PREDICTING PATTERNS OF NEAR-SURFACE AIR TEMPERATURE USING EMPIRICAL DATA OLEG A. ANISIMOV Department of Climatology, State Hydrological Institute, 2nd Line V.O., 23, 199053 St. Petersburg, Russia E-mail:
[email protected]
Abstract. The signal of recent global warming has been detected in meteorological records, borehole temperatures and by several indirect climate indicators. Anthropogenic warming continues to evolve, and various methods are used to study and predict the changes of the global and regional climate. Results derived from GCMs, palaeoclimate reconstructions, and regional climate models differ in detail. An empirical model could be used to predict the spatial pattern of the near-surface air temperature and to narrow the range of regional uncertainties. The idea behind this approach is to study the correlations between regional and global temperature using century-scale meteorological records, and to evaluate the regional pattern of the future climate using regression analysis and the global-mean air temperature as a predictor. This empirical model, however, is only applicable to those parts of the world where regional near-surface air temperature reacts linearly to changes of the global thermal regime. This method and data from a set of approximately 2000 weather stations with continuous centuryscale records of the monthly air temperature was applied to develop the empirical map of the regional climate sensitivity. Data analysis indicated that an empirical model could be applied to several large regions of the World, where correlations between local and global air temperature are statistically significant. These regions are the western United States, southern Canada, Alaska, Siberia, southeastern Asia, southern Africa and Australia, where the correlation coefficient is typically above 0.9. The map of regional climate sensitivity has been constructed using calculated coefficients of linear regression between the global-mean and regional annual air temperature. As long as the correlations between the local and global air temperature are close to those in the last several decades, this map provides an effective tool to scale down the projection of the global air temperature to regional level. According to the results of this study, maximum warming at the beginning of the 21st century will take place in the continental parts of North America and Eurasia. The empirical regional climate sensitivity defined here as the response of the mean-annual regional temperature to 1 ◦ C global warming was found to be 5–6 ◦ C in southern Alaska, central Canada, and over the continental Siberia, 3–4 ◦ C on the North Slope of Alaska and western coast of the U.S.A., and 1–2 ◦ C in most of the central and eastern U.S.A. and eastern Canada. Regions with negative sensitivity are located in the southeastern U.S.A., north-western Europe and Scandinavia. The local tendency towards cooling, although statistically confirmed by modern data, could, however, change in the near future.
1. Introduction General concern that an increasing concentration of CO2 in the atmosphere may result in higher air temperature was expressed by Arrhenius at the end of the 19th century (Arrhenius, 1896, 1907). One hundred years after Arrhenius the imporClimatic Change 50: 297–315, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.
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tance and global nature of this problem was recognised by the world scientific community, which formulated the task to predict climate change in the 21st century and to evaluate the potential environment and socio-economic impacts. Climate change prediction can be addressed by three main tasks. The first is to study the sources and sinks of greenhouse gases and aerosols under anthropogenic forcing. The second is to calculate their future concentrations in the atmosphere and to convert it to radiative forcing. The third task is to predict the parameters of climate using data on the chemical composition of the atmosphere. Although the range of uncertainties associated with the first two problems is still large, it is possible to construct several emission scenarios, which result in distinctly different radiative forcing. Such scenarios have been developed and used in general circulation models (GCMs) to predict changes of climate in the 21st century (Kattenberg et al., 1996). Under equal emission scenarios GCMs, including the recent generation of coupled ocean-atmosphere models, produce quite different patterns and magnitudes of warming depending on the parameterizations of the processes in the climate system, internal parameters of the models and computational details (Barnett et al., 1999, 2000; Greco et al., 1994). Corrections and adjustments have been made to improve the ability of the GCMs to reproduce the modern climate during the control integrations (Gates et al., 1996). Several models have been constructed to better account for effects associated with specific components of the climate system, i.e., sea ice, permafrost, vegetation, and others focused on the processes in specific regions, i.e., in the Arctic (Lynch et al., 1995). However, such improvements do not allow narrower ranges of uncertainty on global or hemispheric scales thus bringing the dominant position of GCMs in the provision of scientific knowledge on future climate change into question (Shackley et al., 1998). In particular, the range of the climate sensitivity generated by GCMs is still 1.5 to 4.5 ◦ C (Kattenberg et al., 1996). Independent criteria and methods are needed to