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Present and Future of Modeling Global Environmental Change: Toward Integrated Modeling, Eds., T. Matsuno and H. Kida, pp. 311–337. © by TERRAPUB, 2001. Earth System Modeling—An Integrated Assessment Tool for Environmental Studies R. A. PIELKE, Sr. Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, U.S.A. Abstract—This paper overviews a wide spectrum of influences on the Earth’s climate system. These include external effects, such as variations in the solar energy output, and internal influences. Internal influences include both natural and human-caused effects. The feedbacks associated with the Earth’s climate system are nonlinear and occur over a range of time and space scales. As a result, our ability to predict the future of climate is hindered, and perhaps impossible. We need to assess integrated Earth system climate models to assess the limits of predictability, to evaluate when sudden transitions might occur, and to evaluate the vulnerability of the climate to human-caused and natural forcings. These subjects are discussed in this paper. INTRODUCTION The papers presented at the 14th Toyota Conference further demonstrate that climate processes involve complex, nonlinear interactions within and between the atmosphere, oceans, land, and ice-covered surfaces. The prediction of future climate, as modified by human activities, therefore, is a much more complicated process than implied, for example, by the Intergovernmental Panel on the Climate Convention (IPCC, 1992, 1996) and the United States National Assessment. The potential accuracy of climate prediction is limited because of the presence of feedbacks within and between the components of the climate system which operate on a variety of time scales. Moreover, these feedbacks can produce rapid transitions within the climate system (e.g. Claussen et al., 1999). As discussed by Pielke (1998, 2000), these feedbacks result in climate prediction being a much greater challenge than weather prediction. With weather prediction (as challenging as it is), many feedbacks can be ignored since their time scale is longer than the time period for which a prediction is being made. In addition, direct human intervention in the climate system occur on the same time scale as the climate forecast, yet this human activity has in the past been unpredictable. Table 1 summarizes an undoubtedly incomplete variety of direct human disturbances, and feedbacks within the climate system, which place limitations on the predictability of climate. It is clear from this table that climate prediction 311 Synoptic-scale snowfall and snowmelt (Liston and Sturm, 1998) Biophysical effect of CO2 (Sellers et al., 1992) Biophysical effect of H2 O Ice Surfaces Land Surface Atmosphere Turbulent heat and moisture fluxes (Pielke, 2000) Indirect effect of aerosols as CCN and IN (Rotstayn, 1999) Oceans/Lakes Short-term hours and less Seasonal high latitude sea-ice melting/freezing (Pielke et al., 2000; Parkinson, 1995) Biogeochemical effect of CO2 (Eastman et al., 2001) Seasonal high latitude snow cover variations (Pielke et al., 2000) Biogeochemical effect of H2 O (Lu et al., 2001) Biogeochemical effect of Nitrogen deposition (Vitousek et al., 1997) Sea surface temperature changes (e.g. El Niño) (Castro et al., 2001) Direct effect of aerosols on the radiation budget (Kaneyasu et al., 2000) Medium-range weeks to years Landscape change (Chase et al., 2000a) North Atlantic Conveyer Belt (Gray et al., 1997) Solar output changes (Bard et al., 2000) Radiative effect of CO2 (Shine and Forster, 1999) Introduction of other anthropogenic trace gases (IGACtivities, 2000) Continental ice cap changes in size Long-term decades to several Table 1. Examples of effects on the global-scale climate system. A recent cite for each is listed as an example of papers that demonstrate the importance of these effects. 312 R. A. PIELKE , Sr. Earth System Modeling—An Integrated Assessment Tool 313 involves much more than accurate knowledge of the level of carbon dioxide (or any other subset of climate forcing) in the future and its influence on the atmospheric radiative budget. This perspective has received confirmation even from proponents of the IPCC perspective. Trenberth (1999) stated that “Improving predictions of what the climate will be, not just with idealized scenarios but taking all factors (including solar radiation, land use changes, aerosol effects, and biogeochemical feedbacks) into account is a high priority.” Shine and Forster (1999), in their paper, which discusses the effect of human activity on radiative forms of climate change, state that “...climate change can occur due to nonradiative processes, such as land use changes...” Lorenz (1979) proposed the concept of forced and free variations of weather and climate. He refers to forced variations as those caused by external conditions, such as changes in solar irradiance. Volcanic aerosols also cause forced variations. He refers to free variations as those that “are generally assumed to take place independently of any changes in external conditions.” Day-to-day weather variations are presented as an example of free variations. He also suggests that “free climate variations in which the underlying surface plays an essential role may