Multi-Model Projection of July August Climate Extreme Changes over China under CO 2 Doubling. Part II: Temperature

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1 ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 28, NO. 2, 2011, Multi-Model Projection of July August Climate Extreme Changes over China under CO 2 Doubling. Part II: Temperature LI Hongmei 1,2 ( ), FENG Lei 1,2 ( ), and ZHOU Tianjun 1 ( ) 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing Graduate University of Chinese Academy of Sciences, Beijing (Received 5 May 2010; revised 18 October 2010) ABSTRACT This is the second part of the authors analysis on the output of 24 coupled climate models from the Twentieth-Century Climate in Coupled Models (20C3M) experiment and 1% per year CO 2 increase experiment (to doubling) (1pctto2x) of phase 3 of the Coupled Model Inter-comparison Project (CMIP3). The study focuses on the potential changes of July August temperature extremes over China. The pattern correlation coefficients of the simulated temperature with the observations are , which are higher than the results for precipitation. However, most models have cold bias compared to observation, with a larger cold bias over western China (>5 C) than over eastern China (<2 C). The multi-model ensemble (MME) exhibits a significant increase of temperature under the 1pctto2x scenario. The amplitude of the MME warming shows a northwest southeast decreasing gradient. The warming spread among the models ( 1 C 2 C) is less than MME warming ( 2 C 4 C), indicating a relatively robust temperature change under CO 2 doubling. Further analysis of Geophysical Fluid Dynamics Laboratory coupled climate model version 2.1 (GFDL-CM2.1) simulations suggests that the warming pattern may be related to heat transport by summer monsoons. The contrast of cloud effects also has contributions. The different vertical structures of warming over northwestern China and southeastern China may be attributed to the different natures of vertical circulations. The deep, moist convection over southeastern China is an effective mechanism for transporting the warming upward, leading to more upper-level warming. In northwestern China, the warming is more surface-orientated, possibly due to the shallow, dry convection. Key words: extreme temperature, coupled climate model, projection, CO 2 doubling Citation: Li, H. M., L. Feng, and T. J. Zhou, 2011: Multi-model projection of July August climate extreme changes over China under CO 2 doubling. Part II: Temperature. Adv. Atmos. Sci., 28(2), , doi: /s x. 1. Introduction Extreme temperature change has more noticeable effect on human life and society than the mean condition. Observational evidence over continental China indicates that extreme indicators, based on both absolute value and percentile, present significant decadal variability; decreasing temperature trend occurred in the 1960s and an increasing temperature trend occurred in the 1990s (see Fig. 3.1 of Li, 2007). The significant cooling region of the lower-end temperature indices was located over the middle-lower Yangtze River valley, and the higher-end cooling region was located Corresponding author: ZHOU Tianjun, zhoutj@lasg.iap.ac.cn to its north (Li, 2007). In addition, the extreme temperatures show an obvious seasonal cycle and interannual variability (Li et al., 2009). Asymmetry is also reflected in the extreme temperature changes. During the past 40 years in China, the mean minimum temperature has increased significantly; however, the mean maximum has not changed significantly (Zhai and Pan, 2003). Under a global warming scenario, Huang et al. (2010) found a close relationship between global extreme temperature anomalies and that in three key regions of China: northeastern China and its coastal areas, the high-latitude regions above 40 N, and southwestern China. China National Committee for International Association of Meteorology and Atmospheric Sciences (IAMAS), Institute of Atmospheric Physics (IAP) and Science Press and Springer-Verlag Berlin Heidelberg 2011

