1. Introduction
The East Asian summer monsoon (EASM) is the most important climate system over East Asia. Its interannual variation affects the livelihoods of billions of people in many countries, including China, Japan, and South Korea. For example, the anomalous EASM activities in 1998 and 2012 have led to serious flooding along the Yangtze River valley and North China, respectively, causing huge economic loss and casualties (Zhou et al. 2009, 2013). Hence, understanding the mechanisms of the interannual variation of EASM is important to conduct short-term climate prediction and reduce the damage caused by drought or flood.
Climate models are useful tools to understand the mechanism of EASM. Many studies have been devoted to the simulation of EASM and its interannual variability by atmospheric general circulation models (AGCMs) or coupled general circulation models (CGCMs) (Kang et al. 2002; Zhou and Li 2002; Chen et al. 2010; Zhou and Zou 2010; Zou and Zhou 2013; Huang et al. 2013). Through many years of development, the basic features and interannual variation of EASM can be captured well in the current models. For example, the climatological low-level southerly wind and high-level westerly jet over East Asia are well reproduced in both AGCMs and CGCMs (Song et al. 2013; Song and Zhou 2013). At the interannual time scale, the relationship between EASM and ENSO is also well captured (Sperber et al. 2013). However, there is still much uncertainty in the EASM simulation for models phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Huang et al. 2013). For the model uncertainty, there are three sources: internal variability of the climate system, the model spread, and the scenario uncertainty (Hawkins and Sutton 2009). For EASM simulation, the uncertainty mainly means that large intermodel spread exists in both the climatology and interannual variation. Some common biases in the AGCMs and CGCMs include the missing mei-yu/changma/baiu rainfall band (28°–38°N, 105°–150°E) and northward shift of western North Pacific subtropical high (WNPSH).
As noted by Wang et al. (2005), the air–sea coupling is important for EASM simulation, since atmospheric feedback on sea surface temperature (SST) is critical over this region. Through comparing the coupled and uncoupled simulations from a regional climate model, Zou and Zhou (2013) found that both the climatology and the interannual variability of rainfall over the northwestern Pacific (NWP) are improved in the coupled simulation. Up to now, there are few studies about the role of air–sea coupling in reducing the uncertainty of EASM simulation.
In the CMIP5, the EASM in the CGCMs and AGCMs are evaluated (Sperber et al. 2013; Song and Zhou 2014). However, their potential differences are not compared systematically under the same observation metrics. In addition, Song and Zhou (2014) proposed the following Indian Ocean–western Pacific anticyclone (IO–WPAC) teleconnection based on CMIP5 AGCMs: The tropical eastern Indian Ocean (TEIO) precipitation increases as a response to the local SST warming anomaly; subsequently, the released heat triggers the Kelvin wave emanating into the western Pacific and maintains the WPAC. This teleconnection highlighted the role of TEIO in the EASM simulation based on the AGCMs, but whether it holds in the CGCMs deserves further study.
The motivations of this study are 1) to assess the performances of CMIP5 CGCMs in the simulation of EASM climatology and interannual variability, especially for the IO–WPAC teleconnection, and 2) to explore the role of air–sea coupling in the simulation of EASM for both the climatology and interannual variability. To achieve these goals, 34 CMIP5 CGCMs and 17 corresponding AGCMs are analyzed. We show evidences that El Niño–Southern Oscillation (ENSO)-induced IO–WPAC teleconnection is also important for EASM in the CGCMs. Further, both the climatology and interannual variability of EASM are better simulated in the CGCMs than those in the AGCMs. For the climatology, the local cold SST biases in the CGCMs reduce the surface evaporation and intensify the WNPSH. For the interannual variability, the air–sea coupling intensifies the IO–WPAC teleconnection and enhances the EASM simulation.
The remainder of this study is organized as follows: Section 2 describes the model, observational datasets, and analysis method. The climatology of EASM in CGCMs and AGCMs are displayed and compared in section 3. Section 4 displays the interannual variability of EASM in CGCMs and its skill origins. The differences between CGCMs and AGCMs in the interannual variability of EASM are investigated in section 5. Finally, the summary is given in section 6.
