Causes of Interannual and Interdecadal Variations of the Summertime Pacific–Japan-Like Pattern over East Asia

Li Tao Key Laboratory of Meteorological Disaster, Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Tim Li International Pacific Research Center, and Department of Atmospheric Sciences, University of Hawai‘i at Mānoa, Honolulu, Hawaii

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Yuan-Hui Ke Hainan Meteorological Observatory, Haikou, Hainan, China

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Jiu-Wei Zhao College of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

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Abstract

A Pacific–Japan (PJ) pattern index is defined based on the singular value decomposition (SVD) analysis of summertime 500-hPa height in East Asia and precipitation in the tropical western North Pacific (WNP). The time series of this PJ index shows clearly the interannual and interdecadal variations since 1948. Idealized atmospheric general circulation model (AGCM) experiments were carried out to understand the remote and local SST forcing in causing the interannual variations of the PJ pattern and interdecadal variations of the PJ-like pattern. It is found that the PJ interannual variation is closely related to El Niño–Southern Oscillation (ENSO). A basinwide warming occurs in the tropical Indian Ocean (TIO) during El Niño mature winter. The TIO warming persists from the El Niño peak winter to the succeeding summer. Meanwhile, a cold SST anomaly (SSTA) appears in the eastern WNP and persists from the El Niño mature winter to the succeeding summer. Idealized AGCM experiments that separate the TIO and WNP SSTA forcing effects show that both the remote eastern TIO forcing and local WNP SSTA forcing are important in affecting atmospheric heating anomaly in the WNP monsoon region, which further impacts the PJ interannual teleconnection pattern over East Asia. In contrast to the interannual variation, the interdecadal change of the PJ-like pattern is primarily affected by the interdecadal change of SST in the TIO rather than by the local SSTA in the WNP.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Li Tao, taoli@nuist.edu.cn

Abstract

A Pacific–Japan (PJ) pattern index is defined based on the singular value decomposition (SVD) analysis of summertime 500-hPa height in East Asia and precipitation in the tropical western North Pacific (WNP). The time series of this PJ index shows clearly the interannual and interdecadal variations since 1948. Idealized atmospheric general circulation model (AGCM) experiments were carried out to understand the remote and local SST forcing in causing the interannual variations of the PJ pattern and interdecadal variations of the PJ-like pattern. It is found that the PJ interannual variation is closely related to El Niño–Southern Oscillation (ENSO). A basinwide warming occurs in the tropical Indian Ocean (TIO) during El Niño mature winter. The TIO warming persists from the El Niño peak winter to the succeeding summer. Meanwhile, a cold SST anomaly (SSTA) appears in the eastern WNP and persists from the El Niño mature winter to the succeeding summer. Idealized AGCM experiments that separate the TIO and WNP SSTA forcing effects show that both the remote eastern TIO forcing and local WNP SSTA forcing are important in affecting atmospheric heating anomaly in the WNP monsoon region, which further impacts the PJ interannual teleconnection pattern over East Asia. In contrast to the interannual variation, the interdecadal change of the PJ-like pattern is primarily affected by the interdecadal change of SST in the TIO rather than by the local SSTA in the WNP.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Li Tao, taoli@nuist.edu.cn

1. Introduction

The interannual variations of elongated rain belts over East Asia (called mei-yu, baiu, or changma) have been extensively investigated by many previous works (e.g., Tao and Chen 1987; Nitta 1987; Huang and Li 1988; Chang et al. 2000a,b; Wang et al. 2000, 2003). Based on the analysis of satellite cloud amount, sea surface temperature (SST), and geopotential height data, Nitta (1987) found a pressure seesaw pattern between the tropical western North Pacific (WNP) and Japan, and named it the Pacific–Japan (PJ) teleconnection pattern. Huang and Li (1988) further studied the characteristics of this teleconnection pattern in East Asia and its relationship with Northern Hemisphere planetary wave propagation, calling it the East Asia–Pacific (EAP) pattern. Essentially, the two patterns pointed out the same phenomenon (hereafter we refer to this pattern as the PJ pattern). It has been shown that the variability of the PJ pattern has a profound impact on the climate variation in East Asia as well as summer TC activity over the WNP (Choi et al. 2010; Kim et al. 2012; Zhan et al. 2011; Tao et al. 2012).

Various theories have been proposed to understand the formation and maintenance of the PJ pattern. For example, Nitta (1987) and Huang and Li (1988) suggested that the PJ pattern results from Rossby wave energy dispersion due to anomalous heating over the Philippines, caused by the local SST anomaly (SSTA) forcing. The wave propagation leads to the meridional dipole of rainfall anomalies between the Philippines and Japan/central China. Lau (1992) suggested that anomalous heating in the WNP near Philippines might trigger a teleconnection pattern influencing East Asia and North America. Kosaka and Nakamura (2006) suggested that the PJ pattern might be regarded as an unstable mode under East Asian summer mean flow. They found that the PJ pattern was maintained by dry energy conversion and interactions with moist processes. Hirota and Takahashi (2012) suggested that the PJ pattern can be considered an internal atmospheric mode. In this pattern, the moist processes strengthen the circulation anomalies, and the Rossby waves propagate northward in the lower troposphere and southeastward in the upper troposphere. Ohba and Ueda (2006) suggested that convective activity around the Philippines is highly correlated with the east–west gradient of SST between the north Indian Ocean and the WNP, and assessed relative contributions of the TIO and WNP SST anomalies in forcing the rainfall around the Philippines. Xie et al. (2009) and Wu et al. (2009a) showed that the basinwide warming in the tropical Indian Ocean (TIO) during the El Niño decaying summer exerted an anomalous anticyclone in the Philippine Sea. This anomalous anticyclone could further excite a PJ pattern over East Asia, leading to an enhanced rainband over the middle and lower reaches of the Yangtze River of China and southern Japan. Wu et al. (2010) stressed the role of a cold SSTA in the WNP in maintaining the anomalous anticyclone in early summer. Kosaka et al. (2013) emphasized the internal atmospheric dynamics in generating a PJ pattern. Some recent studies (e.g., Chowdary et al. 2012; Hu et al. 2014; Kubota et al. 2016; Xie et al. 2016) suggested that there is an interdecadal change of the interannual TIO–ENSO–PJ pattern relationship. The PJ index is significantly correlated with ENSO or TIO after the late 1970s but insignificant for the period from the 1950s to late 1970s. Xie et al. (2010) suggested that the El Niño–induced IO warming persists through boreal summer only after the late 1970s because the thermocline in the southwestern TIO shoals to strengthen a thermocline feedback.

