Different ENSO Teleconnections over East Asia in Early and Late Winter: Role of Precipitation Anomalies in the Tropical Indian Ocean and Far Western Pacific

Tianjiao Ma aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Wen Chen aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollege of Earth and Planetary Sciences, University of the Chinese Academy of Sciences, Beijing, China
cDepartment of Atmospheric Sciences, Yunnan University, Kunming, China

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Shangfeng Chen aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Chaim I. Garfinkel dFredy and Nadine Herrmann Institute of Earth Sciences, Hebrew University, Jerusalem, Israel

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Shuoyi Ding eInstitute of Atmospheric Sciences, Fudan University, Shanghai, China

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Lei Song aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Zhibo Li fDepartment of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

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Yulian Tang aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bCollege of Earth and Planetary Sciences, University of the Chinese Academy of Sciences, Beijing, China

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Jingliang Huangfu aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Hainan Gong aCenter for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

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Wei Zhao gNational Meteorological Center, China Meteorological Administration, Beijing, China

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Abstract

This study aims to better understand the ENSO impacts on climate anomalies over East Asia in early winter (November–December) and late winter (January–February). In particular, the possible mechanisms during early winter are investigated. The results show that ENSO is associated with a Rossby wave train emanating from the tropical Indian Ocean toward East Asia (denoted as tIO-EA) in early winter. This tIO-EA wave train in El Niño (La Niña) is closely related to a weakening (strengthening) of the East Asian trough, and thereby a weakened (strengthened) East Asian winter monsoon and warm (cold) temperature anomalies over northeastern China and Japan. By using partial regression analysis and numerical experiments, we identify that the formation of tIO-EA wave train is closely related to precipitation anomalies in the tropical eastern Indian Ocean and western Pacific (denoted as eIO/wP). In addition, the ENSO-induced North Atlantic anomalies may also contribute to formation of the tIO-EA wave train in conjunction with the eIO/wP precipitation. The response of eIO/wP precipitation to ENSO is stronger in early winter than in late winter. This can be attributed to the stronger anomalous Walker circulation over the Indian Ocean, which in turn is caused by higher climatological SST and stronger mean precipitation state in the Indian Ocean during early winter.

© 2022 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: Wen Chen, cw@post.iap.ac.cn

Abstract

This study aims to better understand the ENSO impacts on climate anomalies over East Asia in early winter (November–December) and late winter (January–February). In particular, the possible mechanisms during early winter are investigated. The results show that ENSO is associated with a Rossby wave train emanating from the tropical Indian Ocean toward East Asia (denoted as tIO-EA) in early winter. This tIO-EA wave train in El Niño (La Niña) is closely related to a weakening (strengthening) of the East Asian trough, and thereby a weakened (strengthened) East Asian winter monsoon and warm (cold) temperature anomalies over northeastern China and Japan. By using partial regression analysis and numerical experiments, we identify that the formation of tIO-EA wave train is closely related to precipitation anomalies in the tropical eastern Indian Ocean and western Pacific (denoted as eIO/wP). In addition, the ENSO-induced North Atlantic anomalies may also contribute to formation of the tIO-EA wave train in conjunction with the eIO/wP precipitation. The response of eIO/wP precipitation to ENSO is stronger in early winter than in late winter. This can be attributed to the stronger anomalous Walker circulation over the Indian Ocean, which in turn is caused by higher climatological SST and stronger mean precipitation state in the Indian Ocean during early winter.

© 2022 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: Wen Chen, cw@post.iap.ac.cn

1. Introduction

El Niño–Southern Oscillation (ENSO) is the most prominent atmosphere–ocean coupled climate phenomenon in the tropics on interannual time scales. It is well known that the climate impact of ENSO is not only confined to the tropics, but also spreads globally to extratropical regions (e.g., Horel and Wallace 1981; Rasmusson and Wallace 1983; Ropelewski and Halpert 1987; Deser and Wallace 1990; Chen et al. 2000; Wu et al. 2003; Xie et al. 2009; Zhou and Wu 2010; Cai et al. 2011; Ding et al. 2017; Zhao et al. 2020).

East Asia is one of the extratropical regions affected by ENSO during boreal winter. El Niño events tend to weaken the East Asian winter monsoon (EAWM), leading to anomalous warm temperatures and increased rainfall in East Asia, whereas La Niña events generally have the opposite effect (e.g., Zhang et al. 1996; Wu et al. 2003; Chen et al. 2013; Chen et al. 2014; Jia et al. 2014). It is well established that ENSO affects the East Asian winter climate by generating an anomalous anticyclone/cyclone over the western North Pacific (e.g., Zhang et al. 1996; Wang et al. 2000; Wu et al. 2017; Zhang et al. 2017). The anomalous anticyclone (cyclone) is a Rossby wave response to the colder (warmer) sea surface temperature (SST) anomalies in tropical western Pacific associated with El Niño (La Niña). Southerly (northerly) wind anomalies to the northwestern edge of the anomalous anticyclone (cyclone) cause a weakening (strengthening) of the northerly monsoon flow over East Asia.

Recently, several studies have shown that the impacts of ENSO on East Asian winter climate may differ from early to late winter. Here, the periods early and late winter refer to the months of November–December and January–February, respectively. Tian and Fan (2019) showed that the prediction skill of the EAWM is higher in early winter than in late winter. They attributed the change in EAWM prediction skill to the strong (weak) relationship between ENSO and the EAWM in early winter (late winter). Some studies have reported that ENSO induces an anomalous anticyclone over the Kuroshio Extension region in early winter; however, this anomalous Kuroshio anticyclone suddenly disappears in January (Son et al. 2014; Kim et al. 2018). The change in the anomalous Kuroshio anticyclone leads to a stronger relationship between ENSO and precipitation anomalies in the Korean Peninsula in early winter that weakens in late winter (Son et al. 2014).