2 NO. 2 LI ET AL. 449 Potential future changes of climate extremes under global warming have been of great concern to both the scientific community and society. Climate system models have played crucial roles in understanding and simulating past, present, and future climates (Zhou et al., 2007). Coordinated by the World Climate Research Program s (WCRP s) phase 3 of the Coupled Model Intercomparison Project (CMIP3), more than 20 state-of-the-art coupled global climate system models have been involved in the modeling activities for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). Model projections of future climate change under different scenarios are available ( ipcc.php). This modeling project reduces the uncertainty of future climate change projection compared to the simulation of only one specific model. In an attempt to assess the potential changes of summertime climate extremes over China in a warmer climate scenario, the output of CMIP3 models of future climate-change projection under CO 2 doubling is analyzed. In the first part of this study (Li et al., 2010a), we have shown an increasing of total and extreme precipitation under a warmer climate, and the consistency of extreme precipitation change among the models is higher than that of total precipitation amount. In this part of the study, we focus on the potential changes of temperature extremes over contiguous China. Based on the projection of global coupled models, dramatic increases in temperature extremes were revealed in many regions of the world (Weisheimer and Palmer, 2005; Tebaldi et al., 2006; Kharin et al., 2007). For the conditions over China, a preliminary analysis using CMIP3 models revealed an increase of warm extremes along the Yangtze River valley (Xu et al., 2009). CMIP3 models are actually lowresolution global climate models. Therefore, complementary regional climate models have been used to perform regional dynamical downscaling. Regional climate model projections also showed more severe warm extremes but less severe cold extremes under a global warming scenario (Gao et al., 2001, 2002; Xu et al., 2006; Zhang et al., 2006). These previous studies, however, mainly presented the results from either a multi-model mean or several specific models. Given the strong limitation of the state-of-the-art climate models in East Asia because of its complex topography (Zhou and Li, 2002; Zhou and Yu, 2006; Li et al., 2010b; Zhou et al., 2009a, b; Chen et al., 2010), it is desirable to examine both the relatively robust aspects (multi-model means) and uncertainties (result spreads among the models) with respect to the projections of future climate change using CMIP3 global models. The rest of the article is organized as follows. Section 2 describes the models, datasets, and methods used in this study. In section 3, the performance of CMIP3 models in simulating the temperature over China is evaluated using the output of the Twentieth- Century Climate in Coupled Models (20C3M). The projected changes of extreme temperature over China based on the output of WCRP CMIP3 1% per year CO 2 increase experiment (to doubling) (1pctto2x) are shown in section 4. The possible mechanism for the temperature change is analyzed in section 5 based on the output of Geophysical Fluid Dynamics Laboratory coupled climate model version 2.1 (GFDL-CM2.1), which shows similarities with the MME and also provides enough data for mechanism analyses. Summary and discussion are given in section Data, models, and methodology The observational dataset used in this study includes the daily maximum temperatures and daily minimum temperatures at 740 stations covering mainland China for the period The corresponding daily precipitation data have been used in the analysis of precipitation extremes (Li et al., 2008). The dataset was developed by National Climate Center of the China Meteorological Administration. In our analysis, the data was quality controlled using the method of Alexander et al. (2006). Details of WCRP CMIP3 models were described in the first part of the study (Li et al., 2010a) and also the CMIP3 data bank ( ipcc.php). In this part of the study, the simulated daily mean and extreme temperatures are used. The 20C3M simulation is used in the evaluation of model performance. The 1pctto2x simulation is used to examine the potential change of extreme temperature over China under a global warming scenario. The output of the preindustrial control run is also used. In order to examine both the relatively robust aspects and uncertainties, we use a total of 24 CMIP3 model outputs. For specific simulations and variables, the data are not complete, and some models do not provide enough data for our analyses. For example, the outputs of 20 models are available for the 20C3M simulation, but the outputs of only 15 models are available for the 1pctto2x simulation. The resolutions of these models are different [see Table 1 of Li et al. (2010a)]. Here we define the extreme temperature indices as the days with temperatures lower than 10th percentile or higher than 90th percentile, centered on a 5-day window for the base period Cold nights are defined as nights when daily minimum tempera-

3 450 PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE VOL. 28 Fig mean July August mean daily maximum temperatures (left) and daily minimum temperatures (right) over China from observations (a and d), MME (b and e), (c) and (f) are their difference between MME and observations. Light (dark) shading indicate areas where the value is larger than 15 C (30 C) in (a and b), 10 C (20 C) in (d and e), and smaller than 5 C (larger than 5 C) in (c and f). ture is lower than its 10th percentile value (hereafter TN10p); warm nights are defined as nights when daily minimum temperature is higher than its 90th percentile value (hereafter TN90p); cold days are defined as days when daily maximum temperature is lower than its 10th percentile value (hereafter TX10p); warm days are defined as days when daily maximum temperature is higher than its 90th percentile value (hereafter TX90p). These are typical indices that have been widely used in international climate extreme studies (Frich et al., 2002; Zhai et al., 2005; Alexander et al., 2006). As emphasized by Li et al. (2008, 2010a), we focus on the July August mean condition, the central period of summertime in the East Asian monsoon domain. The time period considered in this study is for observations and the 20C3M simulation, and the last 20 year simulations of both the 1pctto2x run and the pre-industrial control run. Following Zhou and Yu (2006), we use standard deviation to measure the spread of model simulation results quantitatively. This formula follows: s x = 1 n n (x i x) 2 (1) i=1 where, x i is the individual model result; x is the multi-model mean result; n is the number of models used. When the MME result is larger than the spread among the models, the simulation result is considered to be relatively robust.