In addition to interannual variations, the PJ pattern itself also experienced an interdecadal change. Sun et al. (2014) showed that the summer PJ pattern experienced a marked interdecadal variation around the late 1970s, with its major teleconnection centers shifting southwestward. Huang et al. (2006) indicated that the SST increase over the tropical central and eastern Pacific since the late 1970s was responsible for the interdecadal change of rainfall in China, with increased precipitation over the central and southern China and decreased precipitation in northern China.

The main objective of the present study is to reveal physical mechanisms responsible for the interdecadal variation of the PJ-like pattern and the relative roles of the TIO and WNP SSTA forcing on the interannual variation of PJ pattern. Different from the previous modeling studies that specified the SSTA pattern over an entire domain, we only specify the SSTA in the region where there is a significant positive SST–precipitation correlation, to ensure that the specified SSTA has an active role in impacting the atmospheric circulation.

To describe the PJ interannual and PJ-like interdecadal variations, an appropriate PJ index measuring the strength of the PJ pattern is necessary. Most of previous studies defined the PJ index based on the anomaly values over positive and negative centers (Nitta 1986; Huang and Yan 1999; Wakabayashi and Kawamura 2004). For example, Nitta (1986) defined the PJ index based on the cloud amount differences between a positive center at 16°–20°N, 142°–150°E and a negative center at 32°–38°N, 134°–142°E. Such a definition may pose a serious problem if the anomalous centers shift in different interdecadal periods. In this study, we will define a new PJ index based on singular value decomposition (SVD) analysis.

A key science question to be addressed by the current study is whether the same mechanisms or different ones control the interannual and interdecadal variability of the PJ pattern. To address this question, various idealized numerical experiments will be conducted. Special attention is paid to the relative roles of remote and local SSTA forcing in affecting anomalous heat sources in the WNP and the PJ pattern. The remaining paper is organized as follows. In section 2 the datasets and atmospheric GCM experiments are described. Section 3 introduces the definition of the new PJ index. Section 4 presents observational diagnosis results and numerical model simulation results related to the interannual variation of the PJ pattern. Section 5 investigates the processes related to the interdecadal variation of the PJ-like pattern. A summary and discussion are given in section 6.

2. Data and methods

a. Observational data

The primary data used for this study are the monthly 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005) dataset for a 44-yr period from 1958 to 2001, the monthly National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) atmospheric reanalysis dataset (Kalnay et al. 1996) for a 65-yr period from 1948 to 2012, the National Oceanic and Atmospheric Administration’s (NOAA) Precipitation Reconstruction over Land (PREC/L) dataset with a 0.5° × 0.5° resolution (Chen et al. 2002), the NOAA Precipitation Reconstruction (PREC) dataset with 2.5° × 2.5° resolution (Chen et al. 2002), and the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset (Rayner et al. 2003) for a 65-yr period from 1948 to 2012 with a 1° × 1° resolution. All analyses are focused on the boreal summer period [June–August (JJA)].

b. Model and experiment design

The atmospheric general circulation model (AGCM) used in the study is ECHAM version 4.8 (hereafter ECHAM4), which was developed at the Max Planck Institute for Meteorology (MPI; Roeckner et al. 1996). The AGCM is run at a horizontal resolution of spectral triangular 42 truncation (T42, roughly equivalent to 2.8° × 2.8° in latitude and longitude), with 19 vertical levels in a hybrid sigma–pressure coordinate system extending from surface to 10 hPa. The model physical parameterization schemes include the following: 1) the horizontal diffusion is parameterized by a high-order scheme and a semi-Lagrangian transport scheme is used for water vapor, cloud water, and trace substances; 2) a new vertical diffusion coefficient as functions of turbulent kinetic energy is applied; 3) convection closure for deep cumulus convection is based on convective instability instead of moisture convergence; 4) turbulent fluxes at the surface are calculated from Monin–Obukhov similarity theory with a higher-order closure scheme; and 5) the ECMWF radiation scheme is modified based on water vapor continuum, cloud optical properties, and greenhouse gases.

In the control run (CTRL), the model is forced with the observed monthly climatology of SST and sea ice. Here the climatological SST is calculated based on the period of 1960–99. To study the impacts of remote SSTA forcing over the TIO and the local SSTA effect over eastern of WNP on the PJ interannual and PJ-like interdecadal variation, we carry out two groups of sensitivity experiments with specified SSTA in the TIO and the WNP respectively. Table 1 lists all numerical experiments. Each experiment is integrated for 30 yr, and the last 20 yr are analyzed. Thus, all experiments are equivalent to a 20-member ensemble run.

Table 1.

List of numerical sensitivity experiments.

Table 1.

Lu and Lu (2014) and Kumar et al. (2013) suggested that only those SST anomalies over the oceans where ocean actively affects atmosphere should be used in AGCM modeling. In this study, we use the SSTA over eastern TIO where the SST–rainfall relationship is significant and positive to force the ECHAM4. For PJ interannual variability, we only add monthly regressed SSTA fields from June to August to the climatological SST over the eastern TIO domain (10°S–15°N, 70°–100°E) in the TIO warm run. In the TIO cold run, we add the same SSTA pattern but multiply the climatological SST by −1 over the eastern TIO domain. The difference of the two experiments above reflects the circulation response to anomalous SST forcing in eastern TIO. (By doing so we double the sample number while removing the climatological mean state.) For the PJ-like interdecadal study, we add the monthly regressed SSTA from January to December over the whole TIO domain (10°S–23°N, 50°–100°E) to the climatological SST in the TIO run. Similar we conducted two parallel experiments with the SSTA in the WNP domain (0°–23°N, 120°E–180°). In the WNP cold run, we add the monthly regressed SSTA field from January to December to the climatological SST over the WNP domain. As discussed later, in response to a positive PJ pattern, an anomalous anticyclone appears in the WNP domain. In association with this anomalous anticyclone are a cold SSTA to the east and a warm SSTA to the west. The former plays an active role in inducing the local anticyclone anomaly (i.e., the ocean influences the atmosphere), while the latter plays a passive role (i.e., primarily it is the atmosphere that influences the ocean). Thus, in the WNP cold run, we only include the negative SST anomalies in the WNP domain, while assigning zero SSTA to the region where it is positive. By doing so, we only consider the effect of the local negative SSTA forcing on the WNP anticyclonic response. Similarly, in the WNP warm run, the same SSTA pattern but with an opposite sign is added to the climatological SST over the WNP domain. The difference of the two experiments above reflects the circulation response to anomalous SST forcing in WNP. A Student’s t test is used to examine the statistical significance of the model response. The differences of TIO warm and TIO cold experiments from the control run are examined first. It is found that, to the first order, they are symmetric. The same results are found for the WNP warm and WNP cold experiments.

