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Projection of Future Changes in the Asian Summer Monsoon Under Global Warming Conditions
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PROJECTION OF FUTURE CHANGES IN THE ASIAN SUMMER MONSOON UNDER GLOBAL WARMING CONDITIONS
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11.1 INTRODUCTION Occupying a vast domain including South Asia, the Indian subcontinent, South China Sea (SCS), East Asia, and the western North Pacific (WNP), the Asian monsoon is one of the most important components of the global climate system (Chang, 2004; Wang et al., 2006; Chang et al., 2011). Summer climate variability in Asia is closely related to the variations of the Asian summer monsoon (ASM) system, which is driven mainly by the differential heating of the Indian Ocean and the adjacent Asian landmass, including the snow cover over the Eurasian continent (Hahn and Shukla, 1976; Douville and Royer, 1996). Through the Walker circulation, the El Nin˜o Southern Oscillation (ENSO) also has a considerable impact on the variability of the ASM (Webster and Yang, 1992; Ju and Slingo, 1995; Arpe et al., 1998; Annamalai et al., 2007; Wang et al., 2000). The possible influence of increasing levels of atmospheric trace gases on the Asian monsoon has been one of the focal points of some recent studies. In theory, the rate of surface temperature increase is stronger over land than over oceans in the process of global warming, and monsoon should strengthen accordingly in response to the increased land sea thermal contrast (Ding, 1994; Turner and Annamalai, 2012; Wang et al., 2012a,b; Liu et al., 2014). However, this hypothesis does not consider the interaction of changes in large-scale atmospheric circulations, and whether it is effective for a complicated monsoon (e.g., the ASM) system remains unclear. In the literature, opinion differs on the response of the ASM to increased atmospheric greenhouse gas concentrations based on numerical experiments of individual or multiple climate models. For example, observational analysis by Kumar and Dash (2001) has indicated the role of increased land surface temperature in enhancing the monsoon, but weakening the monsoon and ENSO relationship at interannual timescales over recent decades. Meehl and Washington (1993) and Hu et al. (2000) found an intensification of both the ASM and its variability. The evolution of the ASM has distinct regional features and can be further separated into the East Asian summer monsoon (EASM) and the Indian summer monsoon (ISM). The EASM is projected to strengthen (Li and Zhou, 2010; Sun and Ding, 2011; Chen et al., 2012), strengthen slightly only over SCS (Ueda et al., 2006), vary little
The Asian Summer Monsoon. DOI: https://doi.org/10.1016/B978-0-12-815881-4.00011-1 © 2019 Elsevier Inc. All rights reserved.
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(Kimoto, 2005), and remain normal in terms of intensity (Li et al., 2010); the ISM is projected to significantly strengthen during the Indian monsoon rainfall (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012), slightly increase (Lal et al., 2001), and weaken in the monsoon circulation (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). Given that there are differences in climate models, emissions scenarios, and analysis methods among these studies, and that most do not assess whether climate models can reliably reproduce the present Asian monsoon circulation, it is difficult to obtain a consistent view of the future ISM and EASM. In this chapter, we will use the outputs of reliable climate models to project future changes in the ISM and EASM, including the mean monsoon and its variability, from the perspective of multiple climate models and climate dynamics. Based on all available data of climate models participating in the 5th Assessment Report, Intergovernmental Panel on Climate Change (IPCC, 2013), this study assesses the changes in the EASM and ISM under the representative concentration pathways (RCPs) mid low range scenario (RCP4.5). This chapter is organized as follows. Section 11.2 provides a description of observation data and coupled model intercomparison project phase 5 (CMIP5) simulations used in this chapter. Section 11.3 discusses the models’ ability to simulate the present climatology of the ASM circulation and precipitation, and select reliable models for further analysis. Section 11.4 investigates the possible future changes of the ISM and EASM under the RCP4.5 scenario. Summary and discussions are given in Section 11.5.
11.2 DATA AND MODELS Many climate models have been used to simulate present and future climates in the CMIP5. According to the availability of output data from the CMIP5 historical simulation, and the CMIP5 RCP4.5 simulation (Moss et al., 2010; Thomson et al., 2011), the results of 31 climate models archived in the CMIP5 are applied in this chapter. Basic information about these climate models and experiments is provided in Table 11.1. More details are available at http://cmip-pcmdi.llnl.gov/ cmip5/. In addition, data used to assess the ability of the models include monthly horizontal winds at 850 hPa level (u850 and v850) and sea level pressure (SLP) from the National Centers for Environmental Prediction National Center for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al., 1996). Precipitation data are from the global precipitation climatology projection (GPCP), with a resolution of 2.5 3 2.5 (Adler et al., 2003). These reanalysis and analysis data are treated as observations. A multimodel ensemble mean with the same weights across the reliable models of concern is used to obtain common results of climate models in this chapter. All model and observation data were aggregated to a horizontal resolution of 2.5 3 2.5 . This chapter is primarily focused on the results from the multimodel ensemble, therefore, issues surrounding model uncertainty will not be discussed. Considering that climate change projection in the CMIP5 begins from 2006, and that the NCEP/NCAR reanalysis data are more reliable after 1979 owing to the availability of satellite data, in this chapter the period 1979 2005 was chosen as the reference period.
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Table 11.1 Basic Information on the 31 CMIP5 Models No.
Model Name
Institute/Country
Resolution
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
ACCESS1-0 ACCESS1-3 BCC-CSM1-1 BNU-ESM CanESM2 CCSM4 CESM1-CAM5 CMCC-CESM CMCC-CM CMCC-CMS CNRM-CM5 FGOALS-g2 FGOALS-s2 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-R HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC-ESM MIROC-ESM-CHEM MIROC5 MPI-ESM-LR MPI-ESM-MR MRI-CGCM3 NorESM1-M NorESM1-ME
CAWCR/Australia CAWCR/Australia BCC/China BNU- GCESS/China CCCMA/Canada NCAR/USA NSF-DOE-NCAR/USA CMCC/Italy CMCC/Italy CMCC/Italy CNRM-CERFACS/France LASG-CESS/China LASG-CESS/China NOAA GFDL/USA NOAA GFDL/USA NOAA GFDL/USA NASA GISS/USA NASA GISS/USA MOHC/UK INM/Russia IPSL/France IPSL/France IPSL/France MIROC/Japan MIROC/Japan MIROC/Japan MPI-M/Germany MPI-M/Germany MRI/Japan NCC/Norway NCC/Norway
1.875 3 1.25 1.875 3 1.25 2.8 3 2.8 2.8 3 2.8 2.8 3 2.8 1.25 3 0.94 2.5 3 1.9 3.75 3 7.5 0.75 3 0.75 1.875 3 1.875 1.4 3 1.4 2.8 3 3 2.8 3 1.67 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 1.875 3 1.25 2.0 3 1.5 3.75 3 1.875 2.5 3 1.25 3.75 3 1.875 2.8 3 2.8 2.8 3 2.8 1.4 3 1.4 1.875 3 1.875 1.875 3 1.875 1.1 3 1.1 2.5 3 1.875 2.5 3 1.875
CMIP5, Coupled model intercomparison project phase 5.
11.3 EVALUATION OF MODELS’ SIMULATION The climatology of 850 hPa wind and rainfall in observations and CMIP5 models is shown in Fig. 11.1. In observations (Fig. 11.1A), the strong southwesterly winds flow from the Indian Ocean to the Bay of Bengal (BOB), all the way to the northern SCS, and then turn to eastern China, the
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FIGURE 11.1 The climatological distribution of JJA mean rainfall (shaded; units: mm/day) and 850 hPa wind (vectors; units: m/s) in observations and each CMIP5 model.
Korean peninsula, and Japan. The southwesterly winds affect the climate in East Asia with the southerly winds originating from the northwestern flank of the western Pacific subtropical high (WPSH; the anticyclone circulation over the western Pacific). The monsoon rainfall band is dominated by the WPSH. Corresponding to the strong westerly winds and the WPSH, there are four rainfall centers in GPCP: northern India, the BOB Indochina peninsula, SCS, WNP, and the monsoon rainfall band which is called Meiyu in China, Baiu in Japan, or Changma in Korea (along 30 N). Meiyu is a climatic phenomenon closely related to the East Asian summer monsoon. It is
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called Meiyu in China, Baiu in Japan and Changma in Korea (the introduction of Meiyu has been given in Chapter 3). Many CMIP5 models show similar biases in reproducing the position of the WPSH and the associated magnitude of monsoon rainfall band, as with previous models (Zhou and Li, 2002; Chen et al., 2010). It features a northward shift of the WPSH resulting in an underestimation of rainfall along the monsoon rainfall band from southern China to Japan, and an overestimation of rainfall over the SCS and WNP. Moreover, the rainfall over north India and BOB is also underestimated in most CMIP5 models mainly due to the biases in reproducing the intensity of the cross-equatorial flow over the western Indian Ocean and the associated westerly winds along 10 15 N. The rainfall maximum over two monsoon regions of northern India BOB and from central China to Japan are missed in almost all the models, suggesting that simulation of the ASM rainfall remains a challenge for current climate models (Li and Zhou, 2010; Chen et al., 2000; Liu et al., 2012; Liang et al., 2019). The spatial correlation coefficient (SCC) of climatological JJA mean 850 hPa wind versus rainfall in CMIP5 models is calculated in Fig. 11.2. It is shown that the SCCs of wind are higher than those of rainfall in all the CMIP5 models and are, therefore, indicative of a better reproduction of wind than rainfall. The 31-model ensemble mean has better SCCs of wind and rainfall than other individual models.
