Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment

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1 Climate Dynamics (2005) 25: DOI /s Guiling Wang Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment Received: 14 April 2005 / Accepted: 28 June 2005 / Published online: 6 October 2005 Ó Springer-Verlag 2005 Abstract This study examines the impact of greenhouse gas warming on soil moisture based on predictions of 15 global climate models by comparing the after-stabilization climate in the SRESA1b experiment with the pre-industrial control climate. The models are consistent in predicting summer dryness and winter wetness in only part of the northern middle and high latitudes. Slightly over half of the models predict year-round wetness in central Eurasia and/or year-round dryness in Siberia and mid-latitude Northeast Asia. One explanation is offered that relates such lack of seasonality to the carryover effect of soil moisture storage from season to season. In the tropics and subtropics, a decrease of soil moisture is the dominant response. The models are especially consistent in predicting drier soil over the southwest North America, Central America, the Mediterranean, Australia, and the South Africa in all seasons, and over much of the Amazon and West Africa in the June July August (JJA) season and the Asian monsoon region in the December January February (DJF) season. Since the only major areas of future wetness predicted with a high level of model consistency are part of the northern middle and high latitudes during the non-growing season, it is suggested that greenhouse gas warming will cause a worldwide agricultural drought. Over regions where there is considerable consistency among the analyzed models in predicting the sign of soil moisture changes, there is a wide range of magnitudes of the soil moisture response, indicating a high degree of model dependency in terrestrial hydrological sensitivity. G. Wang Department of Civil and Environmental Engineering, University of Connecticut, 261 Glenbrook Road, U-2037, Storrs, CT 06269, USA gwang@engr.uconn.edu Tel.: Fax: A major part of the inter-model differences in the sensitivity of soil moisture response are attributable to differences in land surface parameterization. 1 Introduction Climate change is one of the top threats for the planet Earth in the twenty-first century. An increasing body of evidence (e.g., Inter-governmental Panel on Climate Changes (IPCC) 2001) supports the notion that observed warming during the past several decades is attributable to human activities, to the increase of anthropogenic greenhouse gases (GHG) in particular. As the concentration of GHG in the atmosphere goes up, temperature is expected to continue to rise well beyond the twenty-first century. Through strong coupling between the Earth s energy and water cycles, the GHGinduced warming will alter hydrological conditions across the global continents. Such hydrological changes can have more profound adverse effect on human welfare than the warming itself. Among them soil moisture changes, which reflect changes in agricultural water availability, are of special concern due to their direct impact on crop productivity and therefore on the global ecosystem s capacity to supply food and other life essentials. Under similar soil-vegetation properties and topographical conditions, soil moisture climatology depends largely on precipitation and the atmosphere s evaporative demand. It is a consensus among different global climate models that temperature will increase across the globe as GHG concentrations go up (Cubasch et al. 2001), causing across-the-board increases of the atmosphere s evaporative demand. However, global warming takes place with a distinctive spatial pattern, and climate sensitivity is highly model-dependent (Cubasch et al. 2001). Therefore the magnitude of warming and subsequently the magnitude of the increase in potential evaporation vary from region to region and from model

2 740 Wang: Agricultural drought in a future climate to model. Model predictions of how precipitation responds to GHG forcing are characterized by even less certainty. In general, warming intensifies the global hydrological cycle (e.g., Milly et al. 2002), thus increasing the globally averaged precipitation, evaporation, and runoff. However, whether precipitation in a specific continental region will increase or decrease and by how much are both model-dependent (Cubasch et al. 2001) and season-dependent. In addition, other factors such as changes in the timing of snow melting and the presence of long soil-moisture memory in some regions further complicate hydrological responses to GHG warming. As a result, it remains a challenge to qualify how soil water availability will change. Whether soil moisture will increase or decrease in a specific region is not a straightforward question to address. Manabe et al. have published a series of papers on the response of terrestrial hydrology (including soil moisture) to global warming based on experiments conducted using various versions of the spectral climate model developed by scientists at the Geophysical Fluid Dynamics Laboratory (GFDL) of NOAA. While their earlier papers (e.g., Manabe et al. 1981, Manabe and Wetherald 1987, Mitchell and Warrilow 1987, Wetherald and Manabe 1999) provided important insights on the physical mechanisms underlying soil moisture response to GHG warming, details of the soil moisture changes have been updated based on a more recent version of the GFDL spectral model (Wetherald and Manabe 2002, Manabe et al. 2004a, b). These papers collectively documented a clear global and seasonal pattern of soil moisture changes and provided mechanistic explanations. This pattern of soil moisture changes is characterized by summer dryness and winter wetness in the middle to high latitudes, and dryness all year in semi-arid regions. Under the influence of GHG forcing, evaporation over oceans increases throughout the year. The seasonal variation of this evaporationincrease is small because the seasonal variation of sea surface temperature is small. The enhanced oceanic evaporation leads to an increase of continental precipitation as atmospheric circulation transports some of the extra water vapor towards land. Over continents in the middle and high latitudes, temperature is very low during winter and very high during summer. As temperature increases, the increase of saturation vapor pressure is much smaller during winter than during summer due to the nonlinearity of the Clausius-Clapeyron equation (Wetherald and Manabe 2002). The result is an evaporation-increase across the middle and high latitudes that is minimal in winter and large in summer. This strong seasonality of continental evaporation-changes and the general increase of precipitation give rise to the predicted winter wetness and summer dryness. Over most semi-arid regions, due to the absence of runoff, soil moisture seeks the level to balance evaporation and precipitation (Milly 1992). Because precipitation changes are quite small compared to the increase of potential evaporation, soil moisture in semi-arid regions tends to decrease in a warmer world. The general mechanisms identified by the aforementioned studies should apply regardless of the modeling framework. In fact, the summer dryness and/or winter wetness in mid-to-high latitudes found in the GFDL spectral model were also predicted by several other models (e.g., Gregory et al. 1997, Douville et al. 2002). However, not all models agree (e.g., Schlesinger and Mitchell 1987, Kellogg and Zhao 1988, Meehl and Washington 1988, Seneviratne et al. 