The mechanisms underlying the changes in relationship between ENSO and East Asian winter climate anomalies from early to late winter are not fully understood. Tian and Fan (2019) suggested that the change in the ENSO–EAWM relationship may be due to differences in the impacts of ENSO on the tropical Walker circulation. During early winter, ENSO induces a double-cell anomalous Walker circulation over the tropical Indian Ocean (tIO) and the tropical Pacific Ocean. The double-cell anomalous Walker circulation gives rise to a systematic wave train pattern, which further exerts a strong influence on the EAWM; however, during late winter, ENSO is accompanied by a single-cell anomalous Walker circulation over the tropical Pacific, and the associated wave train pattern in the Pacific–North American region is too far to the east of the EAWM to strongly affect it. Unlike Tian and Fan (2019), Kim et al. (2018) proposed that the evolution of the ENSO teleconnection over the North Pacific from December to January is contributed by precipitation forcings in the western North Pacific and in the tropical central Pacific. In particular, the sudden disappearance of the Kuroshio anticyclone in January could be due to the decreased precipitation anomalies in the western North Pacific and the increased precipitation anomalies in the tropical central Pacific.

Some studies have shown that the effects of ENSO on winter climate anomalies in East Asia are modulated by Indian Ocean SST or precipitation anomalies. For example, SST warming in the tIO can lead to a strengthening of the anomalous western North Pacific anticyclone associated with ENSO (Watanabe and Jin 2002; Yuan et al. 2012). Shiozaki et al. (2021) divided the impact of ENSO on temperature anomalies in East Asia into warm and non-warm scenarios. They suggested that, compared to the non-warm scenario, the warm scenario is associated with Indian Ocean basin warming during winter and the Indian Ocean dipole mode in the previous autumn. By utilizing reanalysis data and long-term model simulations, Kim and An (2019) reported a strengthening of the anomalous western North Pacific anticyclone when El Niño coexists with the positive phase of Indian Ocean dipole mode. These studies hint that SST and precipitation anomalies in the tIO may play a role in the different ENSO–East Asia relationship between early and late winter; however, little attention has been paid to this possible pathway.

The present study aims to better understand the mechanisms by which ENSO affects climate anomalies in East Asia, with a particular focus on the early winter. Besides the anomalous western North Pacific anticyclone, Tian and Fan (2019) proposed that a systematic wave train pattern also contributes to the close ENSO–East Asia teleconnection in early winter. It is noticed that this wave train pattern has a striking resemblance with a Rossby wave train propagating from the tIO to East Asia, and thereby causes climate anomalies over East Asia. However, the mechanism for the generation of this wave train is not well understood. On the other hand, Zheng et al. (2012) reported that the dominant mode of precipitation anomalies in the tropical Indian Ocean and far western Pacific (tIOWP) can excite the extratropical Rossby wave train that emits from the tIO toward Asia. The above evidence prompts us to investigate possible roles of precipitation anomalies in the tIOWP in relaying the impact of ENSO to East Asian climate in early winter.

To focus on these questions raised above, we organize the rest of the paper as follows. Section 2 describes the data and methods used in this study. In section 3, we first show the ENSO-induced teleconnection patterns over East Asia in early and late winter, respectively, and then analyze the associated climate anomalies. Section 4 elaborates on the mechanism of the ENSO–East Asia teleconnection in early winter. In this section, respective roles of precipitation forcings in the tropical Indian Ocean versus the Pacific Ocean are compared. Possible factors that lead to different ENSO–East Asian climate relationships between early and late winter will be discussed in section 5. A summary and discussion are provided in section 6.

2. Data and methods

a. Data

In this study, the monthly Hadley Centre Sea Surface Temperature (HadISST) 1.1 dataset is employed to monitor the variation of global SST (Rayner et al. 2003). The National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) Reanalysis 2 dataset (Kanamitsu et al. 2002) is used to diagnose variations in the atmospheric circulation. It should be noted that very similar results can be obtained by the ECMWF ERA5 reanalysis. The Global Precipitation Climatology Project (GPCP) dataset (Adler et al. 2018) is utilized to illustrate precipitation anomalies.

The present study focuses on the period 1979–2019 when the datasets are more reliable. We divide the boreal winter into early winter months [November–December (ND)] and late winter months [January–February (JF)], as previous studies have reported that ENSO induces different extratropical atmospheric circulation responses during these two periods (King et al. 2018; Abid et al. 2021). To focus on the interannual variations, linear trends in all time series of the original data are removed (a 9-yr high-pass Lanczos filter would lead to similar results; however, the filter would cause at least 9 years of the total data to be discarded).

b. Indices and wave activity flux

The strength of ENSO is estimated by the Niño-3.4 index, which is defined as the SST anomalies averaged in the region of 5°S–5°N, 170°–120°W. The list of historical El Niño and La Niña events is obtained from the NOAA Climate Prediction Center (CPC). According to the CPC definition, an El Niño or a La Niña event is identified when the oceanic Niño index, which is the 3-month running mean of SST anomalies in the Niño-3.4 region, exceeds the threshold of ±0.5°C for five consecutive overlapping seasons.

For the composite analysis, we first identify the El Niño and La Niña winters according to NOAA/CPC (Table S1 in the online supplemental material). Then, we sum the ND(−1)-averaged and JF(0)-averaged variables during these El Niño (La Niña) winters to obtain the composite results for the early and late winters, respectively. Here, time notations of (−1) and (0) represent the year before and during the peak of ENSO event [e.g., D(−1)JF(0)]. For the regression/correlation analysis, we used the ND-averaged Niño-3.4 index and ND-averaged other variables such as geopotential height, wind, and precipitation to calculate the regression patterns of early winter anomalies. Similarly, we used the JF-averaged Niño-3.4 index and JF-averaged variables to calculate the late winter regressions.