2. Model, observational datasets, and analysis method
The first realizations of historical experiment during 1979–2005 from 34 CMIP5 CGCMs are analyzed in this study (Taylor et al. 2012). To investigate the role of air–sea interaction, the Atmospheric Model Intercomparison Project (AMIP) experiments from 17 AGCMs are also compared to the corresponding CGCMs (boldfaced model names in Table 1). The difference between AGCMs and CGCMs lies in the fact that SST and sea ice are prescribed by observation in the former, while they are generated by the models themselves in the latter. The details of the CGCMs and AGCMs, including the institution, model name expansion, and horizontal resolution (HR), are described in Table 1.
The details of 34 CMIP5 models. The boldface model names indicate that both AGCM and CGCM are used in this study. In the rightmost column, the letters H and L denote the high-skill model and low-skill model, respectively. The correlation coefficients between EASM index and Niño-3.4 index in the previous winter during 1979–2005 in high-skill models and low-skill models are also shown in parentheses in the last column.
The observational and reanalysis datasets used here include the following: 1) atmospheric circulation and air temperature during 1979–2005 from the National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) Atmospheric Model Intercomparison Project II reanalysis (NCEP-2) (Kanamitsu et al. 2002); 2) monthly precipitation during 1979–2005 from the Global Precipitation Climatology Project (GPCP) (Adler et al. 2003); and 3) monthly SST during 1979–2005 from National Oceanic and Atmospheric Administration (NOAA) extended reconstructed SST, version 3b (ERSSTv3b) (Smith et al. 2008).
The climatology and interannual variability of EASM is evaluated in the June–August (JJA) seasonal mean by using the reliable observational metrics. All the datasets are interpolated onto a 2.5° × 2.5° common grid. An EASM index is defined by the difference between 850-hPa zonal wind averaged over 22.5°–32.5°N, 110°–140°E and 5°–15°N, 90°–130°E. Since this index is highly correlated to the first principal component of EASM (Wang et al. 2008), it is considered as a simple representation of the dominant mode of EASM. The most evident features of a dominant mode of EASM are the dipole rainfall pattern and WPAC (Wang et al. 2008). The 850-hPa winds can directly show the WPAC and water vapor transport, so the precipitation and 850-hPa winds are selected to represent EASM. The interannual EASM pattern is got by regressing the precipitation and 850-hPa wind upon the standardized EASM index in the observation and each model. The multimodel ensemble mean (MME) of 34 models is the ensemble mean of 34 interannual EASM patterns, which only removes the model spread. The boreal winter Niño-3.4 index [the SST averaged over the eastern equatorial Pacific (5°S–5°N, 120°–170°W)] is calculated in the observation and each model to represent ENSO.
3. The climatology of EASM simulated by CMIP5 CGCMs and comparison with AGCMs
The climatology of EASM in 34 CMIP5 CGCMs is first assessed (Fig. 1). In the observations, the WNPSH and low-level westerly jet bring much moisture into the EASM region, hence forming three rainfall centers in the tropics (Indochinese Peninsula, Philippines, and western Pacific) and one subtropical rainfall band extending from eastern China to Japan, called mei-yu/changma/baiu. The low-level westerly jet in the MME of 34 CGCMs is slightly stronger than the observations, contributing to the more intense precipitation over the South China Sea. The WNPSH in the MME shifts more northeastward, which also exists in the CMIP5 AGCMs (Song and Zhou 2014) and is noted by many previous studies (Zhou and Li 2002; Chen et al. 2010). Accordingly, the mei-yu/changma/baiu is weak in the MME, but its magnitude seems stronger than the AGCMs (Song and Zhou 2014), especially for the baiu rainfall band.
The climatological distribution of JJA mean precipitation (shaded; mm day−1) and 850-hPa wind (vectors; m s−1) during 1979–2005 in the (a) observations and (b) MME from 34 CGCMs. The skill score of the climatology of (c) precipitation and (d) 850-hPa winds in the MME (denoted by E in the abscissa) and each model (shown from high to low resolutions).