2002). The specific patterns of soil moisture response at regional to continental scales are clearly model-dependent. The multimodel ensemble averaging approach can be very useful in reducing uncertainties related to model dependence. This study examines the response of soil water storage to the increase of atmospheric GHG concentrations based on results from 15 models that participate in the IPCC s 4th assessment. Instead of studying hydrological response based solely on all-model averages, we propose an index to quantify the level of consistency among different models in predicting the direction of responses, and present biased model averages that reflect behaviors of the majority models. Sect. 2 describes the methodology taken and models analyzed. The model consistency index and biased averages of hydrological changes are defined in Sect. 3. Sect. 4 presents results based on model consensus and model inter-comparison. Conclusions and a discussion are given in Sect Models and methodology There are a total of 21 global climate modeling groups participating in the IPCC 4th Assessment. Different groups using different models have carried out the same set of experiments specifying various scenarios of atmospheric CO 2 concentration changes as well as control simulations for both pre-industrial and presentday climates. The experimented CO 2 changes include various SRES scenarios (Nakicenovic and Swart 2000) and idealized 1%-per-year increases, among others. The course of CO 2 change matters if the focus is on quantifying the rate of climate changes. However, at this stage we have yet to address the question of whether soil moisture in any specific region of the globe will increase or decrease as CO 2 concentrations continue to rise. In addition, our analysis indicates that for a same model and over a same region, experiments with different courses and magnitudes of CO 2 concentration-changes produce soil moisture changes that share essentially the same spatial pattern. Therefore, the results of this study regarding the direction of hydrological changes do not depend on any specific scenario of CO 2 changes. On the other hand, the magnitude of soil moisture changes will be analyzed to provide some indication on differences in model sensitivity. Our analyses therefore will be based on the same control and same scenario experiment for all the different models.

3 Wang: Agricultural drought in a future climate 741 We choose the SRESA1b stabilization experiment and compared the after-stabilization climate with the pre-industrial control climate. This choice was made to maximize the number of models included in this analysis at the time of the analysis, output for the preindustrial control and SRESA1b experiment are available from a larger number of models than for any other combination of control and experiment. However, not all models include soil moisture data in their output. Our analysis only includes those models (a total of 15) for which soil moisture data are available for both the SRESA1b experiment and the pre-industrial control simulation at the time of writing. The official names of these models and their originating groups are listed in the following: (1) CCSM3: US, National Center for Atmospheric Research (2) ECHAM5/MPI-OM: Germany, Max Planck Institute for Meteorology (3) FGOALS-g1.0: China, LASG / Institute of Atmospheric Physics (4) GFDL-CM2.0: US Dept. of Commerce / NOAA / Geophysical Fluid Dynamics Laboratory (5) GFDL-CM2.1: same as above (6) GISS-AOM: US NASA / Goddard Institute for Space Studies (7) GISS-EH: same as above (8) GISS-ER: same as above (9) INM-CM3.0: Russia, Institute for Numerical Mathematics (10) IPSL-CM4: France, Institut Pierre Simon Laplace (11) MIROC3.0(medres): Japan, Center for Climate System Research in the University of Tokyo, National Institute for Environmental Studies, and Frontier Research Center for Global Change (12) MIROC3.0(hires): same as above (13) MRI-CGCM2.3.2: Japan, Meteorological Research Institute (14) PCM: US, National Center for Atmospheric Research (15) UKMO-HadCM3: UK Hadley Centre for Climate Prediction and Research / Met Office In the pre-industrial control simulation (referred to as PIcntrl hereafter), CO 2 concentration is prescribed to be at the pre-industrial level (275 ppm); in the SRESA1B stabilization experiment (referred to as SRESa1b hereafter), CO 2 concentration follows the SRESA1B scenario from 2000 to 2100 and stabilizes (at 720 ppm) beyond For all models, the PIcntrl output is available for several hundreds of years. We derive the mean and standard deviation of climate variables for each model based on 100 years of the model integration. For SRESa1b, most models continue to run for over 100 years after the stabilization of CO 2 concentration (i.e., after 2100). For such models, we derive the after-stabilization mean and standard deviation of climate variables based on the 100 years immediately after stabilization, i.e., This is done using results from only one realization when multiple realizations are available. Due to the long averaging period used, there is little difference between climate statistics derived from different realizations of the same model. Models for which data are not available beyond 2100 in SRESa1b include the GISS-AOM model for which two realizations are available, the GISS-EH model for which three realizations are available, and the ISPL-CM4 model for which only one realization is available. For these models, the SRESa1b climate is estimated based on the period , and averaged among multiple realizations when available (i.e., among a total of 50 years for GISS-AOM, 75 years for GISS-EH, and 25 years for ISPL-CM4). Using the period as opposed to causes slight underestimate of the magnitude of climate changes, but otherwise does not influence the results of this study. For each model, the analysis is based on differences between the PIcntrl and SRESa1b climates, which are attributed to the impact of CO 2 increase from its pre-industrial level to approximately 720 ppm. The primary focus of this study is on soil moisture, but precipitation changes will be also analyzed as an important forcing for soil moisture changes. Model outputs are available at subdaily, daily, monthly, and yearly resolutions. Monthly data will be used in this study. Among all the models analyzed, the commonly available variable reflecting the level of soil wetness is the soil moisture content W (in mm). This quantity depends not only on the actual soil wetness but also on the depth of soil in the land surface scheme. The latter varies significantly from model to model. Unless indicated otherwise, our analysis will be based on the mean soil moisture content changes (i.e., SRESa1b-PIcntrl) normalized by the standard deviation of the PIcntrl to eliminate the dependence on soil depth: dw m;k ¼ lðw m;kþ EXP lðw m;k Þ CTRL ð1þ rðw m;k Þ CTRL where m ranges from 1 (January) to 12 (December), k denotes the model name, and l(w m,k ) EXP and l(w m,k ) CTRL are the mean soil moisture content in month m estimated for model k based on the SRESa1b and PIcntrl climates respectively. For consistency, precipitation will be analyzed based on normalized changes dp m,k that is defined similarly to dw m,k. In addition to differences in parameterization of physical and dynamical processes, models also differ in spatial resolution. The resolution of the 15 models range from coarse (e.g., 4 5 for the GISS-AOM) to medium (e.g., for the GFDL models) to fine (e.g., in CCSM3 and finer in MIROC3.0(hires)). To facilitate comparison, the results of all models in our analysis are re-grided to a medium resolution.