therefore be physically possible.” There have been no model experiments to assess climate prediction in which all important atmosphere-ocean-land surface processes were included. Existing papers on this subject have been limited to coupled atmosphere-ocean global models (e.g., Cubasch et al., 1994; Larow and Krishnamurti, 1998) or atmospheric models alone (e.g., Bengtsson et al., 1996). In Bengtsson et al. (1996), the ocean sea surface temperature is prescribed and vegetation effects, in their words, are “grossly simplified.” However, if the ocean and lake surfaces, ice surfaces, and/or land surface change over the same time period as the atmospheric changes, then the nonlinear feedbacks (i.e., two-way fluxes) between the air, land, and water eliminate an interpretation of the ocean-atmosphere and land-atmosphere interfaces as boundaries. Rather than “boundaries” these interfaces become interactive mediums. The fluxes that occur between the atmosphere, ocean and lakes, sea ice, and the land surface, must therefore necessarily be considered as part of the predictive system. On the time-scale of what we typically call short-term weather prediction (days), important feedbacks include biophysical (e.g., vegetation controls on the Bowen ratio), snow cover, clouds (e.g., in their effect on the surface energy budget), and precipitation (e.g., that which changes the soil moisture) processes. This time-scale is already considered an initial value problem (Sivillo et al., 1997). Seasonal and interannual weather prediction include the following feedbacks: biogeochemical (e.g., vegetation growth and senesence), anthropogenic aerosols (e.g., through their effect on the long- and short-wave radiative fluxes), sea ice, and ocean sea surface temperature (e.g., changes in upwelling such as associated with an El Niño) effects. For even longer time periods (of years to decades and longer), the additional feedbacks include biogeographical processes (e.g., changes in vegetation species composition and distribution), human-caused landuse changes, and deep ocean circulation effects Fig. 1. Schematic illustration of direct effects and feedback influences with respect to climate prediction. The length of the arrows has no meaning here, although the possible sign each effect could have is shown (from Pielke, 1998). 314 R. A. PIELKE , Sr. Earth System Modeling—An Integrated Assessment Tool 315 on the ocean surface temperature and salinity. In the context of Lorenz’s (1979) terminology, each of these feedbacks are free variations. Figure 1, adapted from Pielke (1998), schematically illustrates direct and feedback effects on global mean temperature, as an example of the complexity of climate prediction. Using Lorenz’s definitions, the direct effects could be considered forced variations, while the feedbacks are part of the free variations. Each of these time-scales must be considered initial value problems because the predictions are dependent on the initial value for at least some aspects of the ocean-atmosphere-land surface coupled system. A range of recent work (Claussen, 1994, 1998; Foley, 1994; Texier et al., 1997) has shown that the initial specification of the land surface exerts a strong control on the subsequent atmospheric circulation in global climate prediction models. Takata and Kimoto (2000) demonstrate that whether soils freeze or not in the winter has an effect on the subsequent seasonal weather cycles over continental spatial scales. Cubasch et al. (1994) suggests in a greenhouse gas warming experiment with a coupled oceanatmosphere model that the time evolution of the modeled global mean warming is “strongly dependent on the initial state of the climate system.” EXAMPLES OF THE EFFECTS GIVEN IN TABLE 1 Landuse change effects Human-caused landscape change includes deforestation, overgrazing, agricultural activities, urbanization, and reforestation. Leemans (1999), for example, shows that most landuse change occurred in the 1900s and that landuse Fig. 2. Estimate changes in land cover and population from 1700 to 1995. The land use from top to bottom in this figure are “cropland”, “pasture”, “forest”, and “other”, respectively (adapted from Klein, 2001). 316 R. A. PIELKE , Sr. Fig. 3. Model output cloud and water vapor mixing ratio fields on the third nested grid (grid 4) at 21 GMT on 15 May 1991. The clouds are depicted by white surfaces with qc = 0.01 g/kg, with the sun illuminating the clouds from the west. The vapor mixing ratio in the planetary boundary layer is depicted by the green surface with qv = 8 g/kg. The tan surface is the ground. Areas formed by the intersection of clouds or the vapor field with lateral boundaries are flat surfaces, and visible ground implies qv < 8 g/kg. The vertical axis is height, and the backplanes are the north and east sides of the grid domain (from Pielke et al., 1997). Earth System Modeling—An Integrated Assessment Tool 317 change is accelerating (Fig. 2). O’Brien (2000) provides additional information on the accelerating rate of tropical deforestation. Pitman and Zhao (2000), and Chase et al. (1996, 2000a) have presented results that indicate a substantial effect on the Earth’s atmospheric circulation thousands of kilometers from where historical landscape changes occurred. These teleconnections in the