FIGURE 11.2 Scatterplot of the spatial correlation coefficients (SCCs) of climatological JJA mean 850 hPa wind versus rainfall in CMIP5 models. The SCC is relative to NCEP/NCAR and GPCP over the ASM region (15 S 50 N, 30 180 E).
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The ASM is primarily driven by convective, radiative, and sensible heat sources/sinks, among which convective latent heating is the most important. During boreal summer, the most intense convection is observed over two regions, namely the BOB India Arabian Sea and the SCS Philippine Sea (Wang et al., 2001). The heat sources over the BOB and the Philippine Sea affect the ISM and EASM, respectively, because well-defined wind anomalies occur over and to the west of the convective heat sources as a Rossby wave response to the latent heat released in the two convection regions (Matsuno, 1966; Gill, 1980). As the lower-level circulation is dynamically consistent with convective heating in summer and the correlation between interannual variations of the south Asian and WNP convection is statistically insignificant (Wang and Fan, 1999), we focus on 850 hPa winds over the ISM and EASM regions, which represents the changes of monsoon circulation. The spatial distribution of zonal wind at 850 hPa level (u850) within two regions of (5 15 N, 40 80 E) and (20 30 N, 70 90 E) are used to evaluate the simulation of the ISM from CMIP5 models according to previous studies (Wang et al., 2001). Considering that the southerly winds prevail over eastern China and the western side of the WPSH in summer, and there is a clear relationship between the meridional wind and land sea thermal contrasts, the 850 hPa meridional wind (v850) averaged in the region 20 40 N, 110 125 N is used to evaluate the capacities of simulating the EASM in models (Wang, 2000, 2002; Sun and Ding, 2011; Jiang and Tian, 2013). To objectively measure the capacity of the models to simulate the ISM and EASM, the correlation coefficient between simulated and observed climatology of u850 and v850 in regional averages for the period 1979 2005 are calculated model-by-model, as well as the standard deviation and centered root-mean-square error (RMSE) of each simulation with respect to observation based on the ISM and EASM regions. As shown in the Taylor diagram (Taylor, 2001), all 31 climate models with data available (Table 11.1) could reliably reproduce the spatial distribution of u850 over the ISM region (Fig. 11.3A). Correlation coefficients range from 0.17 (GISS-E2-H) to 0.97 (CanESM2), and the values of GISS-E2-H and GISS-E2-R are not statistically significant at the 0.01 t-test significant level. Normalized centered RMSEs range from 0.25 (CanESM2) to 1.04 (GISS-E2-H), with the values of all the models being within 0.25 1.00 except for GISS-E2-H. There is a large spread in the ability of 31 climate models to represent the v850 over the EASM region (Fig. 11.3B). Correlation coefficients range from 0.03 (CMCC-CESM) to 0.87 (MIROC5), and the values of CMCC-CESM, FGOALS-s2, and MRI-CGCM3 are not statistically significant at the 0.01 t-test significance level. Normalized centered RMSEs range from 0.62 (MPI-ESM-MR) to 1.8 (MIROC-ESM), with the values of 13 models being more than 1.00 and those of the other models being within 0.62 1.00. Overall, the ability of most models to simulate the ISM’s circulation is found to be reliable and relatively concentrated, and the ability to simulate the EASM circulation is found to be relatively weak and dispersed. Accordingly, it is necessary to select more reliable models for projecting the future changes of the ISM and EASM. Two preconditions are set to identify reliable models for both the ISM and EASM. First, SCCs had to be positive and statistically significant at the 0.01 t-test significance level; and, second, normalized, centered RMSEs had to be less than 1.00. According to this procedure, and associated with the climatological rainfall and 850 hPa wind simulations over ASM region (Figs. 11.1 and 11.2), 13 CMIP5 models were applied to investigate the possible variation of the ISM and EASM in the future. The ensemble mean of the models is calculated based on the results of the 13 models: ACCESS1-0, BCCCSM1-1, CanESM2, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G,
FIGURE 11.3 Taylor diagram for displaying normalized pattern statistics of climatological u850 (A)within ISM regions of 5 15 N, 40 80 E and 20 30 N, 70 90 E, and (B) v850 within EASM region of 20 40 N, 110 125 E between the 31 CMIP5 models and observations in summer for the reference period 1979 2005. Each number represents a model (see Table 11.1); and observation is considered as the reference (REF). Standard deviation and centered root-mean-square errors are normalized by the reference standard deviation. The radial distance from the origin is the normalized standard deviation of a model; the correlation between a model and the reference is given by the azimuthal position of the mode; and the centered root-mean-square error between a model and the reference is their distance apart. In brief, the nearer the distance between a number and REF, the better the performance of the corresponding model.
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GFDL-ESM2M, IPSL-CM5A-MR, MPI-ESM_LR, MPI-ESM_MR, and NorESM1-ME. Nevertheless, when comparing the ensemble mean results with previous studies, the different model ensembles may explain both minor and major differences. Therefore it is important to develop a set of reasonable metrics for model selection to allow comparison of climate projections between different studies and regions. The use of uniform metrics is an important issue for future climate change research.
11.4 PROJECTION OF THE INDIAN SUMMER MONSOON AND EAST ASIAN SUMMER MONSOON There are more than 20 indices of the ISM and EASM published in the literature (Wang et al., 2008). Considering that the correlation between interannual variations of the South and East Asian convection is statistically insignificant (Wang et al., 1993), two kinds of monsoon indices are selected to measure the variability of the ISM and EASM, respectively. Rainfall anomalies averaged over the core regions of the ISM and EASM are meaningful indices, yet the rainfall data in monsoon regions from models usually have a large model bias (Gao et al., 2011; Liang et al., 2009, 2019; Liu et al., 2015). To obtain more reliable results, corresponding circulation indices are desirable. To choose a circulation index dynamically consistent with convective heating, an ISM index is selected which is defined as the difference of u850 between a southern region (5 15 N, 40 80 E) and a northern region (20 30 N, 70 90 E) (indicated by the two solid-line boxes near India in Fig. 11.5). Such a defined index reflects both the intensity of the tropical westerly monsoon and the lower-tropospheric vorticity anomalies associated with the ISM trough (Wang et al., 2001). The ISM index not only represents well the rainfall anomalies averaged over an extended region including the BOB, India, and the eastern Arabian Sea, it is also highly correlated with the all-India summer rainfall with a correlation coefficient of 0.72 for the 50-year period from 1948 to 1997 (Parthasarathy et al., 1992). The selected index for the EASM is defined by the regionally averaged meridional wind at 850 hPa (v850) within the region of (20 40 N, 110 125 E, Fig. 11.5) (Wang, 2000, 2002; Sun and Ding, 2011; Jiang and Wang, 2005). Due to the EASM determined by the heat contrast between land and ocean, there is an obvious meridional wind circulation in East Asia. To reduce the influence of the differences in the ISM and EASM climatology among the models and their ensemble means, the normalized change of those intensity values relative to the climatology of the reference period 1979 2005 is chosen as the ISM or EASM indices for each model and their ensemble means.
11.4.1 FUTURE CHANGES OF THE INDIAN SUMMER MONSOON AND EAST ASIAN SUMMER MONSOON INDICES The future change of the ISM measured by the ISM index is shown in Fig. 11.4A. It is seen that the ISM intensity slightly decreases over the period 2006 99 relative to 1979 2005 based on the results of either individual models or their ensemble means. Table 11.2 shows the climate tendency rate and trend coefficient of each model and the 13-model ensemble mean of monsoon indices over the period 2006 99. It is seen that the ISM index decreased in 9 of 13 models and increased in
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FIGURE 11.4 (A) The time series of the ISM index as measured by the difference of u850 between two regions (5 15 N, 40 80 E and 20 30 N, 70 90 E) from 13 CMIP5 models. (B) The EASM index as measured by the regionally averaged v850 within the region 20 40 N, 110 125 E. MME, MME_11-year, and MME_trend denote the 13-model ensemble mean, its 11-year smoothing averaged, and long-term trend, respectively.
models of BCC-CSM1-1, CanESM2, CNRM-CM5, and GFDL-ESM2M. However, most of these changes are small. Statistically significant decreased trends at the 0.05 t-test significance level can be identified in GFDL-CM3, GFDL-ESM2G, MPI-ESM-LR, MPI-ESM-MR, and the 13-model ensemble means. On the whole, there is a slightly decreasing change trend for the ISM under global warming based on the 13 reliable CMIP5 models. Such a slight weakening trend is consistent in direction with most previous studies (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). In addition, there are two distinct differences in the future with the comparison to the reference period. First, the averaged intensity of the ISM during 2006 99 is significantly weaker than that in the reference period of 1979 2005. Second, the standard deviation of the ISM index time series as derived from the 13-model ensemble mean decreased by 19.2% relative to the reference period, which implies a significant weakening of interannual variability of the ISM in the future. It disagrees with the increased interannual variability of the monsoon rainfall in the region of 0 20 N, 40 110 E under future global warming as revealed by a coupled model (Hu et al., 2000).
