1. Introduction
The East Asian summer monsoon (EASM) is one of the most dominant climatic systems in East Asia. It affects most parts of China, Japan, and the Korean Peninsula and contributes nearly half of China’s precipitation annually (Ding and Chan 2005; Zhou and Chan 2005; de Carvalho 2016; Chen et al. 2017). Consequently, EASM variability has considerable socioeconomic importance. One of the drivers of the EASM is thermal contrast between the large Eurasian continent and the Pacific and Indian Oceans (Huang et al. 2003). Therefore, understanding the processes of EASM variability associated with Indo-Pacific SST is essential for study of the associated variability of the summer climate in East Asia.
ENSO is regarded as one of the most important factors to influence the EASM (Chen et al. 1992; Shen and Lau 1995; Chang et al. 2000; Wang et al. 2000; Huang et al. 2003; Zhou and Chan 2007; Zhou and Huang 2010; Zhou 2011). On the interannual scale, ENSO affects the EASM through western Pacific anticyclones/cyclones during the ENSO-developing summer (Zhang et al. 1999; Wang and Li 2003), but it also generates an indirect influence through the tropical Indian Ocean (TIO) “capacitor effect” during the ENSO-decaying summer (Xie et al. 2009). In addition, the significant interdecadal variation between ENSO and the EASM has also received considerable attention. Some earlier related studies focused on the strengthening of the ENSO–EASM relationship during the ENSO-decaying phase after the mid-1970s (Wu and Wang 2002; Wang et al. 2008; Xie et al. 2010; Huang et al. 2010). Wang et al. (2008) attributed this decadal shift to the enhanced amplitude and periodicity of ENSO after the late 1970s, whereas Wu and Wang (2002) argued that the mechanisms for this strengthening could be attributed to background change. Other studies considered that this interdecadal change reflected strengthening of the Indian Ocean SST response to ENSO, which accompanied an anomalously strong anticyclone over the western Pacific, enhancing the ENSO–EASM relationship (Xie et al. 2010; Huang et al. 2010). In addition to the decadal change of the relationship between the EASM and the Niño-3 index that occurred in the late 1970s, another decadal change was evident in the early 1990s. Yim et al. (2008) considered that the EASM was connected mainly to SST warming in the Niño-3 area (i.e., 5°S–5°N, 90°–150°W) during 1979–93, which was affected by a combination of developing and decaying ENSO stages. Conversely, the EASM was closely related to the Niño-4 area (5°S–5°N, 160°E–150°W) during 1994–2006. A previous study used a coupled model to reproduce the multidecadal variations of the interannual relationship between the EASM and ENSO; however, the main reason for the decadal variation was attributed to western Pacific SST (Liu et al. 2018). Thus, although many earlier studies have identified decadal variations in the ENSO–EASM relationship, comprehensive understanding of the diverse and complex influencing factors remains lacking.
The Indian Ocean basin mode (IOBM) is the leading mode of TIO SST variability on the interannual time scale. It is also a prominent feature of the observed interdecadal SST trend over recent decades, characterized by basin-scale warming/cooling, which is another factor that influences the EASM (Li et al. 2008). The IOBM peaks in late winter and persists into the following spring and summer (Klein et al. 1999). It emerges mostly as a passive response to heat flux anomalies induced by ENSO, although ocean dynamic processes, especially downwelling Rossby waves, might contribute to part of the SST variability of the IOBM (e.g., Klein et al. 1999; Lau and Nath 2000; Alexander et al. 2002; Xie et al. 2002; Luo et al. 2005). EASM enhancement is favored by Indian Ocean warming that forces both an anticyclonic anomaly over the subtropical western Pacific, which intensifies southwesterly winds toward East China, and a Gill-type response in the upper atmosphere, which intensifies the South Asian high (Yang et al. 2007; Li et al. 2008). The Matsuno–Gill pattern in the upper troposphere and the low-level anticyclone pattern over the subtropical northwest Pacific both play important roles regarding the EASM (Yang et al. 2007; Liu et al. 2018). Previous studies have asserted that the IOBM as a function of ENSO exerts an impact on the EASM; however, Han et al. (2014) considered that the TIO has played a more active role in affecting the Pacific since the early 1990s. Therefore, the primary objective of this study was to explore whether the IOBM generates an independent effect on the EASM in certain periods, with less regulation from tropical Pacific.