4 742 Wang: Agricultural drought in a future climate 3 Consistency index and biased average Due to the strong model dependence of hydrological response to CO 2 concentration increase, different models may predict changes of different signs for the same region and the same variable. Moreover, climate sensitivity differs substantially among models too, leading to a wide range of magnitude in the normalized hydrological changes. The direction of hydrological changes (i.e., increase or decrease) determined based on the all-model average (as presented in Figs. 3a, b, 4a, b) will be unavoidably biased towards models with higher climate sensitivity. In extreme cases where an overly sensitive model predicts a wetter condition while all others predict a drier condition or vice versa, the multiple model average may reflect qualitatively the results from the one exception instead of the majority of the models. Such extreme situations do occur for the models analyzed in this study over some areas, and they occur more frequently in soil moisture changes than in precipitation changes. Simple all-model average can yield misleading results. To quantify the level of consistency among different models in predicting the direction of hydrological changes in a warmer world, we propose a consistency index (I c ) that can be evaluated for each grid point: ( Np NpþNn ðnp > NnÞ Ic ¼ Nn ð2þ ðnp\nnþ NpþNn where Np is the number of models that predict an increase of the hydrological quantity of interest, and Nn is the number of models that predict a decrease. So the sign of Ic indicates the direction of changes predicted by the majority of the models, and the magnitude of Ic reflects the level of majority that is always larger than 50%. Models that make up the majority will be referred to as the majority models. Obviously, majority models differ from minority models in the sign of hydrological response they predict. If at one grid point the number of models that predict a decrease (increase) is larger than the number of models that predict an increase (decrease), the majority models for this specific grid point are the models that predict a decrease (increase). In dealing with climate change predictions from multiple models, several studies (e.g., Raisanen and Palmer 2001, Giorgi and Mearns 2002) in the past used performance-based weighted averaging. For example, Giorgi and Mearns (2002) related the weight of each model to the individual model s reliability in simulating the present climate. Such a reliability-weighted average can be very useful for quantifying the magnitude of certain climate responses after we develop a fairly good understanding of the nature of such responses. For soil moisture however, we have yet to determine the direction of its response to GHG changes. As will be shown in Figs. 1 and 2, the majority-model average and allmodel average of seasonal precipitation climatology are essentially the same. This indicates that models predicting opposite signs of soil moisture changes seem to perform at a similar level in reproducing the control climate. The performance-based ensemble averaging method is therefore not appropriate for this study. To be consistent with our primary interest in the direction of soil moisture changes, here we propose a biased average, which is a simple average among the afore-defined majority models. Obviously, any analysis based on such averages is biased towards the majority. Note that the models that make up the majority can differ from grid point to grid point. At regional to global scales, the biased average can theoretically be influenced by all models and therefore does not reflect the behavior of a specific subset of the analyzed models. It is important to point out here that majority models are not necessarily more reliable than minority models. It is not unlikely that an individual model may differ from the majority models because it includes an improved parameterization not available to others or because it accounts for a relevant mechanism or feedback while others do not. This is especially true for parameterizations related to vegetation and soil. For example, Seneviratne et al. (2005) investigated the behaviors of 8 different AGCMs (some of them are among the 15 analyzed here) related to soil moisture memory issues and found that water-holding capacity of the soil is one important factor that accounts for significant intermodel differences. There is a wide range of complexity of land surface schemes used in the 15 GCMs analyzed in this study. Systematic comparison of their parameterizations is not given here because detailed information is available for only some of the models. 4 Results Before changes in the simulated climate are analyzed, we first examine the pre-industrial climatology of precipitation simulated by the 15 models to gain some confidence in model performance. While different models share similar global patterns of precipitation distribution, the magnitude of precipitation shows considerable model dependence at local to regional scales. Averaged among all of the 15 models, the mean annual precipitation is shown in Fig. 1a. Since observational record for the pre-industrial precipitation is sparse, here we use the observed twentieth-century mean annual precipitation from the CRU data (New et al. 2000) (Fig. 1b) as a surrogate. It is clear that the all-model average of precipitation climatology and the CRU climatology share similar spatial patterns. Significant biases exist in western North America, and biases of smaller magnitude are evident over various areas elsewhere. At both global and regional scales, the all-model average resembles the CRU climatology better than any individual model does (not shown here), reflecting the advantage of multimodel ensemble approach over single-model analysis. Comparison at the seasonal time scale (December-

