1. Introduction
The Indian summer monsoon and East Asian summer monsoon, the two submonsoon components of the Asian summer monsoon, are physically connected (Guo and Wang 1988; Hu and Nitta 1996; Wang et al. 2001; Wu 2002; Lin et al. 2017; Ha et al. 2018). Under the influence of some common factors, such as the circumglobal teleconnection pattern, South Asian high, northwest Pacific subtropical high (NWPSH), and El Niño–Southern Oscillation (ENSO), the Indian summer precipitation and North China summer precipitation vary coherently (Wang and Fan 1999; Wu 2002; Wu 2017; Ding and Wang 2005; Hu et al. 2005; Wei et al. 2015). Guo and Wang (1988) pointed out the significant positive correlation between summer monsoon precipitation in India and North China. The positive correlation has also been confirmed by empirical orthogonal function (EOF) and composite analyses (Kripalani and Singh 1993; Liu and Ding 2008; Lin et al. 2016). Hu and Nitta (1996) and Lin et al. (2016) further noted that the positive correlation is mainly on the interannual time scale.
Among these common factors, ENSO has noteworthy impacts on both Indian and North China summer precipitation and even on global precipitation (Wang et al. 2001; Wu et al. 2003; Hu et al. 2020a). For instance, the Indian summer precipitation and North China summer precipitation are statistically above average during La Niña years, as the La Niña sea surface temperature anomalies (SSTAs) can enhance the Indian and North China summer precipitation by strengthening the NWPSH and the Indo-Pacific Walker cell (Wang et al. 2013; Feng et al. 2014; Huang et al. 2018; Liu and Huang 2019; Yu et al. 2021). Whereas the weak water vapor transportation associated with the weak Indian summer monsoon in El Niño years reduces precipitation in North China (Zhang et al. 1999; Feng and Hu 2004). Later studies showed that when the ENSO signal is removed, the relationship between the Indian and North China summer precipitation is significantly reduced (Hu et al. 2005; Wu 2017). However, in addition to ENSO, Indian and North China summer precipitation are also affected by other factors (Wang et al. 2001), such as SSTAs in the Indian Ocean (Ashok et al. 2001, 2004; Feng and Hu 2004; Wu and Kirtman 2004). For example, the relationship between ENSO and Indian summer precipitation is negatively correlated with tropical Indian Ocean (TIO) SSTAs (Yu et al. 2021), and Indian Ocean dipole (IOD) will counteract the impacts of El Niño on the Indian summer precipitation by adjusting water vapor transportation and Walker cell over India (Ashok et al. 2001, 2004).
However, these boundary forces also experience interdecadal variations. For example, ENSO features have changed around 1977/78, and the warming tendency of Indian Ocean has been observed since mid-1970s (Wu 2002; Roxy et al. 2020). These interdecadal variations may be linked to the interdecadal variations of the summer precipitation connection between India and North China. Specifically, since the twentieth century, the precipitation connections were strong from the early 1950s to the mid-1970s, but weak before the 1950s and from the mid-1970s to the late 1990s (Kripalani and Kulkarni 2001; Wu 2002; Wu and Wang 2002; Feng and Hu 2004). Ashok et al. (2001, 2004) argued that the concurrent positive IOD during El Niño after the 1980s counteracted the impacts of El Niño on the interannual variations of the Indian summer precipitation by adjusting the influences of ENSO on Indo-Pacific Walker cell (Wu and Wang 2002; Wang et al. 2012). The reduction of Indian summer precipitation interannual variations also weaken the circumglobal teleconnection pattern and East Asia–northwest Pacific anticyclone, and then decreases the water vapor transportation to North China, resulting in a weaker relationship between Indian and North China summer precipitation (Wu 2002; Wu and Wang 2002).
What can be concluded from previous studies that the Indo-Pacific Walker cell, NWPSH play important bridge roles in the process of ENSO and Indian Ocean SSTAs affecting the connection between Indian and North China summer precipitation. However, the NWPSH experienced a decadal change around the late 1990s (Huang et al. 2018). It seems that the TIO dominated NWPSH before the late 1990s, and ENSO, changing from eastern type to central type, increasingly affected NWPSH after that (Yu et al. 2010; Wang et al. 2013; Liu et al. 2017; Huang et al. 2018). In addition, the relationship among ENSO and TIO and the Indo-Pacific Walker cell also experienced a decadal transition in the late 1990s (Huang et al. 2018; Liu et al. 2021). The critical factors, influencing the connection between Indian and North China summer precipitation, all experienced interdecadal changes in the late 1990s, but whether the relationship between Indian and North China summer precipitation changed is still a puzzle. If so, what role do ENSO and Indian Ocean play? Understanding these two issues will help us further understand Asian summer precipitation and improve short-term climate prediction. These are the focuses of this work.
The remainder of the article is as follows. Section 2 illustrates the datasets, methods, and some definitions applied in this study. Section 3 introduces the interdecadal variations of precipitation teleconnection and relevant circulation anomalies. A further investigation of the impacts of ENSO and the regulatory roles of SSTAs over the Indian Ocean in sections 4 and 5, respectively. Section 6 presents the discussion and summary.
