science. The retrieval of parameters such as sea surface temperature, oceanic surface wind, atmosphere water content, etc., is a long-term and high-cost proposition. Nevertheless, it is desirable that research in that direction should continue, although it does not appear likely that in the immediate future the microwave approach would yield quantitative information to the degree of accuracy needed for most purposes. APPENDIX. EXPLANATORY NOTES A . 1. General Notation

Notations on the maps are defined as follows: r Average rain rate in millimeters/hour (the figure inside each 4" latitude X 5" longitude grid cell). N Number of observations (available in printout-not shown in map).

A.2. Grid Cell Legend

r Average rain rate in millimeters/hour (corrected to tenths of a millimeter). (r) Same as above, but observations are few ( N < 100). -- No observations ( N = 0). x Excessive rain, indicative of bad data (probably attributable to ice on surface or anomalous mode); r 2 4 mm/hr in weekly maps; r 2 2 mm/hr in monthly, seasonal, and yearly maps. ( x ) Same as above, but observations are few ( N < 100). (blank) Land predominating (more than 75% land in grid cell).

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33 1

A . 3 . Method ofAveraging

The average rain rate in the monthly maps (r)and the average rain rate in the weekly maps (rl,r, ,r, ,etc.) are interrelated in the following manner:

where w is the number of weeks in a month. Note: r is not equal to (rl r, r, r, * * rw)/w. Similarly, the average rain rate in the seasonal and annual maps is

+ + + +- +

or

where n is the number of months and nwis the number of weeks in the season or year, as appropriate. ACKNOWLEDGMENTS My thanks are due to the American Meteorological Society for permission to reproduce Figs. 1- 5 from the Journal of Applied Meteorology and Figs. 8, 10, 17, 19,23,26, and 27 from the Bulletin of the American Meteorological Society. Justus Perthes, Geographische Verlagsanstalt, Darmstadt, Federal Republic of Germany, permitted the reproduction of one oftheir wall maps (Fig. 16). I am indebted to the American Geophysical Union for Fig. 36, which is reproduced from the Journal of Geophysical Research, and to the National Aeronautics and Space Administration for Figs. 7,9, 1 1 - 15,18,20 -22, and 24 from NASA Special Publication410; Figs. 28-31 from NASA Conference Publication-2076; and Figs. 33-35 and 37 from NASA TechnicalMemorandum- 79361.

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Technique for Quantitatively Mapping Rainfall Rates Over the Oceans.” NASA/GSFC Doc. X 91 1-75-72. Goddard Space Flight Center, Greenbelt, Maryland. Wilheit,T. T.,Chang,A.T.C., Rao,M.S.V., Rodgers,E. B.,andTheon, J. S.(1977). Asatellite technique for quantitatively mapping rainfall rates over the oceans. J. Appl. Meleorol. 16, 55 1-560.

Woodley, W. L., Sancho, B., and Miller, A. (1972). Rainfall estimation from satellite cloud photographs. NO’4A Tech. Memo. ERL-OD-11, 1-43. Zwally, H. J. (1977). Microwaveemissivityand accumulation rateofpolar fim. J. Glaciol.18 (79).

Zwally, H. J., and Gloersen, P. (1977). Passive microwave images of the polar regions and research applications. Polar Rec. 18,431 -450. Zwally. H. J., Wilheit, T. T., GIoersen, P., and Mueller, J. L. (1976). “Characteristics of Antarctic Sea Ice as Determined by Satellite-Borne Microwave Imagers,” Proc. Symp. Meteorol. Observ. Space. hlatl. Cent. Amos. Res. Rep. pp. 94-97. Zwally, H. J., Parkinson, C., Carsey, F., Gloersen, P., Campbell, W. J., and Ramseier, R. 0. (1979). “Antarctic Sea Ice Variations, 1973-75.” Roc. NASA Weather Climate Prog. Sci. Rev., 4th. pp. 335 - 340. NASA/Goddard Space Flight Center, Greenbelt, Maryland.

INDEX A Advective-diffusiveocean model, ancient anoxic events and, 6 1 Aerosols, in atmosphere, effects on paleoclimate modeling, 97-98 Africa ancient collision with Europe, 73 C0,-induced temperature changes over, 172, 178,228 C0,-related precipitation, changes in, 193 paleoclimate indicators from, 54, 56, 57 simulated soil moisture, changes in, 209 Alaska paleoclimate indicators from, 56 paleocontinental reconstruction of, 82 paleofloras of, 70 Albian epoch, paleoclimate indicators from, 58 Aleutian low, 90 Alexander Island, paleoclimate indicators from, 56 Alloys fractionation upon solidification of, 4 “mushy zone” formation in, 13 Amazon Basin, glacial epoch of, 53 Ammonia, disappearance from early atmosphere, 99 Ammonites, extinction of, 63 Analog method, of climate estimation, 142 Andes, ancient connection to Antarctica, 82 Angiosperms, as paleoclimate indicators, 43 Angola-Brazil Basin ancient anoxic event in, 60 paleocean temperatures of, 63 Animals, as paleoclimate indicators, 44-45 Anoxic events, in oceans, 78-79 of Cretaceous period, 60-6 1 Antarctica C0,-induced temperature changes in, 172, 177 glaciation beginning in, 54, 121 paleocontinental reconstruction of, 82 progressive separation from Australia, 72, 82, 121

