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.
Domain Warm year Cold year BIO 1983, 1987, 1988, 1991, 1998, 2003, 2005, 2010, 2016 1984, 1985, 1989, 1999, 2000, 2008, 2011, 2012, 2018 NIO 1988, 1991, 1998, 2010, 2016 1989, 1992, 1993, 2008, 2011, 2012, 2014 SIO 1983, 1987, 1988, 1998, 2005, 2010, 2014, 2015, 2016 1984, 1985, 1989, 1999, 2000, 2008, 2011, 2018 TIO 1983, 1987, 1988, 1991, 1998, 2003, 2005, 2010, 2016, 2020 1984, 1985, 1986, 1989, 1993, 1994, 1999, 2000, 2006, 2008, 2011, 2012, 2017, 2018, 2021
Table 1. Warm and cold years derived from the SOM patterns of MAM SST anomalies in different IO 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.
Index Definition WY Vertical shear of zonal wind between 850 and 200 hPa, U850 − U200, averaged over 5°–20°N, 40°–110°E SAM Vertical shear of meridional wind between 850 and 200 hPa, V850 − V200, averaged over 10°−30°N, 70°−110°E EAM Horizontal shear of zonal wind, U200_(40–50N,110–150E) − U200_ (25–35N, 110–150E) SEAM Horizontal shear of zonal wind, U850_(5−15N,90−130E) − U850_(22.5−32.5N,110−140E)
Table 2. Definitions of monsoon indices
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 (RNIO and RSIO) and partial correlation coefficients (PRNIO and PRSIO) 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.
RNIO RSIO PRNIO PRSIO WY −0.297 −0.527* −0.141 −0.473* SAM 0.132 −0.245 0.459* −0.495* EAM 0.262 0.269 0.102 0.120 SEAM −0.332* −0.359* −0.113 −0.183
Table 3. Correlation coefficients (RNIO and RSIO) and partial correlation coefficients (PRNIO and PRSIO) between MAM SST indices and monsoon indices in May. Values marked with * are statistically significant at the 5% significance level
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.
RNIO RSIO PRNIO PRSIO WY −0.007 −0.295 0.205 −0.346* SAM 0.133 −0.046 0.240 −0.207 EAM 0.061 −0.215 0.319* −0.374* SEAM −0.344* −0.422* −0.063 −0.267
Table 4. As in Table 3, but for monsoon indices in JJA
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.
|Domain||Warm year||Cold year|
|BIO||1983, 1987, 1988, 1991, 1998, 2003, 2005, 2010, 2016||1984, 1985, 1989, 1999, 2000, 2008, 2011, 2012, 2018|
|NIO||1988, 1991, 1998, 2010, 2016||1989, 1992, 1993, 2008, 2011, 2012, 2014|
|SIO||1983, 1987, 1988, 1998, 2005, 2010, 2014, 2015, 2016||1984, 1985, 1989, 1999, 2000, 2008, 2011, 2018|
|TIO||1983, 1987, 1988, 1991, 1998, 2003, 2005, 2010, 2016, 2020||1984, 1985, 1986, 1989, 1993, 1994, 1999, 2000, 2006, 2008, 2011, 2012, 2017, 2018, 2021|