2. Dataset and method
a. Dataset
The precipitation data used in this paper includes 1) the precipitation data at 2374 stations in China obtained from the national daily precipitation dataset developed by China Meteorological Administration (CMA), and 2) monthly all-India summer precipitation data for the period 1979–2016 obtained from the Indian Institute of Tropical Meteorology (IITM; Parthasarathy et al. 1995), and 3) the global precipitation data derived by the Global Precipitation Climatology Project (GPCP) (Adler et al. 2003). The SST data are from the United Kingdom Met Office Hadley Center Global Sea Ice and Sea Surface Temperature Dataset during 1979–2019 (HadISST1; Rayner et al. 2003). The monthly mean meteorological fields, including geopotential height, horizontal and vertical wind components, sea surface pressure, air temperature, and relative humidity during the period of 1979–2019 are retrieved from the National Centers for Environment Prediction–Department of Energy (NCEP–DOE) Atmospheric Model Intercomparison Project II reanalysis dataset (Kanamitsu et al. 2002). The 21 models’ historical simulation datasets (1979–2014) of the Atmospheric Model Intercomparison Project (AMIP) of phase 6 of the Coupled Model Intercomparison Project (CMIP6).
b. Method
In this study, ENSO is represented by the Niño-3.4 area’s (5°S–5°N, 120°–170°W) SSTAs and TIO SSTAs index is calculated over 20°S–20°N, 50°–90°E and IOD SSTAs index is the difference between the tropical western Indian Ocean (10°S–10°N, 50°–70°E) and the tropical southeastern Indian Ocean (10°S–0°, 90°–110°E) using HadISST1. The northern part of Eastern China (NEC; because the area studied in this paper is slightly southerly, it is called the northern part of Eastern China, not North China) summer precipitation index is the regional average precipitation of CMA data over 30°–40°N, 104°–120°E from 1979 to 2019. Indian summer precipitation index is defined by the regional average precipitation of GPCP data over 10°–30°N, 70°–90°E from 2017 to 2019, while the IITM data are used before 2017. The NWPSH is defined by summer mean 850 hPa geopotential height anomalies averaged over the maximum interannual variability center (15°–25°N, 115°–150°E) according to Wang et al. (2013). We also discuss the impacts of Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO) on this interdecadal change. The AMO index is defined following Enfield et al. (2001), as averaged North Atlantic basin SSTAs weighted by the cosine of latitude, from the equator to 60°N and from the east coast of North America (80°W) to 0° longitude. (The PDO index was downloaded from https://oceanview.pfeg.noaa.gov/erddap/tabledap/cciea_OC_PDO.htmlTable.)
The time period selected in this paper is the summer (June–August) of the Northern Hemisphere from 1979 to 2019. The anomalies are related to the climatology in 1979–2019. Regression analysis is used and statistical significance is computed using the two-tailed Student’s t test.
3. Interdecadal variations of precipitation teleconnection
The 21-yr sliding correlation between the Indian and NEC summer precipitation shows an interdecadal enhancement trend, and only the correlation between 1999 and 2019 passes the 95% confidence level, with correlation coefficients being −0.03 and 0.44 before and after 1999 (Fig. 1e). Similarly, taking 1999 as the cutoff point of two subperiods, the correlation coefficient of ENSO–TIO reaches the maximum value in the latter period. We also do the same analysis as in Fig. 1e, but first linearly remove the ENSO effect. The 21-yr sliding correlation of the Indian summer precipitation(R − ENSO) and NEC summer precipitation(R − ENSO) also has an interdecadal growth trend (Fig. 1f), but the amplitude is much smaller than the 21-yr sliding correlation of the original series (Fig. 1e). The correlation coefficient between them is −0.12 (0.31) in 1979–98 (PI) [1999–2019 (PII)], and do not pass the confidence level. This indicates that ENSO plays an important role in maintaining the positive correlation between the Indian and NEC summer precipitation in PII. To analyze the interdecadal changes of the relationship between the Indian and NEC summer precipitation, the data period will be divided into two subperiods: PI and PII. The regressions are positive and mostly significant for the NEC in PII (Fig. 1b), but not in PI (Fig. 1a). The regressions in India are rarely significant in PI (Fig. 1c) but significant in most areas in PII (Fig. 1d).
NEC summer precipitation regressed by Indian summer precipitation in (a) PI and (b) PII. (c),(d) As in (a) and (b), but for Indian summer precipitation regressed by the NEC summer precipitation (hatched regions indicate the 90% confidence level, purple boxes indicate the locations of India and the NEC). (e) The interannual variations of Indian (red dashed line) and the NEC summer precipitation (blue dashed line), and the 21-yr sliding correlation (purple solid line) between them. The correlation coefficients for each of the two subperiods (separated by a gray vertical dashed line) are indicated. (f) As in (e), but the effect of ENSO is linearly removed.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
In PI, the Indian summer precipitation is related to the local cyclone and water vapor convergence (Fig. 2a), and the water vapor transportation from the Arabian Sea is the most important, followed by water vapor from the east (Table 1). The water vapor transportation over the NEC is mainly characterized by the input from the south border and the output from the north border (Fig. 2a). The revenue and expenditure of water vapor are roughly offset and accompanied with weak local divergence over the NEC (Fig. 2a; Table 1). The incongruous variations of the water vapor transportation and convergence of the Indian and NEC are consistent with the weak summer precipitation correlation between the two regions during PI (correlation coefficient is −0.03). In PII, the Indian summer precipitation is similarly accompanied with the local cyclone and water convergence. The water vapor transportation weakens in the west and strengthens in the east of India (Table 1). There is not only significant convergence of water vapor but also great net water vapor transportation over the NEC, because the water vapor inputting in the south border increases significantly and the outputting in the north border decreases (Fig. 2b; Table 1). As a result of the net water vapor transportation and convergence, the Indian and NEC summer precipitation increases simultaneously in PII, resulting in the enhancement of summer precipitation teleconnection between India and the NEC (correlation coefficient is 0.44 and passes 95% confidence level).
