Borehole climatology: a new method on how to reconstruct climate

Preface

Three of the major challenges that mankind will come across in the coming decades are increasing energy demand, threatening water shortage, and so far inscrutable climate warming. World climate has been always undergoing significant changes in the past, which were represented by irregular interchanging of colder and warmer cycles of varying duration. At present, however, we witness a pronounced global warming, which seems even to accelerate. Air temperature is rising rapidly as does increase in the weather variability producing frequent extreme events. Six of the 10 warmest years of the twentieth century occurred in the 1990s. Temperatures predicted for the twenty-first century ranges well above the present-day value. If such trend is to continue in the future, serious environmental consequences may be unavoidable. The time period of the last 100-200 years covered by the direct meteorological observations is too short and does not provide material to reliable assess what may happen over the next hundred(s) years. A faithful prediction of the future requires understanding how climate system works, i.e. to reconstruct past climate much further into the past. The estimates of climate variability prior to the existence of an instrumental record of surface temperature are derived from climate proxies. The need to better understand the temperature component of climate models and to extent it into the past inspired the development of a new direct climate reconstruction method (which we call here Borehole Climatology) based on tracing the subsurface "climate fingerprint" left by the past climatic changes. Borehole climatology enables climate reconstruction of the past several millennia, unlike proxy methods provides a direct past temperature assessment and can extend the areal range to the remote regions poorly covered with meteorological observations. The global warming is manifested by increasing mean surface air temperature. Surface temperature changes then propagate downward and impart certain temperature "signature" to the rock strata in the shallow subsurface that can be analyzed to yield direct information on the past climate history. The Earth's subsurface represents, thus, a unique archive of the past climate data, which can be gained by inversion of the present-day temperature-depth profiles measured in boreholes. While the instrumental air temperature records cover only a relatively short period of one or two centuries, the alternative "geothermal" method provides useful research tool to infer paleoclimate variations on the long timescale. Significant progress that has occurred in the borehole climatology in the last two decades was the major motivation for the proposed book. Our book represents the state-of-art of "Borehole Climatology". It explains the principles of the "geothermal" method, gives an account on various techniques of the ground surface temperature reconstruction and summarizes the major results to reveal the climate scenario spanning from Holocene to Recent. On the borehole temperature data taken in various locations all over the world we demonstrate examples of the interaction between the subsurface temperature response to time changes in the ground surface, vegetation

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cover, land-use, and urbanization. An incorporation of the geothermal data into a multiproxy reconstruction provides an independent estimate of the long-term temperature trends. Thus, the worldwide results of the borehole data analyses indicate, for example, that observed increase in the mean air temperature in the twentieth century is likely to be the largest of any preceding century of the past 1000 years. The final goal is to assess the magnitude of the present-day warming and to distinguish between the natural climate variability and the potential human contribution due to environmental pollution. Precise temperature-time monitoring in shallow subsurface can further provide the magnitude of the present-day warming within relatively short time intervals. We are grateful to have found a forum for Borehole Climatology research in this book, and we hope that it will contribute to the continuation and advance of the research work in this area in the future.

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CHAPTER 1

Background and History of the Problem

Weather substantially influences our day-by-day life. The most simple explanation of weather: “It is all that happens outside”. Weather includes the changes of hot and cold or wet and dry. It may be calm or stormy, and clear or cloudy. Weather is a variety of events that occur in the atmosphere all over the world. Broadly speaking, weather is the placeby-place and the day-by-day and/or season-by-season feeling of the state of the atmosphere, when the events take place on relatively small scales both spatially and temporally. Meteorologists record the weather every day. Continuous recording of various weather indices helps to determine the climate of an area. In the every day life people grasp mainly the short-term weather fluctuations, while human perception of climatic changes is rather limited. Climate is a powerful tool for dealing with the weather and represents the average of weather on some spatial scale over a long period of time. It puts somewhat wild, unpredictable everyday weather into long-term perspective. If one knows the climate of some region, he possesses information about what the weather may occur today, a week later, or next year. Climate is the time integral of weather over a period of decades or longer. It accumulates the totality of weather events, thus, may be broadly defined as the long-term behavior of the environmental system. 1.1 The Climate of the Holocene The fact is that Earth’s climate is perpetually changing. Widespread climatologic investigations have shown that climate varies on all timescales from decades to millions of years. The past changes have ranged from slow and gradual to fast and even abrupt. An impact of climate fluctuations on the mankind is extremely significant, sometimes dramatic. Even in relatively recent fifteenth to eighteenth centuries the decrease of the mean annual surface air temperature (SAT) by one or two degrees produced the so-called “Little Ice Age” that had strongly altered many social and economic sectors in Europe. That time the populations died from the crop failure and famine. According to Lamb (1969) “… climatic history must be central to our understanding of human history …”

1

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Fig. 1. Climate for the last 420 000 years: Temperature anomalies are based on Vostok ice core data, Antarctica (adopted from Petit et al., 1999). Reference baseline corresponds to the present global mean air temperature.

Climate can be usually described in terms of normals,1 means and extremes of a variety of weather elements. SAT is typically of interest in any discussions of the large-scale climate variability. It is also a major driving factor in various investigations of the climate impact on many natural and managed systems as well as determining variable for different climatic models. Climate may vary on a large range of temporal and/or spatial scales. Spatial scales may be local (⬍105 km2), regional, continental (10–100 million km2), or global. Temporal scales may vary from relatively short duration (annual/decadal) to long scales comparable with the characteristic times of the geological processes (hundreds of millions of years). On the longer timescale the Earth’s climate roughly represents the alternating of ice ages and interglacials, when the former are characterized by major extension of the polar ice sheets and growth of the mountain glaciers. Figure 1 shows the existing temperature deviations from the long-term temperature average over approximately the past half million years. The temperature anomalies were deduced from the measurements of the isotopic fractionation of oxygen in the ice core from Vostok, the Russian base in Antarctica (Petit et al., 1999; see also the web site of the National Climatic Data Center

1

Climate normals or averages are used to summarize or describe the average climate conditions of a particular location.

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Fig. 2. Climate of the last 50 000 years: Temperature reported for central Greenland (adopted from Alley, 2000). Climate pattern in central Greenland is characterized by a pronounced rapid termination of the ice age about 15 000 years ago, followed by an irregular transient cooling period known as the Younger Dryas, and by abrupt return to the warm interglacial conditions (warming of approximately 0.8 K/yr).

www.ncdc.noaa.gov/paleo/data.html). Temperature anomalies varied between ⫹2 and ⫺8°C reflecting the oscillations in global ice volume with a period of about 100 000 years, although the time pattern is not perfectly regular. Multiple shorter time excursions were superimposed on the long-term cycles. It is obvious that the ice age conditions were characteristic for the most of the last 420 000 years. The short warmer periods (the interglacials) typically continued not longer than few thousands to maximum 15⫺20 thousand years. Figure 2 shows the Greenland ice core data for the last 50 000 years (Alley, 2000). Temperature interpretation is based on stable isotope analysis and ice accumulation data from the GISP2 ice core (central Greenland). Table 1 summarizes the estimated durations of the main events shown in Figure 2. As seen, the peak of the last glacial period occurred 21 000 years ago (the Last Glacial Maximum). That time the continental ice sheet reached to mid-latitudes of Europe and North America (Bradley, 1999; Ruddiman, 2001). This glacial period was somewhat abruptly transformed into the present interglacial not later than 12 000⫺7000 years B.P. Until the 1990s, the general view of climate change was that the Earth’s climate system changes gradually in response to the natural as well as human-induced forcing. However, recent evidences gained from various fields of climatology show that climate may change more rapidly, even abruptly. Greenland ice core record provides a clear picture of such abrupt climate change. One of the best-known and well-studied widespread abrupt temperature decreases is the Younger Dryas cold interval,

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Borehole Climatology: A New Method on How to Reconstruct Climate Table 1. Main paleoclimatic events of the last 125 000 years (guide to terminology) Event Last interglacial peak Last glacial maximum Last glacial Younger Dryas Last deglaciation Holocene “Climatic Optimum” Holocene maximum warming

Estimated age (ka B.P.) ⬃124 ⬃25 to 18 ⬃74 to 14 ⬃12.7 to 11.5 ⬃18 to 10 ⬃10 to present ⬃4.5 to 6 (Europe) ⬃10 to 6 (Southern hemisphere)

when the last warming trend was shortly interrupted by a sudden cooling at about 12 700 years ago. This cooling event was ended even more suddenly about 11 500 years ago (Figure 2). Climate records proved that much of the Northern hemisphere was affected by extremely cold, dry, windy conditions. This event is important because it demonstrates that rapid temperature drops can still occur even during relatively stable and continuous interglacial conditions. The warming was restored at 11 600–10 500 B.P., and this most recent glacial retreat is still going on. The Holocene is the name given to the last approximately 10 000 years of the Earth history, the time since the end of the last major glacial epoch. The unusual, “flat” nature of the last 11 000–12 000 years of the Greenland record represents striking contrast to the periods of cold that had preceded it. Temperature variations over the Holocene period (0.01 Ma to the present) show significantly smaller range in comparison with the early ice age oscillations. However, even such small variations might have significant impact on human civilizations. The Climatic Optimum was the most noticeable period of the mid Holocene. In Europe its maximum was centered around 6000–4500 years B.P., higher SAT by 1 to 2°C existed in some parts of the Earth (particularly in the extra-tropics of the Northern hemisphere). This period coincides with time when the great ancient civilizations were born and flourished. Temperature records of the last two millennia for the Northern and Southern hemispheres and on the global scale are presented in Figure 3 (Mann and Jones, 2003). For more information see also the link of Goddard Institute for Space Studies, New York, (www.giss.nasa.gov/research/paleo). As seen, SATs have changed rather differently in the two hemispheres, and a sharp recent temperature increase in the Northern hemisphere does not bear a resemblance to more gradual increase in the Southern hemisphere. From the review of paleoclimatic data covering the last two millennia (late Holocene) Williams and Wigley (1983), Jones and Mann (2004; see also the references therein) have identified three main climatic excursions. As has been recently demonstrated, the timing of these cold and warm excursions of climate varies geographically over the globe (Crowley and Lowery, 2000). The comparison on the global scale has a trouble in doing because the direct evidence for temperature changes in past few centuries for the Southern hemisphere is sparse. Thus, the timing of the main climatic changes is generally tied on the conventionally-defined European region and/or the Northern hemisphere.

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Fig. 3. Last two millennia multiproxy temperature reconstructions for the Northern and Southern hemispheres and for the global scale (drawn from Mann and Jones, 2003). Temperature anomalies are based on 1961–1990 instrumental reference period. Smoothed course corresponds to 50-year running mean.

The first of the mentioned epochs was a cold period around eighth century, which caused, e.g. renewed ice growth in alpine glaciers and 1–2 m sea level drop below presentday level. This period later changed back and restored warmer conditions between ninth to thirteenth century, the so-called Little Climatic Optimum or Medieval Warm Period (from eleventh to fourteenth centuries) that represented the warmest climate since the Climatic Optimum that occurred at 6000⫺5000 years B.P. At the Medieval Warm Period the warming, however, was not as intensive as under the earlier Climatic Optimum. During this period, global average annual temperature was approximately 1 K (or less) warmer

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than in 1900. That time, for example, the Vikings established a colony in Greenland and the wheat was grown in Norway (64°N latitude). However, the regional evidence of this period is variable, sometimes even unclear. For example, Crowley and Lowery (2000) did not find evidence for warmth in the tropics. The twelfth and fourteenth centuries appeared mainly cold in China (Wang and Gong, 2000). The restricted reconstructions from the Southern hemisphere, such as tree-ring record from Tasmania (Cook et al., 2000), did not confirm any distinct warmer time during the Medieval Warm Period. Generally, this period appeared more evident in areas near and around the North Atlantic. Keigwin and Pickart (1999) hypothesized that the corresponding temperature changes were associated with changes in ocean currents in the North Atlantic, the fact that maintains the role of ocean circulation-related climate variability. On the contrary to the Medieval Warm Period, the position of the Little Ice Age appears to have been much clearer. This time interval represents the greatest glacial advance of the Holocene that have continued from 1300–1450 until 1850–1900 A.D. While the geographic pattern of the Holocene climate fluctuations remains murky, the Little Ice Age and the subsequent warming were really global in their extent. The evidence from mountain glaciers suggests glacier advances in a number of widespread regions, in Europe prior to the twentieth century, as well as in Alaska, New Zealand, and Patagonia (Grove and Switsur, 1994). During the Little Ice Age, average annual air temperatures of the Northern hemisphere were about 1–2 K lower than today, and unusually cold and dry winters prevailed in Europe. That time agricultural productivity dropped significantly, even farming became unmanageable in vast regions in northern Europe. The freezing of the canals in Holland for three months straight as recorded by famous Dutch and Flemish painters can be mentioned in this connection. The Little Ice Age cooling did not represent a one-way story and was sometimes interrupted by several provisional returns of warmth. The regional variability of cold conditions played a significant role. While the hemispherical averages of temperatures for the seventeenth century generally reflect the cold conditions in Eurasia, the nineteenth century cold is mainly associated with the cold climate in North America (Mann and Jones, 2003). Even the timing of peak coldness may depend on the particular season. Since 1850 A.D. the climate is dominated by a clear steady warming trend, which has become known as global warming. Figure 4 shows that the twentieth century SAT has increased by 0.7 K, with about half of that increase occurring since 1978. This warming is particularly noteworthy because the rate of temperature increase is enormously high. In addition, the recent 50–100 years have been the time of unprecedented growth of human activities, accompanied by industrialization, massive deforestation, and other human interferences with the nature with a thoughtful (harmful) effect on the environment. The natural agents, exerting their influence upon climate has been thus “recruiting” with a new powerful mean to produce sizeable changes in the climate. One of the essential problems of the present days is to answer the question to what degree the mankind may be responsible for the present-day climate warming. Is the observed global warming just of natural origin, or does it have certain anthropogenic component? Is the fact that the climate is getting warmer the result of human insensitive approach to its habitat? Is this warming to continue in the future and how serious are the potential environmental consequences? If so, the problem of the worldwide increasing air temperature comes to an end as the strictly scientific discipline, but became the uneasy task for everybody on this planet.

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Fig. 4. Global warming of the twentieth century documented by the mean SAT anomaly (relative to the base period 1961–1990). Figure adopts data from www.cru.uea.ac.uk.

It is true, that some skeptical researchers have debated whether the observed temperature trend is reliable (see Chapter 3) and how the present knowledge is to be extrapolated into future. However, certain evidence of a sizeable warming was reported even after removing data from the urban areas where the “city heat-island effect” could have affected the long-term meteorological temperature data. In most cases, however, the SAT data are consistent with other evidence of warming, e.g. increase of ocean temperatures, shrinking mountain glaciers, decreasing polar ice cover, etc. During this period, the energy reaching the Earth’s surface from the Sun had been measured precisely enough to confirm the conclusion that the reported recent warming has not been occurring just due to solar radiation changes. Although the reason for detected warming is not completely understood, the most of the climatologists interpret it as the result of the increasing concentrations of CO2, CH4, and other greenhouse gases into the atmosphere caused by anthropogenic activity. Greenhouse gases have increased significantly since the Industrial Revolution,2 mostly from burning fossil fuels for energy, industrial activities, and also by transportation (Figure 5, see also Figure 99, Chapter 3). Now the greenhouse gases are at their highest concentration levels in the last 400 000 years and continue to rise. Even when the global 2

Term Industrial Revolution implies a period of rapid industrial growth beginning in the second half of the eighteenth century. It originated in England when the steam engine was invented and later has spread over Europe and the world. It means the beginning of intense use of the fossil fuels with the corresponding emission of carbon dioxide and other numerous anthropogenic influences on the climate system.

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Fig. 5. The comparison of the increase of the greenhouse gases concentration in the atmosphere (bottom panel: CO2 and CH4 data by the Carbon Dioxide Information Analysis Center, CDIAC; http://cdiac.esd.ornl.gov) with the mean annual SAT (top panel).

warming is expedient in some parts of world bringing, e.g. milder winters and longer growing seasons, it may have fatal consequences in others, and globally the expected losses are to outweigh the potential benefits. The Intergovernmental Panel on Climate Change (IPCC; www.ipcc.ch/index.htm) that involves hundreds of scientists and was established to assess scientific, technical, and socio-economic information relevant for the understanding of climate change, predicted that by year 2100 the average global temperature will rise by 1.4 to 5.8 K above 1990 level. The uncertain broad range of possible temperature increase is due to different assumptions considered in the variety of model simulations. Lower boundary indicates that even low climate sensitivity and low economic growth will lead (if no measures are undertaken) to a mean global warming of above 1K, thus surmounting the warmest phase of the Holocene. The IPCC predicted that combined effects of melting ice and seawater expansion from ocean warming may cause the global average sea level rise of approximately 0.1 to 0.9 m between 1990 and 2100. Such rise may bring devastating consequences to coastal communities who will likely experience the loss of their land, increasing flooding due to sea level rise, and more severe storms and surges. Uncertainties remain only about the exact magnitude, rate, and impact of future changes as well as how climate change will afflict different regions. They are stipulated mainly by the lack of sufficient knowledge of how climate could be affected by so-called climate feedbacks (for details see Section 3.4, Chapter 3) and by the difficulty to predict future actions of the society, particularly in the countries of future economic growth and highenergy demands.

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Another important question is how abrupt the future changes will be. Abrupt climate change generally refers to a large shift of climate that takes place so rapidly and unexpectedly (sometimes in the mere span of a decade) that human and/or natural ecosystems have difficulty to adapt. Further definition of “abrupt” or “rapid” climate change is subjective and depends on the long-term temporal pattern of the climate change within which the sudden shift is embedded and/or the sample interval used in a particular study. The shifts from dominantly glacial to interglacial conditions were the most distinct abrupt change over the past half million years. These sudden transitions support the hypothesis that the relatively minor changes in climatic forcing may lead to dramatic response of climate system (e.g. Mikolajewicz et al., 1990). Studying the climate evolution over the last 100 000 years the researchers have discovered repeated examples of abrupt changes like, e.g. the Younger Dryas – the fast slide into and jump out of the last ice age. The termination of the Younger Dryas cold event, for example, is manifested in ice core records from Central Greenland as a near doubling of snow accumulation rate and a temperature shift of approximately 10 K occurring within a decade (Alley, 2000). One of the more recent abrupt climate changes was the Dust Bowl drought, windblown dust, and agricultural decline of the 1930s that displaced hundreds of thousands of people in the American Great Plains. Numerous sudden changes over widespread areas are preserved in paleoclimatic archives and therefore could happen again in future. The likely hypothesis to explain abrupt climatic transitions is that the ocean thermohaline3 circulation switches between different stable modes. Warm climate intervals reflect, e.g. strong deep water formation in the northern North Atlantic and vice versa (Stocker, 2000). It has been suggested that oscillations on such timescale represent an intrinsic feature of the climate system and have persisted throughout the Holocene. If it proves to be the case, any prediction of future climate changes in the North Atlantic region would require accounting for this process. Other forcing can also join in the rapid climate changes. Some short-term, abrupt climate changes, for example, clearly reflect the impact of major volcanic eruptions (Briffa and Osborn, 2002). Growing attention is now to be paid to the possibility of anthropogenic influence on climate that may induce rapid climate changes that are far beyond the range of variability on which the social operating and planning schemes are based. One of the theories, for example, states that the global warming could trigger off the mechanism of abrupt cooling in northern Europe. It has been hypothesized that the melting ice caps will “freshen” the water in the North Atlantic, shutting down the natural ocean circulation that brings warmer Gulf Stream waters to the north. The actual regional drop in temperature may be as high as 6 to 8 K. Such change in the ocean circulation could occur over relatively short period, perhaps within 50 to 100 years. As the present scientists do not know enough about exact mechanisms and details of abrupt climate changes to be able to accurately predict them. The larger and faster the climate change may be more difficult will be the human and natural systems adaptation and stronger expected adversity effects can be expected. Thus, more precise descriptions of the processes causing such changes should be developed. This is especially the case in relation to changes in the magnitude and frequency of extreme events (Knox, 2000). 3 There are three basic processes that make the ocean water circulate, namely tidal forces, winds stress, and density differences. The latter occur due to the temperature (thermo-) and salinity (-haline) differences, thus, the density driven circulation is called the thermohaline circulation.

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Comparing present and past climatic conditions one can conclude that we are indeed fortunate because we are living in one of the warmest and “quiet” periods of the past million years under the milder temperatures that the Earth could provide for human life to flourish. Thus, any significant change of existing climate should be met with an apprehension, and the investigation of past climatic variations holds not only academic interest. This is true especially for the last century when the natural variability of the climate is amplified by the anthropogenic disturbances arising from the drastic transformation of the planetary environment induced by an unimaginable explosion of human activity. Investigations of the past climate may be useful both to understand the present-day climate and its possible future changes, and to test the hypotheses about the causes of the climate change. More climate information from the distant past could be greatly valuable to strengthen our understanding of climate changes and to improve existing models of climate development. In particular, an enhanced effort is needed to expand the geographic coverage, temporal resolution, and variety of the paleoclimatic data. The borehole climatology represents a useful addition to the available array of existing paleoclimate information. Because of numerous boreholes the method is applicable over most continents including polar ice caps. Subsurface temperature records measured in boreholes may represent a useful tool for the past temperature reconstructions in areas less covered by traditional climatic investigations. Borehole geothermometry could also provide data for other purposes, like the atmosphere and land “couplings”. 1.2 Principal Sources of Data on the Earth’s Climate System 1.2.1 Background Climate is variable on all timescales. Its variations represent the complex product of the interaction of Sun and all components of the Earth including atmosphere, oceans, landmasses, snow and ice cover, life, and other structural elements. Geologically, short-term climate changes (⬍120 000 years) occur because of external forcings as well as due to internal factors, both natural and human-induced changes (www.ace.mmu.ac.uk). External causes of climatic changes include changes in the solar radiation and the Milankovitch cycles. Solar radiation is the radiation emitted by the Sun. Its spectral range is determined by the temperature of the Sun. About half of the radiation falls into visible short-wave part of the spectrum, while the other half is mostly in the near-infrared part with a small part in ultraviolet part. The output of energy from the Sun slightly varies over time, changing the total amount of energy absorbed by the Earth atmosphere and thus affecting the climate. The solar activity is linked to the sunspot cycle that occurs with a 22-year periodicity. Quasi-periodic oscillations of the sunspot number with the period of approximately180 years also appear to exist. Figure 6 illustrates the correlation of the global solar irradiance4 reconstructed by Bard et al. (2000) for the last 1200 years and the global temperature anomalies (Figure 3, bottom). The well-known solar minima are centered about 1900, 1810 (Dalton), and 1690 A.D. (Maunder) and correlate with the corresponding temperature falls.

4

Irradiance is the term for the power of electromagnetic radiation that is incident on the surface per unit area. The SI unit for irradiance is W/m2.

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Fig. 6. Correlation between global solar irradiance and global temperature anomalies for the last 1200 years (adopted from data by Bard et al., 2000 and Mann and Jones, 2003).

Appeared near 1200 A.D. maximum is characterized by the irradiance that is comparable or even slightly higher than the present-day level. It can be connected with the Medieval Warm Period, while the Little Ice Age can be attributed to a rather long period of low irradiance between 1450 and 1750 A.D. Notwithstanding that solar activity is recognized as undoubted cause of variations in the climate system (Blackford and Chambers, 1995; van Loon and Labitzke, 1998), its exact role in climate variations on the decadal/centennial timescale is a topic of continuing debate. Crowley and Kim (1996) investigated the correlation of several Northern hemisphere temperature proxy records with solar variability indices and concluded that solar forcing may explain as much as 30–55% of the climate variations on these timescales. The hypothesized source for the rest part of the climate change is internal climate dynamics. Recently van der Schrier and Versteegh (2001) applied new technique to separate solar activity and internal climate dynamics. Based on the 250 years long sunspot record and series of summer temperatures, these authors concluded that for low values of sunspot number the internal climate mechanics dominates, while at high sunspot number internal climate dynamics does not seem so important. Details on how sunspots affect the Earth climate and further references can be found on the web site ⬍www.das.uwyo.edu/⬃geerts/cwx/notes/chap02/sunspots.htm⬎. The Milankovitch theory relates climate variations to the changes of the parameters of the Earth orbit around the Sun, namely to the changes in eccentricity (the shape of the Earth’s orbit), obliquity (the tilt of the Earth’s axis), and in orbital precession (the shifting of the equinoxes). Each variation has its specific time period. For example, the orbit may be more elliptical and/or more circular completing period in about 110 000 years, and the mean annual flux varies as a function of actual eccentricity. These three components of

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the orbital variations affect the total amount of energy received by the Earth, and its seasonal distribution at different latitudes. Fluctuations in solar energy input measured in tens of thousands of years are generally regarded as the cause of major climate fluctuations, and much evidence from paleoclimatic records has been found to support this theory. There is a good correlation between the glacials and periods of low eccentricity. The distribution of the interglacials also shows the evidence of the 41 000 (obliquity) and 21 000 (orbital precession) years cycles. Now scientists have recognized, however, that such orbital variations alone are not enough to account for the whole oscillations in the global climate between ice ages and interglacials (Berger and Loutre, 2002). While external variations may indeed act as a pacemaker for glacial–interglacial transitions, additional climate forcing has been invoked to explain the significant changes in global average temperature up to several degrees. The internal forcing factors include variability of the coupled ocean–atmosphere system, volcanism, producing large eruptions of particulates (dust) and gases into the atmosphere, cryosphere,5 and the land surface. The ocean–atmosphere system represents one of the main constituents of the climate. The atmosphere is involved in practically every physical process of potential importance for climate change. Atmospheric temperature, composition, humidity, cloudiness, and winds determine the global energy fluxes. The atmospheric circulation provides the possibility of rapid propagation of any climate forcing from one part of the Earth to another. The bulk of the energy absorbed by climatic system, much more than absorbed by the atmosphere, is stored at the ocean surface. Because of its huge thermal capacity as well as of its ability to circulate this energy over long timescales, the role of the ocean as the climate forcing factor is extremely important and complex. Warm water moves pole ward whilst cold water returns toward the equator. Energy is also transferred by moisture. The water evaporating from the ocean surface stores heat that is then released when the moisture condenses to clouds and rain. Heat moves also vertically within the oceans. Similarly to the currents in the atmosphere the surface and deep-water currents in the world’s oceans are inter-linked forming the global ocean circulation. Changes in ocean circulation and especially the thermohaline circulation in the North Atlantic have been implicated in abrupt climate changes occurred in the past such as the Younger Dryas. Volcanism can increase the Earth’s albedo (reflectivity) and induces cooling the climate. The cryosphere is the portion of the globe covered by ice and snow. It greatly affects temperature. The sea ice masses increase the reflective capacity of the Earth surface, thus, enhancing cooling. They also insulate the atmosphere from the relatively warm ocean causing steep decline of the winter air temperatures and reducing the supply of moisture to the atmosphere. The water frozen in the glaciers and snow cover on land can melt during warming events with consequent effects on sea level and atmospheric circulation patterns. Snow-covered lands promote cold conditions because of their high reflectivity and because land surface temperatures cannot rise above freezing until the snow melts. The reflectivity of the land surface strongly depends on its cover. While fresh snow reflects more than 90% of the sunlight, the dense forests similarly absorb more than 90% of striking energy. The land surface coverage can also affect cloud formation, precipitation, and the surface water flow, thus, feeding back on climate. 5

Term cryosphere implies the component of the climate system including snow, ice, and permafrost both on and beneath the land and/or ocean surface.

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The main anthropogenic causes of climate change include the emissions of greenhouse gases (carbon dioxide (CO2) and methane (CH4) production), changes in land-use, and the depletion of stratospheric ozone. Greenhouse gases such as carbon dioxide are accumulating in the atmosphere resulting in the increase of air and ocean temperatures. Increased concentration of greenhouse gases in the atmosphere is well documented and its climatic consequences are widely reported in climatic modeling literature (McGuffie and Henderson-Sellers, 2005; Figure 5). The anthropogenic land-use changes include reand deforestation, urbanization, changes in the agricultural practice, desertification, major rivers, and other water masses engineering. The importance of these factors was captured by numerous climate modelers. And finally, the discovery of the Antarctic ozone hole6 in 1985, and more recently, less intensive, but observable ozone depletion over the Arctic (stratospheric ozone represents Earth’s natural protection for all life forms, shielding our planet from harmful ultraviolet radiation) has focused the attention on including the twentieth century ozone destruction in the global climate models. The striking complexity of the temperature records in Figures 1 and 2 probably reflects the complex interactions of all feedback mechanisms.7 Our ability to predict future climate strongly depends on the degree of understanding of the climate system operating mode. Such knowledge can be achieved from the study of the past climate variations and their modeling with appropriate forcing. Comparison of the climate models and simulations of their development with climatic reconstructions can provide constraints on the sensitivity of climate to different forcing (van der Schrier and Versteegh, 2001; Bauer et al., 2003). Complete spatial–temporal pattern of the past climate is thus the clue to successive climate modeling and prediction of possible future climatic changes. It is especially a case in the recent decades when the Earth’s temperature has been increased. Is observed warming an ordinary climatic fluctuation or it is stimulated by intensified anthropogenic activity? How extraordinary is this warming relative to the variations occurred in the pre-industrial times? For understanding of the post-industrial impact to the climate and development of effective adaptation/mitigation strategies as precise as possible knowledge of the early climatic fluctuations is indispensable. Generally, all methods for past climate reconstruction can be classified according to the timescale on which they consider climatic influence:

• short-term (1–1000 years), • medium-term (up to 10 000 years), and • long-term (periods of 100 000 to 1–10 millions years). Paleoclimatologists employ a wide variety of methodological approaches to reveal past climate changes. Except the direct measurements of climatic variables, there are three principal techniques to reconstruct past temperature variations, namely proxy methods, 6 The Antarctic ozone hole is a part of the Antarctic stratosphere where ozone level has dropped to as low as 33% of their pre-1975 value. The ozone hole occurs during September to early December, when strong westerly winds start to circulate around Antarctica and create somewhat similar to an atmospheric container. Over 50% of the lower stratospheric ozone is destroyed in this container. 7 Climate feedback implies such mode of interaction between climate forming processes, when the influence of an initial process triggers changes in other process that return back to the initial process and either intensify (positive feedback) or reduce it (negative feedback).

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inversion of the temperature–depth profiles measured in boreholes, and modeling. Most traditional technique is based on the proxy sources that represent the fingerprint of climate changes on surrounding environment. The nature has provided a lot of indirect recording mechanisms. By analyzing records taken from numerous proxy sources, scientists can extend our understanding of climate far beyond of the approximately 200 years long instrumental recording. All methods possess their own timescale and temporal resolution, their summary is presented in Figure 7. We have a general picture of how climate has changed over the last 150 000 years (through the last glacial–interglacial cycle), but only in terms of very large-scale and of low-frequency changes. The knowledge of climatic changes at higher frequencies, say, variations on the decade to century scale, is very poor, while it is this timescale that is the most important to the current environmental concerns. Contemporary climatic variations must be viewed in the context of changes that have occurred before the global scale potential anthropogenic influence on the environment has started. Paleoclimatic reconstruction over the last millennium requires careful retrieval of all available climatic archives. Even when none of the available approaches to climate reconstruction is free from certain uncertainties, confidence of obtained results can only be provided by comparing several independent sources of information and thus support or verify the reconstruction model.

Fig. 7. Sources of paleoclimatic data and their timescales and temporal resolutions.

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1.2.2 Short-term climate changes Short-term temperature changes can be suitably detected on the base of the instrumental measurements and historical documents. Both sources possess high-resolution up to exact moment of occurrence. Instrumental surface temperature data sets are of primary interest for the recent global warming and for the detection of the 100 year long global temperature trends. The reconstruction is based on the compilation of SAT measured at land stations and ship-based marine sea surface temperature (SST) measurements. The overall characteristic of the instrumental sources is that they present vast volume of data rapidly decreasing in amount and geographical coverage when going back in time. Table 2 presents the evolution of the global instrumental observing system in time. It can suitably be divided into five periods. The first covers the time up to around the second half of the eighteenth century. This period, with a very few exceptions, is covered only by proxy data. The longest instrumental SAT series are available for some locations in Europe and North America back to the mid seventeenth century. Methodical thermometer-based records began at approximately 1850. The second period covers the times, say, from 1760 to 1880 and is characterized by a gradual built-up of surface synoptic network covering the inhabited parts of the globe as well as marine observations along the well-traveled routes. The reasonably coherent surface synoptic network was created in the third period from 1880 to the mid of the twentieth century. However, data sources over tropical regions and the oceans were still generally rare. During the fourth period (1946–1979) meteorologists have had a network of radiosonde stations reasonably covering most of the Northern hemisphere. Finally, the fifth period since 1979 is the only period where there has been a global observing network including the full depth of the atmosphere. Obviously, the instrumental observation window is too short to provide real insight in the longer scale climate variability. Although meteorological records represent the principal and the most reliable data source for the climate change study, they possess numerous shortcomings. Because of the changes in the observing techniques and schedules over the years (e.g. different time of the day for measurements), changes in local exposure due to, e.g. urban development around the site and the re-location of meteorological stations, without overlapping of the record to calibrate new station, the integration of measured quantities into the homogeneous time series is not an easy task even for a single station. Because of significant spatial–temporal variability of the surface temperature, the compiling of the homogeneous records for more or less extensive regions represents a difficult problem and can significantly lower the accuracy of compound SAT series especially when measurements are performed over a century or longer period of time. The compilation of recently homogenized long European SAT series was presented by Table 2. Development of meteorological observing systems Time ⬍1760 1760–1880 1880–1946 1946–1979 ⬎1979

Characteristic observing system Essentially proxy data Built-up of a surface synoptic system(mainly Europe and North America) Basic surface synoptic system ⫹Upper air radiosonde network(mainly Northern hemisphere) Comprehensive global observing system

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Camuffo and Jones (2002). This massif is probably the most reliable volume of shortterm climatologic data. However, even this careful reconstruction is a subject of some uncertainty in its earlier part. Examples of estimates of SAT anomalies extended back to approximately1650 are presented in Figure 8. Smoothed values were calculated from the data by Jones and Moberg (2003). Comparison shows clear differences between the illustrated regions. Besides the limitations imposed by temporal inconsistencies in a single weather station record, the space averaging of single site observations over large territories for global and hemispheric analysis may present certain problem. The most straightforward way to obtain average global surface temperature is to calculate the weighted average of thermometer measurements from the weather stations distributed over the Earth. Weighting procedure is indispensable because the stations are not regularly and/or optimally arranged. Restriction of the observation sites to land and island stations, still large land areas without coverage, the varying number of stations and areas of coverage over the observational period, wide ocean spaces without fixed meteorological stations at all times put difficulties in the way of extracting large-area temperature changes from measurements (Jones et al., 2001). Figure 8 (bottom) shows the result of recent hemispheric averaging of combined land and marine temperature anomalies (model HadCRUT2v; data by Hadley Centre of the UK Meteorological Office, www.cru.uea.ac.uk). According this reconstruction the Northern hemispheric means vary by up to 1K for the recent 150 years. Data indicates colder temperatures in comparison with the base period 1961–1990 in the second half of the

Fig. 8. SAT (relative to base period 1961–90) and their 10-year running means for Central England, Central Europe, Fennoscandia (data by Jones and Moberg, 2003), and Northern hemisphere (see text).

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nineteenth and in the beginning of twentieth century, warmed between 1910 and 1940. Slight oscillations around zero occurred between the years 1940–1975 and then climate has warmed again through 2000. Limited agreement between the Northern hemisphere and the European temperatures is obvious and proves that averages for such extensive units cannot be inferred from a single region series. Using these data, Jones et al. (2001) concluded that average temperature increased 0.6 ⫾ 0.2 K during twentieth century. The uncertainty given for this average reflects the statistical uncertainty in the meteorological station measurements and does not contain such systematic biases, e.g. ocean temperature measurements, urban heat-island effect, etc. The temperature increase is spatially unequal; Arctic regions show the greatest degree of warming, while a little or no warming corresponds to some low latitude areas. Historical data are an important source of detailed information on the millennium scale, particularly for the period from about 1000 A.D. to the beginning of the era of instrumental meteorology. Of course, they are not equivalent in the reliability to the meteorological instrumental measurements. These sources contain generally written records of environmental indicators of climate (parameteorological phenomena) including myths and legends, annals, chronicles and scientific writings, records of social administration and government, commercial and private estate data (crop yields, harvests, and prices), maritime books, early journalism, private papers (diaries, correspondence), etc. Some pictorial documents also can be used as evidence of past climate, e.g. in the work by Camuffo et al. (2003) who studied the increase in the sea level and in flooding tide frequency at Venice on the base of early photographs and the “photographic” paintings by Canaletto and Bellotto. Sometimes climatic information can be extracted from unusual sources, e.g. the cherry tree blossom dates that were recorded at Kyoto, the old capital of Japan, since 812 A.D. (Lamb, 1977). In some cases documentary sources may also be completed by archeological evidence of climate change. Generally, the information fixed in documents does not represent systematic series nor can be readily expressed in terms of standard meteorological variables. Data vary widely in quality. While, for example, the note from the collection by Réthly (1962) on the winter of 1528/29 – “… Suleiman Turkish Emperor came near to occupy Vienna and only extremely cold winter drove his army away” can probably be accepted without limitations, the description of the Italian winter 1132/33 – “… the Po river was frozen to the bottom in its total length. The wine was benumbed with cold even in the deepest cellars” should be interpreted with caution. Historic information often contains exaggerations like “the coldest winter from the beginning of the mankind”. Significant problem represents also that our knowledge of the intellectual and social parameters at which text was written is insufficient. Exact meaning of the words and forms of expression is interpreted in terms of the present scientific knowledge. Initial expressions may be distorted in translated, paraphrased, or summarized sources. Definite shortcoming represents also the fact that significant amounts of historical data can be found only in the regions with well-developed cultural tradition, thus, spatial distribution of this information is generally irregular, when vast areas or even continents can appear as “white spots”. Another limitation of the documentary data is that they require independent calibration to the climatic variables and thus are not comparable in their reliability to instrumental meteorological measurements. However properly evaluated, historical data can yield both qualitative and quantitative information about past climate.

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A wealth of historical data is available for Europe that represents one of the few regions of the world where it may be possible to reconstruct regional climatic variations for the last millennium in season-by-season scale. Outstanding examples of reasonably accurate climatic series obtained from documentary sources are the collection of climatic notes for Hungary and surrounding territories from second to eighteenth centuries by Réthly (1962, 1970), the reconstruction of temperatures prevailing in England and Wales since 800 A.D. (Lamb, 1977), the work by Le Roy Ladurie and Baulant (1980) and Chuine et al. (2004) based on wine-harvest dates in France from fourteenth to fifteenth centuries to the present. The most recent comprehensive review of the documentary data archive was published by Brázdil et al. (2005). Figure 9 demonstrates how historical grape-harvest dates in Burgundy (France) were used to reconstruct summer (April–August) temperature (Chuine et al., 2004). Results reveal generally warm conditions before up to the 1650s and somewhat colder climate since then. Temperatures as high as those reached in the 1990s have occurred several times during reconstructed period. China is another area rich in the documentary climate evidence. The regional instrumental temperature series in China have been extended back over much of the past millennium using documentary data combined with inferences from ice cores and treerings (Wang and Gong, 2000).

Fig. 9. Spring/summer temperature reconstruction based on grape-harvest data in Burgundy (France) from 1370 to 2003 (data by Chuine et al., 2004). Temperatures are given as anomalies with respect to the mean April–August temperature at Dijon for the base period 1960⫺1989, smoothed course corresponds to the 10-year running average.

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1.2.3 Medium- and long-term climate changes The relatively short length of most instrumental records and historical sources restricts the study of climate variability. These data indicate warming trend occurred during twentieth century, however, cannot answer the question whether this warming was unusual and/or extraordinary in the whole Earth’s history. The essential need to prolong directly measured climatic series back into the past inspired the development of various methods for the past climate reconstruction from the traces left by climatic changes in the world. The reconstruction of the past temperature variations on the global/hemispheric scales can be performed by three principal approaches: (1) proxy methods, (2) inversion of borehole temperature logs, and (3) modeling. Different proxy techniques are the oldest and traditional, while the “borehole” method and simulations of the past climate with the state-of-art General Circulation Models (GCM)8 represent recent developments. Threedimensional climate models produce internally consistent simulations that in many features coincide with observed climate and are realistic enough to constitute a surrogate complex for the testing of different reconstruction methods and their basic assumptions (see Section 2.4.4, Chapter 2). The “Simulating the Planet Earth” represents probably the most well known such project. It is connected with the world’s fastest supercomputer developed in Japan. Located at the Earth Simulator Center (ESC) at Yokohama (Japan) it can simulate the complex interactions between the Earth’s atmosphere, ocean, and land for deeper understanding of our planet’s climate, ocean currents, and earthquakes. All global environmental changes can be presented in a one thousand times more detailed grid pattern than that provided by previous supercomputers. This means more precise weather simulations as well as the ability to predict cyclone and typhoon paths. Current GCM simulations can be seen directly in the website www.nec.com/global/features/ index9/index.html. Measurements of borehole temperature profiles are the only direct measurements of the long-term past temperatures in contrast to the proxy indicators that must be interpreted in terms of climate changes using different transfer procedures. Anyhow, it is the proxy reconstructions that provided the most abundant paleoclimatologic database. The essence of the proxy method is the next. Temperature variations cause many changes in the biological and physical environment. Some of these changes are regular enough to be used as quantitative indicators of varying temperature. The “proxy” data is the term used to denote any material that contains indirect signatures of climate. A proxy climate indicator is a local record that is interpreted using physical or biophysical principles to represent some combination of climate-related variations back in time. Paleoclimate proxy indicators have the potential to provide evidence for long-term climatic variations prior to the period of existence of instrumental and documentary records. Generally, proxy methods are classified according the scientific branch that provides the data, e.g. biological, chemical, geological, and/or physical climate-related phenomena. For example, numerous evidence of past climate is interpreted by biological sciences: tree-rings, pollen remains,

8 General Circulation is a term that denotes large-scale motions of the atmosphere and the ocean occurring in response to the differential heating of rotating Earth. The GC computer models are based on numerical solution of fundamental equations for the conservation of mass, momentum, and energy. They also consider the physical processes including sources and sinks of these quantities.

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insect faunas, marine micro-fauna, etc. The family of the existing proxies is continuously growing. Except of well-known records some of them are still under development. For example, according to Weidman and Jones (1994) isotopes from mollusks can help to reconstruct bottom temperatures on the continental shelves of the North Atlantic. Daux et al. (2005) described the possibility to reveal past seasonal distribution of precipitation via oxygen isotope compositions of phosphate that were measured in human tooth enamel. Techniques for obtaining proxy temperature information from all the sources except of boreholes are described in the book by Bradley (1999; see also www.ncdc.noaa.gov/paleo/ proxies.html) and in the web site of the Johns Hopkins University (www.jhu.edu/⬃eps/ paleoguide/archive.html). The comprehensive review is presented in the work by Jones and Mann (2004; see also the references therein). Proxy indicators possess different temporal coverage and resolution. Key aspects of a proxy data source are the minimum sampling interval and date resolution. These factors determine the degree of detail that can be extracted from the record. Some of them keep year-by-year patterns of the past climate, while others because of certain factors, e.g. uncertain radiometric dating, simplified “temporal model” assumptions, etc., cannot provide high-resolution data. Common property of the majority of proxy records is also the diminishing of the resolution into the past. For example, datable stratified systems (tree-rings, varves, ice cores, etc.) can provide time resolution of minimum one year. However, seasonal/annual layers in these records appear clearly within only recent periods and are biased to the past by numerous unrelated to climate factors. The properties of the most commonly used proxy measures are illustrated in Figure 7. High-resolution subgroup includes data resolved on the annual/seasonal or at least on decadal scales (tree-rings, corals, laminated ocean and lake sediment cores, high-resolution ice cores, speleothems,9 etc.). Example of the high-resolution data is presented in Figure 10 that shows five regional reconstructions of summer half-year (April–September) mean temperature anomalies for western North America for the period 1600–1982 by Briffa et al. (1992). Tree-ring data are available from much of the continental land area, they can be accurately dated to an individual year, thus, represent primarily high-resolution source, and by reasonable cross-dating can provide continuous records of up to several thousand years in duration. The essence of the method is that in trees each year’s growth creates a well definite ring. Because tree growth tends to hasten in warm conditions compared to cold weather, the width and density of tree-rings may serve as proxies for average temperature. Tree growth measurements can be made both by taking cores out of living trees and by investigation of cut, dead, and/or fossilized examples. On the other hand, Bradley (1999) has pointed out that the tree growth rarely owes to one climatic variable and generally embraces the full range of such factors as temperature, sunshine, precipitation, humidity, and wind intensity. Temperature and precipitation effects, for example, can be separated only if more than one measure of tree growth is available. Tree-ring characteristics also depend on the climate independent variables including the tree species and age (young trees

9

A speleothem is a term denoting various cave deposits that occur as a complex interaction among rocks, water, and air during cave formation. Samples taken from speleothems can be used as a proxy record of past climate changes.

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Fig. 10. Tree-ring reconstruction of summer temperatures across western North America for the period 1600–1982 and their 10-year running means (based on data by Briffa et al., 1992, 2001).

grow faster that older ones), nutrients in the soil, CO2 concentration, etc. The calibration of tree-ring measurements against climate variables represents heavy problem, since the biological response to climate forcing may change over time. The typical situation is that the calibration is performed using recent, in many cases less than century long meteorological data, and obtained information is then extrapolated on the remaining remote sections of measured tree-ring variables. Generally, average values from the multiple samples per tree and/or multiple trees in the study are calculated. Both procedures

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result in the damping or even loss of the long-term variability. As about tree-ring reconstructions presented in Figure 10, Briffa et al. (1992) have demonstrated that time series possess a good significance over the area 35–55°N, but have a large uncertainty north of the latitude 55°N, especially prior to 1750. All data series exhibit high short-term variability. Temperature anomalies averaged on decadal timescale revealed significant inter-decadal changes. However, centennial trends are expressed very weakly. The range of the temperature variations remains approximately the same during all reconstructed period. As was mentioned above, it is a specific feature of the tree-ring proxy. For these reasons, the tree-ring information appears to be more useful when it is supplemented by other types of proxy information in the “multiproxy” estimates of past climate change (see Figure 11). “Coarser” group of proxies includes past pollen and spore records, earlier tree line position, lake level reconstructions, glacial moraine evidence, most sediment cores, and accumulation ice cores. They are useful to reveal the long-term climate variations on centennial and longer timescales. Typical example of such reconstruction was presented in Section 1.1 (Figure 2). The ice sheets that cover Antarctica, Greenland, the northern archipelagos of Canada and Russia, and the summits of some mountain systems reflect the accumulation of the long year snowfall and can provide several climate-related indicators. In cold dry regions, such as Antarctica and the interior of Greenland, because of

Fig. 11. “Multiproxy” temperature reconstructions for Northern hemisphere by Mann et al. (1998), Crowley and Lowery (2000), and Briffa et al. (2001). All series are anomalies for the 1961–1990 instrumental reference period, and smoothed with a 40-year low-pass filter (www.ncdc.noaa.gov/ paleo/recons.html). Multiproxy reconstruction by Huang (2004) is shown for comparison. As seen, temperature reconstructions based on the borehole data inversion suggest colder conditions in the past.

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insignificant year-by-year evaporation and melt, snow compresses into annual layers of ice. The polar ice caps, for example, have 100 000 of these layers or more. Ice core, cylinder of ice drilled out of glaciers, and polar ice sheets can provide several indicators of climate. Recorded there, stable isotopes of oxygen and deuterium are of primary use for the paleoclimatic reconstructions. The isotopic fractionation of oxygen in ice can then provide a proxy for temperature. The physical rationale for such reconstruction is the next. Almost all water is H216O, but two heavier forms, i.e. HDO (D ⫽ 2H is deuterium) and H218O present in the quantities sufficient to provide measurable basis for the proxy temperature record. In most respects these water molecules are the same as regular water except that because they are heavier, they do not evaporate as readily and condense a bit more easily than H216O water. Generally, the colder the air when the snow fell the richer the concentration of the 16O in the record. The isotope content in ice is determined primarily by the air temperature during snow storms. Warmer air contains a larger fraction of D and O18. When incorporated into a stratified deposit, the (18O/16O) ratio remains frozen. This ratio can be measured very accurately using a mass spectrometer. Over short timescales the change in temperature from summer to winter produces a clear oscillation in the (18O/16O) ratio. This oscillation is used to determine the age of the core at different depths, simply by counting the oscillations. Over longer time periods, this ratio indicates the average temperature in the investigated region between the evaporation site and the coring site. The ratio of 1H / 2H (hydrogen to deuterium) can provide even finer details about source temperature and condensation history. Generally, ice cores can store climate information over more than 105 years. However, significant shortcoming of this kind of proxies is that: (1) they are not good representation of average annual temperature conditions because snow accumulation is seasonal, (2) a change in storm tracks direction could change the isotope signature without temperature change at the given site, (3) the chronology of ice cores is disturbed in depth by horizontal and vertical sinking of the ice, which thins the deep layers to a small fraction of their original thickness, and also by the summer melting of the ice. The resolving power originated from counting of the annual layers, may appear two–three orders poorer in the lower sections of the ice cores (Johnsen et al., 1992). And (4) they are available only from a very small fraction of the Earth. Important characteristic of various proxy data is the degree of their sensitivity to abrupt changes in climate. Pollen method, for example, allows to estimate the total amount of plant growth of given year by the pollen count, and thus, provides valuable information about dominant climatic conditions and their variations. However, plants have very long reaction to climatic “jumps”, thus they are practically insensitive to abrupt changes. On the contrary, many insect populations are extremely temperature-sensitive. Discussion of the details of the specific proxy sources is far over the purpose of this section. Independently of the kind of proxy data the common problems in their using are the dating, lag and response time, degree of stationarity in the nature of the proxy’s response to the climate, and mainly their climatologic interpretation. Since the proxies are indirect traces of climate and only a part of measured variations can be attributed to the climatic changes, they need thorough calibration and validation against independent quantitative climatic information, e.g. with instrumental measurements in the vicinity of proxy site in the intervals of temporal overlapping. Because any single source of paleoclimate information has its limitations, sometimes it is more effective to reconstruct

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large-scale (regional, hemispheric) climate patterns applying multivariate statistical approaches to the combined proxy indicator network (Jones et al., 2001; Mann et al., 1998, 1999, 2003). Such approach is motivated by the fact that each proxy has its specific not overlapping strengths and limitations, and in principle they could assist each other. It is the reason for using a “multiproxy” reconstruction that summarizes advantages of different proxy information and reduces shortcomings of existing methods. Typical “two-proxy” example represents the merging of information from ice cores and deep-sea sediments. Deep-sea sediments accumulate very slowly relative to snow on the ice sheet. This results in much longer records from sediment cores, but with significantly reduced ability to resolve short-term changes. While the ice cores provide the annual and/or even seasonal resolution, the intervals of hundreds to thousands of years might be resolved in a sediment core. On the other hand, ice cores can provide only several hundred thousand years records compared with as long as several million years archived in the sediment cores. Because of this differences ice and sediment cores provide complimentary climate information. In the recent decade, there have been several attempts to combine various types of proxy indicators to create long-scale paleoclimate series. Reliable regional proxy based temperature reconstructions of the past millennium and their comparison with GCM were performed by Crowley and Lowery (2000), Briffa et al. (2001), Mann et al. (1998, 1999), and Mann and Jones (2003), who presented reconstructions of Northern and Southern hemisphere as well as global mean surface temperatures over the past two millennia or so based on high-resolution proxy data, namely historical, tree-rings, ice cores, and sediment records (Figure 3). The latter reconstruction is possibly the most often cited one. It was performed using tree-ring, ice core, coral, and historical records of climate (merged with the recent instrumental observations) and shows temperature variations in the Northern hemisphere over the past millennium (from 1000 A.D. to 1980). Comparison of above mentioned reconstructions is presented in Figure 11 (see also Mann et al., 2000; www.ngdc.noaa.gov/paleo/ei/ei_cover.html). Differences between above three climate histories are evident during the sixteenth and seventeenth, and early nineteenth centuries, where series by Crowley and Lowery (2000) and Briffa et al. (2001) fall outside the uncertainties estimated by Mann and Jones (2003). However, really noticeable feature of all records is so significant recent warming that Mann and Jones (2003) have concluded that indicated by their results late twentieth century warming is unprecedented for the Northern hemisphere at least during the investigated period. Conclusions by Mann et al. (1998, 1999) and Mann and Jones (2003) were supported by similar result by Jones et al. (1998) based on the independent data and methodology. Borehole data by Pollack et al. (1998) independently support this finding for the past 500 years. On the other hand, borehole temperature reconstructions alone as well as including of this source in the multiproxy reconstructions always give a colder past than that suggested by tree-ring and/or by multiproxy data sets. On the integrated (multiproxy ⫹ borehole) reconstruction by Huang (2004) presented for comparison in Figure 11, recent warming clearly appears as a simple continuation of the recovery from the cold conditions prevailed in the sixteenth century (this topic is discussed in more detail in Section 3.3, Chapter 3). Similar conclusions for the Southern hemisphere and on the global scale are less significant because of the sparseness of available proxy data.

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The course of the last millennium temperatures proposed by Mann et al. (1998, 1999) was so unusual (nothing in the first 900 years and dramatic temperature rise in the last 100 years), that it was accepted with caution by some climatologists and induced so trenchant debates that the Mann et al.’s curve was even called the “hockey stick”. The controversy about “hockey stick” can be found at the internet sites by the Marshall’s Institute (www.marshall.org and www.realclimate.org/index.php?m=200502), the World Climate Report (www.worldclimatereport.com), and numerical publications. Independently of the debates on the “hockey stick” this graph has been accepted as a fact by such international communities, e.g. IPCC (www.ipcc.ch) and played an important role in the study of the recent climate change and its consequences (up to Kyoto Protocol). The final point in the “hockey stick” story was placed only recently in the works by von Storch et al. (2004), Moberg et al. (2005), and Hegerl et al. (2007). The authors of the former work were interested in how well the multiproxy temperature reconstruction methodology applied in the creation of the “hockey stick” actually works, especially at multi-decadal and centennial timescales. They have used a coupled atmosphere–ocean model simulation forced with historical changes in solar output and volcanic eruptions and have simulated the surrogate climate for the past 1000 years. Calculated synthetic record was realistic enough and accurately reproduced main climatic episodes of the last millennium. Further, the authors have applied a technique similar to Mann et al.’s, using “proxy” data derived from the surrogate climate record to check how well the Mann et al.’s (1998) methodology could reproduce the actual data from which it was obtained. According to von Storch et al. (2004), the techniques used to construct the “hockey stick” significantly underestimated the real level of variability in the modeled temperature record and the real past climate variations may have been at least two times stronger than that indicated by the reconstructions. It is thus reasonable to conclude that the same techniques applied to the real field data would similarly underestimate the true level of natural variability that will result in too flat course of the past climate history. Even more severe argument against the “hockey stick” was presented in the work by Moberg et al. (2005). These authors have performed two thousands past Northern hemisphere temperature reconstructions using a variety of low- and high-resolution proxy data. Taking it for granted that different kinds of proxy temperature records may be more appropriately related to climatic variations at different timescales, Moberg et al. (2005) have applied a wavelet transform technique that can systematically overcome possible non-stationarities in the data and allows each proxy to explain temperature variations on a timescale that it was most sensitive to. For example, as discussed above, tree-rings capture weakly long-term variations, but are quite powerful for investigation annual-to-decadal scale variability. Low temporal resolution proxies are useful for capturing long-term, multi-century climate variations. By combining information of high-resolution and lowresolution proxies, Moberg et al. (2005) have inferred long temperature reconstruction for the Northern hemisphere (Figure 12). As can be seen, their reconstruction shows larger multi-centennial variability than most of previous multiproxy reconstructions, including, e.g. strong Medieval Warm Period and the Little Ice Age. The natural variation of temperatures in the Moberg et al.’s (2005) reconstruction is two to three times larger than that of the “hockey stick”. On the other hand, it agrees well with the ground surface temperature (GST) reconstructions from borehole data by Huang et al. (2000) (see also Section 3.2 and Figure 94, Chapter 3).

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Fig. 12. Low-frequency component of the Northern hemisphere temperature anomalies (Moberg et al., 2005) compared with the reconstruction by Mann et al. (1998) and borehole ground surface temperature reconstruction by Huang et al. (2000).

Recently Hegerl et al. (2007) have suggested a new calibration method that avoids the loss of low-frequency component in the multiproxy reconstructions. On the basis of updated proxy time series, these authors have reconstructed 1500 years long past temperature variations for the Northern hemisphere on decadal scale of aggregation. Obtained record shows substantial variability over the whole reconstructed period that is very similar to the Moberg et al.’s results. Hegerl et al. (2007) have tested their record with independent temperature reconstructions and with the climate model estimates. Good coincidence was found in both cases. The comparison of the reconstructed by Hegerl et al. (2007) temperature course with borehole estimates by Pollack and Huang (2000) and Pollack and Smerdon (2004) (for details see Section 3.2, Chapter 3) that revealed good agreement of both reconstructions appears to be most important for the borehole climatology. Using a conductive forward model and their SAT estimate as a surface forcing function (for details of calculus see Section 2.5, Chapter 2), Hegerl et al. (2007) have also calculated corresponding subsurface temperature anomaly. Its comparison with the average observed anomaly determined by Harris and Chapman (2001) has shown that the two temperature–depth profiles are almost identical. All above cited works mean probably the end of the “hockey stick” representation and have inspired numerous responses in climatologic community, like “Is the hockey stick broken?” www.tcsdaily.com/article.aspx?id=102704F and www.worldclimatereport.com/ index.php/2005/03/03/hockey-stick-2005-rip. As it is described in Section 3.2 (Chapter 3) (see also Figures 11, 12), borehole temperature reconstructions generally reveal more colder past and the warming that is more gradually distributed over past five centuries.

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Being quite different from the “flat” curve accepted by the “mainstream” of the climatologic community, these results were met with caution. Almost perfect coincidence of the GST reconstructions with the SAT changes presented in the works by Moberg et al. (2005) and Hegerl et al. (2007) represents also powerful verification of the “borehole climatology” with independent method and once more corroborates that borehole temperature reconstruction is a good indicator for the land annual SAT change. In summary we can only mention that considerable scientific efforts have been done to reconstruct past climate from the biological and physical proxy sources. This task is a challenging one and its results represent the subject to many complications and potential uncertainties and confidence can only be gained after comparison of more and more independent sources of proxy data. It is clear that the fundamental limitations (both temporally and spatially) of large-scale proxy-based reconstruction for past centuries arise from increasing sparseness of proxy database available to provide reliable climate information back in time. This database can be completed in space and time to such state when significant improvements will be possible in proxy-based reconstruction of the global climate only through joint efforts of large number of paleoclimate researchers. The compilation of many proxies can somewhat extenuate this problem, however, even a “multiproxy” reconstructions can only give a general understanding of what the climate was like and identify large-scale changes which may be related to climatic forcing of hemispheric or global significance. The “multiproxy” reconstructions are best to indicate climate tendencies or trends rather than exact temperature changes. On the other hand, numerous proxy results indicating similar climatic trends represent a powerful evidence that these tendencies are significant and really occurred, even if the magnitude of the change cannot be quantified.

1.3 Borehole Climatology Geothermics is the sub-branch of geophysics that studies terrestrial heat flow (Kappelmeyer and Haenel, 1974; Haenel et al., 1988; Jessop, 1990). Heat flow is the quantity of heat (generally expressed in mW/m2) transferred from the Earth’s interior to the surface. The major source of the interior heat is the decay of radioactive elements in the Earth’s crust and upper mantle. Up to 70% of continental heat flow may be generated within the upper 10–20 km of the crust; while 96% of the oceanic heat flow comes from below the oceanic crust where the concentration of the radioactive elements is significantly poorer (Kearey and Vine, 1990). The distribution of heat flow is related to tectonic processes in the lithosphere. The average heat flow density is inversely correlated with the geologic age of a given tectonic unit or oceanic crust (Sclater et al., 1980; Condie, 1989). On a regional scale heat flow pattern depends on numerous factors, such as regional differences in crustal radioactivity, fault distribution, hydrogeology, and hydrothermal activity (Cermak, 1983). Knowledge of the subsurface temperature field is central for understanding of practically all geophysical processes. The variation of temperature with depth and amount of heat leaving the Earth’s interior through its surface can be easily measured. Heat flow determinations in boreholes are made by combining sets of temperature–depth profiles and thermal conductivity data by the expression Q ⫽ K (dT/dz), where Q is heat flow, K the thermal conductivity, and T the temperature at depth z. The present

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global heat flow data set was compiled under aegis of the International Heat Flow Commission (IHFC; home-page www.geo.lsa.umich/IHFC) and contains more than 24000 measurements. Its description and analysis can be found in the work by Pollack et al. (1993). Temperatures obtained in boreholes, both the single values from maximumreading instruments and/or continuous temperature surveys (temperature logs), are essential to many areas of scientific research and engineering. Present book is devoted to the so-called ‘geothermal’ or ‘borehole’ method of the climate reconstruction which represents the reconstruction of the past temperature changes from the temperature–depth profiles measured in boreholes. This method principally differs from conventional proxies since it provides direct estimates of the GST histories. Ground surface temperature itself represents one of the important climatic variables, thus, reconstructed GST histories do not require calibration against independent climatologic data. The physics of the phenomenon is the next. At the constant surface conditions the underground temperature is governed by the outflow of heat from the Earth’s interior. For the homogeneous stratum it increases steadily with depth. Temperature changes at the surface slowly propagate downward and appear superimposed on this background geotherm. Figure 13 illustrates the ideal case of the penetration of sudden 1 K increase in the surface temperature into the subsurface with zero temperature gradient. As seen, it creates noticeable curvature of the undisturbed geotherm (sometimes called “U-shape”).

Fig. 13. Subsurface temperature distribution corresponding to a sudden 1 K increase in the surface temperature: 100, 300, and 500-years after its occurrence.

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Surface warming manifests itself as a positive disturbance in the subsurface; cooling shows up as a negative disturbance. The perturbation has the maximum amplitude at the surface, and the subsurface effect is limited to progressively shallower depth ranges as the surface change duration is shortened. The velocity of perturbation is a function of the thermal diffusivity of the medium. For typical Earth’s rocks with diffusivity of ⬃10–6 m2/s perturbation can propagate approximately 20 m in one year and 650 m in thousand years (see examples in Section 2.2, Chapter 2). Thus, 500–650 m deep hole archives climatic information for the last millennium or so. Whereas the depth of a subsurface temperature perturbation is related to the timing of the GST changes, its shape reflects details of the GST history. In other words, the borehole temperature log represents the transformation of the surface climatic events from the time into space coordinates. This transformation is performed by the nature and not by mathematics. Thus, the borehole temperature logging can replace the long-term surface temperature measurements. Well-pronounced “U-shapes” occur only in the ideal case of a single powerful climatic event. Arising sequentially several changes in the surface temperature create more complex and/or less expressed patterns than the strong “U-shapes” presented in Figure 13. Example of the temperature–depth profile simulated for the real GST changes is shown in Figure 19 (Chapter 2). Temperature changes at the Earth’s surface occur at several temporal scales. The oscillations are more regular on diurnal, seasonal, and annual scales. The strongest of these changes are the daily and seasonal variations with the amplitude of approximately10°C and the annual GST oscillations with typical amplitude of 20–30°C. Interannual and long-term temperature change patterns are generally irregular. As the surface temperature signal propagates downward, its amplitude decreases exponentially with depth due to the diffusive process of heat conduction. Each variation vanishes over a vertical distance related to the period of change and to the thermal diffusivity of the ground. Shorter period fluctuations attenuate more rapidly. Thus, the Earth selectively filters out high-frequency component of the surface temperature oscillations, and deeper we go, the more distant past can be inverted (unfortunately also more diffused and less credible). Figure 14 illustrates the amplitude attenuation of the temperature signal when propagating downwards and the delay of its phase by showing the results of the 12-year temperature monitoring at several shallow depths in the experimental borehole Sporilov (Prague, the Czech Republic) (Cermak et al., 2000). The daily temperature wave is practically not observable below 1 m depth. On the other hand, the temperature at 1 m represents integrated average of the daily signal of the previous day. Similarly, annual GST oscillations vanish near approximately 10–15 m depth and are not measurable below this depth. However, temperature measured above this depth is a proper index of the averaged temperature wave of the previous year. The temperature field below the 20–30 m depth is free of any response to the annual and/or shorter temperature variations and contains exclusively the fingerprints of longer scale climatic events with characteristic time of at least several years. Such signal may well characterize the pattern of the long-term climate change. Figure 15 illustrates the amplitude decrement and phase shift of the annual temperature wave with depth in more details. It shows the 1998-year interval of the long-term temperature time series from Sporilov presented in the previous figure. Temperature was monitored at several shallow depths from the surface to 7.5 m. As the

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Fig. 14. Results of 12-year precise temperature monitoring at several shallow depth levels in the Sporilov hole (the Czech Republic; 50.04°N, 14.48°E, 274 m asl.) clearly demonstrate the propagation of the surface temperature signal to the depth.

surface temperature signal propagates downward, it is delayed in time and its amplitude decreases exponentially with depth due to the diffusive process of heat conduction. Each variation vanishes over a vertical distance related to the period of change and to the thermal diffusivity of the ground. Thus, the amplitude of the annual wave decreases to 50% of its surface value at ⬃2 m depth with time delay of about 40 days. It decreases to ⬃15% of its surface value at 5 m depth where it arrives with approximately three months delay. Repeated measurements of borehole temperature logs, e.g. the temperature loggings performed in borehole GC-1 (northwestern Utah) over a span of 14 years by Chapman and Harris (1993) have shown a slowly varying temperature field with remarkable similarity of the measured signals and the replication of their main details, remaining evidently coherent in space and time (Figure 16). Bottom panel (c) of Figure 16 shows the temperature differences between the individual logs (data points) together with the synthetic temperature-difference profile (solid line) computed from the 100 years long meteorological record of SATs in the nearby weather station. The deviations between three temperature logs do not exceed 0.1 K. Synthetic temperature profiles exhibit high correlation with the measured temperature logs. In the case of above mentioned borehole GC-1 synthetic profile represented well systematic negative anomaly and significant curvature in the uppermost 60 m of borehole. This fact indicates that “U-shapes”

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Fig. 15. Results of one-year temperature monitoring at several shallow depths in Sporilov hole. The profiles well illustrate the amplitude decrement and phase delay of the temperature change with depth.

are not occasional variations and really represent the result of the long-term processes of changing climate. This conclusion is supported by repeated temperature logging of borehole Hearst (Canada) that embraces even longer period of time. Three holes together with Hearst site were drilled in northeastern Ontario in 1968 as a part of the heat flow project of the Dominion Observatory. The sites were carefully selected in a relatively flat terrain and in geologically uniform strata. The 600 m deep borehole Hearst is located in a slightly elevated, bushed area at the boundary of large forested and cleared fields. A small nearby lake and swampy area affect the temperatures insignificantly. The first incremental log was measured in 1969 (Cermak and Jessop, 1971). Further virtually continuous loggings were performed in 1985 and in 2000. Temperature measurements are highly precise with absolute accuracy of less than 20 mK for the incremental logs and as small as 10 mK for the continuous logs (for further details see Section 2.4.3). Figure 17 compiles the results of these measurements. As seen, all temperature logs are quite coherent with weak, but clear positive “U-shape” curvature in their uppermost parts. Figure 17 and all similar

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Fig. 16. Measured (a) and reduced (b) temperature profiles for borehole GC-1, NW Utah, USA. Temperature logging was repeatedly performed in years 1978, 1990, and 1992 (data by Chapman and Harris, 1993). Bottom panel (c) shows the temperature differences between the individual logs (data points) together with the synthetic temperature-difference profile (solid line) computed from the 100-year meteorological record of SATs recorded in the nearby weather station.

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Fig. 17. Repeated measured temperature logs (together with the reduced temperatures) performed in the Hearst hole, NE Ontario, Canada (personal communication by W.D. Gosnold and A.M. Jessop, see text).

diagrams below present the temperature log not only on the measured, but also on a reduced scale obtained by subtracting from the measured temperatures, a temperature value ⫽ gradient ⫻ depth (see also Eq. (2.5), Section 2.2). This representation enhances the nonlinearities. The shape of the reduced temperature–depth profiles is more complex than that occurring in the case of the single warming event (Figure 13). The waves of the opposite sign in the reduced temperature profiles hint the presence of the recent warming that may be amplified by the environmental effect of the forest clearing occurred approximately 100 years ago (Wang et al., 1992), subsequent cooling, warming, and cooling again. Examples of the GST history reconstruction for this hole are presented in Section 2.4.3 (Chapter 2). The surface temperature history can be inferred directly from the borehole temperature logs. Earlier the subsurface anomalies were found by forward calculation using appropriate physical models with given surface temperature histories and by selection of those GST history that best explains the measured temperature–depth profile. At present the GST changes are inferred by the more general data inversion techniques. The accuracy of the inversion depends on numerous a priori information, e.g. on the knowledge of conductive properties of the subsurface stratum. This technique is essentially multidecadal and cannot provide information about annual temperature changes or for the times near the present. The advantages of the “geothermal” method are discussed in details in Paragraph 2.2. Here, we would like to point out only the two main advantages: (1) subsurface temperatures are measured directly and on the contrary with the proxy measures their inversion provides a direct evidence of past temperature change at the

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Earth’s surface, (2) because of great number of boreholes the method is applicable over most continents including polar ice caps. From the middle of twentieth century numerous measurements of the temperature profiles in boreholes were performed for the terrestrial heat flow study. The recognition that past climate changes influence the GSTs, penetrate in the subsurface and could be recovered from the temperature–depth profiles measured at boreholes dates back to Lane (1923). The first attempt to infer past climate changes from measured temperature–depth profile dates back to Hotchkiss and Ingersoll (1934) and Birch (1948). More systematic studies of the possibilities of the geothermal method were undertaken only in the early 1970s (Cermak, 1971; Anderssen and Saull, 1973; Beck, 1982). However, even that time climatic perturbations to an otherwise equilibrium geotherm were regarded as “noise”, and it was customary to eliminate them from the temperature profiles measured for the Earth’s heat flow investigations and/or use the lower “undisturbed” sections of the temperature logs for the terrestrial heat flow determination. The real recognition of the method has been gained in the 1980s when the evidence of pronounced last century warming was clearly proved in a number of wells in the Alaskan Arctic (Lachenbruch and Marshall, 1986; Lachenbruch et al., 1988). In the recent two–three decades the geothermal community had undertaken widespread re-examining of existing heat flow data in order to reveal the past GST changes and to construct systematic GST perturbation patterns. The previous “noise” was turned into a valuable signal of the climate change. Further studies of the geothermal method developed in three main directions: 1. Inversion methods – Numerous inverse methods based on different assumptions and used definite a priori knowledge have been developed that time. 2. Climate reconstructions using national borehole database – The ample worldwide geothermal database of temperature logs initially measured and compiled for heat flow studies has proved to be very useful for the GST reconstructions. The intensification of the borehole climate studies was supported by simultaneous significant growth of the global geothermal borehole network. Since the 1990s numerous borehole loggings were performed directly for paleoclimatic reconstructions (for more details visit web sites www.geo.las.umich.edu/climate/index.html and/or www.ncdc.noaa.gov/paleo/borehole/borehole.html). 3. Integration of the obtained GST histories in the traditional paleoclimatic network (Harris and Chapman, 2001; Mann et al., 2003). The first compilation of the studies inferring past climatic changes from underground temperatures has appeared in 1992 (Lewis, 1992). The reconstruction of the GST histories has drawn increasing attention under several international projects in the 1990s. The Project No. 428 carried out in the years 1998–2002 under the UNESCO International Geological Correlation Program “Borehole and Climate” was probably the most important of them. The next after the year 1992 current collection of the borehole climate reconstructions from a number of regions all over the world was compiled by Beltrami and Harris (2001). The analyses of the worldwide borehole data for the large-scale spatial–temporal reconstructions of the Earth’s climate are presented in the works by Pollack et al. (1998), Huang et al. (2000), and Mann et al. (2003). Initially

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the paleoclimatic information was gained from conventional widespread land boreholes. Recently the 50 000 years long GST history was recovered from temperature profiles measured in the ice borehole remained after successfully recovered ice core in the Greenland ice sheet (Dahl-Jensen et al., 1998). The ice borehole logs can provide valuable estimates of past temperature changes in polar environments that are complimentary to proxy reconstructions obtained from ice core oxygen isotopes (for details see Section 2.9). Superdeep boreholes belonging to the International Continental Drilling Program (ICDP; www.icdp-online.de) attracted special attention of the “borehole climatology” community. Geothermal and paleoclimatic investigations are among the most important directions of the ICDP scientific research (Section 3.5). The common merits of the geothermal method are that it is based on a simple physical theory, that the past ground temperature conditions can be derived directly from measured temperature logs and do not need any additional calibration, its ability to recover continuous GST trends over the last millennium or longer, and a rather good terrestrial distribution of boreholes. Among the possible weaknesses is somewhat poor resolution that decreases back in time and non-climatic disturbances that could affect measured temperature–depth profiles. Numerous methods for diminishing of possible biases of the geothermal method are worked out. Obtained GST differs from the SAT that is of general use in meteorology/climatology. This complicates comparison of the GST and SAT based climatic reconstructions. The GST–SAT coupling depends on external factors (e.g. land surface cover and its changes, especially seasonal snow cover variations). Additional studies of this problem include experiments on the monitoring of meteorological and subsurface variables. These experiments were planned to reveal details of the air/ground energy exchange under various surface conditions (Putnam and Chapman, 1996; Beltrami et al., 2000; Cermak et al., 2000; see Chapter 4). Despite the existing sources of bias, the results of the last two decades research confirmed the ability of the method to provide reliable GST history that is consistent with other paleoclimatic information. At present the geothermal method plays a new significant role in the investigations of climate of our planet. Borehole temperature profiles became one of the important sources of climatic information and contributed significantly to our knowledge of the millennial surface temperature changes. Some of the leading scientists give extremely positive evaluation of the geothermal method, e.g. “in my estimation, at least for timescales greater than a century or two, only two proxies can yield temperatures that are accurate to 0.5°C: the reconstruction of temperatures from the elevation of mountain snowlines and borehole thermometry” (Broecker, 2001). Using more modest expressions, one could declare that at present the ‘borehole’ method undoubtedly represents an independent well-developed research tool in the paleoclimatic studies and an important supplement to the climate reconstruction by proxy indicators. The purpose of this book is to present our current best knowledge of the geothermal method for the past climate reconstruction. The book explains the capacity of the subsurface temperature field to “remember” what has happened on the surface and how this memory can be utilized. We therefore describe in details different methods of the GST inversion, make note on the strength and emphasize the potential weaknesses and caveats of the past climate reconstruction from geothermal measurements, particularly with respect to its resolving power, non-climatic disturbances to the measured

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temperature–depth profiles and GST–SAT coupling problems, and discuss the possibilities of further development. Significant part of this work summarizes the major results to reconstruct the climate scenario spanning from Holocene to recent and discusses their role in the improvement of the traditional paleoclimatic patterns. The final goal is to assess the magnitude of the present-day warming and to distinguish between the natural climate variability and the potential human contribution due to environmental pollution. We hope that this book will contribute to advance of the “Borehole Climatology” research in the coming years.

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CHAPTER 2

Climate Change and Subsurface Temperature

While the majority of climatologists are looking up to the sky or plunge themselves in the oceans to understand why and how the climate has changed, an adroit “borehole climatologist” is looking inside deep holes drilled in the ground. The most important research implement of the “Borehole Climatology” is a borehole. Typical borehole site looks like as it is shown in Figure 18. A borehole, usually a small-diameter hole drilled from the land surface to the depth of several tens or hundreds meters, presents a deep narrow shaft in the ground which enables to lower a temperature-measuring device (thermometer) down the hole. The temperature measurements are repeated to progressively greater depths until a long temperature–depth profile is obtained. The thermometer measures the temperature of the borehole filling fluid (usually water), not the surrounding rock, so as to obtain meaningful values of the ambient temperature of the surrounding subsurface strata, the borehole fluid must be in thermal equilibrium with its surroundings. If the hole has been only recently drilled, the fluid may not have time enough to attain thermal equilibrium. Also, any event that subsequently disturbs the bore fluid may cause certain thermal disturbance. The disruption of the thermal equilibriun caused by the drilling process is slowly dissipating; to obtain a reliable precise temperature–depth record a long recovery time (up to several months) is indispensable. Production, i.e. removal of fluid from the borehole, also causes thermal disturbances, so in many cases the oil wells or water-pumping holes are hardly suitable for the borehole climate reconstruction. Temperature logging is actually a part of borehole geophysics, the science that records and analyzes measurements of various physical properties in boreholes. Probes that measure different properties are lowered into the borehole to collect continuous or pointby-point data, so-called geophysical log. These records may be used for various environmental investigations and help better understand the subsurface conditions. Geothermics or geothermal research, the sub-branch of geophysics, is the study of the thermal state of the interior of the solid and of the thermal properties of the Earth material. Knowledge of the subsurface temperature field is central for interpreting and understanding practically all geophysical processes. Temperature log is the temperature record in the borehole and 37

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Fig. 18. The Torun IG-1 (Poland) deep borehole site. Standard measurement technique, when the temperature probe is lowered to the hole with the help of cable on the winch. Temperature data are taken and stored with the pre-selected time interval (usually each 5 s), and depth is simultaneously recorded by computer from the number of revolutions of the pulley.

represents a general tool of geothermics. Examples of the temperature logs measured in boreholes are presented in Figures 16 and 17 (Chapter 1). At the constant surface (temperature) conditions the underground temperature is governed by the outflow of heat from the Earth’s interior. For the homogeneous stratum (constant thermal conductivity of the subsurface rocks) temperature increases steadily with depth, i.e. the geothermal gradient is constant. Temperature changes at the Earth’s surface (as the response to the climate changes) slowly propagate downward into the subsurface and appear as tiny temperature deviations superimposed on the background geotherm. While the part of underground temperature field governed by the heat flow from the Earth’s interior is

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generally steady state, the response to the surface conditions is a transient perturbation. Present precise borehole temperature measurements at depth of up to several hundred meters thus provide an archive of temperature changes that have occurred on the surface in the past. They can be analyzed to yield a ground surface temperature (GST) history over the past few centuries.

2.1 Methods and Technique to Carry Out Borehole Temperature Measurements Temperature data measured in boreholes serve as an input to many fields of the scientific research as well as engineering and exploration, and the techniques and equipment for such measurements are well developed. Among others, Lee and Uyeda (1965) and Jessop (1990) presented detailed review of a history of the geothermal measurement and the interpretation of the heat flow data in terms of the basic geophysical studies. The earliest measurement of the subsurface temperatures started at about 1700s, soon after thermometers had been developed, in mine shafts, tunnels, and/or water wells. Some of the early systematic measurements in boreholes were conducted between 1868 and 1883 under the aegis of the Committee of the British Association for the Advancement of Science (see Bullard, 1965). Initially, these measurements were simply individual readings obtained by the maximum-reading thermometers at shallow depths. The development of the petroleum industry during the second half of the nineteenth century made deep boreholes available for subsurface temperature loggings and, together with the development of electrical-resistance thermometers, significantly improved the accuracy of the measurements. Schlumberger services first introduced the temperature survey, using continuous-recording logging tools, in the late 1930s. Guyod (1946) had presented a series of papers, which discussed the theory and the various current and potential uses of the underground temperature data in the petroleum industry and inspired a widespread application of the temperature logging technique. Haenel et al. (1988) and Jessop (1990) have presented reviews of the methodology and technology of the scientific borehole temperature measurements for the heat flow determination. The most accurate temperature and heat flow data are obtained with high-resolution thermistors sunk into small-diameter, thermally stable boreholes at logging speeds of 10–15 m/min. These data are generally recorded as continuous temperature or temperature gradient logs. The different kinds of the logging tools have a resolution of 1–3 mK with typical accuracy of several hundreds of degree. The borehole GST reconstruction methods deal with very small disturbances to the subsurface temperatures, where even tiny variations of some hundreds of degree are considered significant and accuracy of the measured data is crucial. As mentioned before, the drilling operations disturb the temperature field in the vicinity of the boreholes, while good-quality steady-state data reflecting “formation temperature” are indispensable for proper evaluation of the past climate. The undisturbed borehole temperature can be measured only in the equilibrium conditions after the long period the hole was shut in and drilling mud circulation ceased. The “thermal recovery” time for a borehole may range from a few days for a shallow (100–150 m) well to several months for deeper holes. The main assumptions for the mathematical approximation of the temperature disturbances due to drilling are: (1) drilling was continuous and regular, (2) there is no fluid

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loss, and (3) thermal diffusivity equals that of the surrounding rocks. In this case, the temperature disturbance due to drilling (heat exchange with drilling fluid and frictional heating) Td at a distance r can be approximated by a constant line source (Carslaw and Jaeger, 1962) Td ⫽⫺

 r2  Q , Ei ⫺ 4
K  4 kt 

(1)

where K is the thermal conductivity, k the thermal diffusivity, Q a quantity characterizing the amount of heat released by the heat source, and t the time from the beginning of the disturbing effect. Ei(x) is the exponential integral. If the disturbance lasts from the time th to 0 its value can be expressed as Td ⫽

Q 4
K

    r 2   r2 ⫹ Ei ⫺Ei ⫺  ⫺ 4 kt  .     4 k (t ⫹ t h )  

(2)

For the typical small-diameter boreholes both arguments in the exponential integral will be also small. Thus, the integral allows approximation Ei(⫺x) ⫽ ln x ⫹ 0.577 22. The latter number is the Euler gamma constant. Final expression for Td will be Td ⫽

Q  t ⫹ th  ln . 4
K  t 

(3)

The heat release Q is proportional to the diameter of borehole and the temperature difference between the drilling fluid and surrounding rock. The typical values range in the interval 10 to 20 W/m. In a shallow borehole the temperature of the drilling fluid is close to that on the surface. Additional heating caused by the friction of the drilling bit may slightly increase its value; however, because the temperature in borehole increases with depth, the value of Q may somewhat decrease with depth in case of other conditions being constant. Figure 19 shows the re-establishment of the undisturbed temperature field (having existed prior to the drilling) calculated for different values of the heat released during the drilling process. The magnitude of the borehole temperature disturbance due to drilling decreases almost linearly during the period compared with the drilling time. At t ⫽ th the disturbance falls to approximately 12% of its initial value. For times t ⬎ th the attenuation significantly slows down. The above calculations were performed under assumption of continuous drilling; however, suggested method can be further developed for the case of drilling regime interrupted by several breaks (Štulc, 1995). At a certain stage the above approximation may not be valid (Drury, 1984); this happen when the arguments of the exponential integrals (Eq. (2)) are not small enough due to low circulation time and/or large-diameter boreholes. As shown below, a reliable climate reconstruction using borehole temperature–depth data can be substantially improved by the knowledge of the thermophysical properties

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Fig. 19. Re-establishment of the undisturbed temperature field corresponding to the conditions prior to drilling. Individual curves are marked by the value of heat released by the heat source in W/m.

of the subsurface, namely of the thermal conductivity. Thermal conductivity,1 a basic physical property of rocks, depends on rock/mineral composition. Reliable values of thermal conductivity are helpful for modeling of all kinds of processes of heat transfer in the Earth’s crust. “If the mean conductivity cannot be accurately predicted, even the most sophisticated and appropriate modeling techniques … are not sufficient for accurate temperature predictions” (Blackwell and Steele, 1989). Thermal conductivity determination represents an essential part of the borehole measurements suitably completing the borehole temperature logs. Thermal conductivity is usually determined in laboratory on rock samples collected from the drilled core and worked out in either specific shape or on crushed cuttings using various techniques, e.g. divided bar or needle probe technique (Jessop, 1990). 2.2 Subsurface Temperature Field and its Response to Changing Surface Conditions (Climate) At the first sight, the relation climate change versus subsurface temperature field may be strange. How is it possible that the present-day subsurface temperature measured in the solid surface can reflect a climate change that occurred a long time ago and somewhere high above the Earth’s surface? There is no doubt that the thermal regime 1 Thermal conductivity K is the property of material that indicates its ability to conduct heat. Under pure conductive steady-state conditions, it is defined as a quantity of heat transferred in time t through the layer with thickness L in a normal direction to a surface of area A due to a temperature difference
T: K ⫽ QL/A
T. Typical conductivity of the Earth’s subsurface rocks is in W/mK.

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at the Earth’s surface and in the near-surface shallow depths is controlled entirely by the solar radiation, and the resultant mean surface temperature depends on the longterm budget of the incoming and reflecting radiation. The average energy density of solar radiation just above the Earth’s atmosphere, in a plane perpendicular to the rays, is about 1367 W/m2, a value called the solar constant (although it fluctuates by a few parts per thousand from day to day). The Earth receives a total amount of radiation determined by its cross-section (
R2), but as the planet rotates this energy is distributed across the entire surface area (4
R2). Hence, the average incoming solar radiation (known as “insolation”) is 1/4th the solar constant or ⬃342 W/m2. At any given location and time, the amount received at the surface depends primarily on the state of the atmosphere and the latitude. Temperature (as well as precipitation and wind) is the most important variable, which characterizes the climate. When speaking about the temperature, we usually understand temperature of the air. However, air temperature is not constant and at the given location it shows daily and yearly variations, which may amount from a few degrees up to several tens of degrees. To deal with climate and its changes which cover time spans varying from decades to thousands or millions of years, we may better use a mean annual air temperature as the unit to describe the time variations of temperature. Actually, the World Meteorological Organization (WMO; www.wmo.ch) proposes 30-year time interval as the classical period when defining climate as the statistical system in terms of the mean and variability. However, being important for the conditions existing on the Earth’s surface, the incoming solar radiation is of no practical meaning for the state under the surface. From the surface the temperature is increasing with depth with the rate (geothermal gradient) proportional to the outflow of the thermal energy from the Earth’s interior. Typical geothermal (terrestrial) heat flow on continents equals to 50–60 mW/m2, which is negligible in comparison to the solar flux. This, even relatively, low geothermal outflow can provide significant geothermal gradients corresponding to 20–30 K growth per kilometer. This outflow is governed by the geologic timescale processes; thus, for shorter characteristic time of climatic studies this part of the subsurface temperature field can be assumed to be steady state. For the uniform crust and constant surface temperature the subsurface temperature–depth profile is the combination of the linear increase of temperature with depth plus the transient response to the seasonal temperature variations on the surface. In general, the sinusoidal oscillation of the surface temperature, T(t) ⫽ T0 cos(t), propagates downwards in accordance with the damped wave equation T(t)⫽T0 exp(⫺z) cos(t⫺z), where t is time, z the depth, k the thermal diffusivity,2 and ⫽兹苶 苶兾苶2苶k. If P is the period of surface temperature oscillations, then  ⫽ 2
/P. The wavelength is then  ⫽ 2
/. For a typical value of diffusivity (k ⫽ 10⫺6 m/s), the wavelength of the diurnal oscillation is about 1 m and that of the annual oscillation is about 20 m. At a depth of one wavelength, the amplitude of the oscillation is reduced by a factor of exp(⫺2
) ⫽ 0.002, and is thus negligible for most geophysical purposes. If the (high frequency) daily and annual temperature variations vanish at the depth below this zone of seasonal wave penetration (see Section 1.3, Chapter 3), the (low frequency) long-term climate changes propagate deeper.