3. Observed PJ patterns associated with interannual and interdecadal variations

To describe the PJ interannual and PJ-like interdecadal variations, a new method is used to define the PJ index. Previously, the PJ index was defined based on the values at positive and negative height centers (e.g., Nitta 1986). The problem with the previous method is that if the PJ centers shift (say, on an interdecadal time scale), the index cannot correctly represent the strength of the PJ pattern. On the other hand, it has been shown that the anomalous heating near the Philippines is important in triggering northward-propagating Rossby waves and forming the PJ pattern. Based on the above consideration, we define a PJ index based on the leading SVD mode of the JJA 500-hPa geopotential height field in East Asia and JJA precipitation in the WNP region. The standardized time series of the leading SVD mode for the JJA geopotential height at 500 hPa is defined as the PJ index.

Figure 1 shows the heterogeneous correlation patterns and standardized time series of the leading SVD mode for JJA geopotential height at 500 hPa of ERA-40 over a domain of 10°–70°N, 100°–160°E and JJA precipitation of PREC over a domain of 8.75°–23.75°N, 111.25°–151.25°E from 1958 to 2001. The correlation coefficient of the leading principal components of the two fields is 0.77, indicating that the covariance between the geopotential height field and the rainfall field are high. The squared covariance fraction (SCF) of the leading mode accounts for 91.13%. The percentage of variances explained by the expansion coefficients is 31.58% for the geopotential height field and 53.42% for the precipitation field, respectively.

Fig. 1.
Fig. 1.

Heterogeneous correlation distribution for the first SVD mode of (a) the 500-hPa height field over East Asia and (b) precipitation over the WNP, and (c) the time series of the first PCs of 500-hPa height (solid line) and precipitation (dashed line). Values exceeding the 95% significance level in (a),(b) are shaded. The 500-hPa height is from ERA-40 and precipitation is from PREC.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The main characteristic of the geopotential height field at 500 hPa in the first SVD mode (SVD1) is a “high–low–high” pattern along the meridional direction, with three centers near the Philippines, Japan, and west to the Sea of Okhotsk (Fig. 1a). This pattern resembles well the classical PJ pattern shown by Nitta (1987). Correspondingly, negative precipitation anomalies appear over the Philippine Sea and South China Sea (Fig. 1b). The time series of the leading principal components (Fig. 1c) shows clearly both interannual and interdecadal variability. The interdecadal change of the time series implies that the PJ geopotential height pattern has changed from a meridional tripolar pattern of low–high–low to a tripolar pattern of high–low–high since late 1970s. The physical mechanism responsible for the interannual and interdecadal changes of the pattern is a major subject of the current study.

To examine whether the PJ pattern derived is sensitive to the analysis method, a parallel calculation with use of the empirical orthogonal function (EOF) analysis method is employed for the geopotential height field at 500 hPa. The result shows that the leading EOF pattern is almost same as that extracted from the SVD1, with their pattern correlation coefficient being 0.99.

Part of the upward trend of the PJ index likely comes from the global warming signal since 500-hPa geopotential height represents the air temperature averaged vertically from the surface to 500 hPa, which includes the signal of radiatively forced tropospheric warming. To describe the issues explicitly, we detrend NCEP–NCAR 500-hPa geopotential height from 1958 to 2014 before performing the SVD analysis and the PJ index still shows the interdecadal transition, which transitioned from negative to positive in the late 1970s and then from positive to negative in the late 1990s (Fig. 8a).

We reproduce Nitta’s PJ index based on PREC precipitation from 1958 to 2001 (figure omitted). The PJ index defined by Nitta (1986) is based on cloud amount, which is
eq1
where C(A, B) is the high-cloud amount anomaly averaged in the area of latitude A and longitude B. For comparison between them, we use precipitation of PREC to substitute the high-cloud amount to get Nitta’s PJ index. The correlation coefficient between our PJ index and Nitta’s PJ index is −0.596 for 44 summers of 1958–2001, exceeding the 99% significance level. The positive peak of Nitta’s PJ index become larger since the late 1970s, implying that there has been more rainfall at midlatitudes and less precipitation at low latitudes since the late 1970s, which is consistent with our results. The PJ index we defined here is also significant correlated with those defined by Huang and Yan (1999), Wakabayashi and Kawamura (2004), and Kosaka and Nakamura (2010), with correlation coefficients of 0.71, 0.63, and 0.63, respectively, for 44 summers of 1958–2001 (figure omitted).

Hirota and Takahashi (2012) presented a similar tripolar pattern based on SVD analysis to a correlation matrix of the JJA mean precipitation and the 500-hPa geopotential height over East Asia (0°–90°N, 70°E–170°W) during 1979–2005.