5 Wang: Agricultural drought in a future climate 743 Fig. 1 Pre-industrial climatology of a annual precipitation (in mm), c December January February (DJF) precipitation (in mm/ day), and e June July August (JJA) precipitation (in mm/day) based on the 15-model ensemble average; Climatology of b annual precipitation (in mm), d DJF precipitation, and f JJA precipitation based on the period of the CRU data (New et al. 2000) January-February (DJF) in Fig. 1c, d; June-July-August (JJA) in Fig. 1e, f) indicates that the degree of similarity between the all-model averages and the CRU data during the JJA season is higher than during the DJF season. As explained in Sect. 3, performance-weighted averaging is not appropriate for this study. However, since the biased average proposed in Sect. 3 reflects the majority-model behavior in hydrological response, a relevant question is whether the majority models perform better in simulating the control climate than the simple all-model averages. Figure 2 presents the majority-model average of precipitation in DJF (Fig. 2a, b) and JJA (Fig. 2c, d), with the majority being defined based on the predicted direction of precipitation changes in panels a and c and based on the predicted direction of soil moisture changes in panels b and d. The lack of shading over land areas is due to the lack of a meaningful majority (with a consistency index lower than 60%). Comparing the shaded areas in Fig. 2 and the allmodel averages during corresponding seasons in Fig. 1 indicates that there is little difference between the majority-model averages and all-model averages. The skill of the majority models in reproducing the control

6 744 Wang: Agricultural drought in a future climate a b c d Fig. 2 The majority-model averages of precipitation in DJF (a, b) and JJA (c, d) over land areas where the consistency index among the 15 models exceeds 60% climate is therefore similar to that of the minority models. The response of hydrological processes to GHG increases, as reflected by changes from the PIcntrl to SRESa1b climates, can be strongly season-dependent. Normalized precipitation changes predicted by all of the 15 models demonstrate moderate to strong seasonality. However, for the normalized soil moisture changes, strong seasonality is found in only some of the models, including ECHAM5/MPI-OM, GFDL- CM2.0, GFDL-CM2.1, INM-CM3.0, IPSL-CM4, MIROC3.0(med-res), and PCM. The normalized soil moisture changes predicted by other models either show a very weak seasonality or remain essentially unchanged from January through December. The summer dryness and winter wetness documented by Manabe et al. (2004a, b) and others are therefore apparent in only some of the models. Where seasonal variation of soil moisture changes does exist, the biggest contrast is between summer and winter. Subsequent analyses will therefore be based on precipitation and soil moisture content changes averaged in the (DJF) season and those averaged in the JJA season for every model. 4.1 Consensus among multiple models Changes in the DJF season Figure 3a, b shows the normalized precipitation and soil moisture changes from PIcntrl to SRESa1b during the DJF season averaged among all 15 models. Areas of small changes (less than 0.1 for normalized precipitation changes and less than 0.2 for normalized soil moisture changes) over land are not shaded. Figure 3c, d presents the consistency index (in percentage) for predicting precipitation and soil moisture changes in DJF, where the magnitude indicates the level of the majority (in percentage of all analyzed models) and the sign reflects the direction of hydrological changes predicted by the majority models. Although anything larger than 50% makes a majority, only areas with a majority level higher than 60% (i.e., 9 or more out of 15) are shaded. Based on the definition of the consistency index in Eq. 2, the larger its magnitude is, the more consistent different models are in predicting the direction of hydrological responses. Over areas that are not shaded, the 15 models split at 8 versus 7 in predicting how a specific hydrological variable will change. The biased average, i.e.,

7 Wang: Agricultural drought in a future climate 745 averages, Fig. 3 g, h shows the relative standard deviation, which is the standard deviation among different majority models normalized by the biased average. Bea b c d e f g h Fig. 3 Results on normalized differences in precipitation and soil moisture content during the December January February (DJF) season between the SRESa1b experiment and pre-industrial control (SRESa1b-PIcntrl): 15-model averages of normalized changes in a precipitation and b soil moisture content; consistency index (in %) of the 15 models in predicting the direction of changes in c precipitation and d soil moisture content; the majority-model averages of normalized changes in e precipitation and f soil moisture content; relative standard deviation (among the majority models) of normalized changes in g precipitation and h soil moisture content. In c and d, the magnitude of consistency index reflects the level of the model majority, and the sign indicates direction of changes. Continental areas not shaded in c h indicate places where the 15 models split at 8 versus 7 in predicting the direction of changes average among the majority models, of normalized precipitation and soil moisture changes are presented in Fig. 3e, f. As a measure for the uncertainty of the biased