In the context of global warming, the frequency of extreme El Niño events has increased significantly (Cai et al. 2014; Xia et al. 2017). The SST anomalies related to ENSO warm events after 1990 show different characteristics, and during the entire process of establishment, development, and extinction, the SST anomalies and subsurface temperature anomalies have been maintained in the central Pacific (hereafter referred to as CP El Niño) (Ashok et al. 2007; Larkin and Harrison 2005; Kao and Yu 2009; Kug and Ham 2011; Yin and Zhou 2020). After 1990, in response to decrease of the east Pacific (EP) El Niño and increase of CP El Niño, the associated abnormal convective activity, abnormal cloud shortwave radiation–SST feedback, modified Walker circulation, and abnormal heating positions led to different paths of Rossby wave propagation into higher latitudes (Weng et al. 2007). The different features of development and decay between EP El Niño and CP El Niño could exert different influences on TIO SST variability (Tao et al. 2014), which might affect the EASM–IOBM relationship. Therefore, a secondary objective of this study was to explore how the IOBM might connect to the EASM after El Niño decadal change.
In this paper, the characteristics and potential causes of the interdecadal change in the relationship between tropical Indo-Pacific SST and EASM are investigated. The data and methods used in the study are described in section 2. Section 3 confirms the strength of the interdecadal relationship between the EASM and IOBM (as well as the associated rainfall and circulation patterns) after the early 1990s but vice versa for the relationship between EASM and the Niño-3 index prior to the 1990s. Section 4 outlines how the IOBM influenced the EASM independently after the interdecadal shift of the early 1990s. Section 5 discusses plausible reasons for the interdecadal strengthening in the EASM–IOBM relationship. Finally, a summary and discussion are presented in section 6.
2. Data and methods
Regression anomalies (color shading) of 200-hPa zonal wind (m s−1) against the JJA-mean mei-yu–changma–baiu, which refers to the rainfall over 27.5°–32.5°N, 105°–120°E and 30°–37.5°N, 127.5°–150°E. The dots indicate the 95% confidence levels based on a two-sided Student’s test. The dashed green rectangles indicate the areas used to define the EASMI. The time period is 1958–2014.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
The Niño-3 index is defined as SST anomalies averaged over the eastern equatorial Pacific (5°S–5°N, 90°–150°W). Here, the Northern Hemisphere seasons of autumn, winter, spring, and summer are defined as September–November (SON), December–February (DJF), March–May (MAM), and JJA, respectively. In the figures, letters C and A represent the anomalous cyclone and anticyclone, respectively. Hereafter, any month in an El Niño onset year is identified by the suffix 0 [e.g., January(0)], whereas any month in an El Niño decay year is identified by the suffix 1.
The rainfall dataset comprised information from 597 meteorological observing stations distributed across China. This dataset, provided by China’s National Climate Center, was verified as a credible dataset following examination and calibration using a homogeneity test, extreme test, and temporal consistency test (Zha et al. 2019). In addition, JRA-55 monthly reanalysis data with 1.25° × 1.25° horizontal resolution were used (Kobayashi et al. 2015). The monthly global Precipitation Reconstruction (PREC; Chen et al. 2002) dataset is used, with a 2.5° × 2.5° horizontal resolution. Monthly average SST data (HadISST version 1.1) with 1° × 1° horizontal resolution were obtained from the Hadley Centre (Rayner et al. 2003). This study focused on the period 1977–2014.
Analyses based on correlation, EOF, and numerical simulations were conducted in this study. To confirm the statistical analysis results, we conducted model experiments using a linear baroclinic model with specified anomalous heating. The model was a primary equation model (Watanabe and Kimoto 2000; Watanabe and Jin 2003) with T42 horizontal resolution and 20 sigma levels in the vertical. Our experiments specified anomalous warming (1.2°C day−1) over the eastern TIO (10°S–10°N, 80°–120°E) and anomalous cooling (−1.0°C day−1) over northeastern Asia (33°–47°N, 110°–140°E) at the center of the regions at sigma levels 0.95 and 0.85 (Fig. 12b), anomalous warming (1.2°C day−1) over the eastern TIO (10°S–10°N, 80°–120°E) at the center of the regions at sigma level 0.95 (Fig. 13a), and anomalous cooling (−1.0°C day−1) over northeastern Asia (33°–47°N, 110°–140°E) at the center of the regions at sigma level 0.85 (Fig. 13d), respectively. The model was integrated for a 65-day period and we show the mean output of days 31–60 in later figures (see Figs. 12c,d and 13b,c,e,f).
