Impact of sea surface temperature trend on late summer Asian rainfall in the twentieth century

JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 4256 4266, doi:10.1002/jgrd.50386, 2013 Impact of sea surface temperature trend on late summer Asian rainfall in the twentieth century Qiying Bian 1,2 and Riyu Lu 1 Received 7 September 2012; revised 7 March 2013; accepted 2 April 2013; published 28 May 2013. [1] The impact of the global sea surface temperature (SST) warming trend, which is the leading mode of SST variability, on late summer Asian rainfall is analyzed based on the simulations of five atmospheric general circulation models, which are performed by the U. S. Climate Variability and Predictability Drought Working Group. Our evaluations of the model outputs indicate that these models roughly capture the main features of climatological rainfall and circulations over Asia and the western North Pacific (WNP), but they simulate a too strong monsoon trough and a too northward shifted in the subtropical anticyclone in the WNP and fail to reproduce the rain belt over East Asia. It is found that all of the models simulate an intensified WNP subtropical high (WNPSH) in late summer, an enhanced precipitation in the tropical Indian Ocean and the maritime continent, and a suppressed precipitation in the South Asian monsoon region, the South China Sea, and the Philippine Sea, when the models are forced with the SST trend, which is characterized by a significant increase in the Indian Ocean and western Pacific. All these changes are suggested to be dynamically coherent. The warmer SST trend in the Indian Ocean and western Pacific may suppress precipitation over the Philippine Sea and thus result in a lower tropospheric anticyclonic circulation over the subtropical WNP. The warmer SSTs in the Indian Ocean may also be responsible for the anomalous easterlies and resultant less rainfall over the South Asian monsoon region. The precipitation changes forced by the SST trend are similar in the maritime continent but show an apparent difference over East Asia, in comparison with the observed rainfall trend over lands. The possible reasons for this difference are discussed. Citation: Bian, Q., and R. Lu (2013), Impact of sea surface temperature trend on late summer Asian rainfall in the twentieth century, J. Geophys. Res. Atmos., 118, 4256 4266, doi:10.1002/jgrd.50386. 1. Introduction [2] The rainfall that occurs during the Asian summer monsoon has great economic and climatic importance for more than three billion people living in the Asian continent, and the trend in this rainfall can strongly affect the occurrence of long-term droughts or floods, possibly leading to severe social impacts. Therefore, numerous studies have investigated the trends in summer rainfall in Asia. For example, Zhai et al. [2005] showed that summer precipitation in China had an increasing trend in the lower reaches of the Yangtze River and western China and a significant decreasing trend over North China from 1951 to 2000. This pattern was also reported by Yatagai and Yasunari [1994], 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. 2 University of Chinese Academy of Sciences, Beijing, China. Corresponding author: R. Lu, Institute of Atmospheric Physics, Chinese Academy of Sciences, PO Box 9804, Beijing 100029, China. (lr@mail.iap.ac.cn) 2013. American Geophysical Union. All Rights Reserved. 2169-897X/13/10.1002/jgrd.50386 who analyzed the tendency of summer rainfall variation over China and Mongolia from 1951 to 1990. [3] It would be natural to speculate that long-term changes in oceanic conditions, as a major factor modulating climate variation, might influence these trends in rainfall. Some observational studies have suggested that the warming trend of central and eastern tropical Pacific sea surface temperatures (SSTs) might be related to more rainfall over the Yangtze River valley and less rainfall over North China since the 1970s [Gong and Ho, 2002;Huang et al., 2006; Zeng et al., 2007]. In addition, the warming trend of SSTs in the tropical western Pacific and Indian Ocean might also contribute to the rainfall trend in China and East Asia, for instance, the positive summer rainfall trend in central eastern China and negative rainfall trend in parts of North and Northeast China and Southwest Islands of Japan [Hu, 1997; Hu et al., 2003; Yang and Lau, 2004; Yoo et al., 2006]. [4] Since the causality between SST and rainfall variation is not easily diagnosable through observational analyses, some other studies have investigated the role of SST in forcing long-term variation in summer rainfall and rainfallrelated circulations by examining model simulations. Zhou et al. [2009] analyzed the simulated results of atmospheric general circulation models (AGCMs) forced by observed 4256