The wave activity flux (WAF) defined by Takaya and Nakamura (2001) is used to diagnose the propagation of Rossby waves. This wave activity flux is applicable for quasigeostrophic eddies on a zonally varying basic flow, and is independent of the wave phase. For the calculation of the wave activity flux, please refer to Eq. (38) in Takaya and Nakamura (2001).

c. Partial regression

Partial regression analysis is employed to isolate the impacts of different factors (e.g., X and Y). The isolated variations in X that is independent of Y can be obtained from calculation of a residual X (X|res):
X|res=XbY.
Here, b is the least squares regression coefficient between the time series of X and Y.

d. Linear baroclinic model

A linear baroclinic model (LBM) is employed in this study to analyze the linear response of the atmospheric circulation to steady tropical precipitation forcings. This LBM is developed by the University of Tokyo and the National Institute for Environmental Studies. Details of the model formulation are given in Watanabe and Jin (2002). Many previous works have employed the LBM to help understand the linear response of the extratropical atmospheric circulation to a prescribed tropical forcing, such as heating associated with ENSO (e.g., Jia et al. 2015; Ma et al. 2018).

In this study, we adopt a dry model configuration and a time integration method to obtain the steady atmospheric response to tropical forcings. Our experiments use a T42 horizontal resolution and 20 vertical levels on σ surfaces. We linearize the model about basic states computed from the climatology of early and late winter from the NCEP–DOE reanalysis 2 for 1979–2019. The time integration is continued up to 30 days, while the responses averaged from days 10 to 20 are shown (Watanabe and Jin 2002).

The prescribed diabatic heating source in the LBM experiment is calculated from the precipitation anomaly by the regression analysis [following the method of Kim et al. (2018)]; that is, a prescribed heating denoted as H(x, y, z) is calculated as follows:
H(x,y,z)=PRCP(x,y)Weight(z),
where PRCP(x, y) indicates precipitation anomalies obtained from the regression analysis; Weight(z) represents the vertical profile of the heating with a gamma shape peaking at σ = 0.45 (about 400–500 hPa).

3. ENSO teleconnection over East Asia in early and late winter

We begin by examining the composite anomalies of geopotential heights at 200 hPa (Z200) for El Niño and La Niña events during early winter (ND), late winter (JF), and the entire winter [December–February (DJF)] (Fig. 1). We immediately observe that the responses of the Z200 anomalies over East Asia differ remarkably between early and late winter.

Fig. 1.
Fig. 1.

Composite anomalies of (a),(b) ND-averaged, (c),(d) JF-averaged, and (e),(f) DJF-averaged Z200 (m; contour interval = 10 m) for (left) El Niño and (right) La Niña winters. Heavy and light shading in red (blue) indicate positive (negative) Z200 anomalies significant at the 95% and 90% confidence levels, respectively.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

In the early winter of El Niño events, a wave pattern is observed from the tIO toward East Asia, with negative Z200 anomalies over southern China and positive Z200 anomalies over Japan (Fig. 1a). During the early winter of La Niña, a similar pattern of Z200 anomalies is found, but with opposite sign (Fig. 1b). We will refer to this as the tIO–East Asia (tIO-EA) wave train. In the late winter of El Niño, the positive Z200 anomalies near Japan disappear (Fig. 1c). Instead, an anomalous positive Z200 center and an anomalous negative Z200 center take their place over central Asia and southeastern China, respectively. For the late winter of La Niña, significant Z200 anomalies appear over the area around Novaya Zemlya; meanwhile, negative and positive Z200 anomalies appear over East Asia (Fig. 1d).

The ENSO teleconnection in the midlatitudes has nonlinear and asymmetric features (Hoerling et al. 1997). We further estimate the linear and nonlinear components of ENSO teleconnection over East Asia by the composites of El Niño minus La Niña and El Niño plus La Niña anomalies, respectively (Fig. S1 in the online supplemental material). From Fig. S1b, the composite ND Z200 anomalies over East Asia for El Niño + La Niña are very weak. This result indicates that the nonlinear component of ENSO teleconnection over East Asia during early winter is small. In contrast, for late winter, we observe large Z200 anomalies over the high latitudes of Eurasia in the composite of El Niño + La Niña (Fig. S1d). The amplitude of these Z200 anomalies is comparable to that in the composite of El Niño − La Niña (Fig. S1c). The results suggest that the nonlinear component of ENSO teleconnection over high-latitude Eurasia during late winter is as important as the linear component. Regarding the asymmetry of atmospheric responses to ENSO, Feng et al. (2017) have suggested that the background state plays a vital role. In addition, responses of stratospheric polar vortex to ENSO, especially the stratospheric sudden warming events, have shown large asymmetry (Garfinkel et al. 2012). Thus, the atmospheric responses in tropospheric extratropics including East Asia to ENSO via the stratospheric route may be asymmetric. These together may help us understand the asymmetric responses in the troposphere to ENSO in late winter. However, there is another critical and interesting question: Why does the atmospheric response to El Niño and La Niña show strong linearity in early winter? This issue needs a comprehensive study in the future.

Figures 1e and 1f show the DJF-averaged Z200 anomalies during El Niño and La Niña events, respectively. We can see that a DJF-mean analysis may obscure some features of the ENSO teleconnection over East Asia in early and late winters. ENSO is a major driver of EAWM interannual variability and the key ingredient used in seasonal forecasts of winter climate in East Asia. A separation of early and late winter analysis may help us better understand the impacts of ENSO on East Asia climate, and is helpful in improving subseasonal to seasonal forecasts of East Asian wintertime climate.