model, for example, produce major shifts in the polar jet stream, with substantial higher latitude regions of warming and cooling. Other examples of the influence of landscape change on the climate system include Pielke (2000), Bonan (1997), Lauenroth et al. (1999), Pitman et al. (1999), Wang and Eltahir (2000a, b), Baron et al. (1998), Kabat (2001; see section A), Skinner and Majorowicz (1999), Sisk (1999), and Lewis (1998). Burke et al. (2000) show how the heterogeneity of the landscape can influence GCM modeled atmosphere circulations. Figure 3 provides an example of the important role of land surface on deep cumulus convection. Figure 3 (top) is the model result for a specific date of cumulonimbus development over the central Great Plains of the United States using the current landscape. Figure 3 (bottom) uses the natural landscape. Pielke et al. (1997) provides a discussion of this experiment, while Shaw et al. (1997), Grasso (1996), and Ziegler et al. (1997) provide validation of the results for the current landscape situation. Clearly, the alteration of the landscape (with a reduction in transpiration in the natural landscape) has resulted in a reduction in cumulonimbus cloud activity in the model. The published assessments of land-surface temperatures also ignored humancaused landscape changes, except for urbanization (Easterling et al., 1999). Crowley (2000), and Delworth and Knutson (2000) for example, ignore landuse change effects on temperature trends in their assessments of climate change. Crowley ignores this effect in his conclusions regarding temperature changes over the past 1000 years! These changes can affect both the local and regional temperature record as the landscape cover is altered. Pielke et al. (1999), for example, can explain the observed warming and drying in July and August in south Florida as due to the conversion of the natural landscape to its current human-dominated form. Pielke et al. (2000a) documented substantial spatial variation in temperature trends in eastern Colorado, some of which are undoubtedly associated with landscape change in the 20th century. For instance, Segal et al. (1988, 1989) and Stohlgren et al. (1998), document the major role of irrigation on the climate in this region including a cooling of summer maximum temperatures along the eastern slope of the Colorado Rocky Mountains. Without considering how landscape change has influenced surface temperature change, why should it be concluded that surface temperature data sets should be able to document trends due to increased anthropogenic greenhouse gases and aerosols? Global and regional temperature trends assessed using the NCEP Reanalysis 1000–850 mb thickness values for the period 1979–1996 are consistent with trends evaluated for the lower troposphere using MSU satellite data (Pielke et al., 1998a, b; Chase et al., 2000b). Both data sets found no consistent global temperature change in the lower troposphere during this period. In contrast, the 318 R. A. PIELKE , Sr. Fig. 4. RAMS/GEMTM coupled model results—the seasonal domain-averaged contributions to maximum daily temperature, minimum daily temperature, precipitation, and leaf area index (LAI) due to: f 1 = natural vegetation, f2 = 2 × CO2 radiation, f 3 = 2 × CO2 biology, f12 = interaction of natural vegetation and 2 × CO 2 biology, f 13 = interaction of natural vegetation and 2 × CO 2 biology, f 23 = interaction of 2 × CO 2 for radiation and biology, and f 123 = the interaction of all three factors (from Eastman et al., 2001). surface data shows warming. If each data set are accurate, as implied by the recent NRC Panel on the reconciliation of MSU and surface data (NRC, 2000; Hurrell and Treberth, 1996), the radiative effect of increased carbon dioxide and other greenhouse gases and aerosols must be processed differently by the real climate system, than represented in the GCMs. Landuse change is one possible reason for the differences, since the surface temperature data would respond more to local landscape changes. Biogeochemical effects As carbon dioxide concentrations continue to increase, this enrichment will provide a more favorable environment for plant growth with, as of yet, unknown consequences on the Earth’s climate system. Anthropogenic nitrogen deposition (and other trace gas deposition) also significantly alters the biochemistry of the vegetation. Hayden (1998) summarizes ecosystem feedbacks on climate at the landscape scale. Doherty et al. (2000) and de Noblet-Duchoudré et al. (2000) demonstrate the significant role of vegetation feedbacks within the climate system. Earth System Modeling—An Integrated Assessment Tool 319 In the central grasslands of the United States, using a coupled atmosphericbiogeochemical model Eastman et al. (2001) found that the effect of doubled carbon dioxide would be improved water use efficiency and greater vegetation growth. The net effect, on time periods as short as a growing season according to the coupled models, would be a cooler daytime environment and a warmer nighttime. Figure 4 reproduced from Eastman et al. (2001) shows that landuse change and the enrichment effect of carbon dioxide have more immediate effects on the regional weather during the growing season, than the radiative effect of carbon dioxide. While this result certainly