verify GCMs and to check the consistency of their results, and pivotal role in any such effort must belong to analysis of empirical climate data (Goody et al., 1998). Several empirical methods are used to study past and present climates. Climates of past geological epochs are reconstructed using fossil palaeo-environmental data. Vertical profiles of the ground temperature in boreholes provide records of air and surface temperatures on the time scale of several centures. Climatic change over the last century is available from meteorological observations. Results obtained using these methods provide empirical evidence that the global climate system possesses properties of natural variability and cyclicity, which complicate the detection of any climate change signal and attribution of observed changes as the effect to any specific factor (Barnett et al., 1999). There is, however, a growing confidence that changes in global and regional temperatures have already exceeded the limit of natural climate variability, and are most likely attributable to anthropogenic effects rather than stochastic factors. A significant climate change signal dating from the beginning of the 20th century was
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detected in meteorological records and confirmed by several indirect indicators, including sea ice (Bjorgo et al., 1997; Maslanik et al., 1996; Smith, 1998), ice core data (Thompson et al., 1993), and ground temperature profiles from deep boreholes (Duchkov and Devyatkin, 1992; Gosnold et al., 1997; Lachenbruch and Marshall, 1986). These studies indicate that a 0.5 ◦ C increase of the global-mean air temperature since the beginning of the 20th century produced much higher warming in the Arctic and sub-Arctic than in most other parts of the world. On the North Slope of Alaska and northern Siberia temperature increased by 2–4 ◦ C; this is consistent with expected patterns of anthropogenic climate change. Such conclusions about the attribution of the observed warming is confirmed through comparison of a climate change fingerprint with patterns predicted by comprehensive GCMs driven by the anthropogenically-induced changes in radiative forcing (Andre and Royer, 1999; Barnett et al., 1998, 1999; Hegerl et al., 1996, 1997; Levine and Berliner, 1999; North and Stevens, 1998; Russell et al., 2000; Santer et al., 1995; Stott and Tett, 1998). The signal of anthropogenic warming detected in meteorological records allows construction of an empirical projection of future climate change. Prerequisites for and application of such an approach for the prediction of the air temperature pattern in the first half of the 21st century are discussed in the following sections.
2. Analogs for Future Climate Change from the Past and Present Several methods have been employed to predict future regional climate change using empirical data. The use of palaeoclimatic analogs assumes that climate in the future will be similar to those of past warm periods, including regional details of seasonal air temperature and precipitation. However, changes in the past were caused by many different factors. Elevated concentration of CO2 , variation in incoming solar radiation, Earth orbital parameters, and albedo may produce distinctly different effects on the spatial pattern of climatic parameters. In this paper analysis of the problem is limited to the near-surface air temperature. The warm intervals of the mid-Holocene (5–6 Ky. B.P.), Eemian interglacial (122–125 Ky. B.P.) and mid-Pliocene (3300–4300 Ky. B.P.) are relatively well studied and have been suggested as analogs to future climates (Budyko and Izrael, 1987; MacCracken et al., 1990). In these papers regional scenarios of climate change were obtained through linear scaling of the palaeoreconstructions by the departure of the global-mean annual air temperature and averaging the results over the three warm periods. This approach implicitly assumed that changes of the radiative forcing during past warm epochs, although caused by different factors, produce similar regional patterns of seasonal air temperature. Such a conclusion was based on the qualitative agreement between three patterns of palaeoreconstructed temperature anomalies presented in Budyko and Izrael (1987). A detailed comparison of mid-Holocene, Eemian and mid-Pliocene palaeo reconstructions
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was made by Shabalova and Konnen (1995). They found similarities in the patterns of the zonal-mean anomalies of the seasonal air temperature averaged over large regions and concluded that under certain conditions climate is scalable, i.e., in many cases regional changes of air temperature are proportional to anomalies in the global-mean temperature. A different opinion was expressed by Mitchell (1990), who used a GCM to simulate climates with the Earth’s orbital parameters altered to those appropriate to the mid-Holocene, and with doubled CO2 concentration. The results were different in regional details, which allowed the author to conclude that the midHolocene is by no means a good analog for anthropogenic ‘greenhouse’ warming. Subsequent study found that the regional pattern of the air temperature caused by ‘pure’ anthropogenic CO2 forcing will be further disturbed because of the effect of sulfate aerosols (Mitchell and Johns, 1997). Results obtained with the Canadian Centre for Climate Modeling GCM were similar, indicating descernible discrepancies between the regional details of modern anthropogenic warming and the mid-Holocene (Vettoretti et