8 746 Wang: Agricultural drought in a future climate cause the biased average and its uncertainty are estimated only over regions where the models consistency index is higher than 60%, the lack of shading over land areas in Fig. 3e h is due to the lack of a meaningful majority. In DJF, precipitation increases substantially across the northern middle and high latitudes as well as over the Antarctic. Increases of smaller magnitudes are predicted in tropical Africa and much of South America. Precipitation is predicted to decrease over southwestern North America, Central America, the Mediterranean, the Asian monsoon region, and in some coastal areas of both South America and South Africa. Models are highly consistent in predicting precipitation responses: the consistency index reaches 100% for the precipitation increase over much of the northern middle latitudes, high latitudes in both hemispheres, and tropical East Africa; it reaches 100% as well for the precipitation decrease in southwestern North America and the Mediterranean. Not surprisingly, the biased average of precipitation changes (Fig. 3e), although larger in magnitude than the all-model average, shares the same spatial pattern as the all-model average (Fig. 3a). Due to the general enhancement of potential evaporation rate in a warmer world, soil moisture decreases over both areas of reduced precipitation and areas of slight precipitation increase. Therefore soil moisture is predicted to increase in only part of the areas where precipitation increases. These include a small fraction of South America, tropical East Africa, and part of the northern middle latitudes. The increase of soil moisture is especially large over central Eurasia. In the northern high latitudes, the all-model average shows a decrease of soil moisture despite the significant increase of precipitation. During DJF, models are generally less consistent in predicting soil moisture response than precipitation response. The consistency index for soil moisture changes is especially low over a major part of South America, part of Central and North Africa, the Indian monsoon region, Siberia, and the mid-latitude Northeast Asia, where models split regarding whether soil will get drier or wetter. These areas, with the notable exception of Siberia and Northeast Asia, happen to be places where the all-model average shows a small increase of precipitation. Such a coincidence is probably due to the across-the-board enhancement of potential evaporation that competes with the increased precipitation in determining how soil moisture will respond. Elsewhere in the middle and high latitudes (including most of North America and Eurasia), the majority of the models predict an increase of soil moisture (although the all-model average of changes is negative over some areas). The model consistency level is especially high in predicting the large increase of soil moisture in much of the central Eurasia (Fig. 3d, f). Other areas of soil moisture increase predicted with a high degree of model consistency include the Horn Africa and a small fraction of South America. Models consistently predict a drier future soil condition over southern North America, Central America, a large fraction of South America, the Mediterranean, northern North Africa, South Africa, the Asian monsoon region, and Australia. Soil moisture reduction in these areas results from both reduced precipitation and enhanced potential evaporation. In addition to differences in magnitude across the global continents, the allmodel average and the biased average have opposite signs in northern high latitudes. The uncertainty in predicting the magnitude of precipitation changes is in general lower than that in predicting soil moisture changes (Fig. 3g h). The relative standard deviation among the majority models for precipitation changes is especially small (less than 50%) over the majority of the northern mid- and high-latitudes where the model consistency is high. This does not hold for the uncertainty in predicting soil moisture changes. The relative standard deviation exceeds 50% almost everywhere and exceeds 100% over more than half of the areas where a majority exists Changes in the JJA season Figure 4a, b presents the all-model average of normalized precipitation and soil moisture changes in JJA, Fig. 4c, d shows the consistency index, the corresponding biased averages are presented in Fig. 4e, f, relative standard deviations in Fig. 4 g, h. As in Fig. 3, biased averages and the relative standard deviation among the majority models are estimated only over regions where the models consistency index is higher than 60%. The lack of shading over continental areas indicates small magnitude of changes in Fig. 4a, b and indicates lack of model consistency in Fig. 4e h. In JJA, precipitation is predicted to decrease over an extensive portion of the globe including tropical and mid-latitude North America, Central America, northeastern South America, the Mediterranean, western North Africa, South Africa, and Australia. Models are especially consistent in predicting the precipitation decrease in the Mediterranean, South Africa, and Australia. Precipitation increases across the high latitudes of both hemisphere, but the increase is of a smaller magnitude than during DJF in northern latitudes. Substantial increase is also predicted over a small fraction of South America, the Horn Africa, mid-latitude Northeast Asia, and the Asian monsoon region. Compared with the DJF season, the areas of increased precipitation predicted with a high level of model consistency is much smaller. The majority-model averages and the all-model averages share essentially the same spatial pattern, with the former being large in magnitude. In JJA, the spatial pattern of the all-model average of soil moisture changes is somewhat similar to that in DJF except that the area of soil moisture increase in JJA is much smaller. Moreover, according to Fig. 4b d, over most of the areas where the all-model average yields a wetter condition, the models split regarding the sign of

9 Wang: Agricultural drought in a future climate 747 predicting a strong increase of soil moisture during DJF (Fig. 4d vs. Fig. 3d). Elsewhere over the globe, drier soil conditions are predicted with a high degree of model consistency. The models are almost unanimous in predicting drier conditions over southwestern North America, Central America, part of South America, Siberia, the Meditera b c d e f g h Fig. 4 Same as Fig. 3, but for the June July August season soil moisture changes. Among these areas of low model consistency are Central Africa and the Horn Africa, much of the Middle East, the Indian-Asian monsoon region, a small area in mid-latitude North America, and central Eurasia. Note that the lack of model consistency in central Eurasia during JJA occurs over exactly the same areas where there is high model consistency in

