1. Introduction
The Asian summer monsoon system consists of various regional components (e.g., Tao and Chen 1987; Murakami and Matsumoto 1994), which include the South Asian summer monsoon (SASM), the East Asian summer monsoon (EASM), and the Southeast Asian summer monsoon (SEASM; also called the western North Pacific summer monsoon) (Lau and Yang 1997; Wang et al. 2001; Wang and Lin 2002; Hong et al. 2005; Chang et al. 2005; Wang and Ding 2008; Clift and Plumb 2008; Lee et al. 2014; Hsu et al. 2014). The SASM, which is associated with a meridional land–sea thermal contrast, acts as a typical tropical monsoon with strong vertical zonal-wind shear and abundant summer rainfall over the Bay of Bengal, the Indian subcontinent, and the Arabian Sea. The EASM, on the other hand, is related to a nearly zonal land–sea thermal contrast and characterized by an evident northward rainfall propagation from southeastern China to North China, Korea, and Japan. The SEASM develops with the northward migration of the intertropical convergence zone to the Philippine Sea, displaying a different monsoon feature from its northern counterpart, i.e., the EASM.
The SASM and the EASM are significantly related and interacted on various time scales. Previous studies have demonstrated that the SASM rainfall is positively related to the northern China rainfall but negatively correlated with the rainfalls over the Yangtze River basin and southern Japan (Guo and Wang 1988; Kripalani and Singh 1993; Zhang et al. 1999; Kripalani and Kulkarni 2001; Kim et al. 2002; Wu 2002; Ding and Wang 2005; Hu et al. 2005; Ding et al. 2011; Wu and Jiao 2017; Stephan et al. 2019; Xue et al. 2022). Previous studies have also shown that the variability of tropical Chinese rainfall in May is closely linked to the variation of the following SASM rainfall (e.g., Yang and Gutowski 1992). Two pathways for the SASM–EASM linkage on the interannual time scale have been discussed comprehensively by Wu (2017). The south pathway is mainly through the tropical moisture transport that involves the Pacific–Japan (PJ) pattern (Nitta 1987; Kosaka and Nakamura 2006, 2010) and the change in the western Pacific subtropical high (WPSH) (Krishnan and Sugi 2001), while the north pathway is basically functioning by the middle-higher latitude wave trains including the Silk Road pattern (Enomoto et al. 2003; Wang et al. 2017, 2021; Chowdary et al. 2019; Hu et al. 2020) and the circumglobal teleconnection (Ding and Wang 2005; Zhou et al. 2020) along the upper-level westerly jet stream, which is closely connected with the shift of the South Asian high (SAH) (Wei et al. 2014, 2015, 2019; Xue and Chen 2019; Xue et al. 2021, 2022).
Both external forcings and internal dynamics significantly affect the linkage between regional monsoons (e.g., Biasutti et al. 2018; Ha et al. 2018; Seth et al. 2019; Kosaka 2021; Chen et al. 2023). As a dominant mode driven by anomalous diabatic heating of the SEASM (Nitta 1987; Huang and Sun 1992; Xu et al. 2019), the PJ pattern can induce a see–saw relation between the circulation patterns in the SEASM and the EASM regions. It also exerts a strong impact on SASM rainfall through both atmospheric and oceanic pathways (Srinivas et al. 2018). However, the connection between the SASM and the EASM/SEASM still remains debate. Ha et al. (2018) reassessed the linkage between the Indian and East Asian summer monsoons and found that the link is insignificant due to the compensating effect of decaying or developing El Niño–Southern Oscillation (ENSO). Thus, further investigations of the mechanisms behind the SEASM–SASM linkage cannot only enhance our understanding of monsoon dynamics but also provide useful theoretical basis for improving seasonal prediction of Asian climate.
In this study, we investigate the internal linkage between the SEASM and the SASM and the associated impacting factors thoroughly. The data and method are introduced in section 2. In section 3, the characteristics of SEASM–SASM connection under different circumstances are depicted, and the contribution of different impacting factors and involved physical processes are also illustrated. In the end, overall conclusions and a further discussion are given in section 4.
2. Data and method
a. Data
Monthly atmospheric variables at various pressure levels including geopotential height and winds are obtained from the fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5), with a horizontal resolution of 0.25° × 0.25° (Hersbach et al. 2020). The monthly mean precipitation data with a horizontal resolution of 2.5° × 2.5° are derived from the Global Precipitation Climatology Project (GPCP), version 2.3 (Adler et al. 2003). Monthly sea surface temperature (SST) data with a horizontal resolution of 2° × 2° are from the Extended Reconstructed Sea Surface Temperature (ERSST), version 5 (Huang et al. 2017), provided by the NOAA Physical Sciences Laboratory (PSL), Boulder, Colorado, USA, from the website at https://psl.noaa.gov. The period of 1979–2021 is chosen as our analysis period. The average of December–February (DJF), March–May (MAM), and June–August (JJA) represents the winter, spring, and summer seasons, respectively.
b. Method
To identify the dominant features of summer monsoons over Southeast Asia and South Asia, the empirical orthogonal function (EOF) analysis is performed on summer rainfalls over the SEASM region and the SASM region. The EOF, which is also known as principal component analysis (PCA), can be used to identify the coherent spatial and temporal variations by calculating the eigenvalues and eigenvectors of a spatially weighted anomaly covariance matrix of a field. The first EOF modes of JJA rainfalls in respective SEASM and SASM domains have been mainly discussed in this study since they reflect the most prominent characteristics of the SEASM and the SASM.
To depict the characteristics of the SEASM, the PJ pattern, and ENSO, various indices are analyzed in this study. The definitions of these climate indices are described below.
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The SEASM index is expressed as the horizontal shear of 850-hPa zonal wind between two domains: (90°–130°E, 5°–15°N) and (110°–140°E, 22.5°–32.5°N), as defined by Wang and Fan (1999). A larger value of the index measures the intensification of 850-hPa cyclonic vorticity in the SEASM region.
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The PJ index proposed by Wu et al. (2013) is calculated as the difference in 850-hPa zonal wind anomalies between (105°–145°E, 2.5°–17.5°N), (130°–160°E, 45°–57.5°N), and (112.5°–152.5°E, 22.5°–37.5°N), featuring a meridional dipole structure of precipitation and low-level circulation over the East Asian-Pacific region.
