Relationships between Springtime Sea Surface Temperatures in Different Indian Ocean Domains and Various Asian Monsoons from Late Spring to the Following Summer

The sea surface temperature (SST) of the Indian Ocean (IO) plays a vital role in affecting global climate; thus, the variability of IO SST and its climatic effects have attracted extensive attentions in recent decades (e.g., Shukla, 1975; Saji et al., 1999; Yoo et al., 2006; Yang J. L. et al., 2007; Xie et al., 2009; Chen et al., 2019). Atmosphere–ocean interaction is critical to the interannual variability of IO SST, especially in areas with heavy precipitation (Wu and Kirtman, 2005, 2007). The variability of IO SST manifests a strong interannual component, which is dominated by the Indian Ocean basin mode (IOBM; or the uniform mode) and by the tropical Indian Ocean dipole mode (IODM).

The IOBM is characterized by a uniform warming/cooling at the basin scale, which peaks in late boreal winter and can persist to the ensuing spring and summer (Klein et al., 1999). This SST pattern is highly related to the El Niño–Southern Oscillation (ENSO), the strongest tropical interannual variability (Klein et al., 1999; Yang S. et al., 2007, 2018; Wu et al., 2008; Xie et al., 2009). The uniform warming of the IO SST during the boreal spring is associated with the delayed onset of the Asian summer monsoon (ASM); it strengthens the South Asian monsoon (SAM) in the following summer but weakens the Southeast Asian monsoon (SEAM; Yoo et al., 2006). Observations and ensemble experiments using atmospheric models indicate that the IOBM can prolong the effects of ENSO and influence the subsequent ASM by forcing an anomalous anticyclone over the northwestern Pacific (Yang J. L. et al., 2007; Li et al., 2008). Liu and Duan (2017) pointed out that the warming IOBM can regulate the climate of East Asia by affecting the associated atmospheric circulation, such as the South Asian high (SAH).

The IODM has also attracted considerable attentions. Saji et al. (1999) and Webster et al. (1999) realized that the related ocean–atmosphere interaction in the IO contributes to remarkable climate fluctuations (also see Han et al., 2014). The IODM exhibits strong seasonal phase-locking character, typically occurring, developing, and peaking from the boreal summer to autumn, and decaying in spring (Saji et al., 1999; Li and Mu, 2001); it affects precipitation and atmospheric circulation not only over the IO but also beyond the ocean basin through teleconnections (Behera et al., 1999; Ashok et al., 2001; Behera and Yamagata, 2003; Guan and Yamagata, 2003; Guan et al., 2003; Saji and Yamagata, 2003). It can even influence the ASM in the subsequent year (Yuan et al., 2008; Chen et al., 2021; He et al., 2022).

Previous studies also focused on the roles of SSTs in different IO domains in climate variability. A significant correlation exists between the Arabian Sea SST and Indian summer monsoon precipitation: a warming (cooling) SST increases (decreases) Indian monsoon precipitation (Shukla, 1975; Lau and Yang, 1997; Clark et al., 2000; Levine and Turner, 2012). Compared to the Pacific SST, the northern IO (NIO) SST plays an important role in the biennial transition of the Indian summer monsoon, especially when the ENSO is suppressed (Arpe et al., 1998; Wu and Kirtman, 2007). IO warming acts as a capacitor to anchor atmospheric circulation anomalies over the Indo-Pacific oceans, in which the NIO warming plays the most important role (Yang J. L. et al., 2007; Xie et al., 2009). Wang et al. (2018) found that the NIO SST systemic cold bias in Coupled Model Intercomparison Project phase 5 (CMIP5) models suppressed local convective activity and affected the Matsuno–Gill response in the upper troposphere, leading to decreased Indian monsoon precipitation. The importance of the southern IO (SIO) SST has also been recognized. SIO SST anomalies show a strong relationship with the interannual variability of the ASM, with a warming SST weakening the large-scale monsoon circulation (Zhu and Houghton, 1996). Statistical methods and numerical simulations have been used to explore relationships between different IO domains and the ASM, revealing that the SIO plays an unignorable role in the ASM while the warming in the NIO is beneficial to an earlier onset of the SAM (Terray et al., 2005; Yoo et al., 2006; Li and Yang, 2017).