(a) (b) Figure 1. The (a) first leading pattern of rotated empirical orthogonal functions (REOFs) and (b) associated principal component based on the annual mean sea surface temperature (SST) for the period 1901 2004. The values are scaled, so the product of REOFs gives the unit of C[Schubert et al., 2009]. Indian Ocean-western Pacific SST warming data and suggested that this SST warming is in favor of the western extension of the western North Pacific subtropical high (WNPSH). Particularly, modeling studies indicated that warmer SSTs in the Indian Ocean induce an anomalous easterly in the western Pacific and suppress precipitations over the Philippine Sea in summer and result in a lower tropospheric anticyclonic anomaly over the subtropical western North Pacific (WNP) [Terao and Kubota, 2005; Li et al., 2008; Xie et al., 2009; Zhou et al., 2009]. [5] Other mechanisms, rather than SST forcing, have been put forward to explain the observed precipitation trend in Asia over the last few decades. Ding et al. [2009] and Zhao et al. [2010] proposed that increased snow in the preceding winter andspringoverthetibetanplateaumayinduceananomalous south flood north drought pattern over eastern China in summer through a decrease in land-sea thermal contrast. Furthermore, Duan et al. [2012] suggested that the reduction in sensible heat flux over the Tibetan Plateau in recent decades [Duan and Wu, 2008] has enhanced precipitation in South China and reduced precipitation in North and Northeast China. Wu et al. [2012] proposed that anomalous snow cover over Tibetan Plateau may induce anomalous atmospheric circulation over East Asia and the WNP. On the other hand, some studies have suggested that aerosols or global warming might play a role in influencing the long-term change in rainfall over Asia [Menon et al., 2002; Kimoto, 2005; Li et al., 2007; Meehletal., 2008; Li et al., 2010a, 2010b], but with somewhat controversial results. [6] The global SSTs have increased significantly in the last century [e.g., Tokinaga et al., 2012]. The first leading pattern of SST variability, with warming occurring over most of the global ocean, shows a trend-like change in the last century (Figure 1). But what kind of role does the global SST trend play in influencing precipitation over Asia? The multiple model simulations performed by the U.S. Climate Variability and Predictability (CLIVAR) program [Gutzler and Schubert, 2007] provide us an opportunity to investigate this question. Different anomalous SST patterns, including the SST trend, have been used to force five AGCMs under this program. In this study, the simulated results forced by the SST trend pattern are analyzed to investigate the contribution of global SST trend to the change in precipitation over Asia. [7] We focus on the simulated results of late summer (July, August, and September; JAS). These months represent the rainy season over many parts of Asia. Furthermore, there is a technical reason for choosing these months: The identical SST changes are used to force the AGCMs in each month under the U.S. CLIVAR program, and thus it would be reasonable to expect that SST changes may induce the strongest circulation and precipitation changes during late summer when the climatological SSTs are highest in the oceans adjacent to the Asian continent. Actually, the above speculation can be confirmed by the present results, including figure 5 in this paper, which indicates that there is a clear and strong lower tropospheric anticyclonic anomaly over the WNP in the months of JAS. [8] Previous studies indicated that current AGCMs may not simulate well the Asian summer monsoon [Kang et al., 2002]. Therefore, it is necessary to evaluate the ability of the models used in this study in simulating the Asian summer monsoon, which is illustrated in section 3, before the investigation on the impacts of the SST trend, which is examined in section 4. 2. Data 2.1. Model Outputs [9] The model outputs used in this study were taken from the experiments performed by the Drought Working Group of the U.S. CLIVAR program. These experiments were designed based on the first leading pattern of rotated empirical orthogonal functions (REOFs) on monthly mean SST from 1901 to 2004 [Schubert et al., 2009]. The REOF analysis was performed by using HadISST SST data set [Rayner et al., 2003]. Figure 1 shows the first leading pattern and the associated principal component. This mode is characterized by a global SST warming trend, with warming occurring over almost the entire global ocean, which explains 27.2% of the total annual mean SST variance. The most evident warming in the Northern Hemisphere locates in midlatitude areas along continental coastal lines, such as those in East Asia, North America, and Western Europe. SST warming is also strong in the Indian Ocean and western Pacific. The related principal component shows an increasing trend from 1901 to 2004, which is referred to as the SST trend pattern. In this study, the outputs of two experiments are analyzed. One is a control run, which was forced with monthly varying SST climatology (defined for the period 1901 2004). The other is a sensitive experiment, which was produced by adding the SST trend pattern to the monthly varying SST climatology. 4257