al., 1998). Similarities in regional patterns of air temperature under different radiative forcings play a pivotal role in empirical climate modeling. If the regional temperature reacts linearly to changes of the global radiative forcing caused by different factors, then climate is scalable, in which case gaining insight into the future by using empirical data is feasible. One has to operate with anomalies of air temperature, construct spatial fields using modern or palaeoclimatic data scaled by projections of the global-mean temperature, and to overlay these patterns with regional patterns of modern temperature norms. Some similarities found in palaeo reconstructions of different warm epochs in the past do not provide sufficient arguments either for or against this hypothesis. Such information may be derived from analysis of historical meteorological records. There were two periods in the 20th century when global-mean air temperature rose continuously. The warming between 1920 and 1940 was characterised by a 0.5 ◦ C global temperature increase, while in the subsequent thirty years it decreased by approximately 0.15 ◦ C. Recent warming began in 1972 and continues at present. The global-mean temperature since the beginning of this period has already increased by 0.4 ◦ C; as many studies suggest, these changes may not be explained by climate variability and are most likely attributed to anthropogenic factors (Hegerl et al., 1996, 1997; Wigley et al., 1998). Several early studies reported that changes of the zonal-mean seasonal temperature were similar during the warming in the first half and at the end of the 20th century. This conclusion was reached from analysis of empirical data (Vinnikov, 1986), and comparison of GCM-simulated equilibrium climates under conditions of doubled CO2 , and 2% and 4% increases in the Solar constant (Hansen et al., 1984; Manabe and Wetherald, 1980; Wigley and Jones, 1981). Recent empirical data showed that this preliminary conclusion was wrong, and that the patterns of warming are essentially different. Contemporary warming is greatest
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in mid-latitudes of the Northern Hemisphere, and as follows from the results from comprehensive coupled ocean-atmosphere GCMs, is in accord with a fingerprint of enhanced CO2 forcing. The warming in the first half of the 20th century was most pronounced in the higher latitudes (Nicholls et al., 1996) which is unlikely to have been caused by the anthropogenic factors and may not necessarily be reproduced in the future. Spatial patterns of anthropogenic warming may be studied through comparison of regional temparatures averaged over consecutive time intervals. Changes in annual and seasonal air temperature between 1955–1974 and 1975–1994 have been discussed by Nicholls (1996), the main features being enhanced winter warming over the mid-latitude northern hemisphere continents, and distinct year-round cooling in the north-west North Atlantic and mid-latitudes over the North Pasific. Whether these contemporary changes provide insight into future regional climate is not clear, as the comparison of two time-averaged ‘snapshots’ provides no information on temporal development. Because of the effects of internal stochastic factors in the climate system, the pattern of warming derived from comparison of any two time intervals may be changed in regional details, if compared with a third interval. Consistent patterns of warming with similar regional features in several consecutive time periods are required prerequisites for empirical prediction of climate using the analog approach. In this study air temperature records were used to trace changes in the regional details of the recent warming. Calculations were done using mean monthly air temperature data from approximately 7,000 weather stations of the Global Historical Climatology Network Temperature Database (Peterson and Vose, 1997) and 702 stations with continuous century-scale homogeneous observations at representative sites from the database of the Russian State Hydrological Institute. To minimize biases associated with technogenic influences, most meteorological stations located in the vicinity of big cities and industrial centers were eliminated. Further analysis in this paper was made using data from a subset of 1948 stations with continuous observations over the 1951 to 1997 period shown in Figure 1. Air temperature changes from the reference period 1951–1975 to the periods 1976–1985 and 1986–1997 are shown in Figures 2 and 3. A conclusion in accord with previous results is that climate change is more pronounced in winter than in summer. Summer (i.e., JJA in the northern and DJF in the southern hemisphere) warming over the continents in the last 25 years did not exceed a few tenths of a degree C, which is generally within the range of natural climate variability. Changes of winter and mean annual temperature were more distinct. Regional details include the following: North America. Patterns of temperature anomalies consistently indicate that the recent warmth is greatest in Alaska and central Canada, is less pronounced on the western coast of the U.S.A., and is not detected in the central and eastern parts of the continent. Some minor cooling along the eastern coast in 1976–1985 is detected in the following decade only in the northernmost part of the continent. Remarkably,
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Figure 1. Network of stations with continuous near-surface temperature records over the 1951–1997 period.