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
The climatology simulation of precipitation and 850-hPa winds is evaluated by Eq. (1) (Figs. 1c,d). The precipitation and 850-hPa winds skills in the MME reach 0.83 and 0.96, respectively, higher than any individual model. The model performance is not dependent much on the horizontal resolution, consistent with the AGCMs (Song and Zhou 2014). When compared to the AGCMs (Fig. 3 in Song and Zhou 2014), it seems that both the precipitation and 850-hPa winds have been improved in the CGCMs.
To confirm this hypothesis, we select the 17 CGCMs and AGCMs that share the same atmospheric component. Compared to the observations, both the CGCMs and AGCMs share some similar biases: the cyclone biases are located south of 30°N and the anticyclone biases are located north of 30°N. Accordingly, the positive rainfall biases over the NWP and negative rainfall biases over the mei-yu/changma/baiu are evident (Figs. 2a,b). These common biases in both AGCMs and CGCMs indicate that they may be rooted in the atmospheric model. Compared to the AGCMs, both the circulation and precipitation biases in the CGCMs have been alleviated: the rainfall over the mei-yu/changma/baiu (NWP) has been increased (decreased) and the WNPSH shifts southward (Fig. 2c). In addition, the low-level westerly jet is also improved in the CGCMs compared to that in the AGCMs. To quantify the improvement, the precipitation and 850-hPa winds skill scores in the MME of 17 AGCMs and CGCMs are shown in Fig. 2d. The precipitation skill has been improved from 0.72 to 0.85, while the 850-hPa winds have been improved from 0.80 to 0.91. The skill improvement is reflected in both the pattern correlation and SDR. Compared to the AGCMs, the pattern correlation and SDR is closer to 1 in the CGCMs for both precipitation and 850-hPa winds. The model spread of precipitation and 850-hPa wind skill (represented by the standard deviation of the skills among models) is reduced almost by half in the CGCMs from the AGCMs (0.08 versus 0.14 for precipitation and 0.07 versus 0.15 for 850-hPa wind).
The differences of the climatological distribution of JJA mean during 1979–2005 precipitation (shaded; mm day−1) and 850-hPa wind (vectors; m s−1) between (a) CGCMs and observations, (b) AGCMs and observations, and (c) CGCMs and AGCMs. The black dots indicate that the differences are significant at the 5% level. (d) The skill scores of precipitation and 850-hPa winds in atmospheric models (blue bars) and coupled models (red bar), pattern correlations of precipitation (Pr R) and 850-hPa winds (UV R), and the ratio of precipitation (Pr SDR) and 850-hPa winds (UV SDR) spatial standard deviations of models against the observations.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
Why do the CGCMs improve from the AGCMs in the climatology of EASM? The reason may be rooted in the SST differences between AGCMs and CGCMs (Fig. 3a). The cold SST biases are widespread over the entire Indo-Pacific region, except some positive biases over the tropical western IO (Fig. 3a). The cold biases over the NWP decrease the evaporation, resulting in reduced precipitation, which will enhance the WNPSH. To further confirm this, the scatterplot between the cold SST bias over the NWP and WNPSH bias in the 34 CGCMs is shown in Fig. 3b. The WNPSH bias is negatively related to the SST bias over the NWP, significant at the 5% level. The tropical IO cold bias will not benefit the strengthened WNPSH. Hence, compared to AGCMs, CGCMs improve the precipitation and circulation simulation at the cost of a colder SST bias.
(a) The differences of the climatological SST distribution of JJA mean during 1979–2005 between CGCMs and AGCMs. The black dots indicate that the differences are significant at the 5% level. (b) The scatterplot between the climatological SST bias over the NWP (10°–30°N, 110°–160°E) and the WNPSH bias. The WNPSH is defined by the zonal wind differences between 25°–35°N, 120°–150°E and 10°–20°N, 130°–150°E.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
4. The interannual EASM pattern simulated by CMIP5 CGCMs and its skill origins
The interannual EASM pattern in the observation and simulation is shown in Fig. 4. The most evident features are the WPAC and dipole rainfall pattern (Fig. 4a). This structure is considerably reproduced south of 30°N, but much weaker north of 30°N in the MME of 34 CGCMs (Fig. 4b). Equation (1) is applied to evaluate the simulation skill of interannual EASM pattern in the individual models (Figs. 4c,d). The MME shows the best performances in the interannual EASM pattern, with skill scores of 0.96 in 850-hPa wind and 0.83 in precipitation. The skill score spread among models are large but the model skill is not dependent much on the horizontal resolution. Based on AGCM results, Song and Zhou (2014) suggested that the simulation of IO–WPAC teleconnection is responsible for this spread. Does it still hold for CGCMs? Based on the precipitation skill (Fig. 4c), the highest eight models and lowest eight models (Table 1) are selected to answer this question.