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Table 11.2 The Climate Tendency Rate (CTR, unit: /a) and Trend Coefficients (TC) of Monsoon Indices in the Period 2006 99 by 13 Models and the 13-Model Ensemble Mean (TCs of 0.2 and 0.26 Reach the 0.05 and 0.01 Significance Levels, Respectively) ISM Models ACCESS1-0 BCC-CSM1-1 CanESM2 CMCC-CM CMCC-CMS CNRM-CM5 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M IPSL-CM5A-MR MPI-ESM-LR MPI-ESM-MR NorESM1-ME MME
CTR 0.002 0.005 0.002 0.000 0.005 0.004 0.011 0.007 0.000 0.004 0.007 0.006 0.003 0.006
EASM
TC 0.068 0.138 0.059 0.010 0.158 0.094 0.354 0.246 0.003 0.100 0.190 0.204 0.007 0.248
CTR 0.001 0.004 0.010 0.001 0.001 0.001 0.007 0.002 0.001 0.002 0.003 0.002 0.002 0.005
TC 0.020 0.101 0.406 0.021 0.027 0.029 0.218 0.053 0.021 0.058 0.091 0.086 0.063 0.208
Different from the ISM, Fig. 11.4B shows that there is a slightly increased trend for the intensity of the EASM as a whole, although the increased trend is small. The EASM index increases in 10 of the 13 models, and statistically significant increased trends can be identified in the 13-model ensemble mean and two individual models, namely CanESM2, and GFDL-CM3. There are still three models in which the trend of the EASM index is negative, which implies the future change in the EASM intensity is also model dependent, similar to the ISM. It is indicated to some extent that the previous conclusion on the EASM either strengthening (Ueda et al., 2006; Li and Zhou, 2010; Sun and Ding, 2011; Chen et al., 2012) or having little variation (Kimoto, 2005; Li et al., 2010) from individual or several models is uncertain. On the whole, the EASM intensity is slightly strengthened over the whole period of 2006 99 based on the 13-model ensemble mean result of either individual models or more models with statistically significant positive trends. In addition, it is noted that the standard deviation of the EASM index time series as derived from the 13-model ensemble mean decreases by 16.2% for 2006 99 relative to the reference period, although the long-term trend is positive. That means the interannual variability of the EASM intensity decreases, which disagrees with the increased interannual variability of the EASM rainfall under future global warming, as revealed by 13 CMIP3 models (Lu and Fu, 2010).
11.4.2 FUTURE CHANGES OF MONSOON CIRCULATION AND RAINFALL IN THE ASIAN SUMMER MONSOON REGION Fig. 11.5 illustrates the SLP and horizontal winds at 850 hPa in summer for the period of 1979 2005 from the 13-model ensemble mean (Fig. 11.5A), and the corresponding difference
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FIGURE 11.5 (A) Climatology of horizontal wind at 850 hPa (m/s, vector) and SLP (Pa, shaded) in summer for the period 1979 2005; (B) the corresponding difference between the periods 2070 99 and 1979 2005 from the 13-model ensemble mean. Two red rectangles denote the ISMI of 5 15 N, 40 80 E and 20 30 N, 70 90 E; and the yellow rectangle is the EAMI (20 40 N, 110 125 E).
between the periods 2070 99 and 1979 2005 (Fig. 11.5B). It is shown that the models are generally able to reproduce the distribution of SLP and lower-level wind fields in Asia. Because the land is heated to a greater degree in the summer, it becomes significantly warmer than the oceans.
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The Eurasian continent is controlled by thermal low-pressure systems whereas the oceans are controlled by high-pressure systems. The thermal contrasts due to differences in the warming of the land and oceans drives the monsoon circulation, forming strong southwesterly winds flowing from the Indian Ocean to the BOB, all the way to the northern SCS, and East Asia and the WNP. The southwesterly winds affect the climate in East Asia with southerly winds originating from the northwestern flank of the NWP anticyclone (Fig. 11.5A). During 2070 99, however, under future global warming, the ISM circulation weakens in climate models, which displays that the crossequatorial southerly, the ISM trough, and associated anomalous westerlies between 5 and 15 N, and easterlies over northern India are weakened (Fig. 11.5B). Accordingly, an anomalous anticyclonic circulation occurs over central India. It is also noted that the meridional land sea thermal contrast and thermal effects of the elevated Tibetan Plateau reinforce the interhemispheric thermal contrast resulting from differential solar radiation over Indian monsoon region (Fig. 11.5B). It appears inconsistent with the projected increases in surface land sea thermal contrasts (Turner and Annamalai, 2012; Wang et al., 2012a,b). The monsoon rainfall over the ISM region also significantly increases in the period 2070 99 relative to the reference period (Fig. 11.6), which is consistent with previous studies (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012). Sun et al. (2010) indicated that the ISM intensity positively correlates with the land sea thermal contrast in the lower and upper troposphere before year 2000; thereafter, a reduced upper-tropospheric thermal contrast leads to a weakened ISM circulation, despite an increasing lower-tropospheric thermal contrast. The slight strengthening of the EASM under future global warming is further confirmed by atmospheric circulation changes in the lower troposphere, as large-scale southerly wind anomalies (less than 1 m/s) at 850 hPa prevail over eastern and northeast China in the 13-model ensemble
FIGURE 11.6 The difference of rainfall between the periods 2070 99 and 1979 2005 from the 13-model ensemble mean (unit: mm/day).
11.5 SUMMARY AND DISCUSSION
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mean for 2070 99 relative to the reference period (Fig. 11.4B). This is because, under future global warming, the temperature rises across East Asia and adjacent areas, but with a different magnitude between land and ocean (Sun and Ding, 2011). On the one hand, the rate of warming is faster over East Asia than over the same latitudes of the WNP and hence leads to an increased zonal thermal contrast (Jiang and Wang, 2005). Accordingly, the strengthening of heat low over the East Asian continent is larger in magnitude than the weakening of the WNP subtropical high, leading to an increased zonal SLP gradient and, in turn, southerly wind anomalies over East Asia. On the other hand, the rate of warming is faster over eastern China than over the SCS. This leads to increased meridional land sea thermal and sea level pressure gradients and, hence, southerly wind anomalies over southern China. Overall, these changes in zonal and meridional land sea thermal contrasts across East Asia and surrounding oceans are responsible for the slight strengthening of the EASM. The monsoon rainfall bands of the Meiyu/Baiu/Changma and WNP over the EASM region also increase significantly under global warming (Fig. 11.6), which agrees with the strengthening change of the EASM’s circulation. In previous work performed using the CMIP3 and CMIP5 datasets under global warming, the ISM was projected to significantly strengthen during the Indian monsoon rainfall (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012), slightly increase (Lal et al., 2001), and weaken in the monsoon circulation (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). On the other hand, the EASM was projected to strengthen (Li and Zhou, 2010; Chen et al., 2012), strengthen only over South China (Ueda et al., 2006; Sun and Ding, 2010), vary little (Kimoto, 2005), and remain normal (Li et al., 2010). Such discrepancies are not strange when viewed from this work. That is because that there are differences in climate models, emissions scenarios, analysis methods, and monsoon indices among the studies, and that most of them do not assess whether climate models can reliably reproduce the present Asian monsoon circulation. In addition, the ISM and EASM changes are determined largely by climate models used in the analysis. In other words, those projections are more or less model-dependent, owing to the limited number of climate models.
11.5 SUMMARY AND DISCUSSION Over recent years, many climate models have been used to project future climate change worldwide. Under the most representative RCP4.5 scenario, this chapter assessed the future changes of the ISM and EASM using the data of 13 reliable CMIP5 models. These models were selected from the available 31 climate models used in the IPCC Assessment Report 5 in terms of their ability to simulate the climatology of the ASM in the reference period of 1979 2005. Under future global warming, it was found that there is a slightly decreased trend for the intensity of the ISM’s circulation, with a significant weakening of interannual variability. The spatial distribution displays that the cross-equatorial southerly, the ISM trough and associated anomalous westerly between 5 and 15 N, easterlies over northern India are weakened, and an anomalous anticyclone occurs over central India. But the monsoon rainfall over the ISM region will significantly increase relative to the reference period. On the other hand, the EASM’s circulation and rainfall are strengthened, while the interannual variability of the EASM’s circulation is slightly weakened relative to 1979 2005.
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The spatial distribution data exhibit that large-scale southerly wind anomalies at 850 hPa prevail over eastern and northeast China relative to the reference period. The changes both in zonal and meridional land sea thermal contrasts across East Asia and surrounding oceans are responsible for the slight strengthening of the EASM. Why do the ISM and EASM exhibit differing variations in the future under global warming? The regionality of the Asian monsoon variability is, in part, due to the spatial distribution of the thermal contrast between the ocean and continent. Over India, the meridional land sea thermal contrast and thermal effects of the elevated Tibetan Plateau reinforce the interhemispheric thermal contrast resulting from differential solar radiation. As such, the Indian monsoon is extremely energetic. Over East Asia and its adjacent marginal seas, however, an eastern western land sea thermal contrast dominates, which tends to induce a zonal pressure difference between the Asian continental low and the WPSH. In conjunction with the influence of the northern southern differential solar forcing, the WPSH, along with the EASM trough to its southwest and the subtropical front to its northwest, controls the monsoon circulation over East Asia. From their geographic setting relative to the continent ocean distribution, one would expect that the variability of the ISM and EASM might be dissimilar. The ISM and EASM are driven, in part, by two major convective heat sources. In summer, the two centers are anchored primarily in the BOB and the Philippine Sea, respectively. The changes in intensity and location of the two convection regions have fundamental impacts on the variability of the two monsoon subsystems. Note that the convection in East Asia is more directly affected by the Pacific SST anomaly than the convection over the BOB (Wang et al., 2000). In addition, the ways by which ENSO affects the two convective heat sources are also different (Lau and Nath, 2000; Wang et al., 2000). Therefore, the variations of the ISM and EASM could be very different. It was also noted that the future ASM changes are somewhat model and index dependent. This explains why there are discrepancies on this issue in previous studies, and implies that more emphasis should be given to the ensemble mean of multiple reliable climate models and the accompanying mechanisms. In addition, the horizontal resolution of state-of-the-art climate models is generally higher than before, but their ability to represent the East Asian climate is still inadequate and, hence, their projection is more or less uncertain. Given that the performance of high-resolution regional climate models to simulate the present climate over China is better overall than that of global climate models, and that future changes in monsoon precipitation over China are different between regional and global models (Gao et al., 2012), more attention should be given to dynamic downscaling studies at the regional scale. Finally, we would like to emphasize that there are uncertainties in future emissions or concentration scenarios of greenhouse gases and aerosols, and in radiative forcing scenarios. There are still incomplete aspects in the global climate and Earth system models. The lack of observation data hampers our understanding of climate change on the decadal and longer timescales. Our knowledge of natural climate variability over a range of timescales is also limited. All these factors lead to a level of uncertainty in future climate change projection.