Antarctic Circumpolar Current, beginning of, 121 Antarctic glaciation, in Tertiary period, 7074,121 Antarctic ice sheet, formation of, 73,74 Antarctic Sea, sea ice mapping of, by satellite, 308 Aragonite-to-calciteratio, of shell carbonates, temperature effects on, 4 1 Arakawa- Mintz model (GCM), satellite-derived data coupled with, 326 Arctic C0,-induced temperature changes over, 177 paleoclimate of, 1 15 indicators, 56 Arctic glaciation, initiation of, in ancient times, 74, 121 Arctic Ocean, paleotemperature of, 88 Argentina, paleoclimate indicators from, 57 Asia paleoclimate indicators from, 56 paleofloras of,70 simulated soil moisture, changes in, 208 209,210 Atlantic Ocean ancient anoxic events in, 60 - 6 1 C0,-induced temperature changes over, 178 C02-related precipitation, changes in, 193 evaporites from margins of, 54 paleobathymetry of, 85 -86 paleotemperatures of, 68 pressure oscillation in, 328 satellite-derived rainfall data on, 277, 279 variations in tropical part of, 297-304 surface paleotemperature of, 87, 89 Atlantic-Pacific passage, restriction in ancient times, 74 Atmosphere, aerosol content of, effects on paleoclimate modeling, 97-98 Atmosphere/ocean/ice/land/biomassclimatic system, schematic illustration of, 143

337

338

INDEX

Atmosphere/ocean/sea ice, general circulation model coupled to, 152 Australia C0,-induced temperature changes over, 172, 178 progressive separation from Antarctica, 72, 82 simulated soil moisture changes in, 208 Austral Realm, characteristics of, 57 Averaging method, for rainfall, 33 1

B Barodiffusion, effect on earths core, 10 Barrett’saerial statisticstechnique, for rainfall estimation, 239 Basalts, hot-spot type, chemistry of, 19 Batholiths, D”-originated, 16 Bathymetry of Ocean basins, effects on climate, 76 Bauxites, as paleoclimate indicators, 40, 54 Bay of Biscay, paleotemperatures of, 66 Belemnites, 87 extinction of, 63 Belemnite shell, as standard for oxygen-isotope paleotemperature method, 46, 49, 51 Bellingshausen- Amundsen seas, sea ice mapping of, by satellite, 308 Black Sea, C0,-induced temperature changes over, 172 Boreal Realm, characteristics of, 57 Brachiopods, as paleosalinity indicators, 45 Brazil. C0,-induced temperature changes over, 172, 178 C Calcareous phytoplankton, extinction of, 63 Calcite, in shells, nonequilibrium deposition Of. 48-49 Calcite compensation depth (CCD), in determination of isotopic temperatures, 50-51

Calcium carbonate, deposition in environmental water, 46 Calcrete, as paleoclimate indicator, 40 California, paleocontinental reconstruction of, 82 Campanian period, paleocean temperatures of, 62

Cape -Argentine basin, ancient anoxic event in, 60 Carbon dioxide atmospheric, 36 effect on climate, 77, 79, 80, 120 effect on paleoclimate modeling, 98 101

increase in, 14 1 Carbon dioxide-induced climatic change, 141-235 comparison of model simulations of, 152216 doubling and quadrupling of CO, levels in studies of, 160 equilibrium vs nonequilibrium studies, 156 lag time in, 157 model-dependent results, 2 I7 - 2 19 simulated precipitation changes in, 19 1 206 simulated soil moisture changes, 206-2 16 simulated temperature changes, 165- 190 for CO, doubling, 165- 175, 185-206 for CO, quadrupling, 175 - 185 statistical studies on, 222- 228 time required to reach equilibrium in, 219-221

Carbon-isotope analyses, in paleoclimate studies, 45 method, 52 Carbonates in deep ocean, as buffer for high carbon dioxide in atmosphere, 100 in marine sediments, 4 1 Carboniferous period, glaciations in, 53 Carbon monoxide, disappearance from early atmosphere, 99 Caribbean - Gulf Coast region, paleoclimate indicators from, 58 Caribbean Sea ancient anoxic event in, 6 1 Cretaceous island arcs in ancient times of, 86 paleotemperatures of, 68 Caspian Sea, C0,-induced temperature changes over, 172, 178 Cenomanian period, paleoclimate indicators from, 58 Cenozoic era energy balance models of climates in, 1 15 near-coastal upwelling in, 90

339

INDEX

ocean temperatures of, 66-67 paleoclimates of, 38 Central America, paleocontinental reconstruction of, 82 “Chimney,” in deep-mantle plume, 20 Chlorite, as paleoclimate indicator, 69 Circum-Antarctic current, development of, 73,74