The horizontal distribution of precipitation (shaded; mm day−1) and 850-hPa wind (vectors; m s−1) in JJA regressed on the standardized EASM index during 1979–2005 in the (a) observations and (b) MME. The skill score of the regression pattern of (c) precipitation and (d) 850-hPa winds in the MME (denoted by E in the abscissa) and each model (shown from high to low resolutions).
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
The composite SST, 850-hPa wind, and precipitation are displayed in Fig. 5. In the observations, corresponding to the strong EASM, the SST warming is distributed over the entire IO and cooling over the central Pacific Ocean (Fig. 5a). Corresponding to the IO warming, the precipitation is not uniformly distributed but has two centers over the TEIO and northwestern Indian Ocean (NWIO). The easterly winds occupy the equatorial Indo-Pacific, as the low-level Kelvin wave response to the latent heat released by precipitation anomalies. In the MME, the tropical warming and related TEIO precipitation anomalies are evident. However, the central Pacific cooling is more evident compared to the IO warming and much stronger than the observations (Fig. 5b). This phenomenon is also remarkable in the high-skill and low-skill models (Figs. 5c,d). It suggests that the bias is common among CGCMs, and the reason deserves further investigation. In the high-skill models, the IO warming and related precipitation response are stronger than the MME. However, in the low-skill models, the IO warming is almost missing and the precipitation response is also weaker.
The SST (shaded; K), precipitation (contours; mm day−1), and 850-hPa wind (vectors; m s−1) regressed on the standardized EASM index in (a) observations, (b) MME, (c) high-skill models, and (d) low-skill models. The green (purple) contour lines represent the positive (negative) precipitation anomalies, respectively. The contour interval is 0.40 mm day−1. (e)–(h) As in (a)–(d), but for total tropospheric temperature (shaded; K) and 200-hPa wind (vectors; m s−1).
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
From Fig. 5a, the central Pacific cooling and related negative precipitation anomalies may also trigger the Rossby wave response and contribute to the easterly winds outside of the equator. However, when we consider the total tropospheric heat release (Fig. 5e), it is obvious that the IO warming dominates the tropical climate variation related to the EASM in the summer. The tropospheric warming exhibits Matsuno–Gill (Matsuno 1966; Gill 1980) pattern, with a Kelvin wave wedge penetrating into the western Pacific along the equator and two Rossby wave–like tails over the off-equatorial western IO. Compared to the low-level easterly winds, high-level westerly winds dominate the Indo-Pacific region as the baroclinic response of the Kelvin wave. In the MME, the IO warming is weaker as well as the induced Matsuno–Gill pattern. The Matsuno–Gill pattern related to the central Pacific cooling is comparable to that of the IO warming (Fig. 5f). In the high-skill models, the IO warming and related Matsuno–Gill pattern is enhanced compared to the MME, as well as the high-level Kelvin wave response (Fig. 5g). However, in the low-skill models, the IO warming is missing and the Matsuno–Gill pattern related to the central Pacific cooling dominates the Indo-Pacific region (Fig. 5h).