2

Thermal diffusivity is the ratio of thermal conductivity to volumetric heat capacity (SI units are m2/s).

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In the idealized case, geothermal gradient can be calculated according to the Fourier3 relation G ⫽ Q/K, where Q is terrestrial heat flow and K the thermal conductivity of the medium. In the real case, geothermal gradient depends on local geological structure, e.g. on the composition of rock strata. Under suitable conditions the geological factors affecting the geothermal gradient can be taken into account, so the climate history can be inferred from small temperature anomalies along the depth of borehole. While part of the subsurface temperature field corresponding to the internal processes is steady state, the response to the surface conditions represents a transient perturbation that appears as a disturbance to the background temperature field. Figure 20 illustrates how borehole temperatures can be related to climate change. A sudden warming of the surface by the value of
T will heat up

Fig. 20. The response of the subsurface temperature field to the surface change. Bottom: Typical temperature–depth profile measured in a borehole indicating surface warming/cooling together with the (undisturbed) steady-state geotherm. Top: Disturbances to the geothermal gradient. 3 Jean Baptiste Joseph Fourier (1768–1830) has studied the mathematical theory of heat conduction. He has established the partial differential equation governing heat diffusion and has solved it by means of infinite series of trigonometric functions.

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the near-surface rocks. It creates a temperature profile with curvature like the one shown in Figure 20 (bottom, right) with smaller or even negative thermal gradients. Similarly, cooling produces an opposite effect. It increases geothermal gradient creating temperature profile like the one shown in Figure 20 (top, left). It is the heat conduction that helps to preserve the recollection on the past climate change at depth. The deeper we go, the more remote past history can be studied, even if both the amplitude attenuation and the time delay of the surface event increase with depth. As a simple rule, temperature–depth profiles to depth of 200–300 m record surface temperature trends (climate) over the last two centuries or so; deeper holes may reveal climate history farther back but with sharply decreasing resolution. Under favorable conditions all Holocene climate can be evaluated if the precise temperature log is available to the depth of 1 to 2 km. Perturbations to the conductive thermal regime are generally reasonably noticeable at shallow depth, while deeper temperatures are less affected by climatic variations. Well-developed curvature resembling a “U-shape” occurs only in the ideal case of a sudden relatively pronounced climatic event (see, e.g. temperature logs measured at Cuba, Figure 86, Chapter 3). Gradually changing climate, in reality consisting of several shorter alternating warmer and colder time intervals. creates more complex and less expressed subsurface temperature response than the pattern presented in Figure 20. The magnitude and the shape of the departure of the subsurface temperature from its undisturbed steady-state profile are determined by an amplitude and course of the surface temperature variation (climate). The GST history is recorded in the subsurface. These disturbances can be recollected by solving the ordinary heat conduction equation with appropriate initial and boundary conditions. The heat propagation equation for the source-free laterally homogeneous semi-infinite medium where heat is transferred exclusively by conduction can be written as c

T   T  ⫽ K , t z  z 

(4)

where z is depth, t the time, T(z,t) the temperature, and , c the density and the heat capacity of the medium, respectively. If necessary, the radioactive heat generation A, resulting from radioactive decay of U, Th, and K, can be also included in Eq. (4). However, this procedure is relevant only for deep boreholes. The addition of the term A to the right of Eq. (4) will produce a systematic decrease in geothermal gradient with depth. But the departure from a constant temperature gradient will be too small to be observed at shallow to intermediate-depth holes even for high rates of the heat production. For example, at boreholes with the thermal conductivity of 2–3 W/mK, including heat production of 1–3 W/m3 (the latter value is typical for granites) will produce only 0.002–0.008 K disturbance to the otherwise linear geotherm at depth 100 m and 0.007–0.030 K at depth 200 m, respectively. Initial temperature–depth distribution is T(z, t ⫽ 0) ⫽ T0(z). For the homogeneous strata it is simply T0(z) ⫽ T0 ⫹ Gz, while for a layered slab composed of m layers with constant thermophysical properties Kj and kj ⫽ Kj /cj, (z0, z1), (z1, z2), …, (zm⫺1, zm) it can

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be expressed as (Bodri and Cermak, 1995) T j ( z ) ⫽ T0 ⫹

Q ( z ⫺ z j⫺1 ) ⫹ Kj

j⫺1

∑K i⫽1

Q

( zi ⫺ zi⫺1 ).

(5)

i

Expression (5) suggests the continuity of temperature and of heat flow at the interface between layers. Surface temperature is T(z ⫽ 0,t) ⫽ f(t), t ⬎ 0. Forward calculation is very simple when the surface temperature history f(t) is known. Figure 21 shows the disturbances to the otherwise steady geotherms that develop in the case of a stepwise increase of the surface temperature by
T ⫽ T *⫺T0. The corresponding solution of Eq. (4) at time t ⫽
t can be expressed as (Carslaw and Jaeger, 1962)

 z  T ( z, t ) ⫽ T0 ⫹ Gz ⫹
T ⴱ erfc  ,  2 k
t 

(6)

Fig. 21. The effect of the duration (
t) of a step increase (
T ) in surface temperature on the disturbance penetration depth and the shape of the corresponding geotherms (k ⫽ 10⫺6 m2/s, T0 ⫽ 0°C, G ⫽ 20 K/km,
T ⫽ 3 K).

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where k ⫽ K/c is the thermal diffusivity and erfc(x) the complementary error function of argument x. As seen in Figure 21, a uniform surface anomaly propagates downward. The departure of the disturbed temperature profile from the steady-state geotherm increases, thus, the curvature of disturbed profile decreases with time. The magnitude of the departure of the ground temperature from its undisturbed state is determined by the amplitude of the surface disturbance
T. The velocity of propagation of the surface temperature disturbance depends on the thermal diffusivity of the rocks that is relatively small (⬃10⫺6 m2/s). For such diffusivity a thermal front (i.e. 1% of the surface change) propagates to about 20 m in one year from the beginning of surface warming, 65 m in 10 years, 200 m in 100 years, and 650 m in 1000 years. The real changes in the Earth’s surface temperature occur at different temporal scales. As shown in Section 1.3 (Chapter 1; Figure 14), the most significant and regular of them (daily, seasonal, and annual oscillations) are attenuated at relatively shallow depths of approximately 15–20 m. The longer term variations appear as disturbances to the steadystate temperatures at deeper levels. The combination of the subsequent warming/cooling events on the surface complicates the pattern of disturbances to T⫺z profile. Figure 22 illustrates the disturbances that can occur in the more close to the reality case than that presented in Figure 21. We used in calculations the temperature record for Central England (Chapter 1, Figure 8) as the surface forcing function f(t). This record of annual temperatures exists from the 1659 A.D. The time series is highly variable; the range of temperature oscillations reaches approximately 3 K. Figure 22 shows the departures from the steady-state temperature and geothermal gradients. The shape of the temperature anomaly in this case is more complex in comparison with the profiles shown in Figure 21.

Fig. 22. Steady-state temperature–depth anomaly (left) and geothermal gradient–depth anomaly (right) produced by the climatic temperature forcing corresponding to Central England (Chapter 1, Figure 8). Inset shows the enlarged segment of the gradient anomaly between 50 and 300 m depth.

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Fig. 23. The “thermal memory” of the Earth for the (climate) events on its surface: diffusion of the subsurface temperature perturbation caused by a sudden GST change of duration
t (k ⫽ 10⫺6 m2/s,
T ⫽ 1 K).

Although high-frequency components of GST changes are suppressed by the heat diffusion, calculated temperature–depth profile contains a robust signal of more than three century long climatic history. Negative temperature anomalies and positive gradient in the depth range 50–300 m indicate generally cold conditions in the seventeenth to nineteenth centuries, while the noticeable curvature in the uppermost part of the calculated temperature–depth profile and negative gradients correspond to the rapid warming of the twentieth century. The temperature disturbances propagate downward and slowly fade away. Figure 23 illustrates the downward propagation of the thermal front (1% of the surface temperature change) corresponding to the step-like surface temperature impulse of duration
t. The
T ⫽ 1 K amplitude impulse with duration 10 years propagates to the maximum depth of 170 m and fades away in the underground after approximately 240 years since its cancellation on the surface; 50-year long change can penetrate to approximately 400 m depth and is preserved in the Earth’s interior for approximately 1200-year long period. This example illustrates well the possible length of the surface climatic history that can be extracted from borehole temperature logs as well as the resolution capacity of the borehole climate reconstruction method. Past variations of the GST propagate slowly downward and, although attenuated and smoothed, remain recorded in the subsurface as a perturbation to the steady-state temperature field. Because the transient climatic signals have significantly shorter living time than the geologic heat flow variations, these two signals operate in differing frequency domains and do not mix; thus, it is possible to use borehole temperature profiles for the extraction of the past GST variations. Present rock temperatures measured at depths of up to several hundred meters provide an archive of temperature changes that have occurred on the surface in the past. They can be recovered by an appropriate analysis of

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temperature–depth data and provide the possibility to reconstruct past GST history. The main advantages of this geothermal tool for climate reconstruction are (1) The method is based on a direct temperature–temperature relation. The temperature–depth profiles reflect a direct relationship to the continuous GST forcing. The borehole climate reconstructions do not require any calibration against independent temperature data and contrary to most proxies such reconstruction is free of uncertainties of calibration. Inferred from geothermal data the GST histories can be used as themselves and present a powerful complementary source of information for the verification of proxy records and enable complex multiproxy reconstructions. Recently several groups of climatologists have combined the results of the GST reconstructions with high-resolution proxies such as tree-rings and/or ice core data and achieved both more accurate temperature estimates for the proxy methods as well as better resolution for the geothermal GST histories (Beltrami and Taylor, 1995; Harris and Chapman, 2001, 2005; Huang, 2004; for details see Section 3.3, Chapter 3). (2) The process of heat conduction integrates GST changes continuously in the same manner and thus secures data homogeneity (unlike, e.g. the meteorological data that can suffer from inevitable modification of the recording equipment or station reorganization). (3) Extensive time interval that could be recovered. Temperature–depth profiles contain a robust signal of the long-term surface temperature history. Resolution of the geothermal method covers one to two millennia, in some cases up to the last glacial (Bodri and Cermak, 1997b; Safanda and Rajver, 2001). (4) Continuous rather than short-term sensitivity. The resolution of the geothermal method is essentially multi-decadal. (5) Extensive geographical coverage. Once a borehole is available, temperature log can be obtained easily. The necessary equipments (borehole thermometer or data logger) are relatively inexpensive and common. At present thousands of the borehole temperature logs measured all over the world are available even when not all are useful and serious selection criteria must be applied. The ample worldwide catalog of temperature logs represents a valuable database for comprehensive paleoclimatic investigations. In many cases borehole temperature logs represent the only source of the climatic information for the region that otherwise appeared as a “white spot” on the paleoclimatic map. (6) Minimal anthropogenic disturbances. Many existing meteorological stations were actually located in settlements which later grew to huge population centers; their records may suffer from the induced anthropogenic pollution when local climate became significantly warmer than its surroundings (urban heat island) and the observed data can be mistakenly used. On the contrary, boreholes are usually drilled far from the population centers in remote regions. Climate information stored here contains minimum of such anthropogenic influence.

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The main disadvantages are (1) The decrease of the resolution into the past and progressive smoothing of the amplitude of the recovered temperatures when moving back to the past due to smearing of the climatic signal caused by thermal diffusion. (2) Exact knowledge of the thermal properties of rocks at each borehole site is indispensable to determine uniquely the pattern of the past climatic changes, while this information is not always available in full. This disadvantage, however, is similar to the common insufficiency of the field information in many other geophysical branches. (3) Climate change is related to the mean air temperature, but the inversion of the borehole data provides the GST, which is not identical to the air temperature. The soil–air temperature coupling is complex and dependent on the type of surface topography, albedo, type of bedrock, micro-vegetation cover (and its temporal changes), snow cover, precipitation, changes in water table, etc. The time variations in all of these factors and their reaction to climate and terrain changes make the interpretation of borehole data more complicated. In general, ground temperature is always higher than the air temperature; however, both follow in principle the same trend and the basic features of the climate reconstruction can be well substituted by the GST reconstruction.

2.3 Geothermal Method of Climate Reconstruction: Principles, Resolution, Limitations (Forward and Inverse Techniques, Sources of Perturbation) 2.3.1 Background and history The effect of the past climate changes, namely the last Ice Age, on the temperature gradient was actually recognized by the early heat flow workers (Lane, 1923), and a general mathematical formulation was thoroughly discussed by Carslaw and Jaeger (1962). Only much later it was realized that the problem could be reversed, i.e. from the detailed measured T–z data to infer the past climate. The first attempt dates back to Beck and Judge (1969) who speculated on recent surface temperature variations using data from a borehole drilled on the campus of the University of Western Ontario. Cermak (1971) used T–z profiles from three holes in the Kapuskasing area and reconstructed GSTs for the past millennium in northeastern Ontario, Canada, when the Monte Carlo type statistical method was used to alleviate the calculation instability problems. However, it was not before the late 1980s when Lachenbruch and Marshall (1986) presented clear geothermal evidence from a number of boreholes in Alaska for a recent global warming. This work can be considered as the beginning of the worldwide attention paid to the importance of the temperature logs for the paleoclimate studies. Even when the very first attempt to infer past climate changes from measured temperature–depth profile in inverse problem dates back to Hotchkiss and Ingersoll (1934), the application of the modern geophysical inverse theory to the reconstruction of the GST

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changes from the measured temperature–depth profiles has started with the work by Vasseur et al. (1983), using Backus–Gilbert formalism (Backus and Gilbert, 1967). Since then numerous inversion methods have been developed. The first compilation of the research results inferring wide assessment and comparative study of various methods for the past temperature history reconstruction from underground temperatures has appeared in 1992 (Lewis, 1992). Generally, three basic groups of the inversion methods won widespread popularity: (1) ramp and step models, sometimes referred as the one or last event analysis, (2) inversion techniques employing singular value decomposition (SVD) algorithm, and (3) functional space inversions (FSI). All three algorithms are based on the theory of 1-D heat conduction. 2.3.2 Theory of 1-D heat conduction The problem of heat conduction is formulated for a source-free composite medium and is defined over the time interval [t0 ⫽ 0, tn] and depth interval [z0 ⫽ 0, zm]. We assume that within the medium heat is transferred exclusively by conduction; thus, basic heat equation takes the form of Eq. (4). The surface temperature and temperature at the depth zm (great enough not to be affected by the surface conditions) are, respectively T ( z ⫽ 0, t ) ⫽ T0 , T ( z ⫽ zm , t ) ⫽ Tm

(7)

Within the solid Earth the temperature field is governed by the heat flow from the depth and by the distribution of the thermophysical parameters. All responses to the changing surface conditions are superimposed over the steady-state (initial) internal temperature field U(z) as transient thermal perturbation V(z,t). Thus, the solution of Eq. (4) can be represented as a superposition of two functions (Carslaw and Jaeger, 1962) T ( z, t ) ⫽ U ( z ) ⫹ V ( z, t ), z 僆[ z0 , zm ], t 僆 [t0 , tn ]

(8)

The choice of the value for t0 depends on available T⫺z data. Anyhow, it should be moved sufficiently back into the past such that the thermal regime prior to t0 could be regarded as the steady state. Further, we assume that at the depth zm the climatic perturbation vanishes, i.e., it is not measurable. In this case, the bottom boundary condition will be Tm ⫽ U(zm). Strictly speaking, this assumption fulfills exactly only at zm ;-, but this will not make much difference if the depth zm is large. The equilibrium (initial) temperature U(z) is taken as steady-state temperature for boundary conditions U(z⫽0)⫽T0(t⫽0)⫽U0, K⭸U/⭸z ⫽ Qm (z ⫽ zm), where Qm represents the undisturbed steady-state heat flow at depth zm. In the case of the stratified medium with constant thermophysical properties in each layer U(z) takes the form of Eq. (5). Function V(z,t) represents the transient temperature field due to changing surface conditions; in other words, it is extracted climate signal. The surface temperature perturbation is propagated downward with amplitude attenuation and time delay that increases with depth. The heat equation for V(z,t) is identical to Eq. (4) for the modified surface boundary condition V0(z ⫽ z0,t) ⫽ T0(t)⫺U0, and for zero bottom and initial temperatures.

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The parameterization of the problem (model) is defined by the set of five parameters/functions: K(z), c(z), U0, Qm, and V0(t). Assumption of the source-free medium is sufficient in the most practical cases. If necessary, the rate of the heat production per unit volume can be added to the steady-state equation for the initial temperature U(z). After the choice of the model one can solve the equations for quantities U(z) and V(z,t). It is so-called forward problem. Its solution requires discretization procedure that transforms primary partial differential equations into a set of algebraic equations relating the discretized model m to the vector of the GST values G G ⫽ f (m)

(9)

Depending on the discretization, the forward problem can be solved as accurately as desired. Solution takes analytical form in the case of the layered medium with constant known thermophysical parameters of each layer (Bodri and Cermak, 1995). In this case, K(z) and c(z) can be excluded from m, and Eq. (9) can be written as G ⫽ Dm, where matrix D is generally referred to as the data kernel. The Laplace transformation can be used to integrate Eq. (4). Let T *j⫺1 and Q*j⫺1 be the transforms of the temperature and heat flow at the depth z ⫽ zj⫺1, corresponding to the base of the jth layer, and T j*, Q*j be the analogous values at the depth z ⫽ zj. In the case of perfect thermal contact between the layers we have Tmⴱ Qmⴱ

⫽

Am Cm

Bm A1 L Dm C1

B1 T0ⴱ D1 Q0ⴱ

(10)

where

kj ⫽

Kj c j

, q 2j ⫽

p ,
z j ⫽ z j ⫺ z j ⫺1 , kj

 1  A j ⫽ cosh (
z j q j ), B j ⫽⫺   sinh (
z j q j )  K jqj  C j ⫽⫺ K j q j sinh (
z j q j ), D j ⫽ cosh (
z j q j ) A j D j ⫺ B j C j ⫽1

(11)

and p is the Laplace transform variable. Eqs. (10) and (11) give the transforms of the temperature and heat flow at any point z1, z2, …, zm. Values for intermediate points can then be found from

T ⴱ( z, p) ⫽ T jⴱcosh[q j ( z ⫺ z j )] ⫺

Qj K jqj

sinh[q j ( z ⫺ z j )]

(12)

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Exact evaluation of T and Q leads to rather complicated series of expansions; for shortness, below we present the solution for a homogeneous slab only T ( z, t ) ⫽

2k
zm2

⬁

 ⫺kn2
2 t  n
z  x sin z 2 zm  m

∑ n exp  n⫽1

 kn2
2 K  exp   x 关T0 ( ) ⫺ U 0 兴 d  2 0  zm 

∫

t

(13)

2.3.3 Ramp/step method Solution of the inverse problem consists of three general steps: (1) choice of an appropriate physical model, (2) parametrization of the theoretical equations, and (3) estimation of the parameters. Naturally, this procedure cannot be started from zero and requires including as much a priori information as possible. Each underdetermined problem needs certain assumptions. More complex model implies the greater number of assumptions and/or amount of additional information for the successful past climate reconstruction. Because of the common insufficiency of the field information, simpler models in some cases can be more preferable. The ramp and/or step models that are sometimes referred to as the last event analysis (Lachenbruch and Marshall, 1986) represent the most simple, but robust manner of the parametrization. While other modern inversion techniques allow an arbitrary form of the GST history, the step method assumes the GST changes with time according to a threeparameter law

 t V0 (t ) ⫽ T0 ⫹
T  ⴱ  t 

nⲐ2

(14)

for 0 ⬍ t ⱕ t* and n ⫽ 0, 1, …. The value t* represents the duration of the GST change of the amplitude
T, and the power n determines the shape of this change. At n ⫽ 0 we have a simple step increase/decrease in temperature, n ⫽ 2 represents a linear one, and so on. The solution of this equation for a homogeneous half-space at t ⫽ t* is (Carslaw and Jaeger, 1962) T ( z, t ⴱ ) ⫽ T0 ⫹

 z  Q0 n  z ⫹
T 2 n   ⫹ 1 xi n erfc   2  K  2 kt ⴱ 

(15)

where (x) is the gamma function of argument x and inerfc is the nth time integral of the error function. Formula (15) thus enables us to calculate the subsurface temperature at the end of the GST change. Expression (15) corresponds to a simple four-parameter model m ⫽ [
T, t*, T0, Q0]. In the pioneering work by Lachenbruch and Marshall (1986) quantities T0 and Q0 were defined independently of the parameters
T and t* by the linear fitting to the measured temperature–depth profile immediately below the obviously disturbed nearsurface part of the temperature record. In the more recent works unknown parameters T0,
T, and t* are estimated together with the background heat flow Q0. Using this form

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of parametrization, the inverse problem can be formulated as: given the data T(zk) measured at various depths zk, k ⫽ 0, 1, …, M, find the temperature history characterized by the vector m. To determine the optimum values of the unknown parameters most of the researchers (e.g. Cermak et al., 1992; Safanda and Kubik, 1992) applied the least squares inversion theory proposed by Tarantola and Valette (1982a, b). In this approach, an M-dimensional vector of temperature–depth measurements T is related to parameter vector m by the equation T ⫽ g(m), where g is an M-dimensional vector-function whose components are determined by Eq. (15). Inversion is performed by using the iterative procedure. The advantage of the method is the possibility of quantifying our confidence in the a priori estimates of T and m in a form of their a priori standard deviations as well as of obtaining estimates of the a posteriori standard deviations of m. The a posteriori to a priori standard deviations ratio (SDR) is generally used to characterize the reduction of the uncertainty of the estimated parameters. The ramp/step problem can be extended: (1) for the case of the stratified medium each with constant known thermophysical parameters in the individual layers, and (2) for the multi-step approach V0 (t ) ⫽ T0 ⫹

∑ i

 t
Ti  ⴱ   ti 

nⲐ2

(16)

where t *i and
Ti represent the epochs and magnitude of temperature change (e.g. Beltrami and Mareschal, 1991). For the series of steps of equal duration expressed as departures of the mean temperature value and starting at time tl in the past, present temperature at depth z is given as

T ( z ) ⫽ Tl erfc

z 2 kt



l⫺1

⫹

∑ T erfc 2 i

i⫽1



z k (l ⫺ i ⫹ 1)t

⫺ erfc

  2 k (l ⫺ i )t  z

(17)

The single-step approach may be more suitable in the case when for the area under investigation there is no information on the surface temperature history at all, since it relates the calculated surface change to the conditions averaged for a long prior period. The use of the multi-step model may give a better insight into what really happened. As shown by Putnam and Chapman (1996), a series of step changes can approximate any real surface temperature variation. 2.3.4 Singular value decomposition (SVD) algorithm Singular value decomposition method was first presented in the works by Beltrami and Mareschal (1991), Mareschal and Beltrami (1992), and Wang (1992). Later it was improved in the work by Bodri and Cermak (1995) by including into the analysis various kinds of additional information and the special technique of the GST discretization that ensures optimal choice of the estimated GST vector. As in the previous case, the problem is formulated as the pure conductive 1-D heat transfer of the surface temperature

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variations in a layered slab with the constant, known thermophysical parameters in each layer as described in Section 2.2.2. The model vector can be thus formulated as m ⫽ [U0,Qm,V0(t)]. The surface temperature history is approximated by a series of unequal intervals of constant temperature V0 (t ) ⫽ Vi , ti −1 ⱕ t ⱕ ti , i ⫽ 1, 2, K , N

(18)

The mean values of temperature for the individual time intervals Vi are the unknown parameters of the problem. At such assumption the integral in the forward problem like Eq. (13) can be easily transformed into the series 1 a

N

∑ i⫽1

(Vi ⫺ U 0 )(e ati ⫺ e

ati ⫺1

 n
k  ), where a ⫽    zm 

2

(19)

Borehole temperature data are generally given at discrete zk points (k ⫽ 1, 2, … M). Thus, the solution can be represented by a set of M linear equations in N unknowns Vi Tk ⫽ Aik Vi

(20)

where Tk is the temperature measured in borehole at depth zk and Aik a matrix formed by the values of series similar to Eq. (13) at depth zk for time interval (ti⫺1,ti). When parameters of initial (equilibrium) temperature field U0 and Qm are estimated simultaneously with the GST history V0(t), the vector Vi will consist of (N ⫹ 2) unknowns, and zthe matrix A will contain 1 in its (N ⫹ 1) first column and the thermal resistance R(z) ⫽ 冮0(dz⬘/k(z⬘)) to the depth zi in the (N ⫹ 2) column. At M ⬎ N this yields an underdetermined system of linear equations that can be solved for the unknown parameters Vi by the SVD (Jackson, 1972; Menke, 1989). This general least squares inversion method minimizes both the sum of the squares of deviations of the measured temperature profile from the theoretical model T and the sum of the squares of estimated parameters V 0T V0 (
⫽ AV0 ⫺ T). Mathematically, the SVD procedure can be formulated as follows. Two sets of eigenvectors u and v can be found such that Avj ⫽ juj and ATui ⫽ ivi, where i are the eigenvalues of the matrix A. One shall again assume that there are P non-zero eigenvalues common to the sets of eigenvectors u and v. The set of eigenvectors u represent complete massif of the orthonormal vectors in the data space, while the eigenvectors v are similar set in the “model” space. The P-value (P is less than or equal to the minimum of M and N ) may be interpreted as the potential number of degrees of freedom in the data. The matrix A can be decomposed into the product A ⫽ U
VT, where U is the M ⫻ P orthonormal matrix whose columns are the eigenvectors ui, V an N ⫻ P matrix with the columns of the eigenvectors vi, and
the diagonal matrix of eigenvalues. The solution x can then be written as x ⫽ V
⫺1UTT, where x is the estimate of V0 and
⫺1 a diagonal matrix whose elements are ⫺1 i . According to our previous assumption all P eigenvalues are not zero; thus, the inverse matrix
⫺1 never ceases. However, definite problems may arise also if values of i are non-zero but very small.

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The linear combination of model parameters that have weak impact on the data corresponds to the small singular values i. In the inversion, the data are divided by the smallest eigenvalues. Even for the noise- and error-free data numerical instabilities appear when the ratio of the largest to the smallest eigenvalue exceeds a critical limit (say, 10⫺6; see Section 2.4). The presence of noise in the data intensifies the problem. In this case the inverse matrix A⫺1 exists; however, the uncertainties in the estimated x-vector that for statistically independent data can be calculated as 2

  N ⫺1 var xi ⫽  Vik  k V jk  var(T j )  k ⫽1 j ⫽1   N

∑∑

(21)

will be too large because of the reciprocal eigenvalue in the bracket. This variance can be interpreted as the amplification of the measurement errors in the solution. The order of magnitude of the variance is inversely proportional to the smallest eigenvalue. In practice, it is thus indispensable to eliminate the small eigenvalues from the solution. For the elimination of the parameters that have weak impact on the data the “sharp cutoff” approach is generally used (Wiggins, 1972). Under the sharp cutoff approach we select some dimensionless cutoff value and ignore all eigenvalues whose ratio to the largest eigenvalue is less than this limit. The cutoff value is thus the crucial parameter of the SVD method. It will be shown below that the large values of cutoff tend to smooth the reconstructed curve and move their extremes toward the present. At lower cutoff values, parameters that are weakly represented in solution are better resolved, but their errors will simultaneously grow. Too small cutoff values may lead to the unacceptable error level and to the instability of the solution. Some optimum value must be chosen. Wiggins (1972) suggested the procedure to establish the optimum cutoff. According to his approach one should set an upper limit on the standard deviation of the estimated parameters, and search for the largest number of eigenvalues associated with the solution for which each estimated variance is less than this limit. This then determines the number of degrees of freedom associated with the solution. In other words, the SVD naturally eliminates from the inverse all the oscillations of the surface temperature that the data cannot resolve and yields the smoothed course of the surface temperature. The stabilizing and smoothing of the solution can be achieved also by adding a small constant to each singular value

k


 2k ⫹  2 k

(22)

This procedure does not affect the inversion of the larger singular values, but it stabilizes the inversion of smallest singular values by damping them to zero. The value of damping parameter  (and thus the GST resolution) is determined by the noise level in the data. This procedure generally gives smoother GST histories than the sharp cutoff technique. In principle, there are no exact arguments that could force the choice of either procedure.

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The discretization of time (values of t1, t2, …, tN, Eq. (18)) is an input of the problem. On contrary to the ramp/step method, no explicit constraint is imposed on the surface temperature history. However, excessive number of time intervals in the trial model can result in the instability of the inversion process. On the other hand, we must find the smallest time intervals that can be determined from the given temperature log. The resolution matrix plays an important role in the parametrization of time. In the case of sharp cutoff, the resolution matrix can be defined as R ⫽ VVT. The jth column of this N ⫻ N matrix is the least squares solution for maximizing the jth parameter. At the proper choice of the discretization of time the resolution matrix exhibits delta-like behavior (compact resolution); the column with the best resolving power is nearly always the column with the maximum diagonal element (Bodri and Cermak, 1995, 1997a). Thus, the diagonal elements of the resolution matrix can be used as the measures of the resolving power. When choosing the preliminary discretization scheme, one has to take into account the “thermal” memory of the ground for the events on its surface (see Section 2.2 and Figure 23). One can choose a preliminary timescale with relatively long time intervals gradually increasing toward the past. Subsequently this scale should be refined with the help of the resolution matrix. For such operation a semi-empirical rule that proved to be very effective in the designing of structural models of the Earth from surface-wave and free oscillation data (Wiggins, 1972) can be applied. If, after computing the resolution matrix, we find that a single time interval is nearly perfectly resolved, this means that we have not selected the time interval short enough in the vicinity of the given instant of time to determine the exact shape of the resolution. In such a case the problem should be recomputed for a shorter time interval. In practice the GST reconstruction problem cannot be solved uniquely unless a priori information is incorporated into the analysis from the very beginning (Jackson, 1979). As a priori information the covariance matrices for both measured data and unknown GST are generally used in the SVD approach. For example, one can assume that the measured data are not statistically independent, but are characterized by a positive definite covariance matrix S. The covariance matrix for the data is symmetric (M ⫻ M) matrix, the elements of which are Sij ⫽  2 r (
z )

(23)

where  2 is the standard deviation of the data, r(
z) the autocorrelation function, and
z the depth lag. The autocorrelation function characterizes the range (intensity) of the interdependence of the measured signal. Dependence can be either short-range or longrange. The short-range dependence is characterized by correlations that decrease exponentially fast, while long-range dependence occurs when the correlations decrease like a power function. Our calculations have shown that measured borehole temperatures are characterized by a short-range dependence, thus, by the correlation that decreases exponentially fast r(
z) ⬃ exp(⫺
z /D) (Bodri and Cermak, 1995; Bodri et al., 2001). The decorrelation parameter, D, corresponds to the depth lag at which the autocorrelation falls to (1/e), i.e. defines the distance at which the individual temperature values can be considered statistically independent. This quantity represents an individual characteristic of the given hole. According to the field experience, for the majority of the boreholes the D-value ranges from 50 to 300 m. For boreholes with the fast decorrelation and

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the high number of measured points an advantageous technique of the data thinning (“scarcing”) can be used for inversion. In this case, the original data set of measured temperatures can be divided into subsets, and different parameters of the time discretization of the GST history can be obtained from different sections of the T–z profile. This procedure significantly enhances the reliability of estimated parameters, but requires that the data sets used be statistically independent (Twomey, 1977). Details of application of the data thinning technique are presented in Section 3.1 (Chapter 3) for Finnish boreholes. Another kind of additional information is connected to the interdependence between climatic signals. Highly fluctuating climatic time series exhibit scale invariance or scaling behavior over the wide range of timescales; that is, climatic variations at small scales are related to longer ones by the same scaling law without showing any preferred mode. The investigations of the scaling properties of climatologic data received considerable impetus from the paper by Lovejoy (1982) on the fractal dimension of clouds and rain. By the analysis of meteorological and climatic data using further refined methods, it was concluded that climatic processes exhibit a much more complex structure than previously assumed, where statistical properties at various scales are related through different intensity-dependent dimensions, rather than through a single fractal exponent (Tessier et al., 1993; Lovejoy et al., 2001). The introduction of the multiscaling behavior that may be interpreted as the outcome of a so-called multiplicative cascade process is common to all recent analyses. Generally, such models can be characterized by the range (intensity) of their interdependence, the heaviness of the probability tails, and the degree of nonlinearity. As about the interdependence of the meteorological/climatic signals, the so-called persistence of weather is a well-known phenomenon. If, e.g. given day is sunny and warm, there is high probability that the next day will be similar. Such tendency appears also on the longer scales. Early attempts to quantify this behavior were made in the works by Kutzbach and Bryson (1974) and Hasselmann (1976). Further these investigations were continued in the works by Lovejoy and Schertzer (1986), Ladoy et al. (1991), Bodri (1993), Beran (1994), and Rangarajan and Sant (2004). The common consequence of all research works was the quantitative establishment of the long-range dependence of correlations within different meteorological/climatologic time series that occurs when the correlation decreases like a power function in such a way that the spectrum diverges at low frequencies. Thus, the climate persistence, characterized by the correlation C(
t) of temperature variations separated by the interval
t, follows a power law, C(
t) ⬃
t⫺ . Long-term persistence appears to characterize most climatic phenomena and exists over the spectral range of 1–106 year. Most recent investigations of the weather persistence were performed in the works by Koscielny-Bunde et al. (1998) and Talkner and Weber (2000) using long meteorological temperature records from various climatologic zones in Europe, North America, and Australia. The daily and annual cycles were removed from the data. Investigations with modern detrended fluctuation analysis (DFA) and wavelet techniques that can systematically overcome possible non-stationarities in the data revealed power law correlation decay with roughly the same exponent ⬵ 0.7 (⬇2/3) in the range of time lags between 10 days to at least 25 years. The range of persistence law is limited by the total length of the time series considered. The authors cannot exclude the possibility that it may exceed detected limit. The persistence of the climatic variations can be taken into account by means of the covariance matrix for the

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unknown parameters. The covariance matrix is a symmetric (N ⫻ N ) matrix, the elements of which are


t  Wij ⫽ s2    

⫺

,

(24)

where s is the a priori standard deviation,
t the shift in time, and the characteristic correlation time. Similarly to the introduced above characteristic distance D, the characteristic correlation time corresponds to the practical vanishing of the correlation between climatic events. The values of s and are the input parameters of the problem. As will be shown below, generally, smaller s results in the correspondingly smaller disturbances of the GST, and the longer the time the smoother the obtained solution is. Including an additional information modifies the SVD procedure in such a way that it minimizes both TS⫺1 and V0T W⫺1V0. Further limits may be imposed on the unknown parameters. In most of the inversion problems it is customary to pose some bounds (so-called hard limits) on the values of parameters imposed by the physics of the problem. For example, when determining the Earth’s structure, the densities of the lithospheric rocks may be assumed to vary between 3000 and 4000 kg/m3, and their shear velocities between 4 and 6 km/s. One could treat these statements as pairs of inequality constraints c1 ⱕ bT V0 (t ) ⱕ c2 ,

(25)

where vector b is the moment of solution V0(t) and explores the limits of the solution space. In the case of climatic changes, however, these hard limits tend to be so large that they become irrelevant for practical purposes. The probability distribution of climatic changes may be used to put so-called “soft limits” on the solution. As in the above example with the lithospheric structure, two constants c1 and c2 may be chosen to represent limits on V0(t), but there will be some non-zero probability that V0(t) will violate these bounds. As shown by numerous investigators (e.g. Ladoy et al., 1991; Fraedrich and Lardner, 1993; Olsson, 1995) the cumulative probability distribution (probability that random fluctuation dT exceeds a fixed value T ) of climatic time series generally has a nearly Gaussian shape in the center and a tail (probability of the extreme events) that is “heavier” than would be expected for a normal distribution. The “fat-tailed” probability distributions are general characteristics of climatic time series. When the fluctuations are of this type, the phenomenon is so intermittent that the return times of extreme events are much shorter than those for Gaussian process. We illustrated the difference between Gaussian and the real probability distribution with the use of the two-millennia long homogeneous temperature anomaly time series for Northern Hemisphere developed by Mann and Jones (2003) by using different proxy indicators of climate (Figure 3). The cumulative probability of this data is presented in Figure 24. The difference of the probability tails is clearly visible. For example, the temperature fluctuations corresponding to more than three standard deviations (anomalies of more than ⫺0.016 K or less than ⫺0.504 K) for Gaussian process would have a probability level of 0.0013 that gives the return period for such anomalies of near 770 years.

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Fig. 24. Cumulative probability distributions for annual mean temperature record for Northern Hemisphere presented in Figure 3 (solid line) and for Gaussian process (dashed line). T is expressed in terms of standard units: (T-mean)/s.d. (mean ⫽⫺0.260 K, s.d. ⫽ 0.081 K).

Actually such extreme events occurred eight times in the near 1800-year long Mann and Jones (2003) temperature time series, thus, with much shorter average return period of near 220 years. It should be mentioned that the soft limits result from exact calculations, and not simply from physical plausibility arguments; thus, they seem to be more appropriate than the hard limits. The simplest way to incorporate soft limits in the solution is the examination of the extreme solutions (Jackson, 1979). The maximum (or minimum) value of bTV0(t) is given by Tmax,min ⫽ V0 ⫾ Cb,

(26)

where C ⫽ ATS⫺1A, V0 ⫽ CATS⫺1T, and  ⫽ [Qmax ⫺ Q0)/(bTCb)1/2. In the last expression Q0 ⫽  TS⫺1, where  ⫽ AV0 ⫺ T, and Qmax is the maximum allowable residual criterion for a model that fits the data in a satisfactory way. The value of Qmax should be chosen so that Tmax,min make values of bTV0 to fall as close as possible into the given soft limits.

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Moment vector of the solution b is usually used with the unit coefficients. Some kind of weighting that is reasoned by the well-known progressive smoothing down into the past of the reconstructed climatic fluctuations (see examples presented in the next section) is also possible. Different kinds of additional information are not equally important in all cases. Thus, they may also be weighted for specific problems. 2.3.5 Least squares inversion in functional space (FSI) This algorithm is based on the theory by Shen and Beck (1983, 1991) and modified by Shen et al. (1995). The problem is conventionally formulated as the 1-D pure heat conduction in a laterally homogeneous subsurface with depth-dependent thermal properties (Eq. (4)), where transient component arises from the surface temperature variations. Initial and boundary conditions are the same as described above for SVD and ramp/step methods. It should be mentioned that the use of the 1-D approach is not inspired by the wish to simplify the inversion problem only. An essence of the fact that all inversion techniques are based on the 1-D equation, which obviously neglects the influences of numerous terrain effects such as medium heterogeneity, topography, and groundwater circulation, is that the 2-D inversions will not necessarily improve the GST histories. The 2-D approach will significantly raise the number of degrees of freedom of the inverse problem (underground structure, thermophysical parameters, and pattern of the steady-state temperature field), which implies the corresponding limitation of a priori parameter range treated. As previously, the subsurface temperatures are divided into steady-state and transient components according to Eq. (8). The model is denoted as a set of parameters m ⫽ [K(z), c(z), U0, Qm, V0(t)]. If necessary, the radioactive heat generation can be also included as the parameter into the model m. However, it is justified only for deep holes in the case of the reconstruction of remote GST changes (see Section 2.2 of this chapter). As can be seen, the thermal properties of the medium are formulated as unknown parameters and, in contrast with two previous methods, should be estimated together with the quantities describing the initial thermal state of the medium and the past climate history. In this way the uncertainties of the knowledge of the physical properties of the medium can be also taken into account. It is assumed that the deviation of the true GST history V0(t) from the a priori version V 苶(t) is a stationary Gaussian process with an assumed exponential autocovariogram  ⫺  具[V0 (t ⫹ ) ⫺ V (t ⫹ )],[V0 (t ⫹ ) ⫺ V (t ⫹ )]典 ⫽  2 exp   ,  c 

(27)

where the standard deviation  and the correlation time c characterize a priori constraint imposed on the GST history. An exact shape of the autocovariance function is not a crucial factor of inversion. Thus, in the study by Shen and Beck (1991) the authors used the Hanning window. Wang (1992) applied function 2exp(⫺ 2/ 2c ) at the right side of Eq. (27). The only requirement is that this function should be sufficiently tapered at large . The really important parameters of the autocovariogram are  and c. The former

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is responsible for the amplitude of the GST changes, and the latter controls the smoothness of the quantity [V0(t)⫺V 苶 (t)]. As in the case of SVD method, the correct choice of values for  and c should be based on the use of the existing climatologic records. In its most recent version the FSI can be accomplished in six general steps: (1) Set the model m and define a priori model m0. The values and variances for K(z) and c(z) have been given. It is assumed that they are uncorrelated. To complete m0 one needs the values for remaining parameters U0, Qm, and V0(t). Generally, these quantities are not well known. It is accepted that V0(t) takes small values close to zero, is bounded, and is rather smooth function of time t. Very large a priori standard deviations are assigned to U0 and Qm values to avoid undue bias. (2) Solve the problems posed by the heat conduction Eq. (4) and by its steady-state analog for the steady-state U(z) and the transient components V(z,t) (Eq. (8)) of the temperature field, respectively. Calculated data can be represented as T(z,t) ⫽ Pu[U(z)] ⫹ P[V(z,t)]. This equation describes the “theory” that projects the data on the model, where P and Pu are corresponding projectors. For the discrete data this equation, similar to the SVD, can be written in the algebraic form. However, the fundamental difference between the FSI formulation and the SVD concept is that the former works with operators, while the latter deals with matrices. (3) Calculate the data residual m ⫽ 1/2[T(z,t)⫺T(zk)]T[T(z,t)⫺T(zk)]. The problem of parameter estimation then becomes one of minimizing the weighted least squares misfit function (Tarantola and Valette, 1982a, b; Menke, 1989) 1 1 (m) ⫽ (T ⫺ T0 )T C⫺d1 (T ⫺ T0 ) ⫹ (m ⫺ m 0 )T C⫺m1 (m ⫺ m 0 ) 2 2

(28)

Cd and Cm are a priori covariances describing uncertainties in T(zk) and m0, respectively. The latter equation highlights the fact that for given T0 and Cd, the deviation of the output model from the a priori model depends critically on Cm. It can be illustrated with the simple example. At large Cm (no confidence in the validity of a priori model m0) the second term in the right hand of Eq. (28) will participate only insignificantly in the minimization of (m), so that the initial model become irrelevant. In other words, the model is well resolved by the data. For a small Cm (strong validity of m0), the data will become irrelevant, so the model appears to be poorly resolved by the data. Thus, the application of the FSI strongly depends on the proper assessment of Cm. (4) The quasi-Newton method is applied for minimizing (m) (Shen and Beck, 1991; Wang, 1992). This method can directly compute (approximate) a posteriori model covariance. Its computationally convenient form for jth iteration is m j⫹1 ⫽ m 0 ⫺ C m MTj [ M j C m MTj ]⫺1 ⫻ [T ⫺ T0 ⫺ M j (m j ⫺ m 0 )]

(29)

where M is the derivative operator that maps the model space into the data space, defined by T ⫽ M m. In a functional space formulation the operators M and its

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Borehole Climatology: A New Method on How to Reconstruct Climate transpose MT are not explicitly computed and stored. Only the results of their mapping are calculated. The process of M mapping is presented in detail in the Sections 6 and 8.2 by Shen and Beck (1991) and the mapping of MT in Sections 8.4–8.6 of that work. During the step (4) one should compute the symmetric, positive definite matrix Ej ⫽ Mj CmM Tj ⫹ Cd and solve the vector algebraic equation Ej (dj) ⫽ (mj) for the weighted data residual (dj). (5) Compute the gradient (gj) ⫽ MjT (dj) and model correction (mcorr) ⫽ Cm (gj).

(6) Update the model mj+1 ⫽ m0– (mcorr). The quantification of the uncertainties in the estimated model parameters can be done with the a posteriori covariance operator Cˆm that is given by (Tarantola and Valette, 1982a; Shen and Beck, 1991) C$ m ⫽ Cm ⫺ Cm MT⬁ [ M ⬁ Cm MT⬁ ⫹ Cd ]⫺1 M ⬁ Cm ,

(30)

where M- denotes the derivative operator M evaluated at m ⫽ m-. Eq. (29) corresponds to one iteration of the quasi-Newton expression (28). Generally, the diagonal of Cˆm is interpreted as the variance of m-, and off-diagonal as its covariance. Because of significant computation time needed to examine the whole a posteriori covariance operator, it is common to compute only diagonal entries (the a posteriori variance of m-). They are usually called the SD ratios. The smaller the SD ratio is, the better the estimated parameter is resolved by the measured data. Physically, it means that the data contain sufficient information about this parameter. On the contrary, close to unity SD ratio hints that the data cannot resolve this parameter. 2.3.6 General comparison of the methods Table 3 summarizes the main characteristics of the parametrization schemes and the data inversion for main three methods of the GST reconstruction described above. All methods are based on the 1-D theory of heat conduction and employ the least squares inversion theory. As mentioned above, the use of the 1-D approach is not certainly shortcoming of the inversion problem. The 1-D inversion techniques obviously neglect the influences of numerous 2- and/or 3-D terrain effects such as lateral medium heterogeneity, topography, and groundwater circulation. On the other hand, the development of the 2-D techniques will not necessarily improve the GST histories. The 2-D approach will significantly raise the number of degrees of freedom of the inverse problem (underground structure, thermophysical parameters, and pattern of the steady-state temperature field), while we may only handle finite amount of the measured data. The application of a 2-D approach means that we use more parameters to describe the unknowns than could be uniquely determined by the data. In practice the use of a 2-D approach implies severe limitation of a priori parameter range treated. In all inversion problems some optimal relation between resolution and variance should be established. In other words, one should

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Table 3. Models/parameterization schemes used in the three main inverse methods Property Temporal discretization Spatial discretization

Ramp/step method

Specific heat capacity, c(z)

Step model Homogeneous medium Constant in the medium Known Constant

Heat production rate, A(z)a

Known Constant

Thermal conductivity, K(z)

Bottom heat flow, Qm Steady-state GST U0 GST history V0(t) Additional information

Constant To be estimated Constant To be estimated Polynomial function To be estimated No

FSI

SVD

Finite-difference Finite-element

Steps model Layered medium

Constant in each element To be estimated Constant in each element To be estimated Constant in each element Constant To be estimated Constant To be estimated Piecewise linear To be estimated Yes

Constant in each layer Known Constant Known Constant in each layer Constant To be estimated Constant To be estimated Piecewise constant To be estimated Yes

a

Relevant only for deep boreholes.

answer the question, what is the effective number of degrees of freedom in the data and what parameters can be independently estimated with an acceptable variance. Every method takes the steady-state GST, the equilibrium surface temperature U0, and the basal heat flow Qm as unknown parameters. Together with the thermal conductivity of the medium these quantities control the steady-state temperature profile, and such formulation permits to estimate the parameters characterizing the steady state even for relatively shallow boreholes where estimation of these quantities from the lowermost undisturbed parts of the measured temperature–depth profiles is problematic. From the point of view of the accuracy of the GST reconstruction the SVD and FSI are more effective since they use more complex temporal discretization and incorporate additional information in the analysis, and thus allow the reconstruction of far more detailed GST histories than the ramp/step method. The parametrization scheme applied in the ramp/step method and in SVD uses analytical expressions for the temperature field T(z,t), while in the FSI it can be expressed only numerically. It should be mentioned, however, that the computational efficiency of the methods does not strongly depend on whether the analytical or numerical approximations are used and represent the complex output of the whole parametrization scheme. The ramp/step method and SVD are limited to the problems when the thermal properties of the medium are known. At a first glance it seems to be serious restriction. General insufficient thermal conductivity data in boreholes is a well-known fact. However, in the most field examples errors in K(z) and c(z) are not systematic. In the SVD approach, their effect can be extenuated by imposing appropriate smoothing constraints on the GST history. Thus, notwithstanding the known thermal properties assumption, SVD technique is applicable to a large number of practical cases. The principal distinction from the ramp method and SVD and/or merit of FSI is that the thermal

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properties of the medium are all formulated as unknown parameters and estimated simultaneously with the quantities describing the initial thermal state of the medium and the past climate history. Thus, this method is preferable when the errors in K(z) and c(z) are significant and expected to be systematic. When the thermophysical properties are included in the model as parameters, the problem becomes nonlinear. However, as shown by Shen and Beck (1991) and Wang (1992), for the cases when these quantities are reasonably well known with small uncertainties, the problem is only mildly nonlinear and permits the application of iterative gradient methods to solve the optimization problem described above. It should be also mentioned that for the field examples with exactly known thermal properties the theory becomes strictly linear. Methods of the inversion of the borehole temperature data are all based on the least squares inverse approach. The problem is ill posed; thus, in the real cases the misfit function itself is insufficient to determine the unique and stable solution. The SVD and FSI put definite constraints on the model and employ the concept of incorporating a priori (additional) information to avoid this problem. As will be shown in the next section, a large part of discrepancies in the inverse results obtained by both methods can be attributed to the kinds of constraints and/or the nature of additional information used in the analyses. Unfortunately, the optimal values for the applied constraints depend on the real GST history. Thus, at least some knowledge of the amplitude and timing of the climatic variations that should be reconstructed is indispensable to obtain consistent inversion results. All described methods are based on the least squares technique. Cooper and Jones (1998) have performed a comparison between the effectiveness of the least squares approach and other popular techniques, namely the minimization of the absolute difference between measured data and estimated parameters for inversion of borehole temperature logs that is performed for the purposes of the GST reconstruction. The authors have found that the latter technique requires approximately half the number of iterations to reach the possible minimum error compared to the least squares procedure. According to the above-cited work, the inversion of borehole temperature data in some cases can be significantly improved by the use of techniques other than the standard least squares approach. According to their calculations, exact choice of the inversion technique depends on the statistics of the data. Anyhow, it was used by Cooper and Jones (1998) during all trial runs the best results were obtained by the latter approach combined with some additions that accelerate the procedure in the damped intervals where the model improves only slowly by subsequent iterations. 2.4 Comparison of Ground Surface Temperature (GST) Reconstruction Methods As many geophysical problems, the GST reconstruction involves the estimation of a number of unknown parameters that bear definite relationship to experimental data. These data are generally contaminated by various kinds of random and/or systematic noise as well as may be inconsistent and insufficient for estimation of the unknown parameters. Thus, generally we have strongly underdetermined system; in other words, we would like to draw out the infinity of the details about unknown function from very limited amounts of data. One of the usual ways to overcome this problem is to calculate a family of the trial inversions, compare the interdependence between resolution and

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variance for each case of inversion, and select the run that appears to be most appropriate for the interpretation of the solution. All three above-described methods of the GST reconstruction have treated this problem in their own manner. Existing methods for the GST reconstruction from subsurface temperature–depth profiles are based on the same theory of 1-D heat conduction in a layered medium; thus, we can expect similarity of obtained results in terms of their general features. On the other hand, the GST reconstruction is an ill-posed inverse problem. Its finer output may be method dependent. The inconsistency between GST histories reconstructed by different inverse methods may arise both from the differences in the mathematical/physical approaches used and from the manner in which different kinds of noise (uncertainties) are treated. For comparison and/or joint use of the GST histories inferred by different methods at first the methods themselves should be compared and evaluated. Such comparison including both synthetic and field results has been widely performed at an early stage of the development of “geothermal” method (Beck et al., 1992; Shen et al., 1992). Below we present some examples to illustrate conclusions obtained in above and similar works. We applied two most powerful methods of the GST reconstruction, namely SVD and FSI, to some standard data sets and field data, to illustrate the effects of various constraints on the inferred GST histories. 2.4.1 Effects of smoothing constraints in different methods and the noise in the data: Synthetic examples The most effective way to compare different inversion techniques is to simulate a series of perturbed subsurface thermal regimes using synthetic GST histories, i.e., the true result is known, and to apply the available techniques on those data. Such attempts have been undertaken in numerous works for the simple case of 1-D forward pure conductive model driven by different variants of GST forcing. An inversion approach has been applied to infer GST histories from the simulated profiles and to compare them with the true surface forcing used. The results of such trial numerical experiments have been widely discussed (Beck et al., 1992; Shen and Beck, 1992; Shen et al., 1992). Below we present some illustrations of this approach. The basic synthetic T⫺z data, with which we would like to assess the most effective methods of the GST reconstruction, SVD and FSI, are calculated with the following parameters: K(z) ⫽ 2.5 W/(m K), c(z) ⫽ 2.5 MJ/(m3 K), Qm ⫽ 0, U0(z) ⫽ 4°C. Constant and known thermophysical parameters are chosen to illustrate the effect of the smoothing constraints and not to digress on other influences. The “gate” model of the GST temperature is used to demonstrate the effect of the smoothing constraints in the case of a sudden temperature change. This model has a shape V0(t) ⫽ {4°C at t ⬎ 1600 A.D.; 3°C at 1600 ⱕ t ⱖ1900 A.D., 4°C at t > 1900 A.D.} and roughly corresponds to the Little Ice Age conditions, followed by subsequent warming. Temperature logs were calculated at 5 m interval to a depth of 500 m. The first generated data set G1 is completely noise free, while other profiles were randomly perturbed by a noise with Gaussian distribution with zero mean and standard deviations of 0.01, 0.03, 0.05, and 0.1 K, respectively. Typical measurement error is 0.03 K. The standard deviations were assumed to be independent of the depth. Simulated in this manner T–z profiles are shown in Figure 25.

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Fig. 25. Temperature–depth profile for the “gate” model free of noise (G1) and two synthetic profiles perturbed with Gaussian noise of zero mean and different s.d. of ⫾0.3 K and/or ⫾1.0 K, respectively added to G1. Typical measurement error is usually about 0.03 K.

The GST histories were approximated by a series of individual intervals of constant temperature, when the mean value of temperature in each time interval is an unknown parameter. It is these temperature values that represent the direct result of the inversion procedure. When demonstrating the results graphically, these values were ascribed to the midpoints of the corresponding time intervals and were found as approximated by cubic spline technique (Bodri and Cermak, 1997a). Figure 26 shows the results of GST reconstruction by SVD and FSI for the noise-free G1 data using different smoothing constraints. A priori null GST hypothesis was assumed in the GST reconstructions by FSI technique (no a priori knowledge of the GST history to be estimated). As seen, the true GST history is reasonably well recovered by both inversion techniques. Clearly, the solution depends critically on the cutoff value for SVD and on the values of  and c for FSI. The large values of cutoff tend to smooth the reconstructed curve and move its extremes slightly toward the present (Figure 26a). Too small values may lead to instability of the solution (Figure 26b) with frequent small false extremes. It should be mentioned, however, that these false oscillations do not fog significantly general GST pattern; the long scale course of the GST history is preserved even in the reconstructions calculated with the smallest possible cutoff value. Obviously, some optimum cutoff value

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Fig. 26. The effect of smoothing constraints on the GST histories inferred from G1 temperature–depth profile: (a), (b) SVD, and (c) FSI, respectively. The real GST history is the “gate” model (dashed line). Smoothing constraints are imposed in the form of the cutoff value in SVD and the correlation time c in FSI ( ⫽ 0.5 K). Too small cutoff value may cause the instability of the solution (Panel b).

must be chosen. Wiggins (1972) suggested powerful procedure for its establishment. According to a suggestion of this author that is based on the results of numerous experimental runs, one should set upper limit on the standard deviation of the estimated parameters and search for the largest number of the eigenvalues associated with the solution for which each estimated variance (Eq. (21)) is less than this limit. This then determines the number of degrees of freedom associated with the solution. In FSI method, the constraint imposed on the GST history depends on a priori standard deviation  and correlation time c (Eq. (27)). The former is responsible for the amplitude of the detected GST changes. and the latter controls the smoothness of the quantity [V0(t)⫺V 苶(t)] (Eq. (27)). Both constraints operate together. Too large values assigned to  may turn the autocovariance function into inoperative regime. As will be

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Fig. 27. Method FSI: Effect of varying parameter  (standard deviation characterizing the a priori constraint imposed on the GST history; Eq. (27)) on the noise-free “gate” model (G1) (bottom) and on a sinusoidal model (top). The correlation time c in both cases is 100 years.

shown below, the effect of both parameters on the reconstructed GST history depends on the level of noise in the data. As seen in Figure 26c, the influence of the correlation time c is not so strong when the noise level in the data is low. The influence of the standard deviation  is illustrated in Figure 27 (bottom). In the ideal case, when the T–z profile and the values of thermophysical parameters are noise free and inversion procedure does not introduce discretization/roundoff errors, one may set relatively large value for standard deviation without the risk that the instability of the solution will occur. For the G1 temperature profile (Figure 27, bottom) the instability threshold is as high as 500 K. In the real cases the optimal values for  should be much lower and can be established experimentally. Numerical trial runs have shown that for usual field temperature logs the optimal value is close to 100 K (see also Shen and Beck, 1991, 1992). However, the solution can be regarded as reliable and/or reasonable over a wide range of values for  within this interval. Results of similar calculations for the noise-free “sinusoidal” model are shown in Figures 27 (top) and 28. This model was calculated by the expression V0(t) ⫽ 4°C ⫹ sin(
t/400⫺5
/2) with period of 800 years; t is the time in the years B.P. The oscillations of the surface temperature roughly correspond to the Medieval Warm Period, the Little Ice Age, and the warming since then. Corresponding temperature–depth profile is shown as the inset to Figure 28. Synthetic T⫺z profile used for the GST inversion is completely noise free. This example illustrates the possibility to reconstruct past harmonic oscillations and demonstrates how the damping of high-frequency climatic signal with depth manifests

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Fig. 28. The effects of smoothing constraints in the case of a surrogate climate expressed by a sinusoidal model of the GST with period of 800 years in SVD (bottom), and FSI methods (top), respectively. The calculation was performed with a standard deviation  ⫽ 0.5 K. Estimated GST histories are marked by the applied values of cutoff and correlation time c for SVD and FSI techniques, respectively. The inset shows corresponding noise-free temperature–depth profile.

itself in the reconstructed GST history. Estimations of this more complex GST history show that the T–z profile preserves significant information only about less remote course of the GST. As can be seen, the recent 400–500-year warming trend and the long-term mean temperature (zero-frequency component) can be reliably resolved by both techniques. Calculations show that the acceptable range for the smoothing constraint quantities in both methods depends on the actual GST variations. For the latter more complex GST history the instability of the solution occurs at higher cutoff values for SVD method and correspondingly for lower values of the standard deviation  for FSI technique. The interval of acceptable values for both parameters is smaller than in the previous case, e.g. optimal cutoff values lie in the range 10⫺2–10⫺4. The influence of the correlation time c in FSI method is not so significant in this case. It may change within the same range as in the previous example without risk that the instability in the solution will arise. The influence of the noise in the T–z profiles on the GST reconstructions is illustrated by the next set of reconstructed GST histories. Figure 29 shows GST reconstructions inferred from the temperature logs viewed through a noisy filter. Synthetic

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Fig. 29. The effect of Gaussian noise on the inferred GST histories in SVD (bottom), and FSI (top) methods, respectively. The cutoff value for SVD is 10⫺4, and  ⫽ 0.5 K, c ⫽ 100 years for FSI. The stationary Gaussian noise with standard deviations varying from 0.01 to 0.1 K and zero mean has been superimposed on the “gate” G1 model (Figure 22). For both methods, the results of inversion are practically independent on the noise level, up to standard deviation of 0.04–0.05 K.

temperature–depth profiles simulated for the “gate” model and included different levels of the Gaussian noise (Figure 25) were used as input data. As seen in Figure 29, both methods recovered the “true” gate model by the inversion reasonably well. The interval of the GST history most affected by the noise in the data appears to be the recent past, some 100–200 years. The noise-induced false temperature oscillations are clearly visible in this section of the GST histories reconstructed by both methods. This fact is an obvious consequence of the above-described physical nature of the heat conduction process, when GST variations are attenuated exponentially and smoothed with both depth and time. At the same time the best resolved recent parts of the reconstructed GST curves contain more noticeable fingerprints of noise. The SVD method appears to be more stable to the noise in the temperature logs and is able to tolerate relatively strong noise contamination. As shown, the inversion results are practically independent of the noise level, up to standard deviations close to 0.1 K. The FSI technique appears to be more sensitive to the presence of noise. The amplitude of the GST change is overestimated by 0.5–0.8 K already for the noise s.d. of 0.05 K. The noise-induced instability of the solution occurs for the noise with s.d. values that are slightly above 0.05 K. In principle, the FSI technique sensitivity to the strong noise contamination could be partly suppressed by an accounting of small value to . However, in this case the amplitudes of the GST variations may be underestimated.

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The role of the correlation time c (one of the constraints of FSI) increases in the case of the presence of noise in the data. Figure 30 shows a set of GST histories reconstructed for different values of  and c. The input temperature log was the “gate” model disturbed by the Gaussian noise of zero mean and 0.05 K standard deviation. Small value of  effectively suppresses the amplitude of detected GST changes. The longer correlation time c smoothes calculated climatic history and moves extremes of the GST variations to the past, while shorter correlation time enhances the effects of noise up to the instability of the solution and tends to move reconstructed extremes to the present. To reduce the effect of noise that is generally more prominent at short periods, large c value should be chosen. On the other hand, value of the correlation time cannot be too large, because in this case some important shorter period variations in the estimated GST history can vanish. In SVD, the data are assumed to have equal uncertainties. On the contrary, in FSI data are weighted in accordance with their uncertainties. This allows additional effective constraint on the noise-induced instability by employing greater uncertainties to the nearsurface temperature data. Summarizing above conclusions we can affirm that, notwithstanding that both methods vary significantly in their mathematical calculus, they gave generally similar results in a sense of the broad features of the reconstructed GST histories. The discrepancies in

Fig. 30. The effect of bounding constraints governed by a priori standard deviation  on the inferred GST history (FSI method). Used T–z data are synthetically generated; “gate” model contains Gaussian noise with zero mean and standard deviation of 0.05 K. Small values of  tend to over-smooth GST history, while large values could drive to instability of the solution. The individual curves are marked by the value of the correlation time c.

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the inverse results obtained by both methods are not essential and a large part of them can be attributed to the kinds of constraints and/or the nature of additional information used in the analyses. When the optimal values for constraints are applied, the incoherence between solutions obtained by different methods is minimal. On the other hand, unproved choice of parametrization schemes and a priori constraints can significantly affect both the timing of estimated climatic changes and their amplitude. Unfortunately, the optimal values for essential constraints depend on the real GST history. A priori null GST hypothesis (no a priori knowledge of the GST history to be estimated) was used for the above GST reconstructions. Since the estimated GST models were relatively simple, obtained results reproduced the true climate change sufficiently well. For the real field examples at least some knowledge of the amplitude and timing of the climatic variations that should be reconstructed is indispensable to obtain consistent inversion results. Thus, to achieve optimal results information from other available climatic reconstructions should be incorporated into inversion procedure. More cunning testing of the effectiveness of any technique for the GST reconstruction should include the simulation of the subsurface thermal regimes driven by the state-ofart General Circulation Models (GCM) of surface temperature (Section 2.4.4) and/or perturbed by influences additional to the GST changes and the use of simulated T⫺z profiles to retrieve the real surface forcing. The sensitivity of the inversion techniques to various non-climatic uncertainties is illustrated in the next section.

2.4.2 Effect of systematic errors in thermal conductivity There may be different kinds of the systematic noise in borehole temperature logs; however, one of them is almost common in the geothermal data, namely more or less poor knowledge of the thermal conductivity. Above synthetic examples dealt with the homogeneous strata. As shown in Section 2.2 under pure conductive heat transfer conditions the geothermal gradient is inversely proportional to thermal conductivity; thus, its variations with depth produce corresponding variations from the otherwise linear T⫺z profile (Eq. (4)) that can be misinterpreted in terms of a transient surface temperature. In principle, the GST reconstruction methodology can be readily extended to include thermal conductivity variations. However, most of the temperature logs are accompanied by a few measurements of thermal conductivity and/or the conductivity measurements can be available for some sections of the borehole. Thus, the geophysicists are compelled to treat the subsurface as a homogeneous medium and/or assume its significantly simplified model. For example, erroneous values can be accepted when extrapolating these data on “empty” intervals. The real Earth’s subsurface generally contain significant conductivity variations that can be caused by mineralogy changes and stratigraphic heterogeneity at different crustal levels. Compaction of sedimentary rocks with depth leads to an increase in thermal conductivity through reduction of porosity. Similar changes can be caused by the changes of the fluid saturation of subsurface rocks with depth (low conductivity in unsaturated rock near the surface and higher conductivity in the water-saturated rock below the water table), etc. Poor knowledge of the thermal conductivity of the medium manifests itself as a noise in the interpretation biasing the reconstructed GST history. As shown in the

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previous section random noise does not affect seriously the results of inversion. However, what is about systematic noise? It is clear that the perturbation caused by this kind of disturbance can be removed from the solution only if its exact pattern is known. Of course, in the real field situations such procedure is unreal. Synthetic example below illustrates the influence of the systematic noise on the results of the GST reconstruction. For the simulation below we have assumed three-layer thermal conductivity K(z) ⫽ {2.5 W/mK in the depth interval of 0–100 m; 3.0 W/mK between 100 and 200 m; 2.5 W/mK between 200–500 m depth}. As previously, the noise free T⫺z profile was calculated for the “gate” GST change by 1-D forward heat transfer modeling with 5 m intervals to a depth 500 m. Other parameters remained the same as in the previous examples. The Gaussian noise of zero mean and s.d. of 0.03 K typical for the field temperature measurements was superimposed on the above model. When formulating inverse problem we have taken the medium as homogeneous with constant thermal conductivity of 2.5 W/mK. Figure 31 shows the results obtained from this mistaken assumption. As seen, the systematic bias in temperature conductivity pattern has only a weak impact on the GST histories reconstructed by SVD method. The noise free and contaminated with Gaussian noise T–z profiles gave quite coherent inversion results. Influence of the systematic errors is somewhat stronger in the case of FSI. The GST histories

Fig. 31. The effect of a systematic error in thermal conductivity on the inferred GST history. The true model is 2.5 W/mK except between 100 and 200 m where it is 3 W/mK. A uniform value of 2.5 W/mK was assumed in the inversion: SVD method (bottom) and FSI method (top). Both noise-free T–z profile and a profile contaminated with Gaussian noise with zero mean and s.d. ⫽ 0.03 K were used for the inversion. The GST history for homogeneous medium (grey line) reconstructed by FSI method is included for comparison (top).

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calculated for mistaken one-layer thermal conductivity and using real three-layer model are comparable only for the noise-free data. To avoid instability during FSI of the T⫺z profile contaminated with Gaussian noise, the imposing of stronger bounding and smoothing constraints on the GST history ( ⫽ 0.1 K, c ⫽ 500 years) was indispensable. Resulting GST curve appears to be over-smoothed. On the other hand, FSI method provides the possibility to treat systematic errors in thermal conductivity that is unattainable for SVD technique. Namely, it can formulate thermal conductivity as a parameter with uncertainty that should be estimated together with the GST history. On the contrary, the ramp/step method and SVD are limited to the problems when the thermal properties of the medium are assumed to be known. Synthetic example below illustrates the potential advantage of formulating the thermal properties of the medium as the model parameters to be estimated. The noise-free T–z profile was calculated for the above three-layer model (“real conductivity” in Figure 32). When reconstructing GST history by FSI method (Figure 31, top), a priori constant conductivity of 2.5 W/mK was assigned and the thermal conductivity distribution was formulated as the parameter to be estimated simultaneously with the GST history. Figure 32 shows the results of inversion obtained for the thermal conductivity profile. As seen, estimated course of the thermal conductivity approaches the real variations of this quantity well.

Fig. 32. Real thermal conductivity–depth profile and its estimated modification by the FSI technique.

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Summarizing above results illustrating the use of SVD and FSI approaches, we should mention that as it was expected, both methods gave generally similar GST histories. Applied in the above models systematic uncertainty in the thermal conductivity values was sufficiently small and its effect was to a degree absorbed by the influence of the T–z noise. Including K(z) as a parameter to be estimated can somewhat improve the GST history. The advantages of this approach may appear more obviously, when the systematic noise is stronger. All existing techniques of the GST reconstruction from subsurface T–z profiles are based on the 1-D theory of conductive heat transfer. At given depth the medium is assumed to be horizontally homogeneous, and the variations of the thermophysical parameters are supposed to be only vertical. Deviations from this assumption manifest themselves as noise in interpretation. Lewis and Wang (1992) described effects of spatial and temporal variations of the terrain (upper boundary conditions) and have concluded that potentially such kind of noise can give erroneous GST estimations. According to Lewis and Wang (1992), it is these effects that may be responsible for observed deviations in the GST histories inferred from temperature logs measured in closely spaced boreholes and occurrence of so-called “spaghetti diagrams” – tangling of the superposed GST curves that reflects high regional variability of the results (Figure 33, top; see also Section 3.1.1, Chapter 3). Shen et al. (1995) investigated an influence of possible spatial heterogeneity of the thermal properties of the medium (3-D thermal conductivity structure). The authors performed a set of numerical experiments with synthetic T–z profiles. The 3-D subsurface model was obtained on the cubic 10 ⫻ 10 m grid by perturbing initially homogeneous subsurface by Gaussian noise with zero mean and s.d.⫽ 0.25 W/mK. Synthetic T–z profiles were calculated for above subsurface structure using 3-D heat conduction equation. The first set of profiles (“without signal”) was constructed for zero surface temperature boundary conditions to reveal the properties of noise misinterpreted as signal. These profiles have helped to recognize how strong is the influence of the subsurface heterogeneity on the GST history as well as to reveal most effective constraints (standard deviation of the measured temperatures, uncertainties in a priori values of thermal conductivity, etc.) that can suppress this noise. Numerical trial runs have shown that possibility to obtain “spaghetti diagrams” increases when a priori constraints are too severe; thus, small variations in measured temperatures and thermophysical properties are taken as significant for the reconstructed GST history. The authors have shown that extending constraints on thermal conductivity is a more effective way to suppress the influence of noise arising from the 3-D effects rather than change of constraints on the borehole temperatures. They also determined a range of constraints that appear the best for effective noise suppression. Annual temperatures reconstructed for North America from subsurface data were used as the surface boundary conditions for second set of T–z profiles (“with signal”). This experiment was essential, because too wide a priori constraints may lead to a loss of signal and smoothing of inverted GST history. Numerous inversions of profiles “with signal” under a wide range of values assigned to a priori conditions supported conclusions based on the calculations using “without noise” data and have identified final range of constraints for the reasonable suppression of noise and effective signal recovery. The inversions performed using an appropriate range of constraints significantly attenuated the tangling of the GST histories, although small variations still remain.

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Fig. 33. Transient GST histories of 22 boreholes from central and eastern Canada calculated by the FSI algorithm. Top: The relatively narrow constraints on a priori information resulted in the “spaghetti diagram”. Bottom: Effective attenuation of tangling was achieved by the use of optimal constraints that took into account the possible spatial conductivity heterogeneities. (Redrawn from Shen et al. (1995).)

The authors applied this information for the re-processing of the temperature logs of 22 boreholes from central and eastern Canada. As seen in Figure 33 (top), merged together earlier GST reconstructions by different authors exhibit real chaos and are not easy for comparison and determination of averaged climate history in the area. Shen et al. (1995) suggested that at least a part of the disorder observable in Figure 33 (top) is attributable to the insufficient suppression of the representational noise. Their re-processing with detected optimal constraints provided more consistent results. The higher coherency obtained for closely located boreholes as well as for combined GST histories of all holes (Figure 33, bottom) revealed considerably simpler picture than the previous pattern. Average GST history for central and eastern Canada consists of some 1–4 K warming that began in nineteenth century. Part of this warming may be interpreted as the recovery from the earlier colder period.

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2.4.3 Using additional information: Field example The testing of the inversion procedures using real temperature logs is somewhat difficult because of absence of exact knowledge of the GST history in the borehole site against which inferred results could be calibrated. If inversion methods gave different results, it is quite difficult to define whether a technique has provided better estimation of the GST history. Thus, further field examples will be used not for the testing of the methods, but only for the illustration of the effectiveness of incorporation of different kinds of additional a priori information to stabilize and uniquely determine the solution. Both SVD and FSI methods differ in their theoretical approach, parametrization, and the way of parameter estimation. Common characteristic of both algorithms is that they incorporate a priori (additional) information to achieve optimal results. For a final test of the inverse methods we use a real temperature log measured in borehole Hearst (eastern Canada). This borehole was chosen not only because of the high-quality temperature logs and heat conductivity data available, but also because of its “historical” value. In the 1970s this borehole enabled one of the first practical attempts to assess the past climate history from subsurface temperature data (Cermak, 1971). The GST reconstructions were repeated further in numerous works (Nielsen and Beck, 1989; Shen and Beck, 1992; Cermak et al., 2003). Three holes including Hearst site were drilled in northeastern Ontario in 1968 as a part of the heat flow project of the Dominion Observatory. The sites were carefully selected in a flat terrain and in geologically uniform strata. The 600 m deep borehole Hearst (49.69°N, 83.54°W) is located in a slightly elevated, bushed terrain at the boundary of large forested and cleared fields. A small nearby lake and swampy area affect the temperatures insignificantly. The site is apparently free of the groundwater disturbances. The first incremental log was measured in 1969 (Cermak and Jessop, 1971). Further virtually continuous loggings were performed in 1985 and in 2000 (Nielsen and Beck, 1989). Temperature measurements are highly precise with the absolute accuracy of less than 20mK for the incremental logs and as small as 10 mK for the continuous logs. Figure 17 (Chapter 1) shows results of these measurements (Cermak et al., 2003). As seen, all temperature logs are quite similar with the clear positive “U-shape” curvature in their uppermost parts. Figure 17 and all similar diagrams below present the temperature log not only on the measured, but also on a reduced scale obtained by subtracting from the measured temperatures a temperature value ⫽ gradient ⫻ depth (see also Eq. (5), Section 2.2). This representation enhances the nonlinearities. The shape of the reduced temperature–depth profiles is more complex than that occurring in the case of the single warming event (Figure 13). The waves of the opposite sign in the reduced temperature profiles hint the presence of the recent warming that may be amplified by the environmental effect of the forest clearing that occurred approximately 100 years ago (Wang et al., 1992), subsequent cooling, warming, and cooling again. The 192 measurements for thermal conductivity, rather regularly distributed over the length of the hole, are available. The conductivity is almost constant at 3.23 ⫾ 0.09 W/mK. The specific heat capacity is also relatively constant with an estimated mean value of about 2.5 MJ/(m3 K). The mean rate of the heat production is 0.86 W/m3 (Jessop and Lewis, 1978). For the purpose of the present analysis heat production of this rate has negligible effect on the inversion results.

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Fig. 34. GST histories reconstructed for the temperature log measured in borehole Hearst (SVD method). The use of cutoff values of 10⫺3–10⫺4 resulted to GST histories showing the “Medieval Warm Period” centered near 1200 A.D. and the “Little Ice Age” at about 1650 A.D. Early reconstruction by Cermak (1971) based on the Monte Carlo solution is shown for comparison.

As mentioned above, diffusion process never retains sharp signals; thus, estimated GST histories are relatively smooth with increasing duration and decreasing amplitude of the climatic events into the past. Figure 34 shows GST histories inferred by SVD technique using different cutoff values. For comparison the earliest evaluation of the GST history by the Monte Carlo method (Cermak, 1971) is also shown. Except for the more pronounced appearance of the Medieval Warm Period in the Cermak’s reconstruction, the coherence of results given by both methods is high. The use of cutoff values of 10⫺3–10⫺4 leads to similar GST histories with the “Little Climatic Optimum” centered near 1200 A.D. and the “Little Ice Age” near 1650 A.D. At smaller cutoff values (ⱕ10⫺6) the solution has unreliable amplitude and/or becomes unstable. The coincidence of the measured and a posteriori T–z profiles is quite high. The root mean square (rms) misfit equals to 0.01–0.015K for different cutoff values. It is an essential feature of the GST reconstructions and reflects the underdetermined nature of the inverse problem. In other words, the measured and calculated (a posteriori) T–z profiles fall close to each other even for significantly differing GST histories. Above GST reconstructions were performed without use of a priori additional information. The next calculation illustrates the advantages of including additional independent knowledge in the inversion procedure. As additional information we used the information on the decorrelation of the measured data and on the persistence of the climate changes (Section 2.3.4). As described in this section, the autocorrelation function for the most temperature logs approaches zero exponentially (short-range dependence): r(
z) ⫽ exp(⫺
z/D) where r(
z) is the autocorrelation function,
z the depth lag, and d the characteristic correlation distance (Figure 35). Parameter D corresponds to the depth lag at which the autocorrelation decreases to (1/e), i.e., it defines the distance at which the individual temperature values can be considered statistically independent. For the majority of the boreholes the D-value ranges from 100 to 300 m (Bodri and Cermak, 1997a). Longest decorrelation distances are characteristic for boreholes drilled in ultrabasic rocks.

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Fig. 35. Autocorrelation of measured temperatures at borehole Hearst in the depth interval 20–600m and its exponential fitting.

The effect of different factors on the correlation distance was studied in a number of synthetic examples in the work by Bodri and Cermak (1995). Decorrelation distance depends on the distribution of the thermophysical properties of the medium, and also on whether fluid circulation is present or not. Generally, an environment with fluid circulation has a smaller D-value compared with an environment with no circulation (Bodri and Cermak, 2005a). The presence of a strong climatic signal in measured underground temperatures can also significantly affect the D-value. The advantages of treating the additional information are illustrated in Figure 36. As seen, an augmentation with additional information does not significantly change inferred GST history especially in its more recent part; however, it helps to recover less smoothed GST history. Under FSI runs, two sets of values for a priori constraints for  and c were assigned (Figure 37). Two GST histories were calculated to reveal the uncertainty about the optimal values for the smoothing parameters. Applied values can be regarded as the upper and lower bounds for possible constraints. The GST reconstruction inferred by SVD technique with cutoff value of 10⫺4 is shown for comparison. Once parameters are appropriately chosen the two methods have provided very similar results. All reconstructions revealed cold conditions prior to 1800 A.D. There is also a slight decrease of temperature since about 1964 and 1976 for SVD and FSI reconstructions, respectively. The difference in the onset time of the recent cooling occurs because of the instability induced by noise. Because of the heat diffusion and the uncertainties of measured temperatures, the time span for which the GST history can be reconstructed is limited. Its length is influenced by the depth and quality of measurements and also by the magnitude of the climatic signal that is to be reconstructed (Clow, 1992). The further we go back to the past, the less detail can be distinguished and a smoother course of the true temperature is obtained. This finding can be illustrated in terms of the resolving power of the SVD inversion

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Fig. 36. Effect of the additional information used in the SVD method. An augmentation with additional information provides less smoothed GST history. (Demonstrated on the Hearst data.)

Fig. 37. The GST histories reconstructed for Hearst data by the FSI method using two sets of a priori constraints. The SVD reconstruction (cutoff ⫽ 10⫺4) is shown for comparison.

method (Section 2.3.4). As described in that the given section, the resolution matrix of the unknown parameters can be defined as R ⫽ VVT, where V is a matrix whose elements are the eigenvectors. The jth column of matrix R represents the least squares solution for maximizing the jth parameter. At proper choice of the discretization of time the resolution matrix exhibits delta-like behavior (compact resolution) when the column with the best resolving power is nearly always the column with the maximum diagonal element. Thus, the diagonal elements of the resolution matrix can be used as the measure of the resolving power. It can vary between 1 (perfect resolution) and 0 (no resolution). The resolution was shown to depend on the shape of the surface temperature history, and is also a complex function of many borehole specific parameters, such as accuracy and vertical spacing of the temperature measurements, distribution of thermal conductivity measurements, and the level of noise in the data (Clow, 1992; Bodri and Cermak, 1995); thus, it should be established for each borehole individually.

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Fig. 38. Top: Resolving power of 100, 500, and 1000 year time intervals versus time (Hearst data). Bottom: Resolving power of 50-year intervals versus time without and with incorporating additional information on the interdependence of temperature measurements and on the climate changes (curves are labeled 1 and 2, respectively).

The diagrams of the variation of resolution back in time calculated for borehole Hearst (Figure 38 (top)) illustrate the most prominent property of resolving power of the geothermal method, namely the general fast decrease in resolution into the past. The variance of the jth parameter can be estimated by Eq. (21). A 100-year long event that occurred 300–500 years ago can be resolved with the relative variance of 10–15%. For as early as 2000–3000 years ago, it is only possible to resolve a 500-year interval with the same reliability, and the corresponding duration of event is 1000 years if it occurred 7000–9000 years ago. In other words, the further back we go into the past the less detail can be resolved and the smoother trend of the real temperature conditions on the Earth’s surface can be obtained. However, as mentioned in Chapter 1, such diminishing of the resolution into the past represents a common property of the majority of proxy methods for the paleoclimatic reconstructions. Compared to the variety of proxy climatic reconstruction methods, the resolving power of the geothermal method is lower for the recent 50–100 years and is comparable with other paleoclimatic reconstructions when detecting more remote climatic events (Figure 7, Chapter 1).

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Generally, the Hearst data permits to assess past climatic changes of one millennium or so. Figure 38 (bottom) illustrates the change of resolving power for 50-year time interval calculated for the Hearst hole, i.e. a rapid decrease in resolution with increasing time. The reliability to determine short GST change is about 10–15% for an event, which occurred 50 years ago; for an event that occurred 200 years ago, it is possible to resolve a 50-year interval with the same reliability, and a 200-year interval for an event that occurred 800 years ago. Incorporation of additional information can improve the resolving power of the SVD method. As demonstrated in Figure 38, the ability to resolve 50-year time intervals after incorporating the information about interdependence of temperature measurements and climate changes exhibits an improvement of almost 10% at a time of 0–50 year B.P., and of 23–35% at the interval of 100–150 year B.P. We have compared two of the most powerful methods for the GST inversion using the subsurface temperature–depth profiles. Methods differ in both their parametrization and the technique of parameter estimation. The incorporation of a priori information to obtain a stable and unique solution is central for both techniques. In spite of the theoretical differences between both approaches, their application to synthetic and field examples gives generally similar results in the case of the appropriate choice of the stabilizing constraints. Summarizing conclusions are the next: (1) Large part of discrepancies in the inverse results can be attributed to different constraints imposed on the GST to smooth and stabilize the inverse solution. In general similar results were obtained by two methods when equivalent assumptions were used. (2) In principle, FSI technique allows incorporation of the thermophysical properties as the parameters to be estimated and weighting of the contribution of the data and a priori model and thus appears possessing potential to give better inversion results. However, exact knowledge of the weights/uncertainties of the data as well as a priori model is indispensable to realize this potential, while researchers generally have no sufficient a priori information in their disposal. (3) The computational advantages when incorporating additional information are obvious. Including additional information can improve the resolution and significantly enlarge the extent of the climatic history that can be recovered by the inversion. The more complete is a priori knowledge about past climatic changes from independent complementary sources, the more reliable GST histories can be inferred from borehole data.