Circumequatorial current, disruption of, in Tertiary period, 7 1 Clausius-Clapeyron equation, 205 Clay minerals in marine sediments, 4 1 as paleoclimate indicators, 40, 69 as salinity indicators, 4 1 Climate carbon dioxide-induced changes in, 141235

models of, 36 - 37 Climate estimation by analog method, 142 mathematical models for, 143- 152 by physical method, 142 Climatic change, long-term, forcing mechanisms in, 74- 80 Clouds as diagnostic variables in climate estimation, 146 C0,-induced changesand, 161,164,172, 173, 178, 187 use in rainfall estimation, 239, 293

water in, satellite-derived data on, 320321,324

Coals, as paleoclimate indicators, 39-40, 54, 56

Coastline, changes in, during geologicaltime, 83

Coefficient of material diffusion, for liquids, 8 Computer in studies of mantle convection, 22 use in climate estimation, 146 use in rain mapping, 262 -263 Continent- ocean positions,constant changes in, effects on climate, 76 Continents ancient sites of, 41-42 reconstruction of, 80 - 86 surface elevation of, 148 Cooling of earth, 2

Corals as paleoclimate indicators, 57, 94 as paleosalinity indicators, 45 Coral Sea, paleoceanography of, 7 1 Core (of earth) cooling of, 4,22 - 23 hydrostatic balance of, 6 - 7 inner age of, 5 boundary, see Inner-core boundary glassy transition of, 15 mushiness of, 14 seismic model of, 14 structure of, 12- 15, 24-25 iron in, 4 -mantle boundary, see Core-mantle boundary (CMB) models of, 1 outer stable layer in, 10 structure of, 6 - 12,24 preferred parameter values for, 6 steady state, erroneous concepts of, 2 structure of, 1- 34 ‘summary, 24-25 velocity structure at top of, 10 Core-mantle boundary (CMB) structure of, 15 temperature increment at, 16 thermal gradient at base of, 18 Core paradox, 3 Coriolis parameter, changes in, effect on climate, 75, 94, 296 Cretaceous period atmosphere-ocean system of, 38 atmospheric carbon dioxide levels in, 100101

foraminifera from, 50 fossil family survival from, 43 isotopic composition of ocean water in, 48 marine biogeography of, 67-68 paleoclimate indicators of, 42,43, 46, 121 marine isotopic temperature record, 6 1 67

oxygen-isotope studies, 46 paleotemperature studies on, 52 sea level changes in, 77 sea surface temperature in, 87, 88 Cretaceous-Tertiary boundary event faunal group extinction in, 63

340

INDEX

hypotheses on causes of, 64,65 Crocodiles, as paleoclimate indicators, 44 Cyclones in paleoclimates, I 12 satellite-deriveddata on, 3 I 1 in South Atlantic. 294

D D layer of earth’s mantle deepmantle plumes and, 19-20 structure of, 15- 18 as thermal boundary layer, 15- 16,23, 24 Days per year, in ancient times, 94,95 Deep-sea cores, in paleoclimate studies, 4 1, 45, 50, 58 Deep Sea Drilling Project (DSDP), Ocean floor studies in, 5 I Dendrites, from inner core, 14- 15 Density, as diagnostic variable in climate estimation, 146 Desert dunes, ancient, paleowind direction markers on, 40 Detritus, wind-transported, as temperature indicator, 4 1 Diamictites, as paleoclimate indicators, 4 1 Dinosaurs extinction of, 63,64 temperature tolerances of, 44 Diurnal variation in oceanic rainfall, 297,327 explanations for, 303 Dolomite, as salinity indicator, 41 Downslope slide deposits, 41 Drake Passage, closure of, in ancient times, 73

E Earth cooling of, 2 rate, 2 core of, see Core (of earth) radioactive heating of, 2 in steady state, erroneous notion of, 2 thermal history of, 1 , 2 1 - 27 computer studies, 22 Earth-orbital parameters efftfect on climatic change, 75 in paleoclimate modeling, 96-97 Echinoderms, as paieosalinity indicators, 45 ElectricallyScanning Microwave Radiometer (ESMR), 239-336

brightness temperature from conversion to rain rate, 246-247 factors contributing to, 243 -245 description of system, 24 1 - 246 precipitation parameters derived from, 239-336 advantages, 240,3 17- 3 18 computer use in, 262-263 data collection errors, 260 diagnostic studies, 327 diurnal variation, 297, 303, 327 error analyses, 262 extended forecasts, 328-329 histograms, 327 interannual variation, 295 -296, 327 intercomparison, 268 - 276 oscillations in, 303 problems in, 258-260 radar compared to, 249 -252 suitability, 245 - 246 telecommunications, 328 verification by experiment, 252-257 retrieval of other geophysical parameters by, 317-324 SMMR, 318-319 techniques in, 322-324 storm structure studies by, 308 - 3 1 1 in qualitative estimation of rainfall over land areas, 31 1- 317 sea ice mapping by, 304-308 what it measures, 242-243 Electric fields, in core studies, 1 Ellesmere Island, paleotemperature indicators from, 70 El Niiio phenomenon, satellite-derived data and maps on, 282, 285-287, 295296,328 Energy, from gravitational separation, 25 -27 Energy balance models (EBMs) comparison of, for C0,-induced climate changes, 153- 155, 189- 190,229 of paleoclimates, results of, 1 15 - 1 18 as thermodynamic climate models, 144 Eocene epoch atmospheric carbon dioxide levels in, 101 glaciations in, 53, 121 ocean temperatures in, 7 1, 121 sea surface temperatures in, 87 - 89 Eocene-Oligocene boundary cooling event, 65, 66, 70