10 748 Wang: Agricultural drought in a future climate ranean, part of West Africa, South Africa, and Australia, with the degree of dryness varying from region to region (Fig. 4d, f). Most of these regions are predicted to become drier in future during the DJF season too (Fig. 3d, f). They are therefore the hot spots for agricultural drought in a future world with CO 2 -induced warming. Despite the high degree of model consistency in predicting the direction of changes over many regions, the standard deviation of the magnitude of changes among the majority models is very high in the JJA season. This is especially true for soil moisture changes, and the relative standard deviation exceeds 100% over the majority of the northern mid- and high-latitudes. Uncertainty in predicting the magnitude of precipitation changes in the JJA season is also higher than in the DJF season, with the relative standard deviation exceeding 50% almost everywhere Synthesis While the models agree with each other that soil will become drier in some areas and wetter in others during DJF, a drier condition is predicted during JJA over almost all places where there is a moderate or high level of consistency among the different models. By comparing Fig. 4 and Fig. 3, it is clear that soil moisture during winter (i.e., DJF in northern hemisphere and JJA in southern hemisphere) tends to change towards the same direction as precipitation does, which is consistent with the relatively small magnitude of evaporative increase during cold seasons. During summer, despite the increase in precipitation over many regions, the dominant response is a decrease of soil moisture, consistent with the relatively large magnitude of potential evaporation-increase during warm seasons. In other words, the impact of increased potential evaporation due to higher air temperature outweighs the impact of precipitation increase during warm seasons. Therefore the general mechanisms responsible for summer dryness and winter wetness documented by Manabe et al. (2004) apply. However, the result is not always summer dryness and winter wetness in the middle and high latitudes. Major areas of exception exist in more than half of the models analyzed in this study, as evident in Fig. 3d and 4d. These areas include Siberia, the majority of midlatitude Eurasia, and a small fraction of mid-latitude North America. During DJF over Siberia and mid-latitude Northeast Asia, despite the fairly large increase of precipitation (Fig. 3a, e) and the relatively small increase of potential evaporation expected in a cold environment, there is no model consensus of how soil moisture will respond (Fig. 3d). Similarly, over the majority of central Eurasia in JJA, although precipitation changes are small and potential evaporation is expected to be substantially enhanced in a warm environment, there is also no model consensus on soil moisture changes (Fig. 4d). Slightly over half of the models predict a future dryness during winter in Siberia and northeastern Asia, and a future wetness during summer in most of central Eurasia. This counterintuitive response of soil moisture described above is likely to have resulted from the season-to-season carryover effect of the soil storage, sometimes referred to as the inter-season soil moisture memory (e.g., Koster and Suarez 2001, Seneviratne et al. 2005). Specifically, over Siberia and northeastern Asia, as GHG concentration increases, soil moisture decreases in JJA (Fig. 4d, f) by so much that the soil storage depleted during summer may not be fully replenished before soil becomes frozen again in the fall, which eventually causes year-round dryness in many models. Similarly, over much of central Eurasia and as a response to the increase of GHG concentration, soil moisture increases in DJF (Fig. 3d, f) by so much that the increased water storage in the soil sustains through the enhanced evaporation during summer, eventually leading to year-round wetness in many models in JJA. As examples, Fig. 5 shows the seasonal cycles of soil moisture simulated by CCSM3 for SRESa1b (solid line with open circles) and PIcntrl (solid line with asterisks) averaged over a small area in central Eurasia (Fig. 5a) and over part of Siberia (Fig. 5b). CCSM3 is one of the models that do not show an obvious summer dryness and winter wetness W (mm) W (mm) J F M A M J J A S O N D a b Area: 60W80W, 45N60N Area: 100W120W, 60N75N 850 J F M A M J J A S O N D Fig. 5 Seasonal cycles of soil water content averaged over a small area in central Eurasia a and over part of Siberia b, simulated by the CCSM3 model. Solid lines with open circles are results for the SRESa1b after-stabilization climate and solid lines with asterisks are for the pre-industrial control climate

11 Wang: Agricultural drought in a future climate 749 signal in the middle and high latitudes. The mechanism underlying the expected summer dryness and winter wetness causes the increase of soil moisture to be smaller in summer than in winter or causes the decrease of soil moisture to be larger in summer than in winter. As a comparison, Fig. 6 presents the same quantity but simulated by the GFDL-CM2.1 model, one of the models that show a clear summer dryness and winter wetness signal. In South Europe and the southern part of mid-latitude North America, the majority of the models predict year-round soil dryness. The level of model consistency in predicting such dryness is especially high in South Europe (Figs. 3d, 4d). Figure 7 shows the soil moisture seasonal cycle averaged over South Europe (10W-45E, 36N-50 N) for the PIcntrl and the SRESa1b climates simulated by CCSM3 and GFDL-CM2.1. Both models predict year-round dryness with no clear seasonality in the amount of soil moisture decrease over this region, which contrasts their differences shown in Figs. 5 and 6. In the mid-latitude