2.4.4 Recent testing of borehole inversion methods in simulated climates The reconstruction of the past temperature variations on the global/hemispheric scales is performed by three principal approaches: proxy methods, inversion of borehole temperature logs, and modeling. Different proxy techniques are the oldest and traditional, while the “borehole” method and simulations of the past climate with the state-of-art GCM represent recent developments. The first attempts to decipher certain information on the GST changes from underground temperatures dates back to the early 1970s, and the

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corresponding inversion methods become generally known in the mid-1980s. The first compilation of the studies inferring past climatic variations from underground temperatures edited by T. Lewis has appeared in 1992 (Lewis, 1992). Together with other topics it gathered numerical comparisons/testing of different GST inversion techniques. The goal of these investigations was to prove the ability of the “borehole” method for reliable reconstruction of the past climate change (e.g. Beck et al., 1992; Shen et al., 1992). The attempts to validate and to refine “borehole” method and/or answer numerous questions arising during further development of the techniques and drawing more and more field data in the processing continued permanently for the recent two–three decades. Simultaneously numerous attempts were undertaken to bring together/compare/combine results of different approaches and to integrate them into the complex multi-dimensional paleoclimatic network. Probably the most recent testing of the possibility of the GST reconstruction from borehole temperature logs was performed in the work by González-Rouco et al. (2006; see also the references therein). This attempt has been inspired by the recent comparative studies of various global/hemispherical paleoclimatic reconstructions and somewhat different magnitudes of the past temperature changes (especially in the earlier parts of the records from the sixteenth to eighteenth centuries) of the averaged GST histories in comparison with climatic trends defined from proxy records (Briffa and Osborn, 2002). The global and/or hemispheric scale temperature histories for the several past centuries based on borehole measurements suggest colder past conditions than the reconstructions based on the multiproxy data. For example, tentative hemispheric GST history by Huang et al. (2000) (Figure 94, Chapter 3) revealed a much colder Little Ice Age of approximately ⫺0.8 to ⫺1.0 K in comparison with ⫺0.2 K given by the Mann et al.’s (1998, 1999) multiproxy compilation (for details see Section 3.3, Chapter 3; Mann et al., 2000; www.ngdc.noaa.gov/paleo/ei/ei_cover.html). Results by Briffa et al. (2001; see also Figure 11, Chapter 1) are somewhat closer to the Huang et al.’s (2000) conclusions and give temperatures of the Little Ice Age by 0.3–0.6 K lower than the present, while Crowley and Lowery (2000) proposed, rather, a warming of ⬃0.2 K from 1000 to 1400 A.D., cold conditions of ⬃⫺0.3 K up to 1900, and rapid warming of 0.4–0.8 K in the twentieth century. Two reconstructions for Europe using independent proxies by Luterbacher et al. (2004) and Guiot et al. (2005) have detected almost similar twentieth century warming of 0.25 and 0.27 K, respectively. Mann et al. (2003) have tried to re-assess the coupling of the borehole and traditional proxy data and have re-calibrated the GST history using the twentieth century SAT data. This procedure somewhat increased possible warming to 0.2–0.4 K. Another distinction of the “borehole” and proxy reconstructions is that the amount of warming obtained by Huang et al. (2000) is more regularly distributed over the past five centuries, while in other works the twentieth century warming appears as a continuation of the trend that started only in the nineteenth century. Similar inconsistency was found also among different proxy series. Figure 39 shows comparison of three multiproxy SAT anomaly series for the Northern Hemisphere. Pattern by Esper et al. (2002) represents tree-ring temperature reconstruction, while the compilation by Mann et al. (1998) is based on multiproxy data (tree-rings, ice cores, corals, historical documents, and instrumental data). Reconstruction by Huang (2004) merges multiproxy and borehole sources (for details see Section 3.3, Chapter 3). It was proposed that the inconsistency in the pre-instrumental period between “geothermal” and

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Fig. 39. Comparison of the annually resolved five-century multiproxy reconstructions for the Northern Hemisphere by Mann et al. (1998), Esper et al. (2002), and Huang (2004). Pattern by Huang (2004) integrates also borehole data. Temperatures are shown as anomalies with respect to the 1961–1980 mean.

multiproxy time series could at least in part arise from the significant role that tree-ring information plays in the former reconstructions (Huang et al., 2000). As known, centennial trends are expressed very weakly in tree-ring series (see Figure 9 and Section 1.2.3, Chapter 1). For that very reason Esper et al. (2002) applied a powerful method for the regional calibration of tree-rings that keeps long-term trends better than the method used by Mann et al. (1998) and thus obtained larger variability in the past temperature time series. The early seventeenth century SAT anomaly estimates of these authors diverge by about 0.7 K from those by Mann et al. (1998). Later re-calibration of the data has reduced these differences to only 0.35 K (Briffa and Osborn, 2002). Even bearing in mind turbid complex of reconstruction uncertainty, the curve by Esper et al. (2002) contains evidence for more pronounced climate oscillations in the past millennium than has been previously accepted by the multiproxy reconstructions. Because the divergence in the amplitude of the temperature variation is an extremely important difference, detection of a fair scatter among various published estimates was followed by lengthy discussions. Their effect can be found in the works by Mann and Hughes (2002), Cook et al. (2004), and Esper et al. (2005). The researchers have exchanged their views at numerous conferences (e.g. Session PP19: Climate Change in the Recent

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Past: Integrating Meteorological, Proxy, Borehole, and Modeled Climate Reconstructions; AGU Fall Meeting, December 2005, San Francisco, CA; www.agu.org/meetings/fm05), as well as on the different web sites of professional climatologists, e.g. the Real Climate (www.realclimate.org./index.php?p⫽253), and/or more descriptive the ClimateAudit (CA) and the European Tribune: www.eurotrib.com/story/2006/2/12/19444/8696). The argument on the subject “Borehole versus proxies” has represented only a part of the above “big discussion” on the reliability of different paleoclimate reconstructions. Mann et al. (2003) have optimized the Huang et al.’s (2000) data and partly corrected them later (Jones and Mann, 2004). However, Pollack and Smerdon (2004) did not accept their optimization (for more details see Section 3.2, Chapter 3). Guiot et al. (2005) on the basis of 222 borehole temperature profiles inferred averaged GST history for Europe that indicated ⬃0.5 K higher temperature rise in comparison with their reconstruction based on multiproxy sources. The average of the multiproxy temperature anomalies for the 1500–1700 A.D. segment is ⫺0.1 ⫾ 0.5 K. The average borehole temperature for this period equals to ⫺0.45 K; thus, it is still within the lower boundary of the confidence interval. The amount of warming calculated for Europe is somewhat smaller than Huang et al.’s (2000) value for the Northern Hemisphere. In spite of the higher coincidence obtained after 1750 A.D., Guiot et al.’s (2005) statement was that “borehole temperature reconstruction is not perfect”. The main benefit of this trenchant discussion was probably that it has impelled the researchers to re-assess the skill of different methods for the past climate reconstruction. The most advantageous testing strategies for the ability of the “borehole” method to draw out past GST changes from T–z profiles were applied in the recent works by Beltrami et al. (2006) and González-Rouco et al. (2006), whose authors used simulated subsurface T⫺z profiles forced by the GCM as a substitute of the real climate and applied inversion technique to reconstruct GST histories from calculated profiles. Modern 1000-years long ECHO-g ocean–atmosphere GCM models were used as a surface forcing to the forward models of heat conduction. These models have included the 1000-years long external forcings (solar irradiance, radiative effects, and volcanic aerosols) as well as the anthropogenic influence (greenhouse gas concentration increase) and were complex enough to provide insight into intrinsic properties/possibilities of the inversion technique and to test the correctness of the GST reconstruction. Control and two trial simulations with the same external forcing and different initial conditions were considered. The 600 m deep T–z profiles were gained from the 898 land terrestrial grid boxes and reflected averaged Northern Hemisphere land conditions. To investigate influence of the spatial distribution and surface coupling of borehole sites similar profile was calculated also on the base of the 177 grid boxes reflecting the real borehole distribution. The SVD inversion was applied to these T–z profiles to infer GST history. Recovered from simulated T–z profiles GST histories were compared with the climate model variations used as a surface forcing. Results have shown that in spite of different disturbing factors (e.g. dating of the temperature logs and non-equal depth) and irregular (somewhere sparse) geographical distribution, GST histories return adequately the filtered version of the real climate change. The numerical experiment described above has proved that the SVD inversion technique is powerful enough to reproduce the main features of the multi-century climatic trends. In the case of satisfactory quality of the borehole temperature logs and the proper

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treatment of possible uncertainties, borehole method itself could detect major climatic excursions of the past. It is extremely important that none of the reconstructions based on different models show any evidence of an overestimation of the magnitude of the past climatic change, while it was this difference that has inspiredthe discussion as well as the additional testing of the “borehole” method mentioned above. For the recent two–three decades boreholes distributed over the entire continents were recognized as a great tantamount source for new information in numerous “white spots” of the global map of the paleoclimatic change. Researchers from many countries mastered routine application of the geothermal method and a sizeable number of results have been published. It appears that the boreholes can provide information previously unavailable in character and spatial distribution. Further investigations, however, have added a grain of salt to initial enthusiasm for the “geothermal” climate reconstruction. No doubt, in many cases different inversion methods gave equally good coherent results. However, in a number of situations inversion techniques gave poor results. These failures were attributed to an impact of numerous non-climatic influences on subsurface temperatures that can disturb the ideal heat conduction regime described by Eq. (4). That time potential environmental disturbances to the subsurface climatic archive were recognized as well as the necessity of the careful analysis of the potential perturbations for each individual temperature log. An influence of terrain on the GST has been discussed in detail by Lewis and Wang (1992), and examples of the effects on ground temperatures of spatial distribution of differing terrains, temporal changes in terrain, and subsurface fluid flow have been presented. Since then numerous investigations have been carried out concerning the influence of different local effects on the subsurface temperatures and the ways to recognize these anomalies and reveal reliable GST histories from disturbed temperature logs. The next sections contain the summary of these efforts.

2.4.5 Interpreting ensembles of borehole temperature logs The above sections were devoted to the techniques of the GST inversion from the temperature–depth profiles measured in individual boreholes. As in all branches of geophysics, an extraction of climatic signal from borehole temperature logs is complicated by the presence of noise in the data. The principal sources of noise are of three types: (1) the measurement errors, (2) the representation errors, i.e. the simplification of the mathematical model and its departure from the conditions existing in the real geophysical systems, and (3) terrain effects causing both secular and provisional changes on the ground–air boundary. Since in most of the field situations detailed information that permits sure correction of measured profiles is not available, the development and application of various techniques for suppression of noise and enhancement of the signal have received special importance. Numerous studies in other geophysical branches have shown that the analysis of multiple observations can be more preferable to suppress the effect of the random noise in the data than the use of single measurements. The basic idea of this approach is that the signal can be enhanced and/or noise can be attenuated by the interpretation of the available data together as an ensemble. This approach is widely employed as simultaneous inversion using weighted staking of seismic reflection data (Fatti et al., 1994; Larsen et al., 1999; Margrave et al., 2001; Ryberg et al., 2005). Results have shown that in the

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presence of random noise the combination of the data volumes provides more accurate results than the techniques using individual data. Joint processing effectively suppresses the noise without unnecessary suppression of the signal. The unknown parameters are better constrained and, in spite of the noise present, are more reliably estimated. The advantage of such approach in borehole climatology can be illustrated as follows. Let us assume that the climate in some wide area is characterized by a secular change in temperature that is archived in the underground. It is this signal that should be recovered by the GST reconstruction procedure. Generally, borehole site represents a variety of environmental conditions. Boreholes are drilled into different rock types, on the heights or valleys, embracing a variety of hydrologic regime and surface conditions from bare soil to the dense vegetation. Thus, temperature–depth profiles from different boreholes may contain local non-climatic perturbations to the long-term transient climate signal. All boreholes would unlikely have the same topography and vegetation cover, subsurface structure, and hydrologic regime. If different kinds of noise appear randomly in the data ensemble, an analysis of combined T–z profiles would likely result in the common regional signal enhancement and suppression of noise. A signal common to all the boreholes can safely be attributed to the regional climate change. Combined analysis of borehole data can be performed using two different strategies: (1) simultaneous inversion of the temperature logs from several boreholes, and/or (2) averaging of the individual GST histories. Both procedures differ conceptually. Simple averaging of the GST histories inverted from the single-hole logs can be accomplished without limitations, while the simultaneous inversion of several T–z profiles can be performed exclusively under assumption of the presence of common transient climate signal in all jointly analyzed temperature logs. Beltrami and Mareschal (1993) have extended conventional SVD technique and suggested the multi-inversion approach. The method was verified using 21 temperature logs sampled across the whole eastern and central Canada and yielded generalized GST history for this region. Later Clauser and Mareschal (1995) have performed the testing of this method by the simultaneous inversion of borehole temperature logs from Central Europe. Both studies have supported an increase in the resolution under multi-inversion approach, when common climatic signal can be fully unraveled. Beltrami et al. (1997) has presented detailed description of the simultaneous inversion of borehole temperature data for reconstruction of the GST history in the SVD context. Pollack et al. (1996) have extended the simultaneous inversion approach for the FSI technique. Their method was used for the joint processing of the borehole temperature logs in numerous studies. Thus, Majorowicz and Safanda (2001) have constructed composite surface temperature history from simultaneous inversion of T–z profiles from 43 boreholes located at the western Canadian Basin. The field situations favorable for the simultaneous inversion strategy include: (1) repeated temperature logs from a single borehole, (2) a suite of boreholes from a single site, and (3) a suite of boreholes from a wider region with similar climatologic and environmental changes. The mathematical procedure is especially obvious in the case of the SVD inversion. As previously (see Section 2.3.4), an unknown GST history V0(t) is approximated by N intervals of constant temperature (Eq. (18)). In the case of a single borehole the matrix Aik (Eq. (20)) contains M rows and N columns, where M is the number of temperature measurements in the single-hole. For L holes with Mi measurements in each of them, the matrix A will consist of Li⫽1 Mi rows and N columns, containing series

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similar to Eq. (13) calculated for all given depths in each given borehole. When parameters of initial (equilibrium) temperature field for each borehole U0 and Qm are estimated simultaneously with the GST history V0(t), the vector Vi will consist of (N ⫹ 2L) unknowns, and the matrix A will contain two additions (see Section 2.3.4). The first M1 elements of the (N ⫹ 1) column will be equal to 1 and all other to 0, the following M2 elements in (N ⫹ 2) column are 1 and all other elements are 0, and so on. Similar addition can be constructed for the thermal resistances4 to the depths zi in all given boreholes. Further inversion procedure is the same as for the single-hole SVD case. The efficiency of a simultaneous inversion in the noise suppression is clear. Of course, the GST histories obtained by merging data sets that simultaneously combine a number of T–z profiles and conductivity data with different terrain/microclimate effects and noise level, have typically larger data misfits than the individual holes. In the cases when obtained data misfits are too large, it can mean that a common climatic signal may not present in the data. On the other hand, testing of the simultaneous inversion technique conducted in the work by Beltrami et al. (1997) using SVD approach for both synthetic noisy and noise-free data as well as for the field examples containing common climatic signal have shown that staking temperature perturbations from L boreholes can increase the stability of the solution and resolution of the inversion and improve the signal to noise ratio L. Calculations by Beltrami et al. (1997) revealed the dependence of the by a factor 兹苶 resolving power from the noise level. Generally, composite surface temperature history obtained by simultaneous inversion was comparable with the GST curve obtained by inversion of the single log with the lowest noise. A definite problem for the simultaneous inversion represents the fact that not all available borehole temperature logs were measured using the same sampling interval. In this case the composite GST history was not close to anyone of the individual surface temperature histories and was weighted to the temperature logs with the finer sampling. Thus, temperature logs with similar sampling should be used for simultaneous inversion to avoid possible bias. Temperature logs with large sampling intervals can be interpolated for finer distances. On the other hand, the simultaneous use of the temperature logs of different lengths does not represent serious restriction. Such temperature–depth profiles contain the surface climate history of different time spans. Numerical experiments by Beltrami et al. (1997) have shown that because shallow boreholes does not archive an information on remote GST changes, merging of the shallow- and deep-hole data for simultaneous inversion does not improve the course of the past GST history obtained from the deep holes. On the other hand, this procedure can specify better the recent GST history and/or to improve the estimates of the heat flow obtained from the shallow-borehole data. An averaging of the GST histories reconstructed from the individual borehole temperature logs represents another possible kind of the ensemble interpretation and suppression of the random noise in the geothermal data. As mentioned above, the principal difference between simultaneous inversion and averaging of the individual GST histories is that the averaging can be performed without restrictions, whereas the former procedure provides good results only for boreholes that contain common climatic signal. On the other hand, the simultaneous inversion takes into account the data uncertainties 4

Thermal resistance is the ability of a material to resist the flow of heat. It represents the reciprocal of thermal conductivity and is measured in km/W.

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and the borehole sampling and depth. These effects cannot be easily captured in the simple averaging of the single GST histories. Comparing both techniques, Pollack et al. (1996) have concluded that for the three field situations enumerated above they yield closely identical results. Diverging GST histories were obtained when merging a suite of boreholes from the vast areas that have experienced different surface temperature variations over their different parts. The simultaneous inversion estimate in this case appears to give biased GST history. If both procedures, the GST averaging and simultaneous inversion, exhibit different results it generally notifies that an assumption of a common transient climatic signal in processed boreholes may be invalid. Numerous examples of the application of both procedures to the worldwide database of borehole temperature logs are presented in Section 3.2 (Chapter 3). Recently Chouinard and Mareschal (2006) have compared again different approaches of the GST inversion from ensembles of borehole T–z profiles. They used temperature logs measured in boreholes in two Canadian regions: northwestern Ontario and northern Manitoba/Saskatchewan. Using these data the authors have performed three experiments: (1) simultaneous inversion of all available profiles, (2) screening of the profiles for the possible non-climatic disturbances and simultaneous inversion of the undisturbed profiles, and (3) averaging of the individual inversions. Results of experiments have shown that at least for above two regions the averaging of the individual inversions gives less resolved GST histories than the simultaneous inversion of the same temperature–depth profiles. For example, well resolved by the simultaneous inversion the Little Ice Age appears much weaker in the GST curve calculated by averaging the individual GST reconstructions. Similarly less visible is the fingerprint of the recent warming. On the other hand, the difference between results of the simultaneous inversion of all temperature logs and only selected profiles, which were assumed to be free of the non-climatic influences, was far not so significant than the authors had anticipated. Generally, the most informative results with maximum resolution were obtained from the simultaneous inversion of a few noise-free profiles.

2.5 Ground–Air Temperature Coupling: Pre-Observational Mean Temperature (POM) Borehole temperature measurements contain direct information on the GST history. The GSTs represent important climatic variable; thus, in principle they need no calibration with the independent data. On the other hand, it is the air column temperatures, including the most important surface air temperatures (SAT) taken at screen height (1.5 m above the ground surface), that are typically of interest in discussions of climate variability. The SAT responds to the convective heat transfer in an atmospheric boundary layer, while the GST represents a continuously integrated ground temperature variations in the vicinity of the borehole that occur mainly by conduction process. Thus, both massifs of the data are complementary, but independent data sets that provide measure of the surface temperature and its change through the time in different frequency domains. Once we are sure that we have reliable methods to infer the GST history from borehole logs, further task should be the relation of the GST to the SAT changes. This ensures that the climate change will be tackled with more confidence.

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The problem of coupling of the GST and SAT has arisen from the very beginning of the borehole climatology. The fact of the systematic difference between the GST and SAT, namely that the soil may be warmer than the air has been revealed already in the early work by Chang (1958), who demonstrated that a greater part of the solar radiation is absorbed by the Earth’s surface rather than by the atmosphere. The micrometeorological processes near the Earth’s surface causing higher “thermal capacity” of the ground were investigated in the work by Deacon (1969). Figure 14 (Chapter 1) illustrates the air and ground temperature oscillations measured during 12-year temperature monitoring at several shallow depths in the experimental borehole Prague-Sporilov (the Czech Republic) (Cermak et al., 2000). The annual wave is seen as the most important variation. In addition to an annual cycle, ground temperature exhibit a daily cycle and variations associated with changes in weather. These variations are confined to the near-surface zone. The filtering of the high-frequency components and the lag of the ground response with respect to air temperature variations is apparent in the temperature record presented in Figure 15 (Chapter 1). The daily temperature wave and the weather cycles are practically not observable below about 0.5 m and approximately 1 m depth, respectively. Figure 40 shows monthly averaged GST change in Eilat area (Israel). Ground temperatures were measured at 2 cm, 20 cm, and 1 m depth during the years 1957–1963 (data source: www.fortunecity.com/greenfield/runningbrook/729/id23_m.htm). Temperatures were recorded at 8, 14, and 20 h. This example represents ideal case of the air–ground temperature coupling in warm dry environment without snow cover or freezing. As shown, the coherency of the general course of the near-surface and deeper ground temperatures is practically perfect. On the other hand, deeper ground temperature is higher than the near-surface temperature in winter and is lower in summer. This creates definite attenuation of the total annual range of variation of the GST in comparison with air temperature variations. Due to the fact that the GST is higher than the SAT in winter and is lower in summer, the ground represents potential storage capacity and a source for the heating/cooling. Heat flows out and/or into the ground in the cold and warm seasons, respectively. This phenomenon is referred as the “heat-valve” effect (Gilpin and Wong, 1976). Factors connected to the movements and/or diffusion of air and/or moisture masses (wind, evaporation/transpiration, vertical soaking of soil moisture, and precipitation) tend to equalize air and soil temperatures (Arya, 1988). One of the first empirical long-term relationships between annual mean GST and SAT has been presented by Kukkonen (1987) for the territory of Finland. It is based on the combination of air and ground temperatures measured on the meteorological stations all over the country and borehole temperatures extrapolated to the surface TG ⫽ 0.71 ⫻ TA ⫹ 2.93,

(31)

where TG and TA (°C) are annual mean ground and air temperatures, respectively. As seen on the annual scale ground is warmer than the air. On the other hand, the ground temperature fluctuations are approximately 30% attenuated in respect to the air temperature. The fact that generally the mean annual SAT is lower than the corresponding GST was corroborated by numerous later measurements. Comparison of soil and air temperatures by Chisholm and Chapman (1992) for the Salt Lake City Airport meteorological

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Fig. 40. Monthly averaged ground temperatures measured in Eilat area, Israel. Data are averaged through 1957–1963 period. Ground temperatures were measured at 8, 14, and 20 h at the depths 2, 20, and 100 cm, respectively. (Data source: www.fortunecity.com/greenfield/runningbrook/729/ id23_m.htm.)

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station have shown that the ground is generally warmer than the air by 1–2 K. Similar results was obtained in the work by Schmidt et al. (2001) for Fargo (North Dakota). For the nine-year long record the mean annual average ground temperature was ⬃2 K higher than the air temperature. The same difference was obtained for 1997–1998 years GST–SAT monitoring at the station Pomquet (Nova Scotia). In most of the mentioned locations this difference occurs mainly due to the insulating effect of the snow cover, although such factors as evaporation also play a role. As demonstrated by the regional investigations in Canada, in the regions with insignificant snow cover (e.g. coastal areas) the mean annual GST–SAT difference equals to only 1 K, while in the areas with deep and long duration snow cover (e.g. described below Kapuskasing site) it may reach as much as 5 K. A comparison of mean monthly air and soil temperatures recorded during 1984–1989 period at Salt Lake City Airport has shown that the soil temperatures at all recorded depth (10, 20, 51, and 102 cm) almost perfectly repeat the annual air temperature variations, however, with considerable offset (Chisholm and Chapman, 1992). Repeated model studies have revealed that on the long scale mean annual GST corresponds linearly to the mean annual surface temperature (Baker and Ruschy, 1993; Putnam and Chapman, 1996; Gosnold et al., 1997; Harris and Gosnold, 1999; Majorowicz and Safanda, 2005). This statement can be confirmed by the Granger causality test (see Section 3.4.5, Chapter 3). For this analysis we have used reconstructions of the annual global surface temperature over the last five centuries (1500–1980), based on the multivariate calibration of the high-resolution proxy climate indicators (tree-rings, ice cores, corals, and historical documents) combined with the long-term instrumental records by Mann et al. (1998) (Figure 39 of this chapter) and similarly long GST reconstruction based exclusively on the terrestrial borehole data (Mann et al., 2003; Figure 98, Chapter 3). Application of the Granger causality test to these records have shown that on the long scale the SAT series is the Granger cause of the examined GST, and has thus supported strong long-scale GST–SAT coupling (for details see Section 3.4.5, Chapter 3). All above-mentioned investigations have given the confidence that it seems reasonable to consider borehole temperatures as filtered versions of the surface air temperature (SAT). The complementary nature of the GST and SAT has inspired the idea of coupling of the measured temperature logs and the SAT time series for the joint processing. To estimate the magnitude of recent climate change, specifically the amount of the recent global warming, paleoclimate reconstruction from the temperature–depth records can be suitably completed with a long-term meteorological SAT series monitored at the weather stations. This idea was introduced by Harris and Chapman (1995, 1997) and provided a useful tool for the assessment of the so-called pre-observational mean temperature (POM) that represents the temperature conditions existing before the routine instrumental observations actually started some 100–250 years ago, i.e. the value against which the twentieth century climate warming is usually referenced. Coupling the inverted borehole temperature logs with the SAT series provides a more realistic benchmark than the models based on the inverted borehole data themselves. For the 1-D case of the purely conductive heat transfer POM can be obtained by comparing the temperature log measured in a borehole with synthetic temperature–depth profile, corresponding to the solution of the 1-D heat conduction equation in a horizontally

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layered half-space that describes purely conductive heat transport with no heat sources taken into account (Eq. (4), Section 2.2). The surface boundary conditions corresponding to the observed SAT series are

POM, 0 ⬍ t ⱖ t0  T ( z ⫽ 0, t ) ⫽  , TSAT , t ⱖ t0 

(32)

where TSAT is the SAT temperature time series, and t0 the time when the SAT record started. It is assumed that the interval (0, t0) is long enough. Thus, at constant temperature before t0 and for an absence of other effects the initial temperature–depth profile, T–z, at time t0 represents a steady-state temperature field corresponding to the constant heat flow from depth. The approximate duration of t0 can be estimated from the expression for the characteristic time of the thermal relaxation t0⬃L2/4k, where L is the characteristic length and k the thermal diffusivity. When k equals to 10⫺6 m2/s for a 100–200 m deep borehole t0 achieves approximately 100–300 years. As in the previous processing examples, measured temperatures can be converted into reduced temperatures by removing the quasisteady state part from the measured temperature log. The reduced temperatures contain only temperature “disturbances”, ideally in absence of substantial topographic elevation and other disturbing factors the subsurface climate recollection alone. Since the boreholes have different depths, the measurements to the depth z ⫽ 兹4苶kt 苶苶*, where t* is the time from the beginning of the meteorological record to the date of borehole logging, are generally taken for the inversion. This procedure avoids the biasing due to different borehole depths (Harris and Chapman, 1997). To estimate the POM-value, the standard least-square inversion analysis can be used, which minimizes the sum of the squared differences between reduced and synthetic temperature–depth profiles. Inverted data are sensitive to the calculated POM-value; in the absence of non-climatic disturbances the POM-value can be assessed quite accurately, which is otherwise not possible by using the SAT record alone. Below we illustrate the application of the method to the data from Canadian borehole Hearst (49.69°N, 83.54°W), the GST reconstructions for which were presented in the Section 2.4.3. Generally, results of the meteorological temperature measurements are representative of extensive areas; thus, making POM estimates, there is no need to get results of SAT measurements of especially close to investigated boreholes meteorological stations and/or to reject from consideration borehole temperature logs where such data does not exist. According to investigations by Hansen and Lebedeff (1987), the correlation coefficient between the annual mean temperature variations for pairs of stations selected at random from among the station pairs with at least 50 common years in their record is above 0.5 within 750 km distances at latitudes 23.6–44.4°N and within 1250 km distances for latitudes 44.4–64.2°N for each direction defined by 45° intervals. At middle and high latitudes the correlations approach unity as the separation between the stations becomes small. Of course, local specific conditions, such as vegetation cover, slope orientation, presence of large water body, etc., may produce lateral variations in the GST of up to several degrees over a short distance (e.g. Blackwell et al., 1980). In such cases the SAT records from the nearby meteorological station should be used to calculate the POMvalue (Harris and Chapman, 1997).

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Fig. 41. Annual mean SAT record at the meteorological station Kapuskasing (Canada) for the 1918–2001 period. Data are shown as temperature anomalies from the base period 1961–1990. POM – pre-observational mean temperature.

As a representative SAT record we have taken mean annual temperatures measured at meteorological station Kapuskasing (49.42°N, 82.38°W) (Figure 41). The homogeneous SAT series exists there from 1918. The record reveals certain warming with the mean rate of 0.015K/year characteristic for the most of the twentieth century. In the last few decades, the general warming has been accelerated and its rate for the period 1970–2000 was almost triplicate (0.047K/year). Both Kapuskasing and Hearst boreholes are located in a bushed area that was formed after clearing of surrounding forests approximately 100 years ago. Large cleared fields are situated approximately 500m away from Kapuskasing and, according to Wang et al. (1992), may have only small effect of the temperatures. The larger effect from the closer deforestation may be at the Hearst site. The mean temperature anomalies corresponding to the 1918–2000 and 1970–2000 periods equal to 0.52 and 0.76K, respectively. Temperature logging of the Hearst borehole was performed three times (for details see Section 1.3 (Chapter 1). Figure 17 (Chapter 1) compiles the results of these measurements. As shown, all temperature logs are quite similar with a weak but clear positive “U-shape” curvature in their uppermost parts that hints the presence of the recent warming. For inversion we used temperature–depth data only from below 20 m depth to exclude any seasonal temperature variations. The reducing parameters (T0 – surface temperature and G – geothermal gradient) were calculated by the linear regression of the deepest part of the T–z record. Reduced temperature obtained by subtracting background thermal field from the measured temperature log is shown in Figures 42 and 43. It is curved and systematically positive above 100–150 m depth indicating recent climatic warming. Chisholm and Chapman (1992) have demonstrated high sensitivity of the borehole temperature profiles to the POM-values. This statement is illustrated in Figure 42 that shows the observed and synthetic reduced temperatures for the three different POMvalues. The degree of conformity between the real and simulated models is usually characterized by the sum of the squares of deviations between measured and synthetic temperature logs. We have calculated root mean square (rms) misfits for wide spectra of

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Fig. 42. Combined meteorological and geothermal data were used to infer the POM-value for Hearst hole; reduced temperatures compared with synthetic transient temperature–depth profiles calculated for three choices of POM for the time prior to 1918 (see text). The inset shows the rms misfit as a function of POM and illustrates the best fit for POM ⫽⫺1.98 K.

Fig. 43. Left: Reduced temperature profile for Hearst hole compared with synthetic temperature profile computed for the best-fit POM-value. Right: “Left-over” temperature.

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possible POMs to determine the best fit. Generally, preferred value of estimated parameter corresponds to the minimum of rms misfits. As seen in Figure 42, small differences in POM-value cause significantly poorer fit to the observed reduced temperatures. Even 0.5 K difference in the POM-temperature is critical to obtain a good fit with the observed reduced temperatures. For the Hearst hole the best fitting reduced temperature is POM ⫽⫺1.98 ⫾ 0.01 K (rms misfit ⫽ 0.095 K). A sharp extreme in the misfit diagram (Figure 42, inset) indicates the character of the POM as a robust temperature estimate. Obtained POM-value is almost 2.5 K lower than both 1918–2000 and 1970–2000 temperature means, indicating significantly colder pre-1918 conditions. As shown in Figure 43, there is a satisfactory coincidence between both the amplitude of warming and the depth of perturbed temperatures. The “left-over” temperature residuals, calculated as the difference between reduced and the synthetic best-fit POM–SAT temperature, does not exceed ⫾0.1 K below 100 m depth and reach 0.5 K in the uppermost part of the borehole. In most cases POM coupled with the SAT measurements explains 80–90% of the transient borehole temperature signal (Harris and Chapman, 1997; Bodri et al., 2001). Larger “left-overs” were obtained, e.g. during the POM estimations from a suite of Cuban boreholes (Bodri and Cermak, 2001), where coupled POM–SAT data explained not more than 50–60% of the transient borehole temperature signal. This indicates that for definite sites, at least some portion of the borehole temperatures cannot be explained by the SAT origin and reflects also specific terrain effects. Larger magnitude of the “left-overs” in the uppermost part of the borehole Hearst can likely be attributed to the local different impact of deforestation detected by Wang et al. (1992) at the Hearst and Kapuskasing sites. Essential requirements for the correct POM determination are: (1) the pure conductive regime in the subsurface and (2) the persistence of the land–atmosphere boundary layer conditions, thus a “constant” SAT–GST coupling mode. As known, the process of the heat exchange at the land surface is a complex function of the coupled atmospheric–plant–soil interactions; thus, in principle the response of the land surface to the atmospheric forcing may be time-dependent even at the annual and longer scales of aggregation. Drastic changes in the near-surface hydrology (evaporation and transpiration system), albedo,5 and even surface roughness change that accompanies extensive forest clearing, all have significant impact on the ground–air temperature coupling and therefore on the POM estimate. These problems will be discussed in the next section. 2.6 Ground–Air Temperature Coupling: Effect of Various Environmental Changes 2.6.1 Background The above-mentioned investigations by Baker and Ruschy (1993), Putnam and Chapman (1996), Gosnold et al. (1997), and Harris and Gosnold (1999) have given the confidence that it seems reasonable to view borehole temperatures as the filtered versions of the 5 Albedo is an important concept in climatology and represents a dimensionless measure of the surface/body reflectivity. It may be also expressed as a percentage from 0 to 100% and is determined as the ratio of total electromagnetic radiation reflected to the total amount incident upon it. The average albedo of the Earth is about 30%.

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surface air temperature (SAT). Modeling of the GST–SAT coupling by González-Rouco et al. (2003, 2006) using surrogate SAT simulations have shown that at long timescales the GST represents a good SAT indicator, and their variations practically repeat each other (for details see Section 2.4.4). However, observations do not support this conclusion unconditionally and at all timescales. Recent studies have revealed that in certain regions and under certain conditions the GST does not track accurately the SAT changes, especially at the short timescales. In the recent decade, the problem of the GST–SAT coupling represented the target of continuous study by several research groups. Factors affecting ground temperature can be subdivided into three general categories: (1) meteorological, (2) terrain, and (3) subsurface thermophysical properties. Large spatialscale GST differences are determined primarily by meteorological factors: solar radiation, air temperature, and precipitation through the processes of absorption/reflection/emission of solar short-wave and/or thermal infrared radiation and conductive coupling of ground–air temperature. Except for the factors mentioned above, thermal balance in the ground surface can be affected by numerous secondary processes. Basic agreement between reconstructed GST histories and available SAT records has been documented by numerous investigations at different spatial scales all over the world (see the references above). On the other hand, a strong correlation between both signals has been questioned by Majorowicz and Skinner (1997), Majorowicz and Safanda (2005), and Mann et al. (2003), especially for the northern locations with prolonged snow cover in the winter. The latter authors have argued that in such areas ground loses significant part of the information about air cooling in the winter months because snow insulates and reflects the incoming radiation. The GST–SAT decoupling can also arise due to latent heat effects of freezing/thawing processes. The GST–SAT differences at the daily and seasonal timescales are well documented. Except for the influences mentioned above, the oneby-one coupling between the GST and SAT can be also affected by such processes as the partitioning of moisture content between infiltration/evaporation/runoff, the biological processes, e.g. seasonal vegetation changes, chemical weathering and other long-term land surface changes, and other factors that are not directly connected to the climate. The summary of all processes creates the heat flux at the ground surface and thus affects the GST–SAT coupling. The investigations of the ground–air temperature correlation are performed in two main related and/or complementary directions: (1) Empirical site-specific observations of the GST–SAT coupling at specific locations using monitoring of the air/subsurface temperatures and other meteorological conditions. A comparison of soil and air temperatures provides a direct test of details of their coupling at shorter timescales (from daily to annual). (2) Development of numerical models to simulate both short- and long-scale active processes at the level of air–ground interaction and in the subsurface. (3) Collection of high-quality measurement data. The International Heat Flow Commission global geothermal data set (www.geo.lsa.umich.edu/IHFC) contains over 10 000 worldwide measured borehole temperature logs. GST reconstructions inferred from these data can be compared with the SAT measurements as well as with proxy sources available in the same locations during periods of overlap.

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Borehole Climatology: A New Method on How to Reconstruct Climate Such comparative studies can provide information on the longer scale GST–SAT coupling. It should be mentioned that because of suspected non-climatic disturbances, up to now not more than 10% of geothermal data set was used for paleoclimate reconstructions (Nitoiu and Beltrami, 2005).

Despite that studies enumerated above represent different spatial and/or temporal scales, they complement each other. Empirical correlations established by comparison of the meteorological and geothermal data provide an experimental basis that could be simulated by numerical models primarily concentrated on the physics of various near-surface processes. The investigations by Zhang et al. (2001) represent typical example of the latter-kind research. The authors have examined records of soil temperature at several depths and have compared them with the main climatic variables (air temperature, precipitation, snowfall, and snow thickness data) at Irkutsk (Russia) over the 100-year long period from 1898 to 1995. The relationship between air temperature and soil temperature was proved to be so complex that, using the words by the authors, “changes in air temperature alone cannot explain the changes in soil temperatures in this region”. This research has captured almost all important sources of uncertainties that subsurface temperatures could contain. One of the surprising observations was, e.g. that summer soil temperatures decreased by up to 4°C while summer air temperatures slightly increased. In other cases, when winter air temperatures have oscillated in the narrow range from 4 to 6°C, the rise of soil temperatures was even higher and reached as much as 9°C. Possible explanations for these phenomena suggested by the authors have included: (1) an increase in summer rainfall and (2) an increase in early winter snowfall coupled with an earlier increase in spring snow melt, respectively. The authors have concluded that the changes in soil temperature represent a combined complex output of the SAT and precipitation variations, especially of the snowfall and snow cover on the ground surface, and have warned that “when changes in soil temperature are used as the evidence of climatic warming, caution is required”. They also emphasized that revealed surface warming of permafrost at high latitudes and subsurface ground warming in wide areas elsewhere in the world may be misleading and/or occasional because air temperature alone cannot explain such ground warming. Similar studies by Baker and Ruschy (1993) and Putnam and Chapman (1996) have detected an air–soil temperature offset, when the ground was generally warmer by 1–3 K as well as the seasonal differences in the detected offset. These and other works on this topic have attracted attention for the possible serious shortcomings that the GST histories inferred from borehole temperature logs may contain. The next sections are devoted to the detailed discussion of this problem. 2.6.2 Snow cover and ground freezing Winter snowfall as well as seasonal freezing and thawing cycles of soil can strongly influence the thermal and hydrological characteristics of the uppermost ground layers. Impact of these processes on the surface energy and moisture balance at least on the short scales may be quite serious. Latent heat exchanges at the ground surface and snow cover insulates it from air temperature variations. Because the thermal conductivity of frozen soil is larger compared to an unfrozen state, freezing significantly increase the soil heat flow.

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Simultaneously it reduces hydraulic conductivity thus decreasing infiltration that can lead both to the more runoff and/or higher uppermost soil moisture content caused by restricted drainage (Williams and Smith, 1989). Traditionally, investigations of seasonal freeze/thaw oscillations are performed by in situ measurements and numerical modeling of the ground–air temperature coupling, and thus reflect mainly site-specific and short timescale features of the process (Beltrami, 2001a; Schmidt et al., 2001). The effort to extend existing results to broader spatial regions and to investigate how precisely the GST and SAT signals track each other on the seasonal scale was undertaken in the work by Gosnold et al. (1997). The authors compared the GST record with the air temperatures along transect of the Northern Plains between southern Manitoba and northern Texas (approximately 33–49°N) and examined the nature of the ground–air coupling. Flat topography and geology of this area ensured favorable conditions for borehole temperature reconstruction free of potential topographic disturbances, microclimate, and groundwater effects. Criteria for the borehole screening also included surfaces as uncultivated grassland, shale bedrock, and sites remote from the regions of intensive anthropogenic activity that could result in transient variations of microclimate. For the first test the set of 29 boreholes was selected. The GST reconstructions were performed using FSI method. All obtained GST histories indicated prominent warming trend over the last century. Its amount depended on the latitude; greater warming was detected for the northernmost boreholes. The comparison of the GST histories and SAT was performed using data from 55 stations of the United States Historical Climatology Network (U.S.HCN; http://cdiac.esd.ornl.gov/r3d/ushcn/ushcn.html) situated in the same region. The HCN SAT data series are approximately century long and extend to at least 1994. The latest dates of borehole logging were 1994 and/or 1995. Similarly to the GST reconstructions the HCN temperature data have shown warming during the past century and strong latitudinal trend of its amount. Comparison of both datasets revealed coincidence between amplitude of GST and SAT warming south of about 45°N. On the other hand, the GST reconstructions have shown much stronger warming north of this latitude. The authors have performed modeling of the GST–SAT coupling by repeated calculations and used more than 100-year long SAT signal as a forcing to the 2-D conduction in the subsurface. The FSI of the generated synthetic T–z profiles has given an amount of GST warming similar to the temperature change determined by regression of the SAT records, and thus corroborated the one-by-one coupling between the ground and air temperatures under pure conductive regime of the heat transfer. To interpret obtained inconsistency of the GST and SAT north of the 45°N latitude Gosnold et al. (1997) have tested the data from a network of automated weather stations situated in the investigated area and the results of continuous monitoring of the SAT and soil temperatures at 10 cm depth at automated weather stations installed at three locations with different latitudes at Texas (33.1°N), South Dakota (43.7°N), and Manitoba (49.6°N). The results of this monitoring experiment have shown that the mean annual soil–air temperature differences arise primarily during the separation of both temperatures in winter. This conclusion can be illustrated with the time series of temperatures recorded during similar monitoring experiment that was carried out by the Research Group of the Geophysical Institute of the Czech Academy of Sciences at the microclimate station Prague-Sporilov, the Czech Republic (50.04°N, 14.48°E, 274 m asl). The monitoring has

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been running continuously since the summer of 2002. Four different surface types were investigated: bare soil, sand, grass, and asphalt. Air temperatures at 5 and 200 cm above the surface as well as the soil temperature at depth levels of 2, 5, 10, 20, and 50 cm were recorded at 5 min intervals. The every 3-year (2003–2005) air temperature averages were surface dependent, but appeared lower than the soil temperature means for all four types of the surface. Thus, the differences between air temperature and soil at 2 cm depth amounted to 1.4–1.6 K, 1.8–2.0 K, 0.2–0.4 K, and 4.1–4.8 K for bare soil, sand, grass, and asphalt, respectively. This result hints that on the annual scale the soil is warmer than the air and corroborates similar observations mentioned above by Baker and Ruschy (1993) and Putnam and Chapman (1996) who have detected that the ground is generally warmer than the air by 1–3 K. The inter-annual variability of measured in Prague microclimatic station difference is also surface type dependent and ranges within the first tenths of degree Kelvin. Figure 44 (See Plate 1 of Colour Plate Section) shows temperature variations for some shallow sensors in the Prague-Sporilov hole during the first quarter of the year 2005. It illustrates well the influence of the snow cover on the GST–SAT coupling. As seen, the magnitude of the GST–SAT difference exhibits significant variations. Subsurface heat conduction as well as the factors connected to the movements and/or diffusion of air and/or moisture masses (wind, evaporation/transpiration, vertical soaking of soil moisture, and precipitation) tend to equalize air and soil temperatures. Thus, soil temperatures generally follow the air temperature course when average SAT is above 0°C. The one-byone GST–SAT coupling violates below zero temperatures in the presence of snow cover, because it insulates the ground surface and reduces heat loss (the condition at the measurement site was not enough cold for the soil freezing). It is noticeable that perfect coupling is restored almost immediately after snow cover was thawed (see, e.g. time interval between February 1 and 15). Similar monitoring experiment was performed at the station Potucky (the Czech Republic 50.43°N, 12.78°E, 864 m asl). It is situated in the Ore Mts. forming the natural border between North Bohemia and Germany. The suite of boreholes is located on small territory in the close vicinity of the forested area of coniferous woods. The subsurface temperature monitoring at several shallow depths began in 2003 (for details see Section 4.2, Chapter 4). Figure 45 shows results obtained during autumn 2003 to spring 2004. Temperature was recorded at 2 cm depth and 5 cm height above ground surface to detect the effect of snow cover on shallow subsurface temperatures. The record completely corroborates the results of the Prague-Sporilov monitoring. The coupling of the temperatures is almost perfect in fall and spring and breaks down during most of the winter. The presence of the snow cover in the 2003–2004 winter and absence of really cold temperatures at Potucky station prevented occurrence of soil freezing. Thus, the winter decoupling of the GST–SAT that is seen in Figures 44 and 45 can be attributed exclusively to an influence of the snow cover. Smerdon et al. (2004, 2006) have generalized results of above-cited and similar monitoring experiments. Except for the Czech records mentioned above, the authors have used temperature time series measured during monitoring experiments at Fargo (North Dakota), Cape Henlopen State Park (Delaware), and Cape Hatteras National Seashore (North Carolina). All sites represent different kinds of subsurface strata and/or climatic settings located within the mid-latitude zone from 35 to 50°N, and thus can be used also for the spatial decisions. Similarly to the Czech records, the North American time series

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Fig. 44. Time series of air (at the height 2 m) and soil temperatures (at 2 cm depth) recorded under different surfaces at Prague-Sporilov station. Soil temperatures follow SAT at temperature above 0°C, but are decoupled when the surface is covered by snow. (See Plate 1 of Colour Plate Section).

Fig. 45. Time series of air (at 5 cm above surface) and soil (at 2 cm depth) temperatures recorded at Potucky microclimatic station during the 2003–2004 winter.

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represent several years of simultaneous air and soil temperature monitoring at different heights/depths, and are particularly suitable to reveal the differences between annual GST and SAT signals. Thorough examination of the records has shown that on the annual scale GST signal (even somewhat attenuated and insignificantly phase shifted) follows well the SAT variations. The slight differences between annual GST and SAT signals may occur in both winter and summer seasons. Their amount depends on the site location and its climate as well as on the terrain characteristics. Thus, the study by Smerdon et al. (2004) has demonstrated that the GST–SAT decoupling at Fargo occurs mainly during the winter, whereas at Capes Henlopen and Hatteras observed attenuation of the GST signal has taken place during the summer season. The seasonal partitioning of the GST–SAT decoupling is caused mainly by the corresponding partition of the summer precipitation and snow. While the Fargo location is characterized by the modest rainfall and significant amount of snow, the Cape Henlopen and Hatteras stations have negligible or no snowfall. Similarly to the Czech monitoring results, the North American stations inferred influence of the snow cover on the GST–SAT coupling. According to the results by Smerdon et al. (2004, 2006), in all investigated locations snow cover has affected heat transfer in the surface in such a manner that mean daily soil temperature under snow cover was warmer relative to the SAT. The experiments described above have also detected finer features of the GST–SAT decoupling during cold season, e.g. dependence of the temperature of the soil covered by snow on the thickness of snow layer, the snow quality, both air and ground temperatures before a snowfall, the presence of the vegetation cover as well as the thermophysical properties of the soil. Effect of the snow cover thickness is only of secondary importance. Numerical modeling by Gosnold et al. (1997) of the GST–SAT tracking in the presence of the snow cover has detected that the winter soil temperatures are more sensitive to the presence or absence of snow rather than to the variations in its thickness. Thus, the exact amount of the winter snowfall is not likely a decisive factor of the GST–SAT coupling during the winter. Snow pack control on the soil–air temperature tracking in other seasons was studied in the work by Grundstein et al. (2005). Annual coupling of the GST and SAT was investigated using the soil–air temperature measurements performed during 1990–2002 at Fargo (North Dakota) as well as numerical simulations based on the snow pack physical model. In accordance with the conclusions of the previously discussed studies, Grundstein et al.’s research has corroborated that the GST–SAT decoupling in the investigated location appears to be visible only during winters when dense, thick snow cover, and its long persistence cause strong insulation of the ground. In the late autumn and/or early spring the snow is thin and has a low density. It gives imperceptible thermal insulation and does not break one-by-one GST–SAT coupling. The Czech monitoring experiments described above have supported the influence of the type of surface on the ground–air temperature tracking. Thus, the grasslands preserve the snow cover longer than the bare surfaces, where the snow is not isolated from the ground heat flow. Combining the snow cover with the grass provides better insulation and the temperature under such surface remains above zero (see Section 2.6.3). The rate of snow melting was proven to be also surface dependent. The thickest snow cover is characteristic for the grass and the thinnest can be found in asphalt. The monitoring results mentioned above are more representative of the mid-latitude seasonal GST–SAT relationships. As at the Prague-Sporilov station, winter temperatures

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at the investigated locations are generally not low enough for the soil freezing. At highlatitude regions where SAT temperature for a long time remains far below 0°C, the effect of freezing may even surpass the influence of the snow cover, Gosnold et al. (1997) have interpreted systematic northward increase of GST–SAT difference in the North America, which is revealed in their work, as the result of the northward increase in the duration of snow cover and often occurrence of the ground freezing. While on frosty days the air temperature may be significantly negative, latent heat released during freezing of soil moisture makes soil temperature remain at 0°C level until the whole moisture content has frozen. This is so-called “zero-curtain effect” that is caused by transfer of latent heat during freezing and thawing of water contained in the rock or soil. The degree of saturation and the thickness of the saturated soil represent the main factors controlling the duration of freezing process. Since near-surface soils often freeze before snow covers the land surface and durable soil freezing (sometimes for weeks to months) is a more often phenomenon than continuous snow cover, the freezing effect appears to be of greater influence on the GST–SAT decoupling. According to the observations by Gosnold et al. (1997), the onset of the strong soil–air temperature decoupling does not always correlate with the variations of snow cover; however, in all cases it coincides with the beginning of the soil moisture freezing. Recent results of the continuous temperature monitoring at the Czech micrometeorological stations confirmed conclusions by Gosnold et al. (1997). Effect of the soil freeze/thaw events on the GST is reflected in the early section of the time series, presented in Figure 46. This diagram displays the air temperatures measured at 5 cm above the surface and the ground temperatures registered at depths of 2, 10, and 50 cm at Potucky station. The end of October and especially the beginning of December were characterized by the absence of snow and by the two episodes of the sharp fall of the air temperature well below 0°C. In the time intervals of strong air temperature decrease ground temperature at shallow depths of 2 and 10 cm have remained almost constant and close to 0°C and 1–1.5°C, respectively, illustrating the above-mentioned “zerocurtain effect” that occurs mainly due to latent heat released from the freezing of soil.

Fig. 46. Time series of air (at 5 cm height) and soil temperature changes at 2, 10, and 50 cm depth at Potucky station during October–December 2003.

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Fig. 47. Behavior of the soil temperature at different depth levels below sand and grass surfaces at Prague-Sporilov station during freezing cycles of February 2006 (See Plate 2 of Colour Plate Section).

The data indicate that the soil freezing at Potucky station during October–December 2003 did not actually achieve even 10 cm depth. The ground temperature at the uppermost “active” layer is a complex result of the heat transfer from the frozen upper and undisturbed lower layer as well as the heat release from advancing freezing front. That time there was no snow cover at the station; thus, time series in Figure 46 reflect pure influence of the freeze/thaw processes on the GST. Irregular monthly air surface temperature variations are significantly attenuated at the depth of 50 cm and occur with time delay of days. Temperatures at that depth are lower than the highest positive air temperatures by approximately 3–4 K and may be higher than the lowest negative air temperatures by 8–10 K. Figure 47 (See Plate 2 of Colour Plate Section) shows the behavior of the ground temperature under sand and grass surfaces during February 2006 at the Prague-Sporilov station. Due to heavy frosts and absence of snow in January, the subsurface temperature below both surfaces has dropped below the freezing point. Temperature at 20 cm depth was quite stable at 0°C and ⫺0.3°C under the grass and the sand, respectively. The higher temperature under the grass occurs due to an insulation effect of the vegetation cover, which is mentioned above. In the first half of February, when the SAT was relatively low slightly oscillating around zero, the GST under both surfaces remained practically constant. Its sharp decrease between 2 and 5 cm depths was observed only between February 14 and 15 and was given by a similar drop of the SAT. During the second half of February, when the air temperature increased above zero, the subsurface temperature change under the sand surface generally repeated the SAT course. However, the phase changes of soil water substantially reduced the GST variations. The surface temperature variations vanished at the interface of the frozen and thawed soil layers that remained at zero temperature. Temperature at 20 cm depth was practically constant, which hints that all heat coming from the surface was spent in melting the soil water between 10 and 20 cm (Figure 47, left). Under the grass, where insulation of the surface and low thermal diffusivity of the soil slowed down the penetration of the surface warming, at all measured depth soil temperature remained close to 0°C. (Figure 47, right).

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It should be emphasized that the effect of soil freeze/thaw cycles on the GST–SAT decoupling is only of seasonal importance. On the longer timescales it probably does not violate the air–ground temperature correlation in such a strong manner as it seems at a first glance. The widespread investigations of the timing, duration, and areal extent of this phenomenon by Zhang and Armstrong (2001) over the contiguous USA territory using passive microwave remote sensing revealed high variability of the freeze/thaw variations. The onset of soil freeze occurred generally in October–November, while its termination was in March–April. However, it does not mean that the near-surface layer was continuously frozen during this period. Measurements by Zhang and Armstrong (2001) have shown that the number of days with real surface soil freezing varied from several days to as long as 5 months. Majority of the regions experienced less than 60 days of the actual freezing and their occurrence was quite sporadic. Because of the combined effects of the snow cover and latent heat released by freezing, the soil moisture significantly changes with time; thus, this kind of disturbance vanishes during averaging over large temporal/spatial scales and probably cannot create a false systematic secular trend in the GST. Some recent critiques of GST reconstructions were presented in the works by Mann and Schmidt (2003) and Mann et al. (2003). These authors have compared the SAT, GST, and snow cover trends simulated for the latter half of the twentieth century for terrestrial regions of the Northern Hemisphere by means of the GISS ModelE kind of the GCM family, similar to the one described in Section 2.4.4 of this chapter, and argued that the interpretations of the past SAT trends using GST reconstructions could be significantly biased by an influence of the snow cover during cold season. According to the calculations of the above authors, air temperatures have a dominant influence on ground temperatures only during warm season, while during cold period snow cover has significantly insulated the ground surface from the SAT changes. This process tends to exaggerate the role of warm season, thus providing a source of possible bias when comparing GST and SAT series (see also examples in Section 3.3, Chapter 3). This conclusion was rejected in the works by Smerdon et al. (2004) and especially by Chapman et al. (2004), who have argued that the statement by Mann and Schmidt (2003) completely contradicts with the results of their monitoring experiments. According to Chapman et al. (2004), the source of discrepancy is an artificial sharp division of the years and corresponding temperatures into cold and warm season performed in the work by Mann and Schmidt (2003), while the nature of the conduction process makes the ground temperatures sensitive to continuous rather than to rapid or even seasonal variations. Separated seasonal anomalies are thus inappropriate for detection of the GST–SAT coupling on the long scales that are used in the climatologic studies. An analysis by Chapman et al. (2004) has proved that the GST–SAT tracking is almost perfect (correlation coefficient equals to 0.97), when temperatures are assessed on at least annual scale and thus summarize/compensate both summer and winter effects. This result is confirmed by the recent millennium long simulation of ground temperatures performed in the works by González-Rouco et al. (2003, 2006), who used simulated subsurface T–z profiles forced by the GCM as a substitute of the real climate and applied inversion technique to reconstruct GST histories from the calculated profiles (for details see Section 2.4.4 of this chapter). Modeling results of these authors have proved the fact that the air and ground temperature variations are practically identical on centennial and longer timescales.

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2.6.3 Effect of precipitation In meteorology, precipitation means any form of water, whether liquid or solid, that falls from the clouds and reaches the ground. Precipitation is a major component of the hydrologic cycle, and is responsible for depositing most of the fresh water on the Earth. It also represents the main component of weather. Precipitation occurs in a variety of forms, however, generally as rain and snow. An influence of the winter snow cover on the GST–SAT tracking has been discussed above. What about an influence of the summer rains? Summer soil temperature is controlled by the combined effect of air temperature variations and soil moisture content. An increase in rainfall during summer season would increase both the surface wetness and the soil moisture. This will result in more energy consumption for evaporation and thus cause cooling of the ground surface and soil. It is so-called soil moisture feedback (Yasunari et al., 1991; Matsuyama and Masuda, 1998). In principle, soil moisture feedback mechanism may explain soil cooling during summer, when air temperatures increase. Zhang et al. (2001) have detected clear negative correlation between monthly precipitation and soil temperature at 40 cm depth (greater monthly precipitation with lower soil temperature, and vice versa) in the 100-year long (1980–1990) meteorological time series measured at Irkutsk, Russia. Existing monitoring experiments as well as the above-described numerical simulation by Mann and Schmidt (2003), Mann et al. (2003) have shown that anyhow the air temperatures have a dominant influence on ground temperatures during the warm season. Thus, revealed by the same monitoring experiments possibility of the GST–SAT decoupling during warm periods of the year likely represents the far weaker effect than the decoupling of both temperatures in the cold season. While winter snowfall as well as seasonal freezing and thawing cycles are the main reasons responsible for the breaking of the one-by-one GST–SAT tracking during cold season, the rainfall can produce definite air–ground temperature differences at warm conditions. Precipitation is one of the main factors determining the subsurface thermal regime because it affects the amount of soil moisture and therefore the amount of energy removed from the soil by latent6 and sensible7 heat fluxes. Figure 48 illustrates the types of energy balance at dry and moist ground surfaces. Expression Q* ⫽ H ⫹ LE ⫹ G combines the components of the total heat balance in the air–ground system, where Q* is available net radiation, H the sensible heat, LE the latent heat, and G the subsurface (ground) heat. The latter three variables represent the major categories of the total energy use. Sensible heat is strongly conditioned by the temperature gradient between ground surface and air, while the ground heat flux depends on a similar gradient between the surface and the subsurface. When the evaporation of the water takes place, the positive latent heat flux (LE⫹) occurs in the ground surface. This means that the surface loses energy to the air above. Thus, evaporation is a cooling process for the ground surface. 6

Latent heat flux is the flux of heat from the Earth’s surface to the atmosphere that is associated with the change of states or phase, e.g. with evaporation of water at the surface. Term “latent” is used because this energy does not increase the temperature of water molecules and is only stored in molecules to be released later during the condensation process. 7 Sensible heat is the heat energy transferred between the Earth’s surface and air when there is a temperature difference between them. According to the direction of the temperature gradient, this flux can warm the ground or air.

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Fig. 48. Types of energy balance at the ground surface during warm period.

Various processes operating in the vicinity of the ground–air boundary influence the heat flux balance that cause corresponding changes in both the SAT and GST. Thus, in some cases the GST may reflect the energy balance at the Earth’s surface, rather than the SAT variations. Two of the diagrams in Figure 48 illustrate the differences between heat fluxes for dry and moist surfaces occurring in the daytime. In both cases equal amounts of incoming heat Q* are conducted down to the subsurface. No latent heat transfer occurs without available moisture content, which means the absence of the latent flux from the dry surface. Most of the energy Q* is transferred by sensible flux (H⫹) that results in warmer air temperatures above dry surface. At moist surface the share of sensible heat is lower, while significant amount of the available radiant energy is used for evaporation of surface water, thus creating relatively cooler air than that above dry soil. It should be mentioned that latent heat flux always has priority. If moisture is available for evaporation, this process (LE⫹) takes preference over warming of the air (H⫹) and /or warming of the ground (G⫹). At night the processes reverse. Because the thermophysical properties of the subsurface rock, such as thermal conductivity and heat capacity, depend on the water content, the rainfall can influence not only the energy balance of the ground surface–air system, but also thermophysical and/or hydrological characteristics of the ground. Regions with low porosity and permeability will likely not be significantly affected, while less consolidated medium will experience more pronounced changes. Primarily influence of the precipitation on the GST–SAT coupling occurs on the very short timescales (directly during and after rain events) through, e.g. advective transport of heat by falling water that may significantly contribute to the development of shallow subsurface temperatures. For example, boreal forest sites in interior Alaska and NW Canada exhibited rapid but short soil warming of several degrees in response to summer precipitation events (Hinkel et al., 1997). Figure 49 displays temperature difference between the ground surface and 2 cm depth measured in dry and rainy periods at Prague-Sporilov. During 10-day interval with no rain the differences have shown quasi-periodic oscillations with maximum positive values in the daytime and negative values at night (compare with Figure 40 of this chapter). The range of variations reached ⬃9 K. The temperature differences were negative after rain events both during

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Fig. 49. Time series of temperature difference between ground surface and 2 cm depth temperature at Prague-Sporilov station; comparison of rainy decade with dry period. Top panel also shows total rainfall amount.

day and at night (air temperature at wet surface is lower than that at 2 cm depth, e.g. June 30–July 2 and/or July 5–6; Figure 49, top). Its variations were significantly reduced and ranged within only ⬃3–4 K. On the other hand, evaporation proceeds relatively quickly; thus, depending on the rain strength the “dry” regime was restored 1–2 days after rainfall. The role of precipitation appears to be far more important on seasonal and/or annual scales because of its possible seasonal persistence. In the mid-latitudes snowfall and soil freezing (especially the latter process) represent generally sporadic events. As mentioned in the previous section, their effect on the GST–SAT decoupling is not perceptible already under decadal averaging. On the contrary, rainfall occurs more regularly during much of the summer and its annual distribution remains preserved for the long periods. The Prague site represents typical example of the seasonal timing of precipitation. Daily precipitation at Prague has no significant linear long-term trend. However, it has revealed a certain seasonal character that was preserved for a longer time (Bodri et al., 2005); the wetter season falls during May–August period and the precipitation minimum occurs in winter. This conclusion is confirmed by the meteorological observations in the nineteenth to twentieth centuries on the monthly scale of aggregation (Figure 50). The increase in precipitation in “wet” years occurs mainly due to its significant growth in summer period, when the actual monthly amounts of precipitation can be a few times higher than the average. Prevalence of summer precipitation is a specific feature of the hydrologic cycle in the Czech Republic and is preserved for at least 180-year long period. Similar persistence of the annual distribution seems to be common feature of the precipitation in many regions. According to Lin et al. (2003), there were no significant changes in the seasonal distribution of precipitation

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Fig. 50. Averaged monthly precipitation at Prague-Ruzyne. The 2214 months between 1805 and 1989 were used for averaging. (Data source: The Global Historical Climatology Network, GHCN 1; www.worldclimate.com.)

in the USA and Canada for at least twentieth century. The influence of the precipitation on the GST–SAT relationship may be even more perceptible in the tropics, where evapotranspiration8 is potentially significant year round. Except for the cold season GST–SAT decoupling, the above-cited studies by Smerdon et al. (2004, 2006), which generalized the results of temperature monitoring at four microclimatic stations, have detected the GST–SAT discrepancies during warm period that occur as a result of the changes in surface energy balance caused by the rainfall. Precipitation spans a wide range of 52–115 cm/year at four investigated locations with significantly different amounts of the rain and snow related parts. Thus, four data sets reflect the local climate conditions that may represent a base for comparison. Figure 51 shows time series of daily averages of air temperature (at 5 cm height) and soil temperatures at 5, 100, 200 and 500 cm depth measured at station Prague-Sporilov during the “rainy” year 2000. The amount of precipitation is presented on the histogram below. Detectable high-frequency oscillations of the air temperature record in summer (Figure 51) are caused mainly by the rains that change the moisture content of the soil and correspondingly both latent and sensible heat flow at the ground surface. General influence of precipitation at short timescales is to increase latent heat flux and to decrease sensible heat flux. As seen, rainfall events are accompanied by corresponding changes of both air and ground temperatures. The main observation about summer GST–SAT interrelation is that the rainfall does not cause total decoupling of both temperat-ures similar to that occurring in the winter due to snow cover and freezing/thawing cycles. The air

8 Evapotranspiration represents the sum of evaporation and plant transpiration. The former process accounts for the movement of water to the air from the surfaces, while the latter process is responsible for the water movement within plants and for its loss through plant leaves. Types of vegetation and land use, percentage of soil cover, level of plant maturity as well as meteorological variables (solar radiation, temperature, humidity, wind) are among the factors that affect evapotranspiration.

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Fig. 51. Time series of daily averages of air temperature (5 cm height) and soil temperatures at the surface and at 5, 100, 200, and 500 cm depth measured at station Prague-Sporilov during year 2000. The histogram below shows precipitation amount.

temperature record at 5 cm height and the GST records at the air–soil interface practically repeat each other. Correlation of both temperatures amounts to 0.96. The ground temperature at 20 cm depth slightly fluctuates around air temperature. Depending on the moisture content, the differences between both temperatures may be positive or negative and reach several degrees of Celsius. It causes some offset and attenuation of the GST variations in comparison with air temperature. However, the correlation of both temperatures still remains high and equals to 0.82. This hints that in summer the GST–SAT decoupling does not appear too serious even in the years with large magnitude and high frequency of the precipitation events. Surface temperature variations are still visible at 1 m depth, where they appear as a muted version of surface temperatures. Correlation between air temperatures and temperatures at 1 m depth is 0.69. At least part of the lower correlation between SAT and ground temperatures at deeper levels can be attributed to the phase lag of the GST and SAT occurring during depth propagation of the surface temperature signal rather than to the precipitation influence. Recently, Rybski et al. (2003) have proposed the powerful method for the phase synchronization in different meteorological records. This method can be applied to complex signals and enables to reveal relations between two records by focusing on the phases of the fluctuations in each record. The application of this method to above time series of the SAT and GST at 1 m depth has shown that (1) non-shifted case does not correspond to the best synchronization and (2) best phase synchronization can be found only for a certain time lag of approximately 7 days. When time series were delayed by this interval obtained correlation increased to

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0.79. An approximate value of phase delay t0 can be estimated from the expression for the characteristic time of the thermal relaxation t0⬃L2/k, where L is the characteristic length and k the thermal diffusivity. When k equals to 10⫺6 m2/s, for a 1 m depth t0 reaches approximately 11–12 days; thus, it is comparable with the best phase calculated by the phase synchronization technique. Surface temperature oscillations in Figure 51 are practically imperceptible at 2 m and deeper levels. Ground temperatures below 1 m depth are steadily lower than air temperatures from May to September and are higher than the air temperatures from November to February. At shallow depths variations of the GST around SAT are more erratic. At the shallow subsurface soil temperatures remain steadily higher than air temperatures only during November–February (Figure 51). During most of the year shallow GST irregularly oscillates above and below air temperature depending on the temporal pattern of the rainfall. These oscillations likely will disappear under long-scale averaging. Investigated in the work by Smerdon et al. (2006) stations Cape Henlopen (Delaware) and/or Cape Hatteras (North Carolina) are considerably warmer sites with over twice as much mean annual precipitation as Prague. They exhibit even larger oscillations of the soil temperatures around the air temperatures than the Prague-Sporilov station (Smerdon et al., 2004, 2006). However, irregular interchange of these differences up and down of the air temperature course can scarcely represent the serious failure of the hypothesis about one-by-one GST–SAT coupling on the long-term scales. Smerdon et al. (2006) performed the quantification of the influence of meteorological conditions on the GST–SAT difference by a multivariate regression technique. The analysis of these authors has shown that (1) the differences between ground and air temperatures can be explained in terms of seasonal changes of meteorological variables and (2) the annual GST–SAT differences (
GST–SAT) can be closely estimated by using of the meteorological information alone. The authors suggested the expression including two predictors
GST⫺SAT ⫽  ( P ⫻ SATs ) ⫹ (SD ⫻ SATw ) ⫹ ,

(33)

where (, ) are regression coefficients, P the cumulative precipitation in months without snow, SD the total number of days with snow cover of more than 2.5 cm, and SATs and SATw are mean air surface temperatures during June, July, and August (summer) as well as during December, January, and February (winter), respectively. Term  is associated with the white noise characteristics. For example, at station Fargo mean annual snowfall and rain precipitation amount as well as the number of days with snow cover equal to 123 cm, 52 cm, and 96 days, respectively. For the 10-year long monitoring series at this station estimations by Smerdon et al. (2006) have given  ⫽ 0.39 ⫾ 0.11 and ⫽⫺1.06 ⫾ 0.11 (for standardized values of variables) with significance levels of 1.0 ⫻ 10⫺2 and 3.3 ⫻ 10⫺5, respectively. Common use of two predictors included in Eq. (33) explains 91% of the total variance of annual GST–SAT differences. Values of regression coefficients  and depend on the local climatic conditions and thus should be determined for each site separately. For example, at Fargo location, where mean annual snowfall is four times larger than at Prague or at Cape Henlopen, the SD represents more important influence than the rainfall amount. This quantity alone explains 67% of the variance in the GST attenuation. The use of only SD and SATw product can explain 77% of the variance in the GST attenuation.

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Alternative correlation analysis using only P and SATs explains only very little part of the variance in the annual GST–SAT attenuation and does not bear statistical significance. As shown by numerous trial runs, expression (33) is the best possible one. Other combination/separation of the quantities and/or addition of other regression terms do not significantly improve prediction results. Detected GST–SAT amplitude decoupling during summer and/or winter can be used to describe quantitatively their tracking on the annual scale. Summer attenuation will decrease mean annual GST relative to the SAT, while winter attenuation will have an opposite effect. The difference between means of the annual SAT signal and the same signal attenuated and/or strengthened in its maximum or minimum can be expressed as  GSTsp ⫺ SATsp   GSTwp ⫺ SATwp  GSTa ⫺ SATa ⫽   ⫹ , 2 2    

(34)

where GSTa and SATa are annual means of the GST and SAT, index “p” means the peak amplitudes, and indices “s” and “w” represent the summer and the winter, respectively. Including in this equation regression coefficients from Eq. (33), one can transform it into GSTa ⫺ SATa ⫽⫺  (SATA ⫻
GST⫺SAT ) ⫹ 冷 冨 (SATA ⫻
GST⫺SAT ),

(35)

where SATA is the year-to-year amplitude of the annual SAT signal. Observations by Smerdon et al. (2006) have revealed 1.5–4.5 K GST–SAT differences at Fargo (North Dakota) in 1981–1989 and 1993–1999 periods. Their variations were erratic and did not exhibit any significant linear trend for approximately two decades of observations. Correlation between differences observed and calculated by expression (35) was 0.86 (significant at the 0.0001 probability level). It was shown that calculated differences explain 73% of the variance in the observed values. The above estimation has supported the hypothesis that meteorological conditions are the dominant causes for the occurrence of the GST–SAT decoupling and that the above empirical regressions represent a useful tool for the investigation of the GST–SAT differences on the longer scales. Application of the long meteorological records to the multivariate expressions (33)–(35) provides the possibility of the
GST–SAT calculation on decadal to centennial timescales. This idea was realized in the work by Pollack et al. (2005) (see Section 2.6.4). While most of the relationships between ground temperature and meteorological variables generally represent the multivariate empirical regressions (like abovedescribed Eq. (33)) and are not focused on the underlying physical processes, England et al. (2003) and Lin et al. (2003) have worked out numerical model that captures effects of rainfall on the GST temperature changes and provides controlled reliable simulations based on the quantitative description of the coupled moisture and energy transport through the air–ground interface. Their methodology uses the Land Surface Process (LSP) model that takes into account vertical energy and moisture transport in soil and vegetation. Its extensive discussion is presented in the works by England (1990),

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Liou and England (1996), Judge et al. (2003), and Lin et al. (2003). Extensive calibration and validation of this model was performed through numerical field experiments (Judge et al., 1999, 2001). The LSP comprises relatively complex and detailed description of the microphysical ground–atmosphere processes using multilayered soil and vegetation. The temperature and moisture profiles of the ground and canopy are determined by the coupled energy and moisture transport based on the changes in infiltration, evaporation, transpiration, and recharge fluxes over time. Infiltration is a positive term (water at the ground surface enters the soil) governed by the hydrological properties of the subsurface and by the difference between total precipitation and its share captured by the canopy. Effect of the runoff is generally not taken into account. It occurs when the precipitation rate exceeds the rate of infiltration, while most of the simulations do not include such extreme precipitation events. Because of an absence of runoff and because surface vegetation characteristics remained constant on the multiyear timescale, all changes in infiltration in the LSP model were caused exclusively by the changes in precipitation characteristics. Both evaporation and transpiration are negative terms that remove moisture from the soil and/or surface canopy by transport of vapor into the atmosphere. Finally, recharge fluxes occur due to the fluid flow at the water table boundary. Depending on the flow direction they can be positive or negative. It should be mentioned that the annual sum of recharge fluxes is small; thus, this latter factor is not as significant as the former processes. In the works by England et al. (2003) and Lin et al. (2003) the 1-D model is developed for the multilayered soil with a two-layer vegetative canopy (grass and thatch) at the surface of the hypothetical location characteristic for the prairie grassland in the state of Kansas, belonging to the Great Plains area of the USA. This environment appears to be the most suitable to distinguish the influence of precipitation from other factors, because it is practically not subjected to snowfall and/or freezing and thus can illustrate well the pure effect of rainfall. Other advantages represent a good knowledge of the soil structure and an abundance of the meteorological data for the credible model forcing. Numerical simulations of the microclimatic ground–air interactions were performed over decadal timescales with minute resolution; thus, the authors have investigated both short- and long-term effect of precipitation on the GST changes. Thermophysical and hydrological properties of the subsurface layers in LSP model vary not only with depth, but also with the temperature and moisture content. The model forcing realizes from the surface and includes down-welling radiation, SAT, humidity, wind, cloudiness, and precipitation. An evaluation of the precipitation changes on the GST was a central goal of above researches. The study has concentrated on four primary characteristics of precipitation: its amount, intensity, frequency, and timing that were considered on the wide range of scales from diurnal to decadal. The SAT and precipitation data for model forcing were taken from the database of the U.S. National Climatic Data Center (NCDC; www.ncdc.noaa.gov). Meteorological time series have corresponded to the above-mentioned southern prairies region of Kansas. Results of numerical experiments have proved that independently of other meteorological processes changes in various rainfall characteristics alone in principle can affect the GST temperature. The range for possible changes in precipitation characteristics was chosen in such a manner that simulation results were able to put upper limits on the

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expectable GST changes introduced by precipitation. The study has revealed the next principal influences of the precipitation on the GST change: (1) To investigate the influence of the precipitation amount on the GST the baseline precipitation distribution was multiplied by factors from 0.5 to 2.0. Increase in the amount of daily precipitation manifests itself in the corresponding cooling and wetting of the ground and reduces mean annual GST. The reason is that enhanced precipitation intensity increases the amount of retained moisture and prolongs the time of the water storage in the ground. Both processes cause an increase of the average annual latent heat flux and a corresponding decrease in the sensible flux. Decrease of the precipitation amount and intensity has the reverse effect and causes warming in the ground. The 100% increase in the amount of daily precipitation in comparison with an average baseline value for the Great Plains area may cause annual GST cooling of ⬃0.5K, while its 50% decrease has resulted in the 0.6K warming. (2) To investigate an influence of the precipitation intensity on the GST its distribution was filtered to either decrease rate of occurrence and increase intensity (increasing variance) or to decrease intensity and increase the frequency of rainfall (decreasing variance). The possibilities varied from the constant drizzle all over the year (standard deviation equals to 0 mm) to the weekly precipitation amount that has fallen during one year (s.d. ⫽ 8.2 mm in comparison to the 6.2 mm for baseline). In all cases an amount of annual precipitation remained constant. Decreasing frequency and increasing intensity of the daily precipitation results in the cooling of the ground and increasing of the soil moisture content. On the contrary, increasing frequency and reduced intensity leads to warming and drying of the ground. In addition, when precipitation intensity decreases, significant part of the available moisture remains at the canopy and does not penetrate into the deeper soil. This causes an increase of the latent heat at the air–surface interface, because evaporation of shallow moisture occurs more rapidly than of that stored at deeper levels. Numerical simulations have shown that the 25% increase in the precipitation variance has cooled the ground by only 0.07 K, while a similar decrease has warmed the ground by ⬃0.3 K. The factors described above thus have stronger impact on the GST than the changes in precipitation intensity. (3) Experiments with diurnal precipitation timing (e.g. daytime or nighttime) have shown that it is not significant for the appearance of the GST changes on the longer timescales. On the other hand, precipitation is not equally distributed also over the year. Similar experiments with seasonal precipitation timing revealed more noticeable relationships between annual precipitation peaks (as presented in Figure 50), seasonal changes of the solar radiation, and the SAT. When precipitation maximum coincides with the maxima of the two latter variables (e.g. in Prague occurring in July), it reduces the mean annual GST and increases the annual soil moisture content. In those locations where precipitation peak is close to the radiation minimum in January precipitation causes the warming and drying of the soil with the corresponding reduced role of the latent heat and increased sensible heat. The physics of the process is the next. As known, recharge rates reach their maximum after precipitation events in the winter, when soil moisture is high and latent heat flux is

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low. When precipitation peak occurs in cold season and thus coincides with the maximum positive recharge rates, significant part of the moisture is removed to the phreatic9 zone as positive recharge. Moisture flow to deeper layers dries the uppermost soil. According to the estimates by England et al. (2003) and Lin et al. (2003), seasonal clustering of precipitation can in principle change the GST by 0.4–0.5 K. Detected influence of the seasonal distribution of precipitation on the magnitude of energy and moisture fluxes at the surface hints that the rough modeling of the precipitation influence on the long-term GST–SAT coupling based only on annual averages may not exactly reflect the consequences of seasonal patterns. Above numerical experiments have shown that estimated maximum GST changes, caused by corresponding changes in the main characteristics of precipitation, may reach tenths of degree. Even though such magnitudes are small, potentially they are not insignificant for detection of the real amplitude of the climate signal. Resulting subsurface temperature–depth profiles have a curvature similar to that caused by the climate change (so-called “U-shapes”; see Figure 20, Chapter 2). This opens the possibility of misinterpreting both effects. At a first glance the problem appears quite serious. However, numerical experiments by England et al. (2003) and Lin et al. (2003) have been performed for the extremely wide range of precipitation characteristics to put upper limits on the possible GST changes. Applied range of precipitation changes significantly exceeded really observed characteristics; thus, calculated amount of the GST disturbance can be taken only as acceptable upper limits. Precipitation influence on the GST does not appear so serious in the real nature. Numerical experiments by England et al. (2003) suggest that much less than half of the GST warming detected for the last five centuries could be credibly attributed to the overall changes in precipitation amount or its redistribution within the year. This conclusion was supported by the Lin et al.’s (2003) estimates, who have found that the GST response for really observed precipitation changes on the long scales will be relatively low. For example, during the twentieth century precipitation has increased by only 5–10% at the territory of United States. The Intergovernmental Panel of Climate Change (IPCC; www.ipcc.ch) has reported an increase of 0.5–1% total (including snowfall) decadal increase in the mid and high latitudes of the Northern Hemisphere. Some of subtropical areas have been subjected to only 0.3–0.5% decadal decrease in precipitation. The minimal increase/decrease factor used in model simulations by Lin et al. (2003) was 25% causing approximately ⫾0.2 K GST change. It is more than twice larger than the actual precipitation increase estimated for North America and/or Northern Hemisphere. Extrapolation of the simulated GST change to the observed precipitation trends gives the values of the GST disturbance of only 0.05–0.10 K. This temperature range is approximately an order of magnitude smaller than the amount of the twentieth century warming. Similar estimates have shown that the changes in the occurrence of extreme precipitation events that were also reported by the IPCC will cause very small changes in the annual GST of the order of hundreds of degree. And finally, no significant changes in seasonal timing of precipitation were documented over at least twentieth century; thus their contribution to the long-scale GST changes appears to be negligible.

9

The phreatic zone represents permanently saturated with groundwater layers of soil or rock below the water table.

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Taken together, described monitoring and modeling results help to understand real influence of the various effects of precipitation on the GST–SAT differences on seasonal and annual scales. An extrapolation of conclusions based on short-scale observations over much longer timescales is complicated. While short-scale GST–SAT differences may achieve several degrees of Celsius with significant and irregular inter-annual variations, the amplitude of the long-term trends is typically an order of magnitude lower. Because the amplitude of inter-annual GST–SAT differences will be smoothed on the long-scale averaging, it is obvious that only secular changes in the GST–SAT differences can violate the use of the GST history reconstructions as reliable estimates of long-term SAT variations. However, because of higher magnitude and irregular inter-annual oscillation of the GST–SAT differences their more weak secular variations may be hidden by the high variability of the short-term pattern. Results of numerical modeling by Lin et al. (2003) suggest that actually observed long-scale precipitation trends can only insignificantly break the GST–SAT coupling. Total effect of the precipitation on the GST is likely incomparable with the GST and SAT changes that occurred during twentieth century. 2.6.4 Effect of surface vegetation Vegetation is the ground cover provided by plants. It may be regarded as the skin of the ground. It influences various processes in the biosphere at wide spatial and temporal scales. Besides that the vegetation regulates numerous biochemical processes (e.g. water, carbon,10 and nitrogen cycles),11 it also influences local and global energy balances that are important for the climate. In forested areas, e.g. not more than 5–20% of the shortwave solar radiation reaches the ground surface (Beltrami, 2001a; Nitoiu and Beltrami, 2005). Thus, the ground temperature exhibits weaker fluctuations in the regions with complete dense tree cover. Removing of this protection layer will be accompanied by a corresponding increase in solar radiation that reaches the ground surface and subsequent re-arrangement of all energy balance components (net short wave radiation, net long wave radiation, latent heat, sensible heat, and ground heat). Vegetation also strongly affects soil characteristics (e.g. soil volume, texture, and composition). Both processes can influence the GST–SAT coupling. Investigations of the GST–SAT coupling by Smerdon et al. (2004, 2006) comprising results of the Czech and the North American monitoring experiments have shown that the differences between soil and air temperatures arise in both winter and summer seasons. While the snow cover/soil freezing are responsible for the decoupling of winter temperatures, summer precipitation reduces soil temperatures relative to SAT through evapotranspiration process. Observations have shown that except for the influence of the summer and winter precipitation and soil freezing/thawing on the GST–SAT coupling, it depends also on the type of the land cover. The above-mentioned monitoring experiment at the Prague-Sporilov site was performed under different surface types. Measurements have detected significant influence of the surface type on the GST–SAT difference. Four types of the surface were chosen for the experiment: the bare soil, the sand, the grass, and 10 In the biosphere carbon cycle represents an exchange of carbon between living organisms and the nonliving environment. 11 The nitrogen cycle describes the transformation of nitrogen and its compounds in nature.

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the asphalt. The 3-year temperature averages indicate that the soil is warmer than the air for all surface types, but the soil (at 2 cm depth) and air (at 5 cm height above given surface) temperature difference was surface cover dependent and amounted to 1.5 K, 1.9 K, 0.3 K, and 4.4 K for the bare soil, the sand, the grass, and the asphalt, respectively. This pattern is valid also for the individual year averages. The inter-annual variability of the GST–SAT differences seems to be of the order of the first tenths of degree of Kelvin. New denser grass was seeded in spring 2004 and since that time the temperature above the grass cover appeared to be higher, probably due to decreased air circulation around the sensor that was partially protected by the grass. The highest difference for the asphalt can be explained by an extremely low albedo of this material that makes it very sensitive to incident solar radiation during the year. The GST of the asphalt in the “sunny” year 2003 was by ⬃0.7 K higher than in more “cloudy” year 2004 and 2005, whereas the air temperature was higher by less than 0.3 K. During the winter, vegetation can give a similar insulating effect as a snow cover, protecting the ground from the weather extremes that induce high rates of heat transfer from and to the atmosphere. For example, studies have shown that forest soils do not really freeze in the winter due to the buffering capacity of forests. During January–February 2006, the weather in Prague-Sporilov site was characterized by heavy frosts and absence of the snow cover (see also Section 2.6.2). As a result, temperature under all surfaces has dropped to near the freezing point. Minimum temperatures at the depth 50 cm under the bare soil, the sand, the grass, and the asphalt were ⫺0.29, ⫺0.35, 0.26, and 0.046°C, respectively. The higher temperatures under the grass are given by the insulation of the vegetation cover and those under the asphalt by the above-mentioned low albedo of this material that helped to absorb sunshine during the frosty, but sunny days. When relating the GST and SAT it is customary to assume that the soil–air temperatures coupling mode remained the same over long time intervals. This assumption could be questioned when the borehole sites were subjected to the drastic vegetation/land use changes. The vegetation changes and their causes are manifold. The processes that lead to the vegetation changes can be characterized as gradual or abrupt. Such processes can produce changes of vegetation structure and/or composition very quickly or for long time periods, respectively. Changes in land cover type may be direct, e.g. agriculture, forest clearing; or indirect as a result of altering disturbance processes, e.g. fire events, landslides, floods, etc. They may be either natural, such as germination, growth, death, or human-induced. All processes can operate over various temporal and spatial scales. Changes in the land cover influence all energy balance components. Exact responses to the land cover change are component specific. For example, both sensible and ground heat fluxes are reduced with an increase in tree canopy. On the contrary, conversion of a forest to short vegetation may raise surface temperatures due to increased sensible heat flux in relative to latent heat flux (Eltahir, 1996). In completely forested areas temperature at ground–air interface is lower than at grasslands or bare soils. Annual ground temperature under complete tree cover is also on average lower, while the soil moisture will be on average higher under such areas cover than under complete grassland. The lower ground and air surface temperature will lead to lower evaporation rates and to decrease the latent heat flux from the ground surface. Among all possible land use changes the influence of the deforestation on the GST–SAT coupling represents probably the best-studied process. Deforestation is the removal of trees

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without sufficient reforestation. It may occur naturally as slow forest degradation or sudden extensive forest fires. Anthropogenic influence means conversion of forests to grassland and/or to arable land as well as urbanization and technological uses. Removal of significant tree masses influences all environmental characteristics, changes air–ground boundary as well as the surface hydrological regime, and thus seriously modifies the surface energy balance (Zeng and Neelin, 1999). It generally provokes noticeable changes in climate. Numerous studies have detected an increase in subsurface temperatures following deforestation (Murtha and Williams, 1986; Cermak et al., 1992; Majorowicz and Skinner, 1997; Zhang et al., 2001; Beltrami and Kellman, 2003; Nitoiu and Beltrami, 2005). Except for direct influences caused by the changes in the surface energy balance, climate changes may occur due to indirect feedbacks of altered bio/geo/chemical processes. Climate changes due to deforestation not only are of only local character, but generally embrace global scales as well (e.g. Chase et al., 2000). Betts (2004) and Betts et al. (2004) have compared the radiative forcing caused by the land use changes with the influence of the greenhouse gases, aerosols, and stratospheric ozone (see Section 3.4.2, Chapter 3) and have concluded that these effects have comparable magnitudes. Common effect of deforestation manifests itself as an increase of surface temperature in tropical and temperate regions (Betts, 2004; Betts et al., 2005). Such changes have been detected in numerous borehole temperature logs. Importance of the account for the deforestation disturbances during GST reconstruction from borehole temperature logs was emphasized in the work by Lewis and Wang (1992). These authors have measured temperature–depth profiles in 11 boreholes located at different Canadian environments. Repeated measurements have shown that average GST depends on the vegetation cover. Thus, in forested areas it is generally 4–5 K cooler than at the bare surface. Similar values were measured in Atlantic Canada (Beltrami and Kellman, 2003) and in British Columbia (Plotnikoff et al., 2002). In the regions subjected to deforestation Lewis and Wang (1992) have collected the evidence that these areas have experienced subsequent GST warming. Numerous further studies have corroborated an increase in subsurface temperatures following tree cover removal (Bentkowski and Lewis, 1992; Majorowicz and Skinner, 1997; Skinner and Majorowicz, 1999; Bodri et al., 2001; Cermak and Bodri, 2001; Beltrami and Kellman, 2003; Lewis and Skinner, 2003; Nitoiu and Beltrami, 2005). Beltrami and Kellman (2003) have performed the monitoring of the soil and air temperatures at three locations in Nova Scotia (Canada) to examine how they follow each other in “field” and forested areas. The “field” surfaces included a clay soil and the grass. High-resolution air–soil temperature monitoring over one-year time interval have shown that the maximum positive differences in soil temperatures between “field” and forested locations occur generally in the warm season (spring and summer) mainly because of direct solar heating of the surface at the “field” sites (the direct solar radiation in the forest amounts to only 5% of that detected at the “field” sites). Because of this effect, the spring thawing has occurred some 2 weeks earlier at the grasslands than in the forests. Differences may reach approximately 8 K at the 0–20 cm depth range, and are of ⬃6 K in the 50–100 cm depth interval. During cold seasons differences are significantly smaller. The authors have performed numerical modeling of soil temperatures. Their model has applied the 1-D conductive heat transfer regime and used air temperature as the surface forcing function. Calculations have indicated that during frost-free season (approximately 290 days in the spring-fall period) the soil thermal regime in the forest floor is directly

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coupled to the temperature at lower atmosphere. The discrepancies between measured and modeled data were insignificant. Decoupling of the measured and modeled temperature time series appears to be more noticeable at “field” sites, where assumption of conductive thermal regime driven by air temperature changes was not valid. Detected misfit clearly followed daily periodicity. The measured-modeled data differences were the largest in the daytime. The explanation of this phenomenon lies in the significant increase of the incident solar radiation on the field surface, such that air temperature forcing alone represents only small fraction of the energy driving thermal regime of the subsurface in the daytime. On the contrary, in forested areas, where direct solar radiation was much smaller than at the field sites, the SAT was the main forcing for the ground temperatures and the GST could be accurately simulated by a pure conductive model. In forested areas the shortscale GST–SAT coupling appears to be one by one, while in open fields GST changes are not properly represented by conductive models with SAT forcing. Because deforestation is a widespread anthropogenic activity at all times and all over the world, many drilling sites may contain such kind of disturbance and need correction to separate influence of the non-climatic energy balance changes superimposed on the climate signal. Indeed, numerous temperature logs have been rejected from the GST history reconstruction because boreholes where they were measured were located in the regions of well-documented strong land use changes. The need to correct borehole temperatures for such perturbations was recognized from the very beginning of the borehole climatology. Lewis and Wang (1992) have suggested simple ramp/step model to correct effect of ground warming observed in several boreholes of British Columbia (Canada) after deforestation. This correction should be applied to the temperature log prior to its use for the GST history reconstruction. Obviously, this model was only a first-order approximation, and was not intended to account for all processes occurring in such areas. Recently, Nitoiu and Beltrami (2005) developed a more detailed method for simulation of the effects of the GST changes caused by deforestation. One of the most influential studies in the history of forest ecology was that performed by Covington (1981), who described a pattern in organic matter storage as a function of the date of forest harvest. This so-called Covington’s curve was based on the study of forest floors in series of northern hardwood stands of different ages in New Hampshire (USA). Since the energy balance at the forest floor is affected by the removal of trees and by the variations of the layer of organic matter at the forest flow, Nitoiu and Beltrami (2005) proposed a model based on the Covington’s curve to describe GST variations following deforestation. For the case of total recovery of initial forest the model is formulated as TG ⫽ At B exp (Ct D ) ⫹ TG0 ,

(36)

where TG is GST, t the time after deforestation (in years), and A, B, C, D, TG0 regression coefficients. Figure 52 shows GST variations caused by deforestation that occurred 50 and 100 years B.P., respectively. As seen, the GST sharply increases immediately after deforestation event, when environment is dramatically declined by the biomass removal and by the mixing of the forest flow into mineral soil during harvesting operations. Temperature disturbance reaches its maximum of 2 K at approximately 15 years after harvest. As the forest floor organic matter recovers, temperature slowly returns to its original value TG0. Full recovery may take decades or even century-long periods. Eq. (36) assumes that the

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Fig. 52. Response of the ground surface temperature to deforestation, two events considered which occurred 100 and 50 years B.P. (model by Nitoiu and Beltrami, 2005; A ⫽ 0.221, B ⫽ 1.24, C ⫽⫺0.0649, D ⫽ 1.063, GST0 ⫽ 0 K).

removed forest re-grows to its original state. This model can be improved for the more common cases when the new forest differs from its pre-harvest state or is transformed into bare soil/grassland. In the latter case temperature increase can be much higher than 2 K, which is shown in Figure 52. Subsurface temperature perturbations due to deforestation can then be simulated by 1-D purely conductive equation of heat transfer using synthetic GST histories, as presented in Figure 52, as the surface boundary condition. Correction for deforestation is performed by removal of the simulated T–z profiles from measured temperature logs. Calculations by Nitoiu and Beltrami (2005) have shown that disturbances due to deforestation propagate to some 150–200 m depth. Correction is the largest in the uppermost 100 m depth interval. Its values depend on the harvest timing and for the recent century events can range between 0.1 and 0.6 K. Effect of deforestation is more serious for the more recent events, while the magnitude of the disturbances caused by remote deforestation is significantly attenuated by subsurface heat diffusion process. In the case of remote deforestation at borehole site the GST histories inferred from corrected and uncorrected T–z profiles were practically indistinguishable. This hints that correction for deforestation is unnecessary in regions that experienced older deforestation. Both numerical simulations and analysis of field examples performed by Nitoiu and Beltrami (2005) have shown that the above method very effectively removes disturbances caused by deforestation. The shortcoming of suggested technique is that exact correction is only possible if timing and character of the land use change is known. In the real field situations the harvested and fully re-grown forest case is far not common. More often the deforestation represents a series of events, whose details are generally not well documented. Poor knowledge of the land use history may be a source of significant bias in applied correction. The GST anomalies may affect not only areas that were really subjected to the land use changes, but also their wide surroundings. Ferguson and Beltrami (2006) have studied transient lateral effects of deforestation. Their numerical simulations have indicated that

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the subsurface temperature anomalies caused by deforestation of vast areas may extend far beyond the cleared region. Thus, for 500 m wide deforested area 0.1 K temperature increase reaches approximately 40 m distance beyond the edge of the cleared ground in 50 years after deforestation and ⬃70 m after 100 years, respectively. The authors have proposed effective technique for correction of the T–z profiles measured in boreholes drilled close to the areas affected by the land use change. Unfortunately, this method could not be applied at all locations. Similarly to the above-described correction the latter technique also requires at least some knowledge of the land use changes. Generally, applied models are based on an assumption of a homogeneous deforestation, while real deforestation takes place preferentially near areas that have already been deforested. This creates a mosaic of forested and deforested patches that further complicates the lateral effect of deforestation. An impact of such localized deforestation on the development of shallow temperature anomalies was studied by Bense and Beltrami (2007). The authors have used a suite of the 2-D models to illustrate thermal effect of the patch-like deforestation. Heat transfer can take place by both conduction and advection due to groundwater flow. Modeling results have shown that the patch-like pattern of deforestation can produce significant temperature gradients in the subsurface. Anomalous gradients can be intensified by the horizontal groundwater flow if its rate is above 10⫺8 m/s. While in the case of pure conductive heat transfer maximum extent of the anomaly would not exceed ⬃50 m (Ferguson and Beltrami, 2006), lateral advection of heat can extend the measured disturbance to several hundred of meters away from the deforested area during 100 years after forest clearing. The measured up- and downstream T–z profiles can exhibit contrasting features notwithstanding that both areas had undergone the same GST changes. The vegetation increase can also affect GSTs. In their recent study Kaufmann et al. (2003) have applied statistical techniques to quantify effect of inter-annual variations in vegetation on the surface temperature for different types of land cover over Northern America and Eurasia. The database included satellite measurements of the surface greenness (interpreted as a proxy for photosynthetically active vegetation) and the groundbased meteorological observations for the years 1982 to 1999. Statistical analysis has shown that summer increase in terrestrial vegetation causes corresponding ground temperature decrease. Reductions in the extent of snow cover during the winter compel temperature to rise. Except for the seasonal vegetation increase, its long-term enlargement (e.g. reforestation process) can cause corresponding long-term decrease in the GST. For example, it is the case for many regions of North America over the past century, where subsistence farming was stopped and previous agricultural land was occupied by the forest (Ferguson and Beltrami, 2006). Other local terrain effects causing spatial and/or temporal variations in the land cover, such as forest fires, can also affect surface temperature and influence underground temperature field (Skinner and Majorowicz, 1999; Lewis and Skinner, 2003). Yoshikawa et al. (2003) have investigated an impact of wildfire on the ground temperature in the boreal forests of interior Alaska. Their experiment has detected significant increase of the near-surface temperatures in a short time after ignition. At 2 cm depth temperature has risen to more than 800°C already in about 10 min after ignition. However, the ground temperature has increased only at the shallowest layer (<15 cm). According to Yoshikawa et al. (2003), most of the heat from the fire is transferred into the subsurface by pure conduction that at the thermal diffusivity of the medium of ⬃10⫺6 m2/s penetrates

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approximately 12 cm during one hour. This is the reason that no significant increase in temperature was detected below 15 cm. Longer term GST changes in burned areas occur mainly due to removal of vegetation and destruction of the organic matter layer covering the forest floor. According to the study by Yoshikawa et al. (2003), this layer is an important thermal insulator. Effect of both factors on the GST is similar to deforestation. The authors have monitored annual GST variations in the regions that were on fire. Comparison with the GST measured at the adjacent control unburned sites has shown that similarly to the case of forest clearing at all investigated locations summer ground temperatures were warmer than at the adjacent unburned control sites by 1–20 K. The differences were less significant during the cold season. In case of fires that have burned during the last 10 years, the GST was higher at the burned sites than that at control sites for the early freezing period in autumn. This difference existed until the subsurface active layer became completely frozen. Results of the above three sections concerning seasonal and terrain effects on the GST–SAT coupling can be summarized as follows: (1) Monitoring results have shown that the SAT forcing represents the main cause for the GST changes. This finding generally supports the use of GST as an indicator of the SAT changes at times prior to the beginning of the instrumental record. (2) Differences between annual GST and SAT signals are closely linked to the processes occurring in the shallow, approximately the upper meter, zone beneath the ground surface. (3) Coupling of soil and air temperatures over a single year is complex. The winter snow cover and freeze/thaw effects represent the dominant influences causing GST–SAT decoupling. Because snow cover insulates the ground in the cold season, its systematic and persistent variations may distort one-by-one air–ground temperature coupling and hinder the direct comparison of both variables. In the regions with short-duration snow cover its random fluctuations tend to vanish in the longer period averages. The summer evapotranspiration likely have weaker effect on the GST–SAT decoupling. (4) On the daily scale the GST may be warmer or cooler than SAT in the winter or summer, respectively. These regular seasonal differences manifest themselves as the attenuation of the annually averaged GST signal in comparison with the SAT. Percent of the GST attenuation may vary from approximately 7–8% to 20–25% (Smerdon et al., 2004, 2006). This effect may be progressively intensified as the seasons become more extreme. The seasonal decoupling have lesser influence on the phase shifts of the GST relative to the SAT. The temperature of the ground surface remains almost in phase with that of the air. Annual GST delay relatively to the SAT variations generally amounts to only 5–8 days and thus can be regarded as negligible for the long-term GST–SAT comparison (Smerdon et al., 2004, 2006). (5) Normally the mean annual GST is higher than the SAT. For the most mid-latitude regions this difference amounts to 1–2 K. This value is higher in the regions with deep, long duration snow cover and/or subjected to extreme soil freeze/thaw cycles.