To show how different the interannual and interdecadal variations of the PJ pattern might be, we separate the original PJ index into interannual and interdecadal components. The interdecadal component is defined as a 5-yr running mean. The subtraction of the original PJ index from the 5-yr running mean series is defined as the interannual component. Figure 2 shows the two PJ time series, regressed 850-hPa wind and 500-hPa height fields based on the interannual and interdecadal PJ indices. The interannual PJ pattern is characterized by an anticyclone–cyclone–anticyclone tripolar pattern with three centers located almost in the same longitudes (south–north orientation) near the Philippines, Japan, and Okhotsk (Figs. 2b,d). The interdecadal PJ pattern, on the other hand, shows a less organized tripolar pattern (Figs. 2c,e), with the lower-latitude anticyclonic center shifting westward and the midlatitude cyclonic center shifting southeastward. The pattern correlation coefficient between the interannual and interdecadal PJ patterns is 0.47 for 850-hPa streamfunction over the domain 10°–50°N, 100°–160°E and 0.59 for 500-hPa height fields over the same domain. In the following, we will describe observed SST and circulation patterns associated with the PJ interannual and PJ-like interdecadal variations and physical mechanisms responsible for their variability.

Fig. 2.
Fig. 2.

(a) The 5-yr running mean PJ index (broken line with filled symbols) and the interannual PJ index (after subtracting the 5-yr running mean series; solid line with clear symbols). (b),(c) Regressed JJA 500-hPa height onto the interannual and interdecadal PJ index, respectively. (d),(e) Regressed JJA 850-hPa wind onto the interannual and interdecdal PJ index, respectively. Values exceeding the 95% and 90% significance levels are shaded with dark and light gray, respectively, in (b) and (d). The number of degrees of freedom for the index of PJ-like interdecadal variability is 3.58; therefore, significance test is not performed in (c) and (e). In (b)–(e), “A” signifies anticyclone and “C” signifies cyclone.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

4. Observed characteristics and mechanisms of the interannual PJ variation

First we examine the observed anomalous circulation and SST patterns associated with the interannual variation of the PJ pattern. Figure 3 illustrates the regressed SST and 850-hPa wind patterns against the interannual PJ index from the preceding winter to the concurrent summer. In the preceding winter, positive SST anomalies appear in the tropical central and eastern Pacific Ocean, the tropical Indian Ocean, and along the East Asian coast, while negative SSTA appears over the Philippine Sea. This SSTA pattern resembles a typical El Niño pattern during its mature phase. An anomalous anticyclone appears over the Philippine Sea. Figure 4 shows that negative rainfall anomalies are collocated with the anomalous anticyclone in boreal winter. The in-phase relationship between the negative precipitation anomaly and the underlying cold SSTA suggests that the negative SSTA plays an active role in inducing the anomalous anticyclone in the WNP through reduced atmospheric heating. Note that the negative SSTA, the anomalous anticyclone, and the negative precipitation anomaly persist in the WNP from the preceding winter to concurrent summer (Figs. 3 and 4), implying that the local SSTA, although weakening through the concurrent summer, plays a role in affecting the circulation and precipitation anomalies in the tropical WNP (Wu et al. 2010).

Fig. 3.
Fig. 3.

Regressed anomalous SST (color shading; °C) and 850-hPa wind (vectors; m s−1; values exceeding the 95% significance level are shown) fields onto the interannual PJ index in (a) preceding winter (DJF), (b) spring (MAM), and (c) summer (JJA). The left and right rectangular boxes are the specified SSTA domains for the TIO and WNP runs, respectively.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

Fig. 4.
Fig. 4.

Regressed precipitation (mm day−1) anomaly fields against JJA interannual PJ index in (a) the preceding winter (DJF), (b) spring (MAM) and (c) summer (JJA).

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The basinwide warming appears over the tropical Indian Ocean during the El Niño mature winter. Prior to this season a dipole SSTA pattern appears in this region, with a cold SSTA occurring in the eastern Indian Ocean. It is a combination of a positive downward shortwave radiative flux anomaly (associated with El Niño–induced downward motion anomaly; Hong et al. 2010) and ocean wave dynamics (Li et al. 2003) that warms up the eastern Indian Ocean and leads to a basinwide warming. As a result, the precipitation anomaly exhibits a dipole pattern during El Niño mature winter, while the SSTA illustrates a basinwide warming pattern. This implies that the SSTA in the western Indian Ocean plays an active role in influencing the atmosphere, while the SSTA in the eastern Indian Ocean plays a passive role and is influenced by the atmosphere (Chen et al. 2016). As a consequence, the basinwide warming is not responsible for the formation and maintenance of the anomalous anticyclone in the WNP during El Niño mature winter.

The basinwide warming over the tropical Indian Ocean persists from the preceding winter to the concurrent summer (Fig. 3). Note that in the El Niño decaying summer, SST anomalies in the eastern Pacific and associated precipitation anomalies in central equatorial Pacific become much weaker, while strong positive precipitation anomalies appear over the eastern TIO. It is likely that this positive heating anomaly drives the atmospheric Kelvin wave response, leading to low-level anticyclonic shear and thus negative boundary layer moisture and midtropospheric heating anomalies over the WNP (Wu et al. 2009a, Xie et al. 2009).

It is worth mentioning that in most of the WNP region during the concurrent summer the precipitation anomaly is negative, while the SSTA is positive (Figs. 3c and 4c). This implies that the area-averaged SSTA does not necessarily force the area-averaged convection anomaly. The result seems contradictory to the previous study of Huang and Lu (1989) and Huang and Li (1988), who suggested that the local SSTA near Philippines is a major forcing for the PJ teleconnection pattern. The negative correlation between SST and precipitation implies that the warm SST near the Philippines is not a cause but a result of the negative precipitation anomaly. The argument is consistent with an observational analysis by Wang et al. (2005), who found that the anomalous warm SST over the Philippine Sea was attributed to increased download shortwave radiation flux due to reduced cloud amount. The AGCM modeling result by Wu et al. (2010) indicated that the cold SSTA in the eastern edge of the anomalous anticyclone could force an anomalous anticyclonic Rossby wave response in the WNP. Wu et al. (2010) also suggested that cool SST in the WNP is crucial to maintain the anomalous anticyclone in early summer, whereas the warm SST in the TIO plays a more important role in maintaining the anomalous anticyclone in late summer.