The above analysis shows that the IO–WPAC teleconnection, which is composed of the IO warming, TEIO precipitation, and Kelvin wave response, plays a key role in the simulation of the interannual EASM pattern. However, where is the skill origin of IO warming simulation? As noted by many previous studies (Yang et al. 2007; Xie et al. 2009; Wu et al. 2009), IO warming is often accompanied by the decaying phase of ENSO. Hence, the simulation of IO warming may be rooted in ENSO. We denote the preceding year as year −1 and the present year as year 0. Thus, the preceding winter [December–February (DJF)] is symbolized as DJF(−1), the present spring [March–May (MAM)] is denoted MAM(0), and the present summer [June–August (JJA)] is denoted JJA(0). The SST in DJF(−1), MAM(0), and JJA(0) related to EASM is shown in Fig. 6. In the observations, the SST in DJF(−1) related to EASM displays an ENSO pattern, in which most evident warming dominates the eastern Pacific. From DJF(−1) to JJA(0), eastern Pacific warming gradually weakens and central Pacific cooling appears in JJA(0). Both IO warming and NWP cooling last from DJF(−1) to JJA(0). The persistence of IO warming is due to the air–sea interaction within the IO (Du et al. 2009) and an important component of IO capacitor effect (Xie et al. 2009). The maintenance of NWP cooling is due to the persistence of WPAC and wind–evaporation–SST feedback, resulting from the climatological trade wind (Wang et al. 2013).
The SST (shaded; K) in (left) the preceding winter, (center) the present spring, and (right) the present summer during 1979–2005 regressed on the standardized EASM index in the (a)–(c) observations, (d)–(f) MME, (g)–(i) high-skill models, and (j)–(l) low-skill models.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
In the MME, EASM is also accompanied by the decaying phase of ENSO, but the SST magnitude is weaker. Correspondingly, IO SST is also weaker from DJF(−1) to JJA(0). The ENSO magnitude and IO warming in the high-skill models are comparable to the observation. However, the EASM is not related to the decaying phase of ENSO in the low-skill models. The IO SST is also weak from DJF(−1) to JJA(0). Bellenger et al. (2013) systematically evaluated the ENSO simulation in CMIP5 models. When comparing with their results, we find that most of high-skill models in our study perform well in the simulation of ENSO, while low-skill models generally have low skills in the simulation of ENSO. For example, FGOALS-g2 is regarded as one of the best models in the ENSO simulation and it also belongs to the best eight models in EASM simulation. Hence, the simulation of ENSO is important for IO warming and the teleconnection to EASM.
To further confirm this result, the precipitation and 850-hPa winds over East Asia regressed on the Niño-3.4 index in the previous winter are shown in Fig. 7. In the summer following El Niño, the circulation and precipitation pattern are similar to the interannual EASM pattern (Fig. 7a versus Fig. 4a), indicating the influence of ENSO’s teleconnection. In the MME, the similar structure is reproduced but with much weaker magnitude (Fig. 7b). The WPAC and dipole rainfall pattern are enhanced in the high-skill models (Fig. 7c), while the circulation and rainfall anomalies are disordered in the low-skill models (Fig. 7d). This further confirms that the teleconnection between ENSO and EASM is important for EASM simulation. Finally, we also calculate the correlation coefficients between the ENSO and EASM in the high-skill and low-skill models (Table 1). In the high-skill models, there are seven out of eight models with positive correlation coefficients significant at the 10% level. In the low-skill models, there is only one model with positive correlation coefficients significant at the 10% level but four models with negative correlations. Hence, from this perspective, it is also evident that ENSO plays an important role in the EASM simulation.
The horizontal distribution of precipitation (shaded; mm day−1) and 850-hPa wind (vectors; m s−1) in JJA regressed on the standardized Niño-3.4 index in the previous DJF during 1979–2005 in the (a) observations, (b) MME, (c) high-skill models, and (d) low-skill models.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
5. The comparison between CGCMs and AGCMs in the interannual EASM pattern
From Figs. 4c,d, it seems that the skill scores in the CGCMs are higher than the AGCMs shown in Song and Zhou (2014). We also select the 17 CGCMs and AGCMs to investigate this issue (Fig. 8). Compared to the observations, the biases in the CGCMs are not evident. However, the circulation and precipitation biases in the AGCMs are large and well organized: the positive rainfall bias and cyclone bias in the south and opposite bias in the north. Hence, compared to the AGCMs, the rainfall and circulation have been improved in the CGCMs. This is also reflected in the area-averaged rainfall anomalies (Figs. 8d,e). For the MME from 17 models, the mei-yu/changma/baiu rainfall anomaly is only 0.10 mm day−1 in the AGCMs but 0.23 mm day−1 in the CGCMs, closer to the observations. The NWP rainfall anomaly is also enhanced from −0.53 to −0.94 mm day−1. There are 13 (15) of 17 CGCMs with higher rainfall magnitude than the AGCMs in the mei-yu/changma/baiu region (NWP).