3. Interdecadal change in the relationship between tropical Indo-Pacific SST and EASM, as well as the associated rainfall and circulation
a. Interdecadal shift between EASM and tropical Indo-Pacific SST
After the early 1990s, the relationship between the EASM and the tropical eastern Pacific weakened (and strengthened for the tropical central Pacific), and the areas of significance moved from the western Indian Ocean to the eastern Indian Ocean (Fig. 2a). There was also an interdecadal shift in the relationship between tropical SST and the EASM in the 1970s. Many previous studies have investigated the earlier interdecadal change (e.g., the ENSO–EASM relationship strengthening during the ENSO decay phase after the late 1970s; Wang et al. 2008; Xie et al. 2010; Li et al. 2016); hence, the focus of this study was the second shift that appeared in the early 1990s.
(a) The 15-yr sliding correlation between the EASMI and simultaneous JJA SST (9.5°S–9.5°N); light (dark) areas represent the 90% (95%) confidence level. (b) The 15-yr sliding correlation between the EASMI and Niño-3; dashed lines represent the 90% confidence level. (c) The EASMI index and JJA Niño-3 index and their correlation between 1977–89 (P1) and 1994–2014 (P2).
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
To determine the specific year of the interdecadal shift in the relationship between the EASM and tropical SST, we calculated the sliding correlation between the Niño-3 index and the EASMI using a 15-yr window. The sliding correlation revealed the same two interdecadal shifts (i.e., increasing correlation in the mid-1970s and decreasing correlation after the early 1990s; Fig. 2b), similar to what was observed in the eastern Pacific (Niño-3 area) shown in Fig. 2a. As the focus of this study was the second shift, we considered the period 1977–2014. This period was split into two epochs: 1977–89, called period 1 (P1), and 1994–2014, called period 2 (P2). The correlation coefficient between the EASMI and the Niño-3 index was 0.56 during P1 and only 0.16 for P2 (Fig. 2c), consistent with the results shown in Fig. 2a.
Correlation analysis was conducted to determine the sea area of greatest importance with regard to the interdecadal shift between the EASM and tropical SST in the early 1990s (Fig. 3). During P1, a close relationship between the EASMI and the ENSO-like SST is prominent in Figs. 3a–c. Moreover, it can be seen that significant correlation with TIO SST gradually increases to reach a peak in the simultaneous JJA (Fig. 3c). When the impact of Niño-3 is removed, the TIO area of significance almost disappears (Fig. 3d), implying that the TIO does not have independent impact on the EASM and that the Niño-3 area is the sea area with the greatest impact on EASM during P1. However, the TIO does appear to make a considerable difference in P2. As shown in Figs. 3e–g, the areas of significance between the EASMI and SST are primarily located in the TIO, and reach a peak in the simultaneous JJA. When the influence of the preceding DJF Niño-3 index is removed, the close relationship is still mainly located in the TIO (Fig. 3h), suggesting that the close relationship between the EASM and the TIO SST reflects less interference from the Niño-3 area, and that the TIO can exert independent impact on the EASM during P2.
Correlation maps between EASMI and SST in preceding (a) DJF, (b) MAM, and (c) simultaneous JJA. (d) JJA partial correlation with removal of influence of DJF Niño-3 index during P1. (e)–(h) As in (a)–(d), respectively, but for P2. Light to dark shading represents the 90%, 95%, and 99% confidence levels.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
It is known that there are two leading modes in the TIO, the IOBM and the Indian Ocean dipole mode (IODM), which can be separated by EOFs. Therefore, it is necessary to identify the mode that exerts greatest impact on the EASM, especially in P2. As can be seen from Table 1, the EASM during P1 is linked closely to the IOBM and SST in the Niño-3 area. However, during P2, the EASM is related closely only to the IOBM, which is consistent with the results shown in Fig. 2a. Therefore, during P2, the TIO has independent impact on the EASM, which is displayed mainly by the IOBM but not the IODM.