INDEX

Equador, C0,-related precipitation changes in, 193 Equation of radiative transfer, 248 Equator, paleoclimate of, 37 Europe C0,-induced temperature changes in, 172, 178 simulated soil moisture changes in, 208209,2 12 Evaporites as paleoclimate indicators, 40,54, 56, 100 F Feldspar in marine sediments, 4 1 as paleoclimate indicator, 69 Ferns, extinction of certain genera of, 64 Ferrous oxide, as proposed light constituent in earth’s core, 8, 1 I Ferrous sulfide, in systems of earth’s core, 8 Fish teeth and bones, paleotemperature determinations using, 5 1 Flemish Cap, paleoclimate indicators from, 57 Foote and du Toit relationship for rain rate, 246,247 Foraminifera extinction of, 64 as paleoclimate indicators, 45, 50-51, 52, 57, 71, 87,90 Forcing mechanisms, in long-term climatic change, 74 - 80 diagram, 78 Forecastsofweather, satelliteuse in, 328 - 329 Fossil faunas, as paleoclimate indicators, 37, 39,40 Fossil floras, as paleoclimate indicators, 37, 39,56 Fossil fuels, atmospheric carbon dioxide increase from, 141 Fossil species, current existence of, 43 Free oscillations, in core studies, 1 Fresnel relations, 247 Fruit, as paleoclimate indicators, 43 G General circulation models (GCMs) ofclimates, 145- 152

34 1

for C0,-induced climate changes characteristics, 162- 163 comparison of, 153- I55 description of, 158- 165 for doubled and quadrupled CO,, 165206 problem reduction in, 229 equations for, 146 grid point models and, 146 oceanic, 151 - 152 ofpaleoclimates, 38, 81, 101, 103-106 comparison with paleoclimaticevidence, 107- 108 modeling strategies, 104- 106 resultsof, 113-115, 119-120, 123 sea surface temperatures from, 86 satellite-derived data use with, 326 slab models coupled with, 150- 15 1 spatial resolution of, 146- 147 subgrid-scale processes serving as parameters for, 149 swamp ocean model coupled with, 105, 150 three-dimensional, 157- 158 two-level atmospheric, 147 variable-depth mixed-layer model coupled with, 151 Geodynamo energy source for, 1 - 3 theory, 4-6,22 Geological time scale, major divisions of, 36, 37 Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model of paleoclimates from, 120 satellite data coupled with GCMs of, 326 studies of C0,-induced climate changes at, 159, 160,218 characteristics, 162- 163 precipitation changes, 193, 194,203 soil moisture changes, 207,2 13-2 15 temperature changes, 165, 168- 175, 181- 185 Geophysics, steady state of core in, 2 Geopotential height, as diagnostic variable in climate estimation, 146 German Weather Service,rainfall maps from, compared with satellite-derived data, 268-276

342

INDEX

Gilda (typhoon), satellite-derived data on, 310 Glacial conditions, diamictites as indicators of, 41 Glacial epochs, paleoclimates of, 53- 54 Glacial moraines, tillites as consolidated, 4 1 Glassy transition, in inner core, 15 Global Atlantic Tropical Experiment (GATE) data on rainfall, 268, 271, 297-302, 327 Global Atmosphere Research Project (CARP), radar rainfall data from, 268 Global Oceanic Rainfall Atlas, from ESMR data, 257-259,263 Global rainfall, satellite-deriveddata on, 277, 28 1 Goddard Space Right Center C0,-induced climate change studies at, 158,228 satellite rainfall experiment at, 252-257 Gondwanaland continents, glacial epochs in, 53 Gravitational constant, 4 Gravitational energy calculations of, 25 - 27 as probable energy source for geodynamo, 2, 3 theory, 4 - 6 Gravitational separation, of core constituents, 4 Gravity, in core studies, 1 Greenhouse effect description of, 14 1 - 142 paleoclimates and, 98 Greenland, paleofloras of, 70 Greenland Sea passage, opening of, 72 sea ice mapping of, by satellite, 307 Grid cell legend, 330 Grid point models, in climate estimation, 146 Griineisen parameter, 3 Gulf of Bothnia, sea ice mapping of, by satellite, 307 Gulf Stream, 297 movement of, in ancient time, 74 proto-, development of, 58 Gymnosperms, extinction of certain genera of, 64 Gypsum-rich horizons, as salinity indicators, 41

H Hadley cell, 296 Hadley circulations, in paleoclimates, 112 Heat flow, in core studies, 1 Heat flux, of inner-core boundary, 9 Heat transport, in earth’s interior, 2 I High-Resolution Infrared Radiometer (HRIR), in rainfall estimation, 240 Himalayas, orogeny of, effect on paleocontinental boundaries, 82 Horizontal velocity, in climate estimation, 146 Hurricanes, satellite-deriveddata on, 308 Hurricane tracks, in paleoclimate records, 110

Hydrodynamic mathematical climate models, 143, 144, 151 Hydrothermal circulations, in core studies, 1 I Ice albedo feedback in C0,-induced temperature changes, 167 Iceland-Faroe Ridge, 72 Ice mapping, by satellite, 304- 308 Illite, 49 in wind-transported detritus, 4 1 India, paleocontinental reconstruction of, 82 Indian Ocean ancient anoxic events in, 40 paleobathymetry of, 86 pressure oscillations in, 328 satellite-derived rainfall data and maps on, 217,280,282,283,284,294-295 sea ice mapping of, by satellite, 308 surface paleotemperature of, 87 Indonesia, precipitation changes in, C0,-related, 193 Inner-core boundary (ICB) convoluted form of, 13 heat flux causes of, 9 inner core growth and, 12 seismic properties of, 14 warming of due to adiabatic compression, 14