Most studies have focused on relationships between the ASM and summer IO SST, although the springtime IO SST is also studies occasionally. It is well known that the ASM develops abruptly in May before it reaches its maximum intensity in summer (Webster and Yang, 1992; Lau and Yang, 1997), although the summer monsoon has not been fully established over some regions by then (Ju and Slingo, 1995; Yang, 1996; Yang and Lau, 1998). Some precursory signals in May, such as the 200-hPa westerly jet stream, are heuristic as indication of the subsequent summer monsoon. It is expected that the anomalies of monsoon development in May would provide an indication for the perturbation of the following summer monsoon. Hence, we are particularly interested in the spring–summer transition in May and the links between spring IO SST and the development of the ASM. There has been increasing interest in applying the artificial neural network approach self-organizing map (SOM) to depict atmospheric and oceanic variability patterns (Tozuka et al., 2008; Morioka et al., 2010; Johnson, 2013; Tan et al., 2020; Zhang et al., 2020). Compared with the more traditional statistical methods such as empirical orthogonal function (EOF) analysis, the SOM can extract asymmetrical features between the positive and negative phases (Liu et al., 2006; Leloup et al., 2007; Tozuka et al., 2008). Thus, we employ the SOM to extract the dominant features of IO SST in this study.

In Section 2, the data and methodology employed are introduced. In Section 3, features of SOM SST patterns are exhibited. In Sections 4 and 5, we display the relationship between spring IO SST and the ASM in late-spring and summer, respectively. In Section 6, relative importance of the SIO and NIO is compared. A summary and discussion are provided in Section 7.

The SST dataset used in this study is the NOAA Optimum Interpretation (OI) SST V2 (Reynolds et al., 2002) with a resolution of 1° × 1°. The precipitation dataset is the Global Precipitation Climatology Project (GPCP) version 2.3 (Adler et al., 2003) at a resolution of 2.5° × 2.5°. Wind, geopotential height, and air temperature fields are from the NCEP/NCAR Reanalysis (Kalnay et al., 1996) with a resolution of 2.5° × 2.5°. These data can be available at https://psl.noaa.gov/. All datasets cover from 1982 to 2021.

To investigate regional IO SST patterns and their links to the ASM, we divide the IO into four domains: basin-wide IO (BIO; 30°S–30°N, 40°–110°E), northern IO (NIO; 0°–30°N, 40°–110°E), southern IO (SIO; 30°S–0°, 40°–110°E), and tropical IO (TIO; 15°S–15°N, 40°–110°E).

The SOM method is a type of unsupervised learning artificial neural network for feature detection (Kohonen, 1998). It can convert a complex data into a manageable number that can be displayed on a low-dimensional array (Johnson et al., 2008). It has been widely applied in climate studies in the past decades (Hewitson and Crane, 2002; Tozuka et al., 2008; Johnson, 2013; Tan et al., 2020). Compared with traditional statistical analysis methods, the SOM technique has apparent advantages. For example, the patterns identified by the nonlinear SOM method are more robust than those identified by the linear principal component analysis (Reusch et al., 2005).

We first remove the trend of SST using a least-square method and then obtain SST patterns for each IO domain using the SOM analysis. Each node on the SOM network corresponds to a vector that assigns linear initial value weight. The Best Matching Unit (BMU) is determined based on the Euclidean distance between the weight vector and the input vector from the SST field. The weights of the nodes in the BMU neighborhood radius are adjusted to be closer to the input vector. Repeated for several iterations, the final BMU obtained determines the clustering of the SST patterns (Peura, 1998; Rousi et al., 2015). More details about the SOM technique and chosen parameters are available in Liu et al. (2006). All SOM calculations in this research are implemented based on the Python package called MiniSom (Vettigli, 2018), available at https://github.com/JustGlowing/minisom/.

Before performing the SOM analysis, it is necessary to determine the SOM grid size, which decides the number of SST patterns (K) that can be obtained. To determine a suitable number of nodes, we consider one dimensional SOM (namely the map size is 1 × K). Several studies proposed various useful methods to determine the value of K (Michelangeli et al., 1995; Christiansen, 2007; Johnson, 2013; Riddle et al., 2013). Here, we select a field significance test that controls the false discovery rate (FDR) to determine the value of K.

Several studies successfully implemented the FDR method to determine whether any two patterns in the SOM are statistically distinguishable from each other (e. g., Johnson, 2013; Li et al., 2015; Guo et al., 2017; Tan et al., 2020). The FDR refers to the expected proportion of false discoveries to the total number of rejections of null (Benjamini and Hochberg, 1995; Wilks, 2006).

We follow the technique in Johnson (2013) to determine the value of K. For one-dimensional SOM of 1 × K, we can obtain K SST anomaly patterns that have $ {C}_{K}^{2}=\dfrac{K\left(K-1\right)}{2} $ pairs of SST patterns to perform the FDR test. The SOM technique is performed for various K numbers to determine the optimal value, where K values are set from 2 to 10 with an interval of 1. Here, the optimal K value (K*) that we determine satisfies the following two criteria: (1) all SOM pairs are statistically distinguishable at the 5% significance level when $ K={K}^{*} $, and (2) there are SOM pairs that are statistically indistinguishable when $ K={K}^{*}+1 $.