4 NO. 2 LI ET AL. 451 Fig mean July August mean daily maximum temperatures over China from individual model simulation (a) (t), units: C. Light (dark) shading indicates areas where the values are larger than 15 C (30 C). 3. Evaluation of model performance We begin our analyses by evaluating model performance over contiguous China. The simulated mean and extreme temperatures are compared with the observation data. The mean July August daily maximum temperatures and daily minimum temperatures from the observation data, the MME, and the differences between MME and observation are shown in Fig. 1. The observation data shows that the relatively high temperatures are located over southeastern and northwestern China and the lowest temperatures are located over the Tibetan Plateau. The broad spatial pattern is well reproduced in most models; however, the difference map between the MME and observation data (Figs. 1c and 1f) shows that the simulated temperature is lower than the observation data, especially over western China, with a cold bias >10 C. This is consistent with the IPCC AR4 (Christensen et al., 2007), which pointed out that the simulated temperatures in most models were too low in all seasons over East Asia, and that the mean cold bias was largest in winter and smallest in summer. The discrepancy between the observation data and the simulated daily minimum temperatures is less than those for daily maximum temperatures, which indicates that models may have better performance in simulating daily minimum temperatures than daily maximum temperatures. The spatial patterns of July August mean daily maximum temperatures simulated by individual models are shown in Fig. 2. Most of the coupled models reasonably reproduce the broad-scale characteristics of the observations; however, the simulated daily maximum temperatures over the Tibetan Plateau are generally lower than the observation data in all the models (except for the CGCM3.1). Calculations also indicate that the spread over western China ( 3 C) is larger

5 452 PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE VOL. 28 Fig. 3. As Fig. 2, but for July August mean daily minimum temperatures. Light (dark) shading indicates areas where the values are larger than 10 C (20 C). than that over eastern China ( 2 C). As stated in the IPCC AR4 (Christensen et al., 2007), the global models had significant problems over the Tibetan Plateau due to the difficulty in simulating the effects of the dramatic topographic relief and the distorted albedo feedbacks created by extensive snow cover. Generally, the models with higher resolutions perform better than those with lower or medium resolutions in simulating the relatively high temperatures over northwestern China. The spatial patterns of July August mean daily minimum temperatures simulated by individual models are shown in Fig. 3. The distribution pattern is similar to that of the daily maximum temperatures. The highest value over southeastern China is reasonably well simulated by all the models; however, most models underestimate the daily minimum temperatures over the Tibetan Plateau. The results of some models are even < 5 C, and the differences between the simulation and the observation data are >10 C. A large discrepancy between the simulated and observed daily minimum temperatures occurs in northwestern China. The values of some models are stronger than the observation data; the values of the other models are, however, weaker than the observation data, indicating a spread among the models. Calculations indicate that the consistency among the models in simulation of the daily minimum temperatures is not as good as that among daily maximum temperatures, with the spread >5 C (3 C) over western (eastern) China. As the analysis of precipitation presented in Part I of the study (Li et al., 2010a), we employ the Taylor diagram to quantitatively evaluate the performances of the models in temperature simulations (Fig. 4). The concentration of dots represents resemblance of model simulations. Clearly, the resemblance of simulated daily minimum temperatures to the observation data is higher than that of daily maximum temperatures. The Taylor diagram shows that most of the pattern correlations between the specific model simu-