Previous observational and modeling studies suggested that both the local cold SSTA in the eastern WNP and the warming over the eastern TIO might affect heating anomalies over the WNP in El Niño decaying summers. The change of anomalous heat source and associated anomalous anticyclone flow in the WNP can strengthen the mei-yu rain belt along the central China and southern Japan through enhanced moisture transport (Chang et al. 2000a,b), which leads to the formation of a meridionally oriented PJ pattern in boreal summer (Fig. 3c). In the remainder of this section, we intend to qualitatively assess the relative roles of remote eastern TIO forcing and WNP SSTA forcing in causing the summer PJ pattern in East Asia through idealized ECHAM4 AGCM experiments (listed in Table 1).

Figure 5 shows the simulated 500-hPa height and 850-hPa horizontal winds and precipitation fields (eastern TIO warm minus eastern TIO cold runs) in JJA. Although the tripolar pattern centered along the coast of eastern Asia shifts eastward, the 500-hPa height field still shows a meridional high–low–high tripolar pattern, which is in general consistent with the observed interannual variation pattern shown in Fig. 2b. In the 850-hPa wind field, the imposed TIO warming causes a significant anticyclone anomaly along the coast of Southeast Asia, a cyclone anomaly east of Japan, and an anticyclonic anomaly near 60°N. The anticyclone–cyclone–anticyclone pattern along East Asia is in general consistent with the observed interannual variation pattern shown in Fig. 2d.

Fig. 5.
Fig. 5.

Simulated (a) 500-hPa height (gpm), (b) 850-hPa streamlines, and (c) precipitation (mm day−1; black dashed lines enclose areas indicating significance at the 95% level) fields in JJA for the interannual TIO SSTA runs (eastern TIO warm minus eastern TIO cold run). In (a),(b), areas with values exceeding the 95% significance level are shaded.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The simulated rainfall pattern (Fig. 5c) resembles the observed pattern (Fig. 4c). For example, the eastern TIO warming increases precipitation over the eastern TIO and the Maritime Continent, but reduces the precipitation over South China Sea and the Philippine Sea where the anticyclonic shear or anticyclonic circulation is located.

Figure 6 shows the simulated summer mean velocity potential and divergent wind fields at 200 and 850 hPa (eastern TIO warm minus eastern TIO cold run). The positive SST anomalies over the eastern TIO cause lower-level convergence and upper-level divergence over the eastern TIO but an opposite divergence pattern over the Philippine Sea. This corresponds to pronounced large-scale upward motion anomalies over the eastern TIO and downward motion anomalies over the WNP. The descending motion anomalies in the WNP are accompanied by a low-level anticyclone anomaly over the Philippine Sea.

Fig. 6.
Fig. 6.

Simulated velocity potential (green contours, 106 m2 s−1) and divergent component of the wind vectors (m s−1) at (a) 850 hPa and (b) 200 hPa in JJA for the interannual TIO SSTA runs (eastern TIO warm minus eastern TIO cold run). Areas with values exceeding the 95% significance level are shaded.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The difference between the eastern TIO warm run and the eastern TIO cold run supports the hypothesis that the eastern TIO warming plays an active role in suppressing the convection and causing an anomalous anticyclone over the Philippine Sea. The suppressed convection over the Philippine Sea may further excite a PJ pattern along the coast of East Asia.

How do the negative SST anomalies over the WNP affect the PJ pattern? Figure 7 shows the simulated summer 500-hPa height and 850-hPa wind and precipitation anomaly fields (WNP cold minus WNP warm run) in response to the cold SSTA in the WNP. In the 500-hPa height response, the imposed WNP cooling causes a significant positive anomaly to the south of 30°N along the coast of East Asia, a negative anomaly between 30° and 50°N, and a positive anomaly at 60°N. The pattern is in general similar to the observed counterparts. The 850-hPa wind response in the tropics is dominated by anomalous northeasterlies south of 20°N from the Bay of Bengal to the date line and an anomalous anticyclone near Taiwan. In the midlatitudes, the model simulates an anomalous cyclonic circulation over Japan and an anomalous anticyclonic circulation near the Sea of Okhotsk. The precipitation anomaly field is dominated by decreased large-scale precipitation over the Bay of Bengal, South China Sea, and Philippine Sea. These negative precipitation regions are the areas where the anomalous low-level anticyclonic circulation is located (Figs. 7a,b). Although the amplitude of the negative SST anomaly in the WNP is attenuating toward the late summer, it attains significant amplitude prior to the WNP monsoon onset (Fig. 3b), which may exert a great impact on the monsoon strength.

Fig. 7.
Fig. 7.

As in Fig. 5, but for the interannual WNP cold run minus interannual WNP warm run.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

To quantitatively measure to what extent the TIO and WNP SSTA forcing contributes to the observed PJ interannual variability, we calculate the pattern correlation coefficient (PCC) and root-mean-square error (RMSE) between the simulated and observed 500-hPa geopotential height fields (Table 2) and 850-hPa streamfunction field (Table 3). Table 2 shows the PCC and RMSE results for 500-hPa geopotential height fields over the East Asia/western Pacific domain (10°–50°N, 100°–160°E). The PCC is 0.53 between the observation (Fig. 2b) and the TIO simulation (Fig. 5a) and 0.71 between the observation (Fig. 2b) and the WNP simulation (Fig. 7a). Based on the pattern correlation, it appears that WNP SSTA forcing reproduces a more realistic PJ pattern than TIO SSTA forcing on the interannual time scale.

Table 2.

Pattern correlation coefficient (PCC) and root-mean-square error (RMSE; gpm) of geopotential height at 500 hPa between the observation and the simulations over the domain 10°–50°N, 100°–160°E.

Table 2.
Table 3.

PCC and RMSE (106 m2 s−1) of 850-hPa streamfunction anomaly fields between the observation and the simulations over 10°–50°N, 100°–160°E.

Table 3.

We also calculated the same PCC as above for the 850-hPa streamfunction fields. The PCC (Table 3) is 0.74 between the observation (Fig. 2d) and the TIO simulation (Fig. 5b) and 0.52 between the observation (Fig. 2d) and the WNP run result (Fig. 7b). The RMSE in the TIO run (0.34 × 106 m2 s−1) appears smaller than that of the WNP runs (0.83 × 106 m2 s−1).The average PCC results for the 500-hPa geopotential height and 850-hPa streamfunction fields over the domain 10°–50°N, 100°–160°E are shown in Table 4. The average PCC is 0.64 between the observation and the TIO simulation and 0.62 between the observation and the WNP simulation. Based on the average pattern correlation, it appears that both the remote Indian Ocean forcing and local SSTA forcing in the WNP play an important a role in inducing the observed interannual variability of the PJ pattern in East Asia.