By using the method for determining K^* based on the FDR, we decide the number of SST patterns in each of the four IO domains (Fig. 1), namely, K^* = 4 for the BIO and TIO, but K^* = 5 for the NIO and SIO, meaning that the optimal K value for the BIO and TIO is 4 while that for the NIO and SIO is 5.

Figure 1.  Number of March–May (MAM) SOM SST cluster pattern pairs in (a) BIO, (b) NIO, (c) SIO, and (d) TIO that are indistinguishable at the 5% significance level.

Figure 2 exhibits the distinguishable SOM SST patterns in different IO domains. Take the BIO SST cluster patterns as an example. Pattern 1 (P1) displays a basin-wide SST warming anomaly with its warmest center in the southwest, which is similar to the IOBM (Yoo et al., 2006; Yang J. L. et al., 2007; Li and Yang, 2017). Correspondingly, pattern 4 (P4) exhibits a basin-wide uniform SST cooling anomaly; and the two remaining patterns present a southwest–northeast dipole-like feature in the tropical/subtropical Southern Hemisphere. Previous studies have revealed a strong association of the IOBM with the ASM, while the relationship between the IODM and the monsoon is relatively weaker (Yang et al., 2010; Liu and Duan, 2017). Therefore, we mainly focus on P1 and P4 for the BIO SST clusters here. Similarly, for the SST clusters in the rest of the IO, we choose only the uniform SST patterns. Figures 2e–i demonstrate the spring NIO SST cluster patterns. P1 (Fig. 2e)/P5 (Fig. 2i) exhibits a uniform warming/cooling anomaly and a larger SST anomaly compared to the other NIO SST patterns. As in the NIO, P1/P5 of the SIO SST exhibits a uniform warming/cooling anomaly, and the TIO SST clusters are similar to the BIO clusters, with P1 and P4 for significant SST anomalies, but P2 and P3 for weaker SST anomalies.

Figure 2.  MAM SST cluster patterns (K) for one-dimensional SOM in (a–d) BIO, (e–i) NIO, (j–n) SIO, and (o–r) TIO. The occurrence frequency for each pattern is labeled at the top of each panel. Stippling denotes the 5% significance level using the two-tailed Student’s t test.

In the next two sections, we examine the connection between springtime SST anomalies in different IO domains and the ASM based on the SST cluster patterns. For convenience, we refer to P1 of each domain as the warm years of the corresponding domain, and refer to P4 of the BIO and TIO and also P5 of the NIO and SIO as the cold years. Table 1 shows the warm and cold years derived from the SOM patterns of spring SST anomalies in different IO domains. Each year from 1982 to 2021 corresponds to a pattern identified by the SOM, according to which we select the warm and cold years of SST anomalies in different domains.

In this section, we focus on the relationship between spring IO SST and Asian climate in May when the ASM develops quickly (Webster and Yang, 1992; He and Zhu, 1996). Figure 3 shows climatology of May precipitation, 850-hPa wind, 500-hPa air temperature and geopotential height, and 200-hPa geopotential height and wind. In Fig. 3a, the Somali cross-equatorial flow is established in May, with lower-tropospheric northwesterly wind over the Indian subcontinent and westerly flow over the northern Arabian Sea. Little precipitation occurs in these regions. The southwesterly wind prevails in the lower troposphere over the Bay of Bengal, Indo-China Peninsula, southern China, and south of Japan, accompanied by heavy precipitation in these regions. At the same time, the ridge line of the western North Pacific subtropical high (WNPSH) is located near 20°N (Fig. 3b). Figure 3c shows that the westerly flow prevails over most of the upper troposphere to the north of 20°N in May. The SAH covers a relatively small area, located over the Indo-China Peninsula. In short, the Asian climate in May shows summer monsoon characteristics at least over the Bay of Bengal, Indo-China Peninsula, and South China Sea.

Figure 3.  Climatology of (a) May precipitation (shading; mm day⁻¹) and 850-hPa wind (vector; m s⁻¹), (b) 500-hPa air temperature (shading; K) and 500-hPa geopotential height (contour; gpm), and (c) 200-hPa geopotential height (shading; gpm) and 200-hPa wind (vector; m s⁻¹).

We perform a composite analysis of precipitation and atmospheric circulation based on warm and cold years of SST to reveal the relationship between SSTs in different IO domains and ASM development. Figure 4 shows the composite results of May precipitation, 850-hPa wind, and 5880-gpm geopotential height contour based on warm years minus cold years in the BIO, NIO, SIO, and TIO.