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(6) Within each region variations in the ground properties and surface characteristics due to environmental changes (vegetation, anthropogenic influence) can cause appreciable local variations. The GST–SAT comparison may be problematic in regions subjected to significant land use changes. Effective correction of the GST data is possible, if the timing and character of the terrain change are known. However, various events occurring on the land surface are generally not well documented. In this case their contribution to the heating/cooling of the surface cannot be readily separated from other surface effects. (7) Fortunately, all detected processes that could break the GST–SAT coupling have only short-term effects on the heat transfer in the subsurface, particularly of daily to seasonal timescales. Most of them occur sporadically, while some of these factors tend to compensate each other under the averaging through the larger spatial scales. In general, detected short-term GST–SAT differences cannot violate the assumption on the tracking of both temperatures on the long timescale.

2.6.5 Long-term soil–air temperature coupling All above details on the GST–SAT coupling concern short timescales (daily to annual). These short-term GST–SAT differences are well documented. They arise due to insulating effect of snow cover, freezing/thawing, evapotranspiration, seasonal changes in vegetation, etc. These effects are well interpreted and most of them can be taken into account by using the reduced thermal diffusivity on shallow subsurface. To jointly interpret results of the GST reconstruction with the SAT measurements and/or proxy data one should detect not only short-term GST–SAT tracking, but also their long-term coupling. If the differences between both kinds of the data are assumed to remain constant in time, the revealed GST–SAT decoupling will be preserved over the long timescales. In the opposite case of only randomly changing from year-to-year differences, detected on the daily to annual scales, GST–SAT departures signify nothing about the changes over centennial scale periods. It likely means that existing interpretation of the GST histories inferred from borehole T–z profiles as an estimate of the long-term SAT trends is correct. It therefore represents a challenge to extrapolate conclusions on the GST–SAT relationship observed on short timescales over much longer periods of time. The above-discussed GST–SAT differences occur because the fact that subsurface temperatures integrate combined surface signal and do not retain the air temperature variations alone. Studies described above dealt with the GST–SAT coupling on short (from daily to annual) timescales at single locations. Comparison revealed differences in the amplitude and phase between two signals that occur both in the winter and in the summer and vary with meteorological conditions. Downward propagation of the surface temperature signals is affected by the snow cover, subsurface freezing and thawing, rainfall, water infiltration and its subsurface migration, vegetation, evapotranspiration, etc. These processes have primarily short-term effect, and observed GST–SAT differences can be successfully approximated using daily meteorological observations (Pollack et al., 2005). On the contrary with short-scale temperature monitoring experiments, borehole temperature–depth profiles are used for estimation of the global and hemispheric temperature on timescales

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of centuries or longer (see Section 3.2, Chapter 3). What is about GST–SAT tracking on the large spatial/temporal scales characteristic for the paleoclimatic studies? All existing interpretations of merged GST and SAT records were based on the assumption that both databases are closely related on the long timescales. Because of detection of the short-term GST–SAT discrepancies, the question about a long-term GST–SAT tracking has arisen and an assumption of their longer scale coupling also has become a subject of discussion. To better understand the long-scale GST–SAT coupling and to document the details of the penetration of the surface signal into the ground, a climate and ground temperature observatory was installed in arid northwest Utah in 1994, and over a decade-long ground temperature monitoring has been performed at Emigrant Pass Observatory (EPO), Utah (Bartlett et al., 2004; Davis et al., 2006). The EPO (41.50°N, 113.68°W, 1750 m asl) represents a standard weather station situated on exposed granitic rock at the top of a 150 m deep borehole (GC-1) drilled in 1978. Results of its repeated temperature logging are presented in Figure 16 (Chapter 1). Inversion of measured T–z profiles inferred surface temperature changes that are closely coherent with those observed at the nearby meteorological station 40 km to the northeast (Chisholm and Chapman, 1992). Observatory consists of an array of thermistor strings in the subsurface. Ground temperatures are monitored at several shallow depths from 2.5 cm to 1 m. Meteorological and shallow ground variables are recorded simultaneously. All data from the EPO since November 2004 is available and can be found on the web site http://thermal.gg.utah.edu/facilities/ epo/EPO_data. The file is automatically updated daily. The combined database gives the opportunity to observe the GST–SAT dependence in near real time and to test theoretical models of the GST–SAT interactions. Over decade-long continuous temperature monitoring has shown that GST variations are influenced mainly by the surface air temperature that explains 94% of the GST variance, by incident solar radiation (accounts for 1.3% of the GST variance) and snow cover. Ground temperatures are generally higher than air temperatures. Daily averaged GST–SAT differences range between ⫹14 and ⫺10 K. They are much lower on the annual scale and vary between only 2.3 and 2.5 K. These differences occur due to the solar radiation effect in the summer and the insulating effect of snow cover in the winter. Much of the inter-annual variations in the GST–SAT difference occur due to the changes in solar radiation. It was shown that incident solar radiation is more important during the summer. On the long scale there is a linear relationship between the GST–SAT difference and solar radiation with the slope of 1.21 K/100 W/m2 and the intercept of 2.47 K (Davis et al., 2006). Because of its low thermal diffusivity snow attenuates surface temperature variations in the winter, but its insulating effect is of only minor influence on the annual GST–SAT coupling at the EPO site (accounts for only 0.5% of the annual GST variance). Using EPO monitoring results Bartlett et al. (2004) have developed two-layered forward numerical model of snow–ground interactions. The model is based on three characteristics of snow cover: (1) the onset time, (2) duration of the snow cover, and (3) its thickness. These parameters are generally available from meteorological and remotely sensed data; thus, the authors have validated their model using the century-long National Weather Service data from numerous sites over North America (see above). Their calculations have verified the applicability of the developed model for the broad spectrum of snow conditions and have confirmed its suitability for the prediction of the GST changes in different environments.

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Fig. 53. Annual means of the difference between air and soil temperature at 50 cm depth for weather station 1 108 487, British Columbia. (Drawn from Beltrami and Kellman, 2003.)

To assess the GST–SAT coupling on the decadal timescale Beltrami and Kellman (2003) have examined 30-year measurements of the air and soil temperatures for the 10 weather stations across Canada. Stations have represented open field locations. Soil temperatures were measured at depths of 5, 10, 20, 50, 100, 150, and 300 cm. Figure 53 illustrates annual means of the differences between air and soil temperature at 50 cm depth at the station 1108 487 (British Columbia). As seen, the differences between soil and air temperature randomly oscillate within approximately 1 K range. This phenomenon was observed at all investigated in the work by Beltrami and Kellman (2003) stations. The linear fitting of the data has revealed insignificant increasing/decreasing trends varying from approximately ⫺9 ⫻ 10⫺2 to 6 ⫻ 10⫺3 K/year. If revealed trends are persistent over the longer periods of time, they could certainly obscure the GST–SAT coupling at a single location, thus affecting an interpretation of the SAT trends based on the GST reconstruction data. However, as seen in Figure 53, the inter-annual variability of detected differences is quite high and the total length of the temperature records is insufficient for definite decisions about long-term persistence of detected trends. If departures from perfect GST–SAT tracking which have been calculated by Beltrami and Kellman (2003) are systematic, they are likely to complicate the comparison of the GST and SAT records spanning decades or centuries. Fortunately, (1) because of spatial variability of the signs of observed trends they will probably compensate each other on the large-scale spatial averaging, and (2) in nature many factors that violate perfect GST–SAT coupling tend to compensate each other and/or vanish under large-scale averaging. The investigations by Smerdon et al. (2004, 2006) have revealed significant site-to-site variability in both the magnitude and the nature of the GST–SAT differences as well as their highly irregular distribution between years of observations. Due to the former property, all successful attempts to suppress the “noise” in the GST reconstructions were based on the spatial averaging and/or on the simultaneous inversion of numerous temperature logs (see Section 3.2, Chapter 3). An absence of the secular trends represents the common feature of all examined records. This hints that limits, within which the soil–air temperature differences will fluctuate,

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may be quite narrow. As shown by Jones and Mann (2004), despite the various possible sources for the GST–SAT decoupling, the apparent discrepancies appear to be diminished when attempts are made to account for spatial sampling biases and/or when estimates of the large-scale century-long SAT trends from the borehole data are performed using spatial regression. The conclusions of the above-mentioned studies were based on over decade and 30-years long temperature monitoring results. Clearly, for the correct detection of the long scale GST–SAT coupling longer time series are indispensable. The waiting for the accumulation of the sufficiently longer data series monitored at the microclimatic stations is not a promising task. Thus, the understanding of the centennial or longer term GST–SAT coupling can be performed by either of two ways: (1) Development of the powerful numerical models to simulate long scale active processes at the level of the air–ground interaction and in the subsurface and comparison between observed and modeled temperatures. Consistency between observed subsurface anomalies and anomalies simulated using meteorological forcing will serve as a good verification of the assumption of one-by-one longterm GST–SAT tracking. (2) The GST reconstructions inferred from borehole data can be compared with the SAT measurements as well as with the proxy sources available in the same locations during periods of overlap. Again, the correspondence of the reconstructed and observed climate changes will help to validate the use of borehole temperature logs for paleoclimate reconstruction. The possibility of the application of the first approach was discussed in the work of Bartlett et al. (2004), who have developed two-layered forward numerical model of snow–ground interactions using the EPO monitoring results. Their calculations have verified the applicability of the developed model for the broad spectrum of snow conditions and have confirmed its suitability for the simulation of the GST changes in different environments (see above). Smerdon et al. (2004, 2006) have estimated the multivariate empirical regressions for the GST–SAT differences and meteorological variables and detected that the amplitude of the GST–SAT decoupling during summer and/or winter can be used to describe quantitatively their tracking on the annual scale. On the basis of these conclusions Pollack et al. (2005) have worked out an effective method for modeling of observed GST–SAT differences using daily meteorological observations. Developed in this work numerical model applies an assumption of the pure 1-D conductive heat transfer with the time-dependent forcing on the surface boundary that equals the SAT. Spatially this model represents thin surficial layer of the variable thermal diffusivity superimposed on the homogeneous half-space. In the work by Pollack et al. (2005) meteorological influences are captured as temporal variations of the thermal diffusivity in the surficial zone by empirically determined values of winter snow cover insulation and the annual variations of latent heat of freezing/thawing, evapotranspiration, and seasonal changes in vegetation. The thermal diffusivity represents the ratio of the thermal conductivity to the volumetric heat capacity. The presence of the snow cover, e.g. is equivalent to a decrease of the thermal conductivity, while the latent heat influence increases the volumetric heat capacity. Both effects thus lead to a decrease in the thermal

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diffusivity of the shallow subsurface. Modeling results have shown that the attenuation of the temperature signal passed through the surficial layer depends on the cause of the thermal diffusivity decrease. Reduction of conductivity, increase of volumetric heat capacity, or a combination of both effects produces different signal attenuation. Validation/testing of this method has proved that the model suggested by the authors provides quite high coincidence between simulated and observed subsurface temperatures, while its parametrization is much simpler than for the most complex land surface processes models that are traditionally used in such studies (Pollack et al., 2005). Because records of various meteorological variables (air temperature, rain, and snow cover) are available on much larger time and spatial scales than the present-day results of temperature monitoring, Pollack et al.’s model can be attested as an extremely useful tool for the future investigations of the effect of the long-term trends in the meteorological time series on the GST–SAT coupling. In their most recent work, Pollack and Smerdon (2006) have examined the influence of the subsurface layer with reduced thermal diffusivity on the downward propagation of the temperature signal with different periods. Preliminary calculations have revealed considerable attenuation of the short-term (daily to monthly) signals, while on the annual scale the ratio of the amplitude of the temperature wave that enters the half-space beneath the layer with reduced thermal diffusivity to that of the signal at the same depth in a homogeneous medium varied in the range of only 0.68–0.95. This ratio was even closer to 1 at the decadal (0.86–1.0) and centennial (0.95–1.0) scales. The major conclusion of this study is that the daily and seasonal GST–SAT decoupling has only little impact on the downward propagation of the temperature signals with decadal to centennial characteristic times relevant to the climate change studies. On the whole, this study thus has corroborated perfect long scale GST–SAT tracking. Typical examples of the second-kind research of the long-term tracking of the surface climate changes by underground temperatures are presented in the works by Huang et al. (2000), Harris and Chapman (2001), Beltrami and Bourlon (2004), and Pollack and Smerdon (2004). All these studies have shown that notwithstanding the short-scale GST–SAT decoupling borehole T–z profiles averaged for the Northern Hemisphere have yielded results consistent with the meteorological SAT records and multiproxy reconstructions (for details see Section 3.3, Chapter 3). Obtained results indicate that on the large temporal/spatial scales ground and air temperatures track each other. Merging of both approaches was performed in the work by Beltrami et al. (2005), whose authors have compared precise temperature logs measured in the cluster of four boreholes in northern Quebec (Canada) with the 70-year long SAT series and records of precipitation from the weather station located only 130 km away from the borehole site. Earlier examination has shown that all temperature logs are generally similar and contain stable and consistent climatic signals (Beltrami et al., 1997). The SAT record exhibits warming trend of about 0.9 K during the last 70 years. The region under investigation is characterized by both significant snow cover in the winter (⬃3 m in average) and the summer rainfall (⬃700 mm). Thus, on the shorter scales GST changes could be perturbed by both influences. Because similar climatic conditions exist over the wide regions of central and eastern Canada, investigated boreholes may be regarded as a representative sample of this vast area. The authors have modeled subsurface temperature anomalies by the 1-D pure conductive equation similar to Eq. (17) (see Section 2.3.3) using SAT record as a forcing function. Comparison of measured and simulated T–z profiles was performed

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by the calculation of the pre-observational mean temperature (POM) that represents background long-term mean temperature prior to the beginning of the meteorological record (for details see Section 2.5 of this chapter). Comparison of the measured and calculated temperature–depth profiles have revealed their almost perfect coincidence. Estimated POM-value was 2.3 K below the SAT mean; thus, it indicated pronounced twentieth century warming. Obtained accurate fit of both measured and calculated temperature profiles using the SAT course as an upper boundary condition means that the SAT forcing is responsible for the major part of the subsurface temperature anomalies, and thus proves that in the investigated location ground has tracked the SAT variations for at least last 70 years. Perfect GST–SAT tracking was not obscured either by high amount of summer precipitation or by the thick snow cover. This and similar studies (see Bodri et al., 2001; Harris and Chapman, 2001) corroborate an assumption that GST follows SAT at least on several decades long temporal scales. This interval can be further prolonged. Described in Section 2.4.4 of this chapter comparisons of the last millennium long GST histories inferred from measured temperature logs (210 logs all over Canada) and from T–z profiles simulated using the state-of-the-art General Circulation Model ECHO-g, which takes into account both the anthropogenic and natural external forcings (Beltrami et al., 2006; González-Rouco et al., 2006), proves that the ground follows the SAT trends even in the millennium-long timescales. Summarizing results of existing studies one can conclude that the GST–SAT differences are apparent only on annual and shorter timescales. The long-term relationships between GST and SAT generally support the hypothesis of their one-by-one coupling and validate the assumption that GST histories can be regarded as a source of information about air temperature changes at times prior to the beginning of the instrumental record. 2.6.6 Other possible terrain effects Except for the above-described meteorological influences and vegetation, subsurface temperature distribution may be affected by many local factors that the researchers generally refer to as “terrain”. This term embraces the topography of given location, type of bedrock, hydrological conditions (water table, rivers, lakes, and swamps), moisture effects, etc. All these factors distort the curvature of the temperature–depth profile and thus can bias the reconstructed GST history. Various possible influences of terrain on the subsurface temperatures were summarized by Lewis and Wang (1992), who have called attention of the “borehole” community to the complex terrain-dependent relationships on the GST at any location and time period and emphasized the need of screening each borehole site and their data for possible terrain effects. These studies were continued in the subsequent years. Topography and the groundwater flow represent probably the most often occurring and/or important of the terrain effects. This section is devoted to the former factor, while the latter effect will be discussed in the next section. An extraction of the past climate changes from the borehole temperature measurements contaminated by various terrain effects represents typical “signal-in-noise” problem. The extraction of a useful signal from the noisy measurements is a basic endeavor in all branches of geophysics. The principal sources of noise are of two types: (1) the measurement errors and (2) the representation errors, i.e. the simplification of the mathematical model and its departure from the conditions existing in the real geophysical

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systems. In the unique problem of the reconstruction of the boundary condition (climatic history) from the subsurface temperature profiles to what this book is devoted, the first kind of noise embraces of the usual errors in the measurement of temperatures, depths, and thermal parameters of the medium. Influence of these errors on the reconstructed GST history is discussed in Section 2.3. Effects of the topography and groundwater flow are typical kinds of the representational errors in the GST history reconstructions. These types of errors arise from the fact that all forward and/or inverse methods of the GST reconstruction are based on the assumption of the exclusive heat conduction (no advection) in a one-dimensional, laterally homogeneous medium. Topography characterizes the land surface in terms of elevation, slope, and orientation. It should be described by at least 2-D models. Such modeling can be easily performed by the forward calculations, while present-day GST reconstruction techniques are developed for the 1-D situation and thus cannot capture effects of topography and/or groundwater flow in principle. However, it should be emphasized that the use of the 1-D approach does not represent unnecessary simplification only. As described in Section 2.3.6 of this chapter, the development of the 2-D techniques will not necessarily improve the GST histories. The 2-D approach will significantly raise the number of the degrees of freedom of the inverse problem (underground structure, thermophysical parameters, and pattern of the steady-state temperature field), while we may only handle finite amount of measured data. The application of a 2-D approach means that we use more parameters to describe the unknowns than could be uniquely determined by the data. In practice the use of the 2-D approach implies severe limitation of a priori parameter range treated. In all inversion problems some optimal relation between resolution and variance should be established. In other words, one should answer the question, what is the effective number of degrees of freedom in the data and what parameters can be independently estimated with an acceptable variance. This was the reason that plausible attempts to minimize an effect of noise included development of different correction techniques or at least qualitative assessment of the reliability of the reconstructed GST histories and possible error sources. The perturbations of a vertical heat flow field by topographical effects have played an important role in the interpretation of the geothermal data since the beginning of the terrestrial heat flow measurements. The works by Königsberger and Thoma (1906) and by Lees (1910) represent the earliest 2-D quantitative analyses of the effect of differently dipping slopes based on forward calculations. Jeffreys (1938) have proposed the method to correct measured temperature gradients for local topography. Bullard (1938) has shown that correction is unnecessary in the case of shallow boreholes and relatively low elevations, while high alpine terrains can produce deep disturbances of the underground temperature field. Lachenbruch (1968), Bodmer et al. (1979), and Turcotte and Schubert (1982) have suggested their own techniques for the calculation of temperature patterns below typical topographical profiles. For a flat rectangle with a sinusoidal surface temperature variation calculated from sinusoidal topography these authors have shown that the amplitude of the perturbation decreases with depth z as exp(⫺2
z/), where  is the wavelength of the surface altitude variation. Thus, in the case of topographical variation at the surface with an amplitude A ⫽ 100 m and  ⫽ 1 km the ground temperature would have been reduced to approximately 0.2 K magnitude at 1 km depth. Blackwell et al. (1980) developed a correction method based on a 3-D approach that has taken into account not only elevation, but also other topographic variables: slope angle and orientation.

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In addition to the steady-state topography effect Birch (1950) has estimated transient effects due to uplift and erosion. These effects, however, represent meaningful correction for terrestrial heat flow, but because they occur on much longer timescales, they are not so important for the GST history reconstruction. The work by Sclater et al. (1970) was among the first attempts to solve the problem numerically, while present-day estimations of the topographic effect are almost exclusively based on the 2- and/or 3-D numerical simulations of the subsurface temperature field (Safanda, 1994; Kohl and Rybach, 1996; Kohl, 1999). To investigate terrain effects on the subsurface T–z profiles an experimental 150 m deep borehole was drilled in the campus of the Geophysical Institute in Prague-Sporilov, the Czech Republic (Safanda, 1994). The borehole is located in the slightly elevated terrain with the height difference of ⬃60 m along 1200 m long slope; thus, measured temperature logs clearly need the topographic correction. Topography effect on the subsurface temperature field was simulated by numerical solution of the 2-D steady-state equation of conductive heat transfer in the heterogeneous medium. The radioactive heat production in the medium was assumed to be zero. Thermal conductivity distribution used in the calculations has corresponded to the real in situ structure. It was anisotropic and represented a mean bedding inclination of 50° to the north and a mean conductivity of 3.2 W/mK along the bedding and a conductivity of 2.2 W/mK in perpendicular direction. The real shape of local topography was used as the surface boundary condition, when the GST was assumed to depend on the altitude and the slope of the surface. An atmospheric lapse rate12 of 5 K/km was applied at the upper surface, while constant heat flow of 60 mW/m2 was assigned at the lower boundary at 5.5 km depth. This value can be interpreted as undisturbed heat flow characteristic for the region under investigation. Figure 54 illustrates an effect of topography on the Sporilov site. Isolines represent the values of the vertical heat flow normalized by the value of the basal heat flow at 5.5 km depth. As shown, even relatively flat topography can significantly distort subsurface heat flow field. Except for the parallel isolines characteristic for the horizontally layered medium, one can observe quite complex heat flow pattern. The southern (higher) edge is 0.5 K warmer, while the northern (lower) edge is 0.5 K cooler that the borehole site. The heat flow pattern is rather complicated with the concentration of isolines at the middle plateau, where the borehole is located. The largest gradients occur in the upper 100 m depth interval directly under the borehole site. The disturbances of less than 0.5% appear only below 500 m depth. This depth thus represents characteristic distance of the penetration of topography effect in the case of the relatively smooth relief and low elevation. Calculated normalized heat flow steadily increases with depth from 0.86 at 25 m to 0.93 at 150 m. These values are 7–14% lower than the basal undisturbed heat flow. Figure 55 illustrates possible influence of different slope configurations and subsurface structure on the vertical heat flow profiles at the borehole site (Safanda, 1994). As previously, all models implied 2-D conduction in the complex layered medium. Curve 1a corresponds to the position below Sporilov borehole shown in Figure 54, and thus represents south–north inclined topography with subsurface layers inclined by 50° to north. Curve 1b illustrates the influence of the opposite subsurface inclination. 12 The adiabatic lapse rate means the temperature decrease in the atmosphere as a function of elevation, assuming that air behaves adiabatically. The atmospheric lapse rate varies with temperature and pressure. In the air saturated with water vapor it is generally near 4.9 K/km.

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Fig. 54. Effect of topography simulated for the GFU-1 site. Isolines represent the pattern of the vertical heat flow normalized to the basal heat flow at 5.5 km depth (Data by Safanda, 1994). The southern/northern slopes are 0.5 K warmer and/or cooler, respectively, compared with the flat terrain.

Fig. 55. Distortion of heat flow in borehole GFU-1 due to the topography (Figure 54) for different models of subsurface structure and conductivity (see text). Profile 1a corresponds to the preferred model.

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It represents the same case as the 1a curve, but with the subsurface layers inclined to south. Distortion obtained in this case is 40% lower than in the previous case. This hints that in some cases topography and subsurface structure can balance the effects of each other. Cases 2a and 2b were calculated for the same conductivity models as 1a and 1b curves under the assumption that the temperature does not depend on the slope orientation, but on the altitude only. Both curves coincide and show still lower topographic disturbances than curve 1b. The above four models have taken into account effects of topography and subsurface anisotropy. Disturbances to the vertical heat flow in the isotropic medium, in other words, an effect of topography alone, are shown by curves 3 and 4, where the GST depends on the altitude or slope orientation, respectively. Significant distortion can be achieved in the case that has taken into account the slope orientation. According to Safanda (1994), the discrepancies between investigated models can be attributed to the asymmetric GST pattern on the edges of the plateau, where borehole GFU-1 is located. Finally, curve 5 represents the kind of the model 4 where a step climatic temperature increase of 1K, which occurred 70 years ago, was added to the GST; thus, it illustrates combined effect of topography and climate change. As seen, the curvature of the profile 5 significantly differs from traditional “U-shape” that occurs due to climatic influence alone (e.g. Figures 13 and 20). All presented examples show that there is a potential to obtain erroneous GST history from inversion of the “raw” temperature–depth profiles and corroborate the necessity of the topographic correction of measured temperatures to improve the fidelity of the estimated GST reconstructions. According to Safanda (1994), curve 1a represents the most realistic model of the topographic effect in the vicinity of the Sporilov borehole (simultaneously it exhibits the largest heat flow distortion within the 20–150m range). This model was used for correction of the measured heat flow values. The magnitude of the topographic correction reached 5–8mW/m2; thus, it composed 8–13% of an undisturbed heat flow. The above study has described the topographical perturbation to the steady-state subsurface temperature field. Some of numerical studies, as presented by Kohl (1999), considered the possible influence of topography on transient temperature signals and thus concentrated on the more real case when climatic disturbance is distorted by the topographic influence. As shown in numerous above-cited studies, the topographic disturbances appear to be most significant in some hundreds meters of the subsurface. This depth interval is strongly affected also by the climatic changes that occurred during the last 100–500 years. The investigations by Kohl (1999) have shown that in many locations topographic effects and GST signals can be mixed and inversion of such temperature logs may yield arbitrary results. In this work, the study of the role of each influence on the measured signal was performed on the basis of numerical solution of the 2-D forward heat transfer problem for synthetic sinusoidal topography with varying amplitudes and wavelengths as well as by the simulation of the real relief types and different kinds of GST history. Such detailed study has provided an estimation of the topographic disturbance for the large number of the field situations. To quantify the errors in the GST reconstructions occurring when temperature logs are used without topographic correction, the vertical temperature–depth profiles were extracted from calculated 2-D patterns. Comparison of the GST histories inverted from T–z profiles with and without correction revealed the errors occurring due to the topographic effect. Numerical trial runs have shown that the interpretation of transient temperature profiles is very sensitive to the surface relief. Experiments with sinusoidal surfaces have revealed

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that the topographic effect became stronger in the case of small wavelength and/or higher altitude topography. Larger wavelengths penetrate deeper into the subsurface. The topographic influence may strongly disturb the subsurface temperature signal, and thus generally results in the erroneous GST histories. Even rather flat topographies with 20 km wavelengths and 100 m amplitudes may introduce disturbances that perceptible distort the course of inverted GST. The T–z profiles measured in boreholes located at elevations tend to pronounce a GST cooling and to reduce warming, while the topographic influence at valleys gives an opposite effect. The most recent GST changes are practically unrecoverable from temperature profiles containing topographic perturbations. On the other hand, more remote and/or strong paleoclimate signals present even in the temperature profiles disturbed by rough relief and can provide correct inversion results when data are properly treated. Major conclusion of this work was that it is useful (in some field cases indispensable) to evaluate the site-specific effects before performing the GST inversion. On the basis of the multiple simulations Kohl (1999) has worked out optimal strategy for the topographic correction. Suggested procedure includes the 3-D numerical simulation of the synthetic steady-state temperature field for the local topographic conditions/subsurface structure and subtracting this effect from the measured temperature–depth profile. Results by Kohl (1999) have shown that in most field cases correction is successful and transient surface temperature changes can be accurately extracted even for the rough Alpine environments. Another approach to treat terrain “noise” in the borehole data was applied in the works by Pollack et al. (1996) and Beltrami et al. (1997) who performed simultaneous inversion of ensembles of borehole logs or alternative approach of averaging the individual inversion results. The authors have shown that for the suite of boreholes from the regions with differing topographic settings, the GST averaging and/or simultaneous inversion of the multiple temperature logs yield closely identical results. Calculations by Kohl (1999) have corroborated the conclusion that if statistically sufficient number of boreholes is considered, reliable GST histories can be inferred without application of a topographic and/or other terrain correction for individual boreholes, because for locations with significantly varying surface shape/cover, the magnitudes of arising disturbances can adjust themselves. However, no general rule can be applied. 2.7 Non-Conductive Heat Transfer Effect on the GST Reconstruction (Groundwater Flow Effects) Similarly to the topography effect the groundwater flow is an important kind of the representational errors arising from the fact that all forward and/or inverse methods of the GST reconstruction are based on the assumption of the exclusive heat conduction (no advection)13 in a one-dimensional, vertically heterogeneous medium (for details see previous section). Conduction theory is an ideal approach for the description of heat transfer in consolidated rock bodies with no significant fluid movement. Natural situations are 13 Advection represents transport of some conserved scalar quantity in a vector field, when the mass moves to a region where this quantity has a different value. Meteorologists deal with the advection of variables like temperature or moisture in the atmosphere caused by movement of air by the wind (e.g. the change in temperature when a warm air moves to cold region). Hydrologists are interested in the advection of heat or pollutants by a fluid movement. Generally, any substance/variable can be advected, in a similar way, by any fluid.

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Fig. 56. Typical geologic cross-section of the subsurface in the vicinity of the borehole.

far more complex. Typical geologic cross-section of the subsurface in the vicinity of the borehole is presented in Figure 56. The departures of the real field situation from an ideal homogeneous layered approach may be caused by the lateral heterogeneity in thermal conductivity (see Section 2.4.2) and other physical properties, topography of the upper boundary, temporal changes in the surface (snow, ice, and vegetation cover; Section 2.6) as well as the convective disturbances to temperatures that can occur due to water flow of two types – within the fluid-filled borehole and regional water flow. The small diameter of most of the boreholes relative to their length prevents the occurrence of any intrahole convection strong enough to disturb the thermal regime significantly (Jessop, 1990). On the contrary, regional water flow effect represents one of the most common and serious obstacles for the GST reconstructions. Groundwater is water located beneath the ground surface in soil pore spaces and in the fractures of the geologic formations. The depth at which soil pore spaces become saturated with water is known as the water table. An aquifer is an underground layer of the water-containing permeable rock. It can also represent less consolidated materials (gravel, sand, etc.) from which groundwater can be extracted using a water well. An example of the regional groundwater flow system is presented in Figure 57. Groundwater is recharged from and/or discharged to the surface naturally. Natural discharge may occur at springs and seeps; it can result in wetlands and waterlogged areas. It should be described by at least 2-D physical models. Such modeling can be easily performed by forward calculations, while present-day GST reconstruction techniques are developed for the 1-D situation and thus cannot capture effects of topography and/or groundwater flow in principle. As shown by Kane et al. (2001), an assumption of pure conductive heat transfer through the medium is justified only for subsurfaces with negligible vertical groundwater flow. Otherwise advective component of the heat transfer associated with subsurface fluid movements is capable of producing the curvature in a temperature–depth profile that can be easily confused with the transient climatic effect. Generally, groundwater effect represents one of the most common and serious obstacles for the GST reconstructions. The influence of the groundwater flow on the subsurface temperature is a consequence of a far more effective heat transfer by moving fluid (advection)

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Fig. 57. Cartoon showing the regional groundwater flow system.

than by the thermal conduction in rocks. Lewis and Wang (1992), citing the field examples, warned that even very small water flows could significantly distort the temperature log and thus affect the reconstructed GST history. They gave an example that reconstructing past climate by using data from a number of boreholes in a relatively small area of Lac Dufault (Canada) revealed the onset times of the recent warming in the unrealistic range of 35–150 years ago. Wang et al. (1994) has indicated that vast amount of Canadian temperature logs was excluded from the GST reconstruction because of obvious groundwater disturbances. Using several synthetic T–z profiles as well as the field example and applying general least squares inversion techniques in the ramp/step method Kukkonen et al. (1994) have simulated a situation of “misinterpreting” the curvature of the temperature logs caused by advective disturbances due to groundwater movement in terms of the GST change. Even this simple model of the 1-D effect in the homogeneous half-space with constant soaking velocity of the fluid has demonstrated that it is possible to obtain GST histories from the boreholes with hydrological disturbances, which are acceptable with respect of our knowledge of recent climatic changes, and thus clearly indicated the probable risk of misinterpretation. In geothermics and borehole climatology hydrologic disturbances are regarded as undesirable anomalies that should be discarded or corrected. On the contrary, hydrologists are interested in the analysis of the temperature anomalies themselves to determine the pattern of the groundwater movements and their rates, groundwater flow and hydraulic conductivity with coupled water flow, and heat transfer models. The principles of using geothermal measurements as a groundwater tracer were worked out in the 1960s (e.g. Stallman, 1965) and were further developed in the recent works. For the climatologists’ consolation it should be mentioned that, alternatively, climatic influence on the temperature profiles was often mistakenly interpreted by hydrologists as a result of advective heat transport (e.g. Ferguson and Woodbury, 2005 and the references therein). Reiter (2004) has examined the effect of participation of both groundwater flow and climate change on the occurrence of the curved temperature logs using data from two boreholes located in central New Mexico and in eastern Canada. He has simulated a number of models that combined different cases of the water flow and the GST change, and has compared them with measured temperature logs. Based on the statistical analysis of the misfits, he has concluded that in many field situations observed subsurface anomalies may be explained by either groundwater flow or the GST change impact.

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Because advection could mask or overwhelm climate-induced ground temperature changes, the negligible hydrological activity in the vicinity of investigated boreholes was thus of key importance for every past climate reconstruction approach until quite recently. In practice the researchers have performed screening of the geothermal data before processing and have used for climatic reconstructions only undisturbed and/or input data corrected for the hydrological disturbances. Most field examples, however, suffer from the general lack of satisfactory hydrological information. Thus, in the majority of the borehole sites the screening procedure represents not an easy task. According to Ferguson et al. (2003) and Reiter (2005), at least some of the GST reconstructions were performed without sufficient justification of the absence of groundwater flow. In practice borehole T–z profiles were often used for opposing task, namely as a groundwater tracer to identify surface water infiltration, flow-through fractures, and flow patterns in groundwater areas (see the review paper by Anderson (2005) and the references therein). Three principal kinds of the groundwater movement in the vicinity of the boreholes are: (1) soaking through permeable formations, (2) up- or down-flow at narrow dipping fracture zones, (3) flow between two aquifers or fracture systems and movement of groundwater within drill hole, and the corresponding temperature–depth profiles. Figure 58 schematically sketches possible kinds of the groundwater movement in the three-layered subsurface. Surface temperature is T0 and undisturbed temperatures at the

Fig. 58. Principal types of groundwater movement in the vicinity of a borehole and corresponding temperature logs with characteristic hydrologic disturbances. Dashed line corresponds to an undisturbed geotherm.

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boundaries of the first and second layers and the second and third layers are T1 and T2, respectively (Figure 58, top-left). The deviations from this undisturbed geotherm caused by the vertically moving water are presented in further panels. Top-center: Upward groundwater flow in the upper layer produces high subsurface temperatures. At the surface increased temperature steeply reduces to a value T0 determined by the current climate. This reduction creates curvature similar to that characteristic for the recent surface temperature cooling (see Figure 20). Top-right: Disturbance is much smaller in the case of upward water movement in the second layer, when the upper layer is impermeable and the heat transfer there realized by pure conduction. Basement temperature T2 remains unchanged and the temperature at the first–second layers boundary slightly increases, producing lower geothermal gradient in the second layer. Bottom: These examples illustrate an influence of the downward groundwater flow causing temperature decrease in the corresponding layers that increases with depth. Based on the field studies, Drury et al. (1984) have suggested effective empirical method to detect water flow in boreholes that intersect a flow zone. These authors have detected principal flow types that generate characteristic signature on the temperature field similar to the one presented in Figure 58 and can be easily observed on the precise temperature–depth profiles measured at closely spaced intervals. Thus, often observable “step” profiles with low or even zero thermal gradients appear for the kind of water movement presented in the bottom- and top-right examples of Figure 58. Sharp discontinuities in a temperature–depth profiles (so-called “spike” anomalies) may reflect the in- and/or outflow of fluid and its movement within the borehole as well as groundwater flow along a fault or narrow horizontal layer. Such anomalies are usually well attributable. On the other hand, vertical groundwater flow can produce smooth curvature of a temperature log that can be undistinguishable from climatic disturbance (see examples presented in Figure 59). Pfister and Rybach (1995) have examined numerous temperature logs from the Marmara region (NW Turkey) characterized by frequent manifestations of anomalously high temperature gradients in the subsurface, thermal springs, and steaming grounds, and also have recommended a high-resolution temperature logging as a powerful tool to obtain information about various groundwater flow regimes. Majorowicz et al. (2006) have analyzed possible influence of the groundwater flow on the temperature logs measured in the Paskapoo Formation in the western part of the Western Canada Sedimentary Basin. This formation represents a mudstone-dominated unit with 5–15 m thick interbedded sand channels that can form separated aquifer units. All 11 available Paskapoo temperature logs exhibit noticeable “U-shape”. However, modeling of the groundwater flow influence has shown that the downward flow in the region creates curvature in T–z profiles similar to the manifestations of recent warming. While the minimal recharge rates of ⬃5 mm/year likely have minimal impact, the models using highest possible recharge rates of ⬃25 mm/year have proved that the subsurface water flow can affect temperature profile and thus be responsible for at least a part of observed curvature. Reiter (2001) has worked out a method for estimation of the horizontal and vertical specific discharge components of groundwater flow from precision subsurface temperature measurements. The procedure includes plotting the vertical temperature gradient as a function of both z and T. Fitting this plane to the data can provide the coefficients for estimation of the vertical and horizontal flow components. The testing of suggested procedure with field examples from different flow zones with a great variety of possible flow characteristics has shown

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Fig. 59. Schematic temperature–depth profiles showing the deviations from the undisturbed gradient caused by warming in the surficial layer and groundwater convection in the geothermal zone. Recharge (downward movement of ground water) results in concave upward profiles, while discharge (upward flow) produces convex upward profiles.

good coincidence of obtained results with available hydrological information. On the other hand, this method takes into account hydrological disturbances alone, and thus can give misleading results, when the temperature log also contains climatic influence. Kohl (1998) first has modeled combined effect of both climatic and hydrological disturbances. He has introduced the problem of the GST reconstruction for the temperature–depth profiles containing hydraulic disturbances by taking into account the combined effect of conductive heat diffusion and advection in a transient state by series of forward synthetic calculations. He has demonstrated that the climate signal could not be completely washed out by hydraulic advection even in the strongly advective dominated system. On the other hand, he has also emphasized that the paleoclimate reconstruction from advectively perturbed data using pure conductive approach may provide significantly distorted results even in the case of a strong climatic event.

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How do groundwater disturbances appear in the borehole temperature logs? Generally, two types of fluid circulation in the subsurface occur: (a) forced convection driven by a fluid pressure gradient such as topographically induced convection, and/or (b) free convection resulting from the temperature gradient and producing fluid density variation in a strata heated from below. In the first case (Figure 57), the flow never creates closed convective cells otherwise characteristic for the free convection. Induced flow driven by the gravity force is directed towards the topographic lows with the minimum hydraulic potential. The vertical velocities of fluid flow in this case are enhanced in comparison with the flow rates typical for a free convection in a flat terrain (Bodri, 1994). The basic theory of the heat transfer as applied to the ground water problems can be formulated as follows. If the heterogeneous crust has no major fractures, it can be simulated as a porous medium; that is, replacing the existing network of fractures for flow with a continuous porous medium having effective hydraulic properties. Such “equivalent” porous medium is not capable to interpret adequately small-scale measurements; however, it can successfully be used for the large-scale modeling, when only “average” behavior is required (De Marsily, 1985). Consider an equivalent porous medium saturated with a single-phase fluid and assume that the medium remains more or less stationary for a long time. The fluid (water) is an incompressible liquid whose density depends upon temperature according to the law  ⫽ f [1⫺(T⫺Tr)]. The reasons for the fluid flow are, on one hand, the pressure gradients and, on the other, the external gravity forces. The 3-D system of balance equations of mass, momentum, and energy for natural convection in a non-deformable rock matrix in Boussinesq approximation14 can be written as (Ene and Polisevski, 1987) divv ⫽ 0, k v ⫽⫺ {gradP ⫺ gf [1 ⫺  (T ⫺ Tr )]},  T (c)m ⫹ (c)f vgradT ⫽ div( K m gradT ) ⫹ (1 ⫺ )A t

(37)

where t is the time, v Darcy velocity,  the dynamic viscosity of fluid, k the permeability tensor,  the total porosity, P pressure, T temperature, Tr uniform reference temperature,  the volumetric coefficient of thermal expansion, K the thermal conductivity, (c) volumetric heat capacity, A the radioactive heat generation, and g acceleration due to gravity. Subscripts “f ” and “m” denote the fluid and the medium, respectively. The second equation represents the generalized form of the Darcy’s law. This law can be simplified for incompressible fluids as v ⫽⫺

kf g gradh, 

(38)

where h is the hydraulic head. Concerning boundary conditions, on impervious boundaries we assume that the normal component of fluid velocity will vanish. At a free surface the pressure P is equal to the atmospheric pressure at any point of the free surface. Expressed in hydraulic head it can be written as h ⫽ z. Boundary conditions for 14 The Boussinesq approximation is used in the fluid dynamics and states that for buoyancy-driven flow the density differences are small and can be neglected, except where they appear in gravity terms. This approximation is quite accurate for a variety of natural flows, and makes mathematical modeling simpler.

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temperature in the 1-D case remain as previously a prescribed temperature on the free surface and prescribed heat flow on the lower boundary. Figure 59 illustrates possible disturbances to the conductive geotherm that can occur in zones of the vertical fluid flow. Calculations were performed with the 1-D analog of Eq. (37), that takes into account advective heat transport in the porous medium with thermal conductivity and volumetric heat capacity of 2.5 W/mK and 2.5 ⫻ 106 J/m3 K, respectively, and volumetric heat capacity of fluid 4.187 ⫻ 106 J/m3 K. Geothermal gradient equals 20 K/km. Initial surface temperature was 0°C, and sharp temperature increase of 1°C occurred 50 years B.P. Relatively high Darcy’s velocities of ⫾10⫺8 m/s were used in calculation to emphasize the effect of fluid flow. In the absence of ground water flow, subsurface temperature has curvature (“U-shape”) in the surficial zone and normally follows the linear steady-state geotherm below it. Ground water flow perturbs the geothermal gradient by infiltration of relatively cold fluid in recharge areas and upward flow of warmer fluid in discharge areas, causing concave upward profiles in recharge area and convex upward geotherms in discharge areas. Amplitude of disturbances in this case decreases with depth more slowly than for pure conductive conditions. Figure 60 presents results of numerical trial runs with synthetic temperature logs to demonstrate the impact of hydraulic flow in the case of moderate advection in the stratum. Temperature–depth profiles were generated for the homogeneous half-space with the thermal parameters and surface history used in the example above. For the sake of simplicity the flow velocity was assumed to be steady state and constant all over the investigated depth. The noticeable advective temperature disturbances can occur for flow velocities above 10⫺9 m/s (“moderate” convection). In case of topographically

Fig. 60. Left: Synthetic temperature logs. “Conductive” indicates the solution obtained for pure conductive regime. Other T–z profiles correspond to combined conductive plus advective regimes with different up- and down-flow velocities. Right: Reduced temperature logs. Reduced temperatures for advectively disturbed temperature field were obtained for reducing parameters of T0 ⫽ 0°C, G ⫽ 20 K/km.

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induced convection such velocity corresponds to a hydraulic flow driven by a surface head difference of 100–200 m along 1–3 km long profile in a medium with permeability of ⬃10⫺15 m2. At higher fluid velocities of (1–5) ⫻ 10⫺8 m/s the temperature field is dominated by advection (“strong” convection), which may considerably distort the purely conductive geotherms. The typical T–z profile associated with downward groundwater flow exhibits expressive curvature. Being “corrected” in our case for a constant geothermal gradient (reduced temperature, Figure 60, right) it shows temperature changes similar to the recent warming in the upper part of the profile and cooling in its deeper section. The paleoclimatic signal is not necessarily washed out, but may be significantly distorted (0.5–1 K or even larger differences from the purely conductive geotherm). In case of downward fluid flow the advection lowers the existing temperature gradient and vice versa. Produced by the “moderate” convection hydraulic disturbances to the temperature log are very similar to the purely conductive conditions, and it is not easy to recognize which effect dominates. Bodri and Cermak (2005a) have made an attempt to include advective disturbances due to vertical subsurface fluid flow in the usual 1-D inversion procedure for POM estimates (Section 2.5). This approach seems capable of yielding good results in major groundwater recharge or discharge areas. The series of tests on both synthetic and field examples indicated that the advective/conductive joint inference of the POM-temperature improves the earlier pure conductive models and provides more reliable estimates, thus making possible the use of a vast amount of the previously rejected temperature logs for past climate reconstructions. In this approach the impact of hydraulic flow on the paleoclimatic temperature signal was investigated for a simple 1-D model in case of the semi-infinite medium. Even though it may be far from the realistic 3-D and/or 2-D case presented in Figure 57, such model has a potential to be used as a general approach when fluid flow is suspected to exist in boreholes where temperature log were measured. The 1-D representation of Eq. (37) with constant velocity of fluid was used as the mathematical model. Negative velocity corresponds to the fluid flow towards the surface. The surface boundary conditions remain the same, as in the pure conductive approach; at large depth (reduced) temperature gradient equals zero and the temperature disturbance due to surface climate variations tends to be negligible. As previously, initial T–z profile can be obtained from the assumption of the original steady-state temperature conditions. The unknown parameters are the POM-value and the fluid velocity (v). Similarly to the pure conductive case, both parameters can be estimated by comparing reduced temperatures with synthetic temperature logs using least squares inversion technique. Certain shortcomings of the method are: (1) the assumption of the purely vertical flow, (2) the assumption of constant velocity, which likely is not the case for the most of field situations, and (3) the assumption that the fluid flow conditions have been constant for a long time (at least during the last 100 years or so). The influence of the first assumption is investigated in Bense and Kooi (2004) and Bodri and Cermak (2005a) by means of synthetic models describing the areas where considerable vertical ground fluid flow and surface warming occur simultaneously. As shown, in the discharge and recharge areas estimated with the 1-D approach velocities coincide well with the average vertical velocities of the fluid circulation in the whole investigated depth interval. Modeling results by Lu and Ge (1996) and Reiter (2001) have revealed that horizontal groundwater flow can also produce temperature anomalies that can further be misinterpreted as the result of

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past GST changes. According to the calculations by Bodri and Cermak (2005a), the velocity estimated in zones of sub-horizontal rather than vertical directions of flow is comparable with the mean value of total velocity of the fluid movement in such regions. The differences between the real POM-value and estimated from 1-D model were relatively insignificant even in zones of generally sub-horizontal water movements, where the declination from the pure vertical flow reaches its maximum. As third shortcoming of the method, the fluid flow conditions depend on many factors. They are closely related to recharge of precipitation and can show wide fluctuations; in the media composed of more soluble rocks the dissolution of the material under the fluid flow can increase the permeability of the strata with time; on the contrary, the flow of fluid with high degree of the mineralization can significantly lower the primary high permeability of rocks with time, thus retarding fluid flow, etc. However, as shown by Fetter (1988), most of the local and/or regional flow systems with relatively flat water table in the less permeable strata can be generally considered as possessing a dynamic equilibrium. Drastic changes of the hydrological properties would have to occur before this becomes an important factor. Thus, the third assumption does not present a serious problem. An example in Figure 61 presents a synthetically generated noise free temperature log in a homogeneous half-space described by K ⫽ 2.5 W/mK, (c)m ⫽ 2.5 MJ/m3 K, and G ⫽ 20 K/km. To simulate the surface conditions we used POM of 9.2°C before year 1900 and the surface air temperature since then corresponding to the SAT record observed at the meteorological station Prague-Klementinum (Figure 64). The mean SAT-value for Klementinum for the twentieth century equals to 9.71°C, so the chosen

Fig. 61. Left: Synthetic temperature logs. “Conductive” indicates the solution obtained for a pure conductive regime. Solid black line corresponds to the combined conductive plus advective regimes (down-flow velocity 10⫺9 m/s). Right: Reduced temperature logs. Reduced temperature for advectively disturbed temperature field was obtained for reducing parameters of T0 ⫽ 9.2°C, G ⫽ 20 K/km. “Best-fit” temperature corresponds to conductive regime (for details see text).

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POM-value corresponds to 0.5 K as the last century warming. To calculate synthetic temperature log Tsynt(POM,v) (Figure 61), we used down-flow fluid velocity of 1 ⫻ 10⫺9 m/s. For comparison the geotherm calculated for pure conductive conditions is also presented. As seen, the downward fluid flow lowers the existing temperature gradient. As the calculated synthetic temperature–depth profile is noise free, the estimation of the POM-value and of the fluid velocity, taking into account the advective heat transport, should return the input values. The degree of conformity between the real and synthetic models is usually characterized by the sum of the squares of deviations between measured and synthetic temperature logs. We have calculated root mean square (rms) misfits for wide spectra of possible POM and fluid velocity (v) combinations to determine the bestfit parameters. Generally, preferred values of estimated parameters correspond to the minimum of rms misfits. Figure 62 shows the respective map of the rms misfit as a function of parameters POM and v. As in the previous case of single POM estimation (Section 2.5), the relatively small differences in the estimated parameters can produce significant misfits of the calculated model from the measured temperature log. A single sharp minimum on the rms misfit map implies the stability and uniqueness of the solution and indicates the robustness of the method of joint POM–v estimate. Applying the purely conductive approach to estimate POM-value from the advectively disturbed temperature log we obtain POM-temperature that can differ from its real value. In the above case the POM-value estimated with conductive approach equals to 9.7°C; thus,

Fig. 62. Map of the rms misfit (in Kelvin) as a function of POM-values and fluid velocities for synthetic example shown in Figure 61.

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it is 0.5 K higher that the real value 9.2°C used for the calculation of the synthetic temperature log. Such discrepancy may lead to a misleading interpretation of the twentieth century warming. The value of 9.2°C (used as the pre-1900 temperature) means a 0.5 K warming relatively to 9.71°C, the mean value corresponding to the period 1900–1992, that can be used as the characteristic of the average twentieth century temperature, while the value of 9.7°C calculated under conductive conditions suggests no twentieth century warming at all. The rms misfit between simulated and best-fit calculated temperature logs reaches 0.91 K. As shown in Figure 61 (right), a satisfactory fitting can be achieved only in the uppermost part (40–50 m) of the reduced temperature log. Numerical trial runs with synthetic temperature logs have shown that if the pure conductive approach is used to estimate POM-value from the advectively disturbed temperature log, the corresponding error of the POM estimate depends on the direction and velocity of the hydraulic flow. In the case of fluid up-flow with velocity of 1⫻10⫺9 m/s, the estimated POM-value is 8.4°C, which indicates ⬃1.3 K for the twentieth century warming (rms misfit is high and equals 1.3 K). If the pure conductive approach is applied to estimate POM-value in an advection-dominated system of fluid velocities of 1⫺5⫻10⫺8 m/s, the deviation of the estimated pure conductive POMvalue from the real values corresponding to the down-flow fluid movement may be 1–1.5 K above its true value. Similar, but even more pronounced results could be obtained for the case of upward flow. With the present assumption the maximum permitted velocities of the up-flow movement should not exceed 10⫺8 m/s; higher velocities cannot be applied as such a situation would lead to heating up of the surface and would violate the chosen surface boundary condition. When pure conductive approach is applied to temperature logs affected by strongly advective dominated temperature field, the graph of the rms misfit as a function of POMcan be more complex than a single sharp minimum, presented in Figure 42. It may contain a “flat” extreme and/or two or more local minima, indicating an unstable or a nonunique solution. If such rms pattern is obtained in a practical case, this may indicate a presence of advective disturbances in the measured temperature log. The joint POM–v estimation using field examples is presented below. Temperature–depth measurements were performed for four closely spaced 150 m deep holes near Tachlovice, the site located about 15 km SW of Prague (50.01°N, 14.24°E, 350 m asl). All boreholes penetrated relatively homogeneous stratum of Silurian and Devonian shales. Temperature measurements were taken at 2.5 m depth intervals to the depth 20 m and at 5 m intervals below 20 m. Thermal conductivity was estimated (Bodri and Cermak, 1995) on the basis of the geological survey and amounts to 2.5 W/mK, the value characteristic for typical rocks of the Bohemian massif. All four temperature records are practically identical, all showing a clear “U-shape” (Figure 63), which suggests a recent warming. Minor distortions of the U-shape can be indicative of water movement effects in the top section of the drilled strata of a topographic depression representing a discharge area among surrounding low hills. The borehole sites are located in the slightly undulated terrain on the gently sloping side of the small river. The water flow in pores and in small fractures is driven by a surface head difference of ⬃30–50 m across 3–5 m distance. Relatively low hydraulic conductivity of shales (10⫺8–10⫺4 m/day, Fetter, 1988) retards the flow; thus, relatively low Darcy velocities may be expected in the area.

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Fig. 63. Temperature logs measured in four holes at site Tachlovice (Tach-I to Tach-IV), Czech Republic.

As a representative SAT record we used mean annual temperatures measured at meteorological station Prague-Klementinum (Figure 64). As mentioned above, the mean temperatures corresponding to 1900–1992 and 1960–1992 periods equal 9.71 and 10.07°C, respectively. For inversion we used temperature–depth data only from below 15 m depth to exclude any seasonal temperature variations. The reducing parameters (T0 – surface temperature and G – temperature gradient) were calculated by the linear regression of the deepest part of the T–z records. The estimated POM-values prior to year 1900, taking into account the advective influence, are presented in Table 4. The obtained best-fit values correspond to single minimum in the rms misfit maps (presented in Figure 65) indicating the uniqueness of the solution obtained for the given model formulation. Figure 66 shows the comparison of observed reduced temperatures with the best-fit temperatures calculated for both approaches. As shown, in all cases the inclusion of fluid circulation significantly improved the rms misfit; thus, temperature logs calculated with “conductive plus advective” approach almost perfectly reproduce the observed temperatures. Values of the rms misfit in Table 4 range between 0.06 and 0.08 K. They are 2.5–4 times lower than the rms misfits obtained when we used pure conductive model for the parameter estimation. An additional test of significance of both models can be provided by comparison of the variance of the

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Fig. 64. SAT annual values recorded at the meteorological station Prague-Klementinum. POM1 is the average pre-observational mean temperature corresponding to a pure conductive approach and calculated for all four Tachlovice boreholes; POM2 is the same as POM1, but with the advective disturbances included. Table 4. Reducing parameters and results of POM estimations for boreholes in Tachlovice with pure conductive and conductive/advective approaches Borehole

Tach-I

Tach-II

Tach-III

Tach-IV

G (K/km) T0 (°C) Pure conductive approach POM (°C) rms (K)
a (K) Conductive ⫹ advective POM (°C) v (10⫺9 m/s) rms (K)
(K)

24.36 8.64

22.02 8.94

23.26 8.71

22.93 8.76

9.98 0.205 0.09

10.12 0.276 ⫺0.05

9.98 0.203 0.09

9.97 0.179 0.10

10.34 ⫺2.2 0.055 ⫺0.27

10.62 ⫺2.6 0.076 ⫺0.55

10.33 ⫺1.9 0.071 ⫺0.26

10.27 ⫺1.7 0.068 ⫺0.20


is the amount of warming relatively to 1960–1992 SAT mean, which equals to 10.07°C.

a

differences between model and measurements and the variance of the model. If the phenomenon described by the model plays significant role in the occurrence of the signal, the dispersion of the deviations from the model will be small in comparison with the dispersion of the model. For the Tachlovice boreholes the use of the model taking into account advective disturbances explains 83–95% of the transient signal, while the use of the pure conductive model could explain not more than 27–58% of the temperature signal. In the real field situations, when no or only limited hydrological information is available, the poor coincidence of the reduced and the calculated temperature log under the pure conductive approach may imply on the presence of advective disturbances. The estimated velocities of fluid migration are almost identical for all boreholes and indicate a slow upward movement in the discharge area of topographically induced subsurface convection. All obtained POM-values are similar and somewhat exceed 10°C.

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Fig. 65. Map of the rms misfit (in Kelvin) as a function of the POM and fluid velocities for borehole Tach-I. The position of minimum corresponds to the best fit for POM ⫽ 10.34°C and v ⫽⫺2.22 ⫻ 10⫺9 m/s.

Calculated POM-values for pure conductive approach are lower than those obtained for conductive/advective approach; the differences amount to 0.3–0.5 K (Table 4). Mean POM-values calculated for the Tachlovice borehole T–z data under pure conductive (POM1 ⫽ 10.01°C) and for conductive/advective (POM2 ⫽ 10.39°C) approaches are shown in Figure 64. The difference is less than 0.4 K; however, as this value is comparable with the amount of twentieth century warming, it is significant for the interpretation of the obtained POM-values. The above analysis showed high sensitivity of borehole temperatures to the POM estimate and fluid flow velocity. Even a relatively small difference in POM and/or fluid velocity caused significantly poorer fit with observed data. A well-developed minimum in the misfit function indicates the robustness of the described method. Regardless of being only a first approximation to the full problem, the method offered reasonable POM-values that could explain near 80–95% of the transient borehole temperature signal and enabled to estimate Darcy fluid velocities directly from measured temperature logs. The study suggests that even advectively distorted borehole temperature log may still contain a valuable signal, which can be used in paleoclimate reconstruction to assess the POM-values. Except for extreme cases, such as rough topography, the climate signal cannot be completely “washed out” by hydraulic advection. Bodri and Cermak (2005a) method: (1) can help to distinguish boreholes affected by advective disturbances on the base of temperature log only, when independent hydrologic information is not available, and (2) saves the possibility to include some of the previously rejected temperature logs for the paleoclimate analysis. It is especially important for the regions affected by hydraulic disturbances and/or in the regions with a

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Fig. 66. Reduced temperatures (dots) compared with the best-fit transient temperature logs calculated for pure conductive and/or for conductive/advective approaches for Tachlovice boreholes (Tach-I to Tach-IV).

limited number of borehole temperature logs, where an existing datum may be valuable for the informative reconstruction of the climatic history. The above-cited and similar works represent mainly theoretical and/or local studies of the groundwater flow problem. More comprehensive research was performed in the recent work by Ferguson et al. (2006). To provide general criteria for the screening of the temperature logs for the presence of the groundwater disturbances, the authors have examined what are the hydrological conditions that could produce significant advective disturbances and thus distort climatic signal to an unrecoverable state. The authors have modeled recharge15 area conditions, where advective perturbations are expected to be the largest. The 2-D aquifer models were constructed for the wide range of the 15 Recharge area represents a land area where water reaches the zone of saturation from surface infiltration, e.g. where precipitation soaks through the ground to reach an aquifer. Generally, it is connected with the underground aquifer by a highly porous soil or rock layers. Water entering recharge area may flow for kilometers underground. On the contrary, a net annual transfer of water from the ground to the surface (e.g. to streams, lakes, wetlands) occurs in discharge area. Recharge areas are usually in topographic highs, while discharge areas are located in topographic lows.

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downward fluid velocities from 2 ⫻ 10⫺10 to 2 ⫻ 10⫺8 m/s. To estimate the maximum influence of the possible advective disturbance in every modeled situation, the temperature–depth profile corresponding to maximum downward water flow were chosen for further GST inversions. Applied SVD inversion procedure has assumed pure conductive heat transfer regime; thus, obtained apparent GST histories gave account on the advective disturbances only. Numerical trial calculations have shown that for a 200 m deep and 1 km long basin the groundwater flow can produce a significant perturbation (0.3 K or more) to the GST signal only when the Darcy velocity exceeds 3.3 ⫻ 10⫺9 m/s. Higher velocities were necessary to produce similar perturbation at shallower and/or longer basins. Thus, for a 100 m deep basin of the same length the fluid velocity of more than 2⫻10⫺8 m/s was required. In all cases advectively induced perturbations were significantly lower than a typical climatic signal. The 1.5–3 times larger Darcy velocities would be required to produce an apparent GST history comparable in the order of magnitude with generally observable climatic trends. Because (1) recharge area is generally connected with the underground aquifer by highly porous soil or rock stratum and (2) a net infiltration of rainwater depends on the precipitation amount, Ferguson et al. (2006) have also studied an influence of the hydrogeological parameters of the medium and meteorological conditions on the groundwater disturbances and have concluded that advection will have detectable effect on the GST history only in (1) moderately to highly permeable (k ⬎ 10⫺3 darcy)16 aquifers with (2) high depth to length ratio that (3) can be also characterized by significant amount of precipitation (⬎2000 mm/year).17 Borehole temperature log can be rejected from the GST reconstruction when all three conditions are fulfilled. Their inspection of available database has shown that available temperature profiles were measured in the environments well below these parameters. Moreover, according to the survey by Ferguson et al. (2006) the coincidence of all three conditions occurs only rarely. The above study has indicated that advective disturbances unlikely represent serious problem for the majority of the GST reconstructions that have used the International Heat Flow Commission (IHFC) global database (www.geo.lsa.umich.edu/IHFC/index.html). 2.8 Climate Change and Permafrost In Section 2.6.2 we described the GST–SAT coupling in the case of occasionally frozen soils and have concluded that on the long timescales the effect of soil freeze/thaw cycles on the GST–SAT decoupling is not so important. In the most of the land mass of the Northern Hemisphere actual freezing and their occurrence appears to be quite sporadic, and its effect vanishes during averaging over large temporal/spatial scales and likely cannot create a false systematic secular trend in the GST. What about perennially frozen ground, so-called permafrost? It is frequent at high latitudes and was even more widespread during the last glaciation. The thermal effect of the stored and released latent heat may influence subsurface thermal field. In what way GST–SAT coupling occurs in such environments? Can GST changes be inferred from temperature logs measured in The permeability of a consolidated rock equals to (2.5–3.0) ⫻ 10⫺3 darcy (Fetter, 1988). Average annual precipitation amounts to, e.g. 1190 mm (New York, NY), 818 mm (Toronto, Canada), 752 mm (London, U.K.), 1523 (Tokyo, Japan). 16 17

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boreholes drilled in the deep permafrost? Twenty years ago, Lachenbruch and Marshall (1986) have aroused an interest of the scientific community by highlighting the potential of permafrost borehole temperature data to be used for the climate change reconstruction. Term permafrost means the soil that remains in frozen state for more than two years. Nowadays permafrost conditions characterize about 25% of the land in the Northern Hemisphere (together with discontinuous permafrost and/or glacial ice), including vast regions of Canada, Russia, and Alaska. Most permafrost is located at high latitudes, but there is also alpine permafrost and other small permafrost areas at high altitudes in the mountain chains in both the Northern and Southern Hemisphere. Permafrost was more widespread during the past continental glaciation episodes. Evidence of former existence of permafrost has been found in the regions of North America and Eurasia that are far away from the present permafrost boundary. The physics of the permafrost phenomenon is as follows. At high-latitude regions where the mean annual temperature is below 0°C, some of the ground frozen in the winter does not completely thaw in the summer. The thickness of the permafrost layer is controlled by the energy balance at the soil–air surface and the geothermal gradient (Lachenbruch et al., 1988; Smith and Riseborough, 1996). Annual fluctuation of the air temperature from winter to summer is reflected in an attenuated manner in the subsurface (for details see Section 1.3 and Figures 14 and 15). As the surface temperature signal propagates downward, it is delayed in time and its amplitude decreases exponentially with depth due to the diffusive process of heat conduction. Each variation vanishes over a vertical distance related to the period of change and to the thermal diffusivity of the ground. Shorter period fluctuations attenuate more rapidly. The daily temperature wave is practically not observable below 1m depth (Figure 15, Chapter 1). Similarly, annual GST oscillations are detectable at the 10–15m deep uppermost layer. This interval is known as the active layer. It thaws during summer and generally overlays permafrost (Figure 67). Thickness of the active layer varies by time and location. The thermal regime of the seasonally thawed active layer is highly complex, owing to non-conductive heat transfer processes that operate much of the year (Hinkel et al., 1997). Permafrost represents zone just below the active layer that tends to be ice-rich. The annual temperature variations are not visible in this layer and the temperature gradient corresponding to the outflow of heat from the Earth’s interior becomes discernible. If the permafrost is in the thermal equilibrium, the temperature at the level where annual amplitude vanishes is generally regarded as the minimum temperature of the permafrost. It can vary from close to 0°C at the southern rim of the permafrost area to ⫺10°C in northern Alaska (Hinkel et al., 2001) and ⫺13°C in northeastern Siberia (Pavlov, 1994, 1996). The state of permafrost depends strongly on climate. Global climate models predict that the greatest increase in the average annual SAT will occur at high latitudes over 50°N for the few next decades as anthropogenic carbon builds up in the atmosphere (e.g. Woo et al., 1992; Flato et al., 2000). Most simulations suggest that the warming should be enhanced due to ice-albedo feedback mechanisms.18 Because of expected serious impact

18 Ice (and snow) can have very high albedo. It reflects more than 80% of the incident sunlight, while only some 20% is absorbed. If there is climate change that increases the temperature of the surface it will cause melting that lowers the albedo. It results in more absorbed sunlight increasing melting and lowering albedo even more. It is a straightforward positive feedback mechanism. The net result of such feedback could be a rapid increase of the temperature of the Earth’s surface.

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Fig. 67. Permafrost thermal conditions in winter.

of the global warming on the circumpolar regions, this research is of high priority for the global change research community in the recent time. Long-term monitoring of the permafrost began in the last some decades. In 1967, the Greenlandic Geological Survey (GEUS) have started a monitoring of the permafrost temperatures in borehole from Ilulissat (West Greenland), where 21 thermistors have recorded ground temperatures from 0.25 to 15 m depth (Van Tatenhove and Olesen, 1994; http://nsidc.org/data/docs/ fgdc/ggd631_boreholes_greenland; see also Figure 68). The monitoring experiment continued up to 1982. From the mid-1960s to early 1990s the Geological Survey of Canada (GSC) performed regular ground temperature and permafrost measurements at near 40 deep wells previously used for hydrocarbon exploration at the Canadian Arctic Archipelago to north of 75°N latitude. Since 1990 the GSC has established a set of instrumented sites and boreholes for permafrost thermal monitoring. This system crosses several permafrost boundaries with measurement locations in a variety of climatic and/or environmental conditions (http://gsc.nrcan.gc.ca/permafrost/gtnp). In Canada several groups of researchers perform measurements of permafrost temperatures that began in different times during the last 35 years. The GSC also provides the international data management for the Global Terrestrial Network for Permafrost (GTN-P) borehole temperature monitoring program (see below). The U.S. Geological Survey (USGS) plays an important role in providing field-based data on Arctic–Alaska climate monitoring

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Fig. 68. Top: Temperature monitoring at 0.25 and 1.25 m depth in shallow borehole Illulissat (Greenland) from 6 November 1968 to 15 June 1982. Data are biweekly averaged. Bottom: Thickness of the snow cover. Data by Olesen (2003).

(http://geology.usgs.gov/connections/fws/landscapes/arctic_alaska.htm). The European Union project, Permafrost and Climate Change in Europe (PACE), is collecting data from permafrost boreholes in mountainous regions of Spain, Italy, and Scandinavia. It is expected that this permafrost monitoring network will provide high-resolution data for the permafrost studies under changing climatic conditions both in northern latitudes and at high altitudes of Europe. The goal of the Circumpolar Active Layer Monitoring (CALM) program is to observe the response of the active layer and near-surface permafrost to climate change over multi-decadal timescales (www.udel.edu/Geography/calm). The CALM program began in 1991. Its observational network contains more than 100 boreholes for measurements of the soil and permafrost temperatures. Most CALM sites are located in Arctic and sub-Arctic lowlands, while 20 boreholes arranged in the mountainous regions of the Northern Hemisphere above 1300 m elevation. A new Antarctic borehole network belonging to the CALM currently includes more than 10 sites. The GTN-P was initiated by the International Permafrost Association (IPA) to organize and manage a global database of the permafrost observatories for detecting, monitoring, and predicting climate change. It began in 1992. Some 370 boreholes from 16 countries (the majority from the Northern Hemisphere) have been suggested for the inclusion in the GTN-P borehole temperature monitoring system. For example, in the frames of this experiment temperature measurements were performed in more than 20 boreholes in central East Siberia and in Yamal Peninsula (NW Siberia). The catalog of permafrost measurements from Russia includes 122 boreholes (Melnikov, 1998; http://128.138.135.87/data/ggd316.html). Period of observations was primarily from 1980s to early 1990s. Data from this collection

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were used for the construction of the circum-Arctic map of the permafrost and detection of the ground–ice conditions. It is impossible to enumerate all similar projects; thus, finally we mention only the mountain permafrost temperature monitoring experiment in Mongolia (Sharkhuu and Tumrubaatar, 1998; Sharkhuu, 2003). The broader goal of all these projects is to scrutinize the statement suggested in the earlier works by Lachenbruch and Marshall (1986), Lachenbruch et al. (1988) that the permafrost temperatures represent a reasonably good indicator of climate change. Because the temperature measurements in permafrost are important for quantifying the long-term terrestrial response to the climate change at high latitudes (and/or altitudes), it is therefore crucial that observations and their processing continue over decadal or even longer periods to assess trends and to detect cumulative, long-term climatic variations. In principle, available measurements have supported the sensitivity of permafrost to climate and the GST–SAT coupling in cold regions. Observations by Isaksen et al. (2001) performed during 1998–2000 years have shown that the mean annual GST measured in borehole Janssonhaugen (near Svalbard, Norway) closely correlates with the nearby airport SAT data, indicating a direct linkage between the atmosphere and ground on the annual scale. However, such coherence occurred because of the presence of bedrock close to the surface and practically the absence of a boundary layer of snow, vegetation, organic material, and the mineral soil. The small GST–SAT differences were also found in the regions of the northeastern Canadian High Arctic where active layer is also thin (Taylor et al., 2006). In more compound environments observations revealed regional variability of the warming trends and demonstrated that the variations of the permafrost not only depend on the SAT change, but represent a complex response on the ground composition, microrelief, the moisture and depth of active layer, the amount of ice in the ground, etc. The penetration of the surface temperature changes into the ground also can be affected by the insulating effects of vegetation, organic material, or snow cover. As about the vegetation, the Arctic tundra represents treeless land. No true soil is developed in this environment. However, it generally possesses a well-grown surface organic layer of shallow rooted vegetation with most of the biomass concentrated in the roots. This layer is characterized by high porosity and high hydraulic conductivity. When it is dry, it represents very effective insulator (Hinkel et al., 2001). And it is generally dry, because the climate of Arctic tundra is characterized by low precipitation19 coupled with strong, drying winds. Kane et al. (1990) has found that in northern Alaska only 15–17% (10–15% according to Harazono et al., 1995) of the net solar radiation was used for melting and warming of the active layer during warm season. Snow cover provides an insulating layer on the ground surface. It is actually advantageous to plants defending them from the harsh winter cold. The summarized effect of vegetation and snow cover may suppress the surface temperature signal in permafrost and result in seasonal GST–SAT decoupling. Due to high hydraulic conductivity of the tundra surface vegetation layer, when it is wet, it effectively transfers heat by advection. In a dry, unfrozen state, tundra vegetation is an extremely good thermal isolator. When it is water-saturated, advection within this layer can produce rapid changes in the temperature at the organic–mineral surface layers

19

Yearly precipitation including melting snow is generally of 15–25 cm.

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interface during episodic precipitation events. However, because of the low precipitation, the latter effect likely has no significant influence on the annual and longer scale GST–SAT decoupling as the snow cover. The growing season in the Arctic tundra is extremely short (from 50 to 60 days), and the growth of plant species is slow. Plants more likely reproduce by division and building. If the climate does not vary, such conditions provide plant succession, the long-term stability of the surface layer and, thus, invariable long-term mode of the air permafrost temperature coupling. Because the above-mentioned factors are affected by climate change themselves, an interaction of air and permafrost temperatures implicates composite interdependences. It should be also mentioned that the effect of strong, surface-based atmospheric temperature inversions put difficulties in the way of direct GST–SAT comparison in the Arctic region. Within the range of ground elevations subjected to temperature inversions, mean temperatures may actually increase rather than decrease with increasing elevation. Strength of the winter inversions may reach ⬃2 K/100 m (Bradley et al., 1993); thus, they can successively counteract the usual atmospheric thermal lapse rate. Due to all abovementioned factors, direct comparison of the GST and SAT temperatures in the highlatitude areas represents a very difficult task often giving misleading results. Thus, Taylor et al. (2006) when comparing the GST inferred from the data of five boreholes located at the northeastern Canadian Arctic Archipelago with the mean annual SAT from the neighboring meteorological stations for the 1950–2000 period have found correlation coefficient values ranging from 0.91 through 0.03 to ⫺0.87. In earlier work by Lachenbruch and Marshall (1986) it was emphasized that the GST relates to the base of the active layer and not to the actual ground surface. Significant part of this ambiguity can be elucidated by careful long-term precise monitoring of the ground temperature oscillations and related meteorological variables. Figure 68 shows temperature record from the shallow borehole Ilulissat (Greenland) measured from the late 1968 to 1982 (approximately 5000 days). Air temperature in the area varies from slightly below ⫺30°C to about ⫹15°C. Snow definitely protects the ground from freezing, but regardless of this protection temperature below the surface can freeze down as low as ⫺10°C at 0.25 m or to ⫺8°C at 1.25 m depth. During summer the temperature in the uppermost 1 m can be higher above zero up to depth of 1 m (recorded only in 1969–1972), permafrost never melted below 1.25 m depth (well confirmed for the whole 1969–1982 record), so the surface thickness of alternatively melting and freezing conditions (active layer) is about 1 m. In the areas with continuous permafrost and especially cold climate the thickness of the permafrost can vary between approximately 450–600 m (Barrow and/or Prudhoe Bay, Alaska) to 1500 m (northern Lena and Yana River basins in Siberia). Thus, it is comparable and/or exceeds the depth of the most boreholes. There is a time delay between a change in temperature at the ground surface and the change in permafrost at depth. This lag equals to 2–3 months for the time series monitored at 0.25 and 1.25 m depth (Figure 68). Under typical thermal conditions in the Scandinavian permafrost boreholes the annual wave is delayed by half a year from the surface to approximately 8.5 m depth. Zero annual amplitude depth equals to 18 m (Isaksen et al., 2001). At the base of thick permafrost this lag may be on the order of hundreds to thousands of years, while for thin permafrost it ranges from years to decades. Precise soil temperature and moisture monitoring results at Barrow (Alaska) during 1993–1999 period were used to analyze the interactions within atmosphere–snow–active

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layer–permafrost system (Hinkel et al., 2001). A similar study was performed using data of the ground temperature monitoring near Fairbanks (Alaska) (Kane et al., 2001). Both studies were focused on the detection of the interactions between near-surface ground temperature, the physical properties of this layer, and seasonal variations in the underground heat transfer process. The ground temperature monitoring and soil moisture probe data were supplemented by the SAT temperatures from the neighboring meteorological stations. General patterns of the seasonal GST–SAT coupling measured by Hinkel et al. (2001), Kane et al. (2001) were similar to that described in Sections 2.6.1–2.6.2 (Figures 44–47). The authors have identified four seasonal thermal regimes and demonstrated that seasonal variations are caused primarily by freezing, thawing, and redistribution of water contained in the ground. It was proved that the non-conductive heat transport by water and water vapor plays dominant role in specific periods. The snowfree active layer regime occurs in summer and is characterized by a large daily temperature range and fast response to weather fluctuations. The ground moisture content increases rapidly as the thaw front penetrates downward. Latent heat effects are associated not only with thawing. During this period a significant portion of the absorbed solar energy is expended on evapotranspiration (30–65%), while the ground heat flow is relatively low. The evapotranspiration from the surface and within active layer can significantly lower the mean summer temperature of the near-surface ground; thus, summer represents the period of maximum decoupling of surface and ground temperatures. Effect of the summer precipitation is quite low and incomparable with forest sites in interior Alaska and/or northwest Canada, when soil warming of several degrees can occur in response to precipitation events (Hinkel et al., 1997). In the autumn the active layer refreezes from the top down and from the bottom up. The release of latent heat of fusion slows down the penetration of the freezing front, and the temperature of active layer remains constant during the water–ice conversion. This effect is referred to as the “zerocurtain” (see also Section 2.6.2). When it is at work, the upper permafrost is effectively isolated from the surface temperature variations; thus, the temperature signal is effectively attenuated in the depth. This regime is relatively short in duration (30–40 days). Winter represents the time of freezing regime, “zero-curtain” effect is stopped, and heat is removed more rapidly from the upper layer primarily by conduction. The thickness, thermophysical properties, and duration of snow cover have a strong influence on permafrost. In the winter the ground is warmer than the surface. At sites underlain by permafrost in Alaska, the mean annual GST is generally 3–6 K higher than the mean annual SAT. Daily temperature fluctuations are strongly attenuated under the snow cover. However, the GST–SAT tracing is stronger than in the summer, and synoptic events are visible in the underground temperature record. The cessation of this regime occurs in the spring, when during fast short snowmelt infiltration of the melt water results in rapid warming of the upper underground layer. General conclusion by Hinkel et al. (2001) and Kane et al. (2001) was that in high-latitude regions, where the mean annual temperature is below freezing, the surface temperatures anyhow represent effective upper boundary condition for the deeper temperatures. On the other hand, in the areas with extensive seasonal freezing of the active layer the latent heat of fusion prevents the deeper subsurface from seeing the warmth in the summer. Modeling results by Stieglitz et al. (2003) have specified the role of the show cover in the GST–SAT coupling in the permafrost areas on the longer scales. The authors have

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revealed two main factors influencing the underground temperature, namely the nearsurface air temperature and the temporal variations of snow cover with clear preference of the latter factor. Comparison has shown that on the Northern Slope of Alaska the permafrost temperature changes for the period of 1983 to 1998 are consistent with decadal variability of snow cover. On the other hand, Stieglitz et al. (2003) have also detected that decadal variations in snow cover penetrate to not more than 50–60 m depth and, thus, have little influence on the multi-decadal trends that are archived in borehole temperatures to depth of several hundred meters. The effect of the penetration depth of surface factors that affect the air–permafrost temperature coupling can be illustrated using above Ilulissat (Greenland) data (Figure 68). Maximum and minimum air temperatures were recorded there simultaneously with the ground temperatures. Correlation of biweekly averaged ground temperature series at 0.25 cm depth with maximum and minimum air temperatures for the 1968–1982 time period equals to 0.11 and 0.17, respectively, while similar correlation with temperature measured at 1.25 m depth already reaches 0.31 and 0.41. The correlation between snow cover and ground temperature at 0.25 m depth still equals ⫺0.16, and it falls to zero at the 1.25 m depth. The analysis of more extensive database, namely comparison of the air and permafrost temperatures measured at 1.6 m depth at the Churapcha meteorological station (East Siberia), have shown very weak coupling of both variables on the annual scale (Figure 69). The correlation between both temperatures calculated for the 1957–1992 observational period equals to 0.49, and it is even smaller for shorter averaging intervals (Romanovsky et al., 2000). On the other hand, the correlation of smoothed versions of both temperatures (10-years running means) significantly increases and amounts to 0.87. An improvement of the correlation is more noticeable for the longer averaging intervals. This study supports the ability of permafrost to archive long-term surface air temperature changes and suggests the possibility of exact coupling of the SAT and permafrost temperatures on decadal or longer timescales. According to Romanovsky et al. (2000) decoupling of both temperatures on the annual timescale occurs primarily due to significant inter-annual variability of the snow cover duration and its thickness. This effect will likely be smoothed on the long timescale. Anyhow, it is expected that continuous monitoring of the ground temperatures and related meteorological variables that is carried out at high latitudes/altitudes in the frames of numerous above-mentioned international programs will significantly extend available database and improve our present understanding of the GST–SAT linkage in the permafrost areas. Permafrost is a thermal condition. It is a product of cold climate that compels it to grow from the surface downward, and its formation, persistence, position of the base, and top strongly depend on climate. The air temperature and the length of freezing season are the most important factors that practically determine the existence and stability of the permafrost. The colder the air temperature, whether due to increasing latitude and/or altitude, the more likely permafrost will occur and the thicker it will tend be. This fact can be easily illustrated by examining of the permafrost boundary maps, where the transition from no permafrost to discontinuous and then to continuous zones exhibits clear south–north trend. The coupling between cold climate and permafrost is so strong that the maps of SAT isotherms averaged for long time intervals are often used to delineate various zones of permafrost occurrence (Kudryavtsev et al., 1980). Permafrost is affected by climate variations and thus can be used for detection of the paleoclimate changes.