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The Niño-3.4 index is defined as the area-averaged SST in the region of (170°–120°W, 5°S–5°N), indicating the SST variability in the central-eastern equatorial Pacific. An above-normal index indicates that warming SST appears in the central-eastern equatorial Pacific.
3. Results
a. Characteristics and linkage between the dominant modes of SEASM and SASM precipitations
In this section, we examine dominant features of the SEASM and the SASM as well as their connection on interannual time scale. The first EOF modes and corresponding time series of SEASM and SASM precipitations are shown in Fig. 1. The figure shows that the most dominant EOF mode of JJA precipitation in the SEASM region displays a uniform increasing pattern (Fig. 1a), while that in SASM features a southwest–northeast dipole with a positive center over the northern Bay of Bengal and a negative center over the eastern Arabian Sea and southern India (Fig. 1c). The corresponding time series of the two modes present large interannual variability (Figs. 1b,d). The correlation coefficient between the principal component of the first EOF mode (PC1) of SEASM precipitation and the SEASM index defined by Wang and Fan (1999) is 0.93, which is significantly above the 99.9% confidence level, indicating a tight coupling between SEASM precipitation and monsoon circulation. Features of the first EOF mode of SASM precipitation are overall consistent with those of the first singular value decomposition mode of SASM revealed by Zhang et al. (2022), indicating strengthening and eastward shift of the Indian monsoon trough. It should be mentioned that the relationship between the SASM circulation index proposed by Goswami et al. (1999), which cannot reflect the most dominant mode of SASM precipitation, and the SEASM index is negative but insignificant (correlation coefficient is −0.29). Zhang et al. (2022) also pointed out that most of the existing SASM indices are associated with the second mode rather than the first mode of SASM and the first mode can be well predicted by statistical models. Thus, we choose PC1 of the most dominant mode of the SASM precipitation to measure the intensity of SASM and its relationship with the SEASM.
(a) First EOF mode of SEASM precipitation. (b) Time series of PC1 of the first EOF mode of SEASM precipitation. (c),(d) As in (a),(b), but for SASM precipitation. The topography of the TP above 1500 m is marked by dashed gray contour in (c).
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
The scatter diagram of the standardized time series of PC1 of SEASM and SASM precipitations is displayed in Fig. 2. The correlation coefficient between the two PC1s is 0.66, exceeding the 99.9% confidence level. The criteria of standard deviations of ±0.5 are used for selecting the significant cases in the four quadrants. Standard deviations of ±0.6 and ±0.8 have also been used for sensitivity tests, and similar results are obtained. To retain more samples for further analyses, we have chosen the criterion of ±0.5 standard deviations. Almost all significant cases are located in the first and third quadrants, indicating a robust positive correlation between the most dominant modes of SEASM and SASM precipitations. There are 7 years including 1985, 1990, 1997, 2001, 2004, 2012, and 2018 when both the first EOF modes of SEASM and SASM strengthen simultaneously [i.e., mutually enhanced (ME) SEASM–SASM years] and 9 years including 1980, 1983, 1988, 1995, 1996, 1998, 2007, 2010, and 2020 when both weaken [i.e., mutually weakened (MW) SEASM–SASM years]. Only three cases are located in the second and fourth quadrants, and they would not be taken into account in our further analysis.
Scatter diagrams of standardized PC1s of the first EOF mode of SEASM and SASM precipitations. The three asterisks following the correlation coefficient indicate the significant values exceeding the 99.9% confidence level. Numbers shown in the four quadrants indicate the significant cases for each PC1 exceeding the standard deviations of ±0.5, respectively.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
The spatial characteristics of simultaneous strengthening/weakening of the first EOF modes of SEASM and SASM precipitations are revealed by applying a composite analysis. Figure 3 displays the composite differences in summer precipitation and 850-hPa winds between the 7 ME SEASM–SASM years, 9 MW SEASM–SASM years, and climatology, respectively. In the ME SEASM–SASM cases, a meridional dipole of precipitation and low-level circulation emerges with positive precipitation and cyclonic anomalies over the northern South China Sea–western Pacific and negative precipitation and anticyclonic anomalies to the north (Fig. 3a). Accompanied with the prominent cyclonic system over the northern South China Sea–western Pacific, anomalous low-level divergence and decrease in precipitation appear over the Maritime Continent. Meanwhile, cyclonic curvature forms over the northern Bay of Bengal and anticyclonic curvature appears over southern India and the eastern Arabian Sea, indicating that the Indian monsoon trough is enhanced and shifts eastward with anomalous northwesterly wind over India and anomalous southwesterly wind over the northern Bay of Bengal. It leads to a decrease in precipitation over the eastern Arabian Sea and southern India and an increase in precipitation over the northern Bay of Bengal and northern India. In the MW SEASM–SASM cases, the features are generally opposite to those in the ME SEASM–SASM cases (Fig. 3b). However, compared to Fig. 3a, the distribution in Fig. 3b seems more zonally oriented and equatorial signals are more remarkable. The asymmetry of precipitation and circulation patterns between the ME and MW SEASM–SASM cases indicates that the modulating factors and their relative contributions may be different for the internal coupling processes involved in the ME and MW SEASM–SASM situations.