Figures 2a and 2b show the composite anomalies of Z200 and 200-hPa WAF in El Niño and La Niña early winter, respectively. From Fig. 2a, we immediately observe WAFs from the tIO toward East Asia. In addition, there are also WAFs that spread from the North Atlantic to West Asia, and then turn northeastward to East Asia. The result suggests that the North Atlantic anomalies may also contribute to the tIO-EA wave train. On the other hand, recent studies have shown that atmospheric heating anomalies over the tropical Indian Ocean can excite a Rossby wave train that propagates toward East Asia (e.g., Abid et al. 2021). Also, there is study suggesting that the North Atlantic circulation anomalies may be sourced from a circumglobal teleconnection (CGT)-like wave pattern generated by heating anomalies over the Indian Ocean (Ding and Wang 2005). Thus, it is possible that both the North Atlantic and tropical Indian Ocean heating contribute to the formation of the tIO-EA wave train. Their respective roles will be examined in section 4. For the early winter of La Niña events (Fig. 2b) the patterns of anomalous Z200 and WAFs by and large resemble those in El Niño, but with opposite sign.

Fig. 2.
Fig. 2.

Composite patterns for the anomalous Z200 (contours; contour interval = 10 m) and wave activity flux (vectors; m2 s−2) at 200 hPa for (a) El Niño events in ND and (b) La Niña events in ND. (c),(d) As in (a) and (b), but for JF. Heavy and light shading in red (blue) indicate positive (negative) anomalous Z200 values significant at the 95% and 90% confidence levels, respectively. The vectors are masked out in the following regions: 1) areas where the climatological zonal winds are less than 0 m s−1 (the light-green contours show where U = 0 m s−1); 2) areas where the WAF is smaller than 0.1 m2 s−2; and 3) 10°S–10°N, which is not suitable for quasigeostrophic eddies.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

The spatial pattern of the atmospheric wave train in late winter associated with ENSO is shown in Figs. 2c and 2d for a comparison with that in early winter. From Fig. 2c, we observe that there are two major Rossby wave trains during El Niño late winters, one over central-eastern Eurasia and the other in the PNA region, although the Eurasian one is weaker than the one in the PNA region. The wave train patterns in La Niña late winters are similar to those in El Niño late winters, but with opposite sign (Fig. 2d). Particularly, the above results suggest that spatial pattern of the atmospheric response associated with ENSO over central-eastern Eurasia is obviously different from that in early winter. Marginal northeastward WAFs can also be observed in the Arabian Sea and Bay of Bengal regions in late winter. These fluxes, however, cannot propagate farther into the extratropics. The results seem to indicate that there also exists atmospheric wave propagation from the tropical Indian Ocean, but with much weaker amplitude and no extratropical wave node. The reasons for the weakened northeastward propagation of the wave train and the lack of the extratropical wave node in late winter need further investigation, though they may be partly due to change in the background mean flow (Wang et al. 2019; Chen et al. 2020).

Figures 3a–f show the composite anomalies of ND-averaged 500-hPa geopotential heights (Z500) and 850-hPa horizontal winds (wind850) and air temperature (T850) during El Niño and La Niña events. In the early winter of El Niño, the Z500, wind850, and T850 anomalies reveal a weakened EAWM system, especially in the mid- and high latitudes (Figs. 3a–c). Positive Z500 anomalies near Japan indicate a weakening of the East Asian trough. In association with the weakened East Asian trough, anomalous anticyclonic flow appears in the lower troposphere with a center near Japan (Fig. 3a); furthermore, the southerly wind anomalies on the western edge of the anomalous anticyclone cause warm air temperature anomalies over Japan and northeastern China (Fig. 3c) via meridional advection of warmer and wetter air from lower latitudes (Fig. 3b). For the La Niña early winter, the spatial patterns of Z500, wind850, and T850 anomalies over East Asia are almost identical to those in El Niño, but with opposite signs. This indicates that the teleconnections of El Niño and La Niña over East Asia exhibit symmetric features during early winter.

Fig. 3.
Fig. 3.

Composite anomalies of (left) Z500 (m), (center) 850-hPa winds (m s−1), and (right) 850-hPa air temperature (°C) for (a)–(c) El Niño early winter, (d)–(f) La Niña early winter, (g)–(i) El Niño late winter, and (j)–(l) La Niña late winter. Heavy and light shading in red (blue) in the left column indicate positive (negative) anomalous Z500 values significant at the 95% and 90% confidence levels, respectively. Stippling in the center and right columns indicates winds and temperature anomalies, respectively, significant at the 95% confidence level.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

In the late winter of El Niño, the Z500 anomalies show weak and insignificant variations in the East Asian trough (Fig. 3g). Meanwhile, the anomalous western North Pacific anticyclone (near the Philippine Sea) is confined to south of 30°N (Fig. 3h). This is consistent with the fact that the anomalous western North Pacific anticyclone is an equatorial Rossby wave response to the negative heating anomaly in the Maritime Continent/western Pacific, and it is trapped in the tropics according to the theory of the tropical waves (Paldor 2015, 2019). Correspondingly, impacts of El Niño on low-level temperature anomalies over East Asia are relatively weak during late winter (Fig. 3i). In the late winter of La Niña, we observe notable circulation anomalies over the mid- and high latitudes. The factors causing these circulation anomalies need further investigation, although they may be partly due to the changes in polar vortex (Yu and Sun 2021).

4. Possible roles of precipitation anomalies in the tropical eastern Indian Ocean/western Pacific

It is well established that a tropical–extratropical Rossby wave train can be excited by anomalous latent heating associated with precipitation anomalies (Horel and Wallace 1981; Hoskins and Karoly 1981). To investigate the relationship between the tIO-EA wave train and tropical precipitation forcings, a tIO-EA wave train index that quantifies its time variation is needed. We define a Z200 anomaly-based tIO-EA wave train index: the difference in the area-averaged Z200 anomalies between the regions near Japan (37.5°–47.5°N, 130°–150°E) and southeastern China (25°–35°N, 95°–115°E); these two regions are shown with boxes in Fig. 1a.

Correlation coefficients between the tIO-EA wave train index and precipitation anomalies in early winter indicate a possible source of tropical forcing for the generation of the wave train (Fig. 4a). The distribution of correlation coefficients presents a tripolar pattern in the tropics with significant positive correlations in the tropical western Indian Ocean (wIO) and tropical central–eastern Pacific (tCEP), and significant negative correlations in the tropical eastern Indian Ocean and western Pacific (eIO/wP). Amplitudes of the correlation coefficients in the above-mentioned regions are as high as 0.8, indicating a tight relationship between the tIO-EA wave train and tropical precipitation forcing.