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Annamalai, H., Hamilton, K., Sperber, K.R., 2007. The South Asian summer monsoon and its relationship with ENSO in the IPCC AR4 simulations. J. Clim. 20, 1071 1092. Arpe, K., Du¨menil, L., Giorgetta, M.A., 1998. Variability of the Indian monsoon in the ECHAM3 model: Sensitivity to sea surface temperature, soil moisture, and the stratospheric quasi-biennial oscillation. J. Clim. 11, 1837 1858. Chang, C.-P., 2004. East Asian monsoon. World Scientific, Singapore. Chang, C.-P., Ding, Y., Lau, N.-C., 2011. The Global monsoon system: research and forecast. World Scientific, Singapore. Chen, H., Zhou, T., Neale, R.B., et al., 2010. Performance of the new NCAR CAM3.5 in East Asian summer monsoon simulations: Sensitivity to modifications of the convection scheme. J. Clim. 23, 3657 3675. Chen, H.P., Sun, J.Q., Chen, X.L., 2012. The projection and uncertainty analysis of summer precipitation in China and the variations of associated atmospheric circulation field. Clim. Environ. Res. 17, 171 183. in Chinese. Chen, T.-C., Yoon, J.-H., 2000. Interannual variation in Indochina summer monsoon rainfall: Possible mechanism. J. Clim. 13, 1979 1986. Ding, Y.H., 1994. Monsoons Over China. Springer, Heidelberg, 419 pp. Douville, H., Royer, J.F., 1996. Sensitivity of the Asian summer monsoon to an anomalous Eurasian snow cover within the Meteo-France GCM. Clim. Dyn. 12, 449 466. Douville, H., Royer, J.-F., Polcher, J., et al., 2000. Impact of CO2 doubling on the Asian summer monsoon: Robust versus model-dependent responses. J. Meteorol. Soc. Jpn. 78, 421 439. Gao, H., Yang, S., Kumar, A., Hu, Z.-Z., Huang, B., Li, Y., et al., 2011. Variations of the East Asian Meiyu and simulations and prediction by the NCEP climate forecast system. J. Clim. 24, 94 108. Available from: https://doi.org/10.1175/2010JCLI3540. Gao, X.J., Shi, Y., Zhang, D.F., et al., 2012. Uncertainties in monsoon precipitation projections over China: results from two high-resolution RCM simulations. Clim. Res. 52, 213 226. Gill, A.E., 1980. Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteorol. Soc. 106, 447 462. Hahn, D.G., Shukla, J., 1976. An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. J. Atmos. Sci. 33, 2461 2462. Hu, Z.-Z., Latif, M., Roeckner, E., et al., 2000. Intensified Asian summer monsoon and its variability in a coupled model forced by increasing greenhouse gas concentrations. Geophys. Res. Lett. 27, 2681 2684. IPCC (Intergovernmental Panel on Climate Change), 2013. Climate Change 2013: The Physical Science Basis. In: Stocker, et al., (Eds.), Contribution of Working Group I to the Fifth Assessment Report of the IPCC. Cambridge Univ. Press, Cambridge. Jiang, D.B., Tian, Z.P., 2013. East Asian monsoon change for the 21st century: Results of CMIP3 and CMIP5 models. Chin. Sci. Bull. 58, 1427 1435. Jiang, D., Wang, H.J., 2005. Natural interdecadal weakening of East Asian summer monsoon in the late 20th century. Chin. Sci. Bull. 50, 1923 1929. Ju, J., Slingo, J.M., 1995. The Asian summer monsoon and ENSO. Quart. J. Roy. Meteor. Soc. 121, 113 1168. Kalnay, E., Kanamitsu, M., Kistler, R., et al., 1996. The NCEP/NCAR Reanalysis Project. Bull. Amer. Meteorol. Soc. 77, 437 472. Kimoto, M., 2005. Simulated change of the East Asian circulation under global warming scenario. Geophys. Res. Lett. 32, L16701.
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Kitoh, A., Yukimoto, S., Noda, A., Motoi, T., 1997. Simulated changes in the Asian summer monsoon at times of increased atmospheric CO2. J. Meteorol. Soc. Jpn. 75, 1019 1031. Kumar, J.R., Dash, S.K., 2001. Interdecadal variations of characteristics of monsoon disturbances and their epochal relationships with rainfall and other tropical features. Int. J. Climatol. 21, 759 771. Lal, M., Harasawa, H., 2001. Future climate change scenarios for Asia as inferred from selected coupled atmosphere-ocean global climate models. J. Meteorol. Soc. Jpn. 79, 219 227. Lau, N.-C., Nath, M., 2000. Impact of ENSO on the variability of the Asian Australian monsoons as simulated in GCM experiments. J. Clim. 13, 4287 4309. Li, B., Zhou, T.J., 2010. Projected climate change over China under SRES A1B scenario: multi-model ensemble and uncertainties. Adv. Clim. Change Res. 6, 270 276. in Chinese. Li, J., Wu, Z., Jiang, Z., et al., 2010. Can global warming strengthen the East Asian summer monsoon? J. Clim. 23, 6696 6705. Liang, J., Yang, S., Hu, Z.-Z., Huang, B., Kumar, A., Zhang, Z., 2009. Predictable patterns of the Asian and Indo-Pacific summer precipitation in NCEP CFS. Clim. Dyn. 32, 989 1001. Available from: https://doi. org/10.1007/s00382-008-0420-8. Liang, P., Hu, Z.-Z., Liu, Y.Y., Yuan, X., Li, X., Jiang, X., 2019. Challenges in predicting and simulating summer rainfall in the eastern China. Clim. Dyn., (published online) . Available from: https://doi.org/10.1007/ s00382-018-4256-6. Liu, H.W., Zhou, T.J., Zhu, Y.X., et al., 2012. The strengthening East Asian summer monsoon since the early 1990s. Chin. Sci. Bull. 57, 1553 1558. Liu, Y.Y., Li, W.J., Zuo, J.Q., Hu, Z.-Z., 2014. Simulation and projection of western Pacific subtropical high in CMIP5 models. J. Meteorol. Res. 28, 327 340. Available from: https://doi.org/10.1007/s13351-0143151-2. Liu, Y.Y., Hu, Z.-Z., Kumar, A., Peng, P., Collins, D., Jha, B., 2015. Tropospheric biennial oscillation of summer monsoon rainfall over East Asia and its association with ENSO. Clim. Dyn. 45, 1747 1759. Available from: https://doi.org/10.1007/s00382-014-2429-5. Lu, R.Y., Fu, Y.H., 2010. Intensification of East Asian summer rainfall interannual variability in the twentyfirst century simulated by 12 CMIP3 coupled models. J. Clim. 23, 3316 3331. Matsuno, T., 1966. Quasi-geostrophic motions in the equatorial area. J. Meteorol. Soc. Jpn 44, 25 42. Meehl, G.A., Stocker, T.F., Collins, W.D., et al., 2007. Global Climate Projections. In: Solomon, S., Qin, D., Manning, M., et al., Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, pp. 748 845. Meehl, G.A., Washington, W.M., 1993. South Asian summer monsoon variability in a model with doubled atmospheric carbon dioxide concentration. Science 260, 1101 1104. Moss, R.H., Edmonds, J.A., Hibbard, K.A., et al., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747 756. Parthasarathy, B., Kumar, K.R., Kothawaie, D.R., 1992. Indian summer monsoon rainfall indices: 1871 1990. Meteor. Mag. 121, 174 186. Sun, Y., Ding, Y.H., 2010. A projection of future changes in summer precipiggtation and monsoon in East Asia. Sci. China Ser. D-Earth Sci. 53, 284 300. Sun, Y., Ding, Y.H., 2011. Responses of South and East Asian summer monsoons to different land-sea temperature increases under a warming scenario. Chin. Sci. Bull. 56, 2718 2726. Sun, Y., Ding, Y.H., Dai, A., 2010. Changing links between South Asian summer monsoon circulation and tropospheric land-sea thermal contrasts under a warming scenario. Geophys. Res. Lett. 37, L02704. Taylor, K.E., 2001. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. 106, 7183 7192.