Water vapor fluxes [vectors, 102 kg (m s)−1] and convergence/divergence [shading, 10−5 kg (m2 s)−1] at 1000–300 hPa regressed by Indian summer precipitation in (a) PI and (b) PII. The blue vectors and hatched regions indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Four borders and net water vapor transportation budget (108 kg s−1) over India and the NEC regressed by Indian summer precipitation during the two subperiods. The two regions are marked in Figs. 1 and 2 (purple boxes).
What causes the interdecadal changes of the precipitation teleconnection and relevant water vapor transportation? We can find some clues from the low-level (850 hPa) wind, geopotential height, and surface pressure anomalies related to precipitation (Fig. 3). According to regressions in PI, significantly negative geopotential height, negative surface pressure and cyclone anomalies associated with Indian summer precipitation are present in the southwestern Asia and the southeastern Japan (Figs. 3a,c). These anomalies in the southeastern Japan weaken the water vapor transportation from northwest Pacific to the NEC (Fig. 2a). In PII, the circulation anomalies in southwestern Asia are basically similar to those in PI except for the significant changes over northwest Pacific. The negative geopotential height, negative surface pressure and cyclone anomalies in the southeastern Japan are weakened. Instead, significant positive geopotential height, positive surface pressure and anticyclone anomalies appear in northwest Pacific (Figs. 3b,d). The enhanced NWPSH makes the easterly (southerly) winds in the south (west) of the anticyclone extend westward (northward) to India (the NEC). The strengthened surface pressure contrast, with significant positive anomalies over the ocean and negative anomalies over the land, results in significant southwesterly winds over the NEC (Figs. 3b,d). Thus, more water vapor in northwest Pacific is transported to both India and the NEC, which leads to coherent precipitation variations in the two regions.
850 hPa winds (vectors, m s−1) and the geopotential height (GHT) anomalies (shading, m) regressed by Indian summer precipitation in (a) PI and (b) PII. (c),(d) As in (a) and (b), but for the surface pressure (hPa). The blue vectors and hatched regions indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Why does the NWPSH become stronger in PII? Previous studies have pointed out that ENSO can significantly affect the NWPSH and Indo-Pacific Walker cell and further modulate the summer precipitation in North China and India (Zhang et al. 1996; Wang et al. 2000, 2013; Huang et al. 2018; Yu et al. 2021). Around the late 1990s, ENSO underwent an interdecadal shift (Hu et al. 2020b). Specifically, in addition to the frequency increase and amplitude decrease, the central Pacific–type ENSO occurred more often and the eastern Pacific type less often, the atmosphere–ocean coupling associated with ENSO shifted westward, and a La Niña–like pattern was observed in the tropical Pacific mean state changes (Li et al. 2019; Hu et al. 2020b). Meanwhile, the impacts of ENSO on NWPSH and Indo-Pacific Walker cell seem to have increased (Wang et al. 2013; Liu et al. 2017; Huang et al. 2018; Li et al. 2019; Hu et al. 2020b). Furthermore, compared with other tropical oceans, the tropical Indian Ocean has the strongest warming tendency since the mid-1970s (Roxy et al. 2020; Huang et al. 2021). These interdecadal variations of ENSO and the tropical Indian Ocean may be the sources for the enhanced connection between the Indian and NEC summer precipitation.