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Fig. 69. Mean annual air and permafrost temperature measured at 1.6 m depth (Churapcha meteorological station, East Siberia) (dashed line) and its 10-year running mean (solid line). Redrawn from Romanovsky (2001) (see also http://gsc.nrcan.gc.ca/permafrost/climate_e.php).

Similarly to other temperature logs, inversion of the temperature–depth profiles measured in boreholes drilled in permafrost can provide information on the past GST changes. Mathematical description of the various phenomena occurring in the frozen underground has been presented in numerous works (Carslaw and Jaeger, 1962; Lunardini, 1991; Osterkamp and Gosink, 1991; Rath and Mottaghy, 2003). Detailed modeling of the freezing/thawing processes including heat and mass transport in the permafrost environment is very complex and its physical theory is not fully understood. The heat conduction equation with the phase change is nonlinear and the coupling of the thermal and hydrological processes with climate is complex. For these reasons simplified models are used for description of the thermal state of cold regions. Thus, a simple but effective method for modeling of freezing/thawing processes in the subsurface was suggested by Mottaghy and Rath (2006). The authors have included their scheme into finite-difference

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code SHEMAT (Simulation for HEat and MAss Transport; see also Clauser, 2003) that simulates a wide variety of thermal and hydrogeological processes in both the 2- and the 3-D approach. Significant facility of the modeling procedure represents the fact that in permafrost itself the heat transfer is achieved through pure conduction. Advection is insignificant due to frozen state of water, so that once boundary conditions have been specified, temperature pattern within permafrost and the position of its boundary can be calculated from somewhat modified Eq. (4) (see Section 2.2). The thermal regime of cold regions is strongly affected by the processes of water freezing/melting accompanied by consumption/release of latent heat. This effect is extremely important, since it changes enthalpy by orders of magnitude. The latent heat effect can be included into conventional heat conduction equation by substituting an apparent heat capacity for the volumetric heat capacity of unfrozen stratum. One-dimensional equation of conductive heat transfer that takes into account freezing/thawing in the subsurface can be formulated as a modification of the conventional Eq. (4) (see, e.g. Lunardini, 1991) ⭸  ⭸T ⭸ ⭸T    m (c)m ⫹ i (c)i ⫹ w (c)w ⫹ w  w L ⭸T  ⭸t ⫽ ⭸z  K eff ⭸z  .

(39)

In this equation  is the total porosity, and the indices m, i, and w denote rock matrix, ice, and fluid, respectively, while Keff is the effective thermal conductivity of the matrix filled by water and/or ice (Keff ⫽ mKm⫹wKw⫹iKi). Because ice has a thermal conductivity 3–4 times higher than water, the thawing of the active layer in summer creates progressively thickening lower-conductivity layer between the ground surface and permafrost. Phase change at temperature T * (for water, usually 0°C) is characterized by the latent heat L, and the moisture content is controlled by a function (T )

m ⫽ 1 ⫺ , w ⫽ ⫻ , i ⫽ ⫺ w → m ⫹ w ⫹ i ⫽ 1.

(40)

The quantity  in this equation denotes the fraction of fluid in the pore space of the rock, while the latter constraint implies that the pore space is saturated. Analytical as well as approximate solutions using different functions  have been discussed in numerous works. Recent studies apply numerical approach to the solution of freezing/thawing equations and adopt a half-Gaussian function for  that secures smooth boundaries and facilitates the nonlinear convergence (Lunardini, 1991)    * 2  exp  ⫺ T ⫺ T  , if T ⱕ T ⴱ   w   ⫽    1, if T ⬎ T ⴱ

(41)

Parameter w is usually ⱖ1 K. As in other similar problems, surface boundary condition is T(0,t) ⫽ T0(t), and there are two boundary conditions at the phase boundary at the

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base of permafrost, namely the equivalence of temperatures and the heat flux between permafrost and underlying unfrozen soil. These boundary conditions ensure the continuity of the temperature at the phase boundary as well as a balance of heat conducted to the permafrost boundary, heat conducted away from it, and heat absorbed/released by the phase change. Above equations belong to a class of so-called Stefan20 problems (moving boundary problems; Carslaw and Jaeger, 1962). Their solutions for permafrost were obtained for various basic field situations such as freezing of the half-space with initial temperature gradient, freezing/thawing of the subsurface layer from initial freezing temperature, etc., using approximate methods as well as more exact finite-difference/finite-element approaches. Trial calculations revealed significant deviations of temperature–depth profiles including phase change from that obtained by solution of Eq. (4), which does not take into account effects of emission or absorption of heat. The GST histories inferred from synthetic data have emphasized an importance of including latent heat effect (Mottaghy and Rath, 2006). Figure 70 compares the GST histories reconstructed from synthetic temperature–depth profile simulated using boxcar event scheme and freezing/thawing effects. As shown, inversion performed by means of the above-described

Fig. 70. Inversion of synthetic temperature profile with and without latent heat. Inclusion of the freezing effects appears to be crucial in exact evaluation of the true GST history. (Redrawn from Mottaghy and Rath, 2006.)

20 A large class of problems containing a free or moving boundary is called “Stefan problems” after Austrian scientist Josef Stefan (1835–1893). The original Stefan problem has treated the formation of ice in the polar seas. Nowadays there is a growing interest in Stefan problems.

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technique including latent heat effect provides almost perfect coincidence of the true and the reconstructed GST histories. Exclusion of the latent heat effect gives significantly slowed down GST increase with the glaciation maximum shifted to the present by ⬃10 000 years. It should be mentioned, however, that to enhance latent heat effect the authors have assigned rather high porosity to the medium. Effects of freezing will be reasonably smaller or even negligible for the consolidated rocks. This and former numerical modeling experiments have shown that relatively small changes in surface climate can result in significant changes in permafrost temperatures and that permafrost is highly susceptible to the long-term warming (Lachenbruch and Marshall, 1986; Lachenbruch et al., 1988; Beltrami and Taylor, 1994, 1995; Majorowicz and Judge, 1994; Kukkonen and Safanda, 2001; Safanda et al., 2004). These studies have proved that temperature variations measured in this layer can be used as a powerful indicator of the long-term climate and/or surface energy balance variability. Because heat transfer within thick permafrost occurs almost exclusively by conduction, it is affected primarily by the long-term temperature changes. There are two timescales that are important from the point of view of permafrost response to the surface temperature variations. The first one characterizes the time required for the adjustment of the underground temperatures to new surface conditions after sudden surface temperature change. This time can be estimated as
t ⬃ D2/4k, where D is the permafrost thickness and k its thermal diffusivity (Lachenbruch et al., 1988). For terrestrial permafrost conditions this time can vary from a few years to thousands of years. Table 5 presents the depth of propagation of a temperature wave on frozen ground. As shown, typical moderate depth boreholes are able to record signal of the last glacial. The second timescale characterizes the time that the permafrost thickness needs to respond to the changes in surface temperature. In the case of the step increase of the surface temperature to higher constant value, and after initial response with the characteristic time of
t, the thawing of the permafrost base can be expected. Its rate depends on the heat balance at the phase change boundary and on the ice content in permafrost. Calculations by Lachenbruch et al. (1988) and Ostercamp and Gosink (1991) have shown that the thawing rate is about 1–15 mm/year. Thus, again thousands of years may be required for adjustment of the permafrost thickness to the changed surface temperatures. Such a long characteristic time represents a significant portion of the time intervals between glacial periods of the last million years. Kukkonen et al. (1998) have detected extremely low temperature gradients in a suite of 250–750 m deep boreholes in eastern Karelia (Russia) ranging between 0.8 and 3.7 K/km. Table 5. Depth of propagation of a temperature wave in frozen ground (thermal diffusivity of the frozen strata is 1.3–1.8 ⫻ 10⫺6 m2/s) Depth (m)

Time (years)

12.5–14.5 40–47 125–145 400–500 1250–1450 4000–4700

1 10 100 1000 10 000 100 000

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Area under investigation was situated close to the eastern margin of the Weichselian glacier (see Section 3.5.1, Chapter 3). During the maximum cooling phase (18–20 ka ago) the permafrost in eastern Karelia has extended to 400–600 m depth and was characterized by the temperature of about ⫺10°C. Forward models simulated by Kukkonen et al. (1998) have shown that the long-term effects of the Weichselian glaciation are sufficient to decrease the heat flow values to the measured levels and that detected lowered temperature gradients in the uppermost 1 km can be attributed exclusively to very low ground temperatures during the glaciation and to the thawing of the ancient permafrost. Permafrost probably existed in the area during the whole glaciation period (60–11 ka ago). Its melting in the post-glacial time has consumed heat. This process had retarding effect on the subsurface temperature response to the surface warming. Similarly low temperature gradients in the upper 3 km were reported for more than 300 heat flow measurements in the Urals (Kukkonen et al., 1997). The authors have shown that although this area was outside the glacier, observed phenomenon can be attributed to the periglacial21 conditions during the glaciation. Low thermal gradients were detected in the West-Siberian Platform at latitudes north of 62°N, where even a relic hidden permafrost layer was discovered below the thawed underground in the depth range of 100–300 m (Duchkov and Devyatkin, 1992). Safanda et al. (2004) have investigated temperature–depth profiles measured in a suite of deep (up to 2.3 km) holes located near the Polish–Lithuanian boundary. Measurements revealed a negative temperature gradient in the uppermost 400 m. The study of terrain effects in the vicinity of the boreholes have shown that the climate change can be the only plausible explanation of the peculiarities detected in the temperature logs. An average GST as low as ⫺10°C as well as more than 500 m thick permafrost layer has existed in the area during the last glacial (Hartmann, 1994). Results of forward simulations of the past climate as well as the inverse GST reconstructions have shown that detected course of the T–z profiles can be attributed to the past climate changes alone and, similarly to the above-mentioned work by Mottaghy and Rath (2006), have emphasized the crucial role of the latent heat effect on the coupling of synthetic and measured temperatures. The authors have inferred temperature increase from ⫺10.3°C to ⫹7°C at the end of the glacial (13.7 ka B.P.) and subsequent warming to ⫹8°C in the last 150 years. When the latent heat was not taken into account, the subsurface warming was more rapid and the negative temperature gradients have vanished some thousands of years earlier. According to the calculations by Safanda et al. (2004), observed low subsurface thermal gradients can be partly attributed also to a low (⬃40 mW/m2) terrestrial heat flow incoming from the Earth’s interior in the area under investigation. The reason for the low heat flow values is the low heat production of anorthosites, characteristic rocks of the area. Geothermal gradients measured in the Scandinavian boreholes with the permafrost thickness of 220–380 m have ranged from negative values of ⫺6 K/km to 10–38 K/km. Low geothermal gradients were also detected in a PACE borehole at Stelvio (Italian Alps), on Plateau Mountain (SW Alberta, Canada), at Swedish Lapland, and in certain parts of the Baltic Shield in Norway (Isaksen et al., 2001; and the references therein). Similarly to 21 Several definitions exist for the term periglacial. More conventional one suggests that these environments were located at periphery of past Pleistocene glaciers. Broader modern usage encompasses a wide range of cold but essentially non-glacial climates, regardless of their proximity to glaciers on time or space. More than one third of the Earth’s land surface can be included in this definition.

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Safanda et al. (2004) the latter authors have studied all possible explanations for this phenomenon including terrain effects in the area under investigation and concluded that even in the presence of other influences (e.g. low internal heat production in the plutonic and metamorphic rocks in the mountainous areas of Scandinavia) the impact of paleoclimatic effects on geothermal gradients cannot be ignored. The above described low geothermal gradients are characteristic only for the upper parts of boreholes. Measurements in the deep holes (1 km and deeper) have detected that the low temperature gradients gradually increase with depth and achieve the steady-state values undisturbed by the glacial/interglacial cycles (Safanda and Rajver, 2001; Kukkonen and Joeleht, 2003; Safanda et al., 2004). Numerous temperature logs measured in the boreholes penetrated permafrost were applied for the GST history reconstruction. Using a suite of 20 boreholes that were drilled up to 1800 m depth in the immediate neighborhood of the Kola superdeep hole (see Section 3.5.2, Chapter 3), Rath and Mottaghy (2003) have inferred GST history of the region back to 50 ka B.P. The study has detected that in the area under investigation, the GST increased by 4–5 K since the last glaciation to the Holocene. Somewhat smaller amount of warming than that detected in lower latitudes by other GST reconstructions can be attributed to the insulating effect of the ice cover. The authors also performed the joint inversion of the T–z profiles for seven deepest holes from this database. Reconstructed almost 100 000-year long detailed GST history of the region indicated last glacial maximum with temperature ⫺4 K lower than nowadays, warming that began 15–17 ka B.P. and culminated in 2000 B.P., subsequent cooling with the temperatures 2 K lower than the present level with minimum that occurred 300 years ago, and the warming since then. Recently Mottaghy and Rath (2006) have reprocessed the Poland temperature logs used by Safanda et al. (2004) for the GST reconstructions. The authors have applied the above-described mathematical model of the heat transfer in the freezing/thawing medium with more complex partition function  given by Eq. (41) and have reconstructed the GST history for the last 100 ka. In spite of the different partition function applied in both works as well as the differences in other parameters, in general their findings agree well. The lowest estimated temperatures were ⫺10°C, and 650 m (in comparison with 520 m in the work by Safanda et al., 2004); thick permafrost has disappeared in the region about 4 ka B.P. The GST inversions of temperature logs measured in boreholes drilled in permafrost also have detected more recent climatic changes. An analysis of the T–z profiles from Svalbard boreholes and from holes located in alpine regions of mainland Norway have shown that the permafrost has warmed up over the last 100 years. The reconstruction of the GST history at Janssonhaugen (Svalbard, Norway) from the borehole data has inferred a temperature increase of 1–2 K over the past 60–80 years (Isaksen et al. 2000). An analysis of the temperature–depth profile from the borehole in Juvvasshøe (southern Norway) has indicated a temperature increase of 0.5–1 K over the last 20–40 years (Isaksen et al. 2001). Evidence for secular warming of European mountain permafrost was presented by Harris et al. (2003). The authors have analyzed mountain permafrost temperatures from six boreholes arranged in the latitudinal transect extending from the Alps, through Scandinavia to Norwegian Svalbard. As a part of the above-mentioned PACE European Union research project a number of at least 100 m deep holes were drilled into the frozen bedrock during 1998–2000. All boreholes have exhibited clear “U-shapes”, indicating recent warming. Inversion results have revealed GST warming of ⬃1 K occurred in the last 100 years.

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However, the most noticeable and/or celebrated evidence of the recent climate warming extracted from boreholes drilled in permafrost is connected to Alaskan Arctic region. Permafrost lies beneath about 80% of Alaska’s surface. On the North Slope, permafrost ranges in thickness from about 200 to as much as 700m, having temperature from ⫺8 to ⫺10°C. The geothermal data were obtained from the oil exploration holes distributed all over the Alaskan Coastal Plain and Foothills. Configuration of measured temperature–depth profiles revealed clear curvature towards warmer temperatures (U-shape) in the uppermost 200 m (Figure 71). An analysis of this T–z profiles has provided the first evidence that Alaskan Arctic has warmed by 2–4 K during the twentieth century prior to the mid1980s (Lachenbruch and Marshall, 1986; Lachenbruch et al., 1988). Although the details of the climate warming that time could not be resolved, the recent warming of the permafrost was surely detected. The near-linear character of the deeper permafrost T–z profiles does not necessarily mean that an equilibrium thickness has been attained. Long characteristic times of the permafrost response to climate changes hint that it could still respond (freezing/thawing at the base) to the long-term climate changes that occurred in the last several tens of thousands years. Most recent GST estimates of the same authors using the ramp/step approach corroborated early results and have given 2.7 ⫾ 1.0 K amount of the last century warming. Wide borehole temperature measurements performed over the last two decades in Alaska have shown that permafrost has warmed at all sites along a north–south transect

Fig. 71. Borehole temperature observations from 14 holes in Arctic Alaska (Lachenbruch and Marshall, 1986). The curvature is consistent with a warming at the top of permafrost; the duration of the warming event varies for different sites, but it has a twentieth century onset in general. The shaded region for each curve represents the warming anomaly; the number above each profile indicates the magnitude of the local warming (in °C).

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spanning from Prudhoe Bay to Glennallen (Osterkamp and Romanovsky, 1999). The total magnitude of the warming at the permafrost surface since the late 1960s was proved to be about 2 K. Most recent data indicate that the last increase of the warming rate began on the Arctic Coastal Plane in the mid-1980s and in areas of discontinuous permafrost between 1989 and 1991. Maximum magnitude of warming (3–4 K) was measured at West Dock and Deadhorse near the Arctic Ocean. Detected in Alaska warming can likely be attributed to an anthropogenic influence. Most of the global warming scenarios derived from the GCMs predict that human-induced warming will be amplified in the high-latitude regions with the clear consequences for the permafrost regions (e.g. Woo et al., 1992; Flato et al., 2000). The GCMs predict that the doubling of atmospheric carbon dioxide concentrations should result in the 2–3 times greater warming in the polar regions than the global average. In the discontinuous permafrost region, where ground temperatures are within 1–2 degrees of melting, permafrost will likely disappear. This process will be accompanied by sizable environmental impacts. Its thawing, resulting in warmer soils, will speed decomposition reactions and release additional carbon dioxide and methane into the atmosphere. While permafrost limits water movement, thawing of any permafrost will increase groundwater mobility, the susceptibility of environment to erosion and landslides. Under climate warming, much of this terrain would be vulnerable to subsidence, particularly in areas of relatively warm, discontinuous permafrost. The GST reconstructions used a set of temperature logs measured in 61 (109–620 m deep) holes located in the latitudinal belt between 60° and 82°N in northern Canada have revealed the fingerprints of extensive recent warming (Majorowicz et al., 2004). Results of the simultaneous GST inversions of all borehole data have shown that the GST warming in this area has started in the late eighteenth century and have continued to the twentieth century. Cumulative amount of warming for this period equals to ⬃2K. The GST reconstructions by the same authors for more southern regions of northern Canada (Yukon, Northwest Territories and Northern Alberta) indicated a 1.1–2.5 warming in the twentieth century (Majorowicz and Safanda, 1998; Majorowicz et al., 2002). Recently Taylor et al. (2006) have described results of temperature monitoring at the Canadian High Arctic Permafrost observatories (77°–82.5°N). These sites represent the Canadian contribution to the above-mentioned GTN-P. They lie in the area of rigorous climate with mean annual air temperatures of ⫺18°C that is characterized by low precipitation and snow cover. This latter factor and the lack of vegetation result in a very low insulating effect. Various techniques of borehole measurements were used including single and multiple temperature logging as well as the temperature monitoring in the uppermost ⬃65 m of boreholes. Investigations were concentrated on the detection of contemporary climate change through inversion of subsurface temperature time series (for more details see Section 4.2, Chapter 4) and on the extraction of the climate for the last two centuries to identify the long-term trend. Obtained results have corroborated early conclusions. Inversion resolved the Little Ice Age from the mid eighteenth to mid nineteenth century with the GST temperatures approximately 1K below the long-term mean and subsequent intensive warming that has produced approximately 3 K higher temperatures in the end of the twentieth century. These results generally coincide with similar reconstructions reported for Greenland ice cap GRIP holes and Dye-3 hole in the southeast (see the next section). Simultaneous inversion of the multiple temperature logs from shallower boreholes revealed fine structure of the recent

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warming. Calculations have detected two–three warming/cooling cycles in the GST change during the latter approximately 50 years. In principle, available data, especially originated from the high latitudes, are sufficient to determine regional trends in the permafrost warming and corroborate the possibility of more intensive warming in the circumpolar regions suggested by the GCM simulations. However, from the pioneering work by Lachenbruch and Marshall (1986) to further extensive studies none of the researchers found any significant south–north trend in the magnitude of warming inferred from borehole temperature logs (e.g. Majorowicz et al., 2004). This fact is illustrated in Table 6 that accumulates some results on the permafrost warming magnitude detected in different locations of the Northern Hemisphere. As shown, all GST reconstructions show noticeable temperature increase in different parts of the permafrost environment during the last 30 years; however, any spatial trend is unrecognizable. This hints that the actual pattern of permafrost loss is more complex than a simple uniform northward retreat. Summary: Long ago it was recognized that permafrost contains an abundant data for the proxy climate reconstructions (cryostratigraphic techniques, isotopic analysis, incorporated flora and fauna, etc.). Investigations of the last two decades have proved that temperature–depth profiles measured in boreholes drilled in permafrost contain also direct fingerprints of the past climate change and thus represent a useful tool for reconstruction of the past GST changes in the northern circumpolar areas as well as at highaltitude regions. Under proper choice of the mathematical description of the freezing/thawing processes an inversion of borehole temperatures from permafrost areas can provide accurate GST histories that are consistent with estimates of the temperature change in the permafrost areas gained from conventional proxies. Even if suggested forward and inverse methods for the reconstruction of the subsurface temperature distribution in the deeply frozen medium cannot handle the subtle features of the processes in the uppermost few meters of the ground (active layer); they generally give reliable results when the long-term climate variations associated with the last glaciation are considered. The GST signal for the past few tens thousand years can be reconstructed from the deep boreholes. An ensemble of results reported from the borehole temperature measurements in vast regions of the Alaskan, Russian Arctic, Scandinavian, and European mountains have revealed significant temperature increase since the last glacial as well as the warming during the twentieth century. It was also shown that an impact of the glaciation and fingerprints of the past permafrost can be detected from borehole temperature logs in many permafrost-free regions.

2.9 Climate from Ice Boreholes Detection of the climate change in the circumpolar regions bears great importance. The Polish meteorologist Przybylak (2000) stated, “warming and cooling epochs should be seen most clearly here and should also occur earlier than in other parts of the world”. Hence, as he continues, Earth’s polar regions “should play a very important role in the detection of global changes”. Vast climatic information can be obtained from a variety of stratified deposits, such as deep-ocean and lake sediments, polar ice sheets, speleothems, and peat deposits. This information is probably the most straightforward

Alaskan Coastal Plain and Foothills Barrow Permafrost Observatory, 1950–2003 Trans-Alaska pipeline route Alert, Nunavit 77–82.5°N

2–4

1944–1984

1 0.6–1.5 0.75 3

60–82°N

2

Yukon, Northwest Territories and North Alberta Northern Quebec Juvvasshoe

1.1–2.5

1950–2000 1983–2003 1995–2000 From the late nineteenth century From the late eighteenth century 1980–2002

Janssonhaugen Europe/Sweden Europe/Alps Russia

Asia/Kazakstan Asia/Mongolia

Tarfalaryggen European north, continuous permafrost European north, discontinuous permafrost North of W. Siberia E. Siberia Northern Tien-Shan Khentei and Khangai Mts.

⫺1 0.5–1 0.2 1–2 0.51 0.35 0.1–0.4 1.6–2.8 Up to 1.2 0.3–0.7 1 0.2–0.6 0.3–0.6

Late 1980s–mid-1990s

1973–1992 1970–1995 1980–1990 1960–1992 1973–2002 1973–2003

Reference

Lachenbruch and Marshall (1986) Romanovsky et al. (2002) Osterkamp (2003) Smith et al. (2003) Taylor et al. (2006) Majorowicz et al. (2004) Majorowicz et al. (2002) Allard et al. (1995) Isaksen et al. (2001) Harris et al. (2003) Isaksen et al. (2000) Harris et al. (2003) Harris et al. (2003) Harris et al. (2003) Pavlov (1994) Oberman and Mazhitova (2001) Pavlov (1994, 1996) Romanovsky et al. (2002) Marchenko (2002) Sharkhuu (2003)

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Table 6. Recent warming detected from the permafrost borehole measurements in different locations

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in the case of snow deposited in ice sheets at high latitudes. Polar ice cores contain traces of the past atmosphere – temperature relations, data on precipitation, gas content, and chemical composition as well as a broad spectrum of the early environmental information (for details see Section 1.2.3). Past ice surface temperature reconstructions from the temperature logs measured in the ice boreholes provide valuable estimates of the surface temperature changes in polar environments that are complementary to paleoclimatic records obtained from ice core oxygen isotopes. Ice borehole information could spatially complete information provided by land boreholes. Boreholes drilled through the Greenland and Antarctica ice caps archive the magnitude and timing of climate change over the last 700 ka. Greenland is the world’s largest island. About 81% of its surface is covered by ice creating so-called the Greenlandic ice cap. Climate changes in this area and especially the fingerprints of the global warming are extremely important. Being entirely melted the Greenlandic ice could contribute 6–7 m to the global sea-level rise (Hvidberg, 2000). Numerical simulations of temperature–ice melting relation have indicated that an annual or only summer temperature increase of only by 1 K will result in approximately 20–50% increase of ice melting (Janssens and Huybrechts, 2000). Systematic observations of the Greenland climate thus appear to be of the greatest importance for the studies of polar and global climate change. A unique opportunity for studying the Greenland past climate was offered when in 1966 the U.S. Army Gold Region Research and Engineering Laboratory drilled the first 1400 m long ice core at Camp Century on the Greenland ice sheet (www.ncdc.noaa.gov/paleo/icecore/greenland/gisp/campcentury/campc.html). The oxygen isotope measurement technique applied on the deep ice core provided detailed continuous climatic record over 130 000 years (Johnsen et al., 1970). After that similar ice core projects have been performed in Greenland, Canadian Arctic, and Antarctica. From 1989 to 1994, the U.S. and European scientific communities initiated new intensive ice coring efforts in Greenland. These works, termed as the Greenland Ice Core Project (GRIP; www.ncdc.noaa.gov/paleo/icecore/greenland/summit/document/gripinfo.htm) and the Greenland Ice Sheet Project Two (GISP2; www.gisp2.sr.unh.edu), acquired deep ice cores from on and near the Greenland summit. The objective of both efforts was to reveal continuous, high-resolution, multi-parameter paleoclimatic/paleoenvironmental information stored in the ice. The American–Danish–Swiss GISP1 program began in 1976 and produced a 2037m deep core at the location Dye 3 in southeastern Greenland (65.2°N, 43.8°W; Johnsen et al., 1994). The temperature logging in this deep hole was performed in 1983. The GRIP and GISP2 drilling efforts essentially represent the renewed versions of the GISP1 program. The GRIP site is situated on the ice divide in central Greenland (72.58°N, 37.63°W). The depth to bedrock in this location is approximately 3km, which corresponds to a stratigraphic record of at least 200000 years. This interval includes two glacial/interglacial cycles. The 3029m long GRIP ice core was drilled from 1989 to 1992. The GRIP ice core was successfully recovered in 1992, and 13cm wide liquid filled borehole was left at rest for approximately one year. Temperatures were then measured to the bottom of the hole in 1993, 1994, and 1995 (Dahl-Jensen et al., 1998). The GISP2 ice core (72.60°N, 38.50°W) 3053.4m in depth was recovered after 5 years drilling in 1993. At present it is the deepest ice core extracted from the polar caps. Details of the high-precision temperature logging performed in the GISP2 borehole are presented in the work by Clow et al. (1996).

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All extracted ice cores contain a high-resolution archive of more than 100 000 years of climatic history, embracing at least last interglacial–glacial cycle. It is the longest such record in the Northern Hemisphere. Under multi-institutional efforts scientists have performed a huge number of analyses, such as ice stratigraphy, trapped gases and their stable isotopes, stable isotopes in ice, particulates, major and trace element chemistry of ice, its conductivity, and other physical properties. Probably one of the most important findings of all projects was the recognition of the rapid climate changes at least of the regional extent (Dansgaard–Oeschger events) in the last glacial period (Dansgaard et al., 1993). They have been observed also in the previous ice cores; however, the recent drillings not only confirmed their potential existence, but also have detected such quantitative characteristics as their number and extremely fast onset that have occurred perhaps within a few decades. Investigations suggest that in contrast with the stability of the Holocene conditions, climate of the North Atlantic region is able to reorganize itself rapidly. Except for the recent times, instability was characteristic for the North Atlantic climate over the last 230 ka. This fact hints the possibility of radical climatic changes under anthropogenic influence and present growing atmospheric pollution. Below we concentrate on the investigations that are most important from the topic of this book, namely on the surface temperature history reconstructions performed on the basis of the above-mentioned temperature–depth profiles measured in the holes after ice core recovering. These studies began only in the recent years (e.g. Johnsen et al., 1995; Dahl-Jensen et al., 1998). Figure 72 shows the temperature–depth profiles measured in the Dye3 and GRIP sites. Repeated logging was performed in both holes. The last profiles were used for both GST reconstructions that did not contain drilling disturbances. Measurement precision was ⫾30 mK and ⫾5 mK for the former and the latter temperature logs, respectively. Present mean annual surface temperatures at the sites range between approximately –(20–30)°C and grow to the bedrock to –(10–15)°C.

Fig. 72. The GRIP and Dye 3 temperature profiles. (Redrawn from Dahl-Jensen et al., 1998.)

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Forward modeling results by Dahl-Jensen et al. (1998) have shown that the basal temperatures have remained well below the ice melting point over at least the past 100 ka. Generally, the temperature at the base of the glacier strongly depends on the accumulation and flow of ice and on the geothermal heat flow from below. In Antarctica, e.g. near zero temperature was measured at the base of the moving ice stream at holes B, C, and D (Parizek et al., 2002, and the references therein). Both temperature profiles that are shown in Figure 72 exhibit significant climateinduced disturbances. Because of the nearly flat topography, the homogeneity of the medium and absence of the hydrological disturbances, ice boreholes appear particularly well for the GST history reconstruction. An important shortcoming of the ice boreholes in comparison with the holes drilled in rock is that the ice is moving. Initially, the ice moves downward as the result of snow accumulation at the surface and eventually it creeps laterally to the sea. Thus, temperature distribution along deep ice holes does not represent (like in permafrost regions) the result of pure conductive heat transfer and depends both on the surface temperature and on the geothermal heat flow as the boundary conditions. Except for the climate change measured in the ice boreholes T–z profiles also reflect the ice flow pattern and accumulation rate histories that could not be recovered from the temperature logs and need additional study. Available information can help to simplify physical model used for description of the heat transfer in both holes. Detailed investigations at Greenland summit have shown that (1) the basal temperatures have been below the melting point during the past 100 years and (2) the ice movement has been essentially vertical in the past. Thus, for the surface temperature reconstruction one can use 1-D time-dependent equation of heat transfer 2

⭸T ⭸2 T ⭸T 1 dK  ⭸T  ⫽ k 2 ⫺ vz ⫹ , ⭸t ⭸z c p dT  ⭸z  ⭸z

(42)

where t is time, z the depth, T the in situ ice temperature, and vz the vertical velocity of ice movement. Physical parameters of ice are its density (), thermal conductivity (K), thermal diffusivity (k), and specific heat capacity (cp), respectively. Internal heat production is usually assumed to be zero. The model does not account for the long-term surface elevation changes. Past accumulation rates and ice flow pattern can be found by ice core studies (e.g. Johnsen et al., 1995; Cuffey and Clow, 1997). As previously, the ice surface temperature and the geothermal heat flow from below are regarded as unknowns of the problem (see Section 2.3, Chapter 2). Solution for combined ice flow/heat transport inversion problem was investigated in the work by Dahl-Jensen et al. (1998) on the basis of a Monte Carlo method. Figure 73 demonstrates best-fit surface temperature histories back to 8 ka, which have been obtained in this work. As shown, results for both sites are highly coherent. They show the post-glacial Climatic Optimum that occurred 8000 to 5000 years ago with temperatures 3–4 K higher than now. The surface temperature histories also reveal the Medieval Warm Period around 1000 A.D., while the Little Ice Age has culminated near 1550 A.D. According to Lamb (1969) “climatic history must be central to our understanding of human history”. It is especially a case in such regions as Greenland. Around 1000 A.D. the Vikings have established a colony in Greenland. Thus, in 986 A.D. Erik

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Fig. 73. The reconstructed temperature histories for GRIP and Dye 3: Top: Last 8 ka B.P.; Bottom: last 2 ka B.P. (Data by Dahl-Jensen et al., 1998.)

the Red has led the first settlers from Iceland to southern Greenland and between 986–1000 A.D. more and more inhabitants have arrived from Scandinavia and have gradually settled in the southwest coast of Greenland. During the Little Ice Age the settlers has deserted. “1408 – A wedding is held at Hvalsey Church”. This was the last written record of Greenland’s Norse population. Between 1480 and 1500 the Norse population of Greenland has disappeared (e.g. The Norse History of Greenland; www.greenland-guide.ge/leif2000/history.htm). Four most recent climatic events were warming that culminated around 1730–1740, cold episode that achieved its minimum at 1870–1875, the provisional return of warmth in 1930–1950, and cooling in the last decades. On the long scale results of the GST reconstruction by Dahl-Jensen et al. (1998) show that temperatures in Greenland generally decreased (even not monotonously) since the Climatic Optimum. Measurements of borehole temperatures also have allowed a recalibration of the oxygen isotope–temperature relation for the GRIP ice core. Comparative study has indicated that the temperature change at the end of the last glacial period was more than 20 degrees. Thus, at the time of the last glacial maximum (25 ka) the temperature in central Greenland was by two tens of degree colder that the presentday state. This result was found independently in the GISP2 borehole (Clow et al., 1996). The reconstructed climate history coincides well with the general paleoclimatic trend gained from different proxy sources available in the Arctic region, and can be used to verify previous climatic reconstructions.

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The most recent course of the GST history inferred in Greenland is in good agreement with meteorological measurements in the area. Analysis of the data from eight stations located at coastal southern Greenland for the 1958–2001 period has detected even slight cooling trend in comparison to the global warming by ⬃0.5 K for the same period (Hanna and Cappelen, 2003). Przybylak (2000) has compared data from 37 available Arctic and seven sub-Arctic stations on both the annual and seasonal scales since 1950. Results of this study have shown that in the area under investigation the highest temperatures since the beginning of the instrumental record occurred in the 1930s and even in the 1950s the temperature was higher than in the last 10 years. Temperatures were lowest in the 1960s, while since the mid-1970s the annual mean temperature does not exhibit clear trend. Borehole reconstructions in Greenland can also be compared with the GST histories inferred from other circumpolar boreholes. Taylor et al. (2006) presented GST inversions from three boreholes located in the northeastern Canadian High Arctic (for details see previous section). The 500-year long GST reconstructions resolved cold period that the authors have attributed to the Little Ice Age, during the mid eighteenth to the mid nineteenth century with the GSTs by ⬃1 K below the long-term average as well as the two–three warming/cooling events in the recent 150 years. Notwithstanding that this study area is at a 1400–2100 distance from the Greenlandic borehole sites, results by Taylor et al. (2006) have demonstrated rather good coincidence with the GST reconstructions by Dahl-Jensen et al. (1998). The results show high correlation (0.9) with the GST histories inferred from both the Dye 3 and GRIP data. The reason for such high coincidence is probably the influence of the North Atlantic Oscillation22 (NAO) that significantly affects the mass balance in the area (Hanna and Cappelen, 2003). The Greenland ice cap provides high-resolution climate history; however, the longest records of climate were obtained from the ice boreholes in Antarctica, e.g. 400 ka long Vostok ice core (Petit et al., 1999; see also Figure 1, Chapter 1). The borehole temperature–depth profiles has been measured and successfully applied for the paleoclimate reconstruction in Antarctica (e.g. Salamatin et al., 1994; Dahl-Jensen et al., 1999) using forward approaches and/or inversion based on the Monte Carlo methods. The temperature profile in the 1200 m deep borehole near the summit of Law Dome (66.73°S, 112.83°E) was measured in 1996, 3 years after the cessation of the deep drilling. The 4 ka long surface temperature history has been inferred from the measured temperature log (Dahl-Jensen et al., 1999). The temperature record exhibits two well-developed minima around 1250 and 1850 A.D. Since then temperatures have gradually increased by 0.7 K. Obtained past temperatures show good coincidence with the proxy climate history gained from the stable oxygen isotope 18O record from the same hole. Because high sensitivity of the Antarctic environment to small rises in the annual temperature and its role in the possible global change (e.g. Antarctic ice caps may begin to melt and cause global sea-level rise measured in meters), in the foreseeable future the global warming and/or climate change in this area will be likely a fairly popular scientific topic. 22 The North Atlantic Oscillation (NAO) is the dominant mode of winter climate variability in the North Atlantic region stretching from central North America to Europe and much into northern Asia. It represents a large-scale flow of air between the subtropical high and the polar low. The NAO can be characterized by various types of indices, e.g. by the fluctuations of air pressure between Iceland and Azores.

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Numerous projects, e.g. the 2006–2012 West Antarctic Ice Sheet Divide Ice Core Project; http://waisdivide.unh.edu, are planned to collect various interrelated climatic signals stored in the deep ice holes. Concerning borehole temperature measurements, recently the USGS has suggested a new method to reconstruct past temperature changes in polar regions. This “borehole paleothermometry” technique involves high accuracy (0.0002K) temperature measurements in boreholes drilled into polar ice sheets (or permafrost) and their interpretation. Future collaborative projects intend ice borehole paleoclimatic studies both in central Greenland and at numerous locations in Antarctica (http://esp.cr.usgs.gov/info/glaciers). Reconstructed by this technique 4ka long past temperature trends at Taylor Dome (East Antarctica, 77.83°S, 159.0°E) have shown that this sector of Antarctica has experienced cooling by 1.2K between 4000 and 1000 years ago, in agreement with the cooling which occurred that time in the Northern hemisphere. Further climate history of the area exhibits rapid strong (2K) approximately 500-years long warming. In other words, unlike the Northern Hemisphere, this region did not experience the Little Ice Age. Next short temperature decrease has occurred around 1900. And finally temperatures in the vicinity of Taylor Dome have increased by ⬃0.5K during the last 100 years. In general temperatures are now warmer than they have been over the last 4000 years. Temperatures in the site are still rising today. The course of the GST history revealed for Taylor Dome by precise “borehole paleothermometry” technique generally coincides with those inferred by Dahl-Jensen et al. (1999) using traditional GST reconstruction. As can be seen, there exist noticeable difference between climatic histories detected for the southern and northern polar regions. The reason for such discrepancy is still not clear. On the other hand, southern surface temperature reconstructions concern only a tiny area of the Antarctic; thus, they could not account for the temperature history of the whole continent. For example, detected in the ice boreholes recent warming likely occurred due to the interaction with the southern ocean and is not characteristic for the interior of the Antarctica. Comiso (2000) has analyzed Antarctic temperature data measured at 21 surface stations and from satellite infrared measurements operating since 1979 and has revealed ⫺0.008 to ⫺0.042 K/year temperature decrease. Still higher cooling trends (approximately ⫺0.07 K/year) were reported by Doran et al. (2002) for the period 1986–2000. Further temperature measurements in the ice boreholes together with extensive determinations of the heat flow in the area, which have been planned by the USGS, can likely help to improve our knowledge of the past regional climate patterns and present understanding of the stability of the Antarctic Ice Sheet. There exist also temperature measurements in shallower ice holes that can contribute to better knowledge of more recent climate change. Interesting reconstructions of the historical temperature trend were performed on the basis of the temperature–depth measurements in relatively shallow (120 m deep) borehole that was drilled at the ice divide on the Lomonosovfonna Plateau (one of the highest ice fields in Svalbard) in 1997 (Van de Wal et al., 2001). Radar measurements have detected a ice thickness of ⬃127 m at this site. The ramp/step inversion of the measured T–z profile using simple linear temperature trend assumption and the 1-D heat transfer model (because borehole is situated at the ice divide, the horizontal movement was neglected), described by Eq. (42), has shown that the temperature in the area under investigation has increased with the maximum rate of 0.02–0.025 K/year over the last 100 years. The total possible warming for historical period thus appears to be 2–3 K. Estimated temperature in the nineteenth century appears

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to be 2.4 K lower than the 1912–1996 mean value. Detected amount of cooling is in agreement with the meteorological observations at Svalbard airport. Numerous attempts to reconstruct surface temperatures were performed also for boreholes drilled in high-latitude alpine environments. Probably the most well known of such projects is the 1998–2001 EU-Project “Environmental and Climate records from High Elevation Alpine Glaciers” (ALPCLIM; www.geo.unizh.ch/⬃hoelzle/alpclim.html). The scientific objective of this effort was the exploitation of Alpine glaciers to gain climate related records through the collection and interpretation of isotope-based temperature, atmospheric trace constituents, and especially the en-glacial temperature profiles virtually unexplored in this area. In this project en-glacial temperature profiles were measured with an absolute accuracy of ⫾0.01–0.03 K in a 29 m deep borehole at Seserjoch (4300 m asl, Monte Rosa area), in a 25 m deep borehole at the saddle point of Colle Gnifetti (4450 m asl, Monte Rosa area) and in a 40 m deep borehole on top of Dôme du Goûter (4300 m asl, Mont Blanc area). Firn- and ice-temperature observations from Colle Gnifetti were performed since the early 1980s. This 20-year long time series of en-glacial suggests a surface temperature increase of approximately 0.6 K since about 1990. Obviously, only short scale GST histories could be inferred from the shallow T–z profiles. The GST inversions of the data from the above three sites suggest a surface temperature increase in the order of 0.5–1 K for the last decade.

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Over the 100–200 years’ long instrumental period, climate reconstruction leans against the amount and quality of proxy records and the skill of different techniques to infer climatic information from indirect measurements. Temperature–depth profiles measured in boreholes represent valuable source for paleoclimatic reconstruction that during the last two or three decades significantly contributed to the knowledge of the temperature variations from centennial to millennial timescales. Since sporadic ground surface temperature (GST) reconstructions in the 1970s and the 1980s, a vast amount of borehole temperature log analyses have been performed all over the globe. Because of high scatter in the data quality and different methods used for the GST reconstruction, a heap of obtained results is rather heterogeneous. However, in the regions with the dense coverage and a high quality of T–z profiles, common GST trends are clearly visible in the reconstructions of practically all researchers. 3.1 Timescales of the Reconstructed GST Histories (From Ice Age to the Present) Provided that diffusion is a dominant heat transport process in the Earth, low-diffusivity crustal rocks have a considerable “memory” for the temporal variations of the GST. Fingerprints of the past climatic changes have been found in the temperature–depth profiles measured all over the world in boreholes from the Arctic to the tropic regions. Various methods have been developed in order to extract GST histories from these temperature logs for time intervals ranging from several decades to thousands of years. First attempts to decipher certain information on the GST changes from underground temperatures dates back to the early 1970s, and the corresponding “geothermal” method became generally known in the mid-1980s. The first specialized topical meetings were organized from the early 1990s, when the “borehole method” to reconstruct the past climate changes by the inversion of the borehole temperatures was recognized as a tantamount tool of paleoclimatic studies. Researchers from many countries are now routinely 175

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using this method, and a sizeable number of results have been published as well as regular meetings have been organized worldwide. The first compilation of the studies inferring past climatic changes from underground temperatures has appeared in 1992 (Lewis, 1992). Different methods for the GST inversions were described and applied to the series of regional data. Most of the data came from North America. Three teams from Canada provided the bulk of the results. Also presented were paleoclimatic inversions from Western Utah and Alaska (USA). A few GST histories were inferred from temperature logs measured in Europe (Central France, Germany, and the Czech Republic). Two studies were devoted to the paleoclimatic reconstructions from warmer latitudes in Zaire (Central Africa) and Cuba. Most of the GST histories were relatively short and embraced period of not more than one millennium or so. Sparseness and sometimes clear occasional choice of the temperature logs for the inversions could not permit revelation of larger scale spatial and/or temporal patterns of the GST changes. Only climatic variations in Canada were relatively well documented. On the other hand, results presented in the issue by Lewis (1992) clearly demonstrated that the geothermal method, when used carefully, can provide further voluminous regional data on the paleoclimate change. Investigations have also shown that recent warming over approximately the last century derived earlier from meteorological surface air temperature (SAT) measurements is also seen in the GST histories in many regions. In central and eastern Canada this warming follows strong cold period, the initial warming being a return to the temperature averaged over the last few 100 years. Some of the studies detected good agreement of the past GST changes with the corresponding SATs and with the reconstructions using proxy data. All these works contributed significantly to the field of “Borehole Climatology” research, advancing the application of borehole temperatures to infer GST histories. The reconstruction of the GST histories has drawn increasing attention under several international projects in the 1990s. The project No. 428 “Borehole and Climate” under the UNESCO International Geological Correlation Program was probably the most important of them. The project “Past climate change inferred from the analyses of the underground temperature field: Borehole temperatures and climate reconstructions” was proposed for the period 1998–2002 in order to collect suitable borehole temperature logs from different parts of the world, because their integrated analysis could significantly contribute to better quantitative assessment of the past climate changes and eventually extract the likely manmade (industrial) component of the recent warming from the natural long-term climatic variability. Practically all European countries (including Albania), USA, and Canada were involved in the project, followed by Brazil, India, China, Vietnam, Japan, Morocco, and Egypt. Considerable progress was made especially in Russia, Ukraine and Byelorussia, Romania, and Italy. The general scientific achievement of the project was the compilation of the fully operational database of boreholes with temperature logs and their corresponding GST reconstructions (www.geo.lsa.umich.edu/IHFC/climate). After 1992 the next collection of the borehole climate reconstructions from a number of regions all over the world was compiled by Beltrami and Harris (2001) and generally represented the results of the project No. 428. Published approximately eight years since the first special volume this issue presented wide new collection of works that captured well current directions of research on the Borehole Climatology subject. Little bit early a preliminary analysis of the worldwide dataset for climatic inferences from geothermal

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data have been published (Huang et al., 1997, 2000; Pollack et al., 1998). By the beginning of the twenty-first century the researchers obtained in their disposal wide amount of borehole reconstructions suitable for further conclusions. Boreholes distributed over almost entire landmass of the continents possess immense potential for the new observations of climate signature in many “white spots” of the global paleoclimatic map. At present there are numerical works trying to identify major spatial/temporal patterns of the changes in the global GST dataset. At the outset of discussion of hemispheric and global reconstructions that have been compiled from the analysis of borehole GST inversions, it is useful to list some of the characteristic GST reconstructions. Thus, the rest of this section represents the review of some of the most well known of these attempts. According to their temporal length, all GST histories can be divided into three main groups: 1. embracing the Holocene (0.01 Ma) or so, 2. one or two millennia, and 3. the recent 100–200 years. The first time interval will be described in detail in Section 3.5 with respect to the deep boreholes. In further subsections we present the summary of the 1000–2000 years’ long GST temperature record as well as of the evidence of the recent warming. Most of existing boreholes are generally 500–600 m deep and thus contain information only about approximately the past 500 years. GST history from 1500 to the present is thus especially of interest, because these data represent the bulk of available GST reconstructions. Section 3.2 is devoted to this time interval. 3.1.1 GST changes in the last two millennia (spatial and temporal patterns) Temperature measurements to the depth of 200–300 m are the most common in geothermics, while the data from only 700–1000 m deep holes can reveal climate excursions of the past one to two millennia. Thus, the regions where the GST histories for such long timescales were inferred represent relatively small part of existing inversion results. Below we describe the most important areas where such kind of the GST reconstruction was performed. The Czech Republic Probably the most dense borehole network exists at the relatively small territory (approximately 79 000 km2) of the Czech Republic, landlocked country in Central Europe. There are over 200 temperature–depth profiles in this region measured between 1963 and 1992. Ninety-eight of them, which have been carefully selected (Figure 74), have been used to reconstruct the past GST conditions (Safanda and Kubik, 1992; Bodri and Cermak, 1995, 1997a, 1999; Safanda et al., 1997). Criteria for screening the data were rather severe (see references above). For example, to minimize the possibility of biasing the results by topography and groundwater circulation, the authors have excluded all boreholes located in the mountainous regions and/or displayed evidence of the water movement (see Section 2.7). As shown in Figure 74, the studied area has a typically uneven distribution of data. This clustering, with a few exceptions, is a

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Fig. 74. Map of the Czech Republic showing borehole sites for which temperature logs were processed. (Note: Nearby holes are shown as single dots; triangles show locations of the meteorological stations.)

common feature in the borehole climatology. It is the product of the fact that most temperature loggings were performed in areas of intense industrial interests, such as mineral exploration, coal and oil prospection, and hydrogeological survey. Borehole Holubov (HO-1) represents a typical example of the precise temperature logs and the GST reconstruction in this region of Europe. It is located in southern Bohemia, about 150 km south of Prague. Borehole is situated in a flat area apparently free of topographic and/or groundwater flow disturbance. The high-quality temperature logging was performed in 1972 down to 700 m after the hole had been in equilibrium for several years. The hole penetrated the alternating layers of granulites and peridotites, typical rocks of the ultrabasic structures of the Bohemian Massif. Thermal conductivity and heat production were measured for numerous samples (Cermak, 1975). The mean rate of heat production is 0.63 ⫾ 0.06
W/m3 (Cermak, 1975). For the purpose of the present analysis heat production of this rate has negligible effect on the inversion results. The temperature log has a small but expressive curvature (“U-shape”) in its uppermost part indicating recent warming (Figure 75). The effect of the topography on the measured temperatures was assessed by Safanda and Kubik (1992) to be approximately ⫺0.5 mK/m and relatively constant with depth. Figure 75 also presents the vertical variation of the thermal gradient and heat flow for borehole HO-1. The gradient is low in the uppermost part of the hole, then increases and reaches its “undisturbed” value (compare with synthetic example in Figure 20, Chapter 2). The GST reconstruction is shown in Figure 76. About 1000-year long climatic history can be recovered from measured T–z profile. The characteristic time events deduced from the inversion indicate a warmer period with maximum between 1230 and 1320 A.D., followed by a colder period with a minimum at 1650–1700 A.D., then some warming before 1880,

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Fig. 75. Left: Temperature log (circles) and thermal gradient for borehole Holubov, HO-1 (the Czech Republic) (Bodri and Cermak, 1995). Center: Temperature log on the reduced scale (circles) and thermal conductivity (squares). Right: Vertical variations of heat flow for borehole HO-1.

Fig. 76. Reconstructed GST history for hole Holubov (HO-1) (Bodri and Cermak, 1995). The “Medieval Warm Period” is centered near 1200 A.D. and the “Little Ice Age” near 1650 A.D.

cooling with a minimum at 1950 and pronounced recent warming after 1960. Prior to 1960 the calculated temperature oscillations did not exceed 1.5 K; however, the amplitude of the recent warming may have reached 1.7 K. The reconstructed long-term surface conditions correspond generally well to the Medieval Warm Period and the Little Ice Age. Defining the temporal and spatial nature of climate changes is an important stage in understanding the underlying causes of climate variation. Similar GST histories were

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Fig. 77. Spaghetti diagram: GST histories reconstructed for 98 holes in the territory of the Czech Republic.

derived for all 98 Czech boreholes and used to reconstruct both country-averaged and regional patterns of the respective climate changes. Figure 77 shows transient GST history of 98 boreholes in the Czech Republic. The display of only the transient components of GST enables an easier comparison of the climate change from the regions with different steady-state conditions. The GST histories shown in this figure represent a typical example of the “spaghetti diagrams” characteristic for graphical representation of meteorological/climatologic data (see Shen et al., 1995; Figure 30, Chapter 2 and comments on the web site www.climateaudit.org/?p=10). When all GST histories are drawn, the form usually looks like a bowl of spaghetti. Such diagrams not only are common in the borehole climatology (see, e.g., Figure 11, Chapter 1) but also usually show the results on an almost unintelligible scale. The effectiveness of the spaghetti diagrams is in how it illustrates an amount of possible GST change. In the case of the GST histories an ensemble of curves implies high spatial/temporal variability of the climate changes and/or could suggest that the effects of representational errors may not have been adequately removed (Shen et al., 1995; Gosnold et al., 1997; Cermak and Bodri, 2001). As was shown by Pasquale et al. (2005), the spaghetti diagram of the GST changes reconstructed from the temperature logs measured in the suite of boreholes in northern-central Italy clearly reflects sharp climatic differences between the Tyrrhenian and Adriatic sides of the Peninsula. The Italian orography and the position of the country surrounded by the Mediterranean Sea imply strong effects of the local air circulation that cause high spatial and temporal climate variability. Simple averaging of the GST histories provides highly smoothed regional curves that could not account for the general climate trend in the region.

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Fig. 78. The diagram of occurrences of extremes of climatic events in the past two millennia with the reconstructed air temperatures (50-year running averages) in the Czech Lands plotted as deviation from the 1851 to 1950 mean (Brazdil, 1990).

It can be demonstrated, however, that an ample set of “spaghetti-curves” from extensive area may be confusing only at the first sight and certain general climatic trend can be revealed in almost all cases. One of the more advantageous methods to delineate climatic trends characteristic for the vast investigated area is to combine detected extremes. Thus, Figure 78 summarizes the occurrence times of the extremes of climatic events reconstructed for all 98 inverted GST histories. The times of temperature minima are shown as “cold” and occurrences of maxima as “warm”. As one might expect, Figure 78 exhibits a much more definite pattern of climate changes than the “spaghetti diagram” above. Three early episodes can be distinguished: a cold period between the eighth and tenth centuries, the renewal of general warmth (Medieval Warm Period) with its culmination around 1250 ⫾ 50 A.D., and the cold conditions (the Little Ice Age) with minimum at 1650 ⫾ 30 A.D. Figure 78 also presents the comparison of the reconstructed GST extreme series with the annual air temperature (50-year running averages) reconstructed for the Czech Lands by Brazdil (1990) showing as the deviation from the 1851 to the 1950 mean. Brazdil’s data are based on instrumental observations, and were completed by written historical sources on various indirect indicators of climate, such as grape and hop harvests. The coherence of both series is quite high. The weak appearance of the Medieval Warm Period in the Brazdil’s record may be due to the relative scarcity of the historical data available from this period. While the three early climatic episodes appear as well-marked clusters in the extreme occurrence diagram, the appearance of the Little Ice Age is more erratic and is dispersed over as long as 300 years. Such a feature of the Little Ice Age in the study area is also clearly visible in the climatic record by Brazdil (1990). The cold climate conditions at the territory of the Czech Republic dominated most of the sixteenth and eighteenth centuries, in some cases probably alternated with provisional returns of

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Fig. 79. Comparison of the last 250-year segment of Figure 78 with the mean annual temperatures (10-year running means) for the Prague (Klementinum), Vienna, and Munich meteorological stations.

warmth. The earlier climax of this period around 1650 ⫾ 30 A.D. was observed in the 24 GST histories from different locations, thus believed to be characteristic of most of the territory of the Czech Republic. Since the beginning of the nineteenth century a general warming has dominated the climate pattern interrupted by several shorter, generally not more than decade-long fluctuations of relatively colder and warmer conditions. Using the time occurrence of the reconstructed GST extremes, the next characteristic times of minimum (min) and maximum (max) alternating extremes were found: max 1730 ⫾ 20 A.D., min 1780 ⫾ 10 A.D., max 1820 ⫾ 10 A.D., min 1880 ⫾ 10 A.D., max 1935 ⫾ 7 A.D., min 1943 ⫾ 5 A.D., and max 1976 ⫾ 3 A.D. Figure 79 presents the comparison of the last 250-year segment of Figure 78 with the meteorological series of mean annual air temperatures recorded in Prague, Vienna, and Munich. It shows that the relatively warmer period of the early nineteenth century with a subsequent cooling, indicated by geothermal method, also appears in these records. All records then confirm that the 1880s were the coldest decade. The warmer decade of the 30s and a colder decade of the 40s of the twentieth century, which have been recovered by geothermal method, are confirmed in the instrumental records. On the other hand, certain setbacks of the warmer temperatures in the meteorological series in periods 1948–1953, 1966–1967, 1971–1977, 1981–1983, and 1988–1990, may be regarded as a high-frequency noise in the general warming trend of the twentieth century rather than individual climatic events. These episodes did not appear on the inverted temperature logs but were resolved as a single warming event with its climax around 1976. The derived GST histories have also been used to construct a tentative regional pattern of climatic changes for several distinct climatic epochs on the territory of the Czech Republic. Previously existing information on climate excursions of the last millennium

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for this region was very poor and, like temperature series by Brazdil (1990) presented in Figure 78, was based mainly on the analysis of documentary sources and the instrumental mean annual air temperature records (since 1771) from the meteorological station Prague-Klementinum (Brazdil, 1990; Brazdil and Kotyza, 1995). The climate changes detected by the GST reconstructions were mapped for the following periods: 1100–1300 A.D. (Little Climatic Optimum), 1400–1500 A.D., 1600–1700 A.D. (main phase of the Little Ice Age), and for the most recent climate trend after the year 1960. The rate of the temperature change calculated as derivative of the reconstructed GST history was used as a robust indicator of the climatic trend. As the span of the reconstructed GST history depends on the borehole depth, not all boreholes can cover all time intervals studied. The investigated territory was divided into a regular 1° ⫻ 1° latitude–longitude grid network, and the obtained values were averaged over all grid elements. This procedure enabled to overcome the influence of possible incoherency of GST histories that appeared in the “spaghetti diagram” (Figure 77) as well as the uneven distribution of the drillhole sites. Regional pattern of the average warming rate between 1100 and 1300 A.D. is presented in Figure 80 (top). The existence of the Medieval Warm Period is clear; during this time the entire investigated territory experienced warming, with a maximum rate of up to 0.2 K/100 year, notably in Central Bohemia. A general turn toward colder climate came during 1200–1400 A.D.; the time from 1550 to 1700 A.D. is regarded as the main period of cold climate. However, as was described in Chapter 1 (Section 1.1) the onset of the Little Ice Age as well as its duration was by far not identical throughout Europe. According to the above-mentioned climatic reconstructions by Brazdil (1990), this period was quite erratic at the territory of the Czech Lands and continued at least to the end of the eighteenth century (or even extended into the nineteenth century). Dominant cooling in some cases alternated with provisional returns of warmth (e.g., between 1520 and 1560 A.D. and in the 1670s). To investigate the gradual development of the cold conditions, separate maps were constructed for two stages of this period: (1) 1400–1500 A.D. and (2) 1600–1700 A.D. (Figure 80). These maps illustrate the advance of the cold conditions from the east. While during 1400–1500 A.D. the westernmost regions of the investigated area still showed certain warming trend, possibly the relict of the Medieval Warm Period, between 1600 and 1700 A.D. the entire investigated territory was already subjected to massive cooling. The cooling rates in this period were at least three to five times larger than in the time interval of 1400–1500 A.D. Collection by Lamb (1977) of the winter severity/mildness indices is a useful database for independent comparison of above GST reconstructions. This index represents the excess number per decade of cold winter months (December, January, and February) over months of opposite character. This index may vary between ⫹30 and ⫺30. Excess of cold months is counted negative. Lamb (1977) has gathered collection of indices from 1100 A.D to the 1960s for three positions across Europe near latitude 50°N. The first case embraces the summary of data for the UK (longitude approximately 0°E), the second one corresponds to Germany (approximately 12°E), and the last one is for Russia (approximately 35°E). Extreme decade values in this database range from ⫺28 to 12 over the investigated period. Figure 81 shows the longitude-time map of winter severity index. The findings represented by Figure 80 are in good agreement with the index map. According to Figure 81, the period between 1400 and 1500 A.D. can be characterized

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Fig. 80. Top: Regional pattern of the warming rate (K/100 years) between 1100 and 1300 A.D. in the territory of the Czech Republic. Center: Regional pattern of the climate change rate (K/100 years) between 1400 and 1500 A.D. Bottom: Regional pattern of the climate change rate (K/100 years) between 1600 and 1700 A.D.

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Fig. 81. Winter severity index in different European longitudes near 50°N from 1100 A.D. to 1969. Dark colors indicate colder winters.

by the predominance of cold winters with the clear eastward cooling trend. Extremely cold winters also prevailed all over the 1600–1700 A.D. period. The severity of winters in this period was higher than between 1400 and 1500 A.D. Winter severity rapidly increased to the east. Even when the most borehole GST reconstructions in the Czech Republic, like that presented in Figure 76, have yielded strong ground warming for the whole twentieth century or so, the detailed GST history of the last few decades may be more complicated. The regional trend since approximately 1960 for this region was first reported by Safanda et al. (1995). The authors concluded for the last-century climate conditions: (1) an insignificant change or even cooling in Western Bohemia, (2) 0.2–0.5 K warming over the extensive southern and central parts including the capital Prague, and (3) 0.2–0.3 K cooling in the NE part of the Bohemian sedimentary basin following previous 150–200 years of almost 1 K warming. Comparison of the obtained GST histories with combined air and soil meteorological records disclosed that revealed difference in regional characteristics may have its real regional background.

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Fig. 82. Left: Tentative attempt to construct the regional pattern of the last 30-year warming rate (K/year) in the Czech Republic from GST reconstructions. Right: Map of the recent warming rates obtained from the meteorological mean annual air temperature records. Locations of the meteorological stations are shown in Figure 74.

Figure 82 (left) presents the tentative regional map of the GST change rate since approximately 1960 (Bodri and Cermak, 1999). Warming has been most pronounced around Prague and vicinity and decreased to the south and southwest. Another area of significant warming is located in the easternmost part of the country. The western part of the Czech Republic even goes through an inexpressive cooling (⫺0.007 K/year). Five holes from the western group were investigated by Clauser and Mareshal (1995) and seven by Safanda et al. (1997) who also reported negative rate of the temperature change in the most recent past. To verify the obtained climate history, we used instrumental records of the mean annual air temperature for the period 1961–1996 at 30 local meteorological stations (Figure 74). For each station the linear trend was calculated; these trends then were used to compute contour lines. The resulting map of the “meteorological” warming is presented in Figure 82 (right), being generally of the same order and similar to that given by the “geothermal” data. The most significant warming was characteristic for the central and eastern parts of the Czech Republic, and gradually decreases to the south and north. Figure 83 shows the results of spatial correlation between both patterns. Spatial correlation was calculated from the dense network computed for the construction of contour lines using 3 ⫻ 3 moving window. As shown, both patterns show generally high correlation. An independent analysis of the SAT records from Czech meteorological stations for the same period has revealed warming trends that fall in the interval from 0 to 0.04 K/year with characteristic regional warming rate of 0.0283 K/year (Cermak et al., 2000). Approximately 60% of the data fall within 0.02–0.03 K/year interval. The reasons for the general warming of the last three decades can only be speculated upon. The global warming phenomenon should be mentioned among the possible candidates. On the other hand, the regional distribution of warming presented in Figure 82 gives a certain idea on a potential human impact on climate conditions. The highest indicated rates of the recent GST-warming correspond to the industrial and relatively densely inhabited regions of Prague, northern Bohemia, and Ostrava coal basins; the lowest rates are characteristic for the SW and S slopes of the Bohemian Massif, areas generally forested and less industrialized. An analysis of the spatial pattern of the recent SAT trends by Cermak et al. (2000) has confirmed conclusion by Bodri and Cermak (1999) that more

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Fig. 83. Spatial correlation of warming patterns in Figure 82.

pronounced recent warming is observed in more populated and generally industrialized areas, while lower values occur in areas either agricultural or forested. On the other hand, at least a part of the observed warming can be explained simply as natural short-term oscillation of climate conditions. According to both GST reconstructions and meteorological measurements (see Figure 79), the second part of the nineteenth century can be characterized as one of the coldest periods of the entire Holocene. Part of the observed warming may thus bea natural return of climate from the previous colder conditions to the “normal” level. Finland Examples of the estimates of SAT anomalies for some regions of Europe extended back to approximately 1650 are presented in Figure 8 (Chapter 1). Smoothed values were calculated from the data by Jones and Moberg (2003). Comparison reveals clear differences between illustrated regions. As shown, the range of variations of temperature anomalies is larger and the overall temperature trend is more pronounced in Fennoscandia rather than in Central Europe. On the other hand, the most dominant feature of the SAT at least in Finland is a strong interdecadal variability (Heino, 1994). While the nineteenth century appears as undoubtedly cold, the trends of the next century are less sure. The SAT record does not show any consistent warming or cooling. All existing long-term paleoclimatic reconstructions in Fennoscandia are almost entirely based on the tree-ring proxies. Specific feature of the tree-ring method is that the low-frequency components may be damped because of the long response time of the tree to the weather variations and by the downward trends in growth associated with increasing tree age (see Section 1.2.3). Averaging the tree-width indices from many individual trees to gain continuous standardized record also effectively removes the long-term trends from the reconstructions (Sirén, 1961). Figure 84 presents the longest and most informative tree-ring series for pines at sites distributed over northern Finland for the period 1181–1960 A.D. (Sirén, 1961), i.e., from near the present northern forest limit. The record is highly variable even at the large scales of aggregation. The use of longer averaging

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Fig. 84. Tree-ring width indices (in units of 1000 ⫻ log(Mg), where Mg is geometric mean of the tree-ring widths in millimeters) in northern Finland. (From data by Sirén (1961).)

periods reveals better long-term trends. The difference of this record from 2000 years’ long temperature anomalies time series reconstructed for the Northern Hemisphere (Figure 3, Chapter 1) is obvious. Despite the short 20-year cooling events around 1450 and 1600 A.D., the data do not reveal any pronounced cold period that could be interpreted as the Little Ice Age. Another 1400-year high-quality tree-ring record from Northern Sweden (Briffa et al., 1990) also indicates a relatively short Little Ice Age. Similarly, little evidence was obtained by Briffa et al. (1990) for the existence of the Medieval Warm Period. It is not clear, however, whether these findings can be attributed to the real climatic conditions or to the above-mentioned loss of long-term trends from the tree-ring record. Considerable progress was achieved in the 1990s in borehole paleoclimatic reconstructions for this area, when the data measured for the geothermal heat flow investigation were applied for climate studies. Three research fields were of special interest: (1) modeling of the permafrost in bedrock in northern Fennoscandia and its dependence on climatic variations (see Section 2.8), (2) evaluation of the long-term GST changes in deep boreholes (Section 3.5), and (3) the GST conditions in the last two millennia or so. Modeling of permafrost variations in northern Fennoscandia suggested rapid variations in permafrost thickness during the Holocene depending on the present ground temperature and past climatic variations (Kukkonen and Safanda, 2001). Obtained results have been described in detail in Section 2.8. Observed vertical variation in the heat flow density in the Fennoscandian Shield and in the neighboring parts of the East European Platform was attributed to the major climate change at the Pleistocene–Holocene boundary, and the GST inversion results suggested an average warming of 8.0 ⫾ 4.5 K from the Last Glacial Maximum time (Kukkonen and Joeleht, 2003, and the references therein). A significant vertical variation in heat flow density measured in a 12 km deep hole in the central part of the Kola peninsula was explained as arising due to the very low surface temperatures during the latest glaciation at times more than 10 000 years B.P. (Kukkonen and Clauser, 1994). Attempts to interpret temperature anomalies observed in Finnish deep holes will be described in more detail in Section 3.5.

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Fig. 85. Left: Temperature logs for three Finnish boreholes. Boreholes were logged to 570 m (DH VE-KR-1) and/or 920–950 m depth (DH 679 and DH MHA-4). Only upper 250 m depth interval is shown to emphasize “U-shape” of the profiles. Right: Reduced temperature profiles for Finnish boreholes.

Climate changes of the last 2000 years were inferred from three borehole temperature logs. Boreholes were situated within the narrow (25–27°E) longitude strip in the northern (67.7°N), central (64.6°N), and southern (60.6°N) parts of the Finnish part of the Baltic shield. They were logged in the years 1988–1994 at least one year after the drilling had ceased. The measurements were performed with frequent readings (measured points were separated by a 2.5 m depth interval), and each record contains from 220 to almost 400 individual reading points. Measured temperature–depth profiles and reduced temperatures are presented in Figure 85. The bulk of the observed temperatures represent a quasi-steady-state geothermal field. As previously, to visualize temperature perturbations in measured profiles that might have been caused by climate change we used reduced temperature representation. As shown in Figure 85, departures from the steady-state conditions are significant only in the upper part of each hole up to the depth of 100–150 m. Below this depth reduced temperatures slightly oscillate around zero line. All reduced temperature profiles are curved and systematically positive above 250 m depth with amplitude of 0.5–1 K indicating recent climatic warming. All three temperature logs were inverted individually by SVD technique and for all of them the climatic episodes over the last 2000 years were identified. The method readily allows incorporation of additional information on both the measured data and the climate change. As was shown in Section 2.4.3 (Chapter 2), this procedure increases the number of parameters (individual intervals of constant temperature) that may be estimated. In addition, the information on decorrelation of the measured data and on the persistence of the climate changes has also been described (for details see Section 2.3.4). For all three investigated boreholes, a short-range dependence between data is characterized by a correlation that decreases exponentially fast. An example of

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Fig. 86. Autocorrelation of measured temperatures at borehole DH MHA-4 in the interval of 34–162 m.

the exponential decay of the autocorrelation function of the reduced temperatures is shown in Figure 86. The decorrelation distance, D, corresponds to the depth lag at which the autocorrelation decreases to (1/e), and for all Finnish boreholes this was about 50 m. In other words, the individual measurements separated by this distance can be considered as statistically independent. Investigated boreholes represent relatively rare field example of boreholes with fast decorrelation (for comparison see Figure 35, Chapter 2; for Canadian borehole Hearst decorrelation distance equals 174 m; for the above-described Czech borehole HO-1 D ⫽ 246 m) and high number of measured points. Very effective technique of data thinning (“scarcing”) can be used for inversion of such T–z profiles. In this case the original dataset of measured temperatures can be divided into subsets, and different parameters of the time discretization of the GST history can be estimated from different sections of temperature log. This procedure significantly enhances the number and reliability of estimated parameters but requires that the datasets used be statistically independent (Twomey, 1977). The reconstructed GST histories (Figure 87) appear to be coherent for DH679 and DH VE-KR-1 boreholes situated at more northern latitudes; however, some incoherence arises in the last 300-year part of the GST history obtained for DH MHA-4 hole, which cannot be regarded simply as an artifact of the solution. The GST history reconstructed for another Finnish borehole, Outokumpu (62.72°N, 29.02°E) done by Kukkonen and Safanda (1996), yielded results coherent with the DH MHA-4 GST history presented in Figure 87. These authors revealed cold episode 1000–1200 A.D.,

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Fig. 87. Finland: Reconstructed GST histories.

then warming culminated near 1750 A.D., subsequent cooling with its minimum at the year 1900, and warming since then. Certain explanation of the incoherence of the GST histories shown in Figure 87 can be looked for in the microclimatic local variations. Processing of further borehole temperature–depth profiles from the area will increase our knowledge on the regional variations of the Finnish climate. As about two rest boreholes, the early section of the reconstructed GST histories generally covers a cold interval between approximately 400 and 1000 A.D., followed by a long gradual warming up to 1500–1700 A.D. and a cold period around 1800 A.D. While in Europe cold periods before 1000 A.D. and ca. 1800 A.D. are documented by a variety of proxy records, the fifteenth to sixteenth century warming in Finland appears to be different from the general European trend (see, e.g., Section 1.1, Chapter 1). Period from fifteenth to seventeenth centuries in Europe corresponded to the wellknown cold conditions of the Little Ice Age. It should be mentioned that tree-ring record in Figure 84 also shows the years 1500–1750 as a generally warm time. Provisional return of cold near 1600 A.D. was probably too short to be resolved in the GST reconstructions that integrated all this period as a single warming event. It is not quite clear whether the prolonged time interval with culminating temperatures around 1500–1700 A.D. in Figure 87 can be simply interpreted as the Medieval Warm Period and the Little Ice Age shifted by 100–200 years. Anyhow, some distinction of the Finnish climate compared with the general climate course in Europe and/or the Northern Hemisphere can be observed not only for the last two centuries, but also probably for the longer times. Since there is no long enough series of the instrumental observations of temperature in Finland and/or the proxy records are also geographically and temporally discontinuous, the GST histories extracted from geothermal data may serve as a useful independent dataset to complete significantly the climate history of the region. The amount of temperature logs measured in this area and their high quality provide the possibility to detect even more remote climate events and also their spatial variations.

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Canada Remarkable wide massif of the GST reconstructions for the last millennium exists for the territory of Canada (Beltrami and Mareschal, 1992; Beltrami et al., 1992; Wang et al., 1992; Huang et al., 2000; Pollack and Huang, 2000; Beltrami, 2001; Majorowicz and Safanda, 2001, 2005; see also the references therein). The Canadian Geothermal Data Base consists of the temperature logs measured in more than 1000 boreholes (Wang et al., 1992). Although caution should be exercised to exclude temperature logs that come from boreholes clearly disturbed by terrain effects, this array of data represents possibly the largest regional temperature dataset. The earlier GST histories were evaluated from the inversion of the temperature logs measured in a suite of boreholes from Ontario Province of Canada. Only deep boreholes were used for these GST inversions; thus, calculated GST histories were at least 1000 years long. The temperature–depth profile for borehole Hearst shown in Figure 17 (Chapter 1) is typical for this group. The curvature in the temperature profile is stationary in time and exhibits clear “U-shape”, the signature of recent warming. The GST history inferred from the temperature log measured at Hearst site was discussed in detail in Section 2.4.3 of the Chapter 2 (see also Figures 34–37). It shows slightly warm conditions between 1200 and 1700 A.D., cold period around 1800, and the rapid warming since then. The estimated rise in the GST from 1880 to 1985 A.D. was about 2 K. This trend coincides well with the longest annual mean SAT record in eastern Canada (Parry Sound, northern Ontario). The total SAT increase in this station was about 0.9 K for the last 100 years and agrees well with approximately 1 K average warming estimated for central and eastern Canada from the borehole temperature–depth profiles. Calculated for this area, GST histories are similar in that they all exhibit the common features of the Medieval Warm Period, the Little Ice Age, and a recent warming trend. However, individual reconstructions may differ significantly in terms of exact timing and amplitude of the temperature change. Revealed spatial and temporal variability of the GST histories in Canada is typical for the paleoclimate reconstructions over extensive territories. Later on, the temperature–depth profiles measured in central and western Canada have also been drawn into the investigations. Recently three research groups have analyzed more than 100 borehole temperature profiles at 56 sites distributed from Newfoundland to Manitoba. An analysis of Canadian profiles revealed region-dependent ground surface warming of 1–2 K. The works by Majorowicz and Safanda (2001, 2005) represent typical example of Canadian GST reconstructions. Fifty-one precise temperature logs were measured in the Western Canadian Plains, east of the Cordillera (Canadian Prairies). The majority of boreholes are from Alberta and Saskatchewan. Temperature logging was performed between 1992 and 1996. Figure 88 shows profiles of the transient component of measured temperature logs (reduced temperature). In their uppermost parts all profiles exhibit clear evidence of an extensive recent warming, while the negative temperatures in the 100–200 m depth range signify the earlier cold conditions. Figure 89 shows examples of the GST reconstructions. The scatter of the obtained past climate histories is somewhat lower than that generally appearing in European GST collections. The majority of boreholes include evidence of the cold period between 1850 and 1950 and significant warming since then. To minimize the effects of noise in the individual temperature logs and to obtain a more reliable regional GST model the authors have calculated synthetic transient profiles using boxcar model of the pre-twentieth century climate and the SATs from the nearby weather station as a forcing function and have compared them with the

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Fig. 88. Transient components of the temperature–depth logs for Canadian Prairies boreholes. (Data by Majorowicz and Safanda (2001).)

Fig. 89. Forty-three GST histories inferred from the temperature logs measured in the Canadian Prairies. (Data by Majorowicz and Safanda (2001).)

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Fig. 90. Best POM ⫹ boxcar event possible models for the temperature logs from the Canadian Prairies. The forcing SAT data are from Calmar meteorological station (Alberta, Canada). (Data by Majorowicz and Safanda (2001).)

real transients. The boxcar models of the past temperature preceding recent warming have been considered as a possible first-order past climate approximation explaining well the transient features of the T–z profiles in the northern US plains and grasslands of southern Saskatchewan (Harris and Gosnold, 1999; Majorowicz et al., 1999). Majorowicz and Safanda (2001, 2005) have used the SAT data from Calmar meteorological station (Alberta, Canada; 53.27°N, 113.85°W, 720 m a.s.l.) as a forcing function (see Section 2.5, Chapter 2). Temperature record exists there from 1915 (Figure 90). Mean temperature for the 1915–1993 observational period equals 2.2°C. According to general results of the GST reconstruction (Figure 89), the boxcar event was assumed as representing climatic conditions between 1790 and 1910, while pre-observational mean temperature (POM) is the long-term temperature average before 1790. It was also assumed that the difference between POM and boxcar temperatures is equal to 1 K. Thus, the resulting synthetic transients have depended on only one free parameter, namely the long-term average temperature before the year 1790 (POM). Comparison of the synthetic temperature–depth profiles with the real 51 Prairie transients has shown that satisfactory agreement between both data can be achieved within relatively narrow range of possible POM values (1.15–2.15 K), which indicates approximately 0–1 K and 1–2 K warming in comparison with the 1915–1993 and 1983–1993 Calmar temperature means, respectively (Figure 90). A composite ground-temperature history for southern Canada (Figure 91) was averaged from a number of GST reconstructions. This reconstruction exhibits high coherency with the above-described boxcar ⫹ POM model by Majorowicz and Safanda (2001, 2005). It shows pronounced cold period centered near 1800–1850 A.D. that may correspond to the Little Ice Age and warmer conditions at least since 1000 A.D. However, the most common feature of this long-term GST trend is the total approximately 2 K

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Fig. 91. GST histories for the last millennium years for southern Canada (drawn from the data by Beltrami and Chapman, http://www.esrc.stfx.ca/borehole/borehole.html) and for all territories of Canada. (Data by Beltrami (2001).)

temperature increase from the 1800-year minimum. This rapid warming slowed down and even has turned into ⬃0.25 K cooling in the twentieth century. Even if temperature excursions in the second part of the twentieth century are smaller than the earlier GST changes, they deserve further attention, since such GST course hints that the continuous warming process has been highly colored by temperature fluctuations with frequencies of a decade or decades, and different regions may have experienced quite different conditions. Thus, e.g., the most of warming detected in southern Canada may simply represent recovery from the previous cold conditions. The most recent average GST reconstruction for Canadian territory was calculated by Beltrami (2001); the 112 temperature logs all across the country from Canada’s geothermal database were used. All temperature logs were inverted individually. They were then averaged over the whole investigated region. This total GST history for the territory of Canada embracing the period 980–1930 A.D. is shown in Figure 91. As in the case of southern Canada, the GST history for the whole territory of the country exhibits a marked increase of temperature since about 1800. The parallel calculations by Beltrami (2001) detected a similar increase in the energy stored by the ground. However, both pre-1800 periods characteri-stic of the GST reconstruction for southern Canada are lost in the latter diagram. This implies that these climatic episodes were far not common for the whole extensive territory of Canada. Because the regional spatial and temporal variations of the GST in Canada are well documented, the average climatic trend shown in Figure 91 should be considered only as a highly simplified version of climatic history of the separate regions of the country.

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Authors have interpreted detected warming in the context of increased levels of greenhouse gases (GHG) since the onset of the industrial revolution. On the other hand, at least part of inferred temperature increase could be simple recovery from the previous cold conditions to the level characteristic for the 1000–1400 A.D. period. Examples of the GST reconstructions in permafrost environment of Canadian Arctic and the neighboring regions are presented in Section 2.8 (Chapter 2). The geothermal data were obtained from the oil exploration holes distributed all over the Alaskan Coastal Plain and Foothills. Configuration of measured temperature–depth profiles revealed clear curvature toward warmer temperatures (U-shape) in the uppermost 200 m (Figure 71, Chapter 2). An analysis of these T–z profiles has provided the first evidence that Alaskan Arctic has warmed by 2–4 K during the twentieth century prior to the mid-1980s (Lachenbruch and Marshall, 1986; Lachenbruch et al., 1988). Further GST estimates by Lachenbruch (1994) using the ramp/step approach have corroborated early results and have provided a value of 2.7 ⫾ 1.0 K for the last-century warming. Although the details of the climate warming that time could not be resolved, a recent warming of the permafrost was surely detected. Probably these results represent the most noticeable evidence of the recent climate warming extracted from boreholes. 3.1.2 Recent warming The sizable volume of the boreholes was logged to a depth of 200–300 m. This depth interval preserves a robust signal for the surface temperature trends over the last century or so. Most of the nineteenth to twentieth centuries have actually been covered by instrumental observations (meteorological long-term series), and the geothermal reconstructions for the last 100–200 years could be better used as an addition to existing SAT records and/or for calibrating the method itself rather than independent source of information. The GST reconstructions from relatively shallow boreholes may serve as a valuable estimate of the recent climate changes only in the regions that appear as the “white spots” at the climatic map. Since the beginning of the nineteenth century the global climate has generally been a one-way story, a trend of overall warming following the previous Little Ice Age with probably several shorter, generally not more than decade-long fluctuations of relatively colder and warmer conditions (Figure 4, Chapter 1). Observed global temperature increase during the last 100–150 years has become known as a global warming. Recent variations of the GST changes inferred from borehole T–z profiles have been extensively analyzed over the last 10–15 years in a series of papers. These investigations have shown that the traces of the recent warming (GST history of the past one to two centuries) are common in many borehole temperature records, indicating the temperature rise by 1–2 K over the last century. As described above, most boreholes in the Czech Republic yield GST histories exhibiting significant warming starting not later than some decades ago. A certain geographical pattern exists of regions where the warming rate appears particularly intense and others where it is weaker (Figure 82). Their spatial distribution indicates a possible impact of human activities. Thus, the highest rate of 0.04 ⫾ 0.01 K/year was obtained for the industrial regions of Sudety and in Ostrava coal basin; the lowest rates of less than 0.015 K/year correspond to the southwestern and southern slopes of the Bohemian Massif, areas generally forested. In most regions recently the rate of warming

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has been increasing. In area near Prague the rate of warming for the last 100 years amounted to 0.016 ⫾ 0.006 K/year, which doubled (0.033 ⫾ 0.011) in the last 50 years. The most convincing results showing GST changes for the past five centuries were reported from North America, while European data were more sparse and difficult to interpret. The likely explanation is that the past climate changes were more dramatic in mainly continental conditions of North America, while European climate was strongly affected by the Atlantic weather conditions. There may also be environmental reasons, namely massive total deforestation that occurred in Europe since the Middle Ages1 compared with the later and only partial deforestation in North America. The North American continent represents typical example of the ample twentieth-century warming trend reconstructions by the “geothermal” method, while European data are rather diffused and more difficult to interpret. The last 150-year GST history in Canada involved generally pronounced warming (Figure 91; see also Beltrami and Mareschal, 1991, 1995 and the references therein). The amount of surface temperature increase is 1.5–2°C. Majorowicz and Safanda (1998) and Majorowicz et al. (1999) have analyzed a number of temperature–depth records from 150 to 300 m depth boreholes in western Canada and obtained 300-year long GST histories for the period prior to the instrumental temperature recording. Their GST reconstructions indicates the presence of cold period in the eighteenth to nineteenth centuries with its minimum around 1820 ⫾ 50 K and subsequent warming with a magnitude of 1.9–2.5 K starting in the mid-nineteenth century till present. Results of three-century long GST history reconstructions from the data of boreholes situated in the grasslands of southern Saskatchewan with prevailing semi-desert conditions showed that almost half of the detected warming occurred prior to 1900, thus before the dramatic buildup of the atmospheric GHG (see discussion in Section 3.4). The highest GST warming rates have been observed in the areas where there have been extensive land surface changes, such as forest clearing or forest fires together with conversion of prairie grassland to farming land that occurred prior to 1900. Studies of geothermal data from Alaska provide evidence of unprecedented recent warming (Lachenbruch and Marshall, 1986; see Figure 71, Chapter 2). A series of boreholes distributed across 500 km of the Alaskan Arctic indicate variable but widespread secular warming of 2–4 K at the top of the permafrost near the ground surface. Temperature disturbances in the boreholes extend from the surface to approximately 100 m depth that hints the onset of warming in the early part of the twentieth century. The widespread strong warming detected in Alaska is consistent with the simulations of the greenhouse warming by General Circulation Models (GCM) that predict polar regions to be particularly sensitive to the greenhouse effect (www.giss.nasa.gov/research/modeling/gcms.html). Both regional and site-specific studies of the GST changes for the last 100–200 years exist in the territory of the USA. Investigations in the mid-continent region of North America (Deming and Borel, 1995; Harris and Gosnold, 1999) have shown that the warming characteristic of eastern Canada extends west to the front of the Cordillera. Chisholm and Chapman (1992) and Harris and Chapman (1995) have revealed that the warming in the western United States in and near the Great Basin was only approximately

1 Middle Ages represent a period in the history of Western Europe that began in the fourth and fifth centuries after the disintegration of the West Roman Empire and lasted into the fifteenth century to the period of the Renaissance.

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half of the value that was detected for eastern and central Canada. This fact was also supported by the GST reconstructions by Wang et al. (1994), who analyzed temperature logs from 85 boreholes in eastern and Cordilleran locations. Results have shown that the Cordillera of western Canada has warmed by only ⬃0.8 K, while the warming in the area of the east Cordillera was by ⬃1.5 K. To compare GST and SAT data and to test observed versus predicted climate change on a continental scale, Gosnold et al. (1997) has focused on a 500 km ⫻ 1000 km wide transect in the mid-continent of North America that extends from the Kansas–Nebraska border into southern Manitoba. The 300-year long GST histories, determined from a set of 29 borehole temperature profiles, showed a century-long warming trend that increases systematically with latitude, from ⫹0.4 at 41.1°N to ⫹2.0 K/100 year at 49.6°N. The SAT warming also rises with latitude from ⫹0.5 at 40°N to ⫹1.6 K/100 year at 48.8°N. As described above in the case of the Alaskan Arctic (for details see Section 2.8), these warming trends agree with the regional warming pattern predicted by GCM simulations of global warming. While the GST and the SAT data coincide well in the regions where seasonal ground freezing does not occur, they differ significantly where seasonal ground freezing does occur (see also Section 2.6.2). As explained, the greater GST warming is due to a secular increase in soil moisture that corresponds to an increase in precipitation during the past 50 years. Among the geothermal data from the territory of the USA, the Utah boreholes have a specific interest because high-quality temperature–depth logs are available in the sites with minimal terrain, hydrological and anthropogenic disturbances and where meteorological stations that have operated for about a century are geographically neighbors to borehole sites. Harris and Chapman (1997, 1998) analyzed a number of borehole temperature logs from SE and W Utah (see examples in Figure 16, Chapter 1), which provided generally consistent results suggesting that temperature in the mid-1800s was on average slightly cooler than in the previous centuries, followed by about 0.6 K warming in the twentieth century. Comparison of the calculated transient temperature–depth profiles with the SAT records from nearby meteorological stations indicated that air and ground temperature are well correlated in this area; thus, the POM estimates (see Section 2.5, Chapter 2) yield reliable estimates of the long-term mean temperatures prior to the beginning of SAT records. These baseline temperatures are 0.6 ⫾ 0.2 K cooler than the 1951–1970 average SAT and provide further evidence that twentieth century warming represents a real and significant departure from the nineteenth century ground surface conditions. Not all borehole studies revealed significant GST warming. Chisholm and Chapman (1992) investigated high-resolution T–z profiles in a suite of boreholes in western Utah. The amplitudes of the estimated recent warming ranged between ⫺0.8 and 0.6 K. Two of the boreholes exhibited small or no warming; one borehole even yielded a cooling. The temperature logging was repeated for “critical” boreholes in the years 1978 and 1990. Both logging results gave very similar GST histories. For example, inversion of the temperature–depth profile measured in borehole GC-1 in 1978 with ramp method indicated recent cooling of ⫺0.8 ⫾ 0.3 K over 18 ⫾ 7 years preceding 1978, while the repeated temperature log measured in 1990 have shown ⫺0.7 ⫾ 0.1 K cooling over 47 ⫾ 13 years preceding 1990. One of the weather stations in the area in fact exhibits a cooling for the twentieth century. Authors concluded that in the last century average GST has risen only by 0.3 K in western Utah.