Composite differences in summer precipitation (shading; mm day−1) and 850-hPa winds (vectors; m s−1) (a) between 7 ME SEASM–SASM years and climatology and (b) between 9 MW SEASM–SASM years and climatology. White stippling and blue vectors indicate the values significantly above the 95% confidence level. The topography of the TP above 1500 m is marked by the dashed gray contours.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
b. Possible modulating factors for the ME and MW SEASM–SASM situations
To further investigate the asymmetric features between the ME and MW SEASM–SASM situations and the possible modulating factors, the composite differences in geopotential height at difference levels between 7 ME SEASM–SASM years, 9 MW SEASM–SASM years, and climatology are shown in Fig. 4, respectively. In the ME SEASM–SASM cases, an anomalous stationary wave train with northeastward wave activity fluxes from the SEASM region to the extratropical North Pacific, which highly resembles the PJ pattern, occurs at different vertical levels especially at 500 and 850 hPa, with evident negative centers over the SEASM region and accompanied positive centers around Japan (Figs. 4a,c,e). The low pressure system elongates toward the northern Bay of Bengal, northern India, and the southern Tibetan Plateau (Fig. 4e), because the intensified convection over the SEASM region could force a PJ pattern–like wave train and cause the anomalous cyclone extending westward as a Rossby wave response with westerly anomalies on its southern flank reaching the Arabian Sea. Correspondingly, precipitation increases over the northern Bay of Bengal and northern India. While in the MW SEASM–SASM cases, the circulation patterns are quite different (Figs. 4b,d,f). Midhigh latitude wave train patterns are similar to the North Atlantic–East Asia pattern identified by Chen et al. (2020), but the magnitudes are insignificant at various vertical levels. Positive anomalies dominate over the western North Pacific, the northern South China Sea, the northern Bay of Bengal, and northeastern India. Signals are weakened in midhigh latitudes but intensified in the tropics, implying that the PJ wave train makes relatively small contribution and instead tropical SST anomalies play a substantial role in the MW SEASM–SASM situation (see Figs. 4d,f).
Composite differences in zonal deviation of geopotential height (shading; m) at (a) 200, (c) 500, and (e) 850 hPa between 7 ME SEASM–SASM years and climatology. (b), (d), (f) As in (a), (c), and (e), but for the differences between 9 MW SEASM–SASM years and climatology. Black contours in (a), (b) and (c), (d) indicate the summer climatology of the SAH and the WPSH, respectively. Blue vectors in (c) and (d) present the corresponding wave activity fluxes (m2 s−2) in the region of 10°–60°N, 100°E–120°W. White stippling indicates the values significantly above the 95% confidence level. The topography of the TP above 1500 m is marked by dashed gray contours.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
Thus, the PJ wave train seems to exert an important modulating impact on the ME SEASM–SASM cases. A recent study by Hu et al. (2024) presented the different precipitation and circulation features associated with several PJ indices and revisited the linkage between the PJ pattern and the Indian summer monsoon rainfall. The precipitation and circulation patterns associated with the PJ indices defined by Wu et al. (2013) and Kosaka and Nakamura (2010) are more similar to those in Fig. 3a. Therefore, the index proposed by Wu et al. (2013) has been applied for further analyses. Figure 5 presents the regressed patterns of precipitation, 850-hPa winds, and geopotential height at 200, 500, and 850 hPa against the PJ index. As shown in Figs. 5a–c, the quasi-barotropic wave train pattern exists in the entire troposphere (from 200 to 850 hPa), with negative centers over the northern South China Sea–western North Pacific region, the Bering Sea, and northeastern North America, but positive centers over east of Japan, the Gulf of Alaska, and the extratropical North Atlantic. An anomalous cyclonic circulation expands to northern India and the Tibetan Plateau, inducing intensification and eastward shift of the Indian monsoon trough. Correspondingly, a meridional structure of summer precipitation forms with positive anomalies over the northeastern Bay of Bengal and the northern South China Sea–western North Pacific region and negative anomalies over east of Japan (Fig. 5d).
Regressed summer geopotential height (shading; m) at (a) 200, (b) 500, and (c) 850 and (d) precipitation (shading; mm day−1) and 850-hPa winds (vectors; m s−1) against the PJ index (with linear trend and ENSO signals removed). Black contours represent the summer climatology of SAH in (a) and the summer climatology of WPSH in (b). White stippling and blue vectors indicate the regression coefficients significantly above the 95% confidence level. The topography of the TP above 1500 m is marked by the dashed gray contours.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
On the one hand, the PJ pattern exerts an upstream influence on the SASM by inducing anomalous circulation over the north Indian Ocean, which is consistent with the results of Srinivas et al. (2018) and Kosaka (2021). On the other hand, the heating over the Maritime Continent is reduced by anomalous divergence in the southern flank of anomalous cyclone over the western North Pacific. Correspondingly, anomalous westerlies with anticyclonic curvature form over southern India and vicinity, causing reduced precipitation. Previous studies have demonstrated that the PJ indices containing signals of rainfall anomalies over the Maritime Continent affect the Indian summer monsoon rainfall more effectively (e.g., Hu et al. 2024). In the ME SEASM–SASM situation, accompanied by the PJ-related vertical circulation, anomalous downward motions with reduced rainfall appear over the Maritime Continent (figure not shown), suggesting a potential impact of the reduced heating over the Maritime Continent on the PJ–SASM linkage. The above features of precipitation and circulation are also quite similar to those shown in the Figs. 3a, 4a, 4c, and 4e, indicating that the PJ pattern indeed plays a crucial role in linking the mutual enhancement of the first EOF modes of SEASM and SASM precipitations.
In addition, we select 10 cases without an apparent SEASM–SASM relationship in the first quadrant to further confirm the above result. A comparison of the composite differences in circulation patterns between this “nonrelationship” cases and the ME SEASM–SASM cases indicates that few signals occur over the entire Asian summer monsoon region and the PJ wave train pattern almost disappears in the nonrelationship cases (Fig. 6). Only a stationary wave, which does not pass the statistical significance test, occurs in the higher latitudes. To sum up, the PJ pattern is a dominant modulating factor for the ME of the first EOF modes of SEASM and SASM precipitations.
As in Fig. 4, but for the composite differences for 7 ME SEASM–SASM years and 10 years of “no SEASM–SASM relationship” in the first quadrant.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
As discussed before, an asymmetry of precipitation and circulation patterns exists in the ME and MW SEASM–SASM cases, and the features in the MW SEASM–SASM cases seem more zonally distributed, and equatorial signals are more significant. This implies that tropical SST anomalies exert an impact in the latter cases. The composite differences in SST during the preceding winter and spring and the simultaneous summer are shown in Fig. 7. The change in SST for each season is unapparent in the ME SEASM–SASM cases with insignificant La Niña–like cooling in the equatorial eastern Pacific in the preceding winter (Figs. 7a,c,e). However, in the MW cases, the equatorial eastern Pacific SST presents as El Niño–like warming in the preceding winter and La Niña–like cooling in the simultaneous summer, indicating an ENSO transition from the warm phase to the cold phase (Figs. 7b,d,f). Accompanied with El Niño events, SST warming occurs correspondingly in the tropical North Atlantic and Indian Oceans (e.g., Su et al. 2001; Chiang and Sobel 2002; Chiang and Lintner 2005). Thus, the ENSO transition from warm phase to cold phase could play a major role in the mutual decrease in the first EOF modes of SEASM and SASM precipitations.