[10] Five AGCMs participated in the abovementioned idealized SST experiments. They were the Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model, version2.1 (AM2.1); the National Aeronautics and Space Administration (NASA) Seasonal-to-Interannual Prediction Project, version 1 (NSIPP1); the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS); the National Center for Atmospheric Research (NCAR) Community Atmospheric Model, version3.5 (CAM3.5); and the NCAR Community Climate Model, version3 (CCM3) [Schubert et al., 2009]. The results of idealized runs cover a 50 year period for four models (GFDL, NSIPP1, CAM3.5, and CCM3) and a 36 year period for one model (GFS). Arakawa-Schubert convection scheme is used in the models GFDL, NSIPP1, and GFS, and Zhang and McFarlane scheme is used in the other two models (CAM3.5 and CCM3). Kang et al. [2002] suggested that the models that used the former scheme tend to simulate a too strong precipitation in the Philippine Sea and a too northward shifted WNPSH. The longitude-latitude resolutions of these models range roughly from 1.5 1.5 (CAM, T85) to 3 3.75 (NSIPP1). [11] Because the horizontal resolution differs from model to model, all the data were converted to the longitudelatitude grids of 3 4 to enable a multimodel ensemble (MME) analysis. The MME results were obtained by simply averaging over the outputs of these five models with equivalent weight. The monthly data of horizontal winds and precipitation were analyzed in this study. 2.2. Observational Data [12] The simulating ability of each model was evaluated using the National Centers for Environmental Prediction- National Center for Atmospheric Research (NCEP-NCAR) reanalysis data [Kalnay et al., 1996] and the Global Precipitation Climatology Project (GPCP) precipitation data [Huffman et al., 1997; Adler et al., 2003]. Thirty-one years of NCEP-NCAR data were used in our research (1979 2009) in order to be consistent with the length of the GPCP data. Monthly precipitation data over lands from the Climatic Research Unit (CRU) were used in this study. These data cover the period 1901 2004, which is same as the REOFs analysis performed on SSTs. 3. Brief Evaluation of Models Simulation Ability [13] Figure 2 shows the climatological JAS-mean horizontal winds at 850 hpa in the reanalysis and in each model s control run. In the reanalysis, the westerlies over the northern Indian Ocean and the easterlies over the tropical Pacific meet in the western Pacific (Figure 2a). All of the five models capture this feature, although the NSIPP1 simulates too strong westerlies and the CAM3.5 and CCM3 tend to simulate weaker easterlies. The pattern correlation coefficients between the simulated and observed zonal winds in the domain of the figure range from 0.73 (CCM3) to 0.93 (GFS), and those for meridional winds range from 0.71 (CCM3) to 0.86 (GFS). The pattern correlation coefficients between the MME and observations are 0.89 and 0.87 for zonal and meridional winds, respectively. Over the WNP, the dominant feature of low-level circulation in the reanalysis is an anticyclonic circulation in the subtropics, and eastern China, Korea and Japan are along the western and northern fringes of this anticyclone. All the models capture this anticyclone and simulate well the southerlies over East Asia, which are related closely to the WNPSH and are essential to East Asian climate. [14] Compared to the reanalysis, however, the models exhibit some serious discrepancies in the shape of the WNPSH. These discrepancies can be summarized as follows: (1) all the models overestimate the monsoon trough in the tropical WNP; (2) the range of the WNPSH moves northward by about 5 in the model simulations; and (3) the WNPSH tends to be zonally elongated in the model simulations, rather than elongated in a southwest-northeast direction in the reanalysis. The monsoon trough is confined in the region (120 E 150 E, 10 N 30 N) and tends to be at a northwestsoutheast orientation in the reanalysis, but it expands over this region and tends to be zonally oriented in the simulations. The 850 hpa relative vorticity averaged over the region (120 E 150 E, 10 N 30 N) is 0.8 10 6 in the reanalysis, but as great as 3.2 10 6 in the MME result, with a range from 1.8 10 6 to 6.3 10 6 for the individual models. On the other hand, over the region (110 E 150 E, 30 N 50 N), which can be used to roughly represent the domain of the WNPSH, the averaged 850 hpa relative vorticity is 0.1 10 6 for the MME result and is from 5.2 10 6 to 1.3 