4. The impacts of ENSO
The connection of ENSO with the Indian and NEC summer precipitation also experienced interdecadal variations. Compared with PI, the La Niña SSTAs related to the both Indian and NEC summer precipitation in PII are wider and stronger (Figs. 4a–d), and the correlation between Niño-3.4 index and Indian summer precipitation (the NEC summer precipitation) also increases, with the correlation coefficients being −0.27 (−0.30) in PI and −0.50 (−0.40) passing the 95% (90%) confidence level in PII (Figs. 4e,f). To further validate the precipitation-related SSTAs critical regions, we also calculate the correlation coefficients between the Modoki and Niño-4 indices with the Indian/NEC summer precipitation, respectively. The results show that the correlation coefficients of the Indian, the NEC summer precipitation with Niño-3.4 index are the highest, because precipitation related SSTAs are mainly located in the Niño-3.4 area (Figs. 4c,d). The ENSO influences the Indian and NEC summer precipitation mainly through NWPSH and Indo-Pacific Walker cell (Li and Zhu 2010; Wang et al. 2001, 2013; Huang et al. 2018). The correlation between Niño-3.4 index and NWPSH index increases from −0.0 in PI to −0.41 (passing the 90% confidence level; Fig. 5g) in PII. Here, the La Niña is taken as the example for discussion because of the negative correlation between ENSO and Indian summer precipitation (the NEC summer precipitation). The regressions of the wind, geopotential height, surface pressure, and water vapor flux anomalies onto Niño-3.4 index (multiplied by −1; Fig. 5) are similar to the regressions onto Indian summer precipitation (Figs. 2 and 3). The negative SSTAs and suppressed convection in the central-eastern Pacific strengthen the NWPSH by arousing atmospheric Rossby wave response. Meanwhile, the enhanced Indo-Pacific Walker cell accelerates equatorial easterlies over the northwest Pacific, which generates off-equatorial anticyclonic shear vorticity and strengthens NWPSH (Chung et al. 2011; Wang et al. 2013). Due to enhanced La Niña SSTAs, the westward (northward) extension of the easterly (southerly) wind anomalies on the southern (western) side of the anticyclone strengthen in PII (Fig. 5b), and the water vapor transportation to India and the NEC increases, resulting in a closer connection of precipitation between the two regions in PII (Fig. 5f).
SSTAs regressed by Indian summer precipitation in the (a) PI and (c) PII. (e) The interannual variations of Indian summer precipitation (red dashed line) and Niño-3.4 index (blue dashed line), and the 21-yr sliding correlation (purple solid line) between them. The correlation coefficients for each of the two subperiods (separated by a gray vertical dashed line) are indicated. (b),(d),(f) As in (a), (c), and (e), but for the NEC summer precipitation. The hatched regions indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
850 hPa winds (vectors, m s−1) and the GHT anomalies (shading, m) regressed by Niño-3.4 index (multiplied by −1) in (a) PI and (b) PII. (c),(d) As in (a) and (b), but for the surface pressure (hPa). (e),(f) As in (a) and (b), but for water vapor fluxes [vectors, 102 kg (m s)−1] and convergence/divergence [shading, 10−5 kg (m2 s)−1] at 1000–300 hPa. (g) The interannual variations of Niño-3.4 index (red dashed line) and NWPSH index (blue dashed line), and the 21-yr sliding correlation (purple solid line) between them, the correlation coefficients for each of the two subperiods (separated by a gray vertical dashed line). The blue vectors and hatched regions indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
The La Niña–like mean state changes with negative SSTAs in central-eastern Pacific and positive SSTAs in the western tropical Pacific in PII also favor to increase the Indian summer monsoon precipitation through enhanced Indo-Pacific Walker cell (Wang et al. 2001; Huang et al. 2018). Compared with PI, the lower-layer divergence (upper-layer convergence) over the central-eastern Pacific is stronger, and the strengthened lower-layer convergence (upper-layer divergence) over the Maritime Continent expands northwestward to India in PII (Figs. 6a–d). That enhances the impacts of the tropical Pacific on Indian summer precipitation. Interestingly, there is seems little connection between Indian summer precipitation and the Indo-Pacific Walker cell in PI (Figs. 7a,c), but the connection becomes particularly prominent in PII (Figs. 7b,d).
(a) 850 and (c) 200 hPa velocity potential (VP) (shading, 106 m2 s−1) and divergent wind (vectors, 10−1 m s−1) anomalies regressed by the Niño-3.4 index (multiplied by −1) in PI. (b),(d) As in (a) and (c), but for PII. The blue vectors and hatched regions indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
As in Fig. 6, but regressed by the Indian summer precipitation.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
5. The regulatory roles of SSTAs over the TIO
The SSTAs in the Indian Ocean can also regulate the effects of ENSO on Indian summer precipitation (Ashok et al. 2004; Feng and Hu 2004; Wu and Kirtman 2004; Li et al. 2015; Yu et al. 2021), and it is unclear if such regulatory roles experience interdecadal variations, which may associate with the interdecadal relationship variations between the Indian and NEC summer precipitation. ENSO is mainly related to IOD in PI, while it is related to the TIO SSTAs during PII. The correlations between summer Niño-3.4 index and TIO (IOD) index are stronger (weaker) in PII than that in PI, and the correlation coefficients increase (decrease) from 0.27 (0.57) to 0.54 (0.23) (Figs. 8a–d). The interdecadal variations of connections between ENSO and TIO (IOD) may be the factors leading to the enhancement of the connection between the Indian and NEC summer precipitation in PII.