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In fact, the above-mentioned analyses of more than 100 temperature–depth profiles in North America have proved: (1) the presence of unambiguous ground surface warming during the past 100–150 years, and (2) that the amplitude of this warming varies in the wide range of 0.3–4 K, strongly depending on locality. The fact is that this warming was not derived from SAT records. For example, Karl et al. (1991) after analyzing the meteorological station records for the mid-continent concluded the absence of statistically significant climatic trends. The first attempt of constructing a regional pattern of the recent warming for North America on the basis of estimated GST histories dates back to Deming (1995). He has summarized results of 10 independent investigations of the recent warming including approximately 370 GST reconstructions and has concluded that the revealed twentieth century temperature increase exhibits latitudinal amplification similar to that predicted by GCM. Table 7 represents an updated version of the estimated GST changes in North America embracing regions from Alaska to Texas. Data clearly demonstrate a significant latitudinal trend of the twentieth century warming early revealed by Deming (1995), when temperature increase at Alaskan Northern Slope amounts to 2–4 K and equals to only 0.3 K at Texas. Warming is generally higher in the eastern part of the continent. Results by Deming (1995) (see also the summary in Table 7) show that while an average GST increase in the eastern and in the southeastern part of North America has reached 1–1.5 K in the last 100 years, the characteristic warming rate in the western part of the USA is only half of this value (except of the high latitudes in Alaska). However, this latter trend is not such obvious as the latitudinal temperature increase. Causes of climate change involve any process that is able to alter the balance between energy coming from the Sun and energy leaving the Earth. There are many natural causes of climate change, but recently researchers have become concerned with the anthropogenic influence, especially the effect of atmosphere pollution, on the global climate (for details see Section 1.2, Chapter 1). Thus, the reasons and/or explanations (and all their details) for the twentieth century warming in North America can be looked for in factors related to either climate and its natural variability or population dynamics connected with the transformations of the biosphere by various anthropogenic activities, e.g., deforestations, loss of terrestrial diversity, etc. The most evident influence may be global warming phenomenon. On the other hand, the magnitude of observed warming in North America is still within the range of the estimated natural climate variability for the Holocene. Anyhow, the studies of the borehole temperatures provide a relatively good constraint on the magnitude of warming, as well as the inferences concerning its timing and rate. Obtained evidences can serve as a database for the verification of the theoretical predictions of warming related to the accumulation of GHG in the Earth’s atmosphere through anthropogenic activities. The separation of a likely anthropogenic contribution to the present warming due to industrialization and/or urbanization, or an apparent GST warming due to the change in the surface conditions caused by, e.g., extensive deforestation or land cultivation can be performed by the GST and SAT comparison. For example, change in the vegetation cover may seriously affect the reflective nature of surface; i.e., the soil energy budget, influence evaporation, and as a result increase GST, while the SATs are less affected (see also Section 2.6.4). Data from North America can be completed by the GST reconstructions from Cuba. Very limited meteorological/climatic information exists for this island and surrounding

Site

Southeast Utah Northern Great Plains, USA (NGP) NGP, USA

148.6

2–3

1912

9

68–70

152–162

2–4

1925

21

500–900

42–50

65–81

0–4

20–132 ago

21

?

45–57

70–105

1.5

1860

50–65 51–56 49–68

115–135 110–120 104–136

0.8 1.5–2.5 1.9 ⫾ 0.9

1890 1948 1896 ⫾ 40

39.5–41.5

112–114

0.3

1893

6

39–42 38.5–39 38–39 40–50

112–114 110–111 110–111 96–104

0.6 0.5 0.7 1

1900 1800 1900 1850

3 9 6 45

150 300–500 300–500 ?

Wang et al. (1994) Majorowicz (1993) Majorowicz and Safanda (1998) Chisholm and Chapman (1992) Harris and Chapman (1997) Harris and Chapman (1995) Harris and Chapman (1997) Gosnold (1990)

48–50 46 42–44 48–50 41–46 36.4b

100–103 97 99–102 100–107 100–102 96.7b

1.5 0.6 0.3 2.4 0.7 1.3–1.5

1900 1900 1900 1900 1900 1700–1835

6 1 6 8 20 6

231b 270 207b 95–330 150–710 380b

Harris and Gosnold (1999) Harris and Gosnold (1999) Harris and Gosnold (1999) Gosnold et al. (1997) Gosnold et al. (1997) Deming (1995)

43–46 33.1

68–75 96.5

1 0.3

1875 1900

10 1

213–710 110

Pollack and Huang (1998) Gosnold et al. (1997)

North Central Oklahoma, USA Northeast USA Texas, USA a

Approximate interception date. Averaged value.

b

Number of boreholes

Depth (m) 750

126

150–3000

94 42 35

150–1000 30–220 150–500 160

Reference Lachenbruch et al. (1988) Lachenbruch and Marshall (1986) Beltrami and Mareschal (1991) Wang et al. (1994)

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Time (year)a

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Prudhoe Bay, Alaska North Slope, Alaska Eastern Canada

GST change (K)

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Table 7. Estimated recent GST changes in North America

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territories. On the contrary, borehole network in the region is wide; thus, Cuba represents the typical example of the regions that without GST reconstructions could appear as “white spots” on the world climatic map. Cermak and Bodri (2001) re-opened the earlier interpretation of the 33 borehole dataset from Cuba measured by three joint Czech-Cuban geophysical expeditions between years 1981 and 1986 (Cermak et al., 1991). Boreholes are situated all over the territory of Cuba (Figure 92). Regardless of the borehole position or local geological setting, temperature logs revealed a pronounced anomalous curvature in the upper depth sections of practically all holes visited (Figure 93). The first GST reconstructions were performed using the ramp/step model and revealed 1.6–8.0 K

Fig. 92. Cuba: Location of borehole sites at which temperature–depth profiles were measured.

Fig. 93. The upper part (0–500 m) of temperature–depth records measured in Cuba.

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Fig. 94. GST histories reconstructed for Cuban boreholes.

temperature increase in the interval 30–250 years B.P. (Cermak et al., 1992). When reassessing the anomalous curvature observed in the Cuban temperature profiles, it was felt that the ramp/step model may be too simple and hence more advanced SVD technique was applied. All Cuban “spaghetti-type” GST histories (Figure 94) resemble the results reported by Pollack and Huang (1998, 2000) for cumulative interpretation of worldwide borehole dataset, namely the fact that the twentieth century has been the warmest of the past five centuries (see Section 3.2). Present characteristic warming rate amounts over 0.5 K/100 years since 1900; additional warming of 0.5 K came as a contribution of the four previous centuries (1500–1900). As in the case of the Czech and other regional investigations (see, e.g., Figure 33, Chapter 2 or Figure 77 of this chapter), the Cuban spaghetti diagram is confusing only at the first sight. Certain climatic trend can be delineated when all occurrence times of the individual extremes in the GST curves are plotted as a frequency-time diagram. Figure 95 illustrates the climate reconstruction as a histogram of times of relative temperature maxima (“warm” events) and occurrences of relative temperature minima (“cold” events), regardless of their possible magnitude, grouped in 50-year long intervals. The colder sixteenth to eighteenth centuries together with the recent one or two centuries of temperature recovery and most recent warming are well documented. The mean value of the twentieth century warming for Cuba amounts to 2.2 ⫾ 0.5 K. This value appears quite high in comparison with the amounts lesser than 1 K obtained, e.g., for Utah and Texas (Table 7); however, it coincides well with the warming rates reported at other Caribbean islands by Singh (1997), even when Singh’s data refer shorter time span of the last 40 or 50 years only. The rate of detected warming at Puerto-Rico, Jamaica, Bahamas, Guadeloupe and Trinidad amounted to 0.8–4.6 K/100 years.

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Fig. 95. Cuba: Histogram of the occurrences of climate events (see text).

Cuba is relatively small and narrow; thus, its climate is strongly influenced by the surrounding Atlantic Ocean/Caribbean Sea. According to Hansen et al. (1996), at lower latitudes (approximately between 24°N and 24°S) there is an extensive sustained warming over almost the entire tropical oceans. While global mean surface temperature has risen by about 0.5 K over the last 100 years, the temperature rise in the Caribbean region seems to be somewhat higher, at least 1 K (Singh, 1997). On the other hand, the natural climate variability is masked in this region by high apparent GST warming, a product of systematic clearing of the original tropical forest and consequent exposure of the land to intense solar radiation which increased the surface temperatures by several degrees of Celsius tracing thus the advancement of colonization of the island during the last century. The above-mentioned examples from the North American continent have demonstrated the possibility of the geothermal method for the investigation of recent warming to complete other climate information in the regions where GST reconstructions represent the only available data on the past climate detection. Numerous examples of the recent warming also exist in Eurasia. The paleoclimatic reconstructions using borehole temperature–depth profiles were performed in a broad international co-operation among several European countries. Even arranged in not so dense network as in North America GST histories in Eurasia represent wide amount of climatologic data captured in regions from Atlantic to Pacific Ocean. Most of the calculated GST histories clearly confirmed a general recent warming in the last 200 years. However, because climate of the continent exhibits considerable spatial variability, especially in its EuropeanAtlantic sector, systematic areal trends in the warming amplitude could not have been detected. Thus, further on only the most important GST reconstructions over Eurasia are briefly mentioned. Evidences of the recent warming in Europe are less. They are rather clustered in several countries and show less pronounced amplitude than that detected in North America.

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Data from the Czech Republic and Finland were described in detail in the previous section. Clauser and Mareschal (1995) have inferred the climatic trends of the past 250 years using temperature logs measured in several boreholes of southeastern Germany. Obtained results indicate two main episodes in the averaged GST history, namely cooling that continued from 1750–1800 to 1930–1950 followed by mutual changing of short oscillations of the cold and warm with amplitudes of 0.2–0.5 K. This course of climatic history coincides well with the meteorological SAT records in the area and with independent GST reconstructions for boreholes located in Western Bohemia. Ground temperature histories were inverted from temperature logs measured in two deep boreholes in central France (Mareschal and Vasseur, 1992; see also Section 3.5). Except two remote climatic episodes, this study has identified recent warming over the past 150–200 years. All suitable borehole temperature logs from mainland Portugal were collected and analyzed to assess corresponding GST histories. Results have been compared with the meteorological data (Correia and Safanda, 1999). Extracted GST histories revealed long-term warming that started at the end of the nineteenth century. Further investigations of the T–z profile measured near Evora (Southern Portugal) have indicated warming of about 1 K from the second half of the last century to the middle of the 1990s (Correia and Safanda, 2001). The rate of this warming increased in the recent 10–15 years. Obtained results agree well with the trends observed on almost 150-year long meteorological SAT series recorded at the Lisbon meteorological station. Studies based on the inversion of temperature records measured in boreholes of northwestern Italy showed that the average temperature prior to the beginning of the meteorological recording in the 1830s was surprisingly higher by 0.6 K than that of the 1973–1982 decade (Pasquale et al., 1998). The position of the country surrounded by the Mediterranean Sea implies strong effects of the local air circulation that cause high spatial and temporal climate variability. Further analysis of a suite of boreholes located in the Tyrrhenian as well as in the Adriatic sides of the country demonstrates that the trend of the temperature change on the western side of the Apennines chain differs from that on the eastern side. Since 1750 the western side shows temperature lower than that of the 1990s, with minimum values in the period 1930–1960, followed by an almost linear increase in the GST. Along the eastern side the temperature was always higher than that inferred for the 1970s, with maximum values in the period 1920–1940, which is followed by a sharp temperature decrease. Only since 1970–1980 a local warming phase has started (Pasquale et al., 2005). Seven of the nine processed boreholes in northeastern Slovenia confirmed a warming of about 0.6–0.7 K in the past 100 years; another 2 km deep hole revealed a GST history of the past 20–30 ka (Rajver et al., 1998). Inversion of precise temperature logs measured in 20 boreholes in Hungary revealed rather synchronous warming that culminated near 1850 and cooling since then (Bodri and Dövényi, 2004). Along the northern border of the country an opposite trend is observed. The GST reconstructions based on temperature logs measured in two Hungarian boreholes situated at the lowland of the Carpathian Mts. and two Slovakian boreholes located at some 100–120 km distance NW of Hungarian holes gave quite coherent climatic histories showing cold period around 1800–1850 and pronounced warming since then. The GST histories obtained from seven holes in

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different regions of Romania indicated cooling during the last 150 years for the inner region of the Carpathian Mountains and warming at the same time in the Carpathian foreland. The reconstructions are consistent with the long-term air temperature records (Veliciu and Safanda, 1998). Inversion results from 31 boreholes spread in a 1000 km long, broad N–S oriented strip in the Urals region (Russia) confirmed cold climate conditions 0.5–1.0 K below the long-term mean which culminated in 1700–1750 A.D. followed by more than 1 K recent warming consistent with a 160-year SAT record (Štulc et al., 1998; Golovanova et al., 2001). The Institute of Geology at Novosibirsk (Russia), giving partly contradictory results, reported first practical inversions. Borehole inversions from West Siberia showed last century GST increase of 1.5°C. The permafrost data from the northern regions confirmed a steady general GST increase by 1–5 K beginning in the fifteenth century. However, these results also revealed a slight decrease by 0.3–0.5 K during the last century. Data from two holes drilled in the bottom of the Baikal Lake have also shown certain decrease in the bottom temperature since 1800 till 1910–1930, but a pronounced GST increase by up to 1.5–1.7 K in the last 50–70 years (Duchkov and Sokolova, 1998; see also the next section). Twenty temperature logs measured between 1979 and 1986 are available from the territory of Kamchatka (Smirnov et al., 1991; Sugrobov and Yanovsky, 1993). Later as a part of the 3-year Japanese/Czech/Russian project “Reconstruction of the climatic change from borehole temperature profiles and tree rings in the Kamchatka Peninsula” (2000–2002), precise temperature measurements were performed in a number of holes (Yamano et al., 2002). This project primarily concentrated on obtaining high-quality temperature–depth profiles and verification of the previous measurements in the region. Temperature logs used for the GST reconstructions are shown in Figure 96. Figure 97 shows “spaghetti diagram” of the inverted GST histories. As expected, the recent approximately 200 years’ temperature changes could surely be recovered. Calculated GST changes are in the range of 1.5–2 K. Similar to examples described in Section 1.3 (Chapter 1), boreholes logged with the shift of time gave coherent GST histories. The scatter of all GST curves is somewhat higher and reflects different environmental conditions and history of the Kamchatka Peninsula. Their averaging revealed only the general turn to the warmer conditions from approximately 1950. Obtained results are in good agreement with existing SAT series. Jones et al. (1999) have presented global patterns of the surface temperature change over the past 150 years combined land and marine data on the 5° ⫻ 5° grid box basis. Figure 98 represents one box of this database and shows an estimate of the SAT changes for southern part of the Kamchatka Peninsula. The temperature anomaly time series, reliable back to the beginning of the twentieth century, exhibits marked period of warmth during the last 10–15 years of the record. A slow rise in temperature occurred from the turn of the century to the 1940s and then temperatures remained relatively stable until the second warming phase that began during the mid1970s. Temperature trends over 1978–1997 were 0.05–0.1 K/year for all Kamchatka (significant at the 95% level). Similar warming trends were obtained for all Pacific Ocean at latitudes 40–60°N and in eastern Siberia (Rogers and Mosley-Thompson, 1995). It was this warming that Budyko (1977) and other climatologists have interpreted as the start of a new large-scale climate warming.

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Fig. 96. Temperature logs measured at Kamchatka.

Borehole climate reconstructions in Asia are sparser in comparison with an extensive area of this continent. Near 100 GST reconstructions from the temperature–depth profiles measured in China are compiled in the database by the NOAA Satellite and Information Service and the National Climatic Data Center (www.ncdc.noaa.gov/paleo/ borehole/asi.html). Recent reconstructions in Korea are of special interest because despite intensive borehole logging that continues at present, until recently this country has been a virgin area for such kind of climate reconstructions. Successful attempts of joint study of the climate and groundwater effect on the shallow temperatures were made in Japan (Taniguchi et al., 1999; Uchida and Sakura, 1999). Even when these studies were primarily hydrogeologically motivated, they have recognized the effects of the global warming and the urbanization in the Tokyo metropolitan area on the subsurface T–z profiles. Goto et al. (2002) have inferred approximately 5000-year long GST history from repeated temperature logs measured in 1993 and 2002 in approximately 800 m deep Karasuma borehole, SW Japan. Reconstructed GST histories were highly coherent and represented slow steady warming from 3000 B.C. that culminated around 800 A.D. (Medieval Warm Period). Climatic excursions of the last two millennia were generally similar all over the world and contained the Little Ice Age with minimum temperature between 1400 and 1500 A.D., warm conditions from the middle to late nineteenth century, and approximately 1 K cooling since then. Neither the GST history inferred from 1993 temperature log nor the data measured in 2002 show any signature of the recent warming. Authors have concluded that detected temperature excursions can be partly attributed to the past environmental changes such as temporal expansions of the neighboring Lake Biwa. Recently, Okubo et al. (2003) have performed screening of the 50 borehole temperature logs (boreholes often show signatures of the groundwater movement) and obtained the first GST reconstructions using carefully selected T–z profiles.

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Fig. 97. Reconstructed GST histories for three boreholes at Kamchatka. Individual curves 1, 2, 3, and 4 correspond to FSI inversion with different constraints.

More than 100 GST reconstructions were performed for the borehole temperature logs in India (Sukanta, 2003). These data provide distinct evidence of the GST warming of ⬃0.7 ⫾ 0.1 K in India over the past two centuries. A joint analysis of borehole temperature logs and SAT records revealed POM of 0.5 K lower than the 1961–1990 mean SAT, which supported results of the GST reconstructions. The summary of the past one to two millennia climate excursions revealed by the “geothermal” reconstructions can be generalized as follows. Temperatures were relatively high in the Northern Hemisphere as a whole during the earlier centuries of the millennium. A major difference of this “Medieval Warm Period” from the near global

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Fig. 98. Annual SAT anomalies for the period 1890–1998, relative to 1961–1990 mean, for grid box 50–55°N, 155–160°E (data by Jones et al., 1999; www.cru.uea.ac.uk) and corresponding linear trends.

warming of the late twentieth century is that it was not globally synchronous. Significant warming seems to have occurred in Europe, especially in the regions surrounding the North Atlantic. The Medieval Warm Period was changed by the hemispheric and/or global secular cooling since about 1300–1400 A.D. This cold period known as the “Little Ice Age” has persisted through the Middle Ages up to the nineteenth century. Both spatial and temporal patterns of the Little Ice Age were somewhat erratic including provisional returns of warmth. The peaks of cooling in different regions occurred in substantially different periods of time. In contrast, the twentieth century warming shows a much more homogeneous global appearance, especially in the Northern Hemisphere. Because significantly less data are available for the Southern Hemisphere, both long-term GST excursions as well as the conditions prevailing in the last millennium are less known for this half of the globe. 3.2 Temperature Trends Over Past Five Centuries Reconstructed From Borehole Temperature Data (Spatial and Temporal Patterns) The previous section described collective attempts of the small spatial-scale GST reconstructions, generally in the separate locations or maximum at the country scale. No doubt, the reconstruction of the GST history in a single site and/or in limited area represents an achievement. These studies are crucial for assessing the spatial patterns of climate variability and for evaluation of linkages/differences between regions. The systematizing and synthesis of the results from previous reconstructions to obtain large-scale climatic trends

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is even more difficult and has only recently been achieved after considerable efforts. This section describes this kind of GST reconstructions. As demonstrated in the previous chapter, the resolving power of the geothermal method for paleoclimate reconstruction is relatively low. Further going back into the past, less details can be resolved and only a smoothed trend of real temperature conditions can be gained. Thus, the most promising application of the GST reconstruction method is the last approximately 500 years. The surface temperature signal of this time interval is stored in the uppermost 200–300 m of the temperature–depth profiles. This depth is reached by most of the existing boreholes. The vast amount of the borehole temperature reconstructions of this kind inspired the researchers to accumulate obtained data and generalize them for the large spatial-scale climatic trends. There are numerical works trying to identify major patterns of the climate variability for the last 500 years on hemispheric to global scales. Because individual GST reconstructions exhibit site-to-site variability arising from different local influences (see Chapter 2, and “spaghetti diagrams” in the previous section) that can obscure the real climatic signal, the signal strengthening can be credibly achieved after averaging of a large number of individual results and their statistical analysis. On the other hand, as shown in the work by Beltrami and Burlon (2004), under the restrictions that are essential to obtain robust spatial averages on hemispheric or global scale, the merging of individual GST reconstruction results cannot retrieve reliable information on the climate variations at times before 1500. Information about more remote climate changes can be obtained only from individual borehole inversions. The earliest cumulative analysis, performed by Pollack et al. (1998), used the underground temperature measurements from 358 boreholes in eastern North America (116), central Europe (98), southern Africa (86), and Australia (58). Data were extracted from published works and/or from the database of borehole temperatures. Authors have applied the quality control of the data used. Investigated boreholes were distributed over approximately 40–60°N and 10–40°S longitudes and have represented well both the Northern and Southern Hemispheres. Temperatures at most of the boreholes selected for the analysis were measured to 200–600 m depth, and thus surely included the GST changes of the last five centuries. Authors have examined only century-long trends. Obtained results have shown that approximately 80% of the 358 individual GST reconstructions exhibited a net warming over the past five centuries (Figure 99). The fact that near 20% of the investigated sites indicate a net cooling over the past 500 years reflects regional climate variability. Similar inconsistency can also be found in the meteorological SAT records. On the other hand, 80% warming against 20% cooling hints the universal global nature of the recent warming. Calculated composite temperature change (relative to the present) for the last 500 years is shown in Figure 100 (most recent updated version of this diagram can be found on the web site of the University of Michigan, USA; www.geo.lsa.umich.edu/climate/core.html). This diagram shows a global trend of GST change over the last five centuries, averaged from 358 individual reconstructions. Thick line is the mean surface temperature since 1500 to the present date. The shading represents ±1 standard error of the mean. The evaluated total temperature increase over this time interval equals ⬃1 K. Results also indicate that during the twentieth century alone the surface temperature of the Earth has increased by about 0.5 K; thus, the characteristic velocity of warming for the last century was well above the temperature rise in the previous centuries. Authors affirm that

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Fig. 99. Histogram of cumulative temperature change since 1500 A.D. (Drawn from the data by Pollack et al. (1998).)

Fig. 100. Composite temperature change since 1500 A.D. relative to the present conditions, determined from borehole GST reconstructions. Shaded boundaries represent ⫾1 standard deviation about the mean climatic trend. The 5-year running average of SAT temperature anomalies from the same regions as borehole data is superimposed of the GST trend. (Redrawn from Pollack et al. (1998).)

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the twentieth century has been the warmest of the past five centuries (Pollack et al., 1998). For comparison, SAT anomaly series, averaged using meteorological records from the same regions, is also presented in the figure. The SAT data are shown as anomalies to the 1961–1990 base period, while the GST composite surface temperature anomalies were calculated relative to the present-day level. For this reason the authors have moved the SAT series downward by 0.2 K to ensure better by-eye comparison. As shown in the figure, both records exhibit similar trends. This fact confirms that the borehole temperature reconstructions based on different data and methodology can provide similarly significant results as the meteorological measurements and thus offer an independent verification of the unusual character of the twentieth century climate that has also emerged from the multiproxy studies (Pollack and Huang, 1998). The project “Global Database of Borehole and Climate Reconstruction” was initiated by the Geothermal Laboratory of the University of Michigan (USA) and suggested wide collaboration of the geothermal community (www.geo.lsa.umich.edu/~climate). The goal of this project was the design, collection, and analysis of the geothermal measurements on continents relevant for understanding the nature and causes of climate change for the past five centuries. Proposed main directions of research work were as follows: 1. Performing/collecting the basic geothermal observations including both field and laboratory measurements. Data of at least 200 m deep holes and the measurements embracing depth interval of 20–600 m were included into the database. Because temperatures from the deeper sections of the temperature logs do not contain information on the last five centuries, they were excluded from the consideration. Quality control was performed to find probable inconsistency in the measured data within 20–600 m range. 2. Reconstructing the five-century long GST histories for each site using standardized inversion procedure. Inferred GST histories were then presented as the century-long trends for each of the investigated past five centuries. Such representation emphasizes long-term variations of the GST history. The collection of the century-long GST trends contains valuable information on the past climate changes. It also represents an independent data source that is complementary to high-resolution proxies such as tree rings, ice cores, etc. The first data arrived in 1998. Present-day database contains temperature logs and GST reconstructions of approximately 1000 boreholes (database is continuously growing) contributed by the scientists from countries all over the world. Data represent all continents except Antarctica. The updated version of the map showing locations of the boreholes that have been analyzed to date can be found on the web site of the project. Available data cover well the Eurasia, North America, South Africa, and Australia, while in South America and North Africa (above 0° latitude) there is only 20 and 1 GST reconstruction, respectively. The database develops continuously; thus, at times the attempts are undertaken to revise the earlier results by Pollack et al. (1998) using wider massif of the borehole data. Huang et al. (2000) have assembled vast database of the 616 borehole GST histories (453 are in the Northern Hemisphere and 163 in the Southern Hemisphere) and reconstructed 500-year long average climatic trends on the hemispheric as well as on the

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global scales. Obtained results have corroborated findings of the above-cited work of the same authors (Pollack et al., 1998). The 78% of the investigated borehole temperature logs revealed warming over the past five centuries. This analysis also supported previous conclusions about its value and rate in the 500-year long interval as well as in the twentieth century alone, which were derived on the basis of the smaller amount of 358 boreholes. The authors have proved that the general warming of about 1.0 K is a common climatic tendency of the last five centuries and the twentieth century change is the largest of all the previous four centuries. Almost 0.5 K of the observed temperature increase occurred in recent 100 years, which was the warmest time of the past five centuries. The standard deviation of the derived mean values is lower in the latter analysis using increased number of borehole sites and does not exceed ⫾0.1 K instead of ⫾0.25 to 0.3 K for the previous calculations by Pollack et al. (1998). Thus, this cumulative average of GST history bears more significance. Similarly remarkable is the coincidence of the obtained average GST histories with the trends in the SAT records. The most recent (continuously renewed) global averages of the GST history over the past five centuries are presented on the web site www.geo.lsa.umich.edu/~climate. The hemispheric reconstructions performed by Huang et al. (2000) on the same database revealed that the 500-year long GST warming is somewhat higher in the Northern Hemisphere (1.1 K/500 years) than in the Southern Hemisphere (0.8 K/500 years). Similarly significant is the twentieth century change, namely 0.6 K in the Northern Hemisphere compared with 0.4 K in the Southern Hemisphere. Detected by the authors, “geothermal” trends are highly consistent with 0.56 and 0.47 K/100 year the Northern and Southern hemispheric trends, respectively, calculated from the land SAT data only (Jones et al., 1998, 1999). Regional reconstructions by Huang et al. (2000) also revealed tentative spatial and temporal variability of the GST warming on the continental timescale (Table 8). Results presented in this table show that all continents exhibit the same features as the global average trend, namely the largest warming in the twentieth century than in any of the previous centuries. More rapid warming has occurred in America and Asia; the lowest warming rate is characteristic for Australia. This tendency is also preserved in the last 100 years. It should be mentioned, however, that because of the relative sparse geothermal observations and poor geographical coverage, above conclusions seem to be only tentative, especially for the Southern Hemisphere. Recently, Mann et al. (2003) and Rutherford and Mann (2004) have re-processed the same 453 Northern Hemisphere borehole temperature reconstructions as were used in the Table 8. Continental five- and one-century long cumulative GST changes Continent Europe Asia Africa North America South America Australia

Last 500 years a

0.8 1.2 0.8 1.2 1.4 0.5

Note: Data by Huang et al. (2000). a Temperature change for the five-century long period (K). b Temperature change during the twentieth century (K).

Last 100 years 0.4b 0.6 0.3 0.6 0.7 0.2

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work by Huang et al. (2000) in order to optimally detect the Northern Hemisphere century-long climatic trends for the past five centuries. In the works by Pollack et al. (1998) and Huang et al. (2000) the hemispheric and global GST trends were estimated by a simple arithmetic averaging of the individual GST reconstructions. According to Mann et al. (2003), the estimation of the whole Northern Hemisphere trend by simple averaging of the sparse, irregularly distributed borehole data, when most of them are situated in the extratropical belt, may lead to the noticeable bias. The use of the “optimal detection” technique (see Section 3.4.4) can increase the significance of the estimated climate change. For easier comparison the results of the individual GST reconstructions serving as input data were averaged on the 5° ⫻ 5° grid, which is generally used for the representation of SAT data (Jones et al., 1999). Authors have shown, however, that in their case the procedure of the optimal detection was insensitive to whether the 453 individual GST reconstructions or 94 grid boxes were used for the calculations. The data existed for the 94 grid boxes with 1–22 boreholes per box. The relative error weighting scheme based on occupancy of given grid box was employed. The borehole GST reconstructions can be represented as (M ⫻ N ) matrix B, where M ⫽ 94 (number of grid boxes) and N ⫽ 6 (number of estimated time values: 1500, 1600, 1700, 1800, 1900, and 1980). Because the average logging date is 1978, the recent GST trends in this work are representatives of the 1900–1980 period and not of the whole twentieth century. Applying the singular value decomposition (SVD) of the un-normalized, time-centered data matrix (see Section 2.3.4) one can express the matrix B in an empirical orthogonal eigenvector basis: N

B ⫽ ∑
i uTi v i ,

(43)

i⫽1

where
2i is the relative variance resolved by the ith eigenvector, and ui and vi are its normalized spatial and temporal patterns, respectively. According to Mann et al.’s (2003) estimates, only two eigenvectors appeared to be statistically significant. Thus, e.g., the optimal expression for the spatial pattern of the GST trend in a given century can be represented as a linear function of two spatially centered normalized eigenvectors û1 and û2: I$ ⫽ a u$ 1 ⫹ b u$ 2 ⫹ e,

(44)

where Î is the spatially centered, normalized pattern in the given century, a and b are regression coefficients, and e is the residual error term. The estimation of the coefficients a and b in this case represents simple linear regression procedure (for the details of calculus see Mann et al., 2003). Figure 101 shows comparison of the five-century GST trends for the Northern Hemisphere calculated by Huang et al. (2000) with the areally weighted and averaged borehole reconstruction by Mann et al. (2003) and Rutherford and Mann (2004) (www.ncdc.noaa.gov/pub/data/paleo/borehole/mann2003). The shift of the latter curve occurs because of the difference in the reference periods. While the Huang et al.’s (2000) reconstruction is referenced to the present, Mann et al. (2003) used 1900–1980 projection interval (the mean of 1900–1980 was set to zero) in their calculations. Both results are

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Fig. 101. Comparison of the century-averaged climatic trends for the Northern Hemisphere (Huang et al., 2000) and spatially weighted and averaged borehole reconstruction by Mann et al. (2003).

generally coherent and exhibit five-century warming trend. Estimated warming is somewhat reduced in the amplitude for the latter reconstruction (0.97 K against 1.02 K). As shown in Figure 101, sixteenth to eighteenth century trends practically coincide for both reconstructions. The recent warming in the Huang et al.’s (2000) case is more regularly distributed over the last two centuries, while in the Mann et al.’s (2003) case a bit greater part of it occurred in the twentieth century (0.5 K warming for the whole century and 0.6 K during only 1900–1980 in the former and in the latter reconstructions, respectively). The reason for such discrepancy is that Huang et al.’s (2000) reconstruction does not include significant warming of the last two decades of the twentieth century. This end-of-century bias is the characteristic feature of many GST reconstructions using borehole T–z profiles logged before 1980. Similar coincidence was revealed between reconstructed GST and SAT trends as well as with the proxy-based estimates of the hemispheric climatic trends in the past centuries (Mann et al., 2003; see also the next section). The most recent analysis by Pollack and Smerdon (2004) was primarily concerned with the problems of data aggregation, their gridding, and occupancy-dependent weighting. Averaging of the individual borehole data on the regular grid helps the local noise suppression (see Section 2.4.5). Authors have used 695 individual borehole GST reconstructions and have shown that the averaged five-century Northern Hemisphere and/or global mean trends did not depend essentially on the applied scheme of weighting and aggregation. Independent of the choice of the signal detecting technique obtained in this work large-scale means are generally consistent and equal to approximately 1 K for the wide range of gridding and weighting schemes used. Authors also investigated spatial

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correlation between GST and SAT for different grid sizes. As shown, the correlation appears already at the 5° ⫻ 5° gridding base. However, most of the grid boxes of 5° size contained three or even less boreholes. This quantity is insufficient for an effective smoothing of the site-specific noise in the box-averaged GST histories. Thus, the lowoccupation elements can weaken and/or obscure the spatial GST–SAT correlation. Authors have shown that the correlation can be increased by excluding the low-occupation grid elements from the dataset. Remaining high-occupancy grid boxes (ⱖ3 boreholes) yield a statistically significant GST–SAT correlation. Trends obtained for the 5°- and 30°size datasets are practically similar. The overweighting influence of the grid boxes with the data clustering is of less importance. A little bit different global climatic history of the past five centuries was obtained by Beltrami (2002), who performed GST reconstructions for 826 borehole temperature logs using SVD inversion technique. Later, similar work by Beltrami and Bourlon (2004) was based on the measurements performed at 558 boreholes distributed between 30° and 60°N of the Northern Hemisphere. As in the all above-cited works, T–z profiles for these investigations were taken from the Global Database of Borehole Temperatures. Their spatial distribution and global coverage are generally similar to the previous works. The best coverage is characteristic for North American continent, and approximately 50% of the temperature logs originate from Canada. As in the work by Huang et al. (2000), all temperature–depth profiles were inverted individually and inferred GST histories were then averaged using area-weighted technique. This procedure is indispensable in order to avoid exaggerated representation of the areas with noticeable abundance of boreholes (e.g., some regions of Canada). The latest temperature log was measured in 1999, and the authors have extrapolated calculated global climatic trend to the year 2000. Figure 102 shows globally averaged GST change for the last five centuries and similar result for the Northern Hemisphere.

Fig. 102. Mean global GST history derived from SVD inversion of 826 temperature logs (data by Beltrami, 2002) and mean GST history for the Northern Hemisphere between 30 and 60°N (latitudinal average based on 5° ⫻ 5° grid boxes; Beltrami and Bourlon, 2004).

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The course of the GST history is generally coherent with that detected in the early works except of the provisional cooling in the 1900–1950 (1880–1930 in the later work by Beltrami and Bourlon, 2004) interval that was not found in other works. Total GST warming for the past 500 years, which is equal to ⬃0.9 K, thus coincides well with the value determined by Huang et al. (2000). Independent estimate by Beltrami (2002) has shown that the global average ground heat flow has also increased together with average global GST. According to the study, the total heat absorbed by the ground during the recent 50 years amounts to 7.1 ⫻ 1021 J; thus, it is comparable with the quantities absorbed by the whole atmosphere and/or by the continental glaciers. Cooling event in the end of the nineteenth and first half of the twentieth century has manifested itself as a decrease of ground temperature by 0.2–0.4 K. It was not synchronous over the globe. It is likely the reason that it appears weaker and with phase shift on the global mean in comparison with the Northern Hemisphere GST average. In the Northern Hemisphere this cooling was detected over both North America and Central Europe. This cold event is well visible on the meteorological records from Prague, Vienna, and Munich and on the diagram of occurrences of GST extremes in the territory of the Czech Republic (Figure 75 of the previous section). The reason that this cold excursion was not recovered by other reconstructions (e.g., by Huang et al., 2000) lies probably in the use of simple arithmetic averaging in the above works. If this finding will be corroborated by further studies, it can inspire to reconsider the course of the last 500-year warming trend. According to the results by Beltrami and Bourlon (2004), half of the detected ⬃1K past five centuries’ warming occurred in the recent 50 years. Somewhat different magnitude of temperature changes derived from the GST reconstructions in comparison with climatic trends defined from proxy records has inspired recent discussions and attempts to re-assess the skill of the “borehole” method to draw out past GST changes from T–z profiles. The most recent attempt was undertaken in the work by González-Rouco et al. (2006) and has proved the power of borehole inversion technique to reconstruct long-term climatic trends and/or robustness of existing borehole database to reproduce past SAT variations (for details see Section 2.4.4, Chapter 2). The investigations of the several century regional cumulative GST trends in Russia including Siberia are of special interest. The reasons are: 1. Their important role in the climate system both on the hemispheric and the global scales. According to Cohen et al. (2001), the Siberian high-pressure anomaly is a dominant forcing factor of the winter climate variability in the Northern Hemisphere. 2. Because extensive continental regions in Siberia are relatively weakly affected by the postindustrial anthropogenic activity, the GST trends reconstructed for this region can significantly contribute to ongoing debates over the human influence on the climate (see also Section 3.4). The recent work by Pollack et al. (2003) is concentrated on the past five-century climate changes in the territory of Russia as derived from borehole thermometry. One hundred and one T–z profiles were selected for the GST reconstructions. The investigated sites are generally concentrated in the Ural Mts., NE and SE Siberia. The most significant amount (66 temperature logs) was measured from the southern frontier to polar region of the Ural Mts. Four sites are in the European part of Russia. Figure 103 shows examples of

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Fig. 103. Average GST history of the past five centuries for the Ural Mts., SW and NE Siberia, and “all-Russia” (drawn from the data by Pollack et al., 2003). For comparison similar trend is also shown for North America (30–65°N, 65–125°W). The twentieth century GST trends are compared with those indicated by the SAT observations.

cumulative GST trends for investigated territories as well as averaged for “all-Russia” (Pollack et al., 2003). For comparison, similar data for North America (30–65°N, 65–125°W) and the twentieth century Northern Hemisphere SAT trends are also shown. Results for the Ural Mts. are more significant for the entire Russia. They were calculated from the most robust dataset of 66 boreholes (65% of the Russian data) and show the best coincidence with the GST trend detected for North America. As for the North American reconstructions, the course of the five-century climate is general warming accelerated during the twentieth century. The warming in the recent 100 years reached 0.68 K. This value exceeds the cumulative warming in the four previous centuries. Strong warming trend of 0.22 K/100 years is also characteristic for the nineteenth century. Thus, approximately 80% of the total five-century warming took place in the last 200 years, and over 60% of this warming occurred in the last century. As shown in Figure 103, such general climatic course is also characteristic for the “all-Russia” average GST history. Warming trends obtained for the 500-year period coincide well with the quantities calculated for North America, indicating similar climatic course over both extensive northern continents. The rate of the last 100-year warming coincides in North America and the Urals. The data measured in SW Siberia are relatively sparse and come from different terrains. However, the century-long GST trends calculated from the SW Siberian boreholes are similar to the quantities obtained for the Urals. The total warming is somewhat weaker and is distributed more regularly over the past five centuries. The recent warming is also weaker. However, it is comparable in its magnitude with the warming rate indicated for the Urals region. Here it represents 55% of the total five-century climate warming. An extensive territory of the NE Siberia is represented by the data measured in only 13 holes. All investigated boreholes are drilled in the permafrost. The mean annual surface temperature in these sites remains below 0°C, and the temperature log exhibits below zero values throughout the whole interval of the measurements. The dependence of the permafrost

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in bedrock on the past and presently ongoing climate variations as well as the possibility of the GST history reconstruction from the temperature logs measured in such environment have been discussed in Section 2.8. Because of the relatively small number of borehole results averaged over vast area, the range of uncertainty of the GST reconstructions for NE Siberia is fairly large; thus, the conclusions about the past climate changes are only of tentative character (Pollack et al., 2003). The course of the past five-century climate change in this area is rather distinct from other Russian territory. In contrast with other investigated regions, NE Siberia was exposed to the modest cooling during the sixteenth to eighteenth centuries. Warming with an inexpressive rate of 0.06 K/100 years began here only in the nineteenth century. During the twentieth century this warming rate has increased by almost an order of magnitude to 0.67 K/100 years. This value is comparable with the warming rates characteristic for both “all-Russia” ensemble and North America. Figure 103 also shows a comparison of the twentieth century warming rates calculated from the GST data and the averaged Northern Hemisphere SAT observations (data by Jones et al., 1999; www.cru.uea.ac.uk). These data represent the 5° ⫻ 5° gridded temperature anomalies from the base period 1961–1990. The rates of the twentieth century warming for separate regions were calculated by Pollack et al. (2003) from the data by Jones et al. (1999). They equal to 1.32, 1.54, 0.88, and 1.33 K/100 years for the Urals, SW Siberia, NE Siberia, and “all-Russia” territories, respectively. The distribution of meteorological stations in the area is sparse enough; thus, calculated SAT warming trends can be regarded only as the first-order estimate. For all regions the twentieth century SAT trends exceed the “geothermal” estimate. The reasons for this discrepancy may be the fact that most of boreholes were logged prior to 1983 and therefore do not archive the later climate changes and/or the well-known rapid attenuation of the temperature fluctuations in the first 50 m depth interval, so that temperature logs that begin at greater depth do not contain recent GST excursions. Although Russian GST reconstructions prior to about 1500 A.D. exhibit expanded uncertainties, some important conclusions are still possible. The above results support the finding that the last century warming that occurred in the high latitudes of the Northern Hemisphere comprised Russia also. Here it is not confined to the Arctic and the northernmost regions only, but embraced significant territories up to 45–50°N latitudes. Generally 70–80% of the observed warming occurred in the last 100 years. While the past climatic trends inferred for North America generally suggest one-way story and the twentieth century warming represents simple continuation of the previous long-term warming trend, the Russian GST reconstructions show the twentieth century warming as a continuation of a trend that began only at the start of the nineteenth century. All cited works collectively illustrate that the reconstruction of the century-long trends derived from borehole data over long time periods represents independent and robust source of the paleoclimatic information and may be a useful complementary source to the SAT estimates as well as to traditional proxy reconstructions. Independent estimates of hemispheric and global ground temperature trends over the past five centuries from the information archived in borehole temperature profiles confirm the conclusion of the previous section that the late twentieth century warming is anomalous in a long-term context. Temporal resolution of these GST estimates decreases back in time. Thus, the meaningful comparison of the obtained long-term GST trends with high-resolution temperature estimates based on proxy climate data seems to be very useful.

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3.3 Correlation Between GST Climate Reconstruction, Meteorological Data, and Proxies Paleoclimate reconstructions describing surface temperature course on the hemispheric and/or global scale for the timescales over one to two millennia are generally obtained from traditional proxies as well as from the “geothermal” GST reconstructions. The conformity between GST histories, SAT, and proxy time series was already discussed in Sections 2.5 and 2.6 (Chapter 2). It will be captured in more detail in the present section. It should be declared from the very beginning that the conformity does not mean the identity and/or equality of the investigated sources. It is clear that they are not identical. The question is rather: do these sources show similar trends over long periods of time? The instrumental record alone is clearly not enough to represent few centuries or longer climatic trends. Proxy and borehole paleoclimate reconstructions fill this vacuum. Borehole data are usually interpreted as reflecting true annual temperatures, incorporating all seasons at least on the longer timescales. They may be disturbed by various environmental factors, e.g., change in the land-use practices. However, as shown by Harris and Chapman (2001), this noise can be suppressed by a simple averaging of reduced T–z profiles. The major problems occurring for all proxy indicators are their chronological dating, calibration, and detection of what they really measure. For example, tree-ring records of climate change are an important component of multiproxy records used to infer climate variations, and all Northern Hemisphere temperature reconstructions use at least some tree-ring data. They provide powerful tools for the detection of past temperature variations; they are widespread, well dated, have high resolution, and extend the estimates of climatic variability well beyond the instrumental period. These series are generally calibrated assuming that they reflect annual variations. However, tree growth is more influenced by summer than winter temperatures. Further uncertainties can be introduced by the possible adaptation of the living species to new climate conditions and by expectable nonlinear response to the varying climate on the long timescales (for details see Section 1.2.3). Even if the paleoclimatologists are aware of the shortcomings, the clear meaning of the proxy record is not always described in many research works. To construct credible pattern of the past climate variations the scientists need to compare each other’s interpretations and assumptions. The compilation of a heap of climatic histories seems to be more promising than the use of the single reconstructions. Obviously, the integrated climate history does not represent a simple superposition of different proxy reconstructions and needs a complex technique that consolidates information that different sources contain. An overview of available large-scale climate reconstruction techniques and/or computer codes can be found, e.g., on the web site of the Climate Change Research Section (CCR) of the National Center for Atmospheric Research (www.cgd.ucar.edu/ccr/amman/millennium/recon). This and similar web pages describe the existing multiproxy climate reconstruction methods and provide the corresponding codes that can help to reproduce the individually published reconstructions as well as to assess the behavior of the given method and evaluate its strength and shortcomings. More detailed examination of the calculus for the multiproxy compilation especially for the noisy data can be found in the works by von Storch et al. (2004) and McIntyre and McKitrick (2005a), as well as in the subsequent discussions (Huybers, 2005; McIntyre and McKitrick, 2005b, c; von Storch and Zorita, 2005).

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Below we describe some of the most noticeable examples from the growing amount of the past centuries’ climate reconstructions that merge high-resolution proxy and/or instrumental data with the GST histories. Combination of only two different paleoclimatic data sources appears to be a simpler procedure than the multi-data reconstructions. Two-source merging can be performed by regression of the one source to another. For example, numerous reconstructions were performed by coupling of the instrumental SAT series with the lower resolution proxy indicators (Jones et al., 1998; Mann et al., 1998, 1999). Regression models are generally constructed for the more recent time interval where SAT and proxy data overlap and then extend to remaining proxy sections. Results of such extrapolation, however, imply that the reply of the proxy indicator to the changing climate remains invariable during the long period of time and the coupling of the proxy and SAT over the long-time interval is the same as it was in the period of the instrumental observations. Such simplification holds the source for possible bias. Merging of two equally long climatic series appears to be more promising. Such kind of joint two-source past climate reconstruction was performed by Beltrami and Taylor (1995), who combined borehole CST histories with oxygen isotope data from an ice core. Both measurement sites were closely located in the Canadian Arctic. Because of different resolution of the records (annual versus decadal and longer), such coupling appears to be quite useful and can help to increase the resolution of the GST reconstruction. While the borehole GST histories reflect true annual temperatures at least on the longer scale, the oxygen isotope ratios measured in the ice cores represent mainly summer conditions and summarize responses to various processes. Together with average air temperature at a given time they may contain changes in the water vapor and/or snow history, etc. Thus, comparison with GST series is useful for the ice core data also, since it can make the ice core reconstruction more precise. The main goals of the work by Beltrami and Taylor (1995) were: (1) to calibrate the oxygen isotope data to the GSTs, and (2) to reconstruct GST history with higher resolution than that estimated from borehole data alone. Processing of the data included the estimation of the GST history from the borehole data, low-pass filtration of the oxygen isotope record to make it comparable with the calculated GST course, definition of the GST and filtered isotope data relation, and finally the reconstruction of the GST history from the calibrated oxygen isotope record. Figure 104 shows the oxygen isotope data and the reconstructed GST history. As shown, both datasets are generally coherent; however, high-frequency climate oscillations are lost in the GST diagram. Comparison of the GST with filtered oxygen isotope series on the 25-year scale of accumulation revealed stable linear relation between the two datasets. The oxygen isotope data 18O can be transformed into GST changes TG by the following simple formula: TG ⫽ 2.38 ⫻ 18 O ⫹ 0.046.

(45)

With the help of additional proxy source, smoothed GST history in Figure 104, thus, can be reconstructed with the 25-year resolution. Similar two-source reconstruction was performed by Harris and Chapman (2005) using tree ring and borehole GST histories. Except for the above-mentioned shortcomings of the tree-ring analysis, these data possess high short-term resolution, however,

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Fig. 104. Top: Oxygen isotope data (25 years’ averages) for the Agassiz ice cap core (Ellesmere Island, NWT, Canada) (data by Fisher et al., 1995; www.ncdc.noaa.gov/paleo/icecore/polar/ agassiz/data.html). Bottom: Ground surface GST history for borehole Neil (80.74°N, 83.08°W). (Drawn from the data by Beltrami and Taylor (1995).)

have a strong detrending effect, and thus practically do not reflect low-frequency temperature variations. Borehole GST histories have opposite properties and may serve as a good complement to the tree-ring data because they are capable of capturing lowfrequency events and reflecting real all-year temperature change and do not need calibration. The last but not the least is that both sources cover generally similar geographic areas. On the basis of available borehole temperature logs, Harris and Chapman (2005) have calculated an averaged reduced temperature–depth profile for land areas north of the latitude 20°N. The GST reconstruction performed using this averaged T–z profile has yielded a warming of 1.1 K over the past 500 years. The tree-ring analysis from the same area (Briffa et al., 1992, 2001; see Figure 10, Chapter 1) has indicated considerably less warming for the same period. Merging of both data sources gave high-resolution surface temperature for the past 500 years, which is consistent with both borehole and tree-ring reconstructions without loss of the low-frequency variance. Comparison of borehole data including annual temperature variations with tree-ring reconstruction that reflects mainly warm-season conditions also provided an estimate of the long-term cold season (October–March) temperature variations. It has been demonstrated that continental extratropical Northern Hemisphere annual and cold season temperatures exhibited warming of 0.2 ⫾ 0.1 K and 0.4 ⫾ 0.3 K, respectively, between 1500 and 1856 prior to the start of instrumental SAT record.

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Valuable and probably the clearest comparison can be performed between borehole temperature reconstructions and SAT simply because both datasets are direct temperature measurements and do not need any calibration. Ground temperatures are directly related to the surface temperature forcing. Both sources incorporate climatic variations of all seasons of the year and thus can be interpreted as indicators of real annual temperatures. On the other hand, ground acts as a low-frequency filter that removes information about high-frequency climate variations from subsurface temperature–depth profiles. As a result reconstructed GST histories appear as smooth curves with resolution decreasing into the past. One of the first empirical relationships between annual mean GST and SAT has been presented by Kukkonen (1987) for the territory of Finland. It was based on the combination of air and ground temperatures measured on the meteorological stations all over the country and borehole temperatures extrapolated to the surface: TG ⫽ 0.71 ⫻ TA ⫹ 2.93,

(46)

where TG and TA (°C) are annual mean ground and air temperatures, respectively. As seen on the annual scale the ground is warmer than the air. The ground temperature fluctuations are approximately 30% attenuated with respect to the air temperature. The fact that generally the mean annual SAT is lower than the corresponding GST was corroborated by numerous later measurements (see Section 2.5, Chapter 2). Because of different frequency content of the GST and SAT signals, a direct comparison of the detected climatic trends and/or the use of the above-described simple regression techniques may inspire erroneous conclusions (Harris and Gosnold, 1999). The estimation of POM described in Section 2.5 (Chapter 2) represents a more effective way to compile these two kinds of climatic information. The idea of the joint processing of the measured temperature logs and the SAT was inspired by the above-mentioned complementary nature of the GST and SAT. To estimate the magnitude of recent temperature change, especially the amount of the recent global warming, paleoclimate reconstruction from the temperature–depth records can be suitably completed with a long-term meteorological SAT series monitored at weather stations. This idea was introduced by Harris and Chapman (1995, 1997) and provided a useful tool to assess the POM representing the temperature conditions that existed before the routine instrumental observations actually started some 100–250 years ago, i.e., the value against which the twentieth century climate warming could be referenced. Coupling the borehole temperature logs with the SAT series provides a more realistic benchmark than the models based on the inverted borehole data themselves. Harris and Chapman (2001) inferred mid-latitude (30–60°N) climatic warming combining borehole temperatures with SAT series. For their reconstructions, authors have used temperature logs collected in the Global Borehole Temperature Database (www.geo.lsa.umich.edu/~climate). The measurement dates of the 439 temperature logs processed in this study ranged between 1958 and 1995. The curvature of the profiles was quite variable characterizing both recent warming and cooling similar to an example shown in Figure 108 (see below) for Canadian data. Observed variations have included both the natural climatic excursions as well as the environmental effects. A composite

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reduced temperature–depth profile was calculated by averaging all 439 T–z profiles forward continued to the year 1995. A positive curvature of the resulting profile hints the recent surface warming from the definite long-term mean level. A synthetic transient SAT series was computed by averaging the gridded meteorological data (Jones et al., 2000). The global SAT series from the same database is shown in Figure 4 (Chapter 1). Mean annual temperatures embrace the period 1856–2005. SATs are given as the temperature anomalies in accordance with the base period 1961–1990. For averaging, the authors used only the grid boxes that coincided with borehole locations. Calculated in this manner averaged SAT series was used as a forcing function for the POM estimate. The POM obtained by Harris and Chapman (2001) equals 0.71 K. Such result means that average GST during the long period prior to 1856 was approximately 0.7 K below the 1961–1990 SAT mean. Reconstruction by Huang et al. (2000) based exclusively on the borehole data (see previous section) gives 0.9 K difference (warming) between the 1500–1700 mean temperature and the 1961–1990 reference level, which is very similar to the above POM estimate. According to Harris and Chapman (2001), the combination of the SAT and borehole data to obtain POM value appears to provide better fit to the data than the GST reconstruction alone. Achieved in the recent years abundance of the global databases of high-resolution (annual and/or seasonal) proxy data, together with a few long instrumental and historical climate records available during the past few centuries, gives a possibility to reconstruct spatial patterns of temperature variations several centuries back in time. Reconstructions of the long-scale global/hemispheric trends can place the instrumental observations of the climate variations during the twentieth century in a longer term perspective, and thus provide a more reliable evidence of the role of potential climate forcings over time. The reconstruction of the Earth’s temperature history for the past millennium using a variety of proxy records together with the borehole GST histories is generally connected to the often cited works by Mann et al. (1998, 1999) and Huang (2004) (see also collection of paleoclimate reconstructions presented on the web site by NOAA Satellite and Information Service and National Climatic Data Center, USA; www.ncdc.noaa.gov/paleo). The main goals of the merging different paleoclimatic records are: (1) to obtain better spatial coverage and thus more reliable large-scale estimates, (2) to achieve better temporal resolution, and possibly the last but not the least (3) to verify the reconstructed climatic trends through the use of various independent sources of information. Borehole GST histories represent one of the best sources for the reconstruction of centennial trends. Century-long trends are also well presented in such proxies as the recession of glaciers. Detection of the log-term trends from, e.g., tree-ring data is difficult because of the above-mentioned detrending effect of the tree growth. Decadal/annual/seasonal resolution is provided by tree rings, ice cores, varved sediments, etc. Coupling of different kinds of climatic information can overcome the limitations of the individual proxies and provides useful comparison and validation of individual reconstructions. Combined with instrumental measurements and/or with documentary information, all these sources can significantly contribute to the gaining of reliable climatic histories on the global and/or hemispheric scales. The large-scale reconstruction by Mann et al. (1999) was based on the tree ring, ice core, corals, and historical data, while described in the previous sections reconstructions by Pollack et al. (1998), Huang et al. (2000), Mann et al. (2003), and Pollack and

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Fig. 105. Top: Annual scale temperature reconstruction for the Northern Hemisphere by Mann et al. (1999) for the period 1000 A.D. to 1980. Thick line represents its 50-year running average. Bottom: Integrated temperature reconstruction by Huang (2004). Thick line corresponds to the optimal surface temperature reconstruction by Mann et al. (2003) that uses terrestrial borehole data only. Temperatures are shown as the anomalies according to the 1961–1980 reference mean.

Smerdon (2004) have exclusively used borehole information. An inclusion of the GST reconstructions into the multiproxy and instrumental network was performed in the recent work by Huang (2004). To construct reliable Northern Hemisphere climate change history this research has merged three independent databases, namely 696 borehole GST reconstructions (significant part of them were used in the work by Huang et al., 2000), the twentieth century meteorological record, and the high-resolution multiproxy model by Mann et al. (1999) (Figure 105, top). This latter reconstruction is possibly the most often cited one. It is based on the tree ring, ice core, corals, and historical records of climate and shows the temperature variations in the Northern Hemisphere over the past millennium (from 1000 A.D. to 1980) with the annual resolution. According to the calculations by Huang (2004), borehole data alone suggest a cumulative warming of 0.9 K from the beginning of the sixteenth century to 1980. Obtained temperature increase is consistent with that estimated in the work by Huang et al. (2000) from smaller number of the borehole sites. The range of temperature variations in the record by Mann et al. (1999) does not exceed 0.7 K. It exhibits variable, but generally decreasing trend up to the beginning of the twentieth century and the “hockey stick” shape in the last century (see Section 1.2.3 and Figure 11, Chapter 1). Smaller amplitude of the century-scale

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variability in comparison with that predicted by the geothermal reconstructions (see previous section) hints that this technique reflects well the short-term (annual) oscillations but cannot capture the long-scale trends. Recent studies with the GCM2 also suggest that centennial variations may have been larger (Bauer et al., 2003; Jones and Mann, 2004; von Storch et al., 2004; Zorita et al., 2004; Moberg et al., 2005). The creation of the integrated surface temperature reconstruction consisted of the following main steps (Huang, 2004): 1. Creation of the extended surface temperature history by combining individual borehole estimates and 1900–1980 SAT record. For this purpose all available 696 individual GST reconstructions were gridded and combined with the twentieth century SAT temperatures gridded at the 5° ⫻ 5° area weighting base to prolong the latter database into the past. Data were adjusted according to the 1961–1980 reference period mean and averaged. Obtained SAT–GST surface temperature history is quite similar to those presented in Figure 100. 2. Generalized subsurface temperature anomaly–depth profile was calculated on the base of 1-D pure conductive approach using above extended surface temperature history as the surface boundary conditions. This profile was then used for compiled inversion with the mutiproxy time series (Figure 105, top). 3. As described in Chapter 2, the ability to incorporate additional information is one of the major advantages of both SVD and FSI inversion techniques. The merging of the generalized subsurface T–z profile with the multiproxy reconstruction by Mann et al. (1999) was performed by means of the functional space Bayesian inversion (FSI) (Shen and Beck, 1991) of this signal using annually resolved multiproxy series as the a priori model (see Section 2.3.5). The integrated time series of the temperature change over the last millennium represents the temporal domain of the inversion that should be estimated. It was parameterized3 at annual intervals to ensure readily the incorporation of the annually resolved multiproxy record as an a priori model. Five-century long climate change series is presented in Figure 105 (bottom). The figure shows significant differences from the a priori model. On contrary to the multiproxy series in Figure 105 (top), it exhibits clearly colder past than the climate history by Mann et al. (1998, 1999) and the warming trend that could not be obscured by the high-frequency temperature fluctuations on either the annual or the decadal scales. The period from approximately 1575 to the beginning of the seventeenth century was the coldest during the whole reconstructed time interval, while the twentieth century was far warmer of the past five centuries. As shown, the integrated reconstruction by Huang (2004) coincides well with the optimal surface temperature history by Mann et al. (2003) calculated from 2 General Circulation means the large scale coupled atmosphere–ocean motions arising as a consequence of differential heating on a rotating Earth. They restore the energy balance of the system through the transport of momentum and heat. 3 Parametrization means the technique of the representation such processes that cannot be explicitly resolved by the model on the temporal (or spatial) scale. Parametrization provides the relationship between definite sub-grid and the larger scale for the investigated process (for details see Sections 2.3.4 and 2.3.5 of the Chapter 2).

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the borehole data alone (Figure 105, bottom), as well as with the results of earlier reconstructions described in the previous section. Similarly to the mentioned hemispheric/global scale GST reconstructions, the warming of the twentieth century on the multiproxy results by Huang (2004) is seen more clearly as a simple continuation of recovery from the cold conditions prevailing in the sixteenth century that started far before the onset of the industrialization. On the other hand, warming rate appears to be accelerated in the nineteenth to twentieth centuries. This result corroborates the human-induced forcing of the natural climatic variations. The primary limitations of the large-scale spatial/temporal proxy-based reconstruction arise from an increasingly sparse nature of the available proxy databases back in time. These databases can be extended only through merging of as large as possible results of paleoclimatic studies from different scientific branches. As demonstrated by the above example, the integrated reconstruction, treating together various paleoclimatic data (including borehole climatology), can display information that is weak and/or imperceptible in each record alone. Such integration of available climate histories and the search of common climate signatures in different reconstructions can provide more certain and broad conclusions about global mean temperature changes. So the integration of various paleoclimate reconstructions is more powerful than that either community carries out alone. The improved knowledge of the climate record can help to evaluate less speculative climate trends. Such improvements will lead to further advances in our empirical understanding of climate variations in the past millennium, and will allow for more meaningful comparison with the results obtained from model simulations of the past climate variation and empirical climate variability. 3.4 Is There Any Anthropogenic Component in the Present-Day Global Warming? Evidence From the Underground One of the utilities of the climate reconstructions is their contribution to identifying the causes of climate change. In the above sections we have cited numerous examples (also including borehole climatology) that the Earth’s surface has undergone unprecedented temperature increase over the last century. This climatic trend is known as the global warming. Global temperature has increased by about 0.6 K (⫾0.2 K) since the late nineteenth century. Even with a few interruptions this warming increases continuously. The most recent warming is particularly noteworthy because the rate of temperature increase is enormously high. SAT has increased about 0.2–0.3 K over the past 25 years (the period with the most credible data). Curiously, every single year since 1992 is in the current list of the 20 warmest years on record (http://www.data.giss.nasa.gov/gistemp/2005). Observed warming has not been globally uniform. The recent warmth has been greatest over North America and Eurasia between 40 and 70°N. Some areas (e.g., parts of the southeastern USA) have, in fact, cooled over the last century. The natural patterns of climate have been altered. The anthropogenic origin of the global warming was proposed in 1938 by the English scientist G.S. Callendar (so-called “Callendar effect”). In spite of initial skepticism of scientific community, there is new and stronger evidence that most of the warming over the last decades may be attributable to human activities. Scientists know for certain that human activities are rapidly changing the composition of the Earth’s atmosphere.

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Increasing levels of GHG, like carbon dioxide (CO2), in the atmosphere have been well documented. However, it is not easy to detect to what extent the human-induced accumulation of GHG since pre-industrial times is responsible for the global temperature increase. Like many pioneering fields of the scientific research, the current state of global warming science cannot always provide indisputable answers to our questions. 3.4.1 Background Global warming is a term used to describe the contemporary increase in the average temperature of the Earth’s atmosphere and oceans. Although local temperatures fluctuate naturally, the rising trend that is known as “global warming” can be separated from historical and/or pre-historical climate variations that have occurred naturally. The term “global warming” is usually applied exclusively to the observed rapid global temperature rise during the last 100–150 years, which is believed to be related to an anthropogenic enhancement of the greenhouse effect. It appears that over the past 50 years the average global temperature has increased more rapidly than during the whole recorded paleoclimate history. Linear trends can vary significantly depending on the period over which they are computed. However, the general prediction of the strong and rapid global climatic change seems to bear large level of significance. The debates on the available scientific evidence of the global warming began in the second part of the twentieth century, almost immediately after the discovery of the unusual rising trends in the SAT series. Early chaotic discussions of the observed phenomenon oscillated widely from the hope that most predictions are wrong to the development of various models to explain the possible reasons for the climate change and generate its future scenarios. The Intergovernmental Panel on Climate Change (IPCC; www.ipcc.ch) played an important role in the study of the recent climate change and its consequences. It has been established by the World Meteorological Organization (WMO; www.wmo.ch) and by the United Nations Environment Program (UNEP; www.unep.org) in 1988 to assess scientific, technical, and socio-economic information relevant for the understanding of climate change, its potential impacts, and options for adaptation and mitigation. From 1990, the IPCC has published a series of technical papers and reports comprising the investigations that form standard basis for the reference widely used by scientists as well as by different experts and the policymakers up to the Kyoto Protocol.4 In its third assessment report “Climate Change 2001” (www.grida.no/climate/ipcc_tar) the IPCC affirmed that most of the recent 50-year warming can likely be attributed to the anthropogenic emissions of the GHG and suggested the need for actions to weaken this activity. It was shown that while the past climate history can be explained in the frames of a few well-known natural processes, such as solar variability and volcanic eruptions, none of these processes can be involved in the explanation of the recent dramatic warming. Thus, the modeling of human-created warming mechanisms seems to be indispensable to reproduce the modern temperature history. 4 Kyoto Protocol to the United Nations represents the framework convention on climate change. It was adopted in 1997 in Kyoto (Japan) at the Third Session of the Conference of the Parties to the United Nations. Countries signed this convention agree to reduce their anthropogenic greenhouse gas emission by at least 5% below 1990 level during the 2008–2012 period. The Kyoto Protocol has entered into force in February 2005.

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3.4.2 Greenhouse gases and climate change For about the last decade, there has been an ongoing debate on the contribution of human activities to the global warming of the past century and especially on how anthropogenic activity will contribute to further warming that may occur during the twenty-first century. What is the physical basis for the fear of human-induced changes? Similarly to other living organisms in all epochs, the mankind has influenced surrounding environment. However, an impact of human activities has drastically increased after the Industrial Revolution that began in the mid-eighteenth century in the UK and at the present time embraces the continental and/or global scales. The industrial revolution began with the invention of the steam engine. The most important human activities at present that may have an impact on both regional and global climate are connected with: 1. the combustion of fossil fuels and the biomass burning that produce GHG, 2. the emission of chlorofluorocarbons (CFC) and other halogen compounds that not only are strong GHG, but also play an important role in the depletion of the stratospheric ozone layer, 3. the emission of aerosols (propellants used in aerosol sprays) that affect the composition of the atmosphere, and 4. the change due to urbanization, agricultural practices, and forestry that influence the physical properties of the Earth’s surface. While the effect of other above-mentioned activities is complex and not yet well known, a detailed discussion on the influence of enhanced GHG content was performed in numerous research works. Many chemical compounds found in the Earth’s atmosphere act as GHG. GHG allow sunlight to enter the atmosphere. However, when it strikes the Earth’s surface, some portion of this energy is re-radiated back as infrared radiation (heat). GHG absorb part of this energy, while the remainder escapes back into space. Detaining infrared heat energy they raise the temperature of the lower atmosphere and the Earth’s surface in contact with it. Thus, the role of the GHG is that they trap the heat in the surface–troposphere system. If the atmosphere accumulated all the trapped heat, then the Earth’s temperature would just rise and rise, but it is not the case. Over time, the energy amounts sent from the Sun and radiated back into the atmosphere became roughly equal, thus maintaining the temperature of the Earth’s surface roughly constant at 14–16°C on average. It is the natural greenhouse effect that keeps the Earth’s surface much warmer than it would be if there is no atmosphere. The equilibrium is preserved till the amount of GHG in the air remains the same as well as the amount of heat arriving from the sun is constant. An increase in the concentration of the GHG gives a parallel increase of the infrared opacity of the atmosphere. This so-called radiative forcing causes an imbalance that can be compensated by the corresponding increase of the surface–troposphere system temperature. This is enhanced greenhouse effect. During the last 200 years, mankind has been releasing substantial quantities of extra-GHG into the atmosphere. These extra GHG are trapping more heat; thus, it is expected that the observed average annual SAT increase by about 0.6 K since

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Table 9. Concentration growth of the important atmospheric greenhouse gases (except water vapor) Greenhouse gases Carbon dioxide Methane Nitrous oxide Misc. gases (CFCs, etc.) Total

Pre-industrial levela

Natural additions

Human additions

Total concentration

% of total

288 000 848 285

62 520 577 12

11 880 320 15

368 400 1745 312

99.44 0.47 0.08

25 289 158

0 69 109

2 12 217

27 370 484

0.01 100.00

Note: Data by Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory; http://www.cdiak.esd.ornl.gov. Data represent the year 2000 level. a Concentrations are expressed in parts per billion (ppb).

the late nineteenth century (Figure 4, Chapter 1) may be attributed to the man-made enhanced greenhouse effect. Many gases exhibit greenhouse properties. Some of them (water vapor, CO2, methane (CH4), and nitrous oxide (N2O)) occur in nature, while others are exclusively of anthropogenic origin. Table 9 illustrates the growth of the GHG concentrations since the preindustrial epoch to the year 2000. As observed for the last 250 years, concentrations in the atmosphere have indisputably grown: concentrations of CO2 about 30%, CH4 about 105%, and N2O about 9%. According to the information collected by the IPCC, significant enhancement of anthropogenic GHG emissions is the main cause of this growth. CO2 emissions are closely linked with industrial activities, primarily with the combustion of fossil fuels. For the past 20 years, about three fourth of human-produced CO2 emissions originated from burning fossil fuels. Another important source of CO2 is deforestation. Concentrations of CO2 in the atmosphere are regulated by numerous natural processes, such as plant photosynthesis; their common activity is known as the “carbon cycle”. While the carbon cycle can absorb some of the net 6.1 billion metric tons of anthropogenic CO2 emissions produced each year, an estimated 3.2 billion metric tons is added to the atmosphere annually. Such positive imbalance between emission and absorption amounts results in continuous growth of GHG in the Earth’s atmosphere. The increase in the GHG content together with other human activities affects processes and feedbacks5 in the climate system. However, because of their complicated nature as well as the strong natural variability of the Earth’s climate, it is not easy to determine the extent of the man-made GHG influence on the climate. In computer-based models, rising concentrations of GHG generally produce an increase in the average global temperature. For example, the 18 numerical model runs using 7 independent physical models by Kattenberg et al. (1996) have predicted an equilibrium temperature increase of 2.0 ⫾ 0.6 K in the year 2100 using double the current level of atmospheric CO2. Increasing concentrations of GHG are likely to accelerate the rate of climate change.

5 The interaction between processes operating in the climate system called climate feedback implies that the influence of some of the processes initiates the changes in some other process, and these changes in turn influence the former process. An intensification of the original process corresponds to the positive feedback, while the reduction represents the negative feedback.

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Scientists expect that the average global surface temperature could rise by 0.6–2.5 K in the next 50 years and by 1.4–5.8 K in the twenty-first century, with possible strong regional variations. The most important GHG is CO2, but importance of CH4 is also significant because per kilogram it has 21 times of the effect of CO2 for producing global warming. The CH4 emissions are generally caused by bacteria-induced decay of organic material in anaerobic conditions. Natural wetlands represent the main source for CH4 emissions from decaying organic material. The decay process is also an important anthropogenic CH4 source in the digestive processes and manure of domestic animals, rice cultivation, landfills, and wastewater treatment. At present, CH4 composes 0.5% of total emissions, but it gives about 10% of the radiative forcing6 estimated to be caused by carbon dioxide (data by the Carbon Dioxide Information Analysis Center (CDIAC; http://www.cdiac.esd.ornl.gov). It is assumed that in the future the absolute value of the CH4 forcing will increase but less than that of CO2. N2O (0.1% of total emissions and 296 times per kilogram of the effect of CO2 for producing global warming) is naturally emitted from soils and oceans. The human contribution consists of burning fossil fuels, the use of definite fertilizers, the cultivation of soil, and certain industrial processes (like production of nylon). The most commonly considered indicator of climate change is the SAT. Global temperatures are, in fact, rising. They have increased between 0.3 and 0.6 K in the last 150 years. Even this change has not been monotonic; it is unusual in the context of the last few centuries (see Section 1.2 of Chapter 1). An independent estimate based on the analysis of borehole temperature measurements (Section 3.2) supports the unprecedented character of the recent global warming at least for the last five centuries that is accelerated since the end of the nineteenth century. At first sight these results corroborate the hypothesis of human-induced warming. Long-term paleoclimatic studies also seem to confirm this claim. For example, there have been significant natural variations of CO2 in the geologic past, and these changes are correlated with the general course of climate variations. There is no known precedent for large increases in atmospheric CO2 without simultaneous changes in other components of the carbon cycle and climate system. The illustration of this claim is presented in Figure 106. The bottom image shows the oscillations in the concentration of CO2 in the atmosphere (Vostok ice core, Antarctica, data by Petit et al., 1999). A comparison is shown between observed trends in the CO2 content and the temperature changes estimated for the same location (see also Figure 1, Chapter 1). As shown, temperature changes almost perfectly repeat the CO2 oscillations. On the other hand, on the timescale of the last few thousand years there have been even more pronounced climatic variations during periods when variations in CO2 have been relatively low. For example, from the end of the last glaciation episode about 10 000 years ago until the end of the eighteenth century, the levels of GHG in the atmosphere remained fairly constant, while climate has shown significant oscillations. It is clear that atmospheric GHG does not solely influence global climate. There is still no exact knowledge of how the climate system varies naturally and/or responds to the GHG emissions, 6

Radiative forcing represents a simple measure of the importance of a potential climate change mechanism. It represents the change in the net vertical irradiance due to variations in the internal and/or external forcing of the climate system, e.g., the output from the Sun or change in the carbon dioxide concentration and is measured in W/m2.

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Fig. 106. Comparison of the trends in the surface temperature anomalies (top) and in the atmospheric concentrations of carbon dioxide (bottom) during the last 400 000 years (Vostok ice core, Antarctica; data by Petit et al., 1999). Both curves are highly coherent. Increases in the atmospheric CO2 levels are accompanied by the corresponding warming.

when the equilibrium response of the nonlinear climate system depends in complex ways on various feedbacks. The principal means for understanding climate system response to the GHG forcing is the use of computer models of the Earth’s climate system based on the well-established physical/chemical/biological assumptions and their comparison with the observed/reconstructed paleoclimatic records. Systematic review and evaluation of the existing paleoclimatic data is thus indispensable to produce a consequent and robust paleoclimatic database that may serve as a test target for climate model studies. On the other hand, the models that have been constructed to predict future climate change are necessarily simplified representations of the climate operating system. The climate of the past centuries simulated by three-dimensional coupled atmosphere–ocean models may deviate from the true evolution of the past climate, due to uncertainties in the external forcing, model deficiencies, and internal variability. Thus, the researchers need some sort of testing that will clearly reveal the degree of confidence between models and paleoclimate observations. The progress in reducing uncertainties in the simulation of future climate patterns will also require better understanding of the total behavior of climate system as well as the buildup of GHG in the atmosphere. In the case

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when observed and modeled results appear to be contradictory, robust criteria to reveal the reason for the discrepancy (e.g., shortcomings of the model or quality of the observations used for testing) should be developed. At present, however, the uncertainties in the analysis are considerable and their results are, therefore, only indicative. Local or regional effects can be different from assessed global effects. For example, burning of high-sulfur coal and oil leads to sulfate aerosols, which largely cool the climate in and somewhat downwind of the regions they are emitted. Such regional cooling tends to offset some of the globally averaged warming, but primarily serves to distort the patterns of climate changes relative to the regional patterns that would be produced by GHG-induced changes by themselves (Yang and Schneider, 1997). Simulations of the climate change for specific areas are much less reliable than global ones, and it is unclear whether regional climate will become more variable. Many researchers calculate scenarios for only a few decades because of large uncertainties that accumulate over time. The ability to distinguish a warming trend from natural variability is crucial for an understanding of the climatic response to increasing GHG concentrations. Joint analysis of the borehole and SAT data as well as the complementary use of the past climate reconstructions and model generated synthetic data gives the possibility of interpreting observed twentieth century warming trend in the global warming context. 3.4.3 Was the twentieth century climate unusual? Evidence from the underground In Sections 3.2 and 3.3 we have cited repeated claims of numerous authors about unprecedented nature of the twentieth century warming at least compared with those in the last 500 years. Although the Northern Hemisphere reconstructions prior to about 1400–1500 A.D. exhibit numerous uncertainties, important conclusions are still possible also for more remote epochs. While the warmth early in the millennium (the Medieval Warm Period) approaches mean twentieth century level, the late twentieth century appears quite anomalous. The 1990s was probably the warmest decade in at least a millennium scale. The climatic conditions during the Medieval Warm Period seem to be more spatially variable, while the pattern of the recent warming appears to be generally global. To establish the unusual character of the twentieth century warming, two of the most important questions should be answered: 1. Is the Earth’s surface significantly warmer now than it was in the pre-industrial epoch? If yes, how much warmer is the Earth’s surface now than it was in the preindustrial times? 2. Is the climate course of the last millennium really known to serve as the base for sure comparison? Further important questions that are somewhat beyond the frames of this book may be: 3. Has mankind already changed global climate? 4. Is anthropogenic global climate change in the twenty-first century surmounting at least all Holocene variability?

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With respect to the first question the situation is more or less obvious. Numerous measurements of the land surface air and sea surface temperature (continuously re-examined and updated) revealed a global average rise in the range of 0.3–0.6K between the late nineteenth century and the year 1994. Conclusions based on the conventional temperature observations are supported by the satellite-based data as well as by an indirect evidence such as the decline in the extent and thickness of the Arctic sea-ice cover, melting of the Greenland ice sheet, recession of glaciers (http://www.nsidc.org/data/glims/glaciermelt), etc. The situation with the GSTs is generally similar. The vast volume of the available measurements/GST inversions as well as the wide range of computations and statistical tests applied to this data in the recent years significantly increased the confidence of the conclusions reached. The warming trend in the GST record of the last 100 years is undoubtedly real. Existing estimates fall in the same range as the values calculated from the SAT data. They also show that the GST warming trend of the last century is substantially stronger than that in the whole previous five centuries (Section 3.2). The answer to the second question is not so certain. Is the twentieth century surface temperature warming trend really unusual? With respect to the SATs, we have mentioned above that the IPCC affirmed that most of the recent 50 years’ warming is unprecedented and can likely be attributed to anthropogenic emissions of the GHG. Although this statement was based on the wide-scale research in different scientific branches, it almost immediately became the target of trenchant, still ongoing debates. Their numerous traces can be found, e.g., in the reports by the scientific team of the Marshall Institute, which involves a critical examination of the scientific basis for global climate change policy (www.marshall.org). The primary reason for the IPCC conclusion was a climate reconstruction by Mann et al. (1999) that produced the so-called “hockey stick” diagram (see Figure 11, Chapter 1), which shows the twentieth century as unusually warm compared to preceding times. A new evaluation of the underlying data used to create the diagram by Mann et al. (1999) presented in the work by McIntyre and McKitrick (2003) has raised serious questions about its validity (see also the original papers by McIntyre and McKitrick, 1998, 2005a). Authors have examined the application of the dataset of proxies that were used by Mann et al. (1998, 1999) to reconstruct the temperature record from 1400 to 1980. Their review has found different errors (inappropriate collation, unjustified truncation and extrapolation, use of obsolete data, as well as calculation mistakes). Correction for these shortcomings performed by above authors has revealed that the temperature in the early fifteenth century was actually higher than that in the twentieth century. Recent work by Soon and Baliunas (2003) also comprises definite criticism of the “hockey stick”. Authors conclude that the available climatologic data do not support the hypothesis that the twentieth century was the warmest and/or most unusual of the last millennium. In the subsequent discussion (e.g., Huybers, 2005; McIntyre and McKitrick, 2005b, c; von Storch and Zorita, 2005; see also web sites http://www.meteo.lcd.lu/ globalwarming/hockey_stick/hockeystick01.html and the Mann’s Responses and Counterarguments www.climate2003.com/mann.responses.htm), participants have clearly stated that none of the reconstructions presented the recent warming as a simple turnout from the 14–16th centuries cold conditions, which was not statistically and climatologically meaningful, and argued that the suggestions by McIntyre and McKitrick influence the reconstruction in a minor way and in fact confirm the robustness of the Mann et al.’s (1998) reconstruction within its own framework. The “user-friendly” assessment of the data and

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methods as well as the reliability of Mann et al.’s (1998) reconstruction is also presented on the web site by Mann et al. “Global temperature patterns in past centuries: An interactive presentation (www.ncdc.noaa.gov/paleo/ei/ei_cover.html). This interactive forum allows users to check up directly reconstructed patterns and/or select particular spatial regions and time period of special interest. To firmly answer the second question, it is essential to regard it in the context of the long-term climate variability. Because of their shortness and sparseness, the instrumental temperature measurements can ensure information generally on the higher frequency (seasonal, annual, or decadal) climate change. For example, the investigations of the trends in the high-frequency SAT variability in different regions of the globe by Karl et al. (1995) revealed that the interannual temperature variability is supported by data from the past few decades, while the longer data series indicated that this trend is an artifact. Proxy climate indicators calibrated against temperature time series together with borehole temperature reconstructions can provide data for the confident conclusions of the low-frequency temperature variability. The certainty of these conclusions strongly depends on the accuracy/consistency/completeness of the available paleoclimatic series. Recent development of the paleoclimatology and a number of advances in the new areas (e.g., in the borehole climatology) considerably enriched available information and allowed more meaningful conclusions about spatial and temporal patterns of climate change in the past centuries. The long-term temperature trends over the last millennium or so are evident in many regions as well as on the global and/or the hemispheric scale. Although with definite limitations/uncertainties, the combined multiproxy/borehole temperature history reconstructions can provide global-scale sampling of the general course of the temperature variations over several centuries into the past. Thus, e.g., it was concluded that temperatures in the Northern Hemisphere during recent decades are the warmest in at least six centuries. The latest studies based on the global networks of the multiproxy and/or borehole data have confirmed the early results and have proved their usefulness for describing global and/or hemispheric patterns of climate variability in past centuries (e.g., as described in the works by Mann et al., 1998, 1999; see the previous section). These studies have also provided better comparison of the derived climatic trends with possible physical influences and/or climate forcing (e.g., Crowley and Kim, 1996, 1999; Delworth and Mann, 2000). On the contrary, the data prior to fifteenth to sixteenth century were proved to be too sparse for the firm inferences and could serve only for a few regional reconstructions. Of course, paleoclimate experts should challenge each other’s conclusions and interpretations. Various alternative hypotheses have been proposed to explain modern increase of the global temperatures:

• Most predictions are wrong and the warming is within the range of the natural variability.

• Observed warming represents simple recovery from the previous cold conditions (the Little Ice Age and/or the cold period in the second half of the nineteenth century).

• Recent warming is the result of the changes in solar irradiance, etc.

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It should be mentioned, however, that the explanations of the recent warming include these and other possibilities, but they are not limited to any hypothesis. Climate variations include both natural and anthropogenic factors, and the recent warming reflects an integration of various forcings. As it was shown in the previously cited work by Huang (2004), the integrated surface temperature history calculated by the merging of GST and multiproxy sources coincides in its course with the curve of the radiative forcing, reconstructed by Crowley (2000), that comprises the effects of solar irradiance, GHG, anthropogenic aerosols, and volcanism. This coherence represents the useful validation of the strategy of coupling of the GST–SAT multiproxy information. On the other hand, good agreement between an integrated temperature reconstruction and the radiative forcing corroborates the presence of both natural and human-induced effects in the recent warming. And finally it should be emphasized that the evidence of the unusual character and possible anthropogenic forcing based on the surface air/ground temperature data is only one from a number of independent paleoclimatic researches indicating the strong likelihood that human influences on climate play very important (if not a dominant) role in the observed twentieth century warming of the Earth’s surface. Causes of the twentieth century temperature change can be discovered in more precise manner using optimal detection methodology. Detection and attribution studies probably represent the strongest piece of evidence in support of the above conclusion. These studies demonstrate that the pattern of twentieth century climate change agrees well with that predicted by the modern state-of-the-art numerical models of the climate system in response to the strengthening anthropogenic forcing. 3.4.4 The elements of optimal detection of the climate change and attribution of the causes In the recent years, considerable progress was achieved in attempts to identify an anthropogenic effect on climate using the optimal detection methodology. Detection of the climate changes is the procedure which reveals that the climate is really changed in certain statistical sense. Detection of the anthropogenic changes demonstrates that observed change is significantly different than can be explained by natural internal variability. The detection of a change in climate does not necessarily imply that its causes are understood and/or does not search for the reasons of observed change. Attribution represents the process that looks for the most probable causes of detected climate change. Previously the detection and attribution studies have addressed the simple question: “Have we detected a human influence on climate?” Recently evidence for an anthropogenic contribution to climate trends over the twentieth century is accumulating rapidly (for detailed review see, e.g., IDAG, 2005). Significant progress achieved in this research field in the recent years inspired the re-formulation of the problem. At the present time the question that should be answered is rather: “How strong is the anthropogenic change?” The response to anthropogenic changes in climate forcing is superimposed on the natural, internal, and externally forced climate variability that can occur on similar temporal and spatial scales. Internal climate variability7 manifests itself on the wide range of 7

Internal variability means the part of the climate variability that is not forced by external influences.

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timescales from weeks to centuries and millennia, and the climate is capable to produce the variations of noticeable magnitude without any external influences. Externally forced climate variations may be of two kinds: (1) changes in natural forcing factors, such as solar radiation or volcanic aerosols, and (2) changes in anthropogenic forcing factors that imply increasing concentrations of the GHG, etc. The presence of the natural climate variability means that the detection and attribution of anthropogenic climate change is a statistical “signal-in-noise” problem. Ideally, sure attribution of the detected climate change to anthropogenic causes would require wide series of experiments with the climate system where the probable mechanisms of change are systematically varied to determine the sensitivity of the system to each of them. Of course, such approach to attribution is not possible. In practice, the attribution of observed climate change to a given combination of human activity and natural influences consists of the statistical analysis and assessment of available evidence to test the hypotheses that the observed changes: 1. cannot be attributed entirely to internal variability, 2. are consistent with the estimated responses to the given combination of the anthropogenic and natural forcing, and 3. are not consistent with alternative, physically plausible explanations of recent climate change that do not take into account important elements of the given combination of investigated forcings. In such approach the detection and attribution of the climate change represents the statistical problem that can be solved on the definite level of significance. The elucidation that observed changes do not simply represent a manifestation of internal variability (detection) is thus one of the components of the more complex and demanding process of attribution. The records that are of interest for the detection and attribution of the anthropogenic climate change are approximately 50–100 years’ long. Clearly, the instrumental record is short relatively to this interval. Paleoclimatic reconstructions (including GST histories) can provide time series that are long enough for the estimation of internal climate variability, but a number of problems (limited spatial coverage, temporal inhomogeneity, possible biases in the interpretation of the relationships between proxy indices and climatic variables) make this task difficult. An ongoing progress in the reconstruction of past temperatures, e.g., merging of different proxy series to obtain more certain millennium-long reconstructions of past temperatures with annual or finer resolution described in the previous section, improves the situation. Such kind of paleoclimate reconstructions becomes more and more important for assessing internal climate variability. These time series can also be successfully used for the verification and/or checking of the internal variability estimates from the coupled atmosphere–ocean models to ensure that the calculations do not under- or overestimate the level of internal variability on decadal to century-long timescales. The only mean to quantify/separate internal (“noise”) and the human-forced climate change and/or variability (“signal”) is the use of numerical models of the climate system. As was mentioned above, modeling efforts should be combined with both empirical and statistical techniques. The question formulated above: “How strong is the anthropogenic change?”