North America, the level of model consistency in predicting year-round dryness varies significantly with space, as evident in Figs. 3d and 4d, indicating different spatial patterns of soil moisture changes in different models. Further details for this region will be presented later as a showcase for model inter-comparison (Table 3). W (mm) W (mm) a South Europe, GFDLCM J F M A M J J A S O N D b South Europe, CCSM J F M A M J J A S O N D 100 a Area: 60W80W, 45N60N Fig. 7 Seasonal cycles of soil water content averaged over South Europe for the pre-industrial control climate (solid lines with asterisks) and for the SRESa1b after-stabilization climate (solid lines with open circles): a the GFDL-CM2.1 model; b the CCSM3 model Model inter-comparison W (mm) W (mm) J F M A M J J A S O N D b Area: 100W120W, 60N75N J F M A M J J A S O N D Fig. 6 Same as Fig 5, but for the GFDL-CM2.1 model The results presented in Sect. 4.1 emphasize the similarity among different models without considering the performance of each individual model. When results from different models are compared, it is striking that no single model stays within the majority across all continents, and no single model is excluded from the majority across all continents either. An individual model may predict changes of the same sign as the majority over one region and predict signs opposite to the majority over another region. To quantify the performance of each model against the majority, a similarity index is estimated over areas where the level of model consistency exceeds 60% (i.e., shaded areas in Figs. 3c, d, 4c, d). If Nt is the total number of grid points where the model consistency index exceeds 60%, and the specific model k is among the majority at a number Nk of the Nt grid points, the similarity index for model k can be defined as Nk/Nt. A similarity index of 0.8, for example, means that a model is among the majority over 80% of the areas where a meaningful majority does exist. In addition to differences in the direction of changes, models also differ substantially in the magnitude of such changes, reflecting a wide range of model sensitivity to GHG

12 750 Wang: Agricultural drought in a future climate concentration changes. Here we use the root mean square of the normalized changes for soil moisture and precipitation to demonstrate such differences in model sensitivity. Both the similarity index and the root mean square of normalized changes can be evaluated over the globe and over a specific region of interest as well. Table 1 presents the similarity index followed by the root mean square (in parentheses) for different models and different hydrological variables in DJF and JJA estimated for the global continents. For comparison purpose, the similarity index and root mean square are also estimated for the biased averages (by treating them as if they were results from a super model) and listed in Table 1. From Table 1 it is evident that the inter-model variation of both the spatial pattern and the magnitude are relatively small for the normalized precipitation changes, and such variation is much more pronounced for normalized soil moisture changes. For precipitation changes in both DJF and JJA, except for one outliner (IPSL), the global similarity indices of all other models are close to or significantly higher than 80%, indicating that the spatial pattern of precipitation changes among different models are quite similar. The root mean square of normalized precipitation changes ranges from approximately 0.5 to 1.1. For soil moisture changes in both seasons however, the global similarity index of almost all models (with few exceptions) is lower than that for precipitation changes, indicating that the spatial pattern of soil moisture response is more model-dependent. In addition, the root mean square of normalized soil moisture changes ranges from 1.1 all the way up to 4.6. Compared with the high degree of model similarity in predicting precipitation response, the diverse soil moisture response indicates that differences in land surface schemes contribute significantly to the model dependence of predicted soil moisture changes. Note that results in Table 1 reflect the performance of each individual model at the global scale when measured against the biased average. Their comparison with the biased average may differ significantly from region to region. As an example, Table 2 shows the model similarity index and root mean square for normalized soil moisture changes in DJF and JJA estimated for four specific regions: the United States (130W-60 W, 30N-45 N), the Amazon (90W-30 W, 20S-10 N), Europe (15W-60E, 30N-75 N), and West Africa (20W-15E, 5N-25 N), as visualized in Fig. 8. These regions are chosen because majority of the models predict a future drought but the level of majority is not spatially uniform. The bold fonts in Table 2 highlight models for which the similarity index reaches 90% or higher, suggesting a spatial pattern of soil moisture change signs that is essentially the same as that reflected by Figs. 3f or4f (i.e., the majority); on the contrary, the italic fonts highlight models with similarity index equal to or less than 60%, indicating a spatial pattern that is very different than the majority. For example, the similarity index derived from the CCSM3 model for the JJA season is 0.51 over the US and 0.91 over the Amazon. This implies that CCSM3 predicts a future drought over 51% of the US areas where the majority of the analyzed models predict a drought throughout, and the CCSM3 results agree with the model-majority in the direction of soil moisture changes over more than 90% of the areas in Amazon. It is clear from Table 2 that an individual model can perform similarly to the model-majority in one region and very differently in another, or among the majority models in one season and among the minority in another. For example, the GFDL-CM2.1 model spatial pattern of the direction of soil moisture changes are essentially the same as the majority during both