Table 4.

The average PCC of above height and streamfunction fields.

Table 4.

5. Observed characteristics and mechanisms of the interdecadal PJ-like variation

The time evolution characteristic of the 5-yr running mean PJ index from 1958 to 2014 based on detrended height from NCEP–NCAR shows an interdecadal variation, with shifts from a low phase to a high phase prior to the late 1970s and from a high phase to a low phase in the late 1990s (Fig. 8). Compared to this time series with the preceding winter Niño-3.4 index, detrended SSTA in JJA averaged over the TIO (10°S–23°N, 50°–100°E), and detrended SSTA in JJA averaged over the tropical WNP (0°–23°N, 100°–140°E), one may conclude that the PJ-like interdecadal variation is not caused by El Niño as the Niño-3.4 SST index does not show a significant interdecadal variation. Otherwise, the detrended TIO SST and the tropical WNP SST time series display an interdecadal change in early 1980s and late 1990s. The summer PJ index is significantly correlated with the TIO SST and the tropical WNP SST time series, with correlation coefficients of 0.64 and 0.65, respectively. After a 5-yr running mean is applied to all the time series, the correlation coefficients are 0.40 and 0.55, respectively. After the 5-yr running mean is removed from all the time series, the correlation coefficients of interdecadal PJ index and TIO and the tropical WNP SST are 0.75 and 0.65, respectively.

Fig. 8.
Fig. 8.

Time series of (a) JJA PJ index (black curve) and its 5-yr running mean (gray curve). The detrended NCEP–NCAR 500-hPa height from 1958 to 2014 and precipitation from PREC are applied to obtain the PJ index. (b) Preceding winter (DJF) Niño-3.4 index, (c) JJA SST anomaly averaged in (10°S–23°N, 50°–100°E), and (d) JJA SST anomaly averaged over 0°–23°N, 100°–140°E; (c) and (d) are based on linearly detrended SST from 1958 to 2014.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The significant positive correlations between the interdecadal PJ index and TIO and WNP SSTA time series suggest that they are closely related. Give that a positive interdecadal PJ index corresponds to an anomalous low-level anticyclone over the Philippine Sea (Figs. 2c,e), the positive correlation between the PJ index and the local SSTA suggests that the upward warming trend in the WNP (Fig. 8) is a result of atmospheric forcing. Thus, differently from the interannual time scale, the interdecadal variability of the PJ pattern is likely a response solely to the tropical Indian Ocean forcing. We will explore this hypothesis using idealized AGCM experiments.

Before conducting numerical experiments, we first examine observed anomalous circulation and SST patterns associated with the interdecadal change of the PJ pattern. Figure 9 shows regressed precipitation fields in concurrent summer. The regressed rainfall pattern shows abundant precipitation along middle and lower reaches of the Yangtze River and southern Japan, which is collocated with low-level cyclonic circulation and trough (Figs. 2c,e). Negative precipitation anomalies appear over tropical WNP south of 25°N, where low-level circulation is dominated by anomalous anticyclonic circulation.

Fig. 9.
Fig. 9.

Precipitation (mm day−1) regressed onto the interdecadal (5-yr running mean) JJA PJ index.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

Figure 10 illustrates the horizontal patterns of SST in summer anomalies regressed onto the 5-yr running mean summer PJ index. The interdecadal PJ index is positively correlated with the SSTA in the TIO, most parts of the tropical WNP, and the off-equatorial areas of the tropical central and eastern Pacific Ocean. Negative SST anomalies appear over the midlatitude North Pacific between 20° and 40°N. A weak cold SSTA appears in the eastern WNP domain. The distributions of the regressed SST field of the winter and spring are quite similar to that of the summer (not shown).

Fig. 10.
Fig. 10.

Anomalous SST (°C) patterns in summer (JJA) regressed onto the interdecadal (5-yr running mean) PJ index. The left and right rectangular boxes are the specified SSTA domains for the TIO and WNP runs, respectively.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

To quantitatively examine the relative roles of the TIO SSTA and a weak SSTA in the eastern WNP in causing the interdecadal PJ pattern, we rely on idealized ECHAM4 experiments. Two sets of the AGCM experiments were carried out with imposed different SST anomalies in the TIO and the WNP respectively. In the TIO warm run, we add the regressed SST anomalies over the TIO (10°S–23°N, 50°–100°E) (shown in Fig. 10) to the climatological SST as the model lower boundary condition. In the TIO cold run, the same SSTA pattern with an opposite sign is added to the climatological monthly SST field. In the WNP cold run, we add the negative-only SSTA in the WNP (0°–23°N, 120°E–180°) to the climatological SST. In the WNP warm run, an opposite SSTA pattern over the WNP is specified.

Figure 11 shows the simulated 500-hPa height and 850-hPa wind and precipitation anomaly fields (TIO warm minus TIO cold run) in JJA. In the 500-hPa height field, there is a clear high–low–high pattern in East Asia, oriented in the meridional direction. A significant positive height anomaly appears over South Asia and over the east of the Sea of Okhotsk, while a negative height anomaly appears around Japan. The 500-hPa anomaly field is accompanied by an anticyclone–cyclone–anticyclone pattern along the coast of East Asia at the 850-hPa wind anomaly field. The 850-hPa wind response in the tropics is dominated by anomalous easterlies in the tropics south of 20°N and an anticyclone over the South China Sea. A cyclonic circulation appears over southern Japan, while an anticyclonic circulation occurs over the east of the Sea of Okhotsk. This anomalous low-level circulation pattern resembles well the observed interdecadal wind field (Fig. 2e).

Fig. 11.
Fig. 11.