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Thomson, A.M., Calvin, K.V., Smith, S.J., et al., 2011. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77 94. Turner, A.G., Annamalai, H., 2012. Climate change and the South Asian summer monsoon. Nat. Clim. Change 2, 587 595. Available from: https://doi.org/10.1038/NCLIMATE1495. Ueda, H., Iwai, A., Kuwako, K., et al., 2006. Impact of anthropogenic forcing on the Asian summer monsoon as simulated by eight GCMs. Geophys. Res. Lett. 33, L06703. Wang, B., 2006. The Asian monsoon. Springer, Heidelberg, 679 pp. Wang, H.J., 2000. The interannual variability of East Asian monsoon and its relationship with SST in a coupled atmosphere-ocean-land climate model. Adv. Atmos. Sci. 17, 31 47. Wang, H.J., 2002. The instability of the East Asian summer monsoon-ENSO relations. Adv. Atmos. Sci. 19, 1 11. Wang, B., Fan, Z., 1999. Choice of South Asian summer monsoon indices. Bull. Amer. Meteor. Soc. 80, 629 638. Wang, B., Wu, R., Fu, X., 2000. Pacific-East Asian teleconnection: how does ENSO affect East Asian climate? J. Clim. 13, 1517 1536. Wang, B., Wu, R.G., Lau, K.M., 2001. Interannual variability of the Asian summer monsoon: contrasts between the Indian and the western North Pacific-East Asian monsoons. J. Clim. 14, 4073 4090. Wang, B., Wu, Z., Li, J., et al., 2008. How to measure the strength of the East Asian summer monsoon. J. Clim. 21, 4449 4463. Wang, B., Liu, J., Kim, H., Webster, P., Yim, S., 2012a. Recent change of the global monsoon precipitation (1979-2008). Clim. Dyn. 39, 1123 1135. Available from: https://doi.org/10.1007/s00382-011-1266-z. Wang, H.J., Zeng, Q.C., Zhang, X.H., 1993. The numerical simulation of the climatic change caused by CO2 doubling. Sci. China Ser. B 36, 451 462. Wang, H.J., Sun, J.Q., Chen, H.P., et al., 2012b. Extreme climate in China: facts, simulation and projection. Meteorol. Z. 21, 279 304. Webster, P.J., Yang, S., 1992. Monsoon and ENSO: selectively interactive systems. Quart. J. Roy. Meteor. Soc. 118, 877 926. Zhou, T., Li, Z., 2002. Simulation of the East Asian summer monsoon using a variable resolution atmospheric GCM. Clim. Dyn. 19, 167 180.
FURTHER READING Bueh, C., 2003. Simulation of the future change of East Asian monsoon climate using the IPCC SRES A2 and B2 scenarios. Chin. Sci. Bull. 48, 1024 1030. Ding, Y.H., Ren, G.Y., Zhao, Z.C., et al., 2007. Detection, causes and projection of climate change over China: an overview of recent progress. Adv. Atmos. Sci. 24, 954 971. Ding, Y.H., Wang, Z.Y., Sun, Y., 2008. Interdecadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I. Observed evidences. Int. J. Climatol. 28, 1139 1161. He, L.F., Wu, B.Y., Mao, W.X., 2005. The interdecadal variability of Indian summer monsoon and the climate state shift in North China. J. Trop. Meteorol. 21, 257 264. Tao, S.Y., Chen, L.X., 1987. A review of recent research on the East Asian monsoon in China. In: Chang, C. P., Krishnamurti, T.N. (Eds.), Monsoon Meteorology. Oxford University Press, Oxford, pp. 60 92. Wang, H.J., 2001. The weakening of the Asian monsoon circulation after the end of 1970’s. Adv. Atmos. Sci. 18, 376 386.
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Xu, M., Chang, C.P., Fu, C.B., et al., 2006. Steady decline of East Asian monsoon winds, 1969 2000: evidence from direct ground measurements of wind speed. J. Geophys. Res. 111, D24111. Xue, F., 2001. Interannual to interdecadal variation of East Asian summer monsoon and its association with the global atmospheric circulation and sea surface temperature. Adv. Atmos. Sci. 18, 567 575. Yu, R.C., Wang, B., Zhou, T.J., 2004. Tropospheric cooling and summer monsoon weakening trend over East Asia. Geophys. Res. Lett. 31, L22212. Available from: https://doi.org/10.1029/2004GL021270. Zhang, Q.Y., Tao, S.Y., Chen, L.T., 2003. The interannual variability of East Asian summer monsoon indices and its association with the pattern of general circulation over East Asia. Acta Meteorol. Sin. 61, 559 568. in Chinese.
PROJECTION OF FUTURE CHANGES IN THE ASIAN SUMMER MONSOON UNDER GLOBAL WARMING CONDITIONS
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11.1 INTRODUCTION Occupying a vast domain including South Asia, the Indian subcontinent, South China Sea (SCS), East Asia, and the western North Pacific (WNP), the Asian monsoon is one of the most important components of the global climate system (Chang, 2004; Wang et al., 2006; Chang et al., 2011). Summer climate variability in Asia is closely related to the variations of the Asian summer monsoon (ASM) system, which is driven mainly by the differential heating of the Indian Ocean and the adjacent Asian landmass, including the snow cover over the Eurasian continent (Hahn and Shukla, 1976; Douville and Royer, 1996). Through the Walker circulation, the El Nin˜o Southern Oscillation (ENSO) also has a considerable impact on the variability of the ASM (Webster and Yang, 1992; Ju and Slingo, 1995; Arpe et al., 1998; Annamalai et al., 2007; Wang et al., 2000). The possible influence of increasing levels of atmospheric trace gases on the Asian monsoon has been one of the focal points of some recent studies. In theory, the rate of surface temperature increase is stronger over land than over oceans in the process of global warming, and monsoon should strengthen accordingly in response to the increased land sea thermal contrast (Ding, 1994; Turner and Annamalai, 2012; Wang et al., 2012a,b; Liu et al., 2014). However, this hypothesis does not consider the interaction of changes in large-scale atmospheric circulations, and whether it is effective for a complicated monsoon (e.g., the ASM) system remains unclear. In the literature, opinion differs on the response of the ASM to increased atmospheric greenhouse gas concentrations based on numerical experiments of individual or multiple climate models. For example, observational analysis by Kumar and Dash (2001) has indicated the role of increased land surface temperature in enhancing the monsoon, but weakening the monsoon and ENSO relationship at interannual timescales over recent decades. Meehl and Washington (1993) and Hu et al. (2000) found an intensification of both the ASM and its variability. The evolution of the ASM has distinct regional features and can be further separated into the East Asian summer monsoon (EASM) and the Indian summer monsoon (ISM). The EASM is projected to strengthen (Li and Zhou, 2010; Sun and Ding, 2011; Chen et al., 2012), strengthen slightly only over SCS (Ueda et al., 2006), vary little
The Asian Summer Monsoon. DOI: https://doi.org/10.1016/B978-0-12-815881-4.00011-1 © 2019 Elsevier Inc. All rights reserved.
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(Kimoto, 2005), and remain normal in terms of intensity (Li et al., 2010); the ISM is projected to significantly strengthen during the Indian monsoon rainfall (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012), slightly increase (Lal et al., 2001), and weaken in the monsoon circulation (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). Given that there are differences in climate models, emissions scenarios, and analysis methods among these studies, and that most do not assess whether climate models can reliably reproduce the present Asian monsoon circulation, it is difficult to obtain a consistent view of the future ISM and EASM. In this chapter, we will use the outputs of reliable climate models to project future changes in the ISM and EASM, including the mean monsoon and its variability, from the perspective of multiple climate models and climate dynamics. Based on all available data of climate models participating in the 5th Assessment Report, Intergovernmental Panel on Climate Change (IPCC, 2013), this study assesses the changes in the EASM and ISM under the representative concentration pathways (RCPs) mid low range scenario (RCP4.5). This chapter is organized as follows. Section 11.2 provides a description of observation data and coupled model intercomparison project phase 5 (CMIP5) simulations used in this chapter. Section 11.3 discusses the models’ ability to simulate the present climatology of the ASM circulation and precipitation, and select reliable models for further analysis. Section 11.4 investigates the possible future changes of the ISM and EASM under the RCP4.5 scenario. Summary and discussions are given in Section 11.5.
11.2 DATA AND MODELS Many climate models have been used to simulate present and future climates in the CMIP5. According to the availability of output data from the CMIP5 historical simulation, and the CMIP5 RCP4.5 simulation (Moss et al., 2010; Thomson et al., 2011), the results of 31 climate models archived in the CMIP5 are applied in this chapter. Basic information about these climate models and experiments is provided in Table 11.1. More details are available at http://cmip-pcmdi.llnl.gov/ cmip5/. In addition, data used to assess the ability of the models include monthly horizontal winds at 850 hPa level (u850 and v850) and sea level pressure (SLP) from the National Centers for Environmental Prediction National Center for Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al., 1996). Precipitation data are from the global precipitation climatology projection (GPCP), with a resolution of 2.5 3 2.5 (Adler et al., 2003). These reanalysis and analysis data are treated as observations. A multimodel ensemble mean with the same weights across the reliable models of concern is used to obtain common results of climate models in this chapter. All model and observation data were aggregated to a horizontal resolution of 2.5 3 2.5 . This chapter is primarily focused on the results from the multimodel ensemble, therefore, issues surrounding model uncertainty will not be discussed. Considering that climate change projection in the CMIP5 begins from 2006, and that the NCEP/NCAR reanalysis data are more reliable after 1979 owing to the availability of satellite data, in this chapter the period 1979 2005 was chosen as the reference period.
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Table 11.1 Basic Information on the 31 CMIP5 Models No.