6 NO. 2 LI ET AL. 453 Fig. 4. Taylor diagram of mean July August daily maximum and minimum temperatures, the number marks represent the resemblance between the spatial pattern from single model simulation and observation. The radial distance from the origin indicates the standard deviation of each model simulation, normalized by the observed value. The angle from the horizontal axis represents the inverse cosine of the spatial correlation between the simulation of certain model and the observations. Dots represent daily maximum temperatures and stars represent daily minimum temperatures. lations and the observation data are between 0.6 and 0.9, which are much higher than the corresponding results for precipitation ( ) shown in Part I of this study (Li et al., 2010a). The correlation coefficients for daily minimum temperatures ( ) are larger than those for daily maximum temperatures ( ), suggesting that the performance of the models in simulating the spatial pattern of daily minimum temperatures is better than that of daily maximum temperatures. The high pattern correlation may be affected by the surface altitude. The radial distances are >1.0, which means the spatial variations of temperature over China from model simulation are larger than those of the observation data. 4. Projected future changes Although there are discrepancies between the observation data and the simulations, evaluation of model performance shows that the CMIP3 coupled models simulate the broad characteristics of temperature distribution over contiguous China reasonably well except for the plateau area. Hence, we further examine how the temperature, especially extreme temperature, would change over contiguous China in response to CO 2 doubling. The July August mean daily maximum temperature changes over China under CO 2 doubling relative to the preindustrial control run are shown in Fig. 5. The MME shows a consistent warming trend with its highest value over the northwestern Tibetan Plateau; all the changes are statistically significant at the 5% level (Fig. 5a). A regional climate modeling study by Gao et al. (2003) and an analysis of A1B scenario simulations of IPCC AR4 (Christensen et al., 2007) also showed a larger warming over the Tibetan Plateau compared to surrounding areas due to the effects of greenhouse gases. Giorgi et al. (1997) stated that the greater temperature increase over high-altitude areas can be explained by the decrease in surface albedo associated with melting snow and ice. A further examination of individual model simulations (Figs. 5b p) shows that the locations of the largest warming centers are different among the models. The largest spread is located over the Tibetan Plateau ( 1.9 C), indicating a comparatively larger uncertainty of climate change projection over that area. The July August mean daily minimum temperatures changes over China are shown in Fig. 6. A consistent warming trend is also evident in all the models except for the Bjerknes Centre for Climate Research- Bergen Climate Model (BCM) Version 2 (BCCR- BCM2.0). In the other specific models, the spatial distribution and amplitudes of change in daily maximum temperatures and daily minimum temperatures are similar. The spatial pattern and magnitude of

7 454 PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE VOL. 28 Fig. 5. Simulated changes of July August daily maximum temperatures over China under CO 2 doubling concentration relative to pre-industry control run. (a) MME, (b) (p) individual simulation. Light (dark) shading indicates areas where the values are larger than 2 C (4 C). spread among the models in the simulation of the daily minimum temperatures are almost the same as that of the daily maximum temperatures, being larger over western China and smaller over eastern China (not shown). The daily maximum temperatures and daily minimum temperatures are only part of the extreme temperature indices. We further examine the extreme temperature change in Figs All models (except for BCCR-BCM2.0) show that the cold nights tend to decrease by 5 days in July August over China (Fig. 7). In the meantime, warm nights increase by 30 days under the CO 2 doubling scenario relative to the preindustrial control run (Fig. 8). The MME results show that the increase of warm nights over the Tibetan Plateau is larger than that over the other regions of China. Nevertheless, there are still significant spreads among the spatial distribution of changes among the models (Figs. 8b p). The potential mean-state warming would lead to different change of cold days and warm days. The projected cold days show decreasing trends (Fig. 9), while the projected warm days show increasing trends (Fig. 10) in a CO 2 doubling scenario. The magnitudes of change for cold nights and cold days are almost the same. However, the increase of warm nights based on daily minimum temperatures is much larger than that of warm days based on daily maximum temperatures. This indicates an asymmetric change of warm extreme events. The asymmetric change of the daily maximum and minimum temperatures are universal on the global scale (Karl et al., 1993). Within China, this is the most evident over the Tibetan Plateau and northwestern regions (Ma, 1999; Duan and Wu, 2006; Liu et al., 2006). Observation data indicates that cloud amount greatly affects the change of diurnal temperature range, mainly by influencing the daily maximum temperatures (Karl et al., 1993; Duan and Wu, 2006). A similar result was also found in the climate change projection under the A 2 scenario by model inter-comparison (Lobell et al., 2007). Furthermore, the atmospheric and surface boundary conditions are shown to differentially affect the daily maximum and minimum temperatures, i.e., the desertification would increase the maximum temperatures and decrease the minimum temperatures (Karl et al., 1993). Under CO 2 doubling, the different responses of daily maximum and minimum temperatures to the large-scale climate

8 NO. 2 LI ET AL. 455 Fig. 6. As Fig. 5, but for the daily minimum temperatures. Fig. 7. As Fig. 5, but for the cold nights, units: d. Light (dark) shading indicates areas where the values are smaller than 4 days (larger than 4 days).