Figure 4.  Composite differences in May precipitation (shading; mm day⁻¹) and 850-hPa wind (vector; m s⁻¹) based on warm years minus cold years for (a) BIO, (b) NIO, (c) SIO, and (d) TIO. The red and blue contours denote the composite mean of 5880 gpm at 500 hPa in BIO warm and cold years. Stippling and red vector denote the 5% and 10% significance levels. Values at the upper-right corner of (b), (c), and (d) indicate precipitation pattern correlation coefficients between NIO and BIO, between SIO and BIO, and between TIO and BIO, respectively.

For the BIO, the pattern based on warm years minus cold years (Fig. 4a) exhibits negative precipitation anomalies over the Arabian Sea and Bay of Bengal, and positive precipitation anomalies over the SIO. Simultaneously, the low-level wind over the IO displays a C-shaped pattern (Chen et al., 2019, 2021), meaning that anomalous easterly wind prevails to the north of the equator and anomalous westerly wind, to the south of the equator. The warming of the SIO, particularly in the southwestern IO, enhances local deep convection and causes anomalous northerly wind, which anchor these asymmetric patterns of precipitation and wind (Fig. 4c). These asymmetric wind anomalies weaken the cross-equatorial flow, which is unfavorable for the establishment of large-scale monsoon circulation. Another significant feature of the low-level circulation anomaly is the anomalous anticyclonic circulation over the western North Pacific, which is closely related to the East Asian monsoon (EAM; Wang et al., 2000). In Fig. 4a, the WNPSH expands to the west over the Indo-China Peninsula during the BIO warm years and decreases during the BIO cold years. Correspondingly, the region influenced by the WNPSH shows negative precipitation anomalies. In contrast, southern China on the northern side of the subalpine exhibits positive precipitation anomalies accompanied by anomalous southwesterly wind.

The pattern correlation coefficients of precipitation between BIO and SIO and between BIO and TIO are highly significant at 0.92 and 0.95, respectively. The most significant precipitation anomalies are associated with the SIO SST (Fig. 4c). The centers of SSTs in both BIO and TIO are mainly located south of the equator (Fig. 2). It can be inferred that the SIO SST plays an important role in the relationship between IO SST and the ASM, and that the SIO SST is more representative for IO SST than the SST of the other domains. However, the pattern correlation coefficient of precipitation between Figs. 4a and 4b is only 0.65, which is much smaller compared to the above mentioned two coefficients. The NIO exhibits relatively stronger SST warming in the Arabian Sea and Bay of Bengal compared to the other domains (Fig. 2). The NIO warming weakens the meridional SST gradient and enhances local deep convection, thus intensifying the precipitation over the Arabian Sea and Bay of Bengal (Fig. 4b). The precipitation and wind anomalies over the IO in Fig. 4b do not exhibit an asymmetric mode as in Fig. 4a. Along the African coast of the southwestern IO, southwesterly wind anomalies appear, favoring the development of Somali cross-equatorial flow. Furthermore, the WNPSH (Fig. 4b) enlarges and extends more westward in the warm years, resulting in anomalous southerly winds over the Bay of Bengal, which brings abundant moisture to enhance the precipitation over the northern Bay of Bengal. For the same reason, the rain belt in East Asia shifts northward significantly.

We then perform composite analyses of the atmospheric circulation at 500 and 200 hPa, respectively. In Figs. 5 and 6, the atmospheric circulation anomalies in the mid- and upper-level troposphere display remarkable consistency.

Figure 5.  As in Fig. 4, but for 500 hPa. Stippling denotes the 5% significance level.

Figure 6.  As in Fig. 4, but for 200 hPa. Stippling and red vector denote the 5% and 10% significance levels, respectively.

Figure 5a shows that at 500-hPa, the tropical regions manifest warm anomalies following the BIO SST warming in spring, and the extratropical Northern Hemisphere is mainly characterized by a cold anomaly center over the Tibetan Plateau and a warm anomaly center over the Korean Peninsula. Corresponding to the 850-hPa composite results, Figs. 5c and 5d present very high pattern correlation with Fig. 5a. Similarly, Figs. 6c and 6d also exhibit highly significant pattern correlation with Fig. 6a. With the suppression of deep convective over the Arabian Sea (Fig. 4c) that weakens latent heat in the atmosphere, the cold center over the Tibetan Plateau associated with the SIO SST is larger and extends more southward (Fig. 5c). Correspondingly, the upper-level anomalous cyclone over South Asia associated with the SIO SST is stronger and more widespread (Fig. 6c). All these signals suggest that the warming of IO SST, particularly that of SIO SST, is associated with the suppression of the development of large-scale meridional monsoon circulation.