The differences of the JJA mean precipitation (shaded; mm day−1) and 850-hPa wind (vectors; m s−1) during 1979–2005 regressed on the standardized EASM index between (a) CGCMs and observations, (b) AGCMs and observations, and (c) CGCMs and AGCMs. The precipitation (mm day−1) averaged over (d) mei-yu/baiu/changma (28°–38°N, 105°–155°E) and (e) NWP (10°–20°N, 105°–155°E) regressed on the standardized EASM index in the AGCMs (red bar) and CGCMs (blue bar). The letter M in the abscissa denotes the MME from 17 models, and each number shows different models. The dashed line in (d),(e) indicates the observational magnitude. The black dots in (a)–(c) indicate where more than 80% of the 17 models have the same sign.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
Does the IO–WPAC teleconnection play a role in the improvement from the AGCMs to the CGCMs? Figure 9 shows the IO–WPAC teleconnection in the AGCMs and CGCMs. In both the AGCMs and CGCMs, the EASM is accompanied by the IO warming, inducing the positive precipitation and easterly wind anomalies over the Indo-Pacific. The WPAC extends westward to the eastern IO in both the AGCMs and CGCMs. The associated easterly anomalies on the southern flank weaken the climatological monsoon westerlies, decreasing the evaporation and increasing the SST (Kosaka et al. 2013; Wang et al. 2013). Hence, compared to the AGCMs, the TEIO is warmer in the CGCMs since the SST can be changed by the atmospheric wind anomalies. Accordingly, the significant stronger precipitation anomalies are occupied over the TEIO and induce the stronger easterly wind anomalies (Fig. 9c). Hence, the IO–WPAC teleconnection is intensified in the CGCMs and enhance the EASM. Since the SST anomalies associated with ENSO in the CGCMs are weaker than the observation and decay rapidly (not shown), the intensification may not be due to the effect of ENSO. In contrast, the coupling between WPAC and TEIO SST plays an important role. In addition, the cold SST bias in the NWP may also enhance the WPAC (Wu et al. 2010; Zou and Zhou 2013).
The SST (shaded; K), precipitation (contours; mm day−1), and 850-hPa wind (vectors; m s−1) regressed on the standardized EASM index in (a) CGCMs and (b) AGCMs and (c) their differences. The green (purple) contour lines represent the positive (negative) precipitation anomalies, respectively. The contour interval is 0.3 mm day−1 in (a),(b) and 0.2 mm day−1 in (c). The wind speed less than the 40% of the reference magnitude is omitted. The black dots in (c) indicate where more than 80% of the 17 models have the same sign of the precipitation differences.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
To further demonstrate the role of the TEIO, we first check the relationship between SST and precipitation over the TEIO in 34 CGCMs (Fig. 10a). It is found that they are significantly related, suggesting that the precipitation is the response to the SST. Further, the TEIO precipitation skill is highly correlated to the EASM skill, demonstrating that the TEIO precipitation is important for EASM simulation. Through the comparison between the CGCMs and AGCMs, it is found that the TEIO precipitation in most CGCMs is higher than the corresponding AGCMs and closer to the observations, suggesting that the air–sea coupling improves the TEIO precipitation. Hence, the air–sea coupling enhances the SST and precipitation anomalies over the TEIO and further improves the EASM simulation at the interannual time scale through the intensified IO–WPAC teleconnection.