10 6 for the individual models, all being much stronger than the reanalysis result. [15] The abovementioned discrepancies of the models can be well illustrated by Figure 3, which shows the differences between the simulated and observed 850 hpa winds. The differences are extremely consistent among the individual models over the WNP and East Asia. There is a cyclonic difference in the subtropics for all the models, confirming that the models simulate a too-strong monsoon trough. In the midlatitudes, there is an anticyclonic difference for all the models except GFS. The locations of cyclonic and anticyclonic differences for MME (Figure 3a) are consistent with the abovementioned regions to represent the domains of the monsoon trough and WNPSH. [16] Associated with these discrepancies in the shape of the WNPSH, the lower tropospheric winds also exhibit great differences between simulations and the reanalysis. Corresponding to the overestimation of the monsoon trough in the tropical WNP, the southeasterlies over the subtropical WNP in the reanalysis tend to be easterlies in the simulations. In addition, the southerlies over eastern China are related to southerlies over the South China Sea in the reanalysis, but they are related to the easterlies in the subtropical WNP in the simulations. It should be noted that specific humidity is much greater in the South China Sea than in the subtropical WNP (figure not shown). Therefore, this difference in lower tropospheric winds may lead to decreased rainfall in eastern China in the model simulations. The most serious discrepancy may be the one that appears over the Korean Peninsula and southern Japan. These regions experience southerlies or southwesterlies in the reanalysis, which transport water vapor into these regions. In the simulations, however, these regions are roughly under the ridge of the WNPSH. These insufficiencies in precisely simulating the climatological circulation may affect the models abilities at simulating precipitation. [17] Besides the models insufficiencies in East Asiawestern Pacific region, the biases of lower level circulation 4258

Figure 2. Climatological 850 hpa winds in the reanalysis (a), multimodel ensemble (MME, b) and the five models control runs (c g), averaged over July, August, and September (JAS). Units are in m s 1. also exist in the South Asian monsoon region. The meridional range of the westerlies over the South Asian monsoon region is narrower in the MME simulations (Figure 2). All the models except GFS simulate too narrow westerlies. In addition, the strength of the westerlies tends to be underestimated in the simulations, which can be also illustrated in Figure 3. In MME results, there are anomalous easterlies over most areas of the South Asian monsoon region. The anomalous easterlies appear in the results for all the models except NSIPP1, which overestimates the westerlies over the monsoon region. [18] Figure 4 shows the climatological JAS-mean precipitation in the observation and simulation. In the observation, there is a rain belt locating in the tropics and extending from the west coast of India to the Philippine Sea, with maximum rainfall appearing west of large-scale lands corresponding to the westerlies. This rain belt is captured well by all the models. Rainfall tends to be underestimated in the South Asian monsoon region, which is consistent with the underestimated strength of westerlies over this region. [19] Another rain belt with a slight northeast orientation in the subtropics, which is separated from the tropical precipitation, is also distinct in East Asia and the WNP. However, this subtropical rain belt almost disappears in each of the models results. While precipitations is more than 5 mm d 1 4259

Figure 3. Differences in JAS 850 hpa winds between the control run and reanalysis. (a) is for MME, and (b f) are for individual models. Units are in m s 1. along the East Asian rain belt in observation, all models except GFS simulate the minimum precipitation in this region (less than 3 mm d 1 ). One reason for this discrepancy may result from the models insufficiencies in capturing the lower tropospheric circulation, especially the shape and location of the WNPSH. The location of the northwest fringe of the WNPSH and the associated convergence along this fringe will directly influence the location of the subtropical rain belt. On the other hand, deficiencies in cumulus and cloud parameterization schemes can also contribute to the insufficiency in simulating the location of rainfall. Above all, all of these five models roughly capture the main characteristics of JAS climatological rainfall in tropical Asia, but fail to simulate the subtropical rain belt. 4. Circulation and Rainfall Changes Forced by the SST Trend [20] Figure 5 shows the horizontal wind changes at 850 hpa between the sensitive experiments and the control