Inoceramids, extinction of, 63 Inocerumzcs shells, paleotemperature determinations on. 49, 43 Insects, as paleoclimate indicators, 44

343

INDEX

Intermontane basin deposits, as paleoclimate indicators, 56 Intertropical Convergence Zone (ITCZ) equatorial rain beIt association with, in Africa, 56 satellite-derived data on, 276-282, 290293 Iridium in K-T boundary clays, 64 Irma (typhoon), satellite-deriveddata on, 3 10 Iron alloys, elements in, 8 in earth’s care, 4, 11 - 12 silicon compound of, in systems of earth’s core, 8 J

Jurassic period, paleotemperature indicators of, 51, 52

K Kaolinite as paleoclimate indicator, 40 as salinity indicator, 4 1 Komatiite lavas, mantle rheology and, 24 Kuo- Schneider model (GCM), satellite-derived data coupled with, 326 Kuroshio current. 297 L Lacustrine animals, as paleoclimate indicators, 44 Land areas, qualitative estimation of rainfall over, by satellite, 3 1 1 - 317 Latent heat energy released from, satellitederived data in studies on, 326 release over oceans,239 Laterites, as paleoclimate indicators, 40, 56 Lawrence Livermore National Laboratory (LLNL), CO,-induced climate change studies at, 158, 159 Leaves, as paleoclhate indicators, 43-44,56 Lindemann’s law, 3 Liquids, coefficient of material diffusion for, 8 Liquid-state theory, core cooling and, 3 Liquidus gradient, for earth’s core, derivation of, 3

Lithosphere changes a c t i n g climate, 75 - 76, 8 1 on ocean floor, 85 Lithospheric slabs, lateral heterogeneities of lower mantle and, 2 1,25 Lizards, as paleoclimate indicators, 44 Lower mantle (of earth) discontinuities in, 18 structure of, 1 M Maastrichtian period, paleocean temperatures of, 62, 63, 65 Madagascar paleoclimate indicators from, 57 paleocontinental reconstruction of, 82 Magnesium, of shell cabonate, temperature effects on, 41 Magnetic fields, in core studies, 1 Magnetism, of terrestrial planets, relation to earth’s core, 2- 3 Mantle (of earth) cooling of, 5 -core boundary, see Core - mantle boundary D” layer of, 15 - 18 lower, see Lower mantle Newtonian rheology of, 23 plumes from deep area of, 19-2 1 rheology of, 2 1,24 thermal histories of, 24 viscosity of material in, 17 Mariana Basin, ancient anoxic event in, 60 Marine biogeography, of late Cretaceous period, 67-68 Marine faunas, as paleoclimate indicators, 44-45 Marine sediments as glaciation indicators, 4 1 as paleoclimate indicators, 4 1,46- 52 in studies of paleocean temperatures, 68 69 Marshall -Palmer dropsize distribution, 246, 241,248,257 Mathematical climate models, 143- 152 hydrodynamic type, 143 thermodynamic type, 143 use in climate estimation, 142 Maxwell relation, 8

344

INDEX

Mediterranean Sea, as survivor of “Tethys” ocean, 85, 86 Mesozoic era energy balance models of climates in, I 15 near-coastal upwelling in, 90 paleoclimate indicators from, 58 Messinian salinity event, in Mediterranean, 74 Methane, disappearance from early atmosphere, 99 Miami, rainfall estimation at, radar and satellite data compared, 250-252 Micas, in marine sediments, 41 Microplates, movements of, 82 Mid-Cretaceous period climate of. 38, 54-61 paleocontinental map of, 84 paleogeography of, 110, 1 I8 Mid-Devonian period, glaciations in, 53 Mid-Miocene epoch glaciations in, 53 ocean temperatures of, 67 sea level in, 73 Mid-Permian period, glaciations in, 53 Minerals, in marine sediments, 4 1 Modeling of paleoclimates, 80- 101 boundary conditions, 102 strategies, 101 - 108 survey of results, 108 - 120 Mollusks, fossil species of, 43 Monsoons, 239 forecasting of, 328 in paleoclimates, 1 12 satellite-derivedrainfall maps of, 276, 282 theories of, 295 Montana, plant extinction evidence in, 64 Mountains, pre-Pleistocene, erosion of, 39 Mushy zone formation in alloys, 13 in inner core, 14 seismic properties, 15 N Nannofossil assemblages, as paleoclimate indicators, 57, 58 National Center for Atmospheric Research (NCAR) C0,-induced climate change studiesat 158, 160-162, 218,219 characteristics of, 163