As in Fig. 5, but for the interdecadal TIO warm run minus interdecadal TIO cold run.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The TIO warming causes a positive precipitation anomaly in situ and over the Maritime Continent and a negative precipitation anomaly over the South China Sea and Philippine Sea (Fig. 11c). This precipitation pattern agrees well with the observation (Fig. 9). The anomalous precipitation and low-level wind patterns confirm the heating-induced Kelvin wave response mechanism proposed by Wu et al. (2009a) and Xie et al. (2009).

The simulated velocity potential and divergent wind fields at 200 and 850 hPa in JJA are consistent with the precipitation anomaly pattern (Fig. 12). Associated with positive SST and rainfall anomalies over the TIO, there are low-level convergence and upper-level divergence anomalies in situ. Together with the suppressed convection and anomalous descending motion in the WNP, there are low-level divergence and upper-level convergence anomalies.

Fig. 12.
Fig. 12.

As in Fig. 6, but for the interdecadal TIO warm run minus interdecadal TIO cold run.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

Does the weak cold SSTA in the eastern WNP induce the observed PJ interdecadal pattern? Figure 13 shows the simulated 500-hPa height and 850-hPa wind and precipitation anomaly fields in JJA (WNP cold minus WNP warm run). Because the amplitude of the imposed cooling is too weak and far away, the WNP SSTA forcing cannot induce the PJ-pattern like response over East Asia. Moreover, the cold SSTA in the eastern WNP cannot induce a negative precipitation anomaly over the Philippine Sea, which is clearly shown in the observation (in Fig. 9).

Fig. 13.
Fig. 13.

As in Fig. 5, but for interdecadal WNP cold run minus interdecadal WNP warm run.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

To quantitatively measure to what extent the TIO and WNP SSTA forcing contributes to the observed PJ interdecadal variability, we also calculate the PCC and RMSE between the simulated and observed 500-hPa geopotential height fields (Table 2) and 850-hPa streamfunction field (Table 3). The calculated PCC is 0.29 between the observation (Fig. 2c) and the TIO run result (Fig. 11a) for 500-hPa height anomaly fields over the domain 10°–50°N, 100°–160°E, while the RMSE is 3.92 gpm (Table 2). The PCC becomes even higher (0.50) when using 850-hPa streamfunction anomaly fields at the same domain. As expected, the PCC is much smaller for the WNP SSTA forcing experiments (0.19). The average PCC is 0.40 between the observation and the TIO run result over the same analysis domain (10°–50°N, 100°–160°E) (Table 4). The average PCC is 0.25, which is much smaller for the WNP SSTA forcing experiments.

Based on the above idealized numerical experiments, we conclude that the TIO SSTA change is a primary cause of interdecadal change of anomalous heat source in the WNP and the PJ pattern over East Asia in the late 1970s. The cooling in the eastern WNP does not contribute to the observed change. The steady warming over the Philippine Sea since 1948 (Fig. 8d) is likely a result of the increased downward shortwave radiative fluxes in situ in association with the suppressed precipitation anomaly and the low-level anticyclonic anomaly over the Philippine Sea.

6. Summary and discussion

Based on the SVD analysis of summertime 500-hPa height anomaly field in East Asia and the precipitation anomaly field over the WNP, a new PJ index is introduced. This PJ index well depicts the interannual and interdecadal variations of the PJ pattern over East Asia. In addition to the pronounced interannual variation associated with ENSO, the PJ index has experienced a notable regime shift since late 1970s; that is, the PJ pattern has changed from a cyclone–anticyclone–cyclone meridional tripolar pattern prior to late 1970s to an anticyclone–cyclone–anticyclone tripolar pattern after the late 1970s.

The observational analysis shows that the interannual variation of the PJ pattern in East Asia is closely related to SSTA forcing in both the TIO and WNP. On one hand, the El Niño teleconnection causes the TIO basinwide warming from the El Niño peak winter to the following summer. The enhanced rainfall anomaly associated with the positive SSTA in the TIO during the El Niño decaying summer promotes a baroclinic Kelvin wave response. The low-level anticyclonic shear associated with the Kelvin wave response suppresses convection, leadinh to the formation of an anomalous anticyclone near the Philippine Sea. The negative heat source further excites an anticyclone–cyclone–anticyclone PJ pattern along the coast of East Asia. On the other hand, El Niño heating-induced circulation also causes anomalous cooling in the WNP during El Niño mature winter. This cold local SSTA persists from northern winter to spring. It weakens and shrinks to the eastern WNP in summer, but still impacts the monsoon heating over Philippine Sea in El Niño decaying summer.

The idealized numerical experiments with the ECHAM4 AGCM were carried out to reveal the relative roles of remote Indian Ocean and local WNP SSTA forcing in forcing the anomalous anticyclone in the WNP. The numerical modeling results confirm that both the remote SSTA forcing from the TIO and local WNP SSTA forcing over the WNP equally contribute to the interannual variation of the PJ pattern. The pattern correlation coefficients between the observed and simulated 850-hPa streamfunction anomaly fields are about 0.56 for both the remote and local WNP SSTA forcing experiments.

It is noted that ENSO is not the direct cause of interdecadal variations of the PJ pattern. The interdecadal variation of the PJ pattern is closely related to the interdecadal change of SSTs in the TIO (characterized by an upward warming trend in the TIO since 1948). Based on the observed rainfall–SST relationship, we hypothesize that the SST change in the TIO is a major driving mechanism for the interdecadal transition of atmospheric low-level circulation from an anomalous cyclone to an anomalous anticyclone over the Philippine Sea since the late 1970s; the steady warming over the Philippine Sea, on the other hand, results from the local anomalous anticyclone through increased downward shortwave radiation flux. We tested this hypothesis with idealized ECHAM4 experiments. The numerical experiment results confirmed the hypothesis above. The remote TIO SST forcing, through enhanced local precipitation, induces anomalous anticyclonic circulation over the WNP through a so-called Kelvin wave forcing mechanism suggested by Wu et al. (2009a) and Xie et al. (2009). The negative heat source in the WNP further excites an anticyclone–cyclone–anticyclone PJ pattern over the East Asia. The weak cooling in the eastern WNP has little effect on the PJ interdecadal variation. Therefore, the observational and modeling results indicate different forcing mechanisms for the interannual and interdecadal variations of the PJ pattern.