does not mean we should not be concerned about the radiative effect of increased carbon dioxide and other anthropogenic greenhouse gases over longer time periods, it does mean that the biological effect of enhanced carbon dioxide is likely to be important in influencing climate on the regional scale (and probably global scale) in the long term. Indirect effect of aerosols The collidial stability of clouds that results when pollution cloud condensation nuclei (CCN) are inserted into the atmosphere has been proposed as a major component altering the Earth’s climate system (Pielke, 1984; Cotton and Pielke, 1995, and references therein). In recent work, Rotstayn (1999) alters the parameters in the microphysical representation in a GCM to show, for example, a resultant major shift in the geographical pattern of rainfall in the Equatorial Pacific Ocean. He demonstrates that the generation of rainfall from clouds is a very nonlinear process. Combined with the direct albedo effect of increased CCN on albedo, the net result of this spatially and temporally varying anthropogenic input of aerosols into the atmosphere is a large and nonlinear response within the Earth’s climate system. Other papers, which discuss the significant indirect effect of aerosols on climate, include Lohmann and Feichter (2001), Lohmann et al. (2000), and Rotstayn et al. (2000). Sea-ice feedbacks The melting of sea ice within GCMs is perhaps the largest warming feedback in these models. However, the exposure of the Arctic Ocean could promote enhanced snowfall on the adjacent land masses (Ewing and Donn, 1956). Figures 5(a)–(d) illustrate for the summer the complicated feedbacks between the sea ice and adjacent land masses that can occur. To create these figures, the RAMS model (Liston and Pielke, 2000) was integrated for the warm season of the year with assimilated observed sea ice and with the ice removed. As evident in the figures the near surface air temperatures are actually warmer over the Arctic Ocean (by over 1°C in large areas) when the sea ice absorbs solar radiation and transfers some of this energy as sensible heat back into the atmosphere. Without the sea ice, while the Earth system gains heat through the reduction of reflected solar insolation, the atmosphere is cooler on this time scale. This difference in surface temperature is communicated into the troposphere and could result in a weaker arctic frontal region when sea ice is present, than if it were absent. 320 R. A. PIELKE , Sr. Fig. 5. Simulated mean surface (2 m) temperature (K) for the month of June 1995, for runs (a) without sea-ice data and (b) with sea-ice data. Both model runs used assimilated sea-surface temperature fields and simulated ice-phase physics in the soil column. (c) Mean sea-ice concentration for the month of June 1995 based on NOAA 1° × 1° data, and (d) mean difference (with ice-without ice) in surface temperature for June 1995. (Model runs completed by Liston and McFadden (2000), Colorado State University.) Biogeographic effects The movement of entire biomes based on GCM simulations of atmospheric changes ignores the feedbacks that can occur due to changes in vegetation species composition. Foley (1994), and others have shown that feedbacks from the vegetation are essential for realistic simulations of paleoclimate conditions. Neilson and Marks (1994) found that different water use efficiencies within a biogeographic model produced dramatically different vegetation distributions in response to the same GCM climate change experiment. As an illustration of this effect, Figs. 6 through 8, from Liston et al. (2001) show alterations in snow melt and associated turbulent energy fluxes if a portion of the Arctic tundra were converted to shrubs from tundra (Fig. 6(a) illustrates the Earth System Modeling—An Integrated Assessment Tool 321 Fig. 5. (continued). observed vegetation, while Fig. 6(b) illustrates the vegetation when moist tundra and wet tundra were replaced with shrub tundra). The higher holding capacity of shrubs results in a delayed melt period, as the wind blown snow in the winter is more evenly distributed across the landscape. Even though this experiment did not permit a change in the larger-scale atmospheric wind flow or temperature, a significant feedback still occurred due to the change in vegetation type. Other effects The prevalence of natural and man-caused fires is also a major component of the Earth’s climate system. Many of their effects on the Earth’s environment are summarized in Levine et al. (1999). The biogenic aerosol emitted clearly has a radiative effect, as discussed in Shine and Forster (1999), but the alteration of the land surface energy and water budget is also important, but as of yet, relatively little research has been performed to look at this effect. Amiro et al. (1999) found, for example, that burned regions in the boreal forest of Canada were up to 6°C Fig. 6. Simulated 1987 end-of-winter snowdepth distributions for (a) observed vegetation, and (b) shrub-enhanced vegetation. The wind arrow shows the prevailing storm-wind direction that redistributes the snow. End-of-winter snowdepth histograms for (c) observed vegetation from (a) and (d) shrub-enhanced vegetation (from (b)) (from Liston et al., 2001). 