Fig. 4.
Fig. 4.

(a) Distribution of correlation coefficients between the tIO–EA wave train index and tropical precipitation anomalies in early winter. (b),(c) Composite anomalies of ND-averaged precipitation for the groups of the tIO-EA wave train index > 0.5 and <−0.5, respectively. The yellow dot hatches indicate correlation coefficients significant at the 95% confidence level.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

Figures 4b and 4c show the composite precipitation anomalies in the tropical Indo-Pacific region for the tIO-EA wave train index with >0.5 and <−0.5 groups. We found that the precipitation anomalies for the two groups are largely symmetric in the wIO and eIO/wP regions, whereas they show some asymmetric features in the tCEP. For the group of wave train index > 0.5, the pattern of precipitation anomalies is similar to that associated with El Niño, whereas for the wave index < −0.5 the precipitation anomaly is similar to that associated with La Niña.

In the following, we first examine the possible role of tCEP precipitation anomalies, and then analyze the respective roles of precipitation anomalies in the wIO and eIO/wP regions. Following Abid et al. (2021), a tIOWP dipole precipitation index is defined as the difference in area-averaged precipitation anomalies between the wIO (40°–80°E, 10°S–10°N) and the eIO/wP (90°–140°E, 10°S–10°N) (red boxes in Fig. 4a); a tCEP precipitation index is defined as the area-averaged precipitation anomalies in the region of 180°–130°W, 10°S–10°N (red box in Fig. 4a). The linear correlation coefficients between the tIOWP and tCEP precipitation index in early winter during 1979–2019 reach 0.69. Partial regression analysis is employed to isolate the effects of tIOWP and tCEP precipitation anomalies that are linearly independent of each other. Thus, a tIOWP|res precipitation index is defined as the residual part of the tIOWP precipitation index after linearly removing the tCEP precipitation index (see section 2c); likewise, a tCEP|res precipitation index is also defined. Figures 5a and 5c share a similar tripolar pattern with some subtle differences, consistent with the fact that the precipitation anomalies in the tIOWP are both dependent and independent of those in the tCEP (e.g., Saji et al. 1999; Cai et al. 2011). Comparing Figs. 5b and 5d, we can find that the tIOWP|res precipitation index and the tCEP|res precipitation index can represent quite well the isolated precipitation anomalies in these two regions.

Fig. 5.
Fig. 5.

Distribution of correlation coefficients between the tropical precipitation anomalies and (a) the tropical Indian Ocean–western Pacific (tIOWP) dipole precipitation index, (b) the tIOWP|res precipitation index, (c) the tropical central-eastern Pacific (tCEP) precipitation index, and (d) the tCEP|res precipitation index, in early winter. The yellow dot hatches indicate correlation coefficients significant at the 95% confidence level.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

The respective impacts of tIOWP and tCEP precipitation anomalies on extratropical atmospheric circulations are assessed by regressions of anomalous Z200 and WAF onto the four precipitation indices (Fig. 6). A close comparison of Figs. 6a and 6c reveals that the tIO-EA wave train is more prominently associated with the tIOWP precipitation index, while the PNA wave train is more closely related to the tCEP precipitation index. The relative roles of precipitation in these regions can be isolated more cleanly by regressing onto the tIOWP|res and tCEP|res precipitation indices (Figs. 6b and 6d). The key point is that a clear tIO–EA wave train can be seen in association with the tIOWP|res precipitation index. In sharp contrast, atmospheric circulation anomalies over East Asia are fairly weak in association with the tCEP|res precipitation index.

Fig. 6.
Fig. 6.

Regression patterns of anomalous Z200 and WAF onto the (a) tIOWP dipole precipitation index, (b) tIOWP|res precipitation index, (c) tCEP precipitation index, and (d) tCEP|res precipitation index in early winter. Heavy and light shading in red (blue) indicate positive (negative) anomalous Z200 values significant at the 95% and 90% confidence levels, respectively. The vectors are masked as in Fig. 2.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

The respective roles of each pole in the tIOWP dipolar precipitation anomalies (i.e., the wIO part and eIO/wP part) in generating the tIO-EA wave train are further examined. Here, a wIO index and an eIO/wP index are defined as the west and east part of the tIOWP dipole precipitation index, respectively. The linear correlation coefficient between the wIO and eIO/wP index reaches 0.7 during 1979–2019. A wIO_res index represents the remainder of the wIO index after linearly removing the eIO/wP-correlated part. Likewise, an eIO/wP_res index is obtained.

Figures 7a and 7b show that both the original wIO and eIO/wP indexes are closely related to the tIO-EA wave train. However, the wave train amplitude associated with the wIO_res tends to be weaker than the wIO (Fig. 7c). Figure 7d further shows that the tIO-EA wave train is dominated by the eIO/wP_res. Herold and Santoso (2018) had suggested a damping effect of Indian Ocean warming on the impact of Pacific warming over South Asia and North Africa during extreme strong El Niño winters. Inspired by their study, we further repeated the above regression analysis after linearly removing the Niño-3.4 signal, and the results are almost the same as in Fig. 7 except there is a smaller magnitude of anomalies over the tropics (figure not shown). Hence, the above results suggest that the tIO-EA wave train is dominated by the eIO/wP and that the wIO plays a minor role with a damping over northwestern Pacific region.

Fig. 7.
Fig. 7.