Correlation between EASMI and Niño-3 index, first (IOBM) mode, and second (IODM) mode over TIO (20.5°S–20.5°N, 39.5°–110.5°W) during P1 and P2. One asterisk (*) indicates p > 90%; two asterisks (**) indicate p > 95%.
b. Similar interdecadal shift displayed in EASM-associated precipitation and circulation
Summer rainfall in the monsoon area of China is related to EASM. In Fig. 4a, the rainband (called the mei-yu) marked by the rectangle over the Yangtze River basin is closely related to EASM. The annual regional average sequence displayed in Fig. 4b is referred to the rainfall related to EASM. During P1, the rainfall related to EASM is closely connected to the Niño-3 index (i.e., has a correlation coefficient of up to 0.59; p > 95%). However, in P2, it is linked more closely to IOBM (i.e., correlation coefficient of 0.38; p > 90%), as shown in Table 2, which is in accord with the major results presented in Table 1.
(a) Correlation between EASMI and the 597 station rainfall; the shaded area (black dot) exceeds the 90% and 95% confidence level. (b) Normalized sequence of the sum of station rainfall marked by black dots in rectangle in (a), which is referred to as EASM-related rainfall.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
Correlation between EASM-related rainfall and Niño-3, and between rainfall and the TIO IOBM, during P1 and P2. One asterisk (*) indicates p > 90%; two asterisks (**) indicate p > 95%.
The features of the circulations related to EASM during P1 and P2 are also different. During P1, at the 700-hPa level, the horizontal wind anomaly associated with EASM exhibits a Pacific–Japan (PJ)-like pattern, with anomalous cyclones located in tropics (0°–10°N) and mid–high latitudes (30°–50°N) and an anomalous anticyclone in between 10° and 30°N (Fig. 5a). A similar tripolar pattern also appears in the horizontal wind at the 200-hPa level, albeit with the polarity in the tropics shifted slightly northward and with weaker middle polarity (Fig. 5b). During P1, the PJ-like pattern shows equivalent barotropic characteristics in the lower and upper troposphere. Moreover, the PJ-like pattern can also be discerned in the 500-hPa geopotential height and 2-m air temperature anomalies. However, only the two polarities in geopotential height nearest the equator exceed the 95% confidence level, and only the middle polarity in the 2-m temperature exceeds the 95% confidence level (Fig. 5c). In contrast, the circulation anomalies associated with the EASM differ greatly during P2. The anomalous wind presents a dipole pattern, with a lack of the anomalous cyclone in the tropics, accompanied by a baroclinic structure from low-tropospheric easterly to upper-tropospheric southwesterly wind in the tropics (Figs. 5d,e). However, during P2, the tripole pattern is more significant in terms of the 500-hPa geopotential height anomaly, while the middle polarity in the 2-m temperature anomaly is weakened and the northernmost polarity is enhanced (Fig. 5f).
Regression of (a) 700-hPa horizontal wind (m s−1), (b) 200-hPa horizontal wind (m s−1), and (c) 500-hPa geopotential height (gpm) and 2-m temperature (°C) during P1 on EASMI. (d)–(f) As in (a)–(c), respectively, but for P2. Red vectors, red contours, and dots represent the 95% confidence level. Violet shading highlights the Tibetan Plateau.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
Generally, the PJ-like patterns of anomalies have a certain performance during P1 and P2 but little difference, which can be well extracted by the dominant mode of the 850-hPa vorticity field over the domain 0°–60°N, 100°–160°E based on EOF analysis (Kosaka and Nakamura 2010). Therefore, the first principal component of the EOF analysis was used to represent the PJ index. The calculated value of the correlation coefficient between the PJ index and the EASM during P1 was up to 0.86 (p > 99%); however, the correlation decreased markedly (to 0.60) in P2. The PJ pattern teleconnection is an important atmospheric process via which El Niño exerts influence on EASM and the associated rainfall anomalies (Weng et al. 2007; Feng et al. 2010). Therefore, the decrease in the strength of the relationship between EASM and the PJ index from P1 to P2 suggests reduction of the influence from the Niño-3 area on the EASM, consistent with the results presented in Table 1.
From the analyses above, it can be seen that during 1977–2014, the relationship between the EASM and tropical Indo-Pacific SST has experienced pronounced decadal change. During P1, the EASM was related mainly to the Niño-3 area and IOBM. However, during P2, the EASM was only linked significantly with the IOBM, and the rainfall and PJ-like circulation patterns related to the EASM bore similar resemblance. TIO climate is influenced strongly by El Niño via the atmospheric bridge (Alexander et al. 2002) and ocean channel (Sprintall et al. 2014). Therefore, the IOBM is often considered a medium via which ENSO can influence the EASM, and persistent TIO warming during the summer of El Niño decay exerts strong impact on the monsoon and general circulation over East Asia and the northwest Pacific (Xie et al. 2009; Jiang et al. 2013; Chowdary et al. 2015). However, during P2, the connection between the IOBM and Niño-3 is not obvious. Thus, the question of how the IOBM might exert independent impact on EASM is explored in the following.