9 456 PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE VOL. 28 Fig. 8. As Fig. 5, but for the warm nights, units: d. Light (dark) shading indicates areas where the values are smaller than 5 days (larger than 20 days). Fig. 9. As Fig. 5, but for the cold days, units: d. Shading indicates areas where the values are smaller than 4 days.

10 NO. 2 LI ET AL. 457 Fig. 10. As Fig. 5, but for the warm days, units: d. Shading indicates areas where the values are larger than 15 days. forcing in the coupled models deserves further study. Extreme temperature changes over China have unique spatial distributions, and the model simulations have shown large discrepancies. As the analysis of extreme precipitation presented in Part I of the study (Li et al., 2010a), changes of extreme temperature over five typical sub-regions of contiguous China are calculated (Fig. 11). These five sub-regions are northwestern China (35 50 N, E), Tibetan Plateau (28 35 N, E), northeastern China (43 54 N, E), northern China (35 43 N, E), and southeastern China ( N, E). All the models (except for BCCR-BCM2.0) project more warm days and warm nights and less cold days and cold nights over northwestern China under CO 2 doubling relative to the preindustrial control run (Fig. 11a), and the decreases in cold days and cold nights are <10 days. Over the Tibetan Plateau (Fig. 11b), most models project a decrease of cold nights (except for BCCR- BCM2.0 and CSIRO-MK3.5) and a decrease of cold days (except for CSIRO-MK3.5). All model simulations exhibit an increase of warm nights and warm days; in particular, 12 of 15 models project that the increase of warm nights would >25 days. All model simulations (except for BCCR-BCM2.0) exhibit a decrease in cold nights and cold days and an increase in warm nights and warm days over northeastern China (Fig. 11c). Similar changes are evident in northern China (Fig. 11d) and southeastern China (Fig. 11e), where consistent warming is projected by most simulations. Thirteen of 15 models show that the increase of warm nights would >25 days, with a more consistent projection over southeastern China than the other regions. In summary, most of the model simulations and the MME show consistent warming over contiguous China under CO 2 doubling relative to the preindustrial control run. Although there are discrepancies in spatial distributions of extreme temperature changes among the models, the spread is much smaller than that of extreme precipitation shown in Part I of this study (Li et al., 2010a). The largest spread among the model simulations is located over the Tibetan Plateau. Most models project a decrease in cold nights and cold days, and an increase in warm nights and warm days over contiguous China under CO 2 doubling relative to the preindustrial control run. 5. Possible mechanism for temperature change The MME results of extreme temperature change under CO 2 doubling feature an obviously northwest

11 458 PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE VOL. 28 Fig. 11. Number of model which simulated extreme temperature changes in different change value bin over China, (a) (e) shows the results from 5 sub-regions over China, the locations of sub-regions are shown in (f). The red, light blue, blue and green bar indicates changes of TN90p, TN10p, TX90p and TX10p, respectively. southeast (NW SE) decreasing gradient. To further understand this temperature change, we focus on the GFDL-CM2.1 model for deep analysis. This model produces a similar spatial pattern to that of MME and provides enough data for our further study. The patterns in extreme temperature changes, especially the daily maximum temperature and daily minimum temperature changes, are partly related to the change in mean temperature. A comparison of mean and extreme temperature changes in July August and December January February is shown in Fig. 12. During summer, the daily maximum temperatures show the largest warming amplitude, followed by similar changes in both the mean and minimum temperatures. All three indices show a NW SE decreasing gradient, indicating that the largest climate sensitivity to CO 2 doubling is located over northwestern China. Why is the highest sensitivity located in northwestern China? The larger direct surface radiative forcing associated with the higher altitude and less cloud may be one reason for the largest warming over Northwest China (J. H. Lu, personal communication). Although the water vapor feedback tends to allow greater surface warming across southeastern China, the weaker warming amplitude suggests that the cloud effect may have more negative effect on the warming. In addition, the monsoonal transport also reduces the climate sensitivity over southeastern China and enhances the warming in the north end of the monsoonal domain