SSTAs regressed by Niño-3.4 index (multiplied by −1) in (a) PI and (b) PII. (c) The interannual variations of Niño-3.4 index (red dashed line) and IOD index (blue dashed line), and the 21-yr sliding correlation (purple solid line) between them, the correlation coefficients for each of the two subperiods (separated by a gray vertical dashed lines). (d) As in (c), but for the Niño-3.4 index (red dashed line) and TIO index (blue dashed line). The hatched regions indicate the 90% confidence level, and purple boxes indicate the locations of IOD and TIO.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Previous studies have shown that the convergence and divergence anomalies caused by IOD can significantly regulate the impacts of ENSO on Indian summer precipitation (Ashok et al. 2001, 2004; Zheng et al. 2014; Crétat et al. 2017; Ham et al. 2017). First, it regulates the Walker cell and then affects the water vapor transportation to India and the vertical movements in India. In PI, the negative IOD can produce the positive (negative) velocity potential and convergent wind (divergent wind) anomalies at 850 hPa over the Maritime Continent (tropical west Indian Ocean), while there are mainly negative velocity potential anomalies at 200 hPa over the Maritime Continent (Figs. 9a,c). In addition, the negative IOD will produce westerly wind anomalies over equatorial area, and easterly wind anomalies over India (Ashok et al. 2001), which hinder water vapor transportation from the Arabian Sea to India and suppress Indian summer precipitation (Fig. 9e). However, in PII, the negative IOD is weak and cannot exert sufficient influences on the Walker cell in the Indian Ocean, only causing weak velocity potential and convergent (or divergent) wind anomalies, which eventually lead to less effects on Indian summer precipitation (Figs. 9b,d,f).
(a)–(d) As in Fig. 6 and (e),(f) as in Fig. 2, but regressed by the IOD index (multiplied by −1).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
To verify the regulatory roles of IOD, we further study whether the influences of ENSO on Indo-Pacific circulations will change after linearly removing the influences of IOD. When the influences of IOD are removed, the positive velocity potential and the convergent wind anomalies in the lower layer caused by La Niña move northwestward to India, and their intensities are also enhanced (Fig. 10a). This phenomenon is more obvious at the high level, with the negative velocity potential and divergent wind anomaly center moving westward from the Marine Continent to 60°E (Fig. 10b). This may be related to the velocity potential anomaly center caused by negative IOD is located on the Marine Continent (Fig. 9a,c). Therefore, after removing the influences of IOD, La Niña SSTAs can make the Indo-Pacific Walker cell move westward and induce more obvious upward movements over India (Fig. 10c,d), resulting in an increase of correlation coefficient between Niño-3.4 index and Indian summer precipitation from −0.28 to −0.40 (passing the 90% confidence level) in PI (Fig. 11). However, in PII, the influences of negative IOD on the circulations in the Indian Ocean are weakened (Figs. 9b,d,f), and the correlation between Niño-3.4 index and IOD is also weakened (Fig. 8c). The La Niña SSTAs are stronger during PII and have more obvious influences on the Indo-Pacific circulations and Indian summer precipitation (Figs. 4–6). Therefore, when the influences of IOD are removed, the correlation between Niño-3.4 index and Indian summer precipitation changes little in PII (Fig. 11).
(a) 850 and (b) 200 hPa VP (shading, 106 m2 s−1) and divergent wind (vectors, 10−1 m s−1) anomalies regressed by the Niño-3.4(R − IOD) index (multiplied by −1) in PI. (c) The vertical velocity (shading, 10−2 Pa s−1) and latitudinal–vertical velocity (vectors, m s−1) regressed by the Niño-3.4 index (multiplied by −1) averaged over 10°–30°N in PI. (d) As in (c), but regressed by the Niño-3.4(R − IOD) index (multiplied by −1). The blue vectors and hatched regions and purple lines indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
(a) Correlation coefficients between Niño-3.4 index and Indian summer precipitation index (orange bar), Niño-3.4(R − IOD) index and Indian summer precipitation index (red bar), Niño-3.4(R − TIO) index and Indian summer precipitation index (blue bar) in PI. (b) As in (a), but for PII. (c) Correlation coefficients between the Niño-3.4 index and the NEC summer precipitation index (orange bar), Niño-3.4(R − IOD) index and the NEC summer precipitation index (red bar), and Niño-3.4(R − TIO) index and the NEC summer precipitation index (blue bar) in PI. (d) As in (c), but for PII. The red stars indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
The above mainly introduces the interdecadal changes of the regulatory roles of IOD on ENSO, but the correlation between ENSO and TIO is significantly enhanced in PII (Figs. 8b,d). Xie et al. (2009) demonstrated that TIO SSTAs can induce NWPSH anomalies by stimulating Kelvin wave. However, when TIO SSTAs and ENSO appear simultaneously, it is still unclear whether TIO SSTAs will regulate the influences of ENSO on Indo-Pacific circulations. In PI, TIO negative SSTAs can induce anomalous anticyclone in the Indian Ocean, enhancing the water vapor transportation to India, but also form significant cyclone in the northwest Pacific, weakening the water vapor transportation to the NEC, which are not conducive to the formation of precipitation teleconnection between India and the NEC (Fig. 12a). However, in PII, TIO negative SSTAs can only induce anomalous anticyclone in the Indian Ocean, while they have few influences on northwest Pacific circulations (Fig. 12b). When we remove the effects of TIO SSTAs, the circulation anomalies in the Indo-Pacific Ocean induced by La Niña SSTAs are basically consistent with those without removing the effects of TIO SSTAs (Figs. 12c,d versus Figs. 5a,b). These may be related to the completely opposite interdecadal changes in the relationship between TIO SSTAs and NWPSH and the relationship between Niño-3.4 index and TIO SSTAs. In PI, the correlation between TIO SSTAs and NWPSH is strong (0.62 and passing the 99.5% confidence level; Fig. 12e), but the correlation between Niño-3.4 index and TIO SSTAs is weak (0.27; Fig. 8d). In PII, the correlation between TIO SSTAs and NWPSH is weak (0.23; Fig. 12e), but the correlation between Niño-3.4 index and TIO SSTAs is strong (0.54 and passing the 98% confidence level; Fig. 8d). Therefore, no matter whether the influences of TIO SSTAs are removed, the relationships between Niño-3.4 index and Indian summer precipitation and between Niño-3.4 index and the NEC summer precipitation have almost no change in two subperiods (Fig. 11).