Regression patterns of anomalous Z200 onto the (a) wIO index, (b) eIO/wP index, (c) wIO_res index, and (d) eIO/wP_res index. Hatching indicates the anomalous values significant at the 95% confidence level.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

To further confirm the respective roles of the tropical precipitation anomalies in generating the early winter tIO-EA wave train, numerical experiments are conducted using the LBM (see section 2d). A set of LBM experiments, with prescribed heating obtained from the precipitation anomalies regressed upon the Niño-3.4 index, the tIOWP|res index, and the tCEP|res index, are set up to simulate the responses of atmospheric circulation (Fig. 8). The time integration for each experiment is 30 days, and the response of the atmospheric circulation averaged from day 10 to day 20 is shown.

Fig. 8.
Fig. 8.

Results from the linear baroclinic model (LBM). Response of Z200 anomalies (contours; contour interval = 5 m; averaged over days 10–20) to steady heating anomalies (color; σ = 0.45; K day−1) obtained from the precipitation anomaly pattern regressed upon the (a) Niño-3.4 index, (b) tIOWP|res index, and (c) tCEP|res index. (d),(e) As in (b), but for the wIO warming part and the eIO/wP part, respectively. The red boxes are as in Fig. 1.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

The simulated Z200 anomalies in the LBM associated with the ENSO-heating anomalies are shown in Fig. 8a. The Z200 response in the LBM is consistent with that in the observations (see Fig. 1a); for example, a striking wave train is launched from the tIO and propagates toward East Asia, and upper-level anticyclonic flows occur over the low-latitude central Pacific. However, the response of Z200 in the extratropical North Pacific is weaker than in the observations, including a weaker positive Z200 anomaly center near Japan associated with the tIO–EA wave train and a weaker negative Z200 center near the Aleutian Islands associated with the PNA pattern. The weaker Z200 response in the extratropical North Pacific may be attributed to the lack of interactions between the mean flow and transient eddies in the model, and may also be due to the lack of air–sea interaction in the LBM. Nevertheless, the LBM results show that the tripolar heating anomalies in the tropical Indian Ocean and tropical Pacific generated by ENSO contributes to the formation of the tIO-EA wave train.

The responses of Z200 to the tIOWP|res-related heating anomalies in the LBM are presented in Fig. 8b. It is clear that, in response to the tIOWP heating, a Rossby wave train is excited in the tIO and propagates to East Asia. This model result is consistent with that in the observations.

We have also calculated the wave ray tracing proposed by Zhao et al. (2015) and Li et al. (2015). The ray tracing result confirms that disturbances in the tropical Indian Ocean can propagate across the easterlies and then continue northeastward to the eastern edge of Eurasia (Fig. S4). The above result is consistent with Zhao et al. (2015), who suggested that the southerly flow over the western Indian Ocean and South Asia creates a path for the northward propagation of stationary waves across the easterlies.

Model results regarding the tCEP|res precipitation anomaly forcings are shown in Fig. 8c. From Fig. 5d we can see that the spatial pattern of precipitation anomalies associated with the tCEP|res index shows not only positive values in the tCEP region but also negative values in the western Pacific region (near the Philippine Sea) because precipitation anomalies in those two regions are highly correlated. To better understand the effects of tCEP|res precipitation anomaly forcings, we examined the combined and individual effects of heating anomalies associated with the tCEP|res index in the above two regions. Figure 8c shows the response of Z200 to the tCEP|res-related precipitation anomaly forcings in both regions. The result presents weak negative anomalies south of Japan and weak positive anomalies over the North Pacific. Thus, we observe weak Z200 anomalies over the North Pacific associated with the tCEP|res precipitation anomaly forcings.

The LBM results indicate that the eIO/wP cooling forces a tIO-EA wave train very similar to that forced by the dipolar tIOWP precipitation anomalies, and the wIO heating also forces a wave train but with opposite pattern (Figs. 8d,e). To further check the quantitative contributions of the wIO and eIO/wP, we calculated the area-averaged Z200 anomaly in the two boxes for the three experiments. The results are 5.7 and −10.6 for the north and south, respectively, for the tIOWP dipole forcing; north −1.4 and south 6.9 for the wIO heating; and north 7.1 and south −17.5 for the eIO/wP cooling. Therefore, these LBM results confirm that the eIO/wP cooling plays a dominant role in exciting the tIO-EA wave train, and the wIO heating damps this wave train.

In addition, the main heating sources associated with ENSO appear to be in Pacific Ocean rather than the Indian Ocean (Fig. 9a). Their respective roles have also been examined as shown in Figs. S2c and S2d. Because the main heating sources are observed to be mainly located east of 120°E (Fig. 9a), we divided the Niño-3.4-related heating anomaly at 120°E into an Indian Ocean/far western Pacific sector (30°–120°E) and a Pacific sector (120°E–90°W) to examine their respective roles. As expected, the response of Z200 to the heating anomaly in the Indian Ocean/far western Pacific sector is characterized by a tIO-EA wave train pattern. In contrast, Z200 anomalies induced by the heating anomalies in the Pacific sector mainly appear over the North Pacific, which is in contrast to the tIO-EA wave train pattern generated by the heating anomaly in the tIOWP sector.

Fig. 9.
Fig. 9.

Regressions onto the Niño-3.4 index in early winter for (a) precipitation anomalies (mm day−1), (c) divergent wind (vector; m s−1) and velocity potential (color; m2 s−1) at 200 hPa, and (e) Walker circulation (divergent zonal wind and vertical velocity averaged over 10°S–10°N), with the color fill indicating the vertical velocity, which is scaled by −100. (b),(d),(f) As in (a), (c), and (e), respectively, but for late winter.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

Possible roles of the North Atlantic anomalies (denoted as NAT) in the formation of the tIO-EA wave train are then examined. Figure S5 shows the regression patterns of Z200/WAFs anomalies upon the tIOWP precipitation index and a NAT index, respectively. Here, the NAT index is defined as the area-averaged Z200 anomaly over the region of 50°–70°N, 60°W–0° (red box in Fig. S5b). In Figs. S5a and S5b, similar patterns of Z200/WAFs anomalies are observed over the North Atlantic/East Asia region, and both are similar to those in the El Niño or La Niña composites. A closer comparison reveals that, compared to the tIOWP precipitation anomaly, the NAT anomaly has a stronger effect on the upstream part (i.e., North Atlantic and West Asia), but a weaker impact on the downstream part especially after the turning point ∼60°E (i.e., the tIO-EA wave train). Moreover, the partial regressions show that the tIOWP_precip_res (with the NAT signal removed) can still lead to a strong tIO-EA wave train response (Fig. S5c). In contrast, the NAT_res (with the tIOWP_precip signal removed) has a very weak effect on East Asia (Fig. S5d). For example, the Z200 anomalies over Japan associated with the tIOWP_precip_res index are about 4–5 times greater than those associated with the NAT_res index (∼25 vs ∼5 m).