322 R. A. PIELKE , Sr. Earth System Modeling—An Integrated Assessment Tool 323 Fig. 7. Time evolution of spring snow disappearance for observed vegetation and shrub-enhanced snowcover in 1987 (top) and 1990 (bottom). In the shrub-enhanced simulation, ridges became snow-free earlier (dark shading), and shrub-covered areas became snow-free later (light shading), compared to the observed vegetation simulation (from Liston et al., 2001). Fig. 8. Difference in snowfree date resulting from shrub enhancement for 1987. Positive (negative) numbers indicate the number of days longer (shorter) the vegetation surface remained snowcovered when shrubs were enhanced (from Liston et al., 2001). 324 R. A. PIELKE , Sr. Fig. 9. Carbon sequestration as an integrated Earth system issue. warmer than surrounding areas, and remained warmer even 15 years after the fire. Pinty et al. (2000), in a recent paper, suggests that human-caused fires affect the Earth surface albedo at continental scales. Shepherd (2000) summarizes the role of the stratosphere within the climate system. He discusses the two-way coupling that occurs between the stratosphere and the troposphere which includes both chemical and dynamical interactions. Thus if an anthropogenic perturbation of the troposphere occurs, there are consequences associated with this coupling which reach into the stratosphere, and then feedback into the troposphere. Carbon sequestration To reduce carbon dioxide in the atmosphere, it has been proposed to sequester carbon in the soil through deliberate agricultural management practices (Houghton et al., 1993; Unruh, 1995). However, this procedure has not been assessed in an integrated Earth system perspective (Showstack, 2000). If, for example, water vapor flux into the atmosphere is increased and/or the albedo of the Earth’s surface is reduced, the net radiative effect of sequestering carbon dioxide could be an increase of anthropogenic heating of the atmosphere! Figure 9 illustrates this effect. This example of an Earth system issue illustrates why the evaluation of the carbon cycle must be coupled with the water and energy cycles. They cannot be evaluated independently. The proposed National Science Foundation WaterEnergy-Biota (WEB) initiative (http://cires.colorado.edu/hydrology; James, 2000) recommends this type of integrated Earth system perspective. The recognition that carbon is just one component of the Earth’s environmental system is echoed by Running (2000) who states that “Atmospheric carbon dioxide in itself is not dangerous, it actually helps plants grow faster. But scientists see it as a canary in the coal mine, the leading indicator of other global scale human impacts on the biosphere, the sum total of living organisms on the land and in the oceans.” Earth System Modeling—An Integrated Assessment Tool 325 FALLACY OF DOWNSCALING FOR LONG-TERM CLIMATE MODELING The application of statistical and dynamic downscaling as applied to numerical weather prediction is a very appropriate and valuable tool to improve the spatial and temporal skill of weather projections. However, dynamic downscaling from the radiative effects of CO2 and aerosol GCM sensitivity experiments cannot provide improved skill for several reasons. First, with respect to dynamic downscaling, there is not a feedback upscale to the GCM from the regional model, even if all of the significant large-scale (GCM scale) human-caused disturbances were included. Second, the GCMs also have a spatial resolution that is inadequate to properly define the lateral boundary conditions of the regional model. As shown by Anthes and Warner (1978), the lateral boundary conditions are the dominant forcing of regional atmospheric models associated with propagating features in the polar westerlies. With numerical weather prediction, the atmospheric observations used in the analysis to initialize a model retain a component of realism even when degraded to the coarser model resolution of a global model. This realism persists for a period of time (up to a week or so), when used as lateral boundary conditions for a regional numerical weather prediction model. This is not true with the GCM climate change experiments where observed data does not exist to influence the predictions. A regional model cannot reinsert model skill, that is dependent on lateral boundary conditions, no matter how good the regional model. If this conclusion is disagreed with, the first step to demonstrate that the regional climate model has predictive skill is to integrate an atmospheric GCM with observed SSTs for several seasons into the future. The GCM output would then be downscaled using the regional climate model. There is expected to be some regional skill associated with the seasonal scale inertia of SSTs (Castro et al., 2001), and this needs to be quantified. This level of skill, however, will necessarily represent the maximum skill theoretically even possible with GCMs applied to forecasts years and decades into the future since, for these periods, SSTs must also be predicted, and not specified! Such experiments have not been systematically completed. Indeed, does the concept of predictive skill even make sense when we cannot verify the models until decades into the future (Sarewitz et al., 2000)? The statistical downscaling from large-scale climate change experiments, besides requiring that the GCMs are accurate predictions of the future, also require that the statistical equations that are used for downscaling remain invariant under changed regional atmospheric and land-surface conditions. There is no way to test this hypothesis. In fact, it is unlikely to be valid since the regional climate is not passive to larger-scale climate conditions, but is expected to change over time, both due to direct human-caused landscape changes and in response and feedback to the larger-scale atmospheric conditions. Examples of the current paradigm of downscaling are illustrated in Climate Impacts LINK (1997, their figure 5) and Atmosphere (1999). In the Atmosphere 326 R. A. PIELKE , Sr. Fig. 10. 