It is worth mentioning that there is a bias in the simulated interdecadal precipitation field over the mei-yu front region. Abundant precipitation is observed over the subtropics but not clearly shown in the TIO simulation. Such a bias is likely due to mean state bias. Figure 14 shows the mean precipitation and 10-m wind from the control run (Fig. 14a) and observation [precipitation from Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) data and 10-m wind from ERA-40] (Fig. 14b), as well as their difference (Fig. 14c), during the JJA season. The common features of the westerlies in the north of tropical Pacific and the easterlies in the north of TIO are well reproduced in control run. The drawback of the simulation is the weak southerlies along the East Asian coast and the extension of westerlies from the TIO to the Philippine Sea in the control run, compared to the observation. The weak southerlies along the East Asian coast will cause insufficient rainfall near Japan. This may be one reason that abundant precipitation is observed around Japan but is unclear in the TIO simulation. An et al. (2009) suggested that the adjusted GCM diabatic heating in the tropics would improve the JJA seasonal mean circulation and the mei-yu rainband over East Asia in the GCM. Xie et al. (2016) suggest that the pronounced internal variability of the atmosphere in subtropics and the inadequate representation of subtropical circulation such as the Silk Road pattern also affect the simulation results. Alternatively, other factors may also affect the rainfall along the mei-yu front, such as spring Eurasian snow cover (Yang and Xu 1994; Wu et al. 2009b, Zhang et al. 2013) and Tibetan Plateau warming (Zhang et al. 2004; Wu et al. 2007; Wang et al. 2008). In this study, we use ERA-40 monthly datasets for about 44 years from 1958 to 2001 to study the mechanism of the interdecadal variation of PJ pattern, and results show that the PJ pattern flips over in the late 1970s, which is attributed to the interdecadal variability of TIO SST.

Fig. 14.
Fig. 14.

The JJA mean precipitation (color shading; mm day−1) and 10-m streamlines for (a) the control run and (b) observations (precipitation from CMAP data during 1979–99 and wind from ERA-40 during 1960–99), and (c) their difference.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

The dominant feature of the tropical Indian Ocean SST is the warming trend during the twentieth century (figure omitted), which has been attributed to human activity (Lau and Weng 1999; Du and Xie 2008). However, the pronounced decadal (including multidecadal) fluctuations are also present in TIO SSTs (as shown in Fig. 16). Figure 15 shows the leading EOF of SST for the TIO and Pacific Ocean based on linearly detrended and 8-yr Lanczos low-pass filtered HadISST data in JJA from 1874 to 2011. Figure 15a represents the Indian Ocean basin pattern and explains 64% of the variance and Fig. 15b represents the interdecadal Pacific oscillation (IPO; Zhang et al. 1997) spatial pattern and explains 24% of the variance. Figure 16 shows the leading PC (PC1) for the TIO (black curve) and Pacific (green). It is clearly shown that the interdecadal variation of TIO SST is closely linked with interdecadal variation of IPO, which is consistent with other researchers’ work (Han et al. 2014; Dong et al. 2014). The interdecadal SST pattern in Fig. 10 is similar to the leading EOF over TIO in Fig. 15a, and all present a basin warming pattern.

Fig. 15.
Fig. 15.

(a) The leading EOF of SST for the Indian Ocean, based on linearly detrended and 8-yr Lanczos low-pass filtered HadISST data from 1874 to 2011, which explains 64% of the variance. The filtered SST from 1874 to 2011 is chosen to perform the EOF analysis. The 1870–73 and 2012–15 data are excluded to remove the 8-yr low-pass filter’s endpoint effect. (b) As in (a), but for the Pacific Ocean SST leading EOF, which represents the IPO spatial pattern and explains 25% of the variance.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

Fig. 16.
Fig. 16.

The leading PC (PC1) of 8-yr low-passed SST for the TIO (black curve) and Pacific Ocean (green).

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

Can the interdecadal SST over central Pacific force the PJ-like pattern along the East Asian coast? To clarify this point, another set of experiments for the interdecadal central Pacific (CP) are also carried out in addition to the interdecadal TIO and WNP runs. In the interdecadal CP warm run, we add the monthly regressed SSTA field from January to December over the CP domain (10°S–10°N, 150°E–150°W) to the climatological SST, where the SST–rainfall relationship is positive, to force the ECHAM. In the CP cold run, we add the same SSTA pattern but multiply −1 by the climatological SST over the CP domain. The differences between the CP warm and CP cold experiments are shown in Fig. 17. It is obvious that the positive SST over the center Pacific forces an anomalous cyclone and positive precipitation over the Philippine Sea, rather than an anomalous anticyclone and negative precipitation over Philippine Sea, which is observed as shown in Fig. 9. Over the tropical eastern Pacific Ocean, the SST–rainfall relationship is negative on interdecadal time scales (Figs. 9 and 10), and the positive SST anomalies cannot be used in the AGCM to force the atmosphere because they do not play an active role.

Fig. 17.
Fig. 17.

As in Fig. 5, but for the interdecadal CP warm run minus interdecadal CP cold run.

Citation: Journal of Climate 30, 22; 10.1175/JCLI-D-15-0817.1

All the analysis shows that the TIO SST has pronounced interdecadal variation overlying the warming trend. The interdecadal TIO SST could cause interdecadal convection heating, which could affect the interdecadal variation of anticyclones over the Philippine Sea, and then affect the interdecadal variation of the PJ-like pattern. The interdecadal variation of TIO SSTs is closely linked with the IPO. But the mechanism of how the IPO affects the TIO SST is still unclear.

Acknowledgments

This work was supported by National Key R&D Program 2016YFA0600402, National Basic Research Program of China 2015CB453200, National Natural Science Foundation of China (41375098, 41375095, and 41575043), Jiangsu NSF project BK20150062, Jiangsu Shuang-Chuang Team R2014SCT001, and the Priority Academic Program Development of Jiangsu Higher Education Institute (PAPD). This is School of Ocean and Earth Science and Technology Contribution Number 10227, International Pacific Research Center Contribution Number 1289, and Earth System Modeling Center Contribution Number 185.

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