Composite differences in SST (shading; °C) between 7 ME SEASM–SASM, 9 MW SEASM–SASM years, and climatology for (a),(b) preceding DJF, (c),(d) preceding MAM, and (e),(f) simultaneous JJA, respectively. White stippling indicates the values significantly above the 95% confidence level.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
To further confirm the impact of tropical Pacific SST, an intensity index of SST phase transition is defined by calculating the difference in Niño-3.4 SST between the simultaneous JJA and the preceding DJF (JJA average minus DJF average). A larger value of the index measures a greater intensity of Niño-3.4 SST phase transition from proceeding DJF to the following summer. The standardized time series of the index of SST phase transition intensity is shown in Fig. 8, which displays large interannual variability. We select several significant cases, which include 4 positive Niño-3.4 SST transition years (1989, 1997, 2008, and 2009) and 7 negative years (1983, 1988, 1992, 1995, 1998, 2010, and 2016) by applying the criterion of ±1 standard deviation of the index. Figure 9 shows the composite features of summer precipitation and low-level winds in the 4 positive and 7 negative Niño-3.4 SST transition years, respectively. In the cases of positive SST transition, few signals occur in the Asian summer monsoon region (Fig. 9a), indicating that positive SST transition cannot effectively modulate the Asian summer monsoon. Previous studies have pointed out an asymmetric response of the Asian summer monsoon to El Niño/La Niña, in which La Niña exerts a smaller influence than El Niño (e.g., Hardiman et al. 2018). While in the cases of negative SST transition (i.e., from a warm phase of the Niño-3.4 SST in the preceding winter to a cold phase in the following summer), changes in precipitation and 850-hPa winds are obvious. An anomalous anticyclone with reduced precipitation dominates over the northern Bay of Bengal and the northern South China Sea/western Pacific region, and an anomalous cyclonic circulation with increased precipitation forms over the Maritime Continent, the tropical eastern Indian Ocean, southern India, and the Arabian Sea (Fig. 9b). These features are quite similar to those in the MW SEASM–SASM cases shown in Fig. 3b.
Standardized time series of the intensity of SST phase transition measuring the difference of SST in the Niño-3.4 region between the simultaneous JJA and the preceding DJF.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
Composite differences in summer precipitation (shading; mm day−1) and 850-hPa winds (vectors; m s−1) (a) between 4 positive Niño-3.4 SST transition years and climatology and (b) between 7 negative Niño-3.4 SST transition years and climatology. White stippling and blue vectors indicate the values significantly above the 95% confidence level. The topography of the TP above 1500 m is marked by the dashed gray contours.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
The seasonal evolution of ENSO can cause the formation and maintenance of anomalous western North Pacific anticyclone through suppressed convective heating induced by local SST cooling in the western North Pacific and the capacitor effect of tropical Indian Ocean (e.g., Zhang et al. 1996; Wang et al. 1999, 2000; Wang and Zhang 2002; Yang et al. 2007; Li et al. 2008; Xie et al. 2009, 2016; Wu et al. 2010, 2017). The warming of the tropical Indian Ocean acts as a capacitor anchoring the atmospheric anomalies over the Indo-western Pacific regions through emanating a baroclinic Kelvin wave into the Pacific (e.g., Xie et al. 2009, 2016). This equatorial Kelvin wave induces surface northeasterly wind anomalies and then the corresponding divergence and the anomalous anticyclone over the subtropical northwestern Pacific. Besides, intensified diabatic heating over the Maritime Continent, which is related to the change in the Walker circulation induced by the developed SST cooling in the equatorial eastern Pacific during summer, can also weaken the precipitation over the subtropical western North Pacific through the Kelvin wave but enhance the precipitation over the eastern Arabian Sea and southern India via the westward-propagating Rossby wave. The impacts of tropical Atlantic SST on the Asian summer monsoon have been emphasized by plenty of research (e.g., Chen et al. 2018; Zhao et al. 2020; Feng and Chen 2021). Our recent study found that the increases in tropical Atlantic SST and convection during spring and summer could cause an anomalous barotropic wave train propagating southeastward from eastern North America to East Asia, leading to an eastward extension of the South Asian high and a westward extension of the western Pacific subtropical high (Lu et al. 2023). The anomalous heating over the tropical Atlantic also modulates the Walker circulation through two anomalous vertical cells, with ascending motions over the Maritime Continent and the eastern tropical Indian Ocean, inducing a lower-level anticyclone over Southeast Asia as a Gill-type response. Correspondingly, precipitation decreases over the northern Bay of Bengal and the SEASM region but increases over southwestern India. However, SST warming in other tropical oceans especially that in the tropical Indian Ocean is relatively small during summer. Therefore, the effects of tropical Indian and Atlantic Oceans are not heavily emphasized in our study. It can be concluded that the SST transition from warm phase in the preceding winter to cold phase in the following summer (i.e., a decaying El Niño and developing La Niña–like SST evolution) plays a crucial role in the linkage between the first EOF modes of SEASM and SASM precipitations.
To further confirm the above conclusion, we choose 4 years of “no SEASM–SASM relationship” in the third quadrant to conduct a composite analysis. Compared with the composite differences in circulation patterns in the MW SEASM–SASM situation (Figs. 10a,c,e), few signals occur over the Asian summer monsoon region and the tropical oceans in the “no relationship” cases (Figs. 10b,d,f). Only a stationary wave occurs in the higher latitudes, which is far away from the SEASM and SASM regions. Moreover, compared with significant DJF SST warming and JJA cooling of the equatorial eastern Pacific in the MW SEASM–SASM cases (Figs. 10g,i), little SST change occurs in the “no SEASM–SASM relationship” cases (Figs. 10h,j). Therefore, it is confirmed that the seasonal evolution of equatorial eastern Pacific SST is a dominant modulating factor for the MW of the first EOF modes of SEASM and SASM precipitations.