precipitation changes, 195 soil moisture changes, 2 12 temperature changes, 165, 167, 175179, 183-185, 187, 188 satellite rainfall data coupled with GCMs of, 326 NCAR Community Climate Model, results Of, 119-120 Neogene age, fossil species survival from, 43 New Zealand paleocontinental reconstruction of, 82 paleotemperatures of, 68 Nimbus-5 ESMR description of, 241,259 rainfall data derived from, 264,265,312 Nimbus-6 ESMR, description of, 24 1 -242 Nimbus-7 SMMR, description of, 3 18 - 3 19 Nitrogen, as biolimiting nutrient, 42 Nonglacial epochs, paleoclimates of, 53 - 54 Nora (cyclone), satellite-derived data on, 308 - 3 10 North America paleoclimate indicators from, 56 west coast of, paleoclimate indicators from, 68 Northern Hemisphere, paleofloras of, 70 Nusselt number-Rayleigh number relations, in studies of thermal history of earth, 22,23 0 Ocean@) ancient anoxic events in, 60-61 cooling of, 12 1 circulation of, in paleoclimate modeling, 86-92 deep circulation of, importance of, 106 general circulation models of, 15 1- 152 lithosphere changes in, effects on climate, 76 paleotemperatures of, from general circulation models, 1 13 rainfall maps for, from satellite-derived data, 257-267 rainfall over, satellite-derived data on, 238 satellite-derived precipitation data over, 244-245 improvement, 325 - 326

INDEX

345

surface temperature of, in paleoclimate isotopic composition variation in oceans modeling, 86-92 using, 48 map, 91 shell calcite deposition studies using, 48 Ocean/atmosphere/biosphere system, two 50 principal regimes in, 78-79 Ocean floor P changes in, 8 1, 85 - 86 spreading of, from volcanism, 97 - 98 Pacific Ocean Ocean gateways, paleogeography of, 85 ancient anoxic events in, 60 Ocean/sea ice model, for CO,-induced cliC0,-related precipitation changes in, 193 Cretaceous siliceous sediments from, 42 mate changes, 159- 160 Ocean waters paleobathymetry of, 106 ancient paleoceanography indicators from, 58 isotopic composition variation in, 48 pressure oscillation in, 328 mean isotopic composition of, 47-48 satellitederived rainfall data on, 277, 278, paleotemperatures of, 56-61 282 Oligocene epoch, sea level in, 73 ITCZ characteristics, 290-293 Opaline silica, as paleoclimate indicator, 42 storms of, satellite-derived data on, 308Orbitolina, as paleoclimate indicator, 57, 58 311 Ordovician period, glaciations in, 53 surface paleotemperaturesof, 87,89,90,92 Oregon State University (OSU) Pacific plate, survival of, 85, 86 C0,-induced climate change studies at, Paleobathymetry, in reconstruction of ocean 159-160, 161-162, 218, 219, 220, floor changes, 85-86, 102, 106 22 1 Paleobotany, paleoclimate studies using, 43 characteristics of, 163 Paleoceanography, 52 -74 precipitation changes, 193, 195, 203, Paleocene age 204 fossil genera survival from, 43 soil moisture changes, 208,209 sea surface temperatures of, 88-89 temperature changes, 168, 170, 172- Paleoclimates in pre-Pleistocene ages, 35 175, 177-178, 187 140 Orogeny (mountain building) factors external to the earth in, 75, 121 factors internal to ocean/atmosphere/bioeffects on climate, 76 in GCM modeling of paleoclimates, 1 15 sphere system, 77, 121- 122 Orphan Knoll, paleoclimate indicators from, forcing mechanisms in, 39, 74 - 80 diagram, 78 58 indicators of, 39-52 Oxygen mid-Cretaceous period, 54 -6 1 in ancient atmosphere; 99 modeling of, 38, 80- 101 application of to ancient ocean water, 47-48 boundary conditions, 102, 122- 123 results from, 1 13- 1 15 to mid-Cretaceous period, 59-60 strategiesfor, 101 - 108 in paleotemperature modeling, 96, 122123 results from, 1 13- 115 nonglacial, 121 basic theory of, 46-47 pdeobotanical evidence of, 43-44 ecological factors in, 50-51 paleoceanography in studies of, 52 - 74 in earth’s core, 4 paleozoological evidence for, 44-45 in iron alloy, 8 qualitative evidence for, 39-45, 120-121 sea surface temperature by, 87 quantitative evidence for, 45-52, 121 studies on noncalcite materials, 5 1 Oxygen-isotope paleotemperature method, Paleocontinental maps from mid-Cretaceous period, 84 45,46-52

346

INDEX

modifications to, 82-83 Paleocontinental reconstructions, in paleoclimate modeling, 80 - 82, 122 Paleodepths, of oceans, determination of, 50-51 Paleofloras, as climate indicators, 69- 70 Paleogeography, 55 in paleoclimate modeling, 80-86, 122 Paleontology, use in paleoclimate studies, 42-44 Paleosalinity, indicators of, 41,45 Paleosols, as paleoclimate indicators, 40 Palynomorphs, as paleoclimate indicators, 44 “Panama” sill, removal in Santonian era, 6 I Panama Strait, closure of, 74, 121 Parameterization, in atmospheric general circulation models, 149 PDB standard for oxygen-isotope paleotemperature method, 46 Permian period glaciations in, 53 precipitation simulation on continents of, 109 Phanerozoic era atmospheric carbon dioxide levels in, 100 coal deposits of, 40 Phosphorites, as paleoclimate indicators, 42 Phosphorus, as biolimiting nutrient, 42 Photosynthesis,atmospheric oxygen from, 99 Physical method, of climate estimation, 142 PKJIKP phase of inner core, 14 P K I W phase of inner core, 14 PKP precursors, in core- mantle boundary, 9 Planck’s function for the intensity of radiation by a blackbody, 242,243 Planets, terrestrial, magnetism of related to earth’s core, 2 - 3 Plants, as paleoclimate indicators, 43-44 Plate tectonics hypothesis, 75, 80 paleoclimate studies and, 37-38 Pleistocene age, paleowind indicators of, 42 Plumes from inner cure, 15- 17,22 hot-spot volcanoes and, 19 structure of, 19-21 thermal and dynamical model of, 20 Polar regions, warm paleocfimate of, 37 Poles, ice formation on, sensitivity experiments on, 119 Pollen, as paleoclimate indicator, 43,44, 54 Polynya, determination by satellite, 308