winter temperature in two regions in eastern Alaska and central Canada over the 1986–1995 period was lower than in 1976–1985. Europe. Temperature changes over Europe were generally small. In 1976–1985 year-round cooling was detected in central and eastern Europe and Scandinavia, in the next decade it was traceable only in the northeastern region. Asia. Temperature changes over the coastal zone of the Arctic ocean were different in summer and in winter. Summer temperatures indicate minor cooling in Siberia, Chykotka and central Asia traceable through both periods. Winter temperature was rising continuously through both decades. The greatest warming has evolved progressively in Siberia; in the last decade warmth propagated deep into the continent and reached southeastern Asia. Southern Hemisphere. In the southern and eastern parts of Africa the year-round warmth has expanded with time. Little change has been detected in South America. In the recent decade warming continues to evolve in eastern Australia. However, because of the sparse and irregular station network, the conclusions may change when more data become available. These results show that in some regions, among which are continental central Eurasia and Alaska, patterns of warming averaged over the two periods of time are similar, while in others the spatial distribution has changed. Thus it becomes evident that a straightforward downscaling of the global-mean temperature projections to the regional level using transfer functions constructed from palaeo climatic data, or extrapolation of the temperature records from weather stations is not feasible everywhere. An empirical model of regional climate sensitivity could instead be
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Figure 2. Temperature changes from the reference period 1951–1975 to the period 1976–1985. Upper panel: decadal-mean annual temperature; middle panel: decadal-mean DJF temperature; lower panel: decadal-mean JJA temperature.
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Figure 3. Temperature changes from the reference period 1951–1975 to the period 1986–1997. Upper panel: decadal-mean annual temperature; middle panel: decadal-mean DJF temperature; lower panel: decadal-mean JJA temperature.
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used to predict the pattern of the air temperature field for the prescribed magnitude of global warming.
3. Empirical Model of Regional Climate Sensitivity Predictive empirical modeling of climate requires statistically significant correlations between the regional climate variables and one or several parameters of the global climate, and the ability to predict changes of these global parameters in the future. For this study, an empirical model was constructed to predict the regional climate sensitivity, i.e., the spatial pattern of the mean annual air temperature field under the conditions of the future warmer climate. The model is based on the assumption of a link between the regional (Tr ) and the global-mean (Tgl ) annual (or seasonal) near-surface air temperature, expressed by Tr (t) = ar (t)Tgl (t) + br + n(t) ,
(1)
where ar is the regional climate sensitivity, br is region-specific coefficient, and n(t) is noise attributed to the effects of stochastic non-predictable factors. It is further assumed that the noise can be filtered by averaging over the decadal-scale period of time, ti = ti+1 − ti , i.e., that t i+1 n(t)dt → 0 . (2) ti
Assuming that br does not change with time, (1) and (2) could be used to derive the following equation: Tr (ti ) = ar (ti ) · Tgl (ti ) .
(3)
Here Tr and Tgl are the regional and global-mean annual air temperatures averaged over the decade centered around ti and expressed as anomalies with respect to the prescribed reference period. The purpose of the further analysis is to estimate the accuracy of linear approximations (1) and (3), and to calculate the regional sensitivity, ar . Correlation analysis has been applied to study the statistical link between the variations of the regional and global-mean temperature, using two independent data sets. The set of selected 1948 stations with continuous monthly observations spanning the whole period of recent warming (1960–1997) was used to study changes of the regional temperature. Although efforts were made to create a set of data that adequately represents the thermal regime over the whole globe, the results are not shown over the oceans because of the sparse network of the island stations. Global temperature data calculated using a larger independent network of stations was taken from the LINK database (Viner and Hulme, 1997). Local and global mean
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annual temperatures relative to the 1961 to 1990 reference period were calculated through monthly data and smoothed by an 11-point binomial filter to eliminate the high-frequency component. Correlation coefficients between the regional and global-mean annual temperature, calculated using 1961 to 1997 data, are shown in Figure 4. Within the framework of the empirical approach used in this study, the regional climate sensitivity may only be accurately defined and calculated in those parts of the world where the correlation between local and global annual temperatures is high. These regions include the western United States, southern Canada, Alaska, Siberia, southeastern Asia, southern Africa, and Australia. Labeled contours on the inserts in Figure 4 show correlation coefficients in these regions. In eastern Canada, western Europe and Scandinavia, correlation with the global temperature is low. Regional details are illustrated in Figure 5. The rows in Figure 5 correspond to five numbered regions in Figure 4 with high correlations of local and global temperatures, two stations in each region. Filtered departures of local and global annual air temperature from the 1961–1990 average are shown by solid and dashed curves, respectively. The ratio of the inclination of the curves characterizes the response of the local temperature to changes in the global thermal regime, i.e., the regional climate sensitivity. Regional climate sensitivity was approximated by the calculated coefficients of the linear regression between the departures of the local and global-mean temperature, i.e., ar in Equation (3). Although such a definition provides a somewhat fuzzy metric of the effect changes of atmospheric CO2 content, radiative forcing, and sulphate aerosols have on the regional air temperature, it allows quantitative expression of the statistical relations between the thermal regimes on the regional and global scales. The map of regional sensitivity in Figure 6 could be interpreted as an empirical pattern of the annual air temperature change under the conditions of idealized 1 ◦ C global warming relative to the 1961–1990 reference period. Although the inherent limitations of this method do not allow prediction of changes in all parts of the globe, several essential regional details of contemporary warming are captured. According to the results of this study, warming will be most pronounced in the northern parts of continental North America and Eurasia. Under the 1 ◦ C global warming, temperature increases over the North American continent are projected to be between 5–6 ◦ C in southern Alaska and central Canada, and 3–4 ◦ C on the North Slope of Alaska and western coast of the U.S.A. In the central and eastern U.S.A. and eastern Canada warming is expected to be 1–2 ◦ C, although here the correlation with the global temperature and the significance of projection are low. Minor cooling (0.5–1.0 ◦ C) may take place in the southeastern U.S.A. This is questionable, however, because the local tendency toward cooling, although at present confirmed by high correlation with global thermal regime, could change in the future. In Eurasia the center of warming is located in continental Siberia, where the temperature may rise as much as 5–6 ◦ C. Modern data indicate minor
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Figure 4. Map in the center: correlation coefficient between the regional and global mean annual temperature. Inserts: contours of the correlation coefficient in the selected regions.