12 NO. 2 LI ET AL. 459 Fig. 12. Simulated changes of July August (left) and Dec Jan Feb (right) mean daily temperatures over China by GFDL-CM2.1 under CO 2 doubling concentration relative to pre-industry control run. (a and b) daily mean temperatures, (c and d) daily maximum temperatures, (e and f) daily minimum temperatures. Light (dark) shading indicates areas where the values are larger than 2 C (4 C). (Cai and Lu, 2007). A further comparison of temperature change between winter and summer (Fig. 12b, d, f) shows that the winter monsoon also induces a poleward heat transport, which leads to the amplified warming in high latitudes, and the largest warming center is located over northeastern China. Poleward amplification is one of the most prominent characteristics of global warming (Trenberth et al., 2007). The meridional asymmetry of the climate sensitivity can be successfully reproduced in GCM simulations with the presence of anthropogenic greenhouse gases (Meehl et al., 2000). However, there has been no consensus on the mechanisms of stronger warming in higher latitudes. Changes in ice/snow albedo in high latitudes and evaporation in low latitudes might be the two leading local thermodynamic processes that act to amplify the high-latitude response and reduce the low-latitude response to climate forcing, respectively (Hassol, 2004). Changes in poleward heat transport also contribute to polar warming amplification (Cai and Lu, 2007). The different warming patterns between winter and summer monsoons in Fig. 12 may be related to the different direction of monsoonal heat transport. Additionally, the warming amplitude in

13 460 PROJECTION OF EXTREME TEMPERATURE CHANGES OVER CHINA: TEMPERATURE VOL. 28 Fig. 13. Vertical profiles of simulated changes of July August mean air temperatures averaged over Northwest China (solid line) and Southeast China (dashed line) by GFDL-CM2.1 under CO 2 doubling concentration relative to pre-industry control run. The abscissa axis is the change of air temperature. winter is generally larger than that in summer by >0.5 C. Therefore, the summer monsoonal heat transport may be important in determining the NW SE decreasing gradient of temperature change. The extreme climate changes are closely related to the mean climate change (Meehl et al., 2005; Turner and Slingo, 2009). An examination of the mechanism for the mean temperature change would also enrich our understanding of the extreme temperature change. By comparing the horizontal structures of warming at 700 hpa and 500 hpa (not shown), the main differences are seen in northwestern China and southeastern China. In northwestern China, the warming amplitude at 700 hpa is larger than that at 500 hpa. In southeastern China, the warming amplitude at 500 hpa is larger than that at 700 hpa. No obvious difference is evident in other regions. Therefore, northwestern China and southeastern China are two typical regions that exhibit completely different vertical structures of mean temperature change. The vertical structures of warming over northwestern China and southeastern China are further examined by showing the area-averaged (see Fig. 11) vertical profiles (Fig. 13). The warming changes of temperature over the two regions penetrate the entire troposphere below 200 hpa. In southeastern China, the amplitude of the warming increases with the height. For example, the warming change in southeastern China is <2.0 C at the surface, 2.5 C at 700 hpa, and >3.0 C above 500 hpa. In northwestern China, however, the amplitude of the warming decreases with the height. For example, the warming change is 3.5 C at the surface, but only 2.5 C at 500 hpa. Thus a significant difference exists in the vertical structures of temperature change over the two regions. In the dry areas of northwestern China, few or no moist convections are detected. The dry convection, if any, is usually shallow and is not an effective mechanism for transporting the climate sensitivity upward. Thus the warming is more surface-orientated, as evidenced in Fig. 13. In southeastern China, however, frequent deep, moist convections are found. The deep, moist convection is effective in transporting the climate sensitivity upward. Thereby the warming is more upper-level orientated, as witnessed in Fig. 13. The different vertical patterns of warming clearly show the importance of vertical energy transport, consistent with the conclusions of Cai and Lu (2007) and Lu and Cai (2010). In summary, the response of summer temperature change to CO 2 doubling exhibits a NW SE decreasing gradient. We suggest that this gradient is related to the heat transport of summer monsoons. In our coupled model, more direct forcing of CO 2 doubling due to less cloud leads to stronger surface warming over northwestern China, while the negative effect of cloud on the warming results in weaker surface warming over southeastern China. The deep, moist convection over southeastern China is more effective in transporting the warming upward, evidenced by the contrast of vertical structures of temperature changes over southeastern China and northwestern China. The former increases with the height and is thus more evident at upper levels, while the latter decreases with the height and is therefore more evident at the surface. 6. Summary and concluding remarks We evaluate the performances of the state-of-theart climate models in simulating the present July August mean temperature extremes with daily maximum temperatures and daily minimum temperatures data from both in-situ observations and 20C3M simulations. Potential changes in July August mean temperature extremes over China under CO 2 doubling are estimated with the output of 1pctto2x simulations. Both the 20C3M and 1ptto2x simulations are coordinated by the WCRP CMIP3 for IPCC AR4. The possible mechanism for the change of extreme temperature is further analyzed based on the outputs of GFDL CM2.1, which shows a pattern similar to the MME and provides enough data for our deep analysis. The major results are summarized here. (1) The majority of CMIP3 models reasonably simulate the observed broad-scale spatial patterns of July August temperature characteristics over contiguous China. The results of temperature simulation are obviously better than those for precipitation (see Part I of the study). Almost all of the pattern correlation coefficients of July August mean daily maximum temperatures and daily minimum temperatures between