As in Fig. 4, but for the composite differences in (a)–(f) zonal deviation of geopotential height (shading; m) at 200, 500, and 850 hPa and (g)–(j) SST (shading; °C) during preceding DJF and simultaneous JJA for 9 MW SEASM–SASM years and 4 years of “no SEASM–SASM relationship” in the third quadrant.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
Regressed summer precipitation (shading; mm day−1) and 850-hPa winds (vectors; m s−1) against (a) the PJ index, (b) the SST phase transition index, and (c) the combined index including the signals of both standardized PJ and SST phase transition indices. White stippling and blue vectors indicate the values significantly above the 95% confidence level. The topography of the TP above 1500 m is marked by dashed gray contours.
Citation: Journal of Climate 38, 6; 10.1175/JCLI-D-24-0317.1
4. Conclusions and further discussion
In this study, we have shed light on the physical connection between the most dominant EOF modes of SEASM and SASM precipitations, with a focus on the mutually enhanced and mutually weakened cases of the two modes. The most dominant mode of the SEASM precipitation presents a uniformly increasing pattern, while that of the SASM precipitation features as a southwest–northeast dipole with a positive center over the northern Bay of Bengal and a negative center over the eastern Arabian Sea and southern India. A robust positive relationship exists between these two patterns, with a significant correlation coefficient of 0.66.
However, asymmetric features of precipitation and circulation patterns exist between the ME and MW SEASM–SASM cases. In the ME SEASM–SASM cases, a meridional dipole appears with increased precipitation and cyclonic anomalies over the northern South China Sea/western Pacific but decreased precipitation and anticyclonic anomalies to the north. Accompanied with the prominent cyclonic system over the northern South China Sea/western Pacific, anomalous low-level divergence and decreased precipitation appear over the Maritime Continent. Meanwhile, the Indian monsoon trough is enhanced and shifts eastward with anomalous northwesterly wind over India and anomalous southwesterly wind over the northern Bay of Bengal, leading to decreased precipitation over the eastern Arabian Sea and southern India and increased precipitation over the northern Bay of Bengal and northern India. In the MW SEASM–SASM cases, however, the patterns seem more zonally oriented and equatorial signals are more remarkable although the signs are generally opposite to those in the ME SEASM–SASM cases. This feature suggests that the modulating factors and their relative contributions are different for the internal coupling processes involved in the ME and MW SEASM–SASM situations.
In the ME SEASM–SASM cases, a PJ-like pattern emerges with a huge cyclonic system in the SEASM region and an anticyclone over east of Japan, while the changes in tropical SST are relatively small. Correspondingly, the heating over the Maritime Continent is reduced by anomalous divergence in the southern flank of anomalous cyclone over the western North Pacific. Associated westerly anomalies with anticyclonic curvature elongate to the Arabian Sea and southern India, causing reduced precipitation. These features suggest that the apparent atmospheric variability, i.e., the PJ pattern acts as a dominant modulating factor for the ME situation of the SEASM–SASM linkage under a relatively weak SST background. While in the MW situation, the ENSO-related SST anomalies play a more dominant role in the linkage between SEASM and SASM. The negative evolution of equatorial eastern Pacific SST (i.e., a decaying El Niño and developing La Niña–like SST evolution) can cause the formation of anomalous western North Pacific anticyclone through the suppressed convective heating induced by local SST cooling in the western North Pacific. Accompanied with El Niño, SST warming also occurs in the tropical Indian Ocean and North Atlantic during following spring and summer, leading to the maintenance of the anomalous anticyclone over the western North Pacific through equatorial Kelvin wave, midhigh latitude Rossby wave, and the changes in the Walker Circulation. In addition, the intensified diabatic heating over the Maritime Continent, which is related to the change in Walker circulation induced by developing SST cooling in the equatorial eastern Pacific, can also drive an anomalous anticyclone on its northeast side as a Gill-type response. However, in the cases of positive SST transition, few signals occur in the entire Asian summer monsoon region. Overall, it can be concluded that the mutual enhancement of the first EOF modes of SEASM and SASM precipitations is closely linked through the PJ pattern induced by intensified SEASM convection, while the equatorial eastern Pacific SST transition from a warm phase in the preceding winter to a cold phase in the following summer plays a crucial role in the mutually weakened situation of the linkage between the first EOF modes of SEASM and SASM precipitations.
Although it has been concluded in this study that the evolution of equatorial eastern Pacific SST exerts a dominant influence on the MW SEASM–SASM situation, the relative contributions of SST warming during the preceding winter and cooling during the simultaneous summer still remain unclear, which could be illustrated by conducting model sensitivity experiments with different SST forcings. Moreover, assessment of the performance of current climate models for the asymmetry between the ME and MW SEASM–SASM situations is also important for further research. In addition, it can be noticed that the magnitudes of the signals over the Asian summer monsoon region in the ME SEASM–SASM situation are smaller than those in the MW situation (Fig. 3). As discussed in section 3, the SST pattern acts as an insignificant La Niña–like cooling in the equatorial eastern Pacific in the preceding winter (Fig. 7a) and few signals occur over the entire Asian summer monsoon region in the cases of positive SST transition (Fig. 9a). All these phenomena suggest that the asymmetric effects of La Niña and El Niño events could be one of the reasons for the different magnitudes of climate signals between the ME and MW situations in the Asian monsoon region, which is worthy of further investigation. Relevant studies can lead to a better understanding of the variability of the Asian summer monsoon system, which is conducive to improving the seasonal prediction of the monsoon climate.
Acknowledgments.
The authors thank the three anonymous reviewers who provided valuable comments and suggestions for improving the overall quality of the paper. This study was jointly supported by the National Natural Science Foundation of China (Grant U2242205), the National Key R&D Program of China (Grant 2023YFF0805300), the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant 316323005), the Youth Innovation Team of China Meteorological Administration “Climate change and its impact in the Tibetan Plateau” (Grant CMA2023QN16), the National Natural Science Foundation of China (Grant 42205042), and the Basic Research Fund of the Chinese Academy of Meteorological Sciences (Grant 2022Y010).