850 hPa winds (vectors, m s−1) and the GHT anomalies (shading, m) regressed by TIO index (multiplied by −1) in (a) PI and (b) PII. (c),(d) As in (a) and (b), but regressed by Niño-3.4(R − TIO) index (multiplied by −1). (e) The interannual variations of TIO SSTAs (red dashed line) and NWPSH index (blue dashed line), and the 21-yr sliding correlation (purple solid line) between them, the correlation coefficients for each of the two subperiods (separated by a gray vertical dashed line) are indicated. The blue vectors and hatched regions indicate the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Why does the relationship between ENSO and TIO (IOD) strengthen (weaken) in the latter period? In PI, the ENSO-related positive SSTAs are located on west of 90°E, and only parts of the western Indian Ocean coast pass the 90% confidence level. The east Indian Ocean (east of 90°E) are significant negative SSTAs (Fig. 8a). In PII, the positive SSTAs extend eastward to 100°E, accompanied with the anomaly center expanding eastward into the central Indian Ocean (Fig. 8b). In terms of the spatial distributions of positive and negative anomalies, they are basically the same in two periods, but the ranges and intensity of positive (negative) SSTAs in the later period are enhanced (reduced). This is mainly related to the interdecadal enhancement of the Indian Ocean basin mode (0.6 decade−1, passing the 99.9% confidence level) and the interdecadal weakening of the IOD mode (Figs. 13a,b). Therefore, it is very likely that this is mainly related to the changes of the Indian Ocean itself. And why the latter period NWPSH is mainly affected by ENSO rather than TIO? The prior winter ENSO–summer Indian Ocean coupling is strong and has a significant impact on NWPSH in PI (Xie et al. 2009; Li et al. 2021b). Therefore, NWPSH mainly shows the characteristics of EOF1, which is closely related to the summer positive SSTAs in the Indian Ocean. However, in PII, the coupling is weak between the prior winter ENSO and summer Indian Ocean, and NWPSH mainly presents the characteristics of EOF2, which is related to the summer negative SSTAs in the central Pacific (Huang et al. 2018).
(a) EOF1 and PC1 and (b) EOF2 and PC2 of the tropical Indian Ocean during 1979–2019. The green lines represent linear trends for PC1 and PC2 in (a) and (b).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
To further validate this interdecadal variation and study whether it is symmetric in the El Niño and La Niña backgrounds, we select the 2015 (1985) year in which the ENSO, TIO and IOD indices are all greater (less) than 1 (−1) standard deviation for analysis (Fig. 14i).
(a) The SSTAs, (c) the water vapor fluxes at 1000–300 hPa [vectors, 102 kg (m s)−1] and precipitation anomalies (shading, mm day−1), (e) the 850 hPa GHT (shading, m) anomalies, and (g) the 850 hPa VP (shading, 106 m2 s−1) and divergent winds (vectors, 10−1 m s−1) anomalies in 1985. (b),(d),(f),(h) As in (a), (c), (e), and (g), but for 2015. (i) The standardized Niño-3.4 (blue solid line), TIO (purple solid line), and IOD (red solid line) indices, and the gray dashed lines represent one standard deviation; the green dashed lines represent 1985 and 2015.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Although the central-eastern Pacific are the significant negative SSTAs in 1985 (Fig. 14a), there is cyclonic water vapor circulation over the northwest Pacific, corresponding less water vapor transportation to the NEC (Fig. 14c). This is related to that the remarkable negative SSTAs in the Indian Ocean can induce negative geopotential height anomalies over the northwest Pacific (Fig. 14e), because the NWPSH is mainly affected by the Indian Ocean in PI. In addition, IOD has an important influence on Walker cell in PI, so the rising branch of Walker cell moves eastward to the east of Marine continent, which weakens the influence of Walker cell on Indian summer precipitation (Fig. 14g). This further confirms the above conclusion: the NWPSH is mainly influenced by the Indian Ocean in PI, and IOD could significantly regulate the ascending branch of Walker cell (Figs. 9 and 12).
In 2015, the central-eastern Pacific and the Indian Ocean are marked positive SSTAs in summer (Fig. 14b), and weak anticyclonic (cyclonic) water vapor circulation is over the South China Sea and Philippines (North China to Japan). The NEC is located on the west side of the cyclonic water vapor circulation and affected by the dry and cold north winds, and India is affected by the anticyclonic water vapor circulation, precipitation is less in both regions (Fig. 14d). It is worth noting that there are no significantly negative geopotential height anomalies over the northwest Pacific, which indicates that El Niño SSTAs can weak the NWPSH (Fig. 14f) but the effects of El Niño and La Niña on NWPSH are asymmetrical. Due to IOD has weak influences on Walker cell, so the sinking branch of Walker cell can affect India (Fig. 14h). The extreme case also shows that India–Pacific circulations mainly influenced by ENSO in PII (Figs. 5 and 12).