Precambrian era, glaciations in, 53 Precessional motion, as possible energy source for geodynamo, 2 Precipitation, see also Rainfall C0,-induced changes in, I9 1- 206 satellite-derivedparameters of, 239-336 Pre-Cretaceous period, global climates of, 53-54 Preliminary reference earth model (PREM), data from, application to studies of core- mantle boundary, 16- 17 Pre-Pleistocene ages, paleoclimates in, 35 140 Psychrosphere, 71

Q Quartz, 69 in marine sediments, 4 1 Quartz grains, wind velocity markers on, 40 Quaternary period glacial climates of, 67 paleoclimates of, 37, 53, 121 Queen Charlotte Island, paleoclimate indicators from, 57

R Radar rainfall estimation by, 239 comparison with satellite data, 249-252 Radiative- convective models (RCMs) comparison of, for C0,-induced climate changes, 153-155,157,189-190,229 as thermodynamic climate models, 144145 Rainfall over land areas, qualitative estimation by satellite, 3 1 1 - 3 17 over oceans, maps of from satellite-derived data, 257-267 satellite-derivedparameters of, 239 -336 Rainfall maps, analysis of, 276-290 Rayleigh-Jeans approximation, 242,243 Rayleigh number, high, of constant-velocity fluid, 16 Rayleigh scattering, 249 Red sediments, as paleoclimate indicators, 40-41 Reptiles, as paleoclimate indicators, 44 Rheology, of earth’s mantle, 2 I

INDEX

Rio Grande Rise, breaching of, 72 Rock fragments, in marine sediments, 4 1 Rocks, in studies of paleoclimates, 37 Rossby’s theorem ofconsemation ofpotential energy, 293 Ross embayment, paleotemperature of seas in, 73 Ross Sea, sea ice mapping of, by satellite, 308 Rotation of earth effect on climatic change, 75 importance in paleoclimate modeling, 94 Rudist bivalves extinction of, 63 as paleoclimate indicators, 57 S

Sahara Desert C0,-induced temperature changes over, 172, 178 glacial epoch in, 53 simulated soil moisture changes in, 2 10 San Andreas Fault, 82 Sandstones,desert-type,as paleoclimate indicators, 40, 56 Santonian period, ancient anoxic events in, 61 Satellite-derived precipitation parameters, 239 - 336 by ESMR, 239-336 information from, 238-239 Saturn-like ring, around earth, as proposed mechanism for Eocene- Oligocene boundary cooling event, 72 Scanning Microwave Spectrometer (SCAMS), atmospheric observations made by, 3 18 Scanning Multichannel Microwave Radiometer, rainfall data from, 240, 3 18 319,327 Sea ice in climate estimation, 149, 150, 164 satellite mapping of, 240, 304-308, 325 Sea level, changes, effects on climate, 76-77, 79 Sea of Okhotsk, sea ice mapping of, by satellite, 307 Seasat satellites, SMMR-derived atmospheric data derived by, 3 18- 3 19 Sea surface, paleotemperatures of, 106

347

Sea surface temperature (SST) as boundary for paleoclimate modeling, 122, 124 reconstruction of, 90-92 satellitederived data on, 32 I, 324 in validation of atmospheric general circulation model, 149, 150 Sedimentaryrocks as paleoclimateindicators, 39 Seeds, as paleoclimate indicators, 43 Seismic analyses of D” layer of mantle, 15 - 16 of lower mantle, 25 Seismic data, of deepmantle plumes, 2 I Seismic waves in core studies, 1, 3 inner core, 14- 15 Sensitivity experiments, in modeling of paleoclimates, 107 energy balance models, 115 - 118 results of, 1 15 - 120 Shallow-water marine sediments, as paleoclimate indicators, 4 1 Shatsky Rise, paleotemperature studies on, 59,62 Shells, of marine organisms, oxygen-isotope paleotemperature method applied to, 46 Siberia paleocontinental reconstruction of, 82 paleofloras of, 70 Silica, 7 1 biogenic, in marine sediments, 41 of deep-ocean cherts, paleotemperature determinations on, 5 1 Silicate dusts, from volcanic interruptions, 97,98 Silicate rocks, weathering in paleoclimates,40 Silicon as biolimiting nutrient, 42 in iron alloy, 8 Silurian period, glaciations in, 53 Slab models, general circulation models COUpled with, 150- 151 Smagorinsky-Manabe model (GCM), satellite-derived data coupled with, 326 Smectite in marine sediments, 68,69 as paleoclimate indicator, 40 volcanic origin of, 4 1