Data availability statement.
ERA5 monthly reanalysis data are from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly means?tab=form. GPCP monthly precipitation data can be obtained from the NOAA PSL, Boulder, Colorado, at https://psl.noaa.gov/data/gridded/data.gpcp.html. ERSST.v5 monthly data are provided by the NOAA PSL, Boulder, Colorado, at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html.
REFERENCES
Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.
Biasutti, M., and Coauthors, 2018: Global energetics and local physics as drivers of past, present and future monsoons. Nat. Geosci., 11, 392–400, https://doi.org/10.1038/s41561-018-0137-1.
Chang, C.-P., B. Wang, and N.-C. G. Lau, 2005: The global monsoon system: Research and forecast: Report of the International Committee of the Third International Workshop on Monsoons. Secretariat of the World Meteorological Organization WMO/TD 1266, 553 pp., https://core.ac.uk/download/pdf/36730348.pdf.
Chen, S., R. Wu, and W. Chen, 2018: Modulation of spring northern tropical Atlantic sea surface temperature on the El Niño-Southern Oscillation–East Asian summer monsoon connection. Int. J. Climatol., 38, 5020–5029, https://doi.org/10.1002/joc.5710.
Chen, S., R. Wu, W. Chen, K. Hu, and B. Yu, 2020: Structure and dynamics of a springtime atmospheric wave train over the North Atlantic and Eurasia. Climate Dyn., 54, 5111–5126, https://doi.org/10.1007/s00382-020-05274-7.
Chen, W., and Coauthors, 2023: Recent advances in understanding multi-scale climate variability of the Asian monsoon. Adv. Atmos. Sci., 40, 1429–1456, https://doi.org/10.1007/s00376-023-2266-8.
Chiang, J. C. H., and A. H. Sobel, 2002: Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate, 15, 2616–2631, https://doi.org/10.1175/1520-0442(2002)015<2616:TTTVCB>2.0.CO;2.
Chiang, J. C. H., and B. R. Lintner, 2005: Mechanisms of remote tropical surface warming during El Niño. J. Climate, 18, 4130–4149, https://doi.org/10.1175/JCLI3529.1.
Chowdary, J. S., D. Patekar, G. Srinivas, C. Gnanaseelan, and A. Parekh, 2019: Impact of the Indo-Western Pacific Ocean Capacitor mode on South Asian summer monsoon rainfall. Climate Dyn., 53, 2327–2338, https://doi.org/10.1007/s00382-019-04850-w.
Clift, P. D., and R. A. Plumb, 2008: The Asian Monsoon: Causes, History and Effects. Cambridge University Press, 270 pp.
Ding, Q., and B. Wang, 2005: Circumglobal teleconnection in the Northern Hemisphere summer. J. Climate, 18, 3483–3505, https://doi.org/10.1175/JCLI3473.1.
Ding, Q., B. Wang, J. M. Wallace, and G. Branstator, 2011: Tropical–extratropical teleconnections in boreal summer: Observed interannual variability. J. Climate, 24, 1878–1896, https://doi.org/10.1175/2011JCLI3621.1.
Enomoto, T., B. J. Hoskins, and Y. Matsuda, 2003: The formation mechanism of the Bonin high in August. Quart. J. Roy. Meteor. Soc., 129, 157–178, https://doi.org/10.1256/qj.01.211.
Feng, J., and W. Chen, 2021: Roles of the North Indian Ocean SST and tropical North Atlantic SST in the latitudinal extension of the anomalous western North Pacific anticyclone during the El Niño decaying summer. J. Climate, 34, 8503–8517, https://doi.org/10.1175/JCLI-D-20-0802.1.
Goswami, B. N., V. Krishnamurthy, and H. Annamalai, 1999: A broad-scale circulation index for the interannual variability of the Indian summer monsoon. Quart. J. Roy. Meteor. Soc., 125, 611–633, https://doi.org/10.1002/qj.49712555412.
Guo, Q., and J. Wang, 1988: A comparison of the summer precipitation in India with that in China. J. Trop. Meteor., 4, 53–60.
Ha, K.-J., Y.-W. Seo, J.-Y. Lee, R. H. Kripalani, and K.-S. Yun, 2018: Linkages between the South and East Asian summer monsoons: A review and revisit. Climate Dyn., 51, 4207–4227, https://doi.org/10.1007/s00382-017-3773-z.
Hardiman, S. C., and Coauthors, 2018: The asymmetric response of Yangtze river basin summer rainfall to El Niño/La Niña. Environ. Res. Lett., 13, 024015, https://doi.org/10.1088/1748-9326/aaa172.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Hong, Y. T., and Coauthors, 2005: Inverse phase oscillations between the East Asian and Indian Ocean summer monsoons during the last 12000 years and paleo-El Niño. Earth Planet. Sci. Lett., 231, 337–364, https://doi.org/10.1016/j.epsl.2004.12.025.
Hsu, H. H., T. Zhou, and J. Matsumoto, 2014: East Asian, Indochina and western North Pacific summer monsoon—An update. Asia-Pac. J. Atmos. Sci., 50, 46–68, https://doi.org/10.1007/s13143-014-0027-4.
Hu, P., W. Chen, S. Chen, Y. Liu, L. Wang, and R. Huang, 2020: Impact of the September Silk Road pattern on the South China Sea summer monsoon withdrawal. Int. J. Climatol., 40, 6361–6368, https://doi.org/10.1002/joc.6585.
Hu, P., W. Chen, S. Chen, R. Yang, L. Wang, and Y. Liu, 2024: Revisiting the linkage between the Pacific–Japan pattern and Indian summer monsoon rainfall: The crucial role of the Maritime Continent. Geophys. Res. Lett., 51, e2023GL106982, https://doi.org/10.1029/2023GL106982.
Hu, Z.-Z., R. Wu, J. L. Kinter III, and S. Yang, 2005: Connection of summer rainfall variations in South and East Asia: Role of El Niño–southern oscillation. Int. J. Climatol., 25, 1279–1289, https://doi.org/10.1002/joc.1159.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
Huang, R., and F. Sun, 1992: Impacts of the tropical western Pacific on the East Asian summer monsoon. J. Meteor. Soc. Japan, 70, 243–256, https://doi.org/10.2151/jmsj1965.70.1B_243.