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that the attribution procedure should answer, can be extended as: “Is the magnitude of the response to greenhouse gas forcing as estimated in the observed records consistent with the response simulated by climate models?” The first IPCC Scientific Assessment in 1990 (Houghton et al. 1990; www.ipcc.ch) has detected that the global mean surface temperature has increased by 0.3–0.6K over the last 100 years and that the magnitude of this warming appears to be generally consistent with the predictions of the climate models forced by increasing concentrations of GHG. However, the question that the observed warming (or at least part of it) could be attributed to the enhanced greenhouse effect that time remained unclear. Reasons for this uncertainty were that: (1) there was only limited agreement between model predictions and observations, because climate models were still in the early stages of their development; (2) there was inadequate knowledge of natural variability and influence of other possible anthropogenic effects on climate; and (3) there was scarcity of available observational data, particularly of long, reliable time series. Since 1985–1990 the scientific branch of the climate modeling experienced significant progress. At the present time there are numerous research groups working out both short- and long-scale high-performance models including many components of the climate system. Important results were achieved in the better understanding of the internal variability of the climate system through multi-century model simulations that did not take into account artificial forcing. On the other hand, numerous models have incorporated the climatic effects of different human-induced changes to quantify their possible projections on the future climate. Both theoretical principles of the model construction and results of numerical simulation can be found on numerous web sites, such as the Hadley Centre (www.met-office.gov.uk/research/hadleycentre), the National Center for Atmospheric Research (www.cgd.ucar.edu), or the National Oceanic and Atmospheric Administration (NOAA; www.cdc.noaa.gov). There also exists a vast amount of published works on this topic. The simplest way to attribute observed climatic changes to the anthropogenic effect is the qualitative assessment of consistencies and inconsistencies between the observed data and model projections of anthropogenic climate change. Such studies include generally simple descriptive analysis of climatic variables and models simulated using different forcing schemes. Reliable qualitative detection and attribution can be obtained for climatic variables that possess high climate change signal-to-noise ratios, good spatial data coverage, and consistent signals from different model simulations. Among all climatic variables the SAT appears to be the better database satisfying these requirements. Variations on large spatial scales and time scales of several decades or longer are generally considered to enhance the signal-to-noise ratio. Numerous studies have identified areas of qualitative consistency/inconsistency between observed and modeled climate change. Of course, qualitative studies provide less reliable evidence for an anthropogenic influence on climate than the quantitative attribution techniques; however, they may indicate the possibility for further quantitative detection and attribution study in the areas of already revealed qualitative consistency between observations and models as well as efforts on the improvement of the models in the areas of detected inconsistency. More certain quantitative attribution requires complex mathematical methodology and vast database. Significant progress in this field has been achieved in the recent years. The number of techniques for the quantitative detection of the observed climatic changes to

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the human-induced forcing and corresponding attribution studies has rapidly increased in the last decade. Quantitative studies revealed not only the degree of agreement between observed and modeled climate change, but also the statistical significance of obtained results and the degree to which the final conclusions are independent of the assumptions made in applying the data processing techniques (Hassellmann, 1997; North and Stevens, 1998; Allen and Tett, 1999; Tett et al., 1999, 2000, 2002; Barnett et al., 2000; Hill et al., 2001; Mitchell et al., 2001; Paeth and Hense, 2001; Jones et al., 2003). The “optimal detection” or “optimal fingerprinting” techniques are probably the most popular and are widely used for the surface temperature patterns. These techniques have several slightly different representations (e.g., Hegerl and North, 1997; Jones and Hegerl, 1998; Zwiers, 1999; IDAG, 2005). Both the theoretical base of the optimal detection studies and the applied database of the surface temperature have been extended in the works by Hegerl et al. (2000, 2001), North and Wu (2001), Stott et al. (2001). Optimal detection is a technique that optimizes pattern variability and thus can provide a clearer separation of a climate change fingerprint from natural internal climate variations. It increases the detectability of the forced climate changes through increase of the signal-to-noise ratio by looking at the component of the response away from the direction of highest internal variability. The optimal detection represents a multiple regression between a set of signals derived from model simulations and observations. Its mathematical formulation assumes that a field of n observations y can be represented as a linear combination of candidate signals obtained from climate models g1, %, gm plus noise u: m

y ⫽ ∑ ai g i ⫹ u ⫽ Ga ⫹ u,

(47)

i⫽1

where G ⫽ (g1円%円gm) is the matrix composed of the signal patterns and a ⫽ (a1,%, am) T is the vector composed of the unknown amplitudes. The optimization consists of projection of the observations, signals and noise onto the leading eigenvectors of an estimate of the noise covariance matrix and by weighting down patterns of temperature change with high variability and weighting up low-variability temperature change patterns. The noise covariance matrix can be calculated, e.g., from the long-term model simulation with constant external forcing (Jones et al., 2003). Various permutations of the projected signal patterns are then regressed against the projected observations. This procedure provides sets of regression coefficients (amplitudes). Further generalizations allow, e.g., incorporation of the signal uncertainties into the analysis. The multiple regression procedure consists of: (1) the estimate of the unknown amplitudes a with respect to generalized least squares from observations, and (2) the testing of the null hypotheses that they are zero. If the hypotheses are rejected, the analysis can be continued by attribution consistence test, in a word, by testing the hypothesis that for some combination of the signals the amplitudes are unity. To attribute all or part of the recent climate change to human activity, one also needs to demonstrate that alternative explanations (e.g., pure internal variability and/or purely naturally forced climate change) could not respond to a set of observed changes that can be explained by human-induced influence. It should be mentioned, however, that the

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researchers cannot predict what alternative explanations for observed climate change may be discovered and accepted as plausible, in the future. Thus, some interpretation accepted during the attribution process is never final. This problem is not only common in climatology but also present in all scientific branches that deal with establishing cause and effect using limited base of observations. The possibility of other explanation can never be excluded completely. The hypothesis will appear progressively more valid as numerous alternative explanations are tested and found to be inadequate. Above approach is potentially more informative than the simpler regression techniques because in principle it allows to quantify through associated estimates of uncertainty, how much different factors have contributed to the recently observed climate changes. Such quantification, however, is possible under assumption that important sources of model error (e.g., missing and/or incorrectly represented atmospheric feedbacks) affect primarily the amplitude and not the structure of the response to external forcing. Most of the existing studies suggest that this is the case for the relatively small-amplitude changes observed to date; however, the possibility of model errors changing both the amplitude and the structure of the response cannot be excluded. Probably the most cited works on the optimal detection/attribution of the surface temperature change were published by Tett et al. (2002) and Jones et al. (2003). Former authors have simulated the climatic response to natural (solar irradiance and volcanic activity) and anthropogenic forcing from 1860 to 1997 using coupled atmosphere–ocean GCM. Authors computed a set of models, which incorporated different anthropogenic forcings. One of the models, e.g., has taken into account the influence of the GHG alone, while the other has regarded coupled effects of the tropospheric ozone, GHG, sulfate aerosol, etc. Using the optimal detection analysis, authors have detected the contribution of different forcings to the SAT change. As shown, while natural forcings give a linear trend for the last century close to zero, the warming trend caused by total anthropogenic forcings is equal to 0.5 ⫾ 0.15 K/century. The above analysis has detected that the combination of the natural forcing and the GHG increase as well as the contribution from internal variability is the best explanation for the first half of the twentieth century warming. Warming in the second half of the century was probably caused by the coupled effect of the changes in the GHG, sulfate aerosol, and the stratospheric aerosol due to volcanic eruptions. Jones et al. (2003) have investigated the causes of the surface temperature change over the last four decades using optimal detection methodology. As in the previous work, authors have used a coupled atmosphere–ocean GCM with different sets of forcing. The sets of the signals with the most powerful influence on the climate system included well-mixed GHG, other anthropogenic forcings as well as changes in the solar irradiance8 and volcanic activity. The SAT observations were taken on the latitudinal zonal scales: 90–30°N, 30°N–0°, and 0°–30°S. Because of insufficient number of observations, zone 30–90°S was excluded from the consideration. Obtained results have supported the hypothesis of an anthropogenic influence on the climate. The anthropogenic signals of the GHG and combined response to changes in sulfate aerosol

8 Total solar irradiance is the amount of electromagnetic energy emitted by the Sun over all wavelengths that is received at the top of the Earth’s atmosphere. It measures the solar energy flux in W/m2.

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and tropospheric and stratospheric ozone were robustly detected. As shown, the GHG influence dominated in the last 40 years and has caused as much as 0.56 ⫾ 0.15 K global SAT warming, while the combined effect of changes in sulfate aerosol and ozone produced cooling trend of 0.10 ⫾ 0.01 K at the same time. Response to volcanically produced stratospheric aerosols has resulted in approximately similar cooling of 0.09 ⫾ 0.04 K, and finally 0.12 ⫾ 0.11 K warming of the surface temperatures occurred in response to the changes in solar irradiance. Except for the “optimal detection” techniques, in the recent years there is a growing interest in the use of Bayesian methods for the climate change detection and attribution (Dempster, 1998; Hasselmann, 1998; Levine and Berliner, 1999; Berliner et al., 2000; Lee et al., 2005; Min and Hense, 2005). This interest was inspired by the ability of this formalism to account for uncertainties in various components of the detection and attribution procedure and to incorporate additional information. As shown in Section 2.3.5 (Chapter 2), where similar method was used for the FSI inversion of borehole temperatures, an incorporation of additional information can significantly increase the reliability of the results, in the case of detection and attribution reducing the range of alternative explanations for observed climate change. In most cases additional information includes knowledge about uncertainty and an anthropogenic influence on the climate. This knowledge is expressed through prior distributions that are non-committal on the climate change question. A study by Berliner et al. (2000) has applied a Bayesian framework in a multivariate climate change detection setting. As shown in this work, Bayesian approach allows greater flexibility and strictness in the treatment of different sources of an uncertainty. On contrary to the strong evidence of an anthropogenic influence on the climate of the twentieth century obtained by the optimal detection approach, the evidence from the Bayesian attribution assessment is not so firm. Lee et al. (2005) explain it by the limited length of an available observation record and/or by insufficient account for all possible sources of external forcing. The authors have estimated that the strong evidence from a Bayesian attribution assessment using stringent enough attribution criterion may be available by 2020. Due to subsequent development of the detection/attribution techniques, modeling procedures, and the observational databases, the nature of detected climatic change has been evaluated in more detail in further IPCC Assessment Reports (e.g., Climate Change 2001; www.grida.no/climate/ipcc_tar). Different directions of the evidence on the causes of recent climate change were examined. Below we present the summary of the results obtained for the surface temperatures. (1) Twentieth century climate was unusual. Palaeoclimatic reconstructions for the last 1000 years have indicated that in spite of the large natural climate variability, the twentieth century warming is really unusual, even taking into account uncertainties of the reconstructions. A comparison of empirical evidence with proxy reconstructions and GST histories shows that the natural factors explain relatively well the general temperature trends of the last millennium to the nineteenth century. However, they can hardly account for an unusual warming in the end of the twentieth century (Jones and Mann, 2004). The last two millennia reconstructions did not find an evidence for any earlier periods with warmer conditions than the post1990 period (Moberg et al., 2005).

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(2) Observed warming is inconsistent with the model estimates of the natural internal climate variability. The model estimates of the internal climate variability on the annual to decadal time-scales are generally similar. In some cases they are even larger than those observed. On the longer scales the model estimates of the internal climate variability vary substantially. Estimates from models and observations are uncertain on the century and longer timescales required for its detection. Notwithstanding that the long-scale internal climate variability is uncertain the detection of an anthropogenic signal is insensitive to the model used to estimate internal variability. Recent observed changes cannot be ascribed to internal variability alone even if the amplitude of simulated internal variations is increased by a factor of two or more. The ability of climate models to simulate large-scale temperature changes during the twentieth century, when they include both anthropogenic and natural forcings, and on the contrary, their inability to simulate warming observed over the last half a century, when they do not take into account increasing GHG concentrations, is generally regarded as an evidence for an anthropogenic influence on the global warming. On the whole, changes in the global climate over the twentieth century unlikely can be attributed to the pure internal climate variability. (3) The observed warming (especially) in the second half of the twentieth century appears to be inconsistent with natural external forcing of the climate system. While for earlier periods common influence of solar and volcanic forcing can explain the Medieval Warm Period and the Little Ice Age, externally driven natural climate forcing cannot ensure climate changes comparable in their amplitude and time with the late twentieth century warming (Bertrand et al., 2002). All existing studies reject the natural climate forcing and/or internal variability alone as a possible explanation of the recent climate change. Direct measurements of the solar irradiance exist for only two decades. Longer records (e.g., presented in the works by Hoyt and Schatten, 1993; Lean et al., 1995; Lean, 2000) are the reconstructions based on different assumptions. All these reconstructions exhibit an increase in the amplitude of the total solar irradiance since the Dalton Minimum (Figure 107). Data also suggest that the sharp increase of the overall solar activity during the first half of the twentieth century has practically stopped in its second half. The radiative forcing due to stratospheric aerosols of volcanic activity possesses significant year-by-year variations. In the second half of the twentieth century it was particularly strong during 1961–1965 and 1991–1995 periods (Ammann et al., 2003). The increase in volcanic activity during the past decades would, if anything, produce tropospheric cooling and stratospheric warming, thus the opposite effect to what has occurred over this period. Trends in the volcanic aerosol content together with the small solar irradiance changes during the last two (possibly two to four) decades of the twentieth century indicate that volcanic forcing in this period was negative and could not explain the recent rapid increase of global surface temperature. Not only the surface temperature increase but also changes observed in patterns of vertical atmospheric temperature are similarly inconsistent with the natural forcing trends (Chanin and Ramaswamy, 1999; Jones et al., 2003).

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Fig. 107. Trends in the total solar irradiance since 1750. (Data by Lean (2000).)

(4) Human-induced factors do provide an explanation of the rapid increase of the surface temperature in the twentieth century. Results by optimal detection methods indicate human influence on climate in the surface temperature observations. The model that can give an acceleration of the surface temperature warming during the last three to four decades can be constructed on the base of the GHG forcing. The use of the models considering a number of forced signals as well as an investigation of different sources of the uncertainty has shown that a considerable part of the recent warming can be attributed to the GHG influence. The better coincidence of the observed data with model simulations for the period since 1850 A.D. can be achieved by including the anthropogenic sulfate aerosol forcing. All models produce a response pattern to combined GHG and sulfate aerosol forcing, which is detectable in the nineteenth to twentieth centuries surface temperature record. Because the sulfate aerosol forcing is negative and thus tends to reduce the climate system response, detection of the response to the combined forcing implies the presence of a GHG signal that is at least as strong as a combined signal. For better observation/reconstruction fit the climate models should also include an impact of deforestation (Bertrand et al., 2002). Results of different investigations may vary depending on the details of the analysis, especially as regards the timescales used. For example, an influence of the anthropogenic climate forcing dominates on decadal scales; response to volcanic activity can be better detected on the annual scale, while an incorporation of seasonal information helps in the detection of weaker solar signals (Jones et al., 2003). (5) It is unlikely that detection studies have mistaken a natural signal for an anthropogenic signal. To demonstrate an anthropogenic contribution to climate, it is necessary to rule out the possibility that the detection procedure has mistaken the part or the whole of a natural signal for an anthropogenic change. While estimates of the amplitude of a single anthropogenic signal are generally consistent between different model signals and/or different approaches, joint estimate of the amplitude of several

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signals may vary between models and approaches. Precise separation of the observed warming on human-induced and naturally forced components should be done with considerable care. On physical grounds, natural forcing is unlikely to account completely for observed warming over the last three to four decades, given that it is likely that the overall trend in natural forcing over most of the twentieth century is small or negative. Several studies have involved three or more components – the responses to GHG, sulfate aerosols, and natural (solar, volcanic, or volcanic and solar) forcing. All performed model simulations have detected a substantial GHG contribution over the last 50 years, although in one case the estimated GHG amplitude is inconsistent with observations. Thus, it is unlikely that the researchers have misidentified the solar signal completely as a GHG response. On the other hand, an uncertainty in the amplitude of the response to the natural forcing continues to contribute to an uncertainty in the strength of the anthropogenic signal. (6) Natural factors may have contributed to the early century warming. Most of the discussion presented in this section has been concerned with the evidence relating to a human-induced influence on the late twentieth century climate. Numerical investigations (e.g., Moberg et al., 2005) have revealed a large natural variability in the past climate that likely continues. The observed global mean surface temperature record shows two main periods of warming. Some studies detect a solar influence on surface temperature over the first five decades of the century (Figure 107) with perhaps a small additional warming due to an increase in GHG content, while others suggest that the early warming could be due to a combination of anthropogenic effects and a highly unusual internal variation. Thus, the early century warming could occur due to definite combination of natural internal variability, changes in solar irradiance, and anthropogenic influence. Additional temperature rise characteristic for the second half of the century most likely can be attributed to a substantial warming due to corresponding increase in GHG, partially offset by cooling due to aerosols, and perhaps by cooling due to natural factors toward the end of the century. In the investigations described above, detection and attribution of the causes of the recent climate change were performed by comparison of observed SAT changes with simulated models. Could similar detection/attribution procedure be performed using borehole data? Because the ground acts as the low-pass filter attenuating the subsurface climate signal with depth and time, the resolution of borehole data decreases into the past (Section 2.4.3, Chapter 2). Thus, reconstructed GST histories cannot be directly compared with models. Recently, Beltrami et al. (2006) have suggested a new approach to perform qualitative detection and attribution of the GST changes. The basic steps of the approach developed by the authors are: (1) the use of the simulated model output as a forcing function at the surface (surface boundary condition), (2) the solution of the forward problem to obtain artificial temperature–depth profile, and (3) the comparison of the calculated perturbation with the measured borehole temperature log. Thus, the basic idea of the method by Beltrami et al. (2006) is to propagate modeled surface temperature into the underground and to compare an expected disturbance with the really measured perturbation. Comparison may be fulfilled by traditional statistical techniques used for detection and attribution of SAT signals.

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Authors have tested suggested method using three different model simulations and the temperature logs from numerous boreholes in the territory of Canada. Long-term paleoclimate simulations were performed with the ECHO-g GCM (www.cru.uea.ac.uk/ projects/soap/data/model/echog.htm). The set of models has included coupled atmosphere–ocean interactions and simulated an evolution of the climate system for the period 1000–2000 A.D. The models have taken into account external forcing factors such as solar activity and volcanism as well as the atmospheric CO2 and CH4 concentrations (details can be found on one of the co-author J. Fidel González-Rouco’s web site; http://www.chubasco.fis.ucm.es/~fi/for_wpa01/for_wpa01.html). Ground temperatures that were further used as the surface boundary condition for the calculation of artificial temperature–depth profiles were estimated from a five-layer soil model taking into account influence of vegetation on evapotranspiration,9 run-off as well as the fall, accumulation and melting of snow (Beltrami et al., 2006). Authors have modeled three 1000year long GST records, namely control run (CTRL) that has taken into account only external forcing and two forced simulations (FOR1 and FOR2) with the same external forcing but different anthropogenic influences. The dataset for testing contained 210 Canadian borehole temperature logs. Because of their irregular distribution, the data were arranged into four clusters: British Columbia/Yukon, Manitoba/Saskatchewan, Quebec/Ontario, and Atlantic Canada. Measured temperature logs have exhibited significant differences and for further interpretation authors used averaged subsurface profiles. Figure 108 shows temperature logs measured at Atlantic Canada together with their averaged version and the SAT simulations performed with the CTRL, FOR1, and FOR2 versions of the ECHO-g model. Synthetic temperature–depth profiles were calculated by the 1-D half-space pure conductive forward technique using above simulations as the surface boundary condition. Comparison of the synthetic and average T–z profiles for all four regions is presented in Figure 109. Even examining “by-eye” reveals that in three of the four investigated regions profiles that contain both external and anthropogenic forcings are closer to observed data than the control run that takes into account only external forcing. The external forcing alone can explain neither the curvature of the measured data in the 100–200 m depth range nor the recent warming. This fact hints that the temperature anomalies measured in Canadian boreholes unlikely arose from internal variability of the climate system and that inclusion of the human-induced forcing is indispensable to their realistic explanation. The temperature–depth distribution from British Columbia/Yukon (Figure 109, top-left) is the only one that exhibits good coincidence with all three simulated profiles. As shown, the subsurface temperature anomalies and/or the amount of the recent warming in this area are somewhat lower than those in other investigated regions. In summary, it can be concluded that a qualitative comparison of borehole measurements with GCM for detection/attribution of the anthropogenic changes has shown that similarly to the SAT records underground temperatures are sensitive to the surface forcing and thus can be used for discovering the causes of the recent warming as well as for an improvement of the models for the simulation of the temperature changes. As shown in

9

Evapotranspiration represents the combined process of evaporation from the Earth’s surface and transpiration from vegetation.

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Fig. 108. Subsurface temperature anomaly for Atlantic Canada. Thick line represents averaged temperature–depth profile for the region. Inset shows the 1000 years’ long simulation of SAT temperature for three sets of forcings used in the ECHO-g model (see text). (Redrawn from the data by Beltrami et al. (2006).)

the work by Jones et al. (2003), combination of different kinds of climatic information, e.g., the SAT and the free atmosphere temperature records, has permitted the detection of weak signals that otherwise would not be found using some of these datasets alone. It is clear that as much as possible paleoclimatic reconstructions should be compiled for reliable detection/attribution research. The comparison of the measured versus simulated borehole temperature–depth profiles could represent a valuable independent technique for such studies. The above-described investigations by Beltrami et al. (2006) may be completed/accomplished by the quantitative analysis using traditional statistical techniques. 3.4.5 Granger causality to investigate the human influence on climate Until quite recently the investigations of the occurrence of the global warming due to greenhouse effect were restricted to the estimates obtained using Global Circulation Models and to statistical tests of the measured data. The former method implies well-defined theoretical

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Fig. 109. Comparison of measured (TP) and calculated temperature–depth profles (CTRL, FOR1, FOR2; for details see text) for four regions in Canada: (A) British Columbia/Yukon, (B) Manitoba/Saskatchewan, (C) Quebec/Ontario, and (D) Atlantic Canada. (Redrawn from the data by Beltrami et al. (2006).)

constraints on the observed data, while the latter one represents a useful tool to detect changes in global temperatures and to ascertain the variables responsible for these changes. Most of the univariate/multivariate statistical tests used for the latter studies represent regressions that reflect simple correlations. Granger (1969, 1980) (the year 2003 Nobel Prize laureate in Economic Sciences) has argued that exclusively an interpretation of a set of tests is able to reveal reliable information about causality. In the beginning the Granger causality test was applied only in economics. He has analyzed economic time series with common trends (co-integration). For example, it was proved that a significant increase in the petroleum price has preceded almost all of the post-war economic regressions. Recently the Granger causality analysis has acquired great popularity among the researchers from other scientific branches, especially in climatology. A usual question that arises in time series analysis is whether or not one variable can forecast another. One of the ways to address this question is the Granger Causality Test (GCT). The description of this method and computer codes are presented on numerous web sites (e.g., Statistics & Operation Research (SAS), http://www.support.sas.com/rnd/app/examples/ets/granger or in the lmtest library of the R package, www.usit.uio.no/it/unix/store/proglist/R-lmtest.html). Thus, below we present only a principal description of the method. According to Granger (1969), causality can be defined as follows. A variable Y is causal for another variable X if the knowledge of the past variations of Y can assist in prediction of the future state of X over and above the knowledge of the past variations of

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X alone. In other words, if inclusion Y as predictor improves the prediction of X, then Y is said to be Granger causal for X. Granger causality testing applies only to statistically stationary time series. There are many ways to apply the test of the Granger causality. Below we describe the simple approach that uses the autoregressive specification of the bivariate vector autoregression. The procedure looks like as follows. (1) We select an autoregressive lag p to the time series X (x1, x2, %, xT) and Y (y1, y2, %, yT), and estimate by the ordinary least squares technique following restricted p

xt ⫽ ct ⫹ ∑ ⴱi xt⫺i ⫹ et

(48)

i⫽1

and unrestricted equations p

p

i⫽1

i⫽1

xt ⫽ c1 ⫹ ∑ i xt⫺i ⫹ ∑  i yt⫺i ⫹ ut ,

(49)

where (*) denotes the best linear predictor of xt given its past history; analogously (, ) denote the best linear predictors of xt given its past and the past of yt⫺1, and et, ut represent the error terms. Obviously, the restricted equation does not use information of the dataset Y, while unrestricted equation includes information of both datasets X and Y. (2) Granger causality tests a full model against a null model with no possible causality, where the parameters of interest are set to zero. Thus, the null hypothesis of noncausality will be H0: 1 ⫽ 2 ⫽ % ⫽ p ⫽ 0. We conduct an F-test of the null hypothesis by comparison of respective sums of squared residuals from the restricted and the unrestricted models: T

T

t ⫽1

t ⫽1

RSS1 ⫽ ∑ u$ t2 ; RSS0 ⫽ ∑ e$ t2 .

(50)

If the test statistics S1 ⫽

(RSS0 ⫺ RSS1 ) Ⲑ p ⬃ Fp,T ⫺2 p⫺1 RSS1 Ⲑ (T ⫺ 2 p ⫺ 1)

(51)

is greater than the specified critical value, then the null hypothesis that Y is not the Granger cause of X, can be rejected. This procedure should be repeated for multiple values of lag with each p-value being tested independently of others.

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Borehole Climatology: A New Method on How to Reconstruct Climate It should be mentioned that with lagged dependent variables, such as Granger causality regressions, the test is valid only asymptotically. An asymptotically equivalent S1 value is given by S1 ⫽

T (RSS0 ⫺ RSS1 ) ⬃  2 ( p). RSS1

(52)

Another caveat of the GCTs is that they are sensitive to the choice of lag value and to the methods employed in dealing with the possible non-stationarity of the time series. If the Granger causality is applied, e.g., to the daily time series, one may expect that most of the residual variance will be concentrated at short timescales. In other words the Granger causality statistics will be dominated by the effect of daily prediction errors (Mosedale et al., 2006). The non-stationarity is a common problem of all kinds of the time series analysis. If time series are non-stationary, the model can be applied to temporally differenced quantities, xt ⫽ xt ⫺ xt⫺1, rather than to the original data. An effective approach for the testing of causality that avoids the problems arising from ignoring possible non-stationarity and/or co-integration between series has been proposed by Toda and Yamamoto (1995). A certain lack also represents the general practice of the researchers who investigate a simple story, e.g., Y is Granger cause of X, and do not look the other way. In the real examples, more complicated situations can occur such as neither time series Granger causes the other or that each of them causes the other. Since the 90s of the last century, the GCT has been extensively used in climate research. Among other things it was applied in the concept of coupled climatic processes to investigate the ocean feedback on the North Atlantic Oscillation (NAO) (see Mosedale et al., 2006, and the references therein). It was found that the sea surface temperatures (SST) represent a Granger causal for the daily values of the NAO. Kaufman and Stern (1997) have examined the Northern and Southern Hemisphere instrumental temperature records from 1869 to 1994. Using the Granger causality analysis, they have found that there is a statistical relationship in which the Northern Hemisphere temperature depends on temperature in the Southern Hemisphere and have concluded that the detected causality is the result of anthropogenic climate influence. Almost immediately these attempts attracted benign attention and/or obtained trenchant criticism. The effect of this discussion can be found in the works by Triacca (2001, 2005; http://www.isi-eh.usc.es/ trabajos/122_41_fullpaper.pdf) or on numerous web sites such as “Still waiting for Greenhouse” (www.john-daly.com/granger.htm) and the Union of Concerned Scientists (www.ucsusa.org). The reaction was quite different, somewhere little bit trenchant, e.g., that the GCT is a creature of economic models, and should perhaps have remained so. Most of the researchers have laid stress on the necessity of the proper allowance for the non-stationarity of the data and utilizing more wide climatic databases, e.g., data simulated by global circulation models (Triacca, 2001). Despite the criticism that has been directed against the use of GCT, its merits in the time series forecasting are indisputable, and it is extensively employed in climatology. Probably, it was above discussion that stimulated the interest to this kind of analysis and further application of the Granger causality test in different research fields. In the recent years, the Granger causality analysis has been extensively used to test the hypothesis that GHG are

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responsible for at least a part of the observed twentieth century global warming. Sun and Wang (1996) have suggested that global CO2 emission represents the Granger causal for the global surface warming. Tol (1994) and Tol and de Vos (1993, 1998) have tested the hypothesis that the global mean SAT has increased due to an increase in the atmospheric concentrations of GHG. Obtained results were reliable enough to conclude that at least part of the recent warming with high probability can be attributed to the increase of the atmospheric concentration of CO2. Kaufman and Stern (2002) have found that there is statistically significant relationship between the SAT and the changes in the radiative forcing caused by natural variability and human activity. Combined with other similar investigations, the results mentioned above provide further evidence of the statement discussed in the previous sections that observed over the past 100 years warming trend in global mean temperature is unlikely to be entirely natural in origin. Below we present an example of the application of GCT to demonstrate its pervasiveness in the borehole climatology research. Borehole temperature measurements contain direct information on the GST history. The GSTs represent important climatic variable and thus, in principle, do not need the calibration with the independent data. On the other hand, it is the air column temperatures, including the most important SATs taken at the screen height (1.5 m above the ground surface), that are typically of interest in the discussions of climate variability. The SAT responds to the convective heat transfer in an atmospheric boundary layer, while the GST represents continuously integrated ground temperature variations in the vicinity of the borehole that occur mainly by the heat conduction process. In addition, the GST is significantly influenced by the land surface and soil properties (vegetation, snow cover, etc.; for details see Sections 2.5–2.8, Chapter 2). The problem of coupling of the GST and SAT has arisen from the very beginning of the borehole climatology. The vast volume of repeated studies suggests that on a large scale mean annual GST corresponds linearly to the mean annual surface temperature; thus, it seems reasonable to view borehole temperatures as filtered versions of the SAT. This statement can be confirmed by the GCT. For the analysis below we have used reconstructions of the annual global surface temperature (SAT) over the last five centuries (1500–1980), based on the multivariate calibration of high-resolution proxy climate indicators (tree rings, ice cores, corals, historical documents) combined with the long-term instrumental records by Mann et al. (1998) (Figure 39, Chapter 2) and similarly long GST reconstruction based exclusively on the terrestrial borehole data (Mann et al., 2003; Figure 105, Chapter 3; see also discussion in Section 3.3 of this chapter). The goal is to test the hypothesis that above SAT series (dataset Y) represents the Granger cause of the GST (dataset X), in other words, that the GST changes occur due to the changes in the SAT. Both datasets can be found on the web site www.ncdc.noaa.gov/paleo. Calculated under assumption of the lag length p⫽2 years coefficients of the restricted linear equation for GST series are: 1* ⫽1.968⫾0.013, *2,⫽⫺0.968⫾0.013, ct ⫽0.000191⫾0.000075. Similar coefficients for the unrestricted regression equal: 1 ⫽ 1.969 ⫾ 0.013, 2 ⫽⫺0.969⫾0.013, 1 ⫽0.000124⫾0.000087, 2 ⫽⫺0.000232⫾0.000087, and c1 ⫽0.000182⫾ 0.000071. The F-statistic and the significance F were used to test the validity of the regression. Because calculated F-value in both cases is much higher than the significance F (confidence level equals to 95%), we would reject the null hypothesis (H0 : 1* ⫽ *2 ⫽0 and/or 1 ⫽ 2 ⫽ 1 ⫽ 2 ⫽ 0) and conclude that at least one independent variable in both restricted

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and unrestricted equations is correlated to the dependent variable. Calculated sums of squared residuals are 0.0000151 and 0.0000148, respectively, that gives the value S1 ⫽4.794 (Eq. (51)). Because this value is greater than the specified critical value (Fp,T⫺2p⫺1 ⬵2.996, confidence level is 95%), we can reject the null hypothesis that the used SAT series is not a Granger cause of the examined GST. Although simple statistics presented above does not constitute a total proof of the hypothesis about the GST–SAT causality, and further more accurate application of the Granger causality to this problem (e.g., using multiple lag values) is necessary to obtain unambiguous conclusion, even such simple test was robust enough to support strong long-scale GST–SAT coupling. 3.5 Deep Continental Drilling and Signature of Remote Climate Changes As demonstrated in Chapter 2, under favorable conditions all Holocene climate can be evaluated if the precise borehole temperature log is available to the depth of 1–2 km. On the other hand, Beltrami and Burlon (2004) have shown that under the restrictions that are essential to obtain robust spatial averages on hemispheric or global scale, the merging of the individual GST reconstruction results cannot retrieve reliable information on the climate variations at times before 1500. Information about more remote climate changes can be gained only from individual inversions of temperature logs measured in deep holes. Thus, borehole climatology can provide the reconstruction of remote climatic events only from separate locations where such holes were drilled. On the other hand, an importance of availability of temperature measurements from deep boreholes was recognized by geothermal community long ago. Such studies not only are performed for the knowledge of the composition, structure, and evolution of the Earth’s crust but also can serve many economical purposes, like various deep mining and geothermal energy projects or even the next-generation nuclear waste repository. In the recent decades the deep drilling programs have represented an essential part of the geophysical research. A global overview of major completed as well as on-going deep drilling programs can be found, e.g., on the website of the International Continental Scientific Drilling Programme (the ICDP; www.icdp-online.de). The use of the temperature logs measured in deep holes for the reconstruction of the remote climate change began from the very beginning of borehole climatology. 3.5.1 Late Quaternary GST changes inferred from the deep hole measurements In traditional paleoclimatology the reconstruction of remote climatic changes is based on a variety of proxy records. Because climatic variables are only indirectly reflected in these data and their evaluation requires an interpretation of physical, chemical, and/or biologic phenomena, inferred results may contain systematic biases and errors. To compile the most meaningful and complete climatic history, it is necessary to consider the information of many independent records, and the measurements of underground temperatures in deep boreholes performed worldwide at the recent decades represent a valuable source of paleoclimatic information. In contrast to proxy data that are indirect inferences of the climate change, the subsurface temperatures measured in boreholes

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Fig. 110. Left: The reference Pleistocene climate history used as a forcing function for calculation of subsurface temperature–depth profiles. The ice-base temperature adopted as ⫺1.5° corresponding to glacier thickness of 2–3 km; interglacials are generally warmer by 1° than the reference temperature. Right: transient component of the subsurface temperature calculated in response (1) to the last 0.1 Ma GST variations and (2) to the last 10 000 years temperature change.

directly archive the past GSTs. Because the low thermal diffusivity of rocks, the GST changes propagate slowly downwards and remain recorded as transient perturbations to the otherwise steady subsurface temperature field. For typical rock diffusivities of about 10⫺6 m2/s temperature changes that occurred 1000 years ago penetrate to approximately 250 m depth into the Earth, while temperature excursions that date back to 30 000 years (the last glacial period) produce anomalies at 1300–1400 m depth. Thus, 1.5–2 km deep holes may already yield the GST history up to the last glacial period and subsequent variations of the Holocene climate. Because the depth of the ordinary deep drillholes rarely exceeds 1–2 km the most often time period for the remote GST reconstruction is one to several tens of thousand years. Figure 110 shows the synthetic transient T–z profiles resulting from the long-term GST variations. Profile (1) is based on the last 0.1 Ma changes including the last glacial and major postglacial excursions, while curve (2) represents the underground response to the last 10 000 years (the Holocene). The reference climate history (Figure 110, left) includes two Wisconsin10 glacial stages that lasted to 9500 B.P. and prolonged period of warm conditions with its maximum at the Atlantic Optimum when the warming of approximately 6 K according to the glacial conditions was longed for at least 2000 years. Generally warm interglacial conditions were interrupted by provisional returns of cold, of which the main were the Cochrane re-advance between 7250 and 6000 B.P. and the Sub-Atlantic period. While the effect of the post-Wisconsin warming on the simulated T–z profile is quite strong, the signature of the more recent postglacial excursions is comparatively weak. Maximum disturbance reaches only 0.05 K; thus, the possibility for its reconstruction is not so definite for the warming which terminated the last glacial. 10 The final glacial advance of the Pleistocene in North America, 115 000–10 000 B.P., corresponding to the Scandinavian Weichsel and Alpine Würm.

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As mentioned above (Chapter 2, Section 2.4.3), the reconstructed GST history represents a weighted average. In other words, the further we go into the past the less detail can be resolved and the smoother trend of the real temperature conditions on the Earth’s surface can be obtained. A 100-year long event that occurred 300–500 years ago can be resolved with the relative variance of 10–15%. For as early as 2000–3000 years ago, it is only possible to resolve a 500-year interval with the same reliability, and the corresponding duration of event is 1000 years if it occurred 7000–9000 years ago. Because the cold climate of the Wisconsin glacial has spread approximately over the period of 90 000 years, there is a real chance to reveal it in the GST reconstructions. Together with the average postglacial GST variations obtained GST histories will thus yield an estimate of the glacial/interglacial temperature differences. Similar conclusion was made by Safanda and Rajver (2001) on the basis of synthetic calculations corresponding to the Weichselian ice age in Central Europe. Early attempts of GST inversions of the temperature–depth profiles measured in the deep-drilled holes were made in the works by Bodri and Cermak (1997b), Rajver et al. (1998), Safanda and Rajver (2001). Figure 111 illustrates results of one of these reconstructions. Almost 6-km deep borehole Jablunka (JAB-1) was drilled in the eastern Carpathian flysch zone. This area was outside of the last Eurasian ice sheet (Grosswald, 1980). Temperature logging was performed in 1982 to the depth of about 6000 m with a sampling interval of 50 m (Figure 111). The quality of the temperature log permitted to penetrate into the past up to 30 000 B.C. The reconstruction by Bodri and Cermak (1997b; Figure 111) detected climatic trends that are consistent with other interpretations of the past climatic history. Crowley (1983) has collected the evidence on the climate change in the Holocene. His data indicate two main climaxes during the last glaciation (Late Weichsel): the first 23 000–25 000 years ago, and the second about 15 000–20 000 years

Fig. 111. Left: measured temperature log (solid line) and thermal conductivity (squares) for borehole JAB-1. Right: reconstructed GST history for borehole JAB-1 (solid line). Dashed line represents average height changes of the upper tree line in the Alps and other temperate mountain regions over the last 15 000 years. (Data by Lamb (1977).)

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ago. In the GST history reconstructed for borehole JAB-1 (Figure 111) these intervals have been resolved as a single event. The general background situation at the period of maximum cooling of the last glaciation (about 20 000–18 000 B.P.) seems to have been a drop of the annual mean air temperatures in Central Europe by about 8–14 K compared to the present (Frenzel et al., 1992), which is in good accordance with the values of 9–11 K obtained for the JAB-1 GST history. The latter values were calculated as the temperature differences between mean values for ⫺20 B.C., ⫺15 B.C., and 1900–1982 A.D. periods. The last datum corresponds to the time of borehole JAB-1 temperature logging. The Holocene (the last 0.01 Ma) represents an interglacial epoch. Generally warm conditions have prevailed since the last glacial maximum. The glacial period was transformed into the interglacial in two relatively rapid steps of warming. The first step has begun 13 000 years B.P. and the latter occurred between 10 000 and 7000 years B.P. (Figure 2, Chapter 1). Between these periods there was a brief return to near full glacial climate. The resolving power of the SVD inversion method used for the GST reconstruction presented in Figure 111 appeared too low to detect the sudden climatic cooling that was documented between 11 000 and 10 000 years ago. As shown in Figure 111, except for some less pronounced oscillations, the GST history reconstructed for borehole JAB-1 quantitatively reproduces the generalized climate trend derived from the data of height changes of the upper tree line in the Alps and other temperate mountain regions over the last 15 000 years. The later variations are predominantly due to differences of summer temperature and duration of the season for growth (effective temperature control). Similar information on the climate conditions since the Würm Ice Age (the last 45 000 years) that represents the latest central European and/or Alpine glaciation was inferred from temperature log measured in the 2 km deep borehole Ljutomer, Slovenia (Rajver et al., 1998). The GST history was formally reconstructed for the last 90 000 years; however, because of the progressive decrease in the resolution only climatic episodes of the recent 20 000–10 000 years were clearly outlined. Inversions were performed with both the ramp and FSI method for different values of the standard deviations of a priori conductivity model and measured temperature. The trial runs with different standard deviation values were necessary because of the poor knowledge of the thermal conductivity of rocks at the borehole site. The set of various a priori models was chosen to investigate the quality of estimated GST histories and select the more informative variant of the reconstruction. Figure 112 presents results of inversions obtained by Rajver et al. (1998). Because of the differences in a priori assumptions, estimated GST histories appear more or less smoothed preserving, however, general course of the past climate. The reconstruction revealed glacial maximum 13–14 years ago. The certainty of this event was supported by the results of independent reconstruction with simpler ramp approach. The postglacial warming occurred about 2000–3000 years B.P. and was followed by the longterm “wavy” warming trend with superimposed provisional returns of cold conditions. Attempts to reconstruct climate variations for the past 100 000 years were continued in the work by Safanda and Rajver (2001). Authors have used temperature logs measured in the 1.5–2.5 km deep boreholes from the territory of the Czech Republic and Slovenia. Figure 113 shows GST histories estimated by the joint inversion of three Czech and two Slovenian T–z profiles, respectively. As in the previous figure, different GST histories were calculated using three sets of values of a priori standard deviations for the thermal conductivity and the temperature data. Calculated glacial/interglacial temperature

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Fig. 112. GST histories reconstructed for the Ljutomer deep temperature–depth profile, Slovenia (data by Rajver et al., 1998). GST changes were inverted by ramp/step method as well as by FSI inversion with different standard deviations of a priori conductivity model and measured temperature.

variations for both borehole groups do not exceed 8 K. Inversions performed with highest values of a priori standard deviation for the thermal conductivity gave quite smooth GST histories with variations oscillating in the range of 2–3 K. Preferred values of a priori standard deviation for the thermal conductivity and the temperature data are 0.5 W/mK and 0.2 K, respectively. Inversions exhibit very similar times for the occurrence of the glacial maximum between 19 000 and 10 000 B.P. and rapid warming since then. The range of temperature excursions was significantly lower in the last two millennia. These results agree well with information extracted earlier from the German KTB super deep hole (see below) and borehole JAB-1, where inversion of the T–z profile gave approximately 10 K temperature increase from the last glacial to the present. Above reconstructions also coincide with the most detailed climatic history of the last 100 000 years reconstructed by Zoth and Haenel (1988) on the basis of proxy data series. The GST histories described above probably reflect the Holocene climatic trends typical for the region of Central Europe. Vast deep drilling efforts have been performed in the territory of Russia in the recent one to two decades, and deep temperature logs are available from boreholes of the East European Platform, Middle Asia, West Siberia, Kamchatka, and other regions at up to 5–6 km depths. These drillings were mainly of industrial interest due to the fact that the search for hydrocarbons in the recent years has moved into greater depth. Most of the temperature measurements were performed since 1990s in a short time after drilling, thus, in boreholes that probably did not achieve thermal equilibrium. A successful attempt to reconstruct remote GST changes even on the basis of such less precise

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Fig. 113. GST histories obtained by joint inversion of the three temperature logs from the Czech Republic (top) and two temperature logs from Slovenia (bottom). The GST histories were inverted by FSI technique. Curves are marked by the value of a priori standard deviation of the thermal conductivity and the temperature data.

temperature logs was demonstrated in the work by Demezhko and Shchapov (2001). These authors have inferred 80 000-year long GST history from temperature–depth profile measured in 5.4 km deep hole located in the Middle Urals (Russia). Because only about eight months passed between the logging and the date when the drilling was ceased, the authors have applied correction for drilling disturbances to the measured data (see Section 2.1, Chapter 2). Similar to the above-described reconstructions, inferred GST history included the end of the Würm glaciation, rapid warming about 10 000 years ago (beginning of the Holocene), the Holocene Climatic Optimum (6000–4000 B.P.), and the later major climatic oscillations, such as the Medieval Warm Period that culminated near 1000 A.D. and the Little Ice Age between 1500 and 1800 A.D. Estimated amplitude of the postglacial warming appeared somewhat higher than in the above-cited works and reached 12–13 K. Authors ascribe this inconsistency to the effect of the longterm snow thickness variations (Section 2.6.2). New estimations of the amplitude of the postglacial warming have been made for the South Urals from the temperature–depth profiles measured in 4500 m deep borehole Leuza-1 (the Cis-Ural Trough) and in 2000 m deep Ilmenskaya-1 borehole (55.00°N, 60.17°E, 340 m a.s.l.). Estimations of the postglacial warming reached approximately 11 and 8.5 K, respectively (Golovanova and Valiyeva, 2006; www.ig.cas.cz/activities/abstrakty_total1.htm).

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The longest (100 ka) GST histories were inferred from four deep (1600–2900 m) temperature–depth profiles measured in boreholes situated within the area 50.2–55.5°N, 66.6–102°W in Canada (Rolandone et al., 2003). During the last glaciation this area belonged to the Laurentide11 ice sheet. Ice thickness at borehole locations varied from 1 to 3 km at 21 ka B.P. (Peltier, 2002). Inversion of the measured T–z profiles has yielded the temperature history at the base of the ice sheet and the surface temperature course after the glacial retreat. Calculated GST histories are generally consistent and contain information on the minimum temperature around 20–10 ka B.P. Relatively rapid warming since then has culminated at 3–4 ka, and can likely be attributed to the ice retreat. Further climatic events were cooling with minimum at 1700–1800 A.D. and subsequent warming. Obtained results also indicate slight spatial differences in the basal temperature history across the Laurentide ice sheet. Temperature at the base of the glacier depends on the accumulation and flow of ice as well as on the geothermal heat flow from the Earth’s interior (for details see Section 2.9, Chapter 2). During the last glacial maximum basal temperatures were lower at the southeastern edge of the glacier than southwest of the glacier center. Terrestrial heat flow increases in the SE-SW direction from 34 to 51 mW/m2. According to the studies by Rolandone et al. (2003), the more rapid flow of ice at the southeastern edge of the ice sheet combined with the lower heat flow from below has resulted in colder conditions. The minimum temperatures were reached at different times from 8–12 to 20–30 ka B.P. At all sites basal temperatures were above the melting point throughout 100 ka reconstructed period. The GST values during the last glacial minimum range from ⫺1.5 to ⫹0.4°C. This fact could explain unstable nature of the ice sheet that has been derived from proxy sources (Dyke and Prest, 1982; Licciardi et al., 1998). For comparison, present bedrock temperatures measured beneath the central part of the Greenland ice sheet range between ⫺8 and ⫺13°C (Dahl-Jensen et al., 1998). Some attempts of modeling physical processes and glaciological reconstruction of the Laurentide ice sheet (Liccardi et al., 1998; Marshall et al., 2000; see also the references therein) were made. The latter work has emphasized significant inconsistency in the simulated basal temperatures between models that likely occur due to poorly constrained parameters and physical processes. Results of the GST reconstructions can thus provide additional useful constraint for the glaciological reconstruction of the Laurentide ice sheet history. All described long-term GST histories represent the local-scale reconstructions. As mentioned above, Beltrami and Burlon (2004) have shown that under the restrictions that are essential to obtain robust spatial averages on hemispheric or global scale, the merging of the individual GST reconstruction results generally cannot retrieve reliable information on the climate variations at times before 1500. The reason is a well-known decrease in the signal strengths and resolution of the “borehole” method into the past, smoothing of the resulting GST history obtained from the joint inversion or from the merging of individual GST histories calculated using temperature logs with different noise levels. Disturbances caused by remote climatic events can also be removed by the procedure of steady-state thermal gradient correction below 1 km depth. An attempt of the continental-scale reconstruction of the late Quaternary climate changes was made in

11 The Laurentide ice sheet was a massive ice sheet that covered most of Canada and significant part of the northern US between approximately 90 and 18k years B.P.

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the work by Huang et al. (1997). Authors have suggested the procedure of the GST reconstruction that was believed to avoid above restrictions. Their technique is based on the merging heat flow determinations at different depths in different boreholes and thus requires the availability of the heat flow determinations in deep boreholes. The fact is that two-thirds of all terrestrial heat flow measurements have been performed in less than 1 km deep holes. Situation in North America is even more critical. There 87% of the heat flow determinations originate from less than 500 m deep holes. This puts some limitations on the application of suggested method. For their research Huang et al. (1997) have used more than 6000 continental heat flow measurements compiled by the International Heat Flow Commission (IHFC; www.geo.lsa.umich.edu/IHFC). Most of the data are concentrated in the mid-latitudes of North America and Eurasia; thus recovered from these data GST trend mainly characterizes climate excursions that occurred in the Northern Hemisphere. Available heat flow data were arranged according to the depth range over which the heat flow was determined, and then averaged over 50 m intervals (Figure 114). Calculated standard errors generally reflect the regional variability of the data. Observed slight increase of the standard deviation with depth reflects the diminishing of the heat flow determinations in

Fig. 114. Global representative of the heat flow distribution with depth calculated from ⬃6000 heat flow data in 50 m intervals. Shading means ⫾ 1 standard deviation (data by Huang et al., 1997). Dashed line represents steady-state component of the heat flow.

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Fig. 115. The 20 000-year long GST histories reconstructed from the global heat flow distribution. The null hypothesis means that there was no climate change. Other three curves correspond to the progressive weighting of the data that allows more and more significant deviation from the null hypothesis. Arrows indicate the long-term average surface temperature before the reconstructed 20 000-year period. (Redrawn from Huang et al. (1997).)

the deeper holes. The distribution of globally averaged heat flow with depth exhibits weak decreasing trend, however, with sizeable variations that do not show any clear pattern. Authors ascribed the detected heat flow variations to the climate change. To reveal the corresponding global GST history, they converted heat flow–depth data into temperature–depth profile using expression similar to Eq. (5) (Chapter 2). The GST inversion was performed by means of FSI technique. Although temperature variations in this type of reconstruction are expected to be highly smoothed, an analysis of heat flow measurements as a function of depth yielded a broad enough set of climatic excursions over the last 20 000 years (Figure 115). The null hypothesis means the complete absence of the past climatic change. To investigate the likely range of the GST variations, authors have demonstrated three examples of the possible GST histories that progressively enlarge the amplitudes of the inferred GST change by a greater weighting given to the data. The most reliable range for the GST changes is restricted by the curves “A” and “C”. Of course, it is possible to obtain variations smoother than the curve “A”, but they will require stronger forcing of the null hypothesis and less comprehensive inclusion of the information from observations. More pronounced amplitudes than curve “C” are unrealistic. Inferred long-scale climate course (Figure 115) is generally coherent with the climatic history presented in Figure 110 (left). The calculations indicated low long-term temperatures. The powerful recovery from the previous cold conditions began at least 20 000 years ago. The early to mid-Holocene time appears as a relatively long warm period with its maximum around 8000–6000 years B.P. These warm conditions were changed by the strong cooling that culminated around 2000 B.P. and subsequent fingerprints of the Medieval Warm Period, the Little Ice Age, and the recent warming. Although being relatively smoothed, obtained GST history is clearly coherent with abovedescribed local remote GST reconstructions as well as with the broad outlines of the late Quaternary climate changes revealed by proxy sources.

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Recent investigations by Gosnold (2006) have shown that the amplitude of the postglacial warming in the Northern Hemisphere is of the order of 10–15 K. The author has also brought into attention of the borehole climatologists that the amplitudes of warming may significantly differ between both hemispheres. This study was based on a similar technique of the heat flow determinations in different depths at different boreholes as the previous work. The heat flow-depth variations were calculated from more than 1500 deep European boreholes. In North America, the number of deep holes is significantly lower, and similar depth distribution of the heat flow was obtained from 759 determinations of the IHFC Global Heat Flow Database (www.geo.lsa.umich.edu/IHFC). The author has shown that while average heat flow in Southern Hemisphere shields is approximately 61 mW/m2 (this value falls close to the global estimate presented in Figure 114), this value in Northern Hemisphere shields of similar ages is equal to only 37 mW/m2. According to Gosnold (2006), post-glacial warming may be among the main possible explanations of such a discrepancy. Other possible reasons may be of physical and/or chemical nature. For example, there is some evidence that crustal radioactivity is greater at sites of heat flow measurements in the Southern Hemisphere continents and thus could provide greater contribution to the heat flow. Further thorough investigations need to separate different effects and support the conclusion on the differences in the amount of the climate warming after the last glacial epoch in both hemispheres. Recently, Demezhko et al. (2006) have performed an analysis of the spatial pattern of the Pleistocene–Holocene warming in Northern Eurasia based on the 48 GST reconstructions. This study has found that the amplitude of warming has increased from 8 to 23 K in the SE to NW direction from Greenland to the Urals. Maximum amplitudes were confined to the North Atlantic region. Authors have concluded that detected warming pattern may be a result of the formation of the present system of the warm surface currents in the North Atlantic (the Gulf Stream, North Atlantic, and Norwegian currents). The recent study of ocean circulation in the North Atlantic has found a 30% reduction in the warm currents that carry water north from the Gulf Stream. If the ocean current that gives western Europe its relatively soft climate is becoming weak, it is feared that it might shut down entirely and plunge the continent into a mini ice age12 (Marsh et al., 2005). A significant part of the local remote GST reconstructions was obtained from permafrost areas. Numerous investigations have proved that temperature logs measured in boreholes drilled in permafrost represent a very useful tool for the reconstruction of remote climate changes. Because heat transfer within thick permafrost occurs almost exclusively by conduction, permafrost is affected primarily by the long-term temperature changes. Under proper choice of the mathematical description of the freezing/thawing process, an inversion of borehole temperatures from permafrost areas that nowadays occupy about 25% of the land in the Northern Hemisphere can provide reliable GST histories for the past few ten 100 00 associated with the last glaciation. It was shown that at least 100 ka long GST histories can be reconstructed from the 1.5 to 2 km deep holes (see, e.g., Mottaghy and Rath, 2006; and the references therein). An ensemble of results reported from the borehole temperature measurements in vast regions of the Alaskan, Russian Arctic, Scandinavia, and European mountains has revealed ⬃17–18 K

12

http://terramortis.com/news/2006/01/north-atlantic-30-reduction-in-warm.html.

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temperature increase since the last glacial. The GST reconstructions have also proved that an impact of the glaciation and fingerprints of the past permafrost can be detected from borehole temperature logs in many permafrost free regions (for details see Section 2.8, Chapter 2). Similar to the permafrost boreholes, past ice surface temperature reconstructions from the temperature logs measured in the ice boreholes provide valuable estimates of remote temperature changes in polar environments that are complementary to paleoclimatic records obtained from ice core oxygen isotopes. Ice borehole information could spatially complete information provided by land boreholes. Boreholes drilled through Greenland and Antarctica ice-caps provide the most detailed picture of the timing of climate change over the last 700 ka (for details see Section 2.9, Chapter 2).

3.5.2 Climate signature in superdeep boreholes The success in inferring the remote climatic changes from the temperature data measured in deep boreholes has inspired borehole climatologists for further achievements. Superdeep boreholes belonging to the ICDP (www.icdp-online.de) attracted special attention of the “borehole” community dealing with remote climate changes. Scientific deep drilling is a valuable tool for understanding the Earth’s structure and the ongoing processes. Information from the deep holes provides direct insight into the Earth’s interior and can be used for critical testing of the geophysical/geological models. This information also represents an essential component for a responsible management strategy for the Earth’s natural resources and environment. An interest in the superdeep drilling was also enhanced by the recent proposal to use deep holes (>5 km) as the next-generation nuclear waste repositories. The ICDP is a multinational program to further and fund geosciences in the field of the continental scientific drilling. Currently Austria, Canada, China, Czech Republic, Finland, Germany, Iceland, Japan, Mexico, Norway, Poland, South Africa, and the USA are its members through their National Funding Organizations and/or major research institutions. In addition, UNESCO and some international companies are the associated members. From the very beginning, geothermal and paleoclimatic investigations have appeared among the most important directions of the ICDP scientific research. Numerous processes occurring in the continental crust are temperature dependent. Measurements of subsurface temperature distribution and associated quantities (thermal conductivity, heat production, heat flow) are of vital importance to the understanding of these processes. Paleoclimatic directions include the following research fields: (1) the manner in which Earth’s climate has changed in the recent as well as in the remote past and the reasons for these changes, and (2) the subsurface effects of major impacts on climate and mass extinctions. The German KTB continental deep drilling program represents one of the primary and celebrated attempts of climate investigation in the superdeep holes. The drilling site is located at Oberpfalz area in NE Bavaria (Germany). This region is quite suitable for the study of deep-seated crustal processes. The drill site is located at the boundary between two major tectonic units of the Hercynian fold belt in Central Europe: the Saxothuringian and Moldanubian. The region represents a suture zone formed by the closure of an oceanic basin 320 million years ago. This process gave way to a continent–continent collision and the formation of the huge mountain chain comparable to the present extension

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of the Himalayas. Now the high mountain relief is eroded and previously deeply buried rocks are exposed at the surface (Burkhardt et al., 1989). During the KTB project two deep boreholes were drilled: so-called pilot hole (VB, 4 km) and ultra deep main hole (HB, 9.1 km). Both drill holes are located at a distance of only 200 m from each other. It is the unique constellation of two deep boreholes that are very close at one site. Both holes are drilled in the crystalline metamorphic rocks of the Hercynian continental collision zone, where the dominate rock types are paragneisses and metabasites. First studies for the KTB project began in 1978, and official inauguration of the KTB pilot hole occurred in 1987. The project has included collection, compilation, analysis, and interpretion of a high-quality dataset. Geothermal investigations represented significant part of the KTB scientific program. That time this deep-drilling project has produced one of the world’s best collections of the geothermal data and provided a unique opportunity for the study of heat transfer processes in the deep continental crust. Priliminary research on the GST changes on the 0.01 Ma scale using the KTB-hole information has examined the temperature log measured in the 4 km deep pilot hole. The drilling ceased in 1989, and perturbation to the subsurface temperature field caused by drilling had almost entirely dissipated to the moment of the last temperature logging in 1996 (Huenges and Zoth, 1991). Figure 116 shows examples of the temperature logs measured in the KTB-VB hole. Full set of the temperature logs measured in both the KTB-VB and the KTB-HB drillholes can be found on the web site of the ICDP (www.icdp-online.de/sites/ktb). Except for the small-scale temperature oscillations, the most striking feature of the measured T–z profiles is their distinct non-linearity: the curve is concave. Temperature deficit relative to a linear T–z profile is especially pronounced in the depth range 0.5–3.5 km. It was very enticing to attribute observed curvature to the remote climate change, and a number of forward models were simulated to interpret the curvature of the T–z profile from the KTB drilling site in this context (Rybach, 1992; Jobmann and Clauser, 1994; Kohl and Rybach, 1996). Numerical modeling has demonstrated that the subsurface temperature at the VB-hole bears a clear signature of the paleoclimatic temperature change and quantitatively agrees with the reference climatic series of the last 0.1 Ma for Germany reconstructed by Zoth and Haenel (1988) on the basis of the proxy records. Clauser et al. (1997) have inverted temperature log measured in the 4 km deep KTB pilot hole where temperatures probably were close to the original pre-drilling conditions (Figure 116). Even under simplified approach of the half-space with homogeneous thermal properties, the authors obtained reasonable well timing of the post-glacial warming with amplitude of nearly 10 K. It was also shown that concave shape of the KTB-VB could in principle be explained by the paleoclimatic effect alone. Further investigations have revealed several factors contributing to the thermal field. Probably, one of the major findings of the KTB program was the discovery of the presence of free fluids at significant depths. The KTB researchers expected bone-dry deep crystalline rocks. To their surprise, fluid inflow occurred at several depths from open fractures. Numerous experiments/tests were performed to ascertain the properties of the hydrogeological system at the borehole site. Because the thermal regime in the KTB hole is possibly affected by the groundwater flow, several authors (e.g., Kohl and Rybach, 1996; Clauser et al., 1997) have investigated the thermo-hydraulic field near the KTB in 2- and 3-D approaches. All models were based on detailed knowledge of the geological structure in the drilling site and have taken into account vertical contrasts of rocks with

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Fig. 116. Two temperature logs measured in the KTB pilot hole. (Data from the public database www.icdp-online.de/sites/ktb).

significantly different thermal conductivities as well as information on hydraulic properties which was necessary for an interpretation of advective heat transfer process. Forward numerical experiments by Kohl (1998), who used a complex 3-D transient model of the KTB site accounting for advection, topography, lithologic heterogeneities, and paleoclimatic GST variations, confirmed conclusion made by the earlier research that, together with the lithologic effects, the Pleistocene temperature changes induced by the last glaciation represent the most dominant influence on the temperature field in the KTB. The effect of the thermal advection by subsurface fluid movement is traceable but of minor importance. The author has demonstrated that even in the strongly advection-dominated systems that at certain depth ranges can significantly perturb conductive temperature distribution the paleoclimatic signal cannot be completely “washed out”. On the other hand, the estimation of the paleoclimate fingerprints from advectively disturbed environments cannot be performed using pure conductive approach. For certain recovery of the paleoclimate signal the use of improved advection inversion techniques is indispensable (see Section 2.7, Chapter 2). However, in most of the field situations the construction of both realistic forward models and inversion parametrization schemes represents an extremely complex task because of the lack of sufficient field (especially hydrological) information. At present time, geothermal data from advectively disturbed boreholes can be used rather for the investigation of the temperature-dependent processes occurring in the continental crust than for paleoclimate reconstructions. The Kola superdeep project represents similarly well-known deep-drilling effort. The Kola (SG-3) borehole site is located on the northern rim of the Fennoscandian (Baltic) shield near the Norwegian border at about the same latitude as Prudhoe Bay, whose GST

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reconstruction results were described in Section 3.1.2 (Table 7). It was a Russian-funded project to drill deep into the Earth’s crust. As in the case of the KTB, the Kola project planned wide-ranged geophysical/geological studies. The research areas were: (1) the deep structure of the Baltic Shield, the physical and chemical composition of the deep crust, and the hypothetical transition from granite to basalt; (2) lithosphere geophysics; (3) seismic discontinuities; and (4) the thermal regime in the Earth’s crust. The drilling of the main Kola hole began in 1970, and a number of boreholes were made from a central branch. The deepest of them (SG-3) reached its final depth of 12 262 m in 1994. It is currently the deepest borehole in the world and penetrates about a third through the Baltic continental crust. Extensive geophysical studies have been performed at the project site. Geophysical loggings and other measurements in this hole began almost immediately after the drilling was ceased. Undisturbed temperature–depth profile was measured there in 1998 after four years of continuous shut-in time of borehole (Popov et al., 1999). The Kola drill hole exhibits a considerable variation in the vertical component of heat flow density (Kukkonen and Clauser, 1994; Mottaghy et al., 2005). Measurements revealed significant growth of the vertical heat flow across the borehole. It is about 30 mW/m2 in the uppermost 1 km and equals approximately 70 mW/m2 at 4–5 km depth. Observed variation in the vertical component of the heat flow cannot be attributed to the technical disturbances caused by the drilling procedures, but reflects the complex impact of three main natural processes. The SG-3 hole is located at slightly elevated terrain (150–300 m a.s.l.). Similar to the German KTB holes, the presence of free fluids was indicated in the Kola site down to a depth at least of some kilometers (Huenges et al., 1997). Forward 2-D numerical models by Kukkonen and Clauser (1994) were simulated using the vast available data on lithology, hydrogeology, topography, and the thermophysical structure in the area. Modeling results indicated that contrary to the KTB situation that archives significant paleoclimatic information, the main factors affecting the heat flow at Kola site are advective heat transport (especially in upper 2–3 km) and the complicated crustal structure. The area was covered by the Weichselian glaciation. Kukkonen and Clauser (1994) calculated the paleoclimatic influence using the reference late Pleistocene and Holocene climate history as the forcing function. Their simulations have shown that paleoclimate influence appears to be considerably smaller than the advective and structural effects. Paleoclimatic disturbances to the heat flow decrease rapidly with depth from approximately16 mW/m2 to less than 2 mW/m2 at 1.5 km. This conclusion was supported by the recent investigations of 36 shallow boreholes situated in the vicinity of the Kola SG-3 hole (Mottaghy et al., 2005). Except for the temperature logging, detailed studies of thermal conductivity as well as other important geophysical variables (density, specific heat capacity, radioactive heat generation rate, porosity, and permeability) on numerous samples were performed. Obtained data appear to be in good agreement with the corresponding quantities early measured for the superdeep SG-3 borehole. Detected heat flow values fall in the range of 31–45 mW/m2. Moreover almost all boreholes exhibit significant increase of the heat flow with depth similar to that observed early in the SG-3 hole. For simulation and/or interpretation of observed regularities the authors constructed a realistic 3-D numerical model that incorporated wide amount of available data on the crustal structure at the SG-3 surrounding. Results of numerical trial runs have shown that at least in the upper 4 km advection heat

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transport is the main reason for heat flow growth and the crustal heterogeneity is of only secondary importance. On the other hand, the latter study has shown that at the deeper levels at least half of measured temperature disturbances occur due to the paleoclimate influence. The authors have concluded that the advection of heat by groundwater flow and the paleoclimate play significant role in the downward increase of the heat flow. As in the case of the KTB for the reliable reconstruction of the past climate history both effects should be interpreted together. The above-described results of the geothermal investigations in two superdeep holes probably appear somewhat disappointing concerning the possibilities to infer remote climate change. It is clear as to why numerous measurements/investigations did not provide expected results. Borehole sites were chosen for mainly geotectonic reasons. Due to the lack of previous attempts, both the technical experience of drilling to a great depth and the knowledge of the deep crustal conditions were insufficient in the beginning. For example, numerical ingenuities were applied during the Kola drilling experiment. The main innovation was that, instead of turning the drill bit by rotating the stem, in the Kola well the bit alone was turned by the flow of drilling mud. Thus, it became possible to eliminate rotation of the entire drill string above. The researchers expected to find highly compact rocks at the deep crustal levels. On the contrary, deep rocks were strongly fractured and saturated with water. Initially it was planned that the Kola superdeep hole would be 15 000 m deep. However, mainly because of the higher temperatures that reached 180°C instead of expected 100°C, the final depth did not approach even 13 000 m (after 24-year drilling). Further penetration down to 15 000 m would have meant working at approximately 300°C, and the drill bit could no longer work at such conditions. However, the abundance of the potential sites for the superdeep borehole research and the increased experience in the deep hole drilling permanently support the interest in such studies. An international workshop on continental scientific drilling was held at the GeoForschungZentrum, Potsdam, Germany, from March 30 to April 1, 2005. The purposes of the workshop were: (1) to review and summarize the achievements of the last decade of the ICDP, and (2) to define the opportunities for the future drilling projects addressing a broad set of topics in the earth sciences. The potential sites included subduction zones at the Izu Peninsula (Japan) and/or at Crete, the greatest continental collision zone in the Nanga Parbat region of the Himalayas, etc. The “Climate Change and Global Environment” was declared among the most important scientific research priorities in future. Some of the superdeep drilling programs are currently operating. Thus, e.g., since the 1990s the crater Chicxulub on the Yucatan Peninsula, Mexico, represents the area of extended geophysical and geological research (Hildebrand et al., 1991; Steinich and Marin, 1997). It is assumed that this structure resulted from the impact on the Earth of a large (more than 10 km in diameter) asteroid or comet (Dressler et al., 2004). The study of the impact structure with a diameter of 180–200 km and a center at the port of Chicxulub involved drilling of eight cored UNAM (Universidad Nacional Autónoma de México) boreholes inside the crater and in its immediate vicinity (Urrutia-Fucugauchi et al., 1996) with a depth range between near 60 (UNAM 4) and 700 m (UNAM 7) and culminated by drilling the 1.5 km deep borehole Yaxcopoil-1 (YAX-1) of the Chicxulub Scientific Drilling Project that represents a part of the International Continental Deep Drilling Program (Urrutia-Fucugauchi et al., 2004).

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The precise high-resolution temperature logging was repeated nine times in the period March 2002–February 2004 (Wilhelm et al., 2003, 2004; Popov et al., 2004). The long-term GST reconstructions using the temperature–depth profiles measured in the deep-drilled boreholes in Fennoscandia are of special interest. Two-millennia long GST histories reconstructed in Finnish boreholes have been already presented in Section 3.1.1. The study confirmed certain incoherence of the climatic history in Finland with the results obtained for other parts of Europe. The early section of the reconstructed GST histories covers a cold period between approximately 400 and 1000 A.D., followed by a long gradual warming up to 1500–1700 A.D. and a cold period around 1800 A.D. followed by strong subsequent warming. The fifteenth to sixteenth century warming in Finland appears to be different from the Little Ice Age conditions reported, e.g., for central Europe. During the Weichselian period this area was covered by glaciers. The analysis of the depth dependence of the heat flow in the Fennoscandian Shield and the neighboring parts of the East European Platform has shown its systematic variations with depth (Kukkonen and Joeleht, 2003). The authors have attributed these variations to the long-term climate change during the late Pleistocene glaciation and the Holocene. Inversion of the temperature–depth profiles from a suite of boreholes have shown that the lowest temperatures occurred during the last glacial maximum (⬃20 000 years B.P.) and were followed by the average warming of 8.0 ⫾ 4.5 K approximately 10 000 years B.P. Kukkonen et al. (1994) have estimated the 10 000-year long GST history using T–z data measured in approximately 1 km deep borehole in Lavia, SW Finland. Their reconstruction revealed three steps of the long-term GST history in the region: (1) rapid recovery from the previous cold conditions about 9000 years ago that can be attributed to the retreat of the Weichselian ice sheet, when the temperature increased by approximately 4 K, (2) the warm period that continued from 8000 to 5000 years B.P., and (3) approximately 1K further warming that occurred at the beginning of the twentieth century. This GST history coincides well with the climate course after the latest ice age obtained for southern Finland on the basis of the proxy data (Donner, 1974). The Geological Survey of Finland (GTK) is currently running the Outokumpu Deep Drilling project (www.gsf.fi/projects/o_k_deepdrilling). Drilling at Outokumpu (eastern Finland) site began in April 2004 and was successfully completed in January 2005 at the final depth of ⬃2500 m. The site under investigation belongs to the Paleoproterozoic formation that is well known for its polymetallic massive sulfide ore deposits. It is also one of the oldest ophiolitic formations all over the world. The main reason that has motivated this deep drilling project was the investigation of the deep structure of a classical ore province in the stable Precambrian terrain. On the other hand, it is expected that the Outokumpu deep hole will provide a wide range of research possibilities in numerous scientific branches. The Outokumpu hole is expected to be a deep geolaboratory for various in situ experiments. Among other important activities the research program includes carrying out numerous down-hole temperature logging experiments. The main goal of these measurements is inferring the GST history during the Weichselian glaciation and the Holocene from the geothermal data. Except for the possible new paleoclimatic contributions, results of the planned measurements are expected to provide an improved understanding of heat transfer and fluid flow in the crystalline bedrock. It can be believed that geothermal measurements at the Outokumpu deep hole will turn this site into a reference example of the paleoclimate reconstruction and the heat transfer regimes in the Precambrian crystalline crust.

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These two efforts are neither the first nor the last attempts at drilling superdeep boreholes. The potential of the deep/superdeep holes for the borehole climatology is likely not fully revealed. The selection of scientifically useful sites, drilling of superdeep holes, and their investigation are ongoing and may present with unexpected discoveries. The results described above confirm the possibility to extend the GST history back to the last glacial period. Due to their significant depth extension, the deep holes can reveal information about remote climate changes. In spite of somewhat lower resolving power of the geothermal method compared to most of the proxy indicators and its significant reduction on the timescales of 10 000 years and longer, it is evident that obtained during numerous research efforts GST histories contain clear fingerprints of the last Pleistocene glacial/interglacial transition. The latter event possibly represents the most dominant signal archived in the T–z profiles measured at European deep boreholes. The consistency between GST histories obtained from borehole temperature logs by various authors as well as their coherence with existing independent climatologic records gives strong support to the possibility of using the borehole thermometry database to extract information on remote climate changes.

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CHAPTER 4

Subsurface Temperature Monitoring: Present-Day Temperature Change and Its Variability

4.1 Geothermal Observatories and Subsurface Temperature Monitoring In the previous chapters we have described the reconstruction of the past ground surface temperature (GST) changes from the temperature–depth profiles measured in boreholes. Such profiles actually represent an important part of borehole geophysics, the science that records and analyzes measurements of various physical properties in wells or test holes. The geophysical logging system consists of probes, cable and draw works, and power and processing modules as well as data recording units. Modern logging systems are controlled by a computer. Probes (thermometers) that measure temperature–depth distribution are lowered into the borehole to collect continuous or point-by-point data, socalled temperature log, with one pass of the probe (for details see Section 2.1 and Figure 18). These records may be used for various environmental investigations including paleoclimate reconstructions and assist in better understanding of the subsurface conditions. Because of reduction of the resolving power of the “geothermal” method in the past, the GST reconstructions inferred from borehole temperature logs capture only the general course of the climate variations and not the precise variance or periodic signals clearly presented in the time series of meteorological records. Temperature monitoring represents another data collection method. This measurement scheme applies several temperature sensors (thermistors) fixed at various positions along a cable that is then placed into a borehole for long-term recording of temperatures at selected depths. An interval between neighboring measurements may be from minutes to hours. The sampling design depends on the objectives of the research program. Temperature monitoring is frequently accompanied by additional instruments that determine air temperature and other meteorological variables and/or subsurface parameters such as soil moisture, water level change in borehole, etc. This procedure provides finescale and accurate temperature time series over multiyear time intervals. While temperature logs from deeper holes (ⱖ200–300 m) are used for the GST history reconstructions, the monitoring experiments are generally performed in the shallow (⬍100 m depth) holes. 267

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A special kind of underground measurements represents the soil temperature monitoring. Soil temperature monitoring is the recording of the temperature of soil at specific levels just below the surface. For the measurements at or very near the surface, thermometers can be buried directly into the soil. The main processes affecting soil temperature in the upper meters are solar radiation and heat exchange at the surface. They depend on seasons, physical properties of the soil such as soil type, compaction and moisture content, and vegetation cover. Because atmospheric processes are strongly reflected in soil temperatures, soil temperature monitoring can provide valuable data for the climate change analysis, especially for the detection of the recent climate change magnitude and capturing of the high-frequency climate variability. Soil temperature can also be used to give background data for other environmental monitoring programs such as plant phenology,1 soil decay rate, species diversity, invertebrate2 studies, etc. Recent perspective utility of the soil temperature monitoring is the tracking changes in temperature to determine the effectiveness of greenhouse gas emission reduction measures (see below). It should be mentioned that the borehole and/or soil temperature monitoring represents only a part of the general climate monitoring efforts that include an observation, measurement, and analysis of the past and the present states of climate from systematic networks all over the world. Sure conclusions about an extent of the present climate change and the role of the human activities can be achieved only through an understanding the past climate change and its natural variability. To obtain this information, scientists monitor five components of the climate system: atmosphere, oceans hydrology, land surface, and cryosphere. The land temperature monitoring represents an essential part of the net land monitoring. It can be regarded as an indispensable prerequisite of regional and global environmental studies and management activities. Systematic climate monitoring provides valuable data that can assist in developing climate models for prediction of future trends. Tracking changes in temperature can also help to determine the effectiveness of greenhouse gas emission reduction measures. An establishment of the joint air quality and climate monitoring system in the Black Triangle Region represents a typical example of such efforts. The Polish, Czech, and German border area (so-called Black Triangle) has been recognized as the most heavily industrialized and simultaneously the most environmentally degraded region of Europe. It covers an area of 32 400 km2, and has a population of more than 6 million. The region is one of the largest basins of lignite coal in Europe. Significant amount of sulfur dioxide (SO2) is emitted by area power stations, district heating plants, and other industries. This region accounts for about 30% of Europe’s total SO2 emissions. The member states as well as the EU’s actions have been made to reverse the Black Triangle’s air pollution legacy. The latest data provided by the trans-boundary monitoring system have detected that promising results of this efforts are already in evidence (see, e.g., www.energy.rochester.edu/pl/blacktriangle).

1

Phenology is the study of cyclic events of nature – usually the life cycles of plants and animals – in response to seasonal and climatic changes to the environment. 2 Invertebrate is a term for any animal lacking a backbone. The group includes 97% of all animal species.

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Concerning borehole climatology, subsurface temperature monitoring in boreholes is generally performed in three main related and/or complementary investigation directions: (1) Empirical site-specific observations of the GST–SAT coupling at single sites using monitoring of the air/subsurface temperatures and other meteorological variables. A comparison of soil and air temperatures provides a direct test of details of their coupling at shorter timescales (from daily/annual to decadal) and accounts for how air temperature and other meteorological conditions influence the downward propagation of the surface temperature signal. It is expected that continuous monitoring of the ground temperatures and related meteorological variables that is being carried out at numerous locations in the frames of various international programs will significantly extend available climatologic database and improve our present understanding of the GST–SAT linkage. Examples of such monitoring have been described in Section 2.6 of Chapter 2. (2) Monitoring of the ground temperatures at shallow depths where seasonal/annual temperature variations vanish can serve as an alternative useful tool for a direct quantitative assessment of the global warming rate. For the data collected from climate monitoring to be useful, measurements have to be taken at least over a decade or longer. Any gaps in information make it harder to capture trends and changes in climate. Both above-mentioned kinds of research can assist in resolving the differences between the influence of the past climatic effects and the effects of the present-day air–ground temperature coupling and of how this coupling may change through time. Methods for analysis depend on the specific research questions being asked. (3) An investigation of the shallow subsurface temperature time series can significantly advance our knowledge of the temporal and spatial patterns of the recent changes in the climate variability. A special kind of such research represents permafrost monitoring (see, e.g., web site http://gsc.nrcan.gc.ca/permafrost/canpfnetwork; and Section 2.8, Chapter 2). An analysis of the microtemperature time series monitored at depth in boreholes can also be used successfully in other fields of the geophysical research. Thus, they can help to quantify the stochastic heterogeneity of the temperature signal and provide valuable information on the fine scale features of the heat transfer process in different geological environments (see, e.g., Bodri and Cermak, 2005b). However, these applications lie beyond the scope of our book; thus, next sections will be devoted to the above-enumerated three directions of the climatic research using temperature monitoring data. 4.2 Detection of the Present-Day Warming by Temperature Monitoring in Shallow Boreholes As known, temperature changes at the Earth’s surface occur at various temporal scales. The oscillations are more regular on diurnal, seasonal, and annual scales. Interannual and long-term temperature change patterns are generally irregular. As was demonstrated in

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Sections 1.3 (Chapter 1) and 2.1 (Chapter 2), as the surface temperature signal propagates downward, its amplitude decreases exponentially with depth due to the diffusive nature of the heat conduction process. Each variation vanishes over a vertical distance related to the period of change and to the thermal diffusivity of the ground. Shorter period fluctuations attenuate more rapidly. Thus, the Earth selectively filters out high-frequency component of the surface temperature oscillations, and the deeper we go, the more distant past is archived there (unfortunately also more diffused and less credible). As a part of the UNESCO International Geological Correlation “Borehole and Climate” Program of the IGCP 428 project (for details see Section 3.1, Chapter 3), two experimental shallow boreholes were drilled in two different environments to monitor the depth response of the underground temperature field to changes on the ground surface. Both holes were equipped with a measuring chain of temperature sensor elements at a number of selected depths covering the whole 0–40 m interval. It was expected that several years’ temperature records would provide direct evidence of the decade-scale GST warming. This warming that can be related to the present-day global change was already detected in the territory of the Czech Republic by the more traditional inversion of the borehole temperature logs. Figure 14 (Chapter 1) illustrates the amplitude attenuation of the temperature signal when propagating downwards and the delay of its phase by showing the results of the 12-year temperature monitoring at several shallow depths in the experimental borehole Sporilov (Prague, the Czech Republic) (Cermak et al., 2000). The daily temperature wave is practically not observable below 1 m depth. Similarly, annual GST fluctuations vanish near approximately 10–15 m depth and are not measurable below this depth. The temperature from the 20–30 m depth level is free of any response to the annual and/or shorter temperature variations and contains exclusively the fingerprints of the longer scale climatic trends with characteristic time of at least several years. Such signal may characterize well the pattern of the long-term climate change. Figure 117 illustrates the amplitude decrement and phase shift of the annual temperature wave with depth in more details. It shows the 2003-year segment of the long-term temperature time series from Sporilov presented in Figure 14 (Chapter 1). Temperature was monitored at several shallow depths from 2.5 to 38.3 m. As the surface temperature signal propagates downward, it is delayed in time and its amplitude decreases exponentially with depth (see also Figure 15, Chapter 1). Each variation vanishes over a vertical distance related to the period of change and to the thermal diffusivity of the ground. Thus, the amplitude of the annual wave decreases to 50% of its surface value at ⬃2 m depth with time delay of about 40 days. It already decreases to ⬃15% of its surface value at 5 m depth where it arrives with approximately three months’ delay. Higher frequency oscillations vanish more rapidly. Similar attenuation is observable also in statistical characteristics of the records, e.g., in standard deviations of measured temperatures, the parameters of the linear trends, etc. Monitoring results from the depth interval 25–38.3 m contain fingerprints of the long-term linear warming trend only. Figure 118 compares 10 years’ long monitoring time series measured in Sporilov hole at the surface and at 38.3 m depth. The regular almost linear warming trend of 0.029 K/year is clearly visible at 38.3 m depth. It is not difficult to identify trend parameters in the time series where the trend is monotonous (consistently increasing or decreasing). If the time series contains significant variations over observational period,

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Fig. 117. Results of one-year temperature monitoring at several shallow depths in Sporilov hole (Prague, the Czech Republic). Profiles illustrate the amplitude decrement and phase delay of the temperature change versus depth.

the trend identification is more problematic. Because of the strong and irregular oscillations of the surface temperature, detected at 38.3 m depth, warming tendency is practically not visible in the surface data series. Faulty trend estimates can be obtained by simple linear regression procedure. Even the use of more complex techniques (e.g., different kinds of smoothing and data decomposition into significant components, e.g., Grieser et al., 2002) reveals the warming trend with lower reliability. The situation is similar to that described in Section 3.1.2. An inversion of numerous temperature–depth profiles in North America has revealed the presence of unambiguous ground surface warming during the past 100–150 years with the amplitude varying in the order of

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Fig. 118. Ten-year-long (1994–2003) temperature monitoring series in Sporilov hole. Plot shows temperatures that were measured at the surface and at 38.3 m depth. Line superimposed on the surface temperature series represents an estimate of the linear trend.

0.3–4 K, strongly depending on locality. The fact is that this warming was not derived from the SAT records. For example, Karl et al. (1991), after analyzing the meteorological station records for the mid-continent, concluded an absence of statistically significant climatic trends. As shown in Figure 118, the amplitude of the surface temperature variations does not increase with the overall trend. This means that the variance is not correlated with the mean over the segments of the series (see Section 4.3.1). Detected warming trend is illustrated in more detail in Figure 119 that compares temperatures monitored during 1994–2005 in Sporilov hole at 38.3 m depth with annual average warming rates. For the decade, temperature has warmed from 10.63°C in 1994 to 10.89°C in 2003. The monitoring results exhibit closely parallel linear trends for the individual years for the period from 2000 to 2005 and a progressive rise of the warming rate from 0.0296 K/year in 1994 to 0.0402 K/year in 2003. This warming was not one-way story. Warming was stronger in the year 1996 than in the years 1997–1999. The greatest warming rate of the whole 1994–2005 observational period has occurred in the year 2002. Smaller and less significant mean warming rate of only 0.026 K/year reflects more complex course of the temperature increase on decadal scale. Because of attenuation of high frequencies, trends at all depths in the underground have the same or even 2–3% lower relative error than those calculated from the data monitored in the air. In other words, subsurface trends are determined with the same or little bit higher accuracy as the SAT trends. As the subsurface is seeing more remote events, the amounts of the surface and deeper-measured trends and their timing cannot be compared directly. Figure 120 illustrates the penetration of the linear warming trend occurred at the surface to the depth. This process can be described by Eqs. (2.14) and (2.15) (Section 2.3.3, Chapter 2)

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Fig. 119. Top: results of the one-by-one year temperature monitoring at 38.3 m depth in Sporilov hole for the years from 2000 to 2005. Bottom: comparison of the temperature monitoring results at 38.3 m depth with the mean annual warming rates during 1994–2005 period.

with n ⫽ 2. Velocity of penetration depends on the thermophysical properties of the medium and not on the rate of the surface warming. The fingerprint of the surface warming is already measurable at 1 m depth after ⬃20 days from the beginning of the surface warming, after ⬃220 days at 10 m depth, and after 3 and ⬃7 years at 30 and 50 m depth, respectively. In the case of sustained warming, an amount of the linear trend observable, e.g., at 10 m depth achieves 70% of the surface value after 10–14 years from the beginning of warming. At 30 m depth, warming rate will achieve 50% of the surface value after 25–40 years from the beginning of warming. Comparing the trends detected by Sporilov monitoring experiment with the long-term SAT record at meteorological station Prague-Klementinum (Figure 64, Chapter 2), it can be concluded that today’s subsurface likely reflects strong warming trend that began in the area after the relatively cold 1940s (see also Cermak et al., 2000). As was shown by both the GST reconstructions and the analysis of the SAT data in the territory of the Czech Republic, this warming trend is characteristic for the wide territory surrounding Prague (Section 3.1, Figure 82). An independent analysis of the SAT records from 30 Czech meteorological stations (period 1961–1996) has revealed warming trends that fall in the interval from 0 to 0.04 K/year with characteristic regional warming rate of 0.0283 K/year (Cermak et al., 2000). Approximately 60% of the results fall within 0.02–0.03 K/year interval. An analysis of the spatial pattern of this trend has confirmed the conclusion by Bodri and Cermak (1999) that more pronounced recent warming is observed in more populated and generally industrialized areas, while lower values occur in either agricultural or forested areas.

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Fig. 120. Penetration of the linear warming trend into the subsurface. Curves are labeled by the depth level. After 10–14 years the warming at 10 m depth will achieve about 70% of the value of the surface warming (k ⫽ 10⫺6 m2/s).

Because at least a part of the warming observed at Sporilov can be attributed to an anthropogenic contribution to the local climate in a large urban agglomeration (so-called “urban heat island” effect3), similar monitoring experiment was performed in the Kocelovice site, the Czech Republic (49.47°N, 13.84°E, 518 m a.s.l.; Cermak et al., 2000). The locality represents a rural zone. The 40 m deep borehole is situated at the territory of the meteorological station, and monitoring experiment was put in operation in 1998. Thermistor sensors were fixed in depths of 0.02, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5, 7.5, 10, 15, 20, 25, 30, 35, and 40 m. Air temperature was measured at 0.2, 1, and 2 m. In addition to the temperature measurements, the level of underground water, precipitation, snow cover thickness, wind speed and direction, solar radiation, and air moisture were also registered. The rate of registration was once in an hour. Figure 121 shows the year 2003 temperature increase recorded in the Kocelovice borehole. Detected warming rate was 0.0176 K/year in 1999 and thus was only near 70% lower than that

3 An urban heat island effect (UHI) corresponds to significantly warmer urban agglomeration area than its surrounding countryside. The principal reasons for the UHI are the comparatively warm buildings, significantly differing thermophysical properties of the surface materials used in urban areas (like as asphalt; see results of the monitoring experiments described in Section 2.6.2), and the lack of evapotranspiration (Section 2.6.3). The process of the population agglomeration growth is generally accompanied by a corresponding increase in average temperature that in principle can be confused with the warming trend occurring due to global warming phenomenon.

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Fig. 121. One-year temperature monitoring record at 40 m depth (Kocelovice hole).

observed at Sporilov. It had grown to 0.024 K/year in 2003. These rates, lower than those observed in Sporilov station, coincide well with the lower warming trend obtained for the Kocelovice location by the GST reconstructions as well as with SAT trends presented in Figure 82 (Chapter 3). On the other hand, similarly to the Sporilov site, data from the Kocelovice borehole have shown near 40% decadal increase in the warming rate. This hints that the increase of the amount of warming may represent essential feature on the recent climate change in the Czech Republic. Similar to the results of the GST reconstructions mentioned above, monitoring experiments have revealed spatial dependence of observed recent warming rates, when the highest warming has occurred in the industrialized regions. For further examination of this conclusion, the next monitoring experiment was established at the site Potucky, the Czech Republic (50.43°N, 12.78°E, 864 m a.s.l.). The choice was also inspired by the fact that it is this area where noticeable disagreement between warming trends calculated from the GST and SAT data was detected (Figures 82 and 83, Chapter 3). The borehole Potucky is situated in the western portion of the Ore Mts. (German Erzgebirge, Czech Krušné hory) forming the natural border between North Bohemia and Germany. For the long years, the Ore Mts. area has represented one of the most industrialized regions given by the rich mineral resources, especially the lignite coalfields and connected with them the power and chemical industries. Industrial activity was accompanied by extensive discharges of man-made pollutants into the environment. Acid rain resulting from sulfur dioxide emissions has damaged forests. The problem was particularly serious in North Bohemia during the 1980s due to pollution from the large amounts of fossil fuel used by the neighboring industries and brown coal (lignite) burned by power stations in the former East Germany and southern Poland. Only after 1991, emissions were stopped by the collapse of the emitting industries and by legal reductions of emissions from power plants. However, the long-term damages in the forests, caused by the acidification of the soils, are not yet repaired. High rates of the man-made climate warming may be expected in this territory.