In the mid–upper troposphere, the differences in atmospheric circulation anomalies associated with the NIO SST are more pronounced compared with those in the other domains. Figure 5b exhibits a relatively weaker correlation with Fig. 5a, so does Fig. 6b. With enhanced convective activity over the Arabian Sea, the atmospheric latent heating intensifies, causing warming in the mid and upper troposphere. This provides the NIO with a stronger and more influential heat source for the atmosphere than the other domains. As a response to the enhanced heat source, the upper-level geopotential height field (Fig. 6b) exhibits a significant Matsuno–Gill response (Matsuno, 1966; Gill, 1980), enforcing Kelvin and Rossby waves on the eastern and western sides of the heat source, respectively (Yang J. L. et al., 2007). The Rossby wave to the west manifests itself in the Northern Hemisphere mainly as a positive geopotential height anomaly center over the Arabian Sea. The presence of this positive geopotential height anomaly center over the Arabian Sea contributes to the prevailing anomalous northerly wind over the Indian subcontinent. Furthermore, this Rossby wave over the Arabian Sea forces anomalous teleconnection wave trains in the midlatitudes (Ding and Wang, 2005; Hu et al., 2013), with an intense positive center of geopotential height over the Korean Peninsula. Meanwhile, an anomalous upper-level anticyclone forms over East Asia, centered over Korea. These circulation anomalies over East Asia are conducive to the development of rising motion and to the change in horizontal zonal-wind shear at the upper level (Lau et al., 2000); and all these favor the development of the EAM. The NIO warming SST anomalies can be linked to the development of the EAM by stimulating the Gill response in the IO, which then forces the anomalous midlatitude teleconnection via the Rossby wave.

We also performed a similar composite analysis of 850-hPa wind and precipitation in June based on the warm years minus cold years in each IO domain (figure omitted). In June, the location of EAM rain belt associated with the NIO and SIO shows significant differences. The rain belt associated with the NIO is located from East China to Japan, while that associated with the SIO is located in southern China, with the former being located more northward. In other words, the warming NIO favors the northward shift of EAM rain belt. This also validates the relationship between the IO and EAM development, as proposed above.

To further reveal the link between IO SST anomalies and different Asian monsoon components, we calculate correlation coefficients between different regional SSTs and monsoon indices. The monsoon indices employed are the Webster–Yang index (WY; Webster and Yang, 1992), SAM (Goswami et al., 1999), EAM (Lau et al., 2000), and SEAM (Wang and Fan, 1999), whose definitions are provided in Table 2.

Based on the above composite analysis, we choose the NIO and SIO as representatives to calculate the SST indices, respectively. The NIO SST index is the average of the SST anomalies in the red box in Figs. 7a, b, and the SIO SST index is the average of the SST anomalies in the blue box in Figs. 7c, d.

Figure 7.  (a, b) Composite patterns of MAM SST anomaly (shading; K) for SOM P1 and P5 in NIO, respectively. Stippling denotes the 5% significance level. The NIO index is defined as the average of the SST anomalies within the red box (5°–15°N, 60°–90°E) in (a). (c, d) As in (a, b), but for SIO SST anomaly and for SOM patterns in SIO. The SIO index is defined as the average of the SST anomalies within the blue box (5°–20°S, 55°–80°E) in (c).

Then, we calculate correlation coefficients and partial correlation coefficients (Table 3) of spring SST indices with the monsoon indices in May from 1982 to 2021, respectively. Both the correlation coefficient (R_NIO and R_SIO) and partial correlation coefficients (PR_NIO and PR_SIO) of the SIO SST with the WY index show significant negative relationships, consistent with the results of previous composite analysis, namely, the SIO warming is associated with the suppression of the development of large-scale monsoon circulation. The NIO exhibits a significant positive partial correlation with the SAM after eliminating the effect of the SIO on both the NIO and the SAM, while the SIO shows the opposite, which is also consistent with the previous analysis. Both NIO and SIO SST anomalies display significant negative correlation with the SEAM.

The above correlation and partial correlation analyses further validate the results from the composite analysis. First, the SIO plays an important role in linking IO SST anomalies to Asian climate in May, with SIO SST warming accompanied by a weaker large-scale monsoon circulation. Second, the link between warm SIO SST anomalies and Asian climate provides unfavorable conditions for the establishment of the SAM, while the features for warm NIO SST anomalies are opposite. Furthermore, there appears to be a link between NIO warming and the precursory signals for the development of the EAM. Finally, the Southeast Asian climate in May is related to the SST anomalies in the whole IO rather than to local SST anomalies, with the warming of the IO often accompanied by the weakening of the SEAM in May.