14 NO. 2 LI ET AL. 461 the simulations and observations are between 0.6 and 0.9, which are much larger than that of the precipitation ( ) shown in Part I of this study (Li et al., 2010a). The spatial correlation coefficients between the observation and the simulation for daily minimum temperatures ( ) are larger than those for daily maximum temperatures ( ). Thus the state-ofthe-art climate models have better performances in simulating the spatial pattern of daily minimum temperatures than that of the daily maximum temperatures over contiguous China. (2) The July August temperatures over contiguous China simulated by most models are generally lower than those of observation data. The cold bias in western China is 10 C, which is almost five times larger than the cold bias in eastern China. A large spread (2 C 5 C) among the models could be seen in the simulations of mean states and extreme temperatures. For specific models, the climate mean and extreme indices have similar spatial distributions. The models with higher resolutions generally perform better than those with lower or medium resolutions. (3) Most models and the MME show significant warming of July August mean temperature and an increase of warm extremes under CO 2 doubling relative to the preindustrial control run. The largest warming center is located over the Tibetan Plateau in the MME; however, individual model simulations reveal large discrepancies in the locations of the largest warming center. The magnitudes of the decrease in cold nights and cold days are almost identical, but the increase of warm nights based on daily minimum temperatures (20 40 days) is much larger than the increase of warm days based on daily maximum temperatures (20 25 days), indicating an asymmetric change of extreme warm events. (4) The projected temperature changes over five specific regions of contiguous China, i.e., northwestern China (35 50 N, E), Tibetan Plateau (28 35 N, E), northeastern China (43 54 N, E), northern China (35 43 N, E), and southeastern China ( N, E), are also assessed. These results indicate a homogeneous change; all five sub-regions exhibit similar changes under CO 2 doubling relative to the preindustrial control run, i.e., more warm nights and warm days (increasing by days) but less cold nights and cold days (decreasing by 4 5 days). (5) The response of summer temperature change to CO 2 doubling exhibits a NW SE decreasing gradient, which may be associated with the summer monsoonal heat transport and the contrast of cloud effects in northwestern China and southeastern China. The deep, moist convection in southeastern China is more effective in transporting the warming upward, thus the temperature change over southeastern China is more evident in the upper level. The shallow, dry convection in northwestern China is less effective in transporting the warming upward, thus temperature change over northwestern China is more evident at the surface. The potential change of climate is of great concern to the society. Climate system models have been useful tools for projecting future climate change associated with an increase in greenhouse gases. Information on the projected climate changes should be useful to policymakers. However, given the strong limitations of the state-of-the-art climate models, not only the relatively robust aspects (multi-model mean) but also the uncertainties (spread among the models) in the projections of future climate change with CMIP3 global models should be clarified. Results presented here should be useful in this regard. Given the discrepancies among the climate models, Christensen et al. (2007) reported that the simulated temperatures in most models were too low in all seasons over East Asia. Randall et al. 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