runs in the MME results for each month from January to December. In the tropics, the most distinguished change is the anomalous easterly over the Pacific through the whole year. This anomalous easterly is strongest from July to October, extending to the maritime continent and even to the northern Indian Ocean. In these months, the anomalous easterly extends significantly northward relative to other months and occupies the Philippine Sea. In the subtropical WNP, on the other hand, the circulation changes exhibit a remarkable difference over time. There is a cyclonic change in this region from January to March, and there tends to be an anticyclonic change in most other months. The anticyclonic change is strongest from July to November and exhibits a similarity between the months from July to October. Associated with this anticyclonic change over the subtropical WNP, there is a southerly or southwesterly change over the South China Sea and the East China Sea from July to October. [21] As mentioned in the introduction, this study focuses on the JAS-mean changes forced by the SST trend. Figure 5 indicates that the lower tropospheric wind changes induced by the SST trend are strongest and resemble each other well in the months of July, August, and September. The month of October is excluded from further analysis in this study because October is the month of transition to winter half year. In October, the northerly flows start to appear over 4260

Figure 4. Same as Figure 2, but for precipitation. The contour lines are for 3, 6, 9, 12, and 15, respectively, and values larger than 7 are shaded. Units are in mm d 1. the East China Sea (not shown), which is one of the dominant features for the East Asian winter monsoon. [22] Figure 6 shows the JAS-mean wind differences between the sensitive experiments and the control runs in the MME results and in each model. There is a significant anticyclonic change over the WNP in the MME results, characterized by the significant easterly change in the tropical western Pacific and southwesterly change in the East China Sea. The easterly change extends westward to the northern Indian Ocean, although it is not statistically significant in this region. All the models simulate the anticyclonic change in the WNP and the significant easterly change in the tropical western Pacific. The southwesterly change over the East China Sea is also simulated by each model, despite some differences in the intensity and position of this southwesterly change. [23] Figure 7 shows the JAS precipitation changes in the MME results and in observations. The observed precipitation change is obtained by regressing CRU precipitation, which is only available over lands, onto the PC1 series shown in Figure 1. In the MME result, there is a positive precipitation change extending from the equatorial Indian Ocean to the maritime continent and a parallel negative precipitation change 4261

Figure 5. The monthly difference of 850 hpa horizontal winds between the SST trend experiment and the control run in the MME results. Units are in m s 1. north of that, i.e., over the South Asian monsoon region, the South China Sea, and the Philippine Sea. There is a belt of positive precipitation change in the subtropical Asia and WNP. These simulated changes are similar to the observed changes in the equatorial regions but exhibit apparent differences in other regions. The increasing rainfall occurs in most parts of the maritime continent in observations, consistent with the MME results. The decrease in rainfall over the northern part of the Philippine Islands is also in agreement with observations. However, the observed changes are positive over the Indochina Peninsula and India and are inconsistent with the MME results. The observed strong increase in precipitation over the Korean Peninsula is also missed in the MME results. 5. Discussion [24] The present study indicates that the SST warming trend can induce an anomalous lower tropospheric anticyclonic circulation over the subtropical WNP. This anomalous anticyclone is simulated by all the five models (Figure 6). Previous studies well documented that a positive SST change in the Indian Ocean induces an anomalous easterly in the western Pacific and suppresses precipitations over the Philippine Sea [Terao and Kubota, 2005;Li et al., 2008; Xie et al., 2009; Zhou et al., 2009], resulting in a lower tropospheric anticyclonic change over the subtropical WNP [Lu, 2001;Lu and Dong, 2001]. All the models in the present study, when forced by the SST trend, simulate a negative precipitation change in the tropical WNP (Figures 7 and 8). These present results are consistent with previous results, suggesting that the negative precipitation change over the South China Sea and the Philippine Sea and the anticyclonic change over the subtropical WNP in the present results are