Compared to May, the climatological precipitation in the Asian monsoon regions increases and expands in summer (Fig. 8a). Heavy precipitation centers appear on the west coast of India and the northeast of the Bay of Bengal. The rain band in East Asia also further extends northward. At 500 hPa (Fig. 8b), the WNPSH moves northeastward, with its ridge near 30ºN. There is a warm center over the southern Tibetan Plateau, northern India, and northern Indo-China Peninsula. As depicted in Fig. 8c, the Tibetan Plateau, northern India, northern Indo-China Peninsula, and Iranian Plateau are dominated by the SAH, with the westerly jet stream to the north of the SAH and the easterly jet stream to the south.

Figure 8.  As in Fig. 3, but for JJA.

Composite differences in June–August (JJA) precipitation and 850-hPa wind based on warm years minus cold years are similar to those in May, with the low-level wind anomalies mainly characterized by anomalous anticyclones over the western North Pacific (Fig. 9a). Southern China is influenced by anomalous southwesterly flow to the west of the anomalous anticyclone, which brings water vapor and increases precipitation, while over the Indo-China Peninsula and Bay of Bengal, anomalous easterly winds prevail, weakening the climatological westerlies and suppressing local precipitation. The anomalous winds and precipitation over the IO in summer differ from those in May, due to the change in the background flow after the establishment of the southwest monsoon. An anomalous cyclone forms off the west coast of the Indian subcontinent, causing convergence of water vapor and leading to enhanced precipitation over the eastern Arabian Sea. The precipitation in Figs. 9c and 9d exhibits significant pattern correlation with that in Fig. 9a. In contrast, the correlation coefficient patterns between Figs. 9b and 9a are smaller than the others. The anomalous anticyclone over the western North Pacific is also weaker than the other anticyclones, and thus the anomalous winds are weakened over the Bay of Bengal and southern China (Fig. 9b).

Figure 9.  As in Fig. 4, but for precipitation (mm day⁻¹), 850-hPa wind anomalies (m s⁻¹), and 500-hPa geopotential height (gpm) in JJA.

At 500 hPa (Fig. 10a), the negative anomalous geopotential height over Japan forms a Pacific–Japan (PJ)-like pattern (Nitta, 1989), with a positive anomalous center over the western North Pacific. The BIO SST anomalies are associated with the EAM via this PJ-like pattern. The PJ-like pattern is seen in both Fig. 10c (for the SIO) and Fig. 10d (for the TIO); and it is stronger in Fig. 10c, but relatively weaker in Fig. 10d. Figure 10b (for the NIO), however, presents a different distribution of circulation anomalies. For the NIO, both geopotential height and air temperature show positive anomalies, mainly centered in the midlatitudes of the Northern Hemisphere, without an appearance of the PJ-like pattern. The 500-hPa atmospheric circulation anomalies associated with the NIO SST anomalies tend to spread from the midlatitudes to higher latitudes.

Figure 10.  As in Fig. 5, but for 500-hPa air temperature (K) and geopotential height (gpm) in JJA.

Figure 11 demonstrates that the atmospheric circulation at 200 hPa is highly similar to that at 500 hPa. As in May, positive geopotential height anomalies are manifested almost throughout the tropics (Fig. 11a). However, an anomalous upper-level cyclone prevails over the Sea of Japan, with anomalous westerly wind to its south, in favor of an enhanced westerly jet stream, which is different from that in May (Fig. 6a). The difference in summer upper-level circulation anomalies may be due to the differences in the intensity and position of the subtropical westerly jet stream. The midlatitude teleconnection forced by the Rossby wave over the Arabian Sea is weakened due to the variation of the westerly jet stream in summer. Furthermore, both Figs. 11c and 11d exhibit similar circulation anomalies to those in Fig. 11a, but the intensity of the circulation anomalies is stronger in Fig. 11c compared to Fig. 11d. In contrast, Fig. 11b presents a quite different distribution of circulation anomalies compared to the others. Consistent with the anomalies at 500 hPa (Fig. 10b), the 200-hPa summertime circulation anomalies associated with spring NIO SST anomalies are located mainly in the midlatitudes (Fig. 11b). Distinct from the other domains, the anomalous winds associated with the NIO SST exhibit anomalous anticyclone over East Asia, which enhances the regional horizontal zonal-wind shear.

Figure 11.  As in Fig. 6, but for 200-hPa wind (m s⁻¹) and geopotential height (gpm) in JJA.