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Figure 5. Regional trends of the air temperature for 1950–1997 from selected weather stations. Dashed line – global-mean annual temperature; solid line – local annual temperature (7 years running average). 1 – West coast of the U.S.; 2 – Alaska and northern Canada; 3 – Siberia; 4 – South-Eastern Asia; 5 – Southern Africa and Australia.
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Figure 6. Map of the regional climate sensitivity calculated using the best estimate of the linear regression between the global-mean and regional temperature records over the 1960 to 1997 period.
cooling in central Europe and Scandinavia. Regional projections for the Southern Hemisphere, although shown on the map in Figure 6, were constructed using a sparse network of stations and could change in the future if more data become available. The map in Figure 6 was constructed by estimating the linear regression coefficients ar in Equation (3) using 1960–1997 data, i.e., it represents the best estimate of the present regional climate sensitivity and says nothing about how robust the results are in different parts of the world. This question can be addressed by applying a test for statistical significance. Maps in Figure 7 show the lower (ar −1.96σar ) and upper (ar − 1.96σar ) bounds of the 95% confidence interval for ar (upper and lower panels, respectively). As could be expected from Figure 4, the difference between maps, i.e., the standard error is the lowest in Alaska, central Canada, western coast of the U.S.A., in Siberia and south-eastern Asia.
4. Discussion Results from the empirical climate model described here can be used to construct a regional scenario of climate change for the selected projection of the global-mean
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Figure 7. Lower (ar − 1.96σar ) and upper (ar − 1.96σar ) bounds of the estimated regional climate sensitivity (on the 95% confidence level (upper and lower panels, respectively).
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annual air temperature. Uncertainties are associated primarily with global climate sensitivity and projections of the atmospheric concentrations of greenhouse gases and aerosols. Estimates of the global climate sensitivity generated by GCMs vary from 1,5 ◦ C to 4,5 ◦ C (Kattenberg et al., 1996). Some authors suggest that palaeoclimatic data may provide valuable information for narrowing this range (Budyko and Izrael, 1987; Shabalova and Konnen, 1995). Others, however, argue that because of many inherent limitations of the palaeoanalog method such proxy data are not likely to improve the existing situation (Mitchell, 1990). Complicating the problem, projections of the atmospheric concentrations of the greenhouse gases and aerosols constructed using the ‘business as usual’ and ‘smart World’ scenarios result in quite different radiative forcing in the future (Houghton et al., 1996). One of the methods to predict global-mean temperature is based on a simple 1D climate model with prescribed climate sensitivity driven by projection of the equivalent CO2 concentration, which accounts for the cumulative effect of the atmospheric gases and aerosols on the radiative forcing (Houghton et al., 1997). Several such projections have been constructed and used in calculations with 2,5 ◦ C climate sensitivity predicting approximately 1 ◦ C global temperature increase by the middle of the 21st century (Greco et al., 1994). The map in Figure 6 may be used to scale down this projection to the regional level. It would be appropriate to compare the empirical method presented here with comprehensive theoretical climate models. Changes of the near-surface temperature between 1945 and 1995 produced by ensemble averages from coupled HadCM2, ECHAM4/OPYC, and GFDL R30 models were presented by Barnett et al., 1999, and can be compared with the results of this study: North America. The empirical model predicts a pattern of warming, which has its maximum in Alaska and central Canada, is less pronounced along the western coast of the U.S.A., and is small in central and north-eastern U.S.A. In the southeastern U.S.A. and central Mexico temperature changes are small. A similar result was produced by the HadCM2 model, but two other GCMs predicted somewhat different patterns. The ECHAM4 generated a pattern with cooling in Alaska and south-eastern U.S.A., and warming along the SW-NE transect across the whole continent. Results from GFDL indicated almost homogeneous warming over the North America. Eurasia. Pronounced warming in Siberia was captured by both HadCM2 and GFDL, while ECHAM4 predicted small changes with minor to moderate cooling in continental Siberia and Far East. Cooling in Scandinavia was captured only by ECHAM4, in eastern Europe by ECHAM4 and HadCM2. All models predicted consistent temperature patterns in south-eastern Asia with the warmth decreasing from the south to the north. Given these divergences between the patterns predicted by GCMs, the results of such a qualitative comparison are encouraging and indicate that many essential features of regional climate change are confirmed by several independent methods.