4. Impact of IOBM on EASM during P2
EASM can be influenced by various SST forcing, including the interdecadal Pacific Oscillation and the western North Pacific (WNP) and TIO SST anomalies (Wu et al. 2010; Wang et al. 2012; Yuan and Chen 2013; Zhang et al. 2017); however, the various factors can play different roles in different periods.
Regression of (a) 700-hPa horizontal wind (m s−1), (b) 200-hPa horizontal wind (m s−1), and (c) 500-hPa geopotential height (gpm) and 2-m temperature (°C) on Niño-3 index during P1. (d)–(f) As in (a)–(c), respectively, but for P2 on IOBM. Red vectors, contours, and dots represent the 95% confidence level. Violet shading highlights the Tibetan Plateau.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
(a) Normalized thermal contrast index and its regression with respect to circulation fields, (b) 700-hPa horizontal wind (m s−1), (c) 200-hPa horizontal wind (m s−1), and (d) 500-hPa geopotential height (contours; gpm) and 2-m temperature near surface (shading; °C). Red vectors, contours, and dots represent the 95% confidence level. Violet shading highlights the Tibetan Plateau.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
The dipole pattern is also evident in the cross section displayed in Fig. 8a, with ascending motion at 30°–50°N and subsidence at 10°–20°N, where there are an anomalous cyclone (C) and anticyclone (A), respectively, as shown in Figs. 7b and 7c. Thermal contrast can also induce the similar C–A pattern in the column-integrated water vapor flux, and the significant area impacted by the EASM, marked by the rectangle in Fig. 8b, is located in the area of intersection between the southward and northward water vapor flux. Therefore, the land–sea thermal contrast between the IOBM and the area 33°–47°N, 110°–140°E can produce the major circulation pattern associated with the EASM, and generate the anomalous water vapor intersection, located in just that area, which is influenced significantly by the EASM. Thus, land–sea thermal contrast could be an important influencing factor of the EASM in P2.
Normalized thermal contrast index’s regression with respect to circulation fields: (a) cross section of meridional wind (m s−1), vertical motion (vector; omega multiplied by −100; 0.01 Pa s−1) along 90°–125°E and (b) column-integrated water vapor flux (vector; g kg−1 m s−1) from 1000 to 100 hPa. Red vectors represent the 95% confidence level.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
During P2, the anomalous anticyclone located in Figs. 7b and 7c, along with obvious descending motion in 10°–20°E, is the key anomalous circulation connecting the IOBM and EASM. Because it is mainly located in the land area (20°–30°N, 90°–115°E) at 200 hPa (Fig. 7c), it is different from the western North Pacific anomalous anticyclone (WNPAC) in previous studies, whose formation and maintenance are usually attributed to local wind–evaporation–SST feedback (e.g., Wang et al. 2000; Wang and Li 2003), Rossby waves from cold SST anomalies in tropical central-eastern Pacific (Fan et al. 2013), a warm atmospheric Kelvin wave from the TIO (e.g., Yang et al. 2007; Xie et al. 2009), or local Hadley circulation anomaly (e.g., He and Wu 2014; Chung et al. 2011), and so on. Because the anomalous anticyclone in Fig. 7b can also reappear in simple linear baroclinic model (LBM) experiments, as shown in section 5, which implies that the anomalous anticyclone maybe links to the atmospheric internal dynamical processes. To test this hypothesis, we use the omega equation to explore the relative contributions of atmospheric internal processes and diabatic heating associated with the anomalous subsidence of the anomalous anticyclone over (10°–20°N, 90°–125°E) at 200 hPa. Term A in Eq. (1) is the Laplace term of the omega. The B term is the variation of the absolute vorticity advection with the height, the C term is the Laplacian of the quasigeostrophic temperature advection, and the D term is Laplacian of diabatic heating. The Q in term D can be expressed as in Eq. (2), based on meteorological texts such as Holton (1992). The terms ƒ, Sp, R, P, CP, and Q represent respectively the Coriolis parameter, the static stability parameter, the gas constant for dry air, pressure, the specific heat at constant pressure, and the rate of heating per unit mass due to diabatic processes. Equation (1) is expanded into a perturbation Eq. (3). The B term is expanded into parts B1–B7, and the C term is expanded into parts C1–C6, which help to further explore which terms of vorticity advection, and temperature advection play more major roles in the anomalous vertical motion associated to the anomalous anticyclone in Figs. 7c and 8a.