Model Name
Institute/Country
Resolution
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
ACCESS1-0 ACCESS1-3 BCC-CSM1-1 BNU-ESM CanESM2 CCSM4 CESM1-CAM5 CMCC-CESM CMCC-CM CMCC-CMS CNRM-CM5 FGOALS-g2 FGOALS-s2 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M GISS-E2-H GISS-E2-R HadGEM2-ES INMCM4 IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC-ESM MIROC-ESM-CHEM MIROC5 MPI-ESM-LR MPI-ESM-MR MRI-CGCM3 NorESM1-M NorESM1-ME
CAWCR/Australia CAWCR/Australia BCC/China BNU- GCESS/China CCCMA/Canada NCAR/USA NSF-DOE-NCAR/USA CMCC/Italy CMCC/Italy CMCC/Italy CNRM-CERFACS/France LASG-CESS/China LASG-CESS/China NOAA GFDL/USA NOAA GFDL/USA NOAA GFDL/USA NASA GISS/USA NASA GISS/USA MOHC/UK INM/Russia IPSL/France IPSL/France IPSL/France MIROC/Japan MIROC/Japan MIROC/Japan MPI-M/Germany MPI-M/Germany MRI/Japan NCC/Norway NCC/Norway
1.875 3 1.25 1.875 3 1.25 2.8 3 2.8 2.8 3 2.8 2.8 3 2.8 1.25 3 0.94 2.5 3 1.9 3.75 3 7.5 0.75 3 0.75 1.875 3 1.875 1.4 3 1.4 2.8 3 3 2.8 3 1.67 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 2.5 3 2.0 1.875 3 1.25 2.0 3 1.5 3.75 3 1.875 2.5 3 1.25 3.75 3 1.875 2.8 3 2.8 2.8 3 2.8 1.4 3 1.4 1.875 3 1.875 1.875 3 1.875 1.1 3 1.1 2.5 3 1.875 2.5 3 1.875
CMIP5, Coupled model intercomparison project phase 5.
11.3 EVALUATION OF MODELS’ SIMULATION The climatology of 850 hPa wind and rainfall in observations and CMIP5 models is shown in Fig. 11.1. In observations (Fig. 11.1A), the strong southwesterly winds flow from the Indian Ocean to the Bay of Bengal (BOB), all the way to the northern SCS, and then turn to eastern China, the
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FIGURE 11.1 The climatological distribution of JJA mean rainfall (shaded; units: mm/day) and 850 hPa wind (vectors; units: m/s) in observations and each CMIP5 model.
Korean peninsula, and Japan. The southwesterly winds affect the climate in East Asia with the southerly winds originating from the northwestern flank of the western Pacific subtropical high (WPSH; the anticyclone circulation over the western Pacific). The monsoon rainfall band is dominated by the WPSH. Corresponding to the strong westerly winds and the WPSH, there are four rainfall centers in GPCP: northern India, the BOB Indochina peninsula, SCS, WNP, and the monsoon rainfall band which is called Meiyu in China, Baiu in Japan, or Changma in Korea (along 30 N). Meiyu is a climatic phenomenon closely related to the East Asian summer monsoon. It is
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called Meiyu in China, Baiu in Japan and Changma in Korea (the introduction of Meiyu has been given in Chapter 3). Many CMIP5 models show similar biases in reproducing the position of the WPSH and the associated magnitude of monsoon rainfall band, as with previous models (Zhou and Li, 2002; Chen et al., 2010). It features a northward shift of the WPSH resulting in an underestimation of rainfall along the monsoon rainfall band from southern China to Japan, and an overestimation of rainfall over the SCS and WNP. Moreover, the rainfall over north India and BOB is also underestimated in most CMIP5 models mainly due to the biases in reproducing the intensity of the cross-equatorial flow over the western Indian Ocean and the associated westerly winds along 10 15 N. The rainfall maximum over two monsoon regions of northern India BOB and from central China to Japan are missed in almost all the models, suggesting that simulation of the ASM rainfall remains a challenge for current climate models (Li and Zhou, 2010; Chen et al., 2000; Liu et al., 2012; Liang et al., 2019). The spatial correlation coefficient (SCC) of climatological JJA mean 850 hPa wind versus rainfall in CMIP5 models is calculated in Fig. 11.2. It is shown that the SCCs of wind are higher than those of rainfall in all the CMIP5 models and are, therefore, indicative of a better reproduction of wind than rainfall. The 31-model ensemble mean has better SCCs of wind and rainfall than other individual models.
FIGURE 11.2 Scatterplot of the spatial correlation coefficients (SCCs) of climatological JJA mean 850 hPa wind versus rainfall in CMIP5 models. The SCC is relative to NCEP/NCAR and GPCP over the ASM region (15 S 50 N, 30 180 E).
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The ASM is primarily driven by convective, radiative, and sensible heat sources/sinks, among which convective latent heating is the most important. During boreal summer, the most intense convection is observed over two regions, namely the BOB India Arabian Sea and the SCS Philippine Sea (Wang et al., 2001). The heat sources over the BOB and the Philippine Sea affect the ISM and EASM, respectively, because well-defined wind anomalies occur over and to the west of the convective heat sources as a Rossby wave response to the latent heat released in the two convection regions (Matsuno, 1966; Gill, 1980). As the lower-level circulation is dynamically consistent with convective heating in summer and the correlation between interannual variations of the south Asian and WNP convection is statistically insignificant (Wang and Fan, 1999), we focus on 850 hPa winds over the ISM and EASM regions, which represents the changes of monsoon circulation. The spatial distribution of zonal wind at 850 hPa level (u850) within two regions of (5 15 N, 40 80 E) and (20 30 N, 70 90 E) are used to evaluate the simulation of the ISM from CMIP5 models according to previous studies (Wang et al., 2001). Considering that the southerly winds prevail over eastern China and the western side of the WPSH in summer, and there is a clear relationship between the meridional wind and land sea thermal contrasts, the 850 hPa meridional wind (v850) averaged in the region 20 40 N, 110 125 N is used to evaluate the capacities of simulating the EASM in models (Wang, 2000, 2002; Sun and Ding, 2011; Jiang and Tian, 2013). To objectively measure the capacity of the models to simulate the ISM and EASM, the correlation coefficient between simulated and observed climatology of u850 and v850 in regional averages for the period 1979 2005 are calculated model-by-model, as well as the standard deviation and centered root-mean-square error (RMSE) of each simulation with respect to observation based on the ISM and EASM regions. As shown in the Taylor diagram (Taylor, 2001), all 31 climate models with data available (Table 11.1) could reliably reproduce the spatial distribution of u850 over the ISM region (Fig. 11.3A). Correlation coefficients range from 0.17 (GISS-E2-H) to 0.97 (CanESM2), and the values of GISS-E2-H and GISS-E2-R are not statistically significant at the 0.01 t-test significant level. Normalized centered RMSEs range from 0.25 (CanESM2) to 1.04 (GISS-E2-H), with the values of all the models being within 0.25 1.00 except for GISS-E2-H. There is a large spread in the ability of 31 climate models to represent the v850 over the EASM region (Fig. 11.3B). Correlation coefficients range from 0.03 (CMCC-CESM) to 0.87 (MIROC5), and the values of CMCC-CESM, FGOALS-s2, and MRI-CGCM3 are not statistically significant at the 0.01 t-test significance level. Normalized centered RMSEs range from 0.62 (MPI-ESM-MR) to 1.8 (MIROC-ESM), with the values of 13 models being more than 1.00 and those of the other models being within 0.62 1.00. Overall, the ability of most models to simulate the ISM’s circulation is found to be reliable and relatively concentrated, and the ability to simulate the EASM circulation is found to be relatively weak and dispersed. Accordingly, it is necessary to select more reliable models for projecting the future changes of the ISM and EASM. Two preconditions are set to identify reliable models for both the ISM and EASM. First, SCCs had to be positive and statistically significant at the 0.01 t-test significance level; and, second, normalized, centered RMSEs had to be less than 1.00. According to this procedure, and associated with the climatological rainfall and 850 hPa wind simulations over ASM region (Figs. 11.1 and 11.2), 13 CMIP5 models were applied to investigate the possible variation of the ISM and EASM in the future. The ensemble mean of the models is calculated based on the results of the 13 models: ACCESS1-0, BCCCSM1-1, CanESM2, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G,
FIGURE 11.3 Taylor diagram for displaying normalized pattern statistics of climatological u850 (A)within ISM regions of 5 15 N, 40 80 E and 20 30 N, 70 90 E, and (B) v850 within EASM region of 20 40 N, 110 125 E between the 31 CMIP5 models and observations in summer for the reference period 1979 2005. Each number represents a model (see Table 11.1); and observation is considered as the reference (REF). Standard deviation and centered root-mean-square errors are normalized by the reference standard deviation. The radial distance from the origin is the normalized standard deviation of a model; the correlation between a model and the reference is given by the azimuthal position of the mode; and the centered root-mean-square error between a model and the reference is their distance apart. In brief, the nearer the distance between a number and REF, the better the performance of the corresponding model.
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GFDL-ESM2M, IPSL-CM5A-MR, MPI-ESM_LR, MPI-ESM_MR, and NorESM1-ME. Nevertheless, when comparing the ensemble mean results with previous studies, the different model ensembles may explain both minor and major differences. Therefore it is important to develop a set of reasonable metrics for model selection to allow comparison of climate projections between different studies and regions. The use of uniform metrics is an important issue for future climate change research.