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One of the major differences between the two compared methods is that GCMs are based on a deterministic description of climate processes, while the empirical model may only address the problem of predicting climate change on a statistical basis. Because of this difference, the empirical model captures the stochastic component of climate variability, unlike the situation with theoretical climate models. The spatial resolution of the empirical model depends solely on the availability of data, and while it may provide a high level of detail, due to the statistical nature of the method temporal resolution is low compared to GCMs. Because of the necessity of describing major interactions in the climate system deterministically, GCMs are not likely to achieve a high level of spatial detail. They may, however, be used to calculate the evolution of 3-D fields of major climatic variables with a relatively small (less than one hour) time increment. These differences assign certain priorities to the application of climate-change scenarios derived from the two groups of methods in climate impact studies. Because of the inertia many climate-dependent environmental and socio-economical processes produce explicit reactions to relatively slow changes in the ambient parameters, typically on the order of months to years, and are capable of mitigating the effects of higher frequency climate oscillations. Evaluation of climate change impacts often requires a high level of spatial detail that may be provided by empirical climate modeling. Examples are given in studies of the hydrological impacts of climate change on runoff, soil moisture, and cryosphere, and impacts on energy consumption, agriculture and natural vegetation zonality. Alternatively, when constraints associated with temporal resolution become essential, GCM-based scenarios of climate change are more effective. The general concept of empirical climate modeling using correlations between local and global parameters is somewhat similar to statistical downscaling, which is frequently employed to improve the spatial resolution of results derived from GCMs (Wilby, 1997; Wilby and Wigley, 1997). In both methods the goal is to interrelate the characteristic patterns of simultaneous variations of regional and large-scale, i.e., in the extreme case presented here, global atmospheric parameters. Positive results were obtained in the statistical downscaling of climatic variables, and specifically precipitation, by linking them to large-scale atmospheric flow (Hewitson and Crane, 1996; Storch et al., 1993). There is a wide variety of largescale and global predictors of regional climate change, and much remains to be learned about the transfer functions between scales. Further efforts to minimize these uncertainties should prove to be a profitable line of investigation.
Acknowledgements The author is thankful to V. Poljakov of the State Hydrological Institute for his assistance in computer programming, to S. Tett and two anonymous reviewers for their valuable comments and recommendations, and to F. Nelson and A. Klene of
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the University of Delaware for editorial notes. Special thanks are due to Pavel Groisman, NCDC, for assisting with data collection. The research was supported by the U.S.A. National Science Foundation (grants OPP-9907534 and OPP-9896238), Civilian Research and Development Foundation of the U.S.A. (grant RG1-2078), and International Geological Correlation Program 428 ‘Borehole and Climate’.