We also checked correlations or partial correlations between the time series of tIO-EA wave train index and the tIOWP precipitation index, as well as the NAT index (Table 1). All three tIOWP precipitation indexes are significantly correlated with the tIO-EA wave train index (Table 1, left column). The NAT index shows a close correlation with the tIO-EA wave train index (Table 1, right column). However, the NAT_res index, with the tIOWP precipitation index-correlated part removed, is almost linearly independent of the tIO-EA wave train index (correlation coefficient = 0.07). This implies that the NAT may contribute to forcing the tIO-EA wave train in conjunction with tIOWP precipitation.

Table 1

Correlation coefficients. Two asterisks (**) indicate that the correlation coefficient exceeds the 95% confidence level.

Table 1

Now that we have demonstrated the crucial role of the eIO/wP precipitation anomaly in generating the tIO-EA wave train, we can consider how ENSO impacts on the eIO/wP precipitation. The tropical precipitation anomalies regressed onto the Niño-3.4 index clearly show negative values in the eIO/wP and positive values in the wIO and tCEP (Fig. 9a). These precipitation anomalies are consistent with the descending and ascending branches of the anomalous double-cell Walker circulation over the tropical Indian–Pacific Oceans (Figs. 9c,e). This suggests that the ENSO-induced anomalous double-cell Walker circulation may contribute to the precipitation anomalies in the eIO/wP, and thus are responsible for its effect on the tIO–EA wave train.

5. Possible factors for the difference in ENSO–East Asia relationship in early and late winter

We now consider why the early winter wave train does not persist into late winter. The difference of SST anomalies in El Niño and La Niña during the early and late winter is first examined, respectively (Fig. S7). From Figs. S7a, S7c, and S7e, we can see that SST anomalies in the tropical Pacific exhibit very little difference between the early and late winter of El Niño events. Similarly, the late winter minus early winter SST anomalies during La Niña also show little difference. The above analysis indicates that the evolution of ENSO SST anomalies from early to late winter may not be the dominant factor in causing the distinct teleconnection patterns over East Asia.

Changes in the precipitation anomalies in the eIO/wP region from early to late winter during the ENSO events are shown in Table 2. The result indicates that the eIO/wP area-averaged precipitation anomalies in the early winters are increased by 38% and 27% compared to the late winters of El Niño and La Niña, respectively. Particularly, these early-to-late winter changes are greater in the western part (90°–120°E) than in the eastern part (120°–140°E). The results are consistent with the regime shift of the anomalous Walker circulation (Fig. 9). Hence, the double-cell Walker circulation in early winter tends to enhance the precipitation anomalies in the eIO/wP region. The decreased eIO/wP precipitation anomalies in late winter tend to contribute to the disappearance of the tIO–EA wave train, and thereby a weakening of the ENSO–East Asian teleconnection. In addition, the positive precipitation anomalies in the tCEP are enhanced in late winter, consistent with the study of Kim et al. (2018). They proposed that the enhanced tCEP precipitation anomalies lead to a weakened anomalous western North Pacific anticyclone, and thus also contribute to a weakening of the ENSO–East Asian connection in late winter.

Table 2

Composite anomalies of area-mean precipitation in the eIO/wP region (10°S–10°N, 90°–140°E) for the ENSO events in early and late winter. The left and right values in parentheses indicate the anomalies in the western part (90°–120°E) and eastern part (120°E–140°E), respectively, of the eIO/wP region.

Table 2

An LBM experiment is set up to check the possible impacts of changes in atmospheric background flows on the wave train. The result shows very similar responses of tIO-EA wave train to the eIO/wP forcing under the early and late winter background flows (figure not shown). Thus, the LBM results suggest that the atmospheric background changes may not be a primary cause of the different ENSO–East Asia teleconnections between early and late winter.

To further investigate possible mechanisms for the changes in precipitation anomalies, regressions of SST anomalies onto the Niño-3.4 index in early and late winter are shown in Figs. 10a and 10b. The SST anomalies appear as a zonal dipole in the equatorial eastern and western Indian Ocean in early winter; in contrast, a basinwide SST warming is observed in the Indian Ocean in late winter. The change in SST anomalies reveals the interbasin couplings of ENSO and the Indian Ocean dipole (IOD) and ENSO and the Indian Ocean basin mode from autumn to spring (Saji et al. 1999; Du et al. 2013). Compared with late winter, the dipolar SST anomalies in early winter can help reinforce stronger anomalies of the Walker circulation in the tropical Indian Ocean through positive air–sea interactions. The stronger anomalous Indian Ocean Walker circulation, with stronger ascending and descending branches, provides favorable conditions for the formation of the dipolar precipitation anomaly in the eIO/wP region in early winter. Therefore, we suggest that the ENSO-induced dipolar SST anomalies in the Indian Ocean during early winter may contribute to the precipitation anomalies in the eIO/wP region in early winter.

Fig. 10.
Fig. 10.

Regression patterns of SST anomalies onto the Niño-3.4 index in (a) early winter and (b) late winter; yellow dot hatching indicates correlation coefficients significant at the 95% confidence level. Composite differences between early and late winter for the climatological mean of (c) SST and (d) precipitation. Correlations between SST and precipitation anomalies during (e) ND and (f) JF. Stippling regions indicate values significant at the 95% confidence level.