We also calculate the correlation and partial correlation coefficients between spring SST indices and summer monsoon indices. As shown in columns 2 and 3 in Table 4, only the SEAM is significantly and negatively correlated with the IO SST indices. Similar to May, the partial correlation coefficients of SST indices and the SEAM are all weakened and insignificant. The partial correlation coefficients in columns 4 and 5 show that after eliminating the effect of the NIO SST, a significant positive correlation appears between the SIO index and WY index, suggesting that spring SIO warming is associated with the suppression of the large-scale Asian monsoon circulation. Furthermore, the significant positive partial correlation between the NIO SST and EAM (column 4) responds to the relationship of the NIO SST with the enhanced horizontal zonal-wind shear. In contrast, the SIO SST shows a significant negative correlation with the EAM (column 5), associated with the weakening of horizontal zonal-wind shear over East Asia, as seen above.

Thus, the spring warming in the IO, particularly in the SIO, is associated with the weakening of the large-scale ASM circulation during both the developing and developed monsoon stages. Second, with the northward shift of the subtropical westerly jet stream, the relationship of spring IO SST with summer SAM weakens compared with the monsoon development in May, while the link with the summer EAM strengthens. The warming of NIO (SIO) SST is associated with the strengthening (weakening) of the EAM. In May, the IO SST is closely associated with the development of the SAM, while during JJA it is mainly correlated with the intensity of the EAM. Finally, the spring IO SST is linked to both May SEAM and summer SEAM similarly, with the warming of spring IO SST often accompanied by a later establishment and weaker intensity of the SEAM from May to August.

Here, we further compare the roles of SST anomalies in the northern and southern IO, and explore possible causes of their differences in affecting Asian climate. As shown in Fig. 12a, in the warm NIO SST years, there are two warm anomalous centers to the north and south of the equator. In contrast, in the warm SIO SST years (Fig. 12b), warm SST anomalies are mainly concentrated to the south of the equator, with relatively smaller warm anomalies north of the equator. Next, we remove the years from the time series for warm SIO years when warming also occurs in the NIO to obtain a new time series, which is called the new SIO warm years. As shown in Fig. 12c, compared with the original SIO warm years, the new SIO warm years eliminate most of the NIO warm SST anomalies and preserve only the SIO warm SST anomalies. We then perform a composite analysis of precipitation and atmospheric circulation in both May and summer based on the new SIO warm years minus the original SIO cold years, respectively.

Figure 12.  Composite patterns of MAM SST (shading; K) for (a) NIO P1 and (b) SIO P1, and (c) SST based on new SIO warm years. Stippling denotes the 5% significance level.

After removing the NIO warm years, precipitation decreases over the Arabian Sea and Indo-China Peninsula in May. With the suppression of convection over the Arabian Sea, the atmospheric latent heat becomes weaker and the Matsuno–Gill response to the IO SST warming diminishes. Both the anomalous cold center at 500 hPa (Fig. 13b) and the anomalous cyclone at 200 hPa (Fig. 13c) over the Arabian Sea intensify. The strengthening upper-level cyclone inhibits the evolution of the upward motion in the southern branch of the SAM over the NIO (Wu et al., 2012; Wang et al., 2016, 2018), unfavorable for the development of the SAM and large-scale meridional monsoon circulation. The upper-level circulation response seen here is consistent with the atmospheric response to the cold bias of the NIO SST simulated in Wang et al. (2018) using the Weather Research and Forecasting model.

Figure 13.  Composite differences in (a) May precipitation (shading; mm day⁻¹) and 850-hPa wind (vector; m s⁻¹), (b)500-hPa air temperature (shading; K) and geopotential height in May (contour; gpm), and (c) 200-hPa geopotential height (shading; gpm) and wind (vector; m s⁻¹) based on new SIO warm years minus original SIO cold years. The red and blue contours denote the composite mean of 5880 gpm at 500 hPa in the new SIO warm years and the original SIO cold years, respectively. Stippling and red vector denote the 5% and 10% significance levels. (d–f) As in (a–c), but for JJA.

The summer circulation anomalies based on the new SIO warm years, similarly to those in May, also intensify significantly, especially the PJ-like pattern over the western North Pacific and Japan (Fig. 13e). The strengthening of the upper-level anomalous cyclone over the Sea of Japan is also accompanied by more significant weakening of the horizontal zonal-wind shear over East Asia (Fig. 13f), meaning a much weaker EAM. Such results are similarly and closely linked to the weakening of the Gill response over the IO in the Northern Hemisphere.

After removing the effect of warm SSTs from the NIO and leaving only the effect of warm SSTs from the SIO, the heat source of the Gill response shrinks to the south, resulting in a weakened or even suppressed Gill response in the Northern Hemisphere. This change results in the strengthening of the SIO SST-related atmospheric anomalies, validating our previous conclusion that the NIO and SIO SSTs are inversely related to both development and intensity of large-scale meridional monsoon circulations associated with the SAM and EAM.