1979–1997 trends in degrees C/19 years for December–January–February NCEP 1000– 500 mb layer-average temperature (from Chase et al., 2000b). article, model results are presented for the year 2050 as if they were predictions. Climate Impacts LINK (1999, their figure 3) similarly present model results (in this case their GCM output) for the decades of the 2050s. However, if the Earth system feedbacks occur across the spectrum of space and time scales, and first order human perturbations are left out of the GCMs, they cannot possibly provide plausible forecasts of the Earth system 50 years from now. A more appropriate procedure to use regional climate models is to apply them as sensitivity experiments where the relative effect of regional human disturbance is investigated (e.g., the radiative and biological effect within the regional climate system of increased CO2; landuse change—both historical and estimates of future possibilities; the radiative and biogeochemical effects of air pollution, such as ozone, nitrogen deposition, etc.). The knowledge of these sensitivities would be much more useful to policymakers than the existing downscaled regional climate results, which are inappropriately communicated as projections. Since many decisions are regional and local in scale, the understanding of the involvement of the regional climate system within the entire spectrum of environmental change and variability (and not just the radiative effect of increased CO2 and aerosols) is a more effective methodology to protect society from environmental threats. SUDDEN SHIFTS IN THE EARTH’S CLIMATE The natural Earth’s climate system has abrupt changes over short time periods. Stahle et al. (2000), for example, documents a vast multi-decadal megadrought in the 16th century over North America which would have catastrophic economic impact if it occurred in the next 50 years. This drought far Earth System Modeling—An Integrated Assessment Tool 327 Fig. 11. Simulated January reference height temperature difference (current-natural, C; from Chase et al., 2000a). exceeded any drought of the 20th Century, including the 1930s Dust Bowl years. Song (2000) documents protracted wet and dry periods in China since 1470 A.D. On the more recent time scales, where human intervention within the Earth’s climate system has occurred, there have been diverse regional and temporal trends. Wang and Allard (1995), for example, report on nearly continuous surface air cooling of a region in northern Quebec for the period 1947–1992 despite warming of the western Arctic region of North America. This large spatial variation in temperature trends was confirmed by Chase et al. (2000b) who documented strong lower tropospheric cooling in northeastern Canada from 1979–1997 and strong lower tropospheric warming in northwestern North America (Fig. 10). Chase et al. (2000a) shows that landuse change, particularly in the tropics, could have caused much of the observed lower tropospheric change since 1979 (Fig. 11). Other examples of abrupt changes in climate during the past century include Yonetani and McCabe (1994), Changnon et al. (1993), and Schuster et al. (2000). Schwing and Moore (2000) illustrate how such abrupt changes in climate can have an immediate effect on the biosphere. They documented a change in sea surface temperatures off of the California coast that went in two years from the warmest on record to the lowest in decades. Species composition in zooplankton communities off the Oregon and British Columbia coast shifted from warm water and oceanic species to exclusively subarctic species. Multiple climate equilibrium associated with biosphere-atmosphere interactions are discussed by Claussen (1998), and Wang and Eltahir (2000c), which is one explanation for abrupt climate changes. The concept of chaos, including multiple equilibria, is reviewed in Zeng et al. (1993). Schuster et al. (2000), for example, documented from ice cores in south central North America that the termination of the Little Ice Age occurred abruptly in the decade around 1845 A.D. and continues to the present day in this warmer regime. 