We also validated the conclusions of this article using 21 models (1979–2014, Table 2) of CMIP6. In PI, the average correlation coefficient between the Indian and the NEC summer precipitation for multiple models is 0.18, and the average correlation coefficient between ENSO and IOD is 0.40 (passing the 90% confidence level). The more the models can simulate the positive correlation between ENSO and IOD, the lower the correlation coefficients between the Indian and the NEC summer precipitation, the correlation coefficient between the correlation coefficients of precipitation (y axis) and the correlation coefficients of SSTAs (x axis) in different models is −0.49, passing the 95% confidence level (Fig. 15a). The IOD can cause the Walker cell to move eastward, which is confirmed by the differences of 850 hPa velocity potential and divergent winds regressed by Niño-3.4 index (multiplied by −1) between models with correlation coefficients of ENSO–IOD passing the 90% confidence level and residual models (Fig. 15b). This fully proves that TIO and IOD weaken the effects of ENSO on the India–Pacific circulations, and the Indian and NEC summer precipitation in the PI.
21 models and their names.
(a) Scatterplot of the correlation coefficients between the Indian and NEC summer precipitation (y axis, average correlation coefficient), and the correlation coefficients between ENSO and IOD (x axis, average correlation coefficient and confidence level) of different models in the PI. (b) The differences of 850 hPa VP (shading, 106 m2 s−1) and divergent winds (vectors, 10−1 m s−1) regressed by Niño-3.4 (multiplied by −1) index between models with correlation coefficient of ENSO–IOD passing the 90% confidence level and residual models in the PI. The hatched regions and blue vectors indicate the 90% confidence level in (b).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Although the models can simulate the positive correlation between ENSO and TIO (0.43, passing the 90% confidence level), they cannot simulate the positive correlation between the Indian and NEC summer precipitation (0.14, less than 0.18 in PI) in PII (Fig. 16a). This is because the models underestimate the effects of ENSO on NWPSH and overestimate the effects of TIO on NWPSH (Fig. 16b). Therefore, we composite the Indian, the NEC summer precipitation and TIO anomalies corresponding to the La Niña background (Niño-3.4 index is less than −0.5 standard deviation, NWPSH index is greater than 0.5 standard deviation) and El Niño background (Niño-3.4 is greater than 0.5 standard deviation, NWPSH is less than −0.5 standard deviation), as well as the differences between these two backgrounds (Fig. 16c). In the La Niña background, Indian summer precipitation shows a significant positive anomaly, the NEC summer precipitation is also positive but weak, which is related to the weak positive anomaly of TIO (TIO is not as negative anomaly as Niño-3.4 index). In the El Niño background, Niño-3.4 and TIO indices both show significantly positive anomalies, accordingly, the Indian and NEC summer precipitation both present significantly negative anomalies. The composited differences between the two backgrounds further demonstrate that the Indian and NEC summer precipitation changes consistently only when ENSO and TIO (NWPSH) have the same (opposite) anomalies. In particular, we show the differences of 850 hPa wind and the geopotential height fields between the two backgrounds. The results show that a significant center of geopotential height anomalies is distributed over the northwest Pacific, the south winds on the west side of the anticyclone transport water vapor to the NEC, and the east winds on the south side transport water vapor to India and a large amount of southwest water vapor flow to India. These circulations and water vapor conditions jointly cause rich precipitation in both places (Fig. 16d).
(a) Scatterplot of the correlation coefficients between the Indian and NEC summer precipitation (y axis, average correlation coefficient), and the correlation coefficients between ENSO and TIO (x axis, average correlation coefficient and confidence level) of different models in the PII. (b) Correlation coefficients between ENSO and NWPSH (purple solid line, purple number is the average correlation coefficient), TIO, and NWPSH (red bar, red number is the average correlation coefficient) of different models in the PII. (c) Anomalies of Indian summer precipitation (red bar), the NEC summer precipitation (orange bar), and TIO (blue bar) when Niño-3.4 (green bar) is less than −0.5 and NWPSH (yellow bar) is greater than 0.5 for a La Niña background and an El Niño background, as well as the differences between La Niña background and El Niño background in the PII. (d) The 850 hPa winds (vectors, m s−1) and the geopotential height (shading, m) differences between La Niña background and El Niño background in the PII. The red stars indicate the 98% confidence level in (c). The hatched regions and blue vectors indicate the 90% confidence level in (d).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
6. Discussion and summary
In this section, we discuss the impacts of PDO and AMO on this interdecadal change. Compositing SST differences between the two periods, the North Pacific, northwest Pacific, and the entire Atlantic has significantly positive SSTAs, but the central-eastern Pacific has negative SSTAs (Fig. 17a), which correspond to the positive phase of AMO and the negative phase of PDO. The intersection points of the interdecadal enhancement of the AMO index and the interdecadal weakening of the PDO index is exactly around 1999 (Fig. 17b). Not only the negative phase of PDO can enhance the negative SSTAs in the central-eastern Pacific, but also the positive SSTAs in the tropical Atlantic can further aggravate the negative SSTAs in the central-eastern Pacific by overturning the circulation (Ham et al. 2013).