In the present study, we focus on the relationships of boreal-spring IO SST anomalies with the development and intensity of the Asian summer monsoon. Following Yoo et al. (2006) and Li and Yang (2017), we divide the IO into four domains, and depict the similarities, differences, and relative importance of the links of SSTs in different domains (especially the NIO and SIO) with different components of the Asian summer monsoon. The SOM, an unsupervised artificial neural network analysis, is employed to extract the patterns of spring SST anomalies in different IO domains. For all domains, the important feature of spring SST anomalies is a uniform warming or cooling. The uniform warming/cooling mode of the SIO plays an important role in SST anomalies of the IO. Although we divide the IO into four domains, the SIO can well represent the BIO and TIO; thus, the comparison among various domains can be roughly simplified to the comparison between the northern and southern IO.

A spring warming in the IO, especially in the SIO, is closely linked to the weakening of the development and intensity of the large-scale meridional monsoon circulation; and this relationship is stronger for monsoon development in May. The warming of the NIO is only moderately associated with enhanced large-scale meridional monsoon circulation in summer. Furthermore, the SIO warming is associated with the weakening of SAM development, while the NIO warming enhances the SAM development. In contrast, for summer monsoon intensity, the link of the IO with the SAM is weakened, but that with the EAM is strengthened. Similarly, the warming of the SIO is associated with the weakening of the EAM, while that of the NIO has an opposite effect. Finally, the relationships of spring IO SST with SEAM development and intensity are particularly robust: the IO warming always suppresses the development and intensity of the regional monsoon.

The SIO warming increases the north–south temperature gradient across the IO, which enhances the Northern Hemisphere subtropical westerly jet stream in May, accompanied by anomalous upper-level cyclone and negative geopotential height over South Asia (Fig. 6c). These circulation anomalies are unfavorable to the development and enhancement of the SAH (Yang et al., 2004; Yoo et al., 2006). Through this pathway, the SIO warming is linked to the suppression of the development and strengthening of the Asian meridional monsoon circulation. On the other hand, the NIO warming in spring strengthens the heat source of the atmospheric Matsuno–Gill response, which expands northward. The enhanced heat source makes the Matsuno–Gill response more pronounced, especially for the Rossby wave over the Arabian Sea. This results in positive geopotential height anomalies over the Arabian Sea and Indian subcontinent (Fig. 6b), favoring the development of the SAH and SAM-related rising motions. These differences result in the inverse relationship between SIO and NIO SSTs in their links to the development and intensity of the SAM. The Rossby wave over the Arabian Sea links the IO to the EAM by forcing midlatitude wave trains. With the change in the subtropical westerly jet from May to summer, the connection between IO SST and SAM weakens, but that between IO SST and EAM increases. The warming of the NIO in spring is accompanied by an upper-level anomalous anticyclone over East Asia, which enhances the horizontal zonal-wind shear at 200 hPa in East Asia, implying an enhanced EAM. Against this, the warming of the spring SIO is associated with an anomalous upper-level cyclone over East Asia, which weakens the horizontal zonal-wind shear at 200 hPa, implying a weaker EAM. In addition, the relationship between IO SST anomalies and the SEAM, which is linked via the anomalous anticyclone over the western North Pacific, is robust. The presence of anomalous anticyclone over the western North Pacific suppresses the development of convection over Southeast Asia, resulting in the warm IO, which is often accompanied by a later and weaker establishment of the SEAM.

We also have analyzed the composite results of precipitation and atmospheric circulation based on the warm or cold years in each domain (figures omitted). It is found that the cold SST pattern of the NIO is most unfavorable for the development and enhancement of the SAM and EAM than the cold SST in the other domains. This difference in the relationships of the cold and warm SST patterns with the monsoon is an indication of the advantage that the SOM can provide in terms of identifying the asymmetric features between the positive and negative SST patterns.

In the present study, we find that the TIO and SIO SST patterns are respectively linked to highly consistent precipitation and atmospheric circulation anomalies. Nevertheless, the springtime TIO warming also exhibits specific characteristics that are closely associated with the maintenance of an anomalous anticyclone over the western North Pacific, resulting in heavy precipitation events in central China, such as in the years of 1983, 1998, 2016, and 2020 (Chen et al., 2021; Cai et al., 2022).

We would like to thank the three anonymous reviewers who provided helpful comments and suggestions for improving the overall quality of this article. The MiniSom package is provided by Vettigli (2018) and is available at https://github.com/JustGlowing/minisom/.