The scatterplot of (a) TEIO (15°S–15°N, 80°–100°E) SST vs precipitation anomalies regressed on the standardized EASM index and (b) the TEIO precipitation skill vs EASM skill of the interannual pattern in 34 coupled models. (c) The TEIO-averaged precipitation anomaly in each CGCM (blue bar) and AGCM (red bar) regressed on the standardized EASM index. The letter E in the abscissa indicates the MME from 17 models. The dashed line is the observational magnitude.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
6. Summary
In this study, the climatology and interannual variability of EASM in the 34 CMIP5 CGCMs are evaluated. It is revealed that ENSO-induced IO–WPAC teleconnection is important in the CGCMs. The role of air–sea coupling in improving the climatology and interannual variability of EASM is investigated by comparing 17 AGCMs and CGCMs. For the climatology, the cold SST bias in the CGCMs strengthens the WNPSH. For the interannual variability, the WPAC and TEIO warming forms a positive feedback in the CGCMs. A schematic view of the possible mechanisms is displayed in Fig. 11. The main findings are listed below:
The climatology of EASM is well reproduced in the 34 CGCMs, although the WNPSH shifts more northeastward and mei-yu/changma/baiu is weaker, which also exist in the AGCMs. However, compared to the AGCMs, these biases are improved in the CGCMs. The cold SST biases over the NWP in the CGCMs play an important role in the improvement. They decrease the surface evaporation and enhance the WNPSH.
The interannual EASM pattern is considerably reproduced south of 30°N but much weaker north of 30°N in the 34 CGCMs. Based on the precipitation skill of the interannual EASM pattern, a composite analysis is conducted to investigate the EASM skill origins. The EASM is related to ENSO in the preceding boreal winter. In high-skill models, the spatial pattern and magnitude of ENSO in the preceding winter are comparable to the observations, whereas the SST pattern in the low-skill models is not the ENSO pattern. Further, the summer circulation and rainfall pattern following the preceding ENSO are similar to the interannual EASM pattern in the high-skill models, but they are not well organized in the low-skill models. Hence, the link between ENSO and EASM is important for the EASM in the CGCMs.
The ENSO in the preceding winter induces the IO basin warming in the following summer through the Indian Ocean capacitor effect (Yang et al. 2007; Xie et al. 2009). The TEIO local warming triggers the precipitation anomalies and the heat released warms the troposphere. The Kelvin wave response to the released heat shows low-level easterly winds and high-level westerly winds over the equatorial Indo-Pacific region. These observed features are partly reproduced in the MME but with much weaker magnitude. In the high-skill models, the Matsuno–Gill pattern related to the IO warming is stronger than the MME, although still weaker than the observations. However, in the low-skill models, the IO warming is almost missing and a Matsuno–Gill pattern related to the central Pacific cooling is evident. The central Pacific cooling is overestimated in most models and this bias deserves further study.
Comparing the interannual EASM pattern between the AGCMs and CGCMs, it is found that the WPAC and dipole rainfall pattern are largely improved in the CGCMs. The WPAC extends to the eastern IO in both the AGCMs and CGCMs. The associated easterly anomalies on the southern flank weaken the climatological monsoon westerlies over the TEIO, decreasing the evaporation and increasing the SST. Hence, compared with the AGCMs, the TEIO is warmer in the CGCMs since the SST can be changed by the atmospheric wind anomalies. Accordingly, the significant positive precipitation responses over the TEIO are stronger in most CGCMs than the AGCMs. This will induce stronger low-level Kelvin wave response in the CGCMs. Hence, the IO–WPAC teleconnection is stronger in the CGCMs and enhance the EASM.
The schematic plot of the air–sea coupling’s role in the EASM simulation for both the (a),(b) climatology and (c),(d) interannual variability. For the climatology, compared to the AGCM, the cold SST bias over the WNP in the CGCM decreases the surface evaporation and strengthens the WNPSH, inducing more rainfall over the mei-yu/changma/baiu and less rainfall over the NWP. For the interannual variability, the WPAC extends to the eastern Indian Ocean and associated easterly anomalies on the southern flank weaken the climatological monsoon westerlies over the TEIO. Hence, the TEIO warming anomaly is stronger in the CGCMs than in the AGCMs. The stronger TEIO warming will induce stronger WPAC through IO–WPAC teleconnection, forming a positive feedback.
Citation: Journal of Climate 27, 23; 10.1175/JCLI-D-14-00396.1
Acknowledgments
This work was supported by National Natural Science Foundation of China under Grants 41125017 and 41330423 and the Chinese National Program on Key Basic Research Project (2010CB951904). We acknowledge the climate modeling groups (listed in Table 1) for making available their model output and the World Climate Research Programme (WCRP) Working Group on Coupled Modeling (WGCM), which coordinates the CMIP5 project.
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