credible. Furthermore, all the models simulate more rainfall over the equatorial Indian Ocean and maritime continent (Figures 7 and 8). [25] Based on the present and previous results, therefore, we can conclude that the following physical process may be responsible for the simulated lower tropospheric anticyclonic change over the WNP: relatively stronger warmings in the Indian Ocean and western Pacific induce an easterly change in the western Pacific and reduce precipitations in the tropical WNP, and thus result in an anomalous anticyclonic circulation over the subtropical WNP. The stronger warmings in the Indian Ocean also induce anomalous easterlies over the South Asian monsoon region, possibly due to the weakened land-sea thermal contrast [Wu, 2005]. The anomalous easterlies reduce the strength of westerlies over the South Asian monsoon region and thus result in less rainfall. [26] It has been shown in the preceding section that there are remarkable differences between the simulated and observed precipitation changes in the extratropical WNP and East Asia. There may be various reasons for these differences. First, the evaluation in section 3 suggests that it is still a challenge for AGCMs to simulate the Asian summer circulation and precipitation climatology, particularly for the WNPSH and East Asian rain belt. The AGCMs used in this study overestimate the monsoon trough and simulate a too northward shifted WNPSH (Figure 2). In particular, none of the five models can realistically reproduce the observed rain belt northeastward extended from East China Sea to midlatitude 4262

Figure 6. The difference of JAS-mean 850 hpa winds between the SST trend experiment and the control run of the five models and their MME results. Units are in m s 1. Shading indicates regions of 95% significance level. WNP (Figure 4). Over this region, all the models except GFS simulate minimum precipitations and the simulated rain belts tend to appear over the continental side of East Asia, which is consistent with the previous results shown one decade ago by Kang et al. [2002]. Therefore, it is still necessary to adequately evaluate the model s relevant ability to simulate the climatology before using model s results to investigate changes in climate. The deficiencies of the five models in reproducing the climatological WNPSH and East Asian rain belt may affect the reliable simulations of precipitation changes. The deficiencies in reproducing the climatological westerlies over the South Asian monsoon region, however, seem not to affect significantly the simulations of both circulation and precipitation changes. Despite the remarkable difference in the strength of westerlies over the South Asian monsoon region between the models (Figures 2 and 3), the difference in both circulation and precipitation changes simulated by the models are moderate (Figures 6 and 8). [27] Second, internal atmospheric variability can contribute much to precipitation in Asia and the WNP and result in the differences between the simulated and observed precipitation changes. It has been well documented that the predictability of summer climate in East Asia and the WNP is low, and the majority of extratropical circulation and rainfall variability is determined by internal atmospheric noises [e.g., Wang et al., 1997; Kang et al., 2004; Lu et al., 2006; Wu et al., 2003]. Though these previous studies focused on interannual variability, it can be inferred from the results shown by these studies that internal atmospheric noises may also affect considerably the long-term changing trend in precipitation and make it difficult to detect the forcing effects of SSTs on the precipitation trend, particularly considering that the trend is usually much weaker than interannual variability. [28] Third, the SST trend is not the only reason for the precipitation trend in Asia. Other factors, such as snow cover and human activities, can also have important impacts on the linear trend in Asian rainfall. Lastly, large loadings mainly present in the regions near the coasts, especially in the Northern Hemisphere, in the REOF1 shown in Figure 1a, that is, the SST trend used to force the atmosphere. These significant SST warmings along the coasts may be the results of the warming over the lands, since the warming is 4263