Kim, B.-J., S.-E. Moon, R. Lu, and R. H. Kripalani, 2002: Teleconnections: Summer monsoon over Korea and India. Adv. Atmos. Sci., 19, 665–676, https://doi.org/10.1007/s00376-002-0006-z.
Kosaka, Y., 2021: Coupling of the Indian, western North Pacific, and East Asian summer monsoons. Indian Summer Monsoon Variability: El Niño-Teleconnections and Beyond, J. Chowdary et al., Eds., Elsevier, 263 − 286, https://doi.org/10.1016/B978-0-12-822402-1.00002-8.
Kosaka, Y., and H. Nakamura, 2006: Structure and dynamics of the summertime Pacific–Japan teleconnection pattern. Quart. J. Roy. Meteor. Soc., 132, 2009–2030, https://doi.org/10.1256/qj.05.204.
Kosaka, Y., and H. Nakamura, 2010: Mechanisms of meridional teleconnection observed between a summer monsoon system and a subtropical anticyclone. Part I: The Pacific–Japan pattern. J. Climate, 23, 5085–5108, https://doi.org/10.1175/2010JCLI3413.1.
Kripalani, R. H., and S. V. Singh, 1993: Large scale aspects of India-China summer monsoon rainfall. Adv. Atmos. Sci., 10, 71–84, https://doi.org/10.1007/BF02656955.
Kripalani, R. H., and A. Kulkarni, 2001: Monsoon rainfall variations and teleconnections over South and East Asia. Int. J. Climatol., 21, 603–616, https://doi.org/10.1002/joc.625.
Krishnan, R., and M. Sugi, 2001: Baiu rainfall variability and associated monsoon teleconnections. J. Meteor. Soc. Japan, 79, 851–860, https://doi.org/10.2151/jmsj.79.851.
Lau, K. M., and S. Yang, 1997: Climatology and interannual variability of the Southeast Asian summer monsoon. Adv. Atmos. Sci., 14, 141–162, https://doi.org/10.1007/s00376-997-0016-y.
Lee, E.-J., K.-J. Ha, and J.-G. Jhun, 2014: Interdecadal changes in interannual variability of the global monsoon precipitation and interrelationships among its subcomponents. Climate Dyn., 42, 2585–2601, https://doi.org/10.1007/s00382-013-1762-4.
Li, S., J. Lu, G. Huang, and K. Hu, 2008: Tropical Indian Ocean basin warming and East Asian summer monsoon: A multiple AGCM study. J. Climate, 21, 6080–6088, https://doi.org/10.1175/2008JCLI2433.1.
Lu, M., S. Yang, C. Zhu, J. Wang, S. Lin, W. Wei, and H. Fan, 2023: Thermal impact of the southern Tibetan Plateau on the Southeast Asian summer monsoon and modulation by the tropical Atlantic SST. J. Climate, 36, 1319–1330, https://doi.org/10.1175/JCLI-D-22-0493.1.
Murakami, T., and J. Matsumoto, 1994: Summer monsoon over the Asian Continent and western North Pacific. J. Meteor. Soc. Japan, 72, 719–745, https://doi.org/10.2151/jmsj1965.72.5_719.
Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. J. Meteor. Soc. Japan, 65, 373–390, https://doi.org/10.2151/jmsj1965.65.3_373.
Seth, A., A. Giannini, M. Rojas, S. A. Rauscher, S. Bordoni, D. Singh, and S. J. Camargo, 2019: Monsoon responses to climate changes—Connecting past, present and future. Curr. Climate Change Rep., 5, 63–79, https://doi.org/10.1007/s40641-019-00125-y.
Srinivas, G., J. S. Chowdary, Y. Kosaka, C. Gnanaseelan, A. Parekh, and K. V. S. R. Prasad, 2018: Influence of the Pacific–Japan pattern on Indian summer monsoon rainfall. J. Climate, 31, 3943–3958, https://doi.org/10.1175/JCLI-D-17-0408.1.
Stephan, C. C., N. P. Klingaman, and A. G. Turner, 2019: A mechanism for the recently increased interdecadal variability of the Silk Road pattern. J. Climate, 32, 717–736, https://doi.org/10.1175/JCLI-D-18-0405.1.
Su, H., J. D. Neelin, and C. Chou, 2001: Tropical teleconnection and local response to SST anomalies during the 1997–1998 El Niño. J. Geophys. Res., 106, 20025–20043, https://doi.org/10.1029/2000JD000124.
Tao, S., and L. Chen, 1987: A review of recent research on the East Asian summer monsoon in China. Monsoon Meteorology, C. P. Chang and T. N. Krishnamurti, Eds., Oxford University Press, 60–92.
Wang, B., and Z. Fan, 1999: Choice of South Asian summer monsoon indices. Bull. Amer. Meteor. Soc., 80, 629–638, https://doi.org/10.1175/1520-0477(1999)080<0629:COSASM>2.0.CO;2.
Wang, B., and H. Lin, 2002: Rainy season of the Asian–Pacific summer monsoon. J. Climate, 15, 386–398, https://doi.org/10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2.
Wang, B., and Q. Zhang, 2002: Pacific–East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development. J. Climate, 15, 3252–3265, https://doi.org/10.1175/1520-0442(2002)015<3252:PEATPI>2.0.CO;2.
Wang, B., and Q. Ding, 2008: Global monsoon: Dominant mode of annual variation in the tropics. Dyn. Atmos. Oceans, 44, 165–183, https://doi.org/10.1016/j.dynatmoce.2007.05.002.
Wang, B., R. Wu, and X. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517–1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
Wang, B., R. Wu, and K.-M. Lau, 2001: Interannual variability of the Asian summer monsoon: Contrasts between the Indian and the western North Pacific–East Asian monsoons. J. Climate, 14, 4073–4090, https://doi.org/10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2.
Wang, C., R. H. Weisberg, and J. I. Virmani, 1999: Western Pacific interannual variability associated with the El Niño-Southern Oscillation. J. Geophys. Res., 104, 5131–5149, https://doi.org/10.1029/1998JC900090.