(a) The SST differences between 1999–2019 and 1979–98. The hatched regions indicate the 90% confidence level. (b) AMO index (red dashed line, the red solid line represents the 21-yr sliding average) and PDO index (purple dashed line, the purple solid line represents the 21-yr sliding average).
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
This study revealed the interdecadal enhancement of the summer precipitation teleconnection between India and the NEC after 1999 and examined the associated physical mechanisms. During 1979–98, the La Niña SSTAs have weak effects on NWPSH and Indo-Pacific Walker cell, weakening the water vapor transportation to India and the NEC. In addition, the negative IOD SSTAs further counteract the effects of the ENSO on Indian summer precipitation by inducing the positive (negative) velocity potential and convergent winds (divergent winds) anomalies over the Maritime Continent (tropical west Indian Ocean) at 850 hPa (these are opposite at 200 hPa), and easterly wind anomalies in India. The velocity potential and divergent winds anomalies suppress the upward movements in India and easterly wind anomalies reduce the water vapor transportation to India. At the same time, NWPSH is mainly affected by TIO SSTAs, with cyclone anomaly in the northwest Pacific induced by negative TIO SSTAs weakening water vapor transportation to the NEC. These factors together lead to inconsistent precipitation variations and weak teleconnection between the two regions (Fig. 18a). Since 1999, on the one hand, La Niña SSTAs directly enhance NWPSH by generating the atmospheric Rossby wave response and indirectly enhance NWPSH through generating off-equatorial anticyclonic shear vorticity by accelerating equatorial easterlies, and then transport more water vapor to the NEC (southerly water vapor transportations on the west side of NWPSH) and India (easterly water vapor transportations on the south side of NWPSH). On the other hand, La Niña SSTAs also promote the ascending branch of the Walker cell to strengthen and move westward, reinforcing the Indian convection. Furthermore, the influences of negative TIO (IOD) SSTAs on NWPSH (Indo-Pacific Walker cell) simultaneously decrease, resulting in the relatively enhanced influences of the ENSO on Indian and the NEC summer precipitation (Fig. 18b). Therefore, under the combined effects of the interdecadal variations of the Indian Ocean and ENSO, the summer precipitation teleconnection between India and the NEC is significantly enhanced after 1999.
Diagram of the physical mechanisms of the interdecadal variations of the summer precipitation teleconnection between India and the NEC in (a) PI and (b) PII. The lower layer shows positive (negative) SSTAs as red (blue) shading, anticyclones and cyclones as vectors, and upward (downward) motion as red (green) vertical arrows. The upper layer shows positive (negative) 200 hPa velocity potential as green (yellow) shading and divergent winds as arrows.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0366.1
Previous studies have pointed out that there is a strong coupling relationship between the circumglobal teleconnection (CGT) and summer monsoon precipitation in India and East Asia (Wu 2002; Lin et al. 2017; Zhou et al. 2020), so what role CGT plays in the interdecadal enhancement of the precipitation relationship between India and the NEC is still unknown, which needs further study. The state-of-the-art global climate models can reproduce the large-scale atmospheric circulation and precipitation pattern. However, it is still a challenge to capture regional-scale climate variability, such as summer precipitation in East Asia (Liang et al. 2019). For instance, climate models are unable to predict the teleconnection of summer precipitation patterns in East Asia and the northward advancement of the rainfall belt (Jiang et al. 2005; Gong et al. 2017, 2018; Li et al. 2021a). Li and Lu (2017) argued that the precipitation teleconnections link to the precipitation prediction skills in climate models. We will further examine if current dynamical models can reproduce the summer precipitation connection between India and the NEC and the interdecadal variations.
Acknowledgments.
This work was supported by the State Key Program of National Natural Science Foundation of China (42130610), the General Program of the National Natural Science Foundation of China (42075057, 42275050, and 41975098), the Key Program of Meteorological Research Foundation of Jiangsu (KZ202206, KM202304), and the Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province Grant BE2022612.
Data availability statement.
The daily precipitation data are downloaded from the National Meteorological Information Center, China Meteorological Administration (http://data.cma.cn/data/cdcindex/cid/6d1b5efbdcbf9a58.html). Monthly all-India summer precipitation data are obtained from the Indian Institute of Tropical Meteorology (https://www.tropmet.res.in/data/data-archival/rain/iitm-regionrf.txt). The reanalysis precipitation data in this paper are downloaded from the Version2 Global Precipitation Climatology Project (https://psl.noaa.gov/data/gridded/data.gpcp.html). The reanalysis circulation data are downloaded from the National Centers for Environment Prediction-Department of Energy (NCEP-DOE) Atmospheric Model Intercomparison Project-II reanalysis dataset (NCEP2) (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html). The Ocean data are downloaded from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset (https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html). The PDO index is downloaded from https://oceanview.pfeg.noaa.gov/erddap/tabledap/cciea_OC_PDO.htmlTable. The 21 AMIP models of CMIP6 are available at https://esgf-node.llnl.gov/search/cmip6/.
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