BIAN AND LU: SST IMPACT ON ASIAN RAINFALL [31] We suggest that these differences in rainfall changes are mainly due to the differences in SST forcing between Li et al. [2008] and this study: The idealized SST anomalies are confined to the tropical Indian Ocean in their study, while they are global in our study. The most distinguished SST changes in the present study, besides those in the Indian Figure 7. (a) The JAS-mean precipitation difference between the SST trend experiment and the control run in the MME results. The light and heavy color shadings indicate that there are four and five models, respectively, simulating positive (blue color)/negative (orange color) differences. (b) The observational precipitation regressed onto the first principal component (shown in Figure 1b) from 1901 to 2004. Units are in mm d 1 in Figure 7a and mm d 1 per standard deviation of the first principal component in Figure 7b. more significant over lands than over oceans. The impacts of these SST warmings along the coasts on the atmosphere, therefore, may be overestimated in the present AGCM study. [29] In the present study, we compared MME changes with the observed trend in precipitation, but we found difficulty in obtaining reliable long-term circulation data for a comparison with the simulated trend. Furthermore, there are tremendous differences among different reanalyses even for the tropical feedback processes associated with the strongest interseasonal and interannual signals of the El Niño Southern Oscillation [Kumar and Hu, 2012], and the discrepancies among the various reanalyses may exhibit even larger uncertainties for the long-term changing tendency. [30] Interestingly, there are four common models between this study and Li et al. [2008], i.e., GFS, GFDL, CAM, and CCM3, although the versions may be different. A comparison between their study and this study indicates some notable differences in precipitation changes (see their Figure 2and our Figure 8). In their results, the positive rainfall change tends to be confined to the northern Indian Ocean in the GFS, CAM3, and CCM3 results, and a significant negative change appears over the maritime continent. In contrast, in the present results, the positive precipitation change tends to be strongest in the equatorial Indian Ocean and there is a significant positive change over the maritime continent in these models. Figure 8. The difference of JAS-mean precipitation between the trend experiment and the control run of the five models. The values of contour lines are 0.2, 0.5, 1.0, 1.5, and 2.0, respectively. Units are mm d 1. Shading indicates the regions of 95% significance level, with green color for positive changes and orange color for negative changes. 4264

Ocean, are strong warmer SSTs in the western Pacific, including the maritime continent. Corresponding to these warmer SSTs, there is a positive precipitation change in the maritime continent. This positive precipitation change over the maritime continent is in contrast to the negative precipitation anomaly in Li et al. [2008]. Therefore, it can be implied that the warmer SSTs in the maritime continent may also play a role in affecting precipitation changes in the Indian Ocean and western Pacific. The possible effects of SST anomalies in the maritime continent on the Asian climate have been much less documented in comparison with those of Indian SST anomalies. It would be interesting to investigate the effects of SST anomalies or changes in the maritime continent and to compare with the impacts of anomalous SSTs in the Indian Ocean, although this work is beyond the scope of the present study that focuses on the U.S. CLIVAR experiment results. 6. Conclusions [32] This study investigated the impact of the global SST warming trend on late summer Asian rainfall in the twentieth century by analyzing the simulated results of five AGCMs included in the U. S. CLIVAR Drought Working Group. The pattern of the global SST trend, which is the leading mode of the global SST variability from REOFs, exhibits a warming in almost all the oceans, particularly the Indian Ocean and western Pacific. The impact of the SST trend was estimated by the differences between the results forced by the SSTs with and without the SST trend. [33] All of the five models show a significant lower tropospheric anticyclonic change over the WNP in July-August- September (JAS), forced by the SST trend. This anticyclonic change is resulted from a suppressed precipitation over the South China Sea and Philippine Sea, which is also simulated by all the models. It is suggested that the warmer SSTs in the Indian Ocean and western Pacific induce the suppressed precipitation in the tropical WNP and anticyclonic change in the subtropical WNP. The warmer SSTs in the Indian Ocean may be also responsible for the anomalous easterlies over the South Asian monsoon region and the South China Sea through weakening the land-sea thermal contrast, and result in less rainfall in these regions. [34] The models simulate a positive precipitation change in the subtropical WNP, which is located southward in comparison with the land-observed increasing trend in precipitation. This difference between the simulation and observation may be induced by the models discrepancies in climatology, internal atmospheric variability, impacts of other factors rather than SSTs, and etc. [35] Acknowledgments. 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