11.4 PROJECTION OF THE INDIAN SUMMER MONSOON AND EAST ASIAN SUMMER MONSOON There are more than 20 indices of the ISM and EASM published in the literature (Wang et al., 2008). Considering that the correlation between interannual variations of the South and East Asian convection is statistically insignificant (Wang et al., 1993), two kinds of monsoon indices are selected to measure the variability of the ISM and EASM, respectively. Rainfall anomalies averaged over the core regions of the ISM and EASM are meaningful indices, yet the rainfall data in monsoon regions from models usually have a large model bias (Gao et al., 2011; Liang et al., 2009, 2019; Liu et al., 2015). To obtain more reliable results, corresponding circulation indices are desirable. To choose a circulation index dynamically consistent with convective heating, an ISM index is selected which is defined as the difference of u850 between a southern region (5 15 N, 40 80 E) and a northern region (20 30 N, 70 90 E) (indicated by the two solid-line boxes near India in Fig. 11.5). Such a defined index reflects both the intensity of the tropical westerly monsoon and the lower-tropospheric vorticity anomalies associated with the ISM trough (Wang et al., 2001). The ISM index not only represents well the rainfall anomalies averaged over an extended region including the BOB, India, and the eastern Arabian Sea, it is also highly correlated with the all-India summer rainfall with a correlation coefficient of 0.72 for the 50-year period from 1948 to 1997 (Parthasarathy et al., 1992). The selected index for the EASM is defined by the regionally averaged meridional wind at 850 hPa (v850) within the region of (20 40 N, 110 125 E, Fig. 11.5) (Wang, 2000, 2002; Sun and Ding, 2011; Jiang and Wang, 2005). Due to the EASM determined by the heat contrast between land and ocean, there is an obvious meridional wind circulation in East Asia. To reduce the influence of the differences in the ISM and EASM climatology among the models and their ensemble means, the normalized change of those intensity values relative to the climatology of the reference period 1979 2005 is chosen as the ISM or EASM indices for each model and their ensemble means.
11.4.1 FUTURE CHANGES OF THE INDIAN SUMMER MONSOON AND EAST ASIAN SUMMER MONSOON INDICES The future change of the ISM measured by the ISM index is shown in Fig. 11.4A. It is seen that the ISM intensity slightly decreases over the period 2006 99 relative to 1979 2005 based on the results of either individual models or their ensemble means. Table 11.2 shows the climate tendency rate and trend coefficient of each model and the 13-model ensemble mean of monsoon indices over the period 2006 99. It is seen that the ISM index decreased in 9 of 13 models and increased in
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FIGURE 11.4 (A) The time series of the ISM index as measured by the difference of u850 between two regions (5 15 N, 40 80 E and 20 30 N, 70 90 E) from 13 CMIP5 models. (B) The EASM index as measured by the regionally averaged v850 within the region 20 40 N, 110 125 E. MME, MME_11-year, and MME_trend denote the 13-model ensemble mean, its 11-year smoothing averaged, and long-term trend, respectively.
models of BCC-CSM1-1, CanESM2, CNRM-CM5, and GFDL-ESM2M. However, most of these changes are small. Statistically significant decreased trends at the 0.05 t-test significance level can be identified in GFDL-CM3, GFDL-ESM2G, MPI-ESM-LR, MPI-ESM-MR, and the 13-model ensemble means. On the whole, there is a slightly decreasing change trend for the ISM under global warming based on the 13 reliable CMIP5 models. Such a slight weakening trend is consistent in direction with most previous studies (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). In addition, there are two distinct differences in the future with the comparison to the reference period. First, the averaged intensity of the ISM during 2006 99 is significantly weaker than that in the reference period of 1979 2005. Second, the standard deviation of the ISM index time series as derived from the 13-model ensemble mean decreased by 19.2% relative to the reference period, which implies a significant weakening of interannual variability of the ISM in the future. It disagrees with the increased interannual variability of the monsoon rainfall in the region of 0 20 N, 40 110 E under future global warming as revealed by a coupled model (Hu et al., 2000).
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Table 11.2 The Climate Tendency Rate (CTR, unit: /a) and Trend Coefficients (TC) of Monsoon Indices in the Period 2006 99 by 13 Models and the 13-Model Ensemble Mean (TCs of 0.2 and 0.26 Reach the 0.05 and 0.01 Significance Levels, Respectively) ISM Models ACCESS1-0 BCC-CSM1-1 CanESM2 CMCC-CM CMCC-CMS CNRM-CM5 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M IPSL-CM5A-MR MPI-ESM-LR MPI-ESM-MR NorESM1-ME MME
CTR 0.002 0.005 0.002 0.000 0.005 0.004 0.011 0.007 0.000 0.004 0.007 0.006 0.003 0.006
EASM
TC 0.068 0.138 0.059 0.010 0.158 0.094 0.354 0.246 0.003 0.100 0.190 0.204 0.007 0.248
CTR 0.001 0.004 0.010 0.001 0.001 0.001 0.007 0.002 0.001 0.002 0.003 0.002 0.002 0.005
TC 0.020 0.101 0.406 0.021 0.027 0.029 0.218 0.053 0.021 0.058 0.091 0.086 0.063 0.208
Different from the ISM, Fig. 11.4B shows that there is a slightly increased trend for the intensity of the EASM as a whole, although the increased trend is small. The EASM index increases in 10 of the 13 models, and statistically significant increased trends can be identified in the 13-model ensemble mean and two individual models, namely CanESM2, and GFDL-CM3. There are still three models in which the trend of the EASM index is negative, which implies the future change in the EASM intensity is also model dependent, similar to the ISM. It is indicated to some extent that the previous conclusion on the EASM either strengthening (Ueda et al., 2006; Li and Zhou, 2010; Sun and Ding, 2011; Chen et al., 2012) or having little variation (Kimoto, 2005; Li et al., 2010) from individual or several models is uncertain. On the whole, the EASM intensity is slightly strengthened over the whole period of 2006 99 based on the 13-model ensemble mean result of either individual models or more models with statistically significant positive trends. In addition, it is noted that the standard deviation of the EASM index time series as derived from the 13-model ensemble mean decreases by 16.2% for 2006 99 relative to the reference period, although the long-term trend is positive. That means the interannual variability of the EASM intensity decreases, which disagrees with the increased interannual variability of the EASM rainfall under future global warming, as revealed by 13 CMIP3 models (Lu and Fu, 2010).
11.4.2 FUTURE CHANGES OF MONSOON CIRCULATION AND RAINFALL IN THE ASIAN SUMMER MONSOON REGION Fig. 11.5 illustrates the SLP and horizontal winds at 850 hPa in summer for the period of 1979 2005 from the 13-model ensemble mean (Fig. 11.5A), and the corresponding difference
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FIGURE 11.5 (A) Climatology of horizontal wind at 850 hPa (m/s, vector) and SLP (Pa, shaded) in summer for the period 1979 2005; (B) the corresponding difference between the periods 2070 99 and 1979 2005 from the 13-model ensemble mean. Two red rectangles denote the ISMI of 5 15 N, 40 80 E and 20 30 N, 70 90 E; and the yellow rectangle is the EAMI (20 40 N, 110 125 E).
between the periods 2070 99 and 1979 2005 (Fig. 11.5B). It is shown that the models are generally able to reproduce the distribution of SLP and lower-level wind fields in Asia. Because the land is heated to a greater degree in the summer, it becomes significantly warmer than the oceans.
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The Eurasian continent is controlled by thermal low-pressure systems whereas the oceans are controlled by high-pressure systems. The thermal contrasts due to differences in the warming of the land and oceans drives the monsoon circulation, forming strong southwesterly winds flowing from the Indian Ocean to the BOB, all the way to the northern SCS, and East Asia and the WNP. The southwesterly winds affect the climate in East Asia with southerly winds originating from the northwestern flank of the NWP anticyclone (Fig. 11.5A). During 2070 99, however, under future global warming, the ISM circulation weakens in climate models, which displays that the crossequatorial southerly, the ISM trough, and associated anomalous westerlies between 5 and 15 N, and easterlies over northern India are weakened (Fig. 11.5B). Accordingly, an anomalous anticyclonic circulation occurs over central India. It is also noted that the meridional land sea thermal contrast and thermal effects of the elevated Tibetan Plateau reinforce the interhemispheric thermal contrast resulting from differential solar radiation over Indian monsoon region (Fig. 11.5B). It appears inconsistent with the projected increases in surface land sea thermal contrasts (Turner and Annamalai, 2012; Wang et al., 2012a,b). The monsoon rainfall over the ISM region also significantly increases in the period 2070 99 relative to the reference period (Fig. 11.6), which is consistent with previous studies (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012). Sun et al. (2010) indicated that the ISM intensity positively correlates with the land sea thermal contrast in the lower and upper troposphere before year 2000; thereafter, a reduced upper-tropospheric thermal contrast leads to a weakened ISM circulation, despite an increasing lower-tropospheric thermal contrast. The slight strengthening of the EASM under future global warming is further confirmed by atmospheric circulation changes in the lower troposphere, as large-scale southerly wind anomalies (less than 1 m/s) at 850 hPa prevail over eastern and northeast China in the 13-model ensemble
FIGURE 11.6 The difference of rainfall between the periods 2070 99 and 1979 2005 from the 13-model ensemble mean (unit: mm/day).