5. Possible mechanism of the impact from IOBM on EASM during P2
The preceding analyses established that the EASM is linked closely with the Niño-3 area in P1, and that the impact of TIO SST on the EASM can be attributed to its “capacitor effect” (Xie et al. 2009). In comparison with P1, the EASM in P2 is significantly independently related with IOBM, with less regulation from El Niño–like SST. This is possibly attributable to the weaker impact of CP El Niño on TIO SST anomalies in comparison with EP El Niño (Wang et al. 2013).
During P1 (P2), the standard deviation of the Niño-3 index is 0.77 (0.66), which means weaker variation of ENSO-related SST during P2. In addition to this difference, the IOBM in JJA is related closely to SST in the mid-eastern Pacific from the preceding SON to the simultaneous JJA during P1 (Figs. 9a–d). In the preceding SON, the JJA IOBM has greatest connection with SST in the mid-eastern Pacific and less significant connection with TIO SST (Fig. 9a), which implies that the IOBM in JJA is regulated most significantly by ENSO-related SST. Then, the significant area in the TIO is gradually enlarged (Figs. 9b–d). However, when the signal of the preceding DJF Niño-3 is removed, the pattern in Fig. 9d turns into that in Fig. 9e, with a smaller significant area in the TIO, which implies that the IOBM is influenced significantly by the Niño-3 area in P1.
(a) Correlation maps between JJA IOBM and SST in the preceding (a) SON, (b) DJF, and (c) MAM, and (d) simultaneous JJA during P1. (e) JJA partial correlation with removal of influence of DJF Niño-3 index during P1. (f)–(j) As in (a)–(e), respectively, but for P2. Light to dark areas represent the 90%, 95%, and 99% confidence levels.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
During P2, the connection of the IOBM in JJA with mid-eastern Pacific SST in the preceding SON is significantly weak (i.e., there is almost no significant area in the simultaneous summer). Conversely, the link with preceding TIO SST is stronger, with a greater significant area in the preceding SON (Figs. 9f–i), which means that the IOBM is influenced most significantly by TIO SST in P2. When the signal of the preceding DJF Niño-3 is removed, the pattern in Fig. 9i turns into that in Fig. 9j, with not a distinctly smaller significant area in the TIO, which further verifies that IOBM is weakly influenced by Niño 3 area in P2, the same as the results obtained in Fig. 3.
The composite SST based on El Niño years (i.e., 1977/78, 1982/83, 1986/87, and 1987/88) is shown in Fig. 10. The anomaly patterns exhibit considerable resemblance to those shown in Figs. 9a–d, further confirming that the IOBM in JJA during P1 is connected to ENSO-related SST anomalies. However, during P2, the ENSO-related SST anomalies disappear more rapidly and the positive anomalies change into negative anomalies in the simultaneous JJA (Fig. 11d). Differences in the rate of decay of El Niño might account for the different impact of the Niño-3 area on the IOBM between P1 and P2. This would further confirm that the Niño-3 area imparts influence on EASM and IOBM only as a medium for transmitting the signal of ENSO-related SST anomalies to EASM during P1, whereas the IOBM can exert influence independently on the EASM during P2. In P2, the anomaly of ascent associated with the SST anomalies shown in Fig. 11d is located in the region 10°S–10°N, 80°–120°E (Fig. 12a). Therefore, the sea area of 10°S–10°N, 80°–120°E should be the most important area for exerting impact on the atmosphere during P2, although the entire IOBM generates the significant SST anomalies in Fig. 11d and some of the circulation anomaly shown in Fig. 12a. Therefore, the land–sea thermal contrast between the IOBM and the region 33°–47°N, 110°–140°E can depend on the contrast between the areas 10°S–10°N, 80°–120°E and 33°–47°N, 110°–140°E.