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The Potucky boreholes are situated in the highland region that is characterized by extensive forest cover of coniferous woods and mountain meadows with abundant peat bogs. However, natural woods are spoiled very much by emissions from foothill coal basins. Now intensive work on their recovery is being performed. The suite of shallow boreholes was drilled in Potucky site during 2002, and subsurface temperature monitoring at several shallow depths from 2 cm to 50 m began in 2003. Results of the temperature measurements at 40 and 50 m depth in Potucky borehole from October 2003 to June 2004 are shown in Figure 122. Because of the relatively high thermal conductivity of the subsurface strata in Potucky site (3.2 W/(mK)) in comparison with approximately 2 W/(mK) characteristic for the Sporilov and Kocelovice stations, an annual temperature wave penetrates deeper into the subsurface there. This is the reason that the warming trends detected in Potucky hole at both 40 and 50 m depths do not appear as linear as in Sporilov hole and can be inferred with little bit less significance. As shown in Figure 122, the warming rate calculated for the temperatures measured at 50 m depth is quite high and equals to ⬃0.04 K/year. It is still higher at the 40 m depth where detected warming trend is approximately four times larger than that observed at the same depth at forested and less industrialized southwestern slope of the Bohemian Massif (Kocelovice hole) and approximately two times larger than that measured during the same period in the industrial region of Prague (Sporilov hole). Because the characteristic time of the penetration of the surface temperature signal into the subsurface is inversely proportional to the thermal conductivity of the medium, the warming trend detected in Potucky hole likely reflects more recent events. While the Sporilov and the

Fig. 122. Temperature monitoring at the depth of 40 and 50 m (borehole Potucky, the Ore Mts., Czech Republic) from October 2003 to June 2004. Solid lines represent the estimated linear trends.

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Kocelovice trends can be attributed to the strong warming that began in the area after the relatively cold 1940s, trends detected in Potucky site can be connected with climatic changes of the 1970s and the 1980s. An interpretation of the enormous warming trend detected at Potucky site does not represent an easy task because of the still short observational period but mainly because of the complexity of the processes involved in the local climate changes. The high value of detected warming hints that at least part of it may reflect an influence of the human environmental pollution. However, detailed investigation of all possible forcings reveals the possibility of more complex interdependences. The warming trend observed at Potucky hole can be compared with the long-term SAT measurements at the nearby meteorological station at Fichtelberg, Germany (Figure 123). The Fichtelberg is one of the highest mountains in the German part of the Ore Mts. (50.43°N, 12.90°E, 1213 m a.s.l.). This region is characterized by harsh, cloudy weather with significant precipitation including both wet winters and summers. Long-time annual mean temperature on the Fichtelberg is only 3.2°C. The SAT record exists here from 1891. The local climate has experienced insignificant warming with the rate of 0.0077 K/year for the total observational period from 1891 to 2003. Time interval between 1950 and 1980 was relatively cold. Strong sudden temperature rise with more than 0.05 K/year rate began here since the 1980s. The main temperature increase occurred between 1987 and 1991. It was detected over the whole Central Europe and is known as the “Climate Jump II” (in comparison with the “Climate Jump I” that occurred between approximately 1920 and 1935; see, e.g., Figure 64, Chapter 2). During “Climate Jump II” the GST in Central Europe has

Fig. 123. Mean annual SAT recorded at meteorological station Fichtelberg (Germany) during 1891–2004 and its 10-year running average. Solid lines represent the mean warming rates for the whole observation period and for years 1980–2004.

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increased by approximately 1.2–2 K above the average level for 1950–1985 (Borchert, 2005). This temperature course is quantitatively similar to the global temperature change. However, while the twentieth century global warming rate coincides well with the one observed at the Fichtelberg weather station, the rate of the recent warming in the latter location is approximately four times higher (Figure 4, Chapter 1). The supposed cause of the global warming is the combined effect of the anthropogenic activity and the natural forcing (see Section 3.4, Chapter 3), and it is very enticing to represent an increased warming trend in the study area as man-made, caused by extensive industrial activity. On the other hand, investigations by Borchert (2005, 2006) have revealed significant correlation between recent climate warming and air pollution characteristics with increasing Sun activity, which were observed in Central Europe, represented by increasing sunspot number and flare intensities as well as by decreasing cosmic radiation4 (neutron rates) resulting in reduced cloudiness and corresponding increase of intensity and duration of sunshine. On the basis of the detected correlations, the author has proposed extraterrestrial and not anthropogenic causes for the recent temperature increase in Central Europe, when the transportation and concentration of air pollution may also be strongly affected by external effects. Further studies (including prolonged temperature monitoring) can make causal connections of the recent warming clearer. The success of the monitoring experiments performed in the Czech Republic has inspired an establishment of the joint international monitoring project in the Czech Republic, Slovenia, and Portugal (Safanda et al., 2006). For all the three experiments, 100–200 m deep holes were chosen. Because a thorough thermal equilibrium is an essential requirement to obtain an undisturbed temperature time series suitable for the climate study, only old boreholes that have already achieved thermal equilibrium were used for monitoring experiments. Repeatedly measured temperature–depth profiles revealed fingerprints of an appreciable warming in the uppermost parts of all holes. Temperature monitoring began in the years 2002 (the Czech Republic), 2003 (Slovenia), and 2005 (Portugal) in several depths from 2 cm to 40 m. The air temperatures at 2 m and 5 cm above the ground surface were also measured. Preliminary comparative results are expected to be available in the end of the year 2006. All above-described monitoring experiments have proved that the present-day warming corresponding to the last one to several decades can be reasonably well extracted by precise temperature monitoring at shallow boreholes below the depth of penetration of the seasonal variations. Of course, the detection of the linear trends in the monitoring data sets and their interpretation represent only prelude to the precise analysis of this data. The geothermal inverse theory (e.g., ramp/step method, see Eqs. (2.14) and (2.15), Section 2.3.3) can be used to quantify more precisely the amount and the onset time of the warming trend. Anyhow, even preliminary studies have confirmed the applicability of this kind of temperature measurements for the GST reconstruction. The temperature monitoring in shallow boreholes of 30–50 m depths may be an alternative to the temperature log inversion, routine method of detecting local recent climate changes 4

Cosmic radiation (cosmic rays) is a naturally occurring ionizing radiation coming outside the Earth and filtering through atmosphere. A significant amount of these high-energy particles is discharged by the Sun. Scientists have argued that cosmic radiation can cause the changes in weather, e.g., can cause clouds to form in the upper atmosphere. The cosmic radiation shows an inverse relationship with the sunspot cycle. The reason is that the Sun’s magnetic field is stronger during sunspot maximum and shields the Earth from cosmic rays.

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and for the direct assessment of the present warming rate. Evidence obtained in the shallow subsurface by precise temperature monitoring can also satisfactorily complement meteorological observations. Borehole temperature monitoring in the recent decade became one of the building blocks of the borehole research to help us to understand how the Earth’s climate is changing. A heap of the monitoring experiments is performed all over the world. Except for the detection of the recent warming trends, numerous subsurface monitoring experiments were established for specific climatic applications. Of special interest are reports on the data from so far uncovered areas and attempts to separate the potential man-made components of the global warming from the natural climate variability. Below, we mention some of them. 4.2.1 Emigrant Pass Observatory, Utah Over a decade-long ground temperature monitoring has been performed at the Emigrant Pass Observatory (EPO), Utah (Bartlett et al., 2004, 2006; Davis et al., 2006). To better understand the GST–SAT coupling and to document the details of the penetration of the surface signal into the ground, a climate and ground temperature observatory was installed in arid NW Utah in 1994. The EPO (41.50°N, 113.68°W, 1750 m a.s.l.) consists of a standard weather station situated on exposed granitic rock at the top of a 150 m deep borehole (GC-1) drilled in 1978. Results of its repeated temperature logging are presented in Figure 16 (Chapter 1). Inversion of the measured T–z profiles inferred surface temperature changes that are closely coherent with those observed at the nearby meteorological station 40 km to the northeast (Chisholm and Chapman, 1992). The EPO consists of an array of thermistor strings in the subsurface. Ground temperatures are monitored at several shallow depths from 2.5 cm to 1 m. Meteorological and shallow ground variables are recorded simultaneously. All data from the EPO since November 2004 are available and can be found on the web site http://thermal.gg.utah.edu/facilities/ epo/EPO_data. The file is automatically updated daily. The combined database gives an opportunity to observe the GST–SAT dependence in near real time and to test theoretical models of the GST–SAT interactions. It can also be used for the investigation of the energy balance in the Earth’s surface, reconstruction of the climate change from borehole temperatures, and other geothermal studies. Over decade-long continuous temperature monitoring has shown that the subsurface temperatures at all monitored depths are in general agreement with the air temperature (e.g., correlation coefficients are 0.97 and 0.87 for air-10 cm and air-1 m depth temperatures, respectively). Except for the surface air temperature that explains 94% of the GST variance, the GST variations are influenced by incident solar radiation that accounts for 1.3% of the GST variance and by snow cover. Daily averaged GST–SAT differences range between ⫹14 and ⫺10 K. Observed differences occur due to the solar radiation effect in the summer and the insulating effect of snow cover in the winter. They are much lower on the annual scale and vary between only 2.3 and 2.5 K. In this scale of aggregation, ground temperatures are generally warmer than air temperatures. Much of the interannual variations in the GST–SAT difference occur due to the changes in solar radiation. It was shown that incident solar radiation is more important during the summer. On the long scale there is a linear relationship between the GST and SAT difference and solar

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radiation with a slope of 1.21 K/100 W/m and intercept of 2.47 K (Bartlett et al., 2006; Davis et al., 2006). Because of its low thermal diffusivity, snow attenuates surface temperature variations in the winter, but its insulating effect has only minor influence on the annual GST–SAT coupling at the EPO site (accounts for only 0.5% of the annual GST variance). Using EPO monitoring results, Bartlett et al. (2004) have developed twolayered forward numerical model of snow–ground interactions. Model is based on three characteristics of snow cover: (1) the onset time, (2) duration of the snow cover, and (3) its thickness. These parameters are generally available from meteorological and remotely sensed data, and the authors have validated their model using the National Weather Service data from 23 sites over North America. Their calculations have verified the applicability of the developed model for the broad spectrum of snow conditions and have confirmed its suitability for the prediction of the GST changes in different environments. On the whole, the EPO observations have shown that the GST really tracks the air temperature on the timescales relevant to the climate change studies. The GST reconstructions generally assume that the GST–SAT difference is constant over long timescales and thus the transient temperature changes at the ground surface reproduce the transient SAT changes measured at weather stations. The EPO monitoring results have warranted this assumption over the past decade and thus have given a serious experimental support for the use of the GST histories as a valuable addition to the SAT measurements and multiproxy reconstructions in climate change research. 4.2.2 The Sornfelli borehole In numerous regions, e.g., high elevation sites, islands, flat northern environments, etc., the surface air temperature may represent a complex result of an interaction of some climatic variables. The detection of the real warming trends in such areas and their separation from an impact of the short-term changes of other climatic variables may be quite difficult. Climate monitoring in such locations can help to filter out disturbing effects and identify long-term climatic trends. One of such monitoring efforts is being carried out at the Faroe Islands (Denmark). It is a small group of islands that is situated in the stormiest part of the North Atlantic, midway between Scotland and Iceland. The Faroe Islands are located in a key region for understanding land–atmosphere–ocean interaction in the North Atlantic region. Under the influence of the warm ocean current of the Gulf Stream, the climate is relatively mild for the latitude. On the other hand, because these islands lie in the path of the majority of Atlantic depressions, they are cloudy (daily sunshine in the summer months averages only about 4 h), wet (annual precipitations ranges between 1500 and 2500 mm), and windy throughout the year. The air surface temperatures in the region strongly depend on the wind speed and direction as well as on the cloud and snow cover (Cappelen and Laursen 1998; Humlum and Christiansen, 1998). The Climate Research Station was situated at the summit of Sornfelli mountain (799 m a.s.l.) on the main island Streymoy, and the monitoring was started in November 1999. It is expected that this experiment will provide meteorological data on the arctic climate environment on the Faroes that can then be placed in a wider North Atlantic as well as the Northern hemisphere perspective. Traditionally, meteorological stations in the area are located near sea level, which makes studies of the vertical climate change effects problematic. Data from Sornfelli borehole can allow the calculations of climatic altitudinal gradients, which

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can then be used for additional studies such as the interpretation of the vegetational zonation, soil development and present periglacial processes, and their relation to the past climatic conditions. Because of the cold, wet, and windy climate, as well as expected heavy icing problems, the measurements with standard meteorological equipment are difficult on Sornfelli, if not impossible. Thus, a special meteorological station was constructed, using a drum-shaped housing and internal heating. For the subsurface temperature monitoring, an 11.32 m deep borehole was drilled and thermocouples were installed. The station was redesigned in the spring of 2004, and new instruments and data logging equipment were installed in June 2004. Meteorological data are logged each 30 min. Similar to the EPO data, the Sornfelli monitoring results are regularly published in the web site www.metsupport.dk/data/sornfelli. On-line borehole data are updated every hour except at night local time. 4.2.3 Kamchatka Kamchatka is a peninsula in the Russian Far East comparable in size to Japan and surrounded by the Pacific Ocean and the Bering and Okhotskoe Seas. Human intervention to the atmospheric temperature and environment is expected to be small in Kamchatka because of very sparse population and small industrial activities. The recent climatic trends detected there probably reflect natural climate variability characteristic for the Northern Pacific region and not an anthropogenic influence. As a part of the 3-year joint Japanese–Czech–Russian research project “Reconstruction of the climatic changes from borehole temperature profiles and tree rings in the Kamchatka Peninsula” (2000–2002), precise temperature measurements were performed in a number of holes ( Yamano et al., 2002). This project primarily concentrated on obtaining precise temperature–depth profiles in a number of boreholes, drilled more than 15 years ago, and on verification of the previous measurements in the region. Temperature logs were repeatedly measured in 12 boreholes during 2000–2002 at intervals of a few months to one year (for details see Section 3.1.2, Chapter 3). Data were used to propose a climate model of the last 100–150 years (Figure 97, Chapter 3). All temperature logs have shown a general turn to the warmer conditions since approximately 1950. The most detailed GST history was inferred for a suite of boreholes at Malki location (53.33°N, 153.47°E). Climatic history shows warm period with the maximum near 1850, cold conditions culminating between 1920 and 1950 and pronounced warming of 1.2–1.6 K since then. Obtained results are in good agreement with the existing SAT series. Jones et al. (1999) have presented global patterns of the surface temperature change over the past 150 years’ combined land and marine data on the 5° ⫻ 5° grid box basis. Figure 98 (Chapter 3) shows one box of this database, namely, an estimate of the SAT changes for southern part of the Kamchatka Peninsula. The temperature anomaly time series, available back to the beginning of the twentieth century, exhibits high interannual temperature variation that somewhat attenuated between 1960 and 1990. Warming trend of 0.007 K/year calculated for the interval 1890–1998 is insignificant. The slow temperature rise with warming rate of 0.026 K/year occurred after approximately 1960–1963. It was followed by the marked period of warmth during the last 10–15 years of the record. Similar warming trends were obtained for the Pacific Ocean at latitudes 40–60°N and in eastern Siberia (Rogers and Mosley-Thompson, 1995).

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It was this warming that Budyko (1977) and other climatologists have interpreted as the start of a new large-scale climate warming. Except of the numerous temperature loggings, a high-resolution temperature monitoring of 1 mK accuracy was performed at several selected depth levels in four boreholes (Yamano et al., 2002; see also Bodri and Cermak, 2005b and the references therein). Temperatures in two unstable wells in Elizovo (E-1) and Yugozapadnaya (UZ) sites were monitored at 325 and 108 m depth, respectively. Such high depths were chosen because the scatter of data during repeated borehole logging has exceeded any explainable differences due to instrumental incorrectness and/or field of technical problems. Data loggers for temperature monitoring were installed at the depths of maximum temperature gradient. Temperatures have shown high degree of irregularity over all measured periods within up to several hundreds of degree, but because of relatively large monitoring depths they did not exhibit any significant linear trend that could be attributed to the recent warming. However, monitoring was not performed in vain, because an analysis of these deep microtemperature time series has helped to quantify the stochastic heterogeneity of the borehole temperature signal and provided valuable information on the fine-scale features of the heat transfer process in different geological environments (see, e.g., Bodri and Cermak, 2005b). Temperature loggers were also installed at four shallower depths 25, 30, 35, and 40 m in two boreholes with more stable temperature–depth profiles (Malki-2 and Malki-19) for 10–11 months. Similar to monitoring results described above short-term temperature variations observable in the upper 10–20 m depth interval of Malki holes significantly decayed in comparison with the surface temperature variations. On the other hand, recorded temperature time series have not shown any significant linear trend. Temperature has remained almost constant (within 2 m K). The reason is that the most recent warming that began 10–15 years ago still not penetrated to 25–40 m depth and insignificant warming trend characterized for the most of the twentieth century appeared too weak to be archived in the subsurface. For the same period, temperature was monitored at 50 and 100 cm below the ground surface in the close vicinity of Malki-12 and Malki-19 boreholes with lower accuracy of only 0.1 K. Analysis of these time series have shown that heat transfer in the uppermost ground is pure conductive at Malki-12 location, while small non-conductive component was detected at Malki-19 hole during February to May 2002. This non-conductive disturbance can be related to the freezing/thawing of the soil around 50 cm depth. 4.2.4 Livingston Island, Antarctic At the polar and sub-polar environments there are large areas subjected to high energy transfer in the ground surface. To investigate the surface energy balance in such regions for prognostic research of climate change two shallow boreholes (1.1 and 2.4 m) were drilled in the year 2000 in Livingston Island (South Shetlands, Antarctica; 62.65°S, 60.35°W). Temperature monitoring was performed in four depths during 2000 and 2001 (Ramos and Vieira, 2003; www.igme.es/internet/cnda). Temperature data were collected at 4-h intervals in several shallow depths. Because of quartzite bedrock setting, temperature time series were characterized by an absence of any trace of the phase change processes. Measurements have shown that the subsurface temperature regime is almost exclusively controlled by air temperature, which conductively penetrates to the depth

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with usual loss of amplitude and phase delay. The monitoring results at the Livingston Island’s boreholes bottom have shown only seasonal variations. Future international project plans drilling and monitoring of two new 20 m deep boreholes in Livingston and Deception Islands. Although shallow measurements described above can provide information about the surface ground temperature history only for the short time intervals comparable with the length of the time series, they are useful complements to the longer scale but less wellresolved GST histories inferred from the temperature logs measured in deeper holes. Current temperature monitoring experiments are performed in the single borehole sites. Once trends are detected and local characteristics and causes are identified, these results can be integrated into wider spatial scale network. These results can also be incorporated into other research fields or ecological issues, e.g., the environmental management. 4.3 Recent Climate Variability 4.3.1 Climate change and climate variability For a better understanding of the nature of the climate change, attention is to be focused not only on the evolution of mean climate characteristics, but also on the changes in climate variability, and on climate extremes. The necessity of including the variability characteristics in the climate change studies has been demonstrated in several works (Katz and Brown, 1992; Wilks and Riha, 1996; Rebetez, 1996, 2001; Bodri, 2004; and the references therein). It can be demonstrated that the frequency of climatic extremes is more sensitive to the changes in variability rather than to the mean climate state (Katz and Brown, 1992). Increase or decrease in the frequency of extremes can be enormously large even for relatively small mean changes in climate (Katz, 1999). Rebetez (1996) has shown that climate variability is one of the most important characteristics in the human perception of climate. The potential response of the socio-economic fabrics of the global community to the changes in climate variability may be stronger than to the changes in climatic averages (Rebetez, 1996; Wilks and Riha, 1996), while these changes are completely obscured when examining only the evolution of mean characteristics. In the everyday life, climate change and climate variability are often confused. In its exact mean climate (and any other real-valued random variable) variability refers to the spread of a data set. An assessing of variability generally includes two key components: (1) how spread out are the data values near the center, and (2) how spread out are its tails. The common definitions of the central value that best describes data are their mean, median, and mode. The common numerical measures of the spread are variance, standard deviation, range, average absolute deviation, etc. The changes that are greater than 4 standard deviations are generally referred as extreme events. When assessing variability, variations in the central (typical) state and the spread statistics of the climate should be detected on all temporal and spatial scales beyond that of individual weather events. An analysis of the climate and its variability from observed data is especially challenging in the case of a changing climate. An interaction between mean characteristics of climate and its variability and extremes depends on the statistical distribution of given climatic variable (Meehl et al., 2000). Possible influence of the changes in the mean and variability on climate is illustrated in Figure 124. The climatic temperatures

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Fig. 124. Effect of the change in the mean and in the variance for the standard normal distribution of temperature. “Previous climate” curve corresponds to mean⫽0 and variance⫽1. “New climate” is calculated for the next cases: (A) mean temperature increases (mean⫽1), (B) variance of temperature increases (variance⫽2), and (C) both characteristics increase (mean⫽1, variance⫽2).

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often have a normal distribution (“the bell curve”). The non-stationarity of this distribution implies changes in the mean temperature and/or its variance. Increase in the mean temperature gives more warm conditions and less cold weather (Figure 124, panel A). However, it does not produce any change in climate variability. In other words, the range between the warmest and coldest temperatures does not change. The change in extremes occurs only due to a shift in the distribution without a change in its shape. This means that for the real situations prediction of changes in extremes there is no need of additional study of variability. They could be predicted simply from the changes in longer term monthly, seasonal, or annual means that are generally available (e.g., global gridded data by Jones et al., 1999). On the contrary, an increase in variability without change in average temperature (panel B) produces the change in the shape of the probability distribution resulting in the same increase in the probability of both warm and cold extremes as well as increase in the absolute value of these extremes. Prediction of changes in extremes needs detection of changes in meteorological variables (e.g., indices of extremes) such that determination from station data is not a trivial task. Panels A and B illustrate that the global warming is not equivalent to climate change, and significant climate change can occur without any global warming or cooling. Increase in both characteristics of temperature distribution (panel C) results in an asymmetric increase of the probability of extremes producing more frequent warm events with more extreme hot temperatures. Its influence on cold extremes is far less pronounced. Figure 124 illustrates typical case of the global warming. Obviously, other combinations of changes in the mean temperature and its variability would lead to different patterns of the probability of cold and warm events occurrence. For the climatic variables that, like precipitation, are not well approximated by the normal distribution situation may be far more complex. Consequently, even when changes in temperature extremes were detected, their attribution to the changes in the mean or variance (or both) needs specific analysis and/or some kind of “key test” that may provide an idea on the degree of confidence associated with obtained conclusions. The fact is that Earth’s climate is always changing. It varies on a broad range of timescales and over many orders of magnitude. Climate oscillates on the millennial timescales between ice ages and interglacials causing global scale rearrangements of ice cover and ocean circulation. The shorter scales of its variation embrace periods from centuries like the Medieval Warm Period and the Little Ice Age to decades, as indicated by the temperature changes in the twentieth century. Generally the shorter the timescale, the stronger is the impact expected on a local spatial scale and the longer the timescale, the more is impact on the global scale and resulting socio-economic consequences. For example, long-term climate variations may alter agricultural productivity, land and marine ecosystems together with the resources that supply these ecosystems, while seasonal to interannual climate variations can strongly affect agriculture, the abundance of water resources as well as the demand of energy. On the other hand, different-scale climate variability modes cannot be treated separately. Results of the recent investigations increasingly support that short- and long-term climate variability are intrinsically linked. The climate system is quite complex and highly non-linear. Expected modes of its variability are also complex. Variability may be due to natural internal processes within the climate system (internal variability) and/or variations in natural or anthropogenic external forcing (external variability). An overview of the natural climate variability and its causal

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mechanisms was presented in the pioneering work by Mitchell (1976). It was partly this work that inspired the U.S. National Geophysical Data Center (NGDC) to design an interactive web site “Climate Timeline Tool: What is Variability?” that helps to assess the basic processes and causes of climate variability (www.ngdc.noaa.gov/paleo/ctl/about1.html). This graphic demonstrates also the interactions of variability over varying timescales. Significant climate variations are occurring within the diurnal scale to the 100 000 timescale corresponding to orbital forcing. All these variations have occurred before any anthropogenic influence on the climate system could be in operation. The natural variability of the climatic system itself is quite high. Evidence for such intrinsic variability has been found in observations and coupled general circulation models (Delworth and Mann, 2000). The past three to five decades have seen an increasing recognition that human activities may have substantial effect on the climate system. Recent climate variability may be intensified by the human influence. Because this is one of the great, still unresolved problems of climate science, changes in climate variability and in both weather and climate extremes have received increased interest in the recent decades. Numerous research programs, such as the international program “Climate Variability and Predictability” (CLIVAR; www.clivar.org), the Climate Variability and Trends Group of the NOAA (U.S. National Oceanic and Atmospheric Administration) Air Resources Laboratory (www.arl.noaa.gov/ss/climate), Climate Variability Working Group (CVWG; www.ccsm.ucar.edu/working_groups/Variability/index.html) together with the Intergovermental Panel of Climate Change (IPCC; www.ipcc.ch), were put on operation. The general objective of these efforts is to describe and understand the physical processes responsible for climate variability and predictability on various scales through the collection and analysis of observations and the development and application of models of the climate system. This goal can be achieved in wide cooperation with other relevant climate research and observing programs. Questions that could be addressed with the focused study of borehole temperature monitoring data include: 1. How does climate variability varied? 2. Are these changes consistent in the key regions? 3. In cases when reported variability changes appear to be contradictory, it should be examined where detected differences represent real regional variability or simply reflect the differences in quality of data and/or detection methods used. The answers to these questions will come from the development of the monitoring network and the acquisition data having sufficient length and resolution to provide a base for variability studies. Results from intensive local investigations should be combined for the studies of regional variability change patterns. Future valuable outcome of such efforts may be monitoring time series database similar to already existing “Borehole Temperatures and Climate Reconstruction Database” initiated by the Geothermal Laboratory of the University of Michigan (www.geo.lsa.umich.edu/⬃climate). A systematic review and evaluation of existing data can produce a coherent and internally robust data that will serve as a base for the variability studies, revealing potential forcing mechanisms and modeling of not only a warmer but also more variable future world.

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4.3.2 Trends in the recent climate variability The Earth’s climate system consists of a number of subsystems (atmosphere, hydrosphere, lithosphere, cryosphere, and biosphere) with their own characteristic times of operation from days to millennia. Each subsystem has its own internal variability mode, when some of its parameters change intensively over narrow range of timescales, while others remain constant over fairly long time. These ranges may overlap between subsystems. Due to these complex interactions climate varies on all timescales. For simplicity the vast range of the global variability is studied across the hierarchy of frequency domains with different scales of aggregation (such as intra- and interannual, and interdecadal to multimillennial). These studies have revealed specific features of the variability within distinct frequency bands, e.g., day-by-day versus interannual temperature variability. As about temperature variability, there is a vast amount of research works using surface meteorological observations, upper-air temperatures estimated from radiosondes, satellite-inferred tropospheric temperature trends, and other variables to detect its variability trends. Because we would like to connect these efforts with the possibility of the variability detection from the ground temperature monitoring (multiyear time series), further we will describe only results concerning the high-frequency variability detected from the SAT data that can serve as a background for a comparison with the results obtained from the subsurface temperature monitoring data. Considerable insight into empirical climate variability changes over the last century was obtained from the details of the patterns of annual and seasonal surface temperature variations. Recent studies have detected not only the global scale warmth but also changes in the SAT variability. Most recent efforts significantly advanced our knowledge of the temporal and spatial patterns of climate variability. Results of investigations of the local and spatial patterns of the high-frequency climate variability were presented in numerous works (Karl et al., 1993, 1995, 1999; Balling, 1995; Liang et al., 1995; Kelly and Jones, 1999; Moberg et al., 2000; Grieser et al., 2002; Bodri and Cermak, 2003; Bodri, 2004; Braganza et al., 2004; Seidel and Lanzante, 2004). Most of the authors have used only the twentieth century data. This has helped to avoid bias due to progressively increasing number of measurements during the whole observational period. Given the number of techniques for variability detection in different works, results of the earlier studies have shown significant scatter. Thus, Parker et al. (1994) have compared interannual variability for the global data of seasonally accumulated surface air temperatures for two periods 1954–1973 and 1974–1993 and found small global increase of SAT variability. Especially noticeable increase was obtained for central North America. Jones et al. (1999) have worked with global data and have not detected any change in variability. Investigations by Grieser et al. (2002) based on the monthly averaged European temperatures have shown that at least in this region of the world the SAT variance has mainly decreased or remained constant during the last 100 years. Michaels et al. (1998) have examined monthly averaged SAT data for the 5° ⫻ 5° grid boxes around the world and have detected decrease in the intra-annual variability that prevailed over the past 50–100 years. The authors also have found general decrease in monthly temperature variability for the United States, some regions of the former Soviet Union and China. Mixed trends were detected for Australia.

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More uniform results exist for the high-frequency temperature variability. An analysis by Karl et al. (1995) using SAT data of 1910–1990 observational period has revealed that day-by-day to interannual variability has generally decreased in the Northern hemisphere. Balling (1998), analyzing daily and monthly variability of historical temperature records, has found its overall decrease from 1897 to 1996. Collins et al. (2000) have identified reduced day-by-day variability trends for Australia. Karl et al. (1993), Easterling et al. (1997), and New et al. (2000) have shown that the land surface warming observed over the last 50 years has been accompanied by relatively stronger increases in daily minimum temperatures than in daily maximum temperatures (see also www.ncdc.noaa.gov/oa/climate/mxmntr/mxmntr.html). Thus, the difference that is called the diurnal temperature range (DTR) and represents effective measure of the daily temperature variability has decreased in recent years. Easterling et al. (1997) have revealed a decrease of the DTR from 1950 to 1993 for ⬃4100 stations in both the Northern and Southern hemispheres. A study by Karl et al. (1993) states: “Since 1950 all of the increase of temperature across the U.S.A. is due to an increase in the minimum temperature (about 0.75 K/100 years) with no change in the daily maximum temperature. This caused a decrease in the diurnal temperature range”. Subsequently, similar decrease in daily SAT variability has been observed at other locations and as stronger as one goes towards the Polar Regions. A study by Braganza et al. (2004) has detected strong negative trend of ⬃0.4 K in the DTR over global land areas (gridded SAT data) for the last 50 years. The last 50-year period was chosen by most of the researchers because it has the largest and most consistent data coverage. A study by Braganza et al. (2004) detected that the increase in daily minimum temperature over this period was ⬃0.9 K, while the maximum temperature had risen by only ⬃0.6 K. It now appears that most of the observed global surface warming of recent decades is occurring at night. The studies of the correlation between changes in mean SAT value and its variability are sparse and not as unanimous as the results of the DTR change. Braganza et al. (2004) have shown that observed clear DTR decrease is not spatially uniform. The correlation of the DTR with the mean temperature over all observations of the 1901–2000 period is not significant and equals to only ⫺0.24. Griffiths et al. (2005) have revealed significant location-dependent trends in the DTR in the majority of stations across the broad Asia-Pacific region, as well as the correlation between mean temperature and the frequency of extreme temperature events. This correlation appears stronger in the less populated/urbanized regions. Vincent and Mekis (2006) have examined trends in the mean temperature and the DTR for Canada and have shown that at least for the period 1950–2003 there is significant decrease in the DTR as well as a decrease in the variance of the daily mean temperature. Both trends were location dependent. Observed reductions in daily temperature variability over the last century are large; they unlikely occur due to natural climate variability alone. Numerous attempts were undertaken to capture the correlation between changes in the SAT mean and variability through numerical modeling of the effects induced by humans. The majority of the climate model simulations associated with the build-up of greenhouse gases predicts not only climate warming but also a general decrease in the climate variability (e.g., Karl et al., 1999; McGuffie et al., 1999). Dai et al. (2001) and Stone and Weaver (2002, 2003) have shown that anthropogenic forcing by greenhouse gases and sulfate aerosols

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in General Circulation Models (GCMs) caused small but detectable decrease of 0.2 K/100 years in the global DTR over the twentieth century. The 50-years DTR trends of similar amount were simulated by Braganza et al. (2004). Modeling results have also corroborated that expected DTR decrease is not spatially uniform. Possible reasons for the DTR decrease are: (1) urban heat island effect (see previous Section), (2) an increase in cloudiness, and (3) anthropogenic greenhouse gases and sulfate aerosol emissions. Verdecchia et al. (1994), Stone and Weaver (2002), and Braganza et al. (2004) have shown that the main controlling factors for the DTR are clouds and soil moisture. Because of the number of atmospheric and surface boundary conditions affecting the maximum and minimum temperature, the linkages of the observed changes in the DTR to large-scale anthropogenic climate forcings still remain tentative. Further studies for more sure detection of the temperature variability (including measurement of underground temperature) are indispensable. Detection of the temperature variability does not represent an easy task. Notwithstanding that all variability measures are based on the difference from some reference point, e.g., long-term mean or previous discrete value, there are many ways to define temperature variability. It may be calculation of the change of the magnitude of the DTR, frequency of occurrence of temperature extremes, the difference of the mean temperature from one day to the next, change of the standard deviation of temperature between two adjacent time periods, etc. Results are clearly dependent on the statistics chosen. Thus, for example, the latter technique may cause confounding of the high- and low-frequency variability and is insensitive to the position of large positive and negative departures from the mean within given interval (Karl et al., 1995). For example, two time series 0, 0, 0, 1, 1, 1 and 0, 1, 0, 1, 0, 1 have identical standard deviations, but significantly differing variability. The DTR is highly sensitive to small changes in maximum and minimum temperatures, etc. In addition, because all variance statistics are dependent on the reference level, e.g., mean, the uncertainties in the rate of change of the mean may confound detection of the changes in variance. Moberg et al. (2000) have compared the properties of eight statistical measures of the day-by-day variability using European series of daily averaged surface air temperatures for the period 1880–1998. Two techniques were found to be most powerful for the detection of variability in temperature time series: (1) the intramonthly standard deviation of daily temperature anomalies and (2) suggested in the work by Karl et al. (1995) the mean of a series of values defined by the absolute value of the difference in temperature between two adjacent discrete time periods. The quality of observed data is a vital factor for both methods. For example, the latter procedure is very sensitive to the homogeneity of the temperature series; thus, it can be applied as the diagnostic tool for detection of the changes in the measurement techniques or other inhomogeneities in the temperature time series used. Applying both methods, Moberg et al. (2000) have revealed different behavior of daily variability trends in different parts of Europe. Variability has decreased by 5–10% in the northeast of Europe, has shown change of 0% to ⫺5% in the northwest, and has increased by 5% to the southwest. On a longer timescale, day-by-day temperature variability in winter, spring, and autumn in northern Europe has decreased over the last approximately two centuries. The larger variability in northern Europe before twentieth century can be mainly attributed to a higher frequency of winter extremes.

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General analysis of the climatic temperature change/variability includes decomposition of the observed time series into following significant components: 1. linear trend 2. harmonic components 3. extreme events 4. noise (stationary or non-stationary). Robust estimation of each component in the presence of other components is not a trivial task. It can be performed by various statistical methods, from the linear regression and spectral analysis to far more complicated, e.g., the Generalized Additive Model (GAM; Vislocky and Fritsch, 1995; Grieser et al., 2002). Independently of their capacity/performance all these techniques should answer the following questions: 1. Is there significant linear trend in measured records? 2. Are there significant harmonic components? 3. If so, has any observed cycle changed, e.g., how has changed an amplitude of the annual cycle? 4. Are there extreme events that cannot be explained by the statistical properties of the record? While many time series can be described in terms of two basic classes of components: trends and periodicity, climatic time series contain significant intrinsic stochastic component. Thus, the last but not least question should be: 5. When all significant deterministic components were removed, what is the structure of the remainder stochastic noise? To meet these requirements a flexible stepwise strategy has to be used. Below we present an example of the detection of variability changes in the 8-year-long time series of the GST monitored at station Prague-Sporilov. While borehole GST reconstructions capture low-frequency variability only, temperature monitoring data can be complementary to these long-term trends detecting shortterm variability. Details of the monitoring experiment at Prague-Sporilov site are described in the previous section. The site is located on the top of a low hill in the campus of the Geophysical Institute of the Czech Academy of Sciences on the rim of large urban agglomeration. The temperature has been monitored since 1994 (Cermak et al., 2000) at a number of selected depth/elevation levels below/above the surface. Figure 125 shows results of 8-year temperature monitoring. These data refer to the temperature measurements obtained by zero-depth thermistor sensor installed on the top of a few millimeters of the rotten organic relics upon the compact soil ground. The individual measurements were taken at 15-min intervals and then averaged to 6-h regular grid; the precision of the individual readings is better than 0.01 K. The early years suffered by several data gaps; an uninterrupted continuous record exists only for the period 1998–2001. There were no changes on the observational procedure or in the equipment installation

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Fig. 125. Results of 8-year temperature monitoring of the ground surface temperature at PragueSporilov borehole.

during the whole experiment. The estimates of variability are thus not influenced by any data inhomogeneity problems, which otherwise may seriously bias the results (Moberg et al., 2000). The record of the natural internal variability can be reconstructed by removing estimates of the response to the periodic external forcing (Jones and Hegerl, 1998). The actual character of changes in the temperature variability may be distorted by the annual temperature variations when the slope of the annual cycle is steep in the spring and autumn seasons (Karl et al., 1995; Moberg et al., 2000). To minimize the potential influence of the annual cycle, the measured data, before being processed, should be converted into non-periodic temperature anomalies. Figure 126 shows how this pre-processing works. The measured temperatures were expressed as TLY, where Y ⫽ 1, % , 8 corresponds to years from 1994 to 2001, and index L ⫽ 0, 1, % , 1460 means the serial number of the corresponding 6-h long interval within the respective year. The mean annual cycle contained 1461 points from 0 to 365 days at 6-h intervals and was calculated by averaging 8-year values of 苶 TL ⫽ ᎏ18ᎏ
8y⫽1TLY. The reference temperature was then obtained from this cycle using the mean value, first four harmonics of the Fourier analysis, and the daily wave (wave number 365). Little, if any, additional variance could be explained when higher order harmonics are used. To obtain the temperature anomaly the reference temperature was removed from measured temperature (Figure 127). As seen, temporal oscillations of obtained signal are erratic and do not exhibit apparent regularity, trend, or cyclic pattern. The first insight in the variability of this record can be gained using its probability distribution. Figure 128 presents the comparison of the probability distribution of the Sporilov temperature anomalies record with the normalized standard distribution. Both distributions generally coincide. Prominent feature of the temperature anomaly record is the prevalence of extremes in warm seasons of the year while extremes are relatively rarer

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Fig. 126. The average annual cycle and its Fourier-smoothed representation (thick line).

Fig. 127. Temperature anomalies calculated from the surface temperature monitored in PragueSporilov during the period 1994–2001.

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Fig. 128. Histogram of occurrences of different temperature anomalies in the time series shown in previous figure. The Gaussian distribution is shown for reference.

in cold seasons. The probability distribution of the temperature anomalies (Figure 128) is skewed5 to the right (moment coefficient of skewness is equal to 1.59). More heavy (“fat”) right tail indicates that warmer extremes are more frequent than colder extremes. This finding agrees well with the knowledge obtained for the larger scales of aggregation. An infrequent occurrence of cold extremes in daily temperatures in the last two decades relative to warmer extremes was reported both for local and for global temperatures (Jones et al., 1999; Rebetez, 2001). Data presented here confirmed this fact for the higher frequency variation up to 6-h aggregation level. Note that the temperature anomaly distribution is also more peaked6 with respect to the normal distribution (kurtosis is equal to 9.55). The appropriateness of the existing numerous variability measures is judged by their power to detect/describe the details of variability pattern. The measure used for the detection of the temporal changes in variability was suggested in the work by Karl et al. (1995) and is defined as the absolute value of the temperature difference between two adjacent periods of time. The measure, which we call N-point change, is calculated as the absolute

5 Skewness is a measure of the asymmetry of the probability distribution. A distribution is right-skewed if the right tail (higher values of variable) is longer, and, on the contrary, is left-skewed if the left tail (lower values) is longer. Symmetric distribution looks the same to the left and right of the center and has zero moment coefficient of skewness. Skewness of the normal distribution is 0. 6 Kurtosis is a measure of the “peakedness” of the data relative to a normal probability distribution. Positive kurtosis means that the distribution has a distinct peak near the mean, declines rather rapidly, and has heavy tails. In other words, it means that more of the variance occurs due to infrequent extremes, as opposed to frequent lower size variations. Negative kurtosis indicates a “flat” distribution. Kurtosis of the normal distribution is equal to 0.

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difference between the average for the N-point long sequence that begins at measured point t and the similar average of N anomalies that begins at point t ⫹ t. Generally, t ⱕ N, which implies the possibility of overlapping. The overlapping is useful for longer intervals when the application of the strictly non-overlapping differences artificially constrains their number, and may lead to noisy seasonal estimates. We have temperature anomalies time series T1, T2, T3, % , Ti, % . The measure of variability TN (N-point change) is defined as the absolute difference between the average of a sequence of temperature anomalies for N points that begins at point I and the average for the N-point long sequence beginning at point (i ⫹ N ⫺ k) TN ⫽abs(Ti ⫺Ti⫹ N ⫺k ),

(53)

where Ti ⫽

1 N

i ⫹ N ⫺1

∑ l ⫽i

Tl ; Ti⫹ N ⫺k ⫽

1 N

i ⫹2 N ⫺ k ⫺1

∑

Tl .

(54)

l ⫽i ⫹ N ⫺ k

For the time lag k ⬎ 0 there are partly overlapping running differences (Karl et al., 1995). The measure of variability was calculated successively for the whole temperature time series to obtain time series of variability measure. The values of N were chosen as 1, 2, 3, 4, and 20, 40 corresponding to the averaging intervals from 6 to 24 h as well as to 5 and 10 days. There are some natural separations of the temporal scales of the climate system variability. Perhaps, the most important of them is that between weather and climate. Mainly in technical reasons, weather refers to variability in the climate system at timescales less than about 10–14 days, while the climate variability refers to the longer timescales. While the multihour timescale data aggregation patterns still reflect variability of the weather fluctuations, the 5- and 10-day aggregation can be attributed exclusively to the short-term climate variability. Daily periodic variability (the daily wave) was removed from the observed temperatures (see above); thus, it cannot have any influence on the calculated variability patterns discussed later. Four upper panels of Figure 129 show variability changes on the 6-h to day-by-day scales of aggregation for 8 years. The variability patterns depend on the length of the averaging interval N, and this dependence reflects essential features of the climate dynamics at different timescales. Variability time series for 6-h intervals do not show any significant linear trend, but they exhibit apparent quasi-seasonal oscillations. In all studied time intervals the variability increases during the spring season (partly also in the summer) and decreases in the autumn–winter seasons. Except for extremely variable year 2000, the spring “explosions” in variability are very short. The 12- and 18-h patterns do not offer any special features and represent only a gradual transition from higher to lower frequency variability pattern. Detected quasi-seasonality is relatively less pronounced in the dayby-day variability, the oscillations of which are more irregular. Day-by-day variability rarely falls to very low values and even it does then only for a short time interval. On the other hand, the 24-h variability exhibits a general decreasing trend of ⫺0.038 ⫾ 0.002 K/year similar to what is predicted by the most of greenhouse warming simulations. Climate model simulations associated with the build-up of greenhouse gases predict not

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only climate warming but also a general decrease in climate variability (e.g., Karl et al., 1999; McGuffie et al., 1999). This trend is absent in the higher frequency variability. Such decreasing trend existing during the second half of the twentieth century was reported also in works cited above dealing with the changes in the diurnal range of the SAT. Two lower panels of Figure 129 show variability changes on the 5- and 10-day scales of aggregation for the same 8 years. Surface temperature variability is somewhat higher than at the daily scale; however, its pattern is comparable with the day-by-day variability oscillations. Quasi-seasonal oscillations characteristic for the short-term scales of aggregation are absent, and decreasing trend of the same order as in the variability time series on 18- and 24-h scales of aggregation is preserved. Probably, part of the variability decrease observed in the Sporilov data may be attributed to the urbanization effect (see, e.g., Jones et al., 1990; Griffiths et al., 2005) characteristic for the intensively developing suburban part of city of Prague. However, its most significant part can be attributed to the NAO forcing. As known, the climate of the European-Atlantic sector exhibits considerable spatial and temporal variability. Recent studies have indicated that the variability of atmospheric circulation patterns in the Northern Hemisphere may be affected by the differences in the sea level pressure between the Atlantic Subtropical High centered near the Azores and its Sub-polar Low near SW Iceland. This phenomenon is referred to as the North Atlantic Oscillation (NAO; for more details see www.ldeo.columbia.edu/NAO). It has roughly decadal pattern with a dominant period of 12 years and, as shown by the recent studies, has a strong impact on weather (both temperature and rainfall regimes) and climate from the eastern coast of the United States to Eurasia and from North Africa and Middle East to the Arctic regions especially in the wintertime (see, e.g., Rodwell et al., 1999; Marshall et al., 2001; and the references therein). As shown in the work by Bodri and Cermak (2003) at all frequencies there is a significant correlation between Sporilov variability and the NAO index. At the investigated location correlation is positive and appears more prominently in winter periods, when the NAO control over the weather is stronger. 4.3.3 Structure of the stochastic component of the short-term climate variability There is little doubt that climate change involves a number of non-linear processes. Thus, except for the deterministic trend components, the climate contains significant stochastic part. A deterministic signal is traditionally defined as anything that is not noise (i.e., an analytic signal, or perfectly predictable part, predictable from measurements over any continuous interval, etc.). Deterministic components have reduced the degree of uncertainty and normally correspond to the main modes of the system behavior. They arise as a result of their own physical mechanisms and a sum of contributions from various forcings. Stochastic component (noise) represents an accumulation of random influences (the day-by-day weather variations, stochastic climate change on longer timescales, etc.) superimposed on the deterministic part on the climatic signal. The unpredictable weather fluctuations represent a permanent source of stochastic noise in the climatic time series. Induced by these fluctuations, noise variability can mask climate changes caused by anthropogenic and other deterministic influences, and its presence causes additional challenges to the climate researcher that deals with climate variability.

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Fig. 129. Variations of differences in average temperature anomalies for 6-, 12-, 18-, 24-h and 5-, 10-day averaging intervals and their linear trends (thick lines). Low-frequency changes are highlighted by a Gaussian filter, roughly corresponding to 10-day moving averages.

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On the other hand, the weather noise cannot be regarded as annoying hindrance. It represents essential part of the climate variability. Characteristic timescales of the deterministic and stochastic variability are matching. Sometimes an addition of the stochastic noise can significantly amplify deterministic signal. This so-called stochastic resonance has become widely recognized as a paradigm for noise-induced effects in driven non-linear dynamic systems. This phenomenon has been propounded as, e.g., a possible explanation for the ice ages and the noise-induced transitions in thermohaline circulation. The early work by Hasselmann (1976) has first introduced the idea of the separation of timescales observed in the climatic records and treating their short-term components as stochastic variables. Further studies have indicated that an application of the ideas of stochastic processes provides a useful insight into the climate physics. Weather-induced climate variability can be studied with stochastic climate models using stochastic processes and stochastic differential equations that are able to capture complex patterns of both signal and noise and their “cooperation”. Modes of the stochastic climate variability can be identified by statistical analysis of the observational data. Various tools of mathematical statistics have found wide application in climatologic research. Fractal dimensional analysis represents a powerful tool for the detection of the stochastic component of climate and/or the construction of stochastic terms of the climate models. This analysis (fractal dimension analysis) consists of an assessment of the invariant quantities that arise from the scaling properties of records and is based on the numerical evaluation of variance (a quadratic measure of variability). Fractal dimensional analysis of geophysical time series is a well-established research tool to investigate their dynamics. It was initiated by a series of papers by Mandelbrot and Van Ness (1968) and Mandelbrot and Wallis (1968, 1969) and has been followed by the application of the fractal/multifractal technique to various geophysical processes (Mandelbrot, 1982; Lovejoy and Mandelbrot, 1985; Ladoy et al., 1991; Turcotte, 1992; Schertzer and Lovejoy, 1995). Fractal dimension analysis is particularly well suited for an assessment of the time series variability (Hastings and Sugihara, 1993). Scale invariance has been found to hold empirically for a number of geophysical processes. The mathematical definition of the “simple scaling” or scaling of the increments is as follows. The function Y(x) is termed scale invariant, if it fulfills the condition: Y (
x )⫽
H Y (x ),

(55)

where Y(x)⫽Y(x1)⫺Y(x0), x⫽x1 ⫺x0 and Y(
x)⫽Y(x2)⫺Y(x0 ), x2 ⫽x0 ⫹
(x1 ⫺ x0 ) for arbitrary scale ratios
and x. Equality in Eq. (55) means equality in probability distributions. The random variables u and v are equal in this sense when Pr(u ⬎ q)⫽Pr(v ⬎ q) for any threshold q (“Pr” means probability). The “simple scaling” means that if we scale the coordinate x by means of an appropriate choice of the exponent H, then we always recover the same function. The parameter H is a constant called the-unique-scaling parameter (0 ⱕ H ⱕ 1). An assessment of scaling properties of the climatic time series starts with the assumption that they can be modeled as a stationary stochastic process. There are many standard methods to assess the scaling structure of {Yi}. A typical (and probably simplest) procedure consists in performing a Fourier (spectral) analysis of the time series.

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Spectral analysis is concerned with the detection of cyclic patterns of the data and expresses the amount of variance in a time series that occurs at different frequencies or timescales. In the case of deterministic time series the purpose of this analysis is to decompose a multicyclic time series into a few sinusoidal functions with particular frequency. If a time series represents a complex output of the stochastic process, distinct periodicities are generally absent and power density is distributed across the entire spectrum. In this case, spectral analysis represents a conventional method of analyzing time series data to determine the power (mean square amplitude) as a function of frequency. A stochastic, or noise, signal is fully described by its power spectral density which gives the expected signal power versus frequency. Assuming that a process can be described by a single dimension, H allows one to use the energy spectrum E( f ), where f is the frequency, of the observed variable Y(x) for scaling investigations. The energy spectrum is scaling when it can be described by a power law relationship according to (e.g., Ladoy et al., 1991): E ( f ) ⬃ f ⫺b ,

(56)

where b ⱖ 0. In the simple scaling case, exponents H and b are related according to b ⫽ 2H ⫹ 1. When E( f ) is of this form over given frequency range, fluctuations occur at all scales with no characteristic time and hence within this range the process is scale invariant. Most geophysical time series and particularly climatic time series obey this behavior. Spectra of climatic time series are characterized by two important features: (1) continuity and (2) so-called “red noise” behavior (slope towards longer timescales in the logarithmic representation of the power spectra). The “redness” can be attributed to stochastic mechanisms where random high-frequency fluctuations (e.g., unpredictable weather variations) are being integrated by the components of the climate system with slower response, e.g., ocean, while the low-frequency fluctuations develop and grow in the amplitude with increasing timescale (Hasselmann, 1976). Different values of b represent the cases of the “colored noise”. For example, white noise has equal power density across the entire spectrum (constant energy at all frequencies) as white light. In the logarithmic power spectral density versus frequency diagrams it appears as flat, with b ⫽ 0. Thus, an exponent b can be interpreted as a measure of departure from the non-correlated random white noise. The scaling spectrum with b " 0 has an “excess” of energy at low frequencies and thus is known as a “red noise” (in the sense of Gilman et al., 1963). It got this name after a connection with red light, which is on the low end of the visible light spectrum. In the logarithmic power spectral density versus frequency diagrams, red noise appears as descending line with the slope b. Figure 130 shows different kinds of the time series of the “red noise”. Brownian noise is a kind of signal noise produced by Brownian motion (one-dimensional random walk).7 It is named in honor of Robert Brown (1773–1858), leading British botanist, the discoverer of the Brownian motion. 7

A continuous process {Y(t)} represents a continuous-time random walk or a Brownian process if, for any time step t, the increments y(t) ⫽ y(t ⫹ T) ⫺ y(t) are: (1) Gaussian, (2) of mean 0, and (3) of variance proportional to t (to t2H in the case of fractional Brownian noise).

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Fig. 130. Synthetically generated kinds of the fractional Brownian noise (“red noise”).

An ordinary Brownian noise has b ⫽ 2 meaning that it has more energy at lower frequencies. For the ordinary Brownian noise, the change, or increment, from one moment to the next is random (non-correlated) and normally distributed. For the simple scaling case the coefficient of correlation r of successive increments is equal to 22H ⫽ 2 ⫹ 2r (r ⫽ 2b⫺2 ⫺ 1), where ⫺1/2 ⬍ r ⬍ 1 is independent of the time step t (Hastings and Sugihara, 1993). In the case b ⫽ 2, this equation gives r ⫽ 0. In other words, successive increments are uncorrelated. Because of the absence of the correlation between amplitude of oscillations corresponding to two successive time intervals, such signal is unpredictable. Brownian noise can be produced by integrating white noise. In the intervals of 2 ⬍ b ⬍ 3 and 1 ⬍ b ⬍ 2 stochastic time series exhibit two distinct types of behavior: persistence or antipersistence. Persistence is a presence in time series of significant dependence between observations a long time span apart. Persistence represents a long-range correlated or long memory process and may be characterized by a correlation function decaying hyperbolically as the lag increases, as opposed to the exponential decay of short memory processes. In this case, even

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sufficiently distant from each other, fluctuations are strongly influenced by the longterm, persistent trends. Such records are qualified as less variable. Visual appearance of persistent noise with spectral exponent 2 ⬍ b ⬍ 3 looks like random fluctuations superposed upon a “background” that performs several quasi-cycles. Because the future trend is more likely to follow an established trend, persistent processes are more predictable. In the case of b ⫽ 3, correlation coefficient between two successive increments is 1, and the function is completely differentiable (deterministic). Beran (1994) has characterized the family of strongly persistent time series. They are well known in geophysical and in particular in climatic time series (for more details see Section 2.3.4). The signals with higher b values obey less erratic or more regular, trend-reinforcing behavior. In the signals with b between 1 and 2 (antipersistent), inversely correlated fluctuations dominat and the signal reveals a more “nervous”, rough appearance with frequent reversals. The upper panel of Figure 130 represents the signal with familiar Kolmogorov8 spectrum with b ⫽ 5/3 characteristic for the turbulent wind fluctuations. In spite of the relative complexity of the antipersistent time series, the predictability again increases below b ⫽ 2. It occurs due to inverse correlation of fluctuations in such series. It means that an increase in the amplitude of the process is more likely to lead to its decrease in the next time interval. The scaling regime describes the random part of climate variability: in the range of timescales where the scale invariant law holds, the climatic system has no characteristic timescale, and the climate changes result from the accumulation of random fluctuations. Possible breaks of scaling that are often observed in climatic time series (e.g., Fraedrich and Larnder, 1993; Olsson, 1995) signify the appearance of the basic characteristic timescales of climate system and identify the boundary between the random and deterministic regimes. Due to strong intermittency, scaling studies require vast amount of the measured data and preferably many independent realizations. Some of the analyses using climatic time series have suffered from the shortness and the low quality of the data. Results of precise, decade(s)-long temperature monitoring appear to be especially suitable for this kind of analysis. Below we illustrate an application of above technique using time series of the temperature anomalies recorded at 0.05 m above the ground and at 1 and 10 m depth at Prague-Sporilov. All data are 6-h averaged, and thus still contain significant part of the weather fluctuations. Data were preprocessed in a similar way as the GSTs, shown in Figure 126. As in Figure 127, the temporal oscillations of calculated temperature anomalies are erratic and do not exhibit apparent regularity, trends, or cyclic pattern. Figure 131 shows examples of the power spectra of temperature anomalies measured at Prague-Sporilov at 0.05 m above the surface and at 1 and 10 m depth. All power spectra are similar. There is no evidence of periodic variations at any particular frequency; the background seems to be quite dominant. All spectra exhibit clear red noise behavior over all normalized frequency domain with spectral exponent b between 1 and 2 signifying antipersistence. The exponent b is the largest for the air temperature

8

A.N. Kolmogorov (1903–1987) is a Russian mathematician who made major advances in the fields of probability theory and topology. He has also worked on turbulence, classical mechanics, and information theory. In 1941, Kolmogorov has published a paper in which he derived a formula for the energy spectrum of turbulence.

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Fig. 131. Power spectra of temperature anomalies monitored at Prague-Sporilov at 0.05 m above ground surface and at 1 and 10 m depth. Frequency dependence of the power spectral density variations are shown in a log–log plot. The values are relative: the frequencies are normalized to the lowest frequency in the spectrum, the power spectral density to that at the lowest frequency. Solid lines represent the least squares fit to the data.

anomalies and progressively decreases with depth. This means that the degree of antipersistence (variability) is the highest for the temperatures recorded in the air and decreases into the subsurface because of the well-known gradual filtering out of the high-frequency oscillations. The antipersistence of the temperature time series reflects, in particular, the turbulent nature of the atmospheric and ocean dynamics responsible for weather fluctuations. As the signal penetrates into the surface its probability distribution comes nearer to the Gaussian. Figure 132 shows the histogram of occurrences of different temperature anomalies measured in Sporilov station at 1 m depth. Its comparison with the Gaussian distribution that is shown for reference, as well as with similar diagram calculated for the SAT temperature anomalies (Figure 128), demonstrates that the former histogram is more close to the Gaussian distribution. The right tail (the prevalence of warm extremes) disappears. The skewness of the distribution (degree of asymmetry) is close to zero characteristic for the normal distribution. The distribution is still more peaked than the Gaussian (kurtosis is equal to 4.75). However, this value is two times lower than that obtained for the air temperature anomalies. The differences from the Gaussian distribution appear in the more damped form in the temperature anomalies measured at deeper levels. Above calculations hint that the underground temperature monitoring could provide reliable information on the short-term variability of stochastic component of the surface temperature signal. The Earth smoothes extremes and filters out high-frequency fluctuations; thus, only the most important time resistant irregularities are preserved in the ground temperatures. More complex stochastic model that reproduces the variability and the long-term correlation observed in climatic time series was suggested in the work by Lavallée and Beltrami (2004). The stochastic model proposed by these authors represents a convolution between the Fourier transform of the random variable (white noise) {Xi}, i ⫽ 1, % , N

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and the function with a power law dependence (Eq. (56)) in the frequency space. Its output {Yi} can be presented as

 2(i ⫺1)( j ⫺1)  Yi ⬀ ∑ f ⫺b Ⲑ 2 Ff ( Xi ) exp  , N  j ⫽1 N

(57)

where Ff (Xi) is the discrete Fourier transform of the random variable and j is related to f by f ⫽ 2( j ⫺ 1). In this case, the power spectrum of {Yi} takes the form of Eq. (56). Using this relation, the scaling exponent b of the measured time series can be estimated from observed data. The values of the underlying random variable {Xi} can be calculated from the relationship: Xi ⬀ Fi⫺1  Ff (Yi )⫻ f

bⲐ 2

, 

(58)

where Fi⫺1 is the Fourier inverse. While analysis of the Prague-Sporilov data presented above has assumed the Gaussian distribution of the measured data, in the model by Lavallée and Beltrami (2004) the probability distribution, controlling the variability of stochastic model, is unspecified. When it is identified from the analysis of the probability density function of {Xi}, the statistical properties of the stochastic model can be regarded as completely known.

Fig. 132. Histogram of occurrences of different temperature anomalies in time series monitored in Sporilov station at 1 m depths. The Gaussian distribution is shown for reference.

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The authors applied the model outlined above to the 1500–2000 years’ long dendrochronological time series. Obtained exponent b ranged between ⫺0.5 and ⫺0.7, thus, revealed much less departure from the non-correlated random white noise than PragueSporilov time series described above. Lavallée and Beltrami (2004) have also investigated some possible probability laws including the Gaussian, the Cauchy, and the Lévy distributions (three of the few distributions that are stable and that have probability density functions that are analytically expressible) to find the best fitted to their data probability distribution. The authors have compared the cumulative probability distributions (probability that random fluctuation DT⬘ exceeds a fixed value DT) of the three probability density functions mentioned above. The misfit of the theoretical and measured probability density functions is more obvious in such plots. The cumulative probability distribution of climatic time series generally has a nearly Gaussian shape in the center and a tail (probability of the extreme events) that is “heavier” than expected for a normal distribution (see Section 2.3.4 and Figure 24, Chapter 2). Note that the “fat-tailed” probability distributions are general characteristics of the long-term climatic time series. When the fluctuations are of this type, the phenomenon is so intermittent that the return times of extreme events are much shorter than those for Gaussian process. According to the Gaussian law, very strong fluctuations have almost zero probability of being observed. The Lavallée and Beltrami’s analysis has shown that the stochastic model based on Lévy’s law reproduces the climatic variability archived in dendrochronological time series in the most precise manner. Similar cumulative probability plot presented in Figure 133 was calculated for the ground temperature anomalies monitored at 1 m depth in Sporilov borehole. The misfit of the

Fig. 133. The log–log plot of the cumulative probability distribution for the temperature anomalies measured at 1 m depth at Sporilov site. The Gaussian cumulative probability is given for comparison.

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measured data with the Gaussian law is minimal. It hints that this distribution reproduces measured data with enough accuracy. The “fat-tailed” distributions characteristic for longterm climatologic time series (e.g., Lévy’s law that was found for the dendrochronological time series in the above-cited work) means the higher probability of large fluctuations. The Gaussian distribution characteristic for the underground temperatures reflects the main properties of the heat conduction process: progressive smoothing of the surface signal and filtering out of its high-frequency component. The variability of stochastic component of climate can be studied from different viewpoints. The fractal approach presented above provides the simplest non-trivial example of scale invariance, and is appropriate for dealing with extreme and ubiquitous variability of climate. Numerous similar (mono-) fractal studies were performed in different climatic as well as geophysical fields. An assumption of a unique dimension was abandoned in the later works. More sophisticated statistical summaries consider the multifractal theory in combination with the multiplicative processes similar to energy flux cascade in turbulence (Davis et al., 1994; Schertzer and Lovejoy, 1995, 2004; Lovejoy et al., 2001; Schertzer et al., 2002). Except for the single dimension, it involves a moment scaling function that describes the behavior of the statistical moments at different scales and is able to embrace an entire range of complexity of the geophysical signals. An applicability of the multifractal theory has been thoroughly investigated during the last decade. The discrete wavelet transform (DWT) is a powerful signal processing technique that also offers several advantages over traditional spectral analysis techniques. It can be used for the analysis of the non-stationary time series (one of the primary limitations of Fourier analysis). It is scale adaptive and allows to decompose original time series into a collection of new time series, each of which represents the variability in the signal over a characteristic band of scales. Unlike Fourier coefficients that capture variability over the entire time series, the DWT captures variability associated with their local features giving better estimates of the variance attributable to local, intermittent variations in time series. Further developments of the DWT, e.g., the maximum overlapping discrete wavelet transform (MODWT), provides several advantages over the DWT. Other examples of statistical methods specific to climate research are presented in a book by Von Storch and Zwiers (1999). The book describes applications ranging from simple use of sampling distributions to obtain estimates of the uncertainty of a climatological mean to complex statistical methodologies composing the basis for calculations that are capable of revealing the dynamics of the climate system. In the past decade, climate variability research has made considerable progress in understanding and modeling climate changes on timescales of years to decades. In the previous section we looked briefly at several applications of stochastic processes to the detection of the short-term variability that presents in the time series arising from subsurface temperature monitoring. The examples discussed have shown that while GST reconstructions from the borehole temperature logs represent a useful tool for inferring long-term climate trends, time series resulting from borehole temperature monitoring can be of key importance in assessing the patterns of temporal climate variability. This suggests a direction for future research. The investigations of variability likewise the investigations of warming trends can be used for the validation of the simulated models for various scenarios of greenhouse-gas emission and land use. A detailed understanding of climate variability is also important for the prediction of extreme climatic events.

Conclusions and Perspectives of Future Progress Climate provides/controls certain basic conditions of life to all constituents of the living world. The reconstruction of a long past climate record is necessary for an evidence of the past climatic regimes and for the study of the long-term processes controlling climate change. Investigations of the past climate are also indispensable for both to understand present-day climate and its possible future changes, and to test the hypotheses about the causes of the recent climate change. More climate information from the distant past could be highly valuable to strengthen our understanding of the modern climate changes and to improve existing models of climate development. An ultimate utility of paleoclimate reconstructions can be their contribution to the detection of the causes of climate change. In traditional paleoclimatology the reconstruction of the long climate changes is based on a variety of proxy records. Because climatic variables are only indirectly reflected in these data and their evaluation requires an interpretation of physical, chemical, or biologic phenomena, results may contain systematic biases and errors. To compile the most meaningful and complete climatic history, it is necessary to consider the information of many independent records. Measurements of underground temperatures in boreholes performed worldwide at the recent decades represent a valuable source of the paleoclimatic information. Subsurface temperatures respond to an integrated, continuous temperature change at the Earth's surface. Surface temperature changes penetrate deep into the subsurface. Process of heat conduction in rocks smoothes out high-frequency air temperature oscillations; thus, temperature-depth profiles measured in boreholes preserve information on average surface temperatures over a decade to millennium or longer timescales. Like other approaches to the paleoclimate reconstruction, the "borehole" method has its own strengths and weaknesses. Its major advantage is that in contrast to the proxy data representing indirect inferences of climate change, the subsurface temperatures measured in boreholes directly archive past GSTs. In other words, except for the meteorological instrumental records, borehole temperature-depth profiles contain the most quantitative information on the past climate change. Borehole temperature-depth profiles yield robust long-term temperature trends, but because of the nature of heat conduction in the subsurface with decreasing in time resolution. It is these properties (direct measure of temperature, its continuous recording at the same place, low-pass filtering) that make borehole temperature logs so valuable for the family of the climate change detection techniques. Because of the wide geographic availability of this geothermal archive, subsurface temperatures have significant potential to provide spatial paleoclimatic information. Obviously, it exists practically everywhere beneath the land surface. To get this information one should perform only relatively simple and inexpensive procedures of the borehole drilling and logging. An application of this technique is especially important in numerous, still poorly sampled, regions of Asia and the Southern Hemisphere. During the last decades our understanding of the borehole climatology has improved, and it has taken place among the leading methods of the paleoclimate reconstruction. 305

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Inferring of the GST history from borehole temperatures has become a major endeavor of geothermal research. Numerous analyses of the individual boreholes or local suites of boreholes have inferred temporal patterns of the GST change over various time intervals, while the regional and hemispheric to global ensembles of the GST reconstructions have revealed generalized patterns of the temperature change at the Earth's surface. Globally averaged borehole data have indicated a climate warming in the Northern Hemisphere of about 1 K over the past five centuries, half of which has occurred in the twentieth century alone. Results obtained by various research teams using slightly different techniques of the GST reconstruction show general agreement. Detected warming coincides well also with the climatic trend established by various proxies and in its last section by the instrumental record. Similar trend has been detected in the temperature time series monitored in shallow boreholes. Numerous monitoring experiments and field studies performed by multiple research groups also have documented the fidelity of the GST-SAT coupling. Together with the borehole temperature logging, borehole temperature monitoring in the recent decade became one of the building blocks to help us understand how the Earth's climate is changing. Last decades have been characterized by a remarkable progress in different practical efforts to apply borehole temperature measurements in a number of climatologic problems. Much more work was carried out in the borehole temperature logging, in the extension of the temperature monitoring systems, in the data collection and creation of the database with easy access, and in data evaluation than that was anticipated in the beginning of the borehole climatology. This work is progressively continuing, because borehole climatology indubitably cannot be regarded as an accomplished scientific branch. Studies investigating regional GST variations still have relatively large uncertainty because of the high local-scale microclimate variability as well as the lack of exact techniques for screening out boreholes that are not ideally suited for the climate change reconstruction. Substantial progress should be achieved in the widening of existing database to the regions that still appear as the "white spots" on the climatologic maps, in the development of more powerful mathematical procedures reducing uncertainties in the GST inversions, in robust method of merging of the borehole results with other paleoclimatic information, and in the incorporation of this new knowledge into modeling flamework to better understand how climate variables, which are important to human and natural systems, are affected by the changes in the Earth's system resulting from natural processes and anthropogenic activities. All these studies will assist to better understand and predict future climate change. Considerable uncertainty still remains in the interpretation of the borehole data owing to possible non-climatic environmental influences. Discovering the causes of climate change is tied in part to our ability to discern regional variations in GST histories. There are many questions on the physical nature of the GST changes over the last one to two millennia that cannot be quantitatively and conclusively answered on the current level of knowledge. It is expectable that future international borehole research activities will ensure progressive advancement of the GST reconstruction techniques and results. Combined with other climatologic studies they will provide aggregate, broader answers on numerous questions regarding the real significance of various physical factors of the climate change perspective. The borehole climatology is still under development.

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Subject Index a posteriori covariance, 62 a priori information, 33, 52, 56, 76-77, 82 accuracy, 39, 40, 63, 172 additional information, 52-53, 57-58, 60, 63-64, 72, 77-82, 189, 225,240 adiabatic lapse rate, 130 advection, 121,129, 133-134, 136, 138, 140-141,144, 147, 149, 153, 158, 262-264 Alaska, 6, 49, 107, 121, 150-156, 163-164, 166, 176, 197, 199-200 albedo, 12, 49, 96, 117, 150 amplitude, 29-31, 42, 44, 46-47, 49-50, 52, 61, 64, 67, 70-72, 78, 84, 96, 99, 112, 115-116, 123, 126-127, 129, 140, 150, 154, 179, 189, 192, 199, 203, 214, 224, 239, 241-243, 255,259, 261,270-272, 283, 290, 298-300 amplitude decrement, 29, 31,270-271 annual GST oscillations, 29 Antarctica, 2, 13, 22, 167, 169, 171-172, 211, 230-231,260, 282 anthropogenic component, 6, 226 antipersistence, 299-301 aquifer, 149 Atlantic Subtropical High (ASH), 295 attenuation, 29, 127, 218, 272 autocorrelation function, 56, 78, 190 borehole climatology, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26-28, 30, 32, 34-38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86-88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118-120, 122, 124, 126, 128, 130, 132, 134-136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246,

248-250, 252, 254, 256, 258, 260, 262, 264, 266, 268-270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304-306 borehole filling fluid, 37 geophysics, 37, 267 logging, 34, 93, 99, 206, 282 boundary conditions, 44, 50, 60, 75, 93, 139, 141,158-159, 169, 225, 289 Boussinesq approximation, 139 Brownian noise, 298-299 Canada, 22, 31, 33, 49, 76-77, 87, 92, 94, 107, 109, 118-119, 125, 127-128, 135, 137, 149-151,155, 161,164, 166, 176, 192, 194-195, 197-198, 200, 215, 221, 244-246, 256, 260, 288 characteristic distance, 58, 130 city heat-island effect, 7 climate, 1-32, 34-173, 175-270, 272, 274-290, 292, 294-298, 300, 302, 304-306 change, 3, 8-15, 17, 19-20, 22, 25, 29, 34, 37-39, 41-45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71-73, 75, 77-79, 81-85, 87, 89, 91-93, 95, 97, 99, 101, 103, 105, 107, 109, 111,113, 115, 117-119, 121,123, 125-129, 131-133, 135, 137, 139, 141,143, 145, 147, 149-157, 159, 161,163-165, 167-169, 171-173, 175-176, 179-181,183-184, 188-189, 196-199, 209, 211,213, 216, 218-219, 224-241,243, 250, 252, 256, 258-261, 264-266, 268, 270, 275,277-280, 282-283, 285-286, 295, 300, 304-306 change detection and attribution, 240 feedback, 8, 13, 229 reconstruction, 13-14, 19, 28, 34-35, 37, 40, 47-49, 52, 85-86, 92, 98, 126, 136, 138, 141,147, 165, 171,175-176, 192, 202, 206, 209, 211, 219-220, 222-223, 226, 232-233, 236, 262, 265, 267, 286, 305 331

332

Subject Index

reconstruction database, 286 variability, 2, 6, 15, 19, 36, 89, 171, 180, 199, 203-204, 208-209, 216, 226, 234-236, 240-241,249, 268-269, 279, 281,283, 285-288, 294-295, 297, 300, 304, 306 climate change long term, 19, 29, 42, 163, 265, 270 medium term, 19 short term, 10, 15 climatic time series, 57-58, 290, 295, 297-298, 300-301,303 climatology, 2-4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26-28, 30, 32, 34-38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86-88, 90, 92, 94, 96, 98-100, 102, 104, 106, 108-110, 112, 114, 116, 118-120, 122, 124, 126, 128, 130, 132, 134-136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238-240, 242, 244, 246, 248-250, 252, 254, 256, 258, 260, 262, 264, 266, 268-270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304-306 CO 2 concentration, 7, 229, 230, 23 lf, 249 cooling, 3-4, 6, 9, 12, 29, 33, 43-44, 46, 77, 79, 90, 97, 106, 114, 123, 133, 137, 141, 161-162, 165, 170-173, 179, 182-183, 185-188, 191,195, 198, 204-206, 208-209, 216, 218, 222, 232, 240-241, 243, 253, 256, 258, 285 cosmic radiation, 278 covariance matrix, 56-58, 238 crustal radioactivity, 27, 259 cryosphere, 12, 268, 287 Cuba, 44, 176, 199, 201-203 cutoff approach, 55 Czech Republic, 29-30, 90, 99-100, 108, 130, 145, 176-182, 184-186, 196, 204, 216, 253, 255, 260, 270-271,273-276, 278 Darcy velocity, 139, 149 deep continental drilling, 250 deep boreholes, 250, 253, 257, 260, 261 deforestation, 6, 13, 94, 96, 117-122, 197, 199, 229, 242

differential heating, 19, 225 discharge, 134, 137-138, 140-141,144, 146, 148 discrete wavelet transform, 304 discretization procedure, 51 diurnal temperature range (DTR), 288 drilling, 35, 37, 39-41,119, 167-168, 171, 189, 250, 254-255,260-266, 283, 305 D-value, 56, 78-79 East Siberia, 152, 156-157 England, 7, 16, 18, 46, 112-113, 115 environmental change, 19, 87, 96, 123, 206 evaporation, 23, 90, 92, 96-97, 100, 106-109, 113-114, 117, 199, 244 evapotranspiration, 109, 116, 122-123, 126, 155,244, 274 external forcing, 10, 85, 128, 230-231, 238-241,244, 285, 291 extreme event, 9, 290 finite difference, 63, 159 finite element, 63, 159 Finland, 90, 187-188, 191,204, 222, 260, 265 five-century-long GSTH, 211 forcing function, 26, 46, 118, 127, 192, 194, 223, 243, 251,263 forward technique, 244 Fourier analysis, 291,304 fossil fuels combustion, 229 France, 18, 176, 204 frozen ground, 149, 160 functional space inversion, 50 Gaussian noise, 66, 70-71, 73-75 Gaussian process, 58-60, 303 general circulation, 19, 72, 128, 197, 225, 286, 289 General Circulation Model (GCM), 19, 24, 72, 82, 85, 105, 165, 197-199, 239, 244 Generalized Additive Model (GAM), 290 geothermal gradient, 38, 42-44, 46, 72, 94, 137, 140-141,150, 161-162 geothermal observatory (for climate), 1, 211, 212, 267 geothermics, 27, 37-38, 135, 177 glacial-interglacial cycle, 14 Global database of borehole and climate reconstructions, 211 Global database of borehole temperatures, 215

Subject Index global warming, 6-9, 15, 49, 92, 151, 164, 167, 171,186, 196, 198-199, 206, 222, 226-228, 230, 232, 241,245, 249, 269, 274, 278-279, 285 Granger causality, 92, 245-248, 250 greenhouse gases, 7-8, 13, 118, 196, 228-229, 288-289, 294 Greenland, 3-4, 6, 9, 22, 35, 151-152, 154, 156, 164, 167, 169-172, 233, 256, 259-260 ground freezing, 98, 103, 198 ground surface temperature (GST), 25, 39, 64, 175,267 groundwater circulation, 60, 62, 177 greenhouse effect enhanced, 229, 237 natural, 228 GST history, 29, 33, 35, 44, 48, 52, 54, 57, 60, 63-64, 66-80, 83, 85, 87-89, 116, 119, 128-130, 132, 135, 149, 159, 162, 169, 171-172, 177, 179-180, 183, 185, 190, 192, 195-197, 204, 206, 208, 211-212, 215-218, 220-221,249, 251-253, 255-256, 258, 265-267, 281,306 harmonic components, 290 heat capacity, 42, 44, 63, 77, 107, 126-127, 139-140, 158, 169, 263 conduction, 29-30, 43-44, 48, 50, 60-62, 65, 70, 75, 85-86, 92, 100, 129, 133, 150, 157-158, 249, 270, 304-305 flow, 27-28, 31, 34, 38-39, 42-43, 45, 47, 49-52, 63, 77, 88, 90, 93, 97-98, 102, 109, 129-132, 140, 149, 155, 161,169, 172, 178-179, 188, 216, 256-260, 263-265 propagation equation, 44 heat-valve effect, 90 hemispherical averages, 6 high-frequency climate variations, 222 high-resolution temperature monitoring, 282 historical documents, 15, 83, 92, 249 hockey stick, 25-26, 224, 233 Holocene climate, 6, 44, 250-251,263 human-forced climate change, 236 humidity, 12, 20, 109, 113 hydraulic conductivity, 99, 135, 144, 153 head, 139 hydrocarbon exploration, 151 hydrogeology, 27, 263 hydrothermal activity, 27

33 3

ice core, 2-3, 9, 18, 20, 22-24, 35, 48, 83, 92, 167-172, 21 l, 220, 223-224, 230-231, 249, 260 instrumental data, 83, 220 record, 14, 19, 92, 122, 128, 171,182, 186, 219, 236, 249, 305-306 insulation effect, 104 Intergovermental Panel of Climate Change (IPCC), 8, 25, 115,227, 229, 233, 237, 240, 286 International Geology Correlation Program IGCP, 270 International Heat Flow Commission (IHFC), 28, 97, 149, 176, 257, 259 inverse techniques, 49 inversion, 14, 19, 22, 33-35, 49-50, 52-58, 60, 62, 64-66, 68, 70, 72-74, 77-79, 82-83, 85-89, 93-94, 105, 124-125, 129, 132-133, 135, 141, 145, 149, 157, 159, 162, 164-165, 169, 171-172, 175, 177-178, 188, 190, 192, 198, 204-205,207, 211, 215-216, 225,240, 253-256, 258-259, 262, 265,270-271,278-279 Israel, 90-91 Kamchatka, 205-207, 254, 281 Kola superdeep project, 262 KTB German continental deep drilling, 260 kurtosis, 293, 301 Kyoto Protocol, 25, 227 -

last event analysis, 50, 52 Last Glacial Maximum, 3-4, 162, 170, 188, 253, 256, 265 Late Quaternary GST change, 250 latent heat, 97-98, 103, 105-107, 109, 114, 116-117, 126, 149, 155, 158-161 Laplace transformation, 51 least-squares inversion, 53, 54, 60, 135, 141 Little Climatic Optimum (LCO), 5, 78, 183 Little Ice Age (LIA), 1, 6, 11, 25, 65, 68, 78, 83, 89, 164, 169-172, 179, 181,183, 188, 191, 192, 194, 196, 206, 208, 234, 241,255, 258, 265, 285 mean global GST, 215 Medieval Warm Period, 5-6, 11, 25, 68, 78, 169, 179, 181,183, 188, 191-192, 206-208, 232, 241,255, 258, 285

334

Subject Index

melting, 8-9, 23, 102, 104, 150, 153-154, 158, 161,164, 167, 169, 233, 244, 256 meteorological data, 21, 48, 113, 204, 219, 223, 280-281 record, 15, 30, 32, 93, 110, 112, 128, 185, 204, 211, 216, 224, 267 meteorology, 17, 35, 106 micro-vegetation cover, 49 misfit function, 61, 64, 147 Monte Carlo method, 78, 169, 171 multiproxy data, 24, 83 near-surface temperature, 90, 121 noise free, 66, 68, 74, 88, 143 non-climate disturbances, 35, 93, 98 North America, 3, 6, 15, 20-21, 57, 75, 100, 102-103, 115-116, 121,124, 150, 171, 176, 197-200, 203,209, 211-212, 215-218, 226, 251,257, 259, 271,280, 287 North Atlantic Oscillation (NAO), 171,248 Ocean-atmosphere, 85 ozone hole, 13 paleoclimate, 10, 19, 23-24, 27, 49, 85, 92, 98, 126, 133, 138, 147, 156, 171,176, 192, 209, 219, 222-223,226-227, 231, 234, 236, 244, 262-265, 267, 305 parametrization, 127, 262 past climate reconstruction, 13, 19, 35, 52, 85, 136, 141,220, 232 periglacial, 161, 281 permafrost, 12, 98, 149-166, 169, 172, 188, 196-197, 205,217, 259-260, 269 persistence, 57, 78, 96, 102, 108, 125, 156, 189, 299 perturbation, 29, 34, 39, 43, 47, 49-50, 73, 129, 132, 149, 243, 261 phase shift, 29, 102, 122, 216, 270 phenology, 268 power spectrum, 302 precipitation, 12, 20, 42, 49, 90, 97-98, 100, 102, 106-111,113-116, 127-128, 142, 148-149, 153-155, 164, 167, 198, 274, 277, 285 pre-observational mean temperature (POM), 89, 128, 194 present-day warming, 36, 269, 278 probability distribution, 58-59, 285, 291,293, 297, 301-303 proxy methods, 13, 19, 48, 81-82

radiative forcing, 249 ramp model, 119, 201,202 rainfall, 98, 102, 106-109, 111-114, 123, 127, 295 recent climate change, 25, 92, 172, 196, 227, 236, 238, 240-241,243, 268, 275, 278, 305 Recent Global Warming, 15, 49, 92, 222, 230 recharge, 113-115, 137-138, 140-142, 148-149 recharge flux, 113 remote climate change, 209, 250, 259-261, 264, 266 scaling properties, 57, 297 spectrum, 298 seasonal change, 111, 114, 123, 126 shallow boreholes, 63, 88, 129, 196, 263, 269-270, 276, 278, 282, 306 short-term sensitivity, 48 signal-in-noise problem, 128, 236 singular value decomposition, 50, 53, 213 skewness, 293, 301 Slovenia, 204, 253-255,278 snow cover, 12, 35, 49, 90, 92, 97-98, 100, 102-106, 109, 111, 116-117, 121-124, 126-128, 152-156, 164, 249, 274, 279-280 snowfall, 22, 98, 102, 106, 108, 111, 113, 115 soil moisture, 90, 99-100, 103, 105-106, 114, 117, 155, 198, 267, 289 solar constant, 42 solar irradiance, 10, solar radiation, 7, 10, 236 soil-air temperature coupling, 49, 117, 123 spaghetti diagram, 75-76, 180-181, 183, 202, 205,209 spatial discretization, 63 spatial patterns of temperature variations, 223 steady-state temperature, 46-47, 50, 60, 62-63, 93, 129, 133, 141 step increase, 45 step model, 50, 52-53, 63, 119, 201-202 stochastic component, 295 Sub-Polar Low, 295 subsurface temperature, 10, 26-29, 33, 35, 37, 39, 41-45, 47, 49, 51-53, 55, 57, 59-61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81-83, 85-87, 89, 91, 93, 95, 97-101, 103-105, 107, 109, 111, 113, 115,

Subject Index 117-121, 123, 125, 127-135, 137, 139-141, 143, 145, 147, 149, 151,153, 155, 157, 159, 161, 163-165, 167, 169, 171, 173,222, 225, 244-245, 250-251, 260-261,267, 269, 271,273,275-277, 279, 281-283, 285, 287, 289, 291,293, 295,297, 299, 301,303-305 subsurface temperature monitoring, 100, 267, 269, 271,273, 275-277, 279, 281,283, 285,287, 289, 291,293, 295, 297, 299, 301,303-304 superdeep boreholes, 260 surface air temperature (SAT), 1, 92, 97, 176 surface vegetation effect, 113, 116, 154 surface warming, 29, 43, 46, 98, 104, 141, 161,192, 199, 223, 249, 271,273-274, 288 synthetic temperature profile, 30, 95, 159 temperature disturbance, 39-40, 46-47, 119, 140-141, 197, 264 gradient, 28, 39, 44, 49, 106, 121,129, 137, 139, 141,143, 145, 150, 159-162, 282 logger, 282 monitoring, 29-31, 90, 100, 102-103, 109, 118, 123-124, 126-127, 151-153, 155, 164, 267-273, 275-279, 281-283, 285-287, 289-291,293, 295,297, 299-301,303-304, 306 offset, 98 variability, 234, 287-289, 291,295 temperature-depth profile 14, 26-29, 33-37, 42-44, 47-50, 52, 63, 65-70, 77, 82, 86-89, 92, 93, 95, 115, 118, 123, 128, 132-134, 136-138, 140, 143, 149, 157, 159, 161-163, 165, 168, 171,175, 177, 189, 191, 192, 194, 196, 198, 199, 201, 203, 205, 209, 215, 221-223, 243-245, 251,252, 254-256, 258, 263, 265,267, 271,278, 281,282, 305 temporal discretization, 63

335

terrain effect, 60, 62, 86, 96, 121-122, 128, 130, 161-162, 192 thawing, 97-98, 103, 106, 109, 116, 118, 123, 126, 155, 157-165, 259, 282 thermal conductivity, 27, 38, 40-44, 63, 72-75, 77, 80, 88, 98, 107, 126, 130, 134, 139-140, 144, 158, 169, 178-179, 252-255, 260, 263,276 thermal diffusivity, 29-30, 40, 42, 46, 93, 104, 111,121,123-124, 126-127, 150, 160, 169, 251,270, 280 thermal equilibrium, 37, 150, 254, 278 memory, 47 recovery, 39 resistance, 54, 88 thermohaline circulation, 9, 12, 297 thermometer, 15-16, 37, 48 thermophysical parameters, 50-51, 53-54, 60, 62, 65, 68, 75, 129 transient signal, 146 transpiration, 90, 96, 100, 109, 113, 244 tree-ring data, 20, 219, 221,223 UN Environmental Program (UNEP), 227 United States, 99, 115, 197, 287, 295 urban heat island effect, 274, 289 urbanization, 13, 118, 199, 206, 228, 295 U-shape, 28-31, 44, 77, 94, 115, 132, 137, 140, 144, 162-163, 178, 189, 192, 196 variability measures, 293 volcanic aerosols, 85, 236 water-ice conversion, 155 weather, 1-2, 16, 19-20, 30, 32, 57, 90, 92, 99, 106, 117, 124-125, 127, 155, 187, 192, 197-198, 222, 277-280, 283, 285-286, 294-295,297-298, 300-301 winter severity index, 183, 185 World Climate Report, 25 zero-curtain effect, 103