348

INDEX

“Snapshot” simulations of paleoclimates, 96, 97, 106-107, 123 results Of, 109 - 11 5 Snow mass, in climate estimation, 146, 164 Soil moisture, in climate estimation, 146 C0,-induced changes, 206-216 Soil temperature, in climate estimation, 146 Soils, ancient, see Paleosols Solar luminosity effects on climatic change, 75 in paleoclimate modeling, 94-96 South America glacial epoch in, 53 paleoclimate indicators from, 56 simulated soil moisture changes in, 210, 212 South Atlantic, previously unrecognized rain area in, 29 1- 294 Southern Hemisphere satellite-derived data on rainfall in, 296297 global general circulation and, 327 - 328 Southern Ocean evolution of, 85 paleobathymetry of, 86 paleotemperature of, 7 1 sea ice mapping of, by satellite, 308 Southern pressure oscillation, 328 South Pacific Convergence Zone, ITCZ convergence with, 290 South Pacific ocean, pressure oscillation in, 328 South Tasman Rise,ancient sea over, 73 Spores, as paleoclimate indicators, 44,54 Standard mean ocean water (SMOW), 47,48, 59 Statistical-dynamical models (SDMs) of paleoclimates, 38, 101- 103, 123 Storms, satellite-derived data on, 308- 3 11 Strontium/calcium ratios, in calcite, 50 Structure of earth’s core, 1 - 34 Sulfur in earth‘s core, 4 in iron alloy, 8 Sulfur gases, from volcanic eruptions, 97 Superanomaly method, in studies of carbon dioxide-induced climatic change, 157 Surface albedo as diagnostic variable in climate estimation, 146

importance in paleotemperature reconstruction, 92 GCM modeling, 1 14 Surface elevations, changes in, effects on paleocontinental maps, 83 Surface pressure, in climate estimation, 146 Surface weathering, carbon dioxide levels and, 99 Surface wind, satellite-deriveddata on, 319320, 323-324 Swamp ocean model GCM Ocean modeling based on, 105, 150, 216,219-221 spectral general circulation model of, 1 19 120 T Tanzania, paleoclimate indicators from, 57 Tasman Sea, paleoceanography of, 7 1 Teleconnections, in global precipitation, 328 Temperature in climate estimation, 146 simulated changes in, from atmospheric CO,, 165-190 Tertiary period climates of, 37, 38,42,61-74, 121 marine isotopic temperature record, 61 67 oxygen-isotopestudies, 46 foraminifera from, 5 1 glacial climates of, 67 glacial epochs in, 53-54 global cooling and Antarctic glaciation in, 70-74 isotopic composition of ocean water in, 48 paleotemperature studies on, 52 plant taxa of, as paleoclimate indicators, 43 progressive cooling during, 38 - 39 sea level changes in, 77 Tethyan Realm, 85 characteristics of, 57, 58 Tethys ocean, 82, 85 paleobathymetry of, 106 Thermal convection, as possible energy source for geodynamo, 2 Thermal evolution, of earth, 1, 3 Thermal history, of earth, I , 2 I - 27 Thermodynamic mathematical climate models, 143

INDEX

energy balance models (EBMs), 144 radiative-convective models (RCMs), 144-145

Tillites, as paleoclimate indicators, 41, 53 Topography, in core studies, 1 Toroidal magnetic field, 5 Transfer function, in superanomaly method for climate simulation, 157 Triassic period, precipitation simulation on continents of, 109 Tropical organisms, as paleoclimate indicators, 45 Troposphere, aerosolsin, effect on climate, 98 Turonian period, paleocean temperatures of, 63

Turtles, as paleoclimate indicators, 44 Typhoons, satellite-deriveddataon, 308 - 309

U United Kingdom Metereological Office (UKMO) C0,-induced climate change studies at, 159, 186-188

precipitation changes, 204 United States C0,-related climate changes in, 228 paleofloras of, 70 simulated soil moisture changes in, 208, 212

rainfall over, satellite-derived data on, 315-316

USSR C0,-induced climate change studiesin, 159 paleofloras of, 70

Vertical velocity, as diagnostic variable in climate estimation, 146 Volcanic rocks, use in core studies, 1 Volcanism in ancient Pacific Ocean, 86 effect on climate, 77, 97-98 in Tertiary period, 72 Volcanoes hot-spot type deep-mantle plumes and, 19 molten material from D’ layer from, 20 W

Waldteufel relationship, for rain rate, 246, 247

Walker circulations, in paleoclimates, 1 12 Warm saline bottom-water (WSBW) hypothesis, climatic implications of, 88 Water vapor in climate estimation, 145- 146 satellite-derived data on, 320, 324 Weddel Sea, sea ice mapping of, by satellite, 307 - 308

Westerly winds, in paleoclimates, 1 12 West Pacific Atolls, rainfall data for, 302 White Sea, sea ice mapping of, by satellite, 307

Wind($ detritus transported by, as climate indicator, 41 hypothetical, in mid-Cretaceous period, 111 Wisconsin glacial age, general circulation model applied to, 150 Wood, as paleoclimate indicator, 43

V

Variable-depth mixed-layer model, general circulation model coupled with,15 1

Z Zeolites, in marine sediments, 41

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