SST composite (°C) between SST mean during P1 and the preceding (a) SON, (b) DJF, and (c) MAM. (d) Simultaneous JJA SST during P1, based on El Niño events that occurred in 1977/78, 1982/83, 1986/87, and 1987/88. Dotted areas represent the 95% confidence levels of the two-tailed Student’s t test.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
(a)–(d) As in Figs. 9a–d, but based on El Niño events that occurred in 1994/95, 1997/98, 2006/07, and 2009/10 during P2.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
(a) As in Fig. 10, but for u (m s−1) and omega (multiplied by −100; 0.01 Pa s−1) along 10°S–10°N, and red vectors represent the 95% confidence level. (b) Thermal forcing location, negative temperature forcing over 33°–47°N, 110°–140°E, and positive temperature forcing over 10°S–10°N, 80°–120°E; contour interval is 0.2°C. The (c) 700- and (d) 200-hPa horizontal wind for the mean of the model data for days 31–60.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
When the positive (negative) thermal anomaly is added at 10°S–10°N, 80°–120°E (33°–47°N, 110°–140°E) as described in section 2, a similar dipole pattern appears in the 700- and 200-hPa horizontal wind fields (Figs. 12c,d), consistent with Figs. 5d, 5e, 7b, and 7c. But the anomalous cyclone in the middle-high latitudes in Fig. 12c shifts eastward and southward, and the anomalous anticyclone in the low latitudes in Fig. 12c shifts westward and southward obviously, compared to that in Figs. 5d and 7b. So, the anomalous anticyclone is far from the anomalous cyclone, resulting in the disappearance of convergence zone, locating at 20°–30°N in Fig. 5d (Fig. 12c). Moreover, the 200-hPa circulation anomalies have better resemblance, and the northern anomalous cyclone is reproduced better in comparison with the anomalous low-latitude anticyclone (Fig. 12d), compared to that in 700 hPa shown in Fig. 12c. However, the anomalous anticyclone in the low latitudes is reproduced weakly and more southward in Fig. 12d, compared to that in Figs. 5e and 7c.
When only the negative thermal anomaly is added at 33°–47°N, 110°–140°E, only an anomalous cyclone can be reproduced over 30°–50°N in the 700- (Fig. 13b) and 200-hPa (Fig. 13c) horizontal wind fields. When only the positive thermal anomaly is added at 10°S–10°N, 80°–120°E, as shown in Fig. 13e, only an anomalous anticyclone can be reproduced over the low latitudes at 700 hPa, similar to that in Fig. 12c. As shown in Fig. 13f, in the 200-hPa fields, the anomalous anticyclone is stronger and more northward, compared to Fig. 12d. The land–sea thermal contrast exerts influence on the EASM, with the critical land area 33°–47°N, 110°–140°E mainly associated with the mid-high circulation anomalies and the critical sea area 10°S–10°N, 80°–120°E mainly associated with the low-latitude circulation anomalies.
(a) Thermal forcing location, negative temperature forcing over 33°–47°N, 110°–140°E; contour interval is 0.2°C. The (b) 700- and (c) 200-hPa horizontal wind for the mean of the model data for days 31–60. (d)–(f) As in (a)–(c), but for positive temperature forcing over 10°S–10°N, 80°–120°E.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
6. Summary and discussion
An interdecadal change in the relationship between the EASM and IOBM occurred around the early 1990s, which was manifested as enhanced independent influence of IOBM on EASM. During P1, the EASMI was closely related to both ENSO-related and IOBM SST anomalies. When the signals from Niño-3 were removed, the significance of the IOBM was weakened significantly. This illustrates that ENSO-related SST anomalies represent the most important factor influencing EASM and that IOBM cannot impart independent influence on EASM. In P1, these features were also discerned in EASM-related rainfall and circulation patterns. During P2, the EASM was closely connected to IOBM, which generated a significant impact on the EASM through the land–sea thermal contrast with area 10°–30°N, 110°–140°E; by this, the IOBM generated independent impact on the EASM. The shift in the interannual relationship between the EASM and IOBM could be related to variation in the rate of decay of El Niño from P1 to P2.