328 R. A. PIELKE , Sr. Fig. 12. Schematic of different classes of prediction (from Pielke, 2000). CONSEQUENCES As presented earlier in this paper, neither the GCMs or the regional climate models include all of the significant human effects on the climate system. These additional human perturbations of the climate system, and the variety of feedbacks across a wide spectrum of time and space scales reinforce the conclusion of a “discernible human influence”, but also limit our ability to predict the future climate. The combined effects of human landuse change, the biogeochemical effect on the atmosphere due to increased CO2, and the microphysical effect of pollution aerosols, for example, have not yet been included in these models. As stated by Shine and Forster (1999), who reviewed the effect of human activity on radiative forcing, the conclusions of the IPCC may not prove to be robust when a broader set of human-caused radiative effects are included. Thus, the IPCC model runs should only be interpreted as sensitivity experiments, not forecasts, projections, or even scenarios. They are certainly not Earth system models. Figure 12 presents a schematic where climate model prognostic calculations can be presented as one of four types: • sensitivity studies; • scenarios; Earth System Modeling—An Integrated Assessment Tool 329 Fig. 13. Use of ecological vulnerability/susceptibility in environmental assessment (from Pielke and Guenni, 1999, adapted from Tenhunen and Kabat, 1999). • projections; • perfect foresight. In sensitivity analysis, one of the important forcings on climate is perturbed (e.g., a doubling of CO 2 concentrations and its radiative effect). This is the approach used to produce the IPCC reports. This method could, in principle, generate skillful predictions but only if other climate forcings, such as landuse change and the biological effect of doubled CO2 were unimportant. A scenario approach would apply if all of the important direct and feedback effects on climate were included, yet the results are sensitive to initial conditions. A scenario is then a realization out of an ensemble of possible simulations. If the realizations could cover the ensemble space, then the model results could be considered realistic projections. The term “prediction” is variously misused to describe sensitivity studies and scenarios. In contrast to weather prediction, where we feel that we understand all of the important physical effects and feedbacks, the climate system is a much more complex dynamical system. If skillful climate prediction is not possible beyond some time scale, a vulnerability assessment approach is the preferred scientific approach to provide policymakers useful information (Pielke Jr., 1998). Figure 13 as reproduced from Pielke and Guenni (1999), illustrates an example of this approach with respect to water resources. With this technique, needs of the policymaker are better met in that the focus is on decisions and not predictions (Sarewitz et al., 2000). This vulnerability perspective is further discussed in Pielke and Guenni (1999), and Kabat (2001; section E). The Earth system modeling approach should be constructed around the vulnerability/susceptibility paradigm. All important aspects of the Earth system 330 R. A. PIELKE , Sr. need to be represented instead of having the atmospheric/or ocean-atmospheric GCMs drive the other components of the Earth system. THE EARTH SYSTEM HEAT BUDGET It is useful to consider the Earth’s heat budget as part of a coupled system instead of focusing on “global warming” as primarily an atmospheric focus. Conceptually, the Earth system’s heat budget, when averaged for annual time scales and longer, can be considered with respect to the following bins. Atmosphere: the troposphere predominately, since about 80% of the mass of the atmosphere resides in this layer. Oceans: the ocean and larger, deep lakes can be considered as containing two layers; above the thermocline and the remainder (the deep ocean). Sea ice: heat energy is stored when sea water is frozen, and heat is released when it melts. The thickness of the ice and its areal coverage describe this heat store. Continental glaciers: heat energy is stored when snow accumulates over time and is converted into fresh water ice masses. The heat is released into the atmosphere when melting and evaporation, or sublimation occurs, and into the oceans when icebergs calf into the sea and melt. Land: heat energy is stored in the ground when soil becomes frozen (permafrost develops), and heat is released when the permafrost melts. The amount of heat storage is a function of the wetness of the soil when it freezes, the heat capacity and density of the soil, and the depth of freezing. Heat is also stored when the soil and underlying rocks warm, and heat is released when the soil and subsurface rocks cool. The amount of heat storage depends on the wetness of the soil, and the soil heat capacity and density. Lost to space: heat energy is continuously lost to space. With an increase in heat input within the Earth system, it is inadequately known how the heat lost to space would change. By averaging over annual time periods, shorter-term variability in heat storage (such as winter snows over non-glaciated regions which disappear in the spring) do not affect this budget. Similarly, Antarctic sea ice is predominantly melted and refrozen each year (Gloersen et al., 1992). External to the Earth’s climate system, are the irradiance of the Sun and volcanic eruptions. Human disturbances, such as the effect of an increase in downwelling longwave electromagnetic energy due to CO2 and other anthropogenic greenhouse gases, have their initial effect in the atmosphere. However, interfacial fluxes can transfer this heat energy input