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mean for 2070 99 relative to the reference period (Fig. 11.4B). This is because, under future global warming, the temperature rises across East Asia and adjacent areas, but with a different magnitude between land and ocean (Sun and Ding, 2011). On the one hand, the rate of warming is faster over East Asia than over the same latitudes of the WNP and hence leads to an increased zonal thermal contrast (Jiang and Wang, 2005). Accordingly, the strengthening of heat low over the East Asian continent is larger in magnitude than the weakening of the WNP subtropical high, leading to an increased zonal SLP gradient and, in turn, southerly wind anomalies over East Asia. On the other hand, the rate of warming is faster over eastern China than over the SCS. This leads to increased meridional land sea thermal and sea level pressure gradients and, hence, southerly wind anomalies over southern China. Overall, these changes in zonal and meridional land sea thermal contrasts across East Asia and surrounding oceans are responsible for the slight strengthening of the EASM. The monsoon rainfall bands of the Meiyu/Baiu/Changma and WNP over the EASM region also increase significantly under global warming (Fig. 11.6), which agrees with the strengthening change of the EASM’s circulation. In previous work performed using the CMIP3 and CMIP5 datasets under global warming, the ISM was projected to significantly strengthen during the Indian monsoon rainfall (Kitoh et al., 1997; Ueda et al., 2006; Turner and Annamalai, 2012), slightly increase (Lal et al., 2001), and weaken in the monsoon circulation (Douville et al., 2000; Meehl et al., 2007; Sun et al., 2010; Sun and Ding, 2011). On the other hand, the EASM was projected to strengthen (Li and Zhou, 2010; Chen et al., 2012), strengthen only over South China (Ueda et al., 2006; Sun and Ding, 2010), vary little (Kimoto, 2005), and remain normal (Li et al., 2010). Such discrepancies are not strange when viewed from this work. That is because that there are differences in climate models, emissions scenarios, analysis methods, and monsoon indices among the studies, and that most of them do not assess whether climate models can reliably reproduce the present Asian monsoon circulation. In addition, the ISM and EASM changes are determined largely by climate models used in the analysis. In other words, those projections are more or less model-dependent, owing to the limited number of climate models.
11.5 SUMMARY AND DISCUSSION Over recent years, many climate models have been used to project future climate change worldwide. Under the most representative RCP4.5 scenario, this chapter assessed the future changes of the ISM and EASM using the data of 13 reliable CMIP5 models. These models were selected from the available 31 climate models used in the IPCC Assessment Report 5 in terms of their ability to simulate the climatology of the ASM in the reference period of 1979 2005. Under future global warming, it was found that there is a slightly decreased trend for the intensity of the ISM’s circulation, with a significant weakening of interannual variability. The spatial distribution displays that the cross-equatorial southerly, the ISM trough and associated anomalous westerly between 5 and 15 N, easterlies over northern India are weakened, and an anomalous anticyclone occurs over central India. But the monsoon rainfall over the ISM region will significantly increase relative to the reference period. On the other hand, the EASM’s circulation and rainfall are strengthened, while the interannual variability of the EASM’s circulation is slightly weakened relative to 1979 2005.
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The spatial distribution data exhibit that large-scale southerly wind anomalies at 850 hPa prevail over eastern and northeast China relative to the reference period. The changes both in zonal and meridional land sea thermal contrasts across East Asia and surrounding oceans are responsible for the slight strengthening of the EASM. Why do the ISM and EASM exhibit differing variations in the future under global warming? The regionality of the Asian monsoon variability is, in part, due to the spatial distribution of the thermal contrast between the ocean and continent. Over India, the meridional land sea thermal contrast and thermal effects of the elevated Tibetan Plateau reinforce the interhemispheric thermal contrast resulting from differential solar radiation. As such, the Indian monsoon is extremely energetic. Over East Asia and its adjacent marginal seas, however, an eastern western land sea thermal contrast dominates, which tends to induce a zonal pressure difference between the Asian continental low and the WPSH. In conjunction with the influence of the northern southern differential solar forcing, the WPSH, along with the EASM trough to its southwest and the subtropical front to its northwest, controls the monsoon circulation over East Asia. From their geographic setting relative to the continent ocean distribution, one would expect that the variability of the ISM and EASM might be dissimilar. The ISM and EASM are driven, in part, by two major convective heat sources. In summer, the two centers are anchored primarily in the BOB and the Philippine Sea, respectively. The changes in intensity and location of the two convection regions have fundamental impacts on the variability of the two monsoon subsystems. Note that the convection in East Asia is more directly affected by the Pacific SST anomaly than the convection over the BOB (Wang et al., 2000). In addition, the ways by which ENSO affects the two convective heat sources are also different (Lau and Nath, 2000; Wang et al., 2000). Therefore, the variations of the ISM and EASM could be very different. It was also noted that the future ASM changes are somewhat model and index dependent. This explains why there are discrepancies on this issue in previous studies, and implies that more emphasis should be given to the ensemble mean of multiple reliable climate models and the accompanying mechanisms. In addition, the horizontal resolution of state-of-the-art climate models is generally higher than before, but their ability to represent the East Asian climate is still inadequate and, hence, their projection is more or less uncertain. Given that the performance of high-resolution regional climate models to simulate the present climate over China is better overall than that of global climate models, and that future changes in monsoon precipitation over China are different between regional and global models (Gao et al., 2012), more attention should be given to dynamic downscaling studies at the regional scale. Finally, we would like to emphasize that there are uncertainties in future emissions or concentration scenarios of greenhouse gases and aerosols, and in radiative forcing scenarios. There are still incomplete aspects in the global climate and Earth system models. The lack of observation data hampers our understanding of climate change on the decadal and longer timescales. Our knowledge of natural climate variability over a range of timescales is also limited. All these factors lead to a level of uncertainty in future climate change projection.
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Thomson, A.M., Calvin, K.V., Smith, S.J., et al., 2011. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77 94. Turner, A.G., Annamalai, H., 2012. Climate change and the South Asian summer monsoon. Nat. Clim. Change 2, 587 595. Available from: https://doi.org/10.1038/NCLIMATE1495. Ueda, H., Iwai, A., Kuwako, K., et al., 2006. Impact of anthropogenic forcing on the Asian summer monsoon as simulated by eight GCMs. Geophys. Res. Lett. 33, L06703. Wang, B., 2006. The Asian monsoon. Springer, Heidelberg, 679 pp. Wang, H.J., 2000. The interannual variability of East Asian monsoon and its relationship with SST in a coupled atmosphere-ocean-land climate model. Adv. Atmos. Sci. 17, 31 47. Wang, H.J., 2002. The instability of the East Asian summer monsoon-ENSO relations. Adv. Atmos. Sci. 19, 1 11. Wang, B., Fan, Z., 1999. Choice of South Asian summer monsoon indices. Bull. Amer. Meteor. Soc. 80, 629 638. Wang, B., Wu, R., Fu, X., 2000. Pacific-East Asian teleconnection: how does ENSO affect East Asian climate? J. Clim. 13, 1517 1536. Wang, B., Wu, R.G., Lau, K.M., 2001. Interannual variability of the Asian summer monsoon: contrasts between the Indian and the western North Pacific-East Asian monsoons. J. Clim. 14, 4073 4090. Wang, B., Wu, Z., Li, J., et al., 2008. How to measure the strength of the East Asian summer monsoon. J. Clim. 21, 4449 4463. Wang, B., Liu, J., Kim, H., Webster, P., Yim, S., 2012a. Recent change of the global monsoon precipitation (1979-2008). Clim. Dyn. 39, 1123 1135. Available from: https://doi.org/10.1007/s00382-011-1266-z. Wang, H.J., Zeng, Q.C., Zhang, X.H., 1993. The numerical simulation of the climatic change caused by CO2 doubling. Sci. China Ser. B 36, 451 462. Wang, H.J., Sun, J.Q., Chen, H.P., et al., 2012b. Extreme climate in China: facts, simulation and projection. Meteorol. Z. 21, 279 304. Webster, P.J., Yang, S., 1992. Monsoon and ENSO: selectively interactive systems. Quart. J. Roy. Meteor. Soc. 118, 877 926. Zhou, T., Li, Z., 2002. Simulation of the East Asian summer monsoon using a variable resolution atmospheric GCM. Clim. Dyn. 19, 167 180.
FURTHER READING Bueh, C., 2003. Simulation of the future change of East Asian monsoon climate using the IPCC SRES A2 and B2 scenarios. Chin. Sci. Bull. 48, 1024 1030. Ding, Y.H., Ren, G.Y., Zhao, Z.C., et al., 2007. Detection, causes and projection of climate change over China: an overview of recent progress. Adv. Atmos. Sci. 24, 954 971. Ding, Y.H., Wang, Z.Y., Sun, Y., 2008. Interdecadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I. Observed evidences. Int. J. Climatol. 28, 1139 1161. He, L.F., Wu, B.Y., Mao, W.X., 2005. The interdecadal variability of Indian summer monsoon and the climate state shift in North China. J. Trop. Meteorol. 21, 257 264. Tao, S.Y., Chen, L.X., 1987. A review of recent research on the East Asian monsoon in China. In: Chang, C. P., Krishnamurti, T.N. (Eds.), Monsoon Meteorology. Oxford University Press, Oxford, pp. 60 92. Wang, H.J., 2001. The weakening of the Asian monsoon circulation after the end of 1970’s. Adv. Atmos. Sci. 18, 376 386.
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Xu, M., Chang, C.P., Fu, C.B., et al., 2006. Steady decline of East Asian monsoon winds, 1969 2000: evidence from direct ground measurements of wind speed. J. Geophys. Res. 111, D24111. Xue, F., 2001. Interannual to interdecadal variation of East Asian summer monsoon and its association with the global atmospheric circulation and sea surface temperature. Adv. Atmos. Sci. 18, 567 575. Yu, R.C., Wang, B., Zhou, T.J., 2004. Tropospheric cooling and summer monsoon weakening trend over East Asia. Geophys. Res. Lett. 31, L22212. Available from: https://doi.org/10.1029/2004GL021270. Zhang, Q.Y., Tao, S.Y., Chen, L.T., 2003. The interannual variability of East Asian summer monsoon indices and its association with the pattern of general circulation over East Asia. Acta Meteorol. Sin. 61, 559 568. in Chinese.