CP ENSO and EP ENSO have different amplitudes and durations (e.g., Weng et al. 2007; Wang and Ren 2020). The duration of EP ENSO is 3–7 years, but during P2, with the increase of CP ENSO, the life cycle of ENSO only lasts 2–3 years, which means a faster rate of decay (Wang and Ren 2020). There exists obvious IOBM warming after EP El Niño, while insignificant SST anomalies exist in the Indian Ocean after CP El Niño because of the lack of tropospheric temperature (TT) mechanism, “atmospheric bridge” mechanism, and ocean dynamics (Tao et al. 2014). When IOBM is connected closely with Niño-3, it often influences the WNP anticyclone through warm tropospheric Kelvin and further exerts impact on EASM, as well as other climate systems (Xie et al. 2009). This study emphasized that the thermal contrast between the IOBM and the key area 33°–47°N, 110°–140°E had a large independent impact on EASM during P2, when the IOBM was connected weakly with Niño-3. Especially, the associated temperature and vorticity advection play an important role in the anomalous anticyclone/cyclone appearance, which is linked to the IOBM and EASM.
As shown in Fig. 2a, in addition to the interdecadal shift in the relationship between the EASM and TIO SST that occurred in the early 1990s, significant interdecadal change also occurred in the late 1970s. Some previous studies suggested that the change in the relationship between IOD-like SST and EASM had an important role in the earlier interdecadal shift (i.e., the strong positively correlated relationship between the IOD-like SST anomaly and EASM before the late 1970s, whereas the IOD-like correlation diminished after the late 1970s; Ding et al. 2010). Wang et al. (2020) considered that the change associated with summer monsoon meridional circulation may arise from the interdecadal shift in the leading modes of low-level geopotential height over East Asia-Australia and Indo-Pacific SST anomalies in boreal summer. However, as demonstrated in present paper, the second shift around the early 1990s between EASM and TIO SST depended on a change in the relationship between EASM and IOBM, rather than IOD.
This study, which revealed interdecadal strengthening in the relationship between EASM and IOBM, focused mainly on the idea that thermal contrast triggered the different circulation pattern that influenced EASM during P2, under the background of a faster rate of decay of El Niño in comparison with P1. To verify the robustness of the results obtained, the results in Figs. 14 and 15 are demonstrated in the following.
Correlation maps between EASMI and PREC rainfall during (a) P1 and (c) P2. (b) As in (a), but for partial correlation with removal of influence of DJF Niño-3 index during P1. (d) As in (b), but for partial correlation with removal of influence of DJF Niño-3 during P2. (e) As in (d), but for removal of influence of thermal contrast index. Light to dark shading represents the 90%, 95%, and 99% confidence levels.
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
As in Fig. 4, but for EASMI defined by Lau et al. (2000).
Citation: Journal of Climate 34, 16; 10.1175/JCLI-D-20-0825.1
As shown in Fig. 14, when the station rainfall in Fig. 4 is replaced by PREC rainfall, the rainfall anomalies from the western North Pacific to East Asia demonstrate an obvious PJ-like pattern during P1, similar to the circulation anomalies in the left panel of Fig. 5 (Fig. 14a). When the impact of Niño-3 is removed, the significance area decreases dramatically (Fig. 14b). During P2, the PJ-like pattern also exists, but a little weaker (Fig. 14c), compared to P1. When the impact of Niño-3 is removed, the PJ-like pattern still exists (Fig. 14d), but when the signal associated with thermal contrast index in Fig. 7a is removed, the PJ-like pattern almost disappears (Fig. 14e). These illustrate that EASM-related rainfall is closely related to the Niño-3 area during P1 and related to thermal contrast between the IOBM and critical land region 33°–47°N, 110°–140°E during P2. Similar findings can also be obtained when the EASMI in this paper is replaced by other indices, such as those defined by Huang and Yan (1999) and Lau et al. (2000). It can be seen from Fig. 15 that the major circulation pattern illustrated in Fig. 5 can be largely reproduced when the EASMI is replaced by the index defined by Lau et al. (2000), using the 200-hPa u component of wind over the area 25°–50°N, 110°–150°E. However, a few slight differences are evident in P1, including the eastward shift of the PJ-like wave at 700 hPa and the absence of the anomalous cyclone at 0°–10°N at 200 hPa. During P2, the circulation patterns shown in Figs. 15d–f bear great similarity to those presented in Figs. 5d–f, except for a larger significant area in the 2-m temperature field in the middle polarity and an insignificant area in northernmost polarity (Fig. 15f).
In future work, it would be interesting to explore whether other physical processes influence the EASM–IOBM relationship.
Acknowledgments
We cordially thank all the dataset providers. This work is supported by the National Key Research and Development Program of China (2016YFA0600603) and Projects funded by the China Postdoctoral Science Foundation (2019M660761, 2019M660762, and 2020T130640).
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