into one or more of these heat storage bins. Similarly, landuse change can directly alter the net electromagnetic radiation received at the surface with resultant changes in the heat storage in the atmosphere and in the ground. The transfer between the bins of the heat energy needs to be assessed for different time scales. Existing evidence, such as illustrated on pages 319–328, suggests these transfers are highly nonlinear and illustrate the chaotic dynamical nature of the Earth system. Earth System Modeling—An Integrated Assessment Tool 331 Examples of the assessment of these terms include Levitus et al. (2000) who reported an approximate 2 × 1023 Joules added heat content to the world oceans from 1948 to 1998, the NRC (2000) study which found no statistically lower tropospheric temperature change up to the present, and Nagurnyi et al. (1999) who reported on a 3% change of average ice thickness in the Arctic Ocean basin between 1972–1992. Levi (2000), in contrast, report that the Arctic ice cover is 40% thinner than it was 20 to 40 years ago. Permafrost depth has no trend (http: //www.geography.uc.edu/CALM). Changes of solar luminosity of almost 3 watts per meter squared from 1979 to 1991 have been observed associated with sunspot activity (Parker, 2000). A consequence of considering the Earth’s heat budget in the context of heat pools is that fluxes of heat can occur between different components of the system over a variety of time scales. Heat could be inserted into the atmosphere through enhanced greenhouse gas downwelling, but stored in the deeper ocean. Only when a threshold is passed might the consequences of the heat immediately reappear from the ocean into the atmosphere. Such a potential nonlinear response is quite distinct from the nearly monotonic increase in tropospheric temperature predicted by the IPCC. Such a storage of heat deeper in the ocean was found in the Levitus et al. (2000) study who reported on substantial changes in heat content in the 300 meter to 1000 meter layers of each ocean, and in depths greater than 10000 meters of the North Atlantic. If this is a continuing trend, the deep ocean could provide a long-term storage for heat energy; an effect which has not been represented realistically in any ocean-atmosphere GCM on this time scale. Similarly, the melting of sea ice and/or permafrost could provide a buffer to tropospheric temperature changes until most of the ice melted. Alternatively, the excess heat associated with anthropogenically elevated greenhouse gases could be radiated out into space. Gray (2000, personal communication), for example, proposes that if the hydrologic cycle were invigorated, cumulus cloud updrafts would be more concentrated such that the lower relative humidity in the subsiding air between the clouds would promote increased long-wave radiative loss to space. The understanding of the Earth system including the interactions among the component systems is not understood. However, it is clear that no one component (such as the atmosphere) can be considered, within the context of climate, as separate and distinct entities. This is a major reason why Earth system models are needed as an assessment tool in climate. CONCLUSION This paper briefly overviews the complexity of the Earth’s climate system. Direct anthropogenic and natural effects, and feedbacks within the climate system, occur across the spectrum of time and space scales. The carbon cycle is an important component of the system, but is not the only important driver of long-term climate change. There are important consequences associated with the need to consider the Earth’s climate as a complex, coupled nonlinear dynamical system. First, there 332 R. A. PIELKE , Sr. are several human perturbations to the Earth system in addition to the radiative effect of increased carbon dioxide and other anthropogenic gases, which are equal or larger in their regional and global effect. These perturbations include landuse change, the biogeochemical effect of enhanced carbon dioxide, and the microphysical effect of pollutant aerosols. Second, since the land, oceans and lakes, and sea ice interacts with the atmosphere through complex, nonlinear feedbacks, deterministic prediction beyond some time period is unattainable. At present this limit appears to be the seasonal lead time (e.g., see Landsea and Knaff, 2000). For the periods of decades and longer, the uncertainty associated with human deliberate and inadvertent interaction (such as associated with as of yet undiscovered technology) within the climate system assures that the future system will not be predictable. As a result of this limit on prediction, a broader assessment of risk to climate and other environmental stresses is proposed (Pielke and Guenni, 1999; Kabat, 2001). This vulnerability-sensitivity assessment methodology would permit evaluations of what component of society is most at risk to perturbations in the environment. Policymakers can determine where resources should be placed to minimize the risk, or to insure against loss. Vorosmarty et al. (2000), for example, demonstrates that the greatest threat to drinkable water over the next 50 years is not climate change as produced by any of the GCMs, but population growth. 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