References Andre, J. C. and Royer, L. F.: 1999, ‘Short-Term Climatic Fluctuations and the Interpretation of Recent Observations in Terms of Greenhouse Effect’, C.R. Acad. Sci. II A 328, 261–272. Arrhenius, S.: 1896, ‘On the Influence of the Carbonic Acid in the Air upon the Temperature of Ground’, Philos. Mag. 4, 237–275. Arrhenius, S.: 1907, Das Werden der Welten, Akademische Verlagsgesellschaft, Leipzig, p. 208. Barnett, T. P., Hasselmann, K., Chelliah, M., Delworth, T., Hegerl, G., Jones, P., Rasmusson, E., Roeckner, E., Ropelewski, C., Santer, B., and Tett, S.: 1999, ‘Detection and Attribution of Recent Climate Change: A Status Report’, Bull. Amer. Meteorol. Soc. 80, 2631–2659. Barnett, T. P., Hegerl, G., Knudson, T., and Tett, S.: 2000, ‘Uncertainty Levels in Predicted Patterns of Anthropogenic Climate Change’, J. Geophys. Res. 105, 15525–15542. Barnett, T. P., Hegerl, G. C., Santer, B., and Taylor, K.: 1998, ‘The Potential Effect of GCM Uncertainties and Internal Atmospheric Variability on Anthropogenic Signal Detection’, J. Climate 11, 659–675. Bjorgo, E., Johannessen, O. M., and Miles, M. W.: 1997, ‘Analysis of Merged SMMR-SSMI Time Series of Arctic and Antarctic Sea Ice Parameters 1978–1995’, Geophys. Res. Lett. 24, 413–416. Budyko, M. I. and Izrael, Y. A. (eds.): 1987, Anthropogenic Climatic Change, Hydrometeoizdat, Leningrad, p. 405. Duchkov, A. D. and Devyatkin, V. N.: 1992, ‘Reduced Geothermal Gradients in the Shallow WestSiberian Platform’, Global Planet. Change 6, 245–250. Gates, W. L., Henderson-Sellers, A., Boer, G. J., Folland, C. K., Kiton, A., McAvaney, B. J., Semazzi, F., Smith, N., Weaver, A. J., and Zeng, Q.-C.: 1996, ‘Climate Models – Evaluation’, in Houghton, J. T. et al. (eds.), Climate Change 1995: The Science of Climate Change. Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, U.K., pp. 229–284. Goody, R., Anderson, J., and North, G.: 1998, ‘Testing Climate Models: An Approach’, Bull. Amer. Meteorol. Soc. 79, 2541–2549. Gosnold, W. D., Todhunter, P. E., and Schmidt, W.: 1997, ‘The Borehole Temperature Record of Climate Warming in the Mid-Continent of North America’, Global Planet. Change 15, 33–45. Greco, S., Moss, R. H., Viner, D., and Jenne, R.: 1994, Climate Scenarios and Socioeconomic Projections for IPCC WG II Assessment, Consortium for International Earth Science Information Network, Washington, D.C., p. 11. Hansen, J., Lacis, A., Ring, D., Russell, G., Stone, P., Fung, I., Ruedy, R., and Lerner, J.: 1984, ‘Climate Sensitivity: Analysis of Feedbacks Mechanisms’, in Climate Processes and Climate Sensitivity, Geophys. Monograph 29, 130–163. Hegerl, G. C., Hasselmann, K., Cubasch, U., Mitchell, J. F. B., Roeckner, E., Voss, R., and Waszkewitz, J.: 1997, ‘Multi-Fingerprint Detection and Attribution Analysis of Greenhouse Gas, Greenhouse Gas-Plus-Aerosol and Solar Forced Climate Change’, Clim. Dyn. 13, 613–634. Hegerl, G. C., von Storch, H., Hasselmann, K., Santer, B. D., Cubasch, U., and Jones, P. D.: 1996, ‘Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method’, J. Climate 9, 2281–2306.
314
OLEG A. ANISIMOV
Hewitson, B. C. and Crane, R. G.: 1996, ‘Climate Downscaling: Techniques and Application’, Clim. Res. 7, 85–95. Houghton, J., Meira, L., Griggs, D., and Maskell, K. (eds.): 1997, An Introduction to Simple Climate Models Used in the IPCC Second Assessment Report, IPCC Technical Paper II, p. 47. Houghton, J. T., Meira Filno, L. G., Callander, B. A., Harris, N., Kattenberg, A., and Maskell, K. (eds.): 1996, Climate Change 1995: The Science of Climate Change, Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, U.K., p. 572. Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G. A., Mitchell, J. F. B., Stouffer, R. J., Tokioka, T., Weaver, A. J., and Wigley, T. M. L.: 1996, ‘Climate Models – Projections of Future Climate’, in Houghton, J. T. et al. (eds.), Climate Change 1995: The Science of Climate Change, Contribution of Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, U.K., pp. 285–358. Lachenbruch, A. H. and Marshall, B. V.: 1986, ‘Changing Climate: Geothermal Evidence from Permafrost in the Alaskan Arctic’, Science 234, 689–696. Levine, R. A. and Berliner, L. M.: 1999, ‘Statistical Principles for Climate Change Studies’, J. Climate 12, 564–574. Lynch, A. H., Chapman, W. L., Walsh, J. E., and Weller, G.: 1995, ‘Development of a Regional Climate Model of the Western Arctic’, J. Climate 8, 1555–1570. MacCracken, M. C., Hecht, A. D., Budyko, M. I., and Izrael, Y. A. (eds.): 1990, Prospects for Future Climate: A Special U.S./U.S.S.R. Report on Climate and Climate Change, Lewis, Chelsea, MI, p. 270. Manabe, S. and Wetherald, R. T.: 1980, ‘On the Distribution of Climate Change Resulting from an Increase in CO2 Content of the Atmosphere’, J. Atmos. Sci. 37, 99–118. Maslanik, J. A., Serreze, M. C., and Barry, R. G.: 1996, ‘Recent Decreases in Arctic Summer Ice Cover and Linkages to Atmospheric Circulation Anomalies’, Geophys. Res. Lett. 23, 1677–1680. Mitchell, J. F. B.: 1990, ‘Greenhouse Warming – Is the Midholocene a Good Analog’, J. Climate 3, 1177–1192. Mitchell, J. F. B. and Johns, T. C.: 1997, ‘On Modification of Global Warming by Sulfate Aerosols’, J. Climate 10, 245–267. Nicholls, N., Gruza, G. V., Jouzel, J., Karl, T. R., Ogallo, L. A., and Parker, D. E.: 1996, ‘Observed Climate Variability and Change’, in Houghton, J. T. et al. (eds.), Climate Change 1995: The Science of Climate Change, Cambridge University Press, Cambridge, U.K., pp. 133–192. North, G. R. and Stevens, M. J.: 1998, ‘Detecting Climate Signals in the Surface Temperature Record’, J. Climate 11, 563–577. Peterson, T. C. and Vose, R. S.: 1997, ‘An Overview of the Global Historical Climatology Network Temperature Database’, Bull. Amer. Meteorol. Soc. 78, 2837–2849. Russell, G. L., Miller, J. R., Rind, D., Ruedy, R. A., Schmidt, G. A., and Sheth, S.: 2000, ‘Comparison of Model and Observed Regional Temperature Changes during the Past 40 Years’, J. Geophys. Res. Atmos. 105, 14891–14898. Santer, B. D., Taylor, K. E., Wigley, T. M. L., Penner, J. E., Jones, P. D., and Cubasch, U.: 1995, ‘Towards the Detection and Attribution of an Anthropogenic Effect on Climate’, Clim. Dyn. 12, 77–100. Shabalova, M. V. and Konnen, G. P.: 1995, ‘Climate-Change Scenarios – Comparisons of Paleoreconstructions with Recent Temperature-Changes’, Clim. Change 29, 409–428. Shackley, S., Young, P., Parkinson, S., and Wynne, B.: 1998, ‘Uncertainty, Complexity and Concepts of Good Science in Climate Change Modelling: Are GCMs the Best Tools?’, Clim. Change 38, 159–205. Smith, D. M.: 1998, ‘Recent Increase in the Length of the Melt Season of Perennial Arctic Sea Ice’, Geophys. Res. Lett. 25, 655–658.
PREDICTING PATTERNS OF NEAR-SURFACE AIR TEMPERATURE
315
Storch, H., Zorita, E., and Cubasch, U.: 1993, ‘Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime’, J. Climate 6, 1162–1171. Stott, P. A. and Tett, S. F. B.: 1998, ‘Scale-Dependent Detection of Climate Change’, J. Climate 11, 3282–3294. Thompson, L. G., Mosleythompson, E., Davis, M., Lin, P. N., Yao, T., Dyurgerov, M., and Dai, J.: 1993, ‘Recent Warming – Ice Core Evidence from Tropical Ice Cores with Emphasis on Central-Asia’, Global Planet. Change 7, 145–156. Vettoretti, G., Peltier, W. R., and McFarlane, N. A.: 1998, ‘Simulations of Mid-Holocene Climate Using an Atmospheric General Circulation Model’, J. Climate 11, 2607–2627. Viner, D. and Hulme, M.: 1997, The Climate Impacts LINK Project, University of East Anglia, Norwich, U.K., p. 17. Vinnikov, K. Y.: 1986, Climate Sensitivity, Hydrometeoizdat, Leningrad, p. 224. Wigley, T. M. L. and Jones, P. D.: 1981, ‘Detecting CO2 -Induced Climatic Change’, Nature 292, 205–208. Wigley, T. M. L., Jaumann, P. J., Santer, B. D., and Taylor, K. E.: 1998, ‘Relative Detectability of Greenhouse-Gas and Aerosol Climate Change Signals’, Clim. Dyn. 14, 781–790. Wilby, R. L.: 1997, ‘Non-Stationarity in Daily Precipitation Series: Implications for GCM DownScaling Using Atmospheric Circulation Indices’, Int. J. Clim. 17, 439–454. Wilby, R. L. and Wigley, T. M. L.: 1997, ‘Downscaling General Circulation Model Output: A Review of Methods and Limitations’, Prog. Phys. Geog. 21, 530–548. (Received 21 December 1999; in revised form 25 January 2001)