Wang, L., P. Xu, W. Chen, and Y. Liu, 2017: Interdecadal variations of the Silk Road pattern. J. Climate, 30, 9915–9932, https://doi.org/10.1175/JCLI-D-17-0340.1.
Wang, L., P. Xu, and J. S. Chowdary, 2021: Teleconnection along the Asian Jet stream and its association with the Asian summer monsoon. Indian Summer Monsoon Variability: El Niño-Teleconnections and Beyond, J. Chowdary et al., Eds., Elsevier, 287 − 298, https://doi.org/10.1016/B978-0-12-822402-1.00009-0.
Wei, W., R. Zhang, M. Wen, X. Rong, and T. Li, 2014: Impact of Indian summer monsoon on the South Asian high and its influence on summer rainfall over China. Climate Dyn., 43, 1257–1269, https://doi.org/10.1007/s00382-013-1938-y.
Wei, W., R. Zhang, M. Wen, B.-J. Kim, and J.-C. Nam, 2015: Interannual variation of the South Asian high and its relation with Indian and East Asian summer monsoon rainfall. J. Climate, 28, 2623–2634, https://doi.org/10.1175/JCLI-D-14-00454.1.
Wei, W., R. Zhang, S. Yang, W. Li, and M. Wen, 2019: Quasi-biweekly oscillation of the South Asian high and its role in connecting the Indian and East Asian summer rainfalls. Geophys. Res. Lett., 46, 14742–14750, https://doi.org/10.1029/2019GL086180.
Wu, B., T. Li, and T. Zhou, 2010: Relative contributions of the Indian Ocean and local SST anomalies to the maintenance of the western North Pacific anomalous anticyclone during the El Niño decaying summer. J. Climate, 23, 2974–2986, https://doi.org/10.1175/2010JCLI3300.1.
Wu, B., T. Zhou, and T. Li, 2017: Atmospheric dynamic and thermodynamic processes driving the western North Pacific anomalous anticyclone during El Niño. Part I: Maintenance mechanisms. J. Climate, 30, 9621–9635, https://doi.org/10.1175/JCLI-D-16-0489.1.
Wu, J., X. Xu, F. Jin, and P. Guo, 2013: Research of the intraseasonal evolution of the East Asia Pacific pattern and the maintenance mechanism. Acta Meteor. Sin., 71, 476–491.
Wu, R., 2002: A mid-latitude Asian circulation anomaly pattern in boreal summer and its connection with the Indian and East Asian summer monsoons. Int. J. Climatol., 22, 1879–1895, https://doi.org/10.1002/joc.845.
Wu, R., 2017: Relationship between Indian and East Asian summer rainfall variations. Adv. Atmos. Sci., 34, 4–15, https://doi.org/10.1007/s00376-016-6216-6.
Wu, R., and Y. Jiao, 2017: The impacts of the Indian summer rainfall on North China summer rainfall. Asia-Pac. J. Atmos. Sci., 53, 195–206, https://doi.org/10.1007/s13143-017-0013-8.
Xie, S.-P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo–western Pacific climate during the summer following El Niño. J. Climate, 22, 730–747, https://doi.org/10.1175/2008JCLI2544.1.
Xie, S.-P., Y. Kosaka, Y. Du, K. Hu, J. S. Chowdary, and G. Huang, 2016: Indo-western Pacific Ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci., 33, 411–432, https://doi.org/10.1007/s00376-015-5192-6.
Xu, P., L. Wang, W. Chen, J. Feng, and Y. Liu, 2019: Structural changes in the Pacific–Japan pattern in the late 1990s. J. Climate, 32, 607–621, https://doi.org/10.1175/JCLI-D-18-0123.1.
Xue, X., and W. Chen, 2019: Distinguishing interannual variations and possible impacted factors for the northern and southern mode of South Asia High. Climate Dyn., 53, 4937–4959, https://doi.org/10.1007/s00382-019-04837-7.
Xue, X., W. Chen, S. Chen, S. Sun, and S. Hou, 2021: Distinct impacts of two types of South Asian highs on East Asian summer rainfall. Int. J. Climatol., 41, E2718–E2740, https://doi.org/10.1002/joc.6876.
Xue, X., W. Chen, and S. F. Chen, 2022: Distinct impacts of two types of South Asian high on the connection of the summer rainfall over India and North China. Int. J. Climatol., 42, 8056–8072, https://doi.org/10.1002/joc.7692.
Yang, J., Q. Liu, S.-P. Xie, Z. Liu, and L. Wu, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708, https://doi.org/10.1029/2006GL028571.
Yang, S., and W. J. Gutowski, Jr., 1992: On the relationship between tropical Chinese rainfall and the Indian summer monsoon. J. Meteor. Soc. Japan, 70, 997–1004, https://doi.org/10.2151/jmsj1965.70.5_997.
Zhang, R., A. Sumi, and M. Kimoto, 1996: Impact of El Niño on the East Asian monsoon: A diagnostic study of the ‘86/87 and ‘91/92 events. J. Meteor. Soc. Japan, 74, 49–62, https://doi.org/10.2151/jmsj1965.74.1_49.
Zhang, R., A. Sumi, and M. Kimoto, 1999: A diagnostic study of the impact of El Niño on the precipitation in China. Adv. Atmos. Sci., 16, 229–241, https://doi.org/10.1007/BF02973084.
Zhang, T., X. Jiang, S. Yang, J. Chen, and Z. Li, 2022: A predictable prospect of the South Asian summer monsoon. Nat. Commun., 13, 7080, https://doi.org/10.1038/s41467-022-34881-7.
Zhao, W., W. Chen, S. Chen, S.-L. Yao, and D. Nath, 2020: Combined impact of tropical central-eastern Pacific and North Atlantic sea surface temperature on precipitation variation in monsoon transitional zone over China during August–September. Int. J. Climatol., 40, 1316–1327, https://doi.org/10.1002/joc.6231.
Zhou, F., R. Zhang, and J. Han, 2020: Influences of the East Asian summer rainfall on circumglobal teleconnection. J. Climate, 33, 5213–5221, https://doi.org/10.1175/JCLI-D-19-0325.1.