seasons in the Amazon, and during JJA in the US and West Africa; however, the same model performs very differently than the majority during DJF in the US and West Africa. In other examples, the FGOALS-g1.0 model performs very similarly to the majority in all four regions except for the DJF season over West Africa; and the MIROC3.0(medres) model performs similarly to the Table 1 Model similarity index measured against the majority and root mean square evaluated based on the whole globe for predicted hydrological changes in December January February (DJF) and June July August (JJA) The similarity index (values before the parentheses) reflects the fraction of the global continents where an individual model is among the majority in predicting the direction of hydrological changes; the root mean square (values within the parentheses) measures the magnitude of such changes Models Precipitation DJF Precipitation JJA Soil Moisture DJF Soil Moisture JJA CCSM (0.9) 0.85 (0.6) 0.65 (4.2) 0.77 (4.3) ECHAM5/MPI-OM 0.93 (1.1) 0.86 (0.9) 0.83 (0.9) 0.85 (1.1) FGOALS-g (0.7) 0.84 (0.7) 0.69 (3.9) 0.91 (3.9) GFDL-CM (0.9) 0.82 (0.7) 0.81 (1.3) 0.81 (1.2) GFDL-CM (1.0) 0.87 (0.6) 0.80 (1.6) 0.91 (1.5) GISS-AOM 0.86 (0.6) 0.83 (0.5) 0.61 (3.7) 0.70 (3.5) GISS-EH 0.87 (0.6) 0.78 (0.5) 0.74 (2.0) 0.73 (2.4) GISS-ER 0.79 (0.7) 0.77 (0.5) 0.75 (1.7) 0.68 (1.9) INM-CM (0.7) 0.87 (0.6) 0.80 (3.0) 0.71 (3.0) IPSL-CM (0.9) 0.65 (0.5) 0.74 (3.0) 0.80 (4.8) MIROC3.0(hires) 0.91 (1.0) 0.88 (0.8) 0.89 (3.0) 0.80 (2.8) MIROC3.0(medres) 0.89 (1.1) 0.79 (0.6) 0.85 (3.0) 0.76 (2.5) MRI-CGCM (0.5) 0.86 (0.6) 0.78 (4.6) 0.53 (4.0) PCM 0.92 (0.8) 0.87 (0.7) 0.84 (1.4) 0.72 (1.2) UKMO-HadCM (1.0) 0.90 (0.7) 0.61 (4.0) 0.81 (4.0) Biased average 1.00 (0.8) 1.00 (0.6) 1.00 (2.2) 1.00 (2.4)

13 Wang: Agricultural drought in a future climate 751 Table 2 Same as Table 1, but for soil moisture changes evaluated for four different regions Models US Amazon Europe West Africa DJF JJA DJF JJA DJF JJA DJF JJA CCSM3 0.87(1.0) 0.51(1.2) 0.72(2.2) 0.91(1.8) 0.63(2.5) 0.84(3.1) 0.64(2.9) 0.57(2.2) ECHAM5/MPI-OM 0.74(0.9) 0.95(1.1) 0.77(0.9) 0.82(0.9) 0.71(1.0) 0.76(1.6) 0.71(1.1) 1.00(1.5) FGOALS-g (1.5) 1.00(2.0) 0.82(2.0) 0.97(2.1) 0.92(2.1) 0.95(2.6) 0.60(3.2) 1.00(2.8) GFDL-CM (0.8) 0.90(1.1) 0.88(0.7) 0.85(0.9) 0.85(1.7) 0.82(0.8) 0.60(2.1) 0.98(2.9) GFDL-CM (1.4) 0.98(0.9) 0.91(0.7) 0.97(0.8) 0.74(2.5) 0.86(1.0) 0.67(2.6) 1.00(2.3) GISS-AOM 0.68(1.2) 0.50(1.5) 0.57(1.7) 0.33(2.1) 0.74(3.9) 0.87(3.9) 0.80(3.3) 0.52(2.8) GISS-EH 0.68(1.1) 0.68(1.3) 0.78(1.2) 0.55(1.2) 0.96(2.6) 0.92(3.2) 0.77(1.8) 0.98(1.6) GISS-ER 0.66(0.9) 0.77(1.2) 0.75(0.8) 0.70(1.0) 0.90(3.0) 0.85(3.2) 0.90(1.7) 0.48(1.7) INM-CM (0.5) 1.00(0.7) 0.60(3.1) 0.48(3.6) 0.94(1.5) 0.81(1.9) 0.80(3.3) 0.69(2.8) IPSL-CM4 0.19(2.4) 1.00(8.0) 0.68(5.3) 0.60(5.2) 0.66(2.9) 0.82(6.7) 0.60(4.1) 0.83(1.8) MIROC3.0(medres) 0.93(3.1) 0.96(3.2) 0.78(1.8) 0.97(2.0) 0.93(3.4) 0.84(2.9) 0.40(3.2) 0.42(3.3) MIROC3.0(hires) 0.91(3.8) 0.97(3.4) 0.73(3.3) 0.88(3.8) 0.93(2.5) 0.88(2.5) 0.81(2.4) 0.84(2.5) MRI-CGCM (2.8) 0.80(2.5) 0.68(2.0) 0.88(2.0) 0.81(5.3) 0.63(4.6) 0.69(2.3) 0.95(2.2) PCM 0.92(0.3) 0.63(0.4) 0.65(0.4) 0.72(0.6) 0.95(1.6) 0.93(2.0) 0.51(2.2) 0.40(2.4) UKMO-HadCM3 0.54(0.8) 0.79(0.7) 0.88(1.4) 0.74(2.0) 0.56(4.0) 0.74(4.2) 0.83(1.9) 0.59(1.3) Biased average 1.00(1.3) 1.00(1.8) 1.00(1.4) 1.00(1.5) 1.00(1.9) 1.00(1.6) 1.00(1.8) 1.00(1.6) The bold face fonts highlight similarity indices equal to or higher than 0.9, and the italic fonts highlight similarity indices equal to or lower than 0.6 Table 3 Seasonal and spatial patterns of soil moisture changes over mid-latitude North America, as simulated by each of the 15 models Models DJF JJA Fig. 8 Visualization of the four areas used in Table 2 majority in the US, the Amazon, Europe, but very differently in West Africa. The magnitude of normalized hydrological changes differs from model to model, and for the same model varies with season and region as well, as shown in Table 2 (by the numbers in parenthesies). However, it is evident from Table 2 that the seasonal and spatial variations of the magnitude of soil moisture changes are generally smaller than the model-to-model differences. The large model-to-model differences in magnitudes, as also shown by the large relative standard deviation in Figs. 3 h and 4 h, reflect strong model dependence of the terrestrial hydrological sensitivity to GHG concentration changes, which poses a challenge for evaluating the impact of future climate changes. Note that the similarity index only reflects the fraction of total areas of similarity. It does not carry any location-related information. Two models with the same similarity index can have very different spatial patterns. Using the mid-latitude North America as an example, CCSM3 ± ± ECHAM5/MPI-OM FGOALS-g1.0 GFDL-CM2.0 GFDL-CM2.1 + GISS-AOM ± + ± + GISS-EH + + GISS-ER + + INM-CM3.0 ± IPSL-CM4 + MIROC3.0(hires) ± ± MIROC3.0(medres) ± MRI-CGCM2.3.2 ± ± PCM ± UKMO-HadCM3 + Biased Average ± Here + symbols indicate increase and symbols indicates decrease. Two different symbols when oriented vertically represent a north south spatial pattern (north above south) and when oriented horizontally represent an east west spatial pattern (west left to east). For example, + indicates soil moisture increases in the west and decreases in the east; similarly, ± indicates soil moisture increases in the north and decreases in the south Table 3 describes the different seasonal and spatial patterns of soil moisture changes predicted by each of the 15 models. The spatial patterns predicted by all the models can be categorized into three main different types: a north south pattern, a west east pattern, and all across. The majority-model averages of soil moisture changes indicate an increase in the north and decrease in the south during winter (DJF) and decrease across the US during summer (JJA). During winter, the GFDL- CM2.1 and the UKMO-HadCM3 models both differ significantly from the majority, with the GFDL- CM2.1