Citation: Journal of Climate 35, 24; 10.1175/JCLI-D-21-0805.1

In addition, we examined the evolution of the dipole mode index (DMI; for the IOD; Saji et al. 1999), the tIOWP precipitation index, and the tIO–EA wave train index during ENSO events (Fig. S3). Here, all the monthly data are averaged for two successive months to filter out anomalies possibly from intraseasonal variations such as the Madden–Julian oscillation. The evolution of DMI index shows that the coupling of ENSO and IOD is strongest in September–October (SO) and October–November (ON), and then gradually decreases in early winter (ND) and late winter (JF). The evolution of the tIO–EA wave train index is consistent with that of the tIOWP index. Both indices show the largest anomalies around ON and ND and then decrease, confirming that variation in the tIO-EA wave train is closely related to the precipitation anomalies in the tIOWP.

It is well established that the response of atmospheric circulation to tropical SST anomalies depends not only on the magnitude of the SST anomalies, but also on the climatological mean SST and precipitation (Wu et al. 2006; Ham and Kug 2011, 2015a; Ferrett et al. 2020). According to the Clausius–Clapeyron relationship, saturation water vapor pressure increases exponentially with temperature; thus, in the tropical oceans more (less) latent heating will be released if SSTs are perturbed by an identical amount in a region with a warmer (colder) SST basic state. This stronger latent heat response in a region with a warmer SST basic state will then lead to a stronger remote atmospheric response (Ham and Kug 2011, 2015b). The differences in the basic state of SST and precipitation between early and late winter are shown in Figs. 10c and 10d. Figures 10e and 10f show that the SST–precipitation relationship over the Indian Ocean displays notable differences between early and late winter (i.e., a closer connection during early winter and weaker during late winter). It can be seen that the tropical north Indian Ocean is warmer and wetter in the early winter. On this basis, the dipolar SST anomaly in the Indian Ocean (Fig. 10a) results in a stronger convective response in early winter. The warmer and wetter basic state leads to a stronger atmospheric response to similar amplitude of SST anomalies, which likely contributes to the generation of a tIO–EA wave train. Therefore, the different climatology of SSTs and precipitation in early and late winter in the tropical Indian Ocean also play a role in explaining the different East Asian atmospheric responses to ENSO.

6. Summary and discussion

The present study shows that precipitation anomalies in the tropical Indian Ocean can act as a bridge that “teleconnects” the ENSO signal to East Asia during boreal early winter. It is revealed that ENSO induces different atmospheric circulation responses in early and late winter. In early winter, ENSO causes an extratropical Rossby wave train emanating from the tIO toward East Asia. This tIO–EA wave train is characterized by wedge-shaped anomalous Z200 over the tIO, and alternating negative and positive centers over southeastern China and Japan, respectively. The tIO-EA wave train has an equivalent barotropic structure in the extratropics; thus, the positive Z200 anomalies near Japan cause a weakening of the East Asian trough, lead to a weakened East Asian winter monsoon, and result in warm temperature anomalies over northeast China to Japan via wind-induced temperature advection. In contrast, in late winter, ENSO has a stronger projection on a PNA-like atmospheric wave train, which radiates poleward from the central–eastern Pacific and so has little impact on East Asia. The impacts of ENSO on climate over East Asia in late winter are mainly through the western North Pacific anomalous anticyclone, which is an equatorial Rossby wave response to the SST cooling. The western North Pacific anomalous anticyclone is trapped in the tropics, so its influence on East Asian climate is also limited to the lower latitudes. Our further analyses find that the precipitation anomalies in the eIO/wP play an important role in the formation of the tIO–EA wave train, which can be confirmed by a set of numerical experiments. Meanwhile, the ENSO-induced North Atlantic anomalies may also contribute to the tIO-EA wave train in conjunction with the precipitation anomalies in the eIO/wP.

The changes in extratropical atmospheric response associated with ENSO from early to late winter can be attributed to the systematic shift of tropical precipitation and Walker circulation anomalies. In early winter, ENSO is associated with a double-cell anomalous Walker circulation. The double-cell anomalous Walker circulation is associated with anomalous ascent over the western tIO and tCEP, together with anomalous descent over the eIO/wP. The anomalous descent in the eIO/wP is responsible in exciting the tIO-EA wave train. In late winter, in contrast, ENSO induces a single-cell anomalous Walker circulation mainly over the tropical Pacific Ocean. The changes in the regime of the anomalous Walker circulation lead to a weakening in the eIO/wP precipitation anomalies. The net effect is that a tIO-EA wave train is barely excited in late winter.

Two factors may be responsible for the different atmospheric responses associated with ENSO in early and late winter over the eIO/wP. On the one hand, the interbasin coupling of ENSO with the Indian Ocean dipole mode in autumn can persist in ND, and so the west-warm and east-cold contrast of SST anomalies contributes to the enhancement of precipitation anomalies in the eIO/wP. On the other hand, the basic state of SST and precipitation also plays a role. In particular, the northern tIO is wetter and warmer in early winter than in late winter; consequently, the latent heating anomalies associated with an identical SST anomaly are stronger in early winter, thus leading to a stronger atmospheric response to the underlying SST anomalies, which favors the generation of the tIO–EA wave train.

To check how the Siberian high is affected by El Niño and La Niña during early and late winter, respectively, we conduct a composite analysis of surface level pressure (SLP) similar to that in Fig. 1. Figures S8a and S8b show very weak SLP anomalies over Mongolia and Siberia, which indicates little impact on the early winter Siberian high from El Niño and La Niña. However, El Niño and La Niña tend to induce asymmetric effects on the late winter Siberian high (Figs. S8c,d). Specifically, El Niño has no significant impact on the Siberian high intensity. In contrast, La Niña tends to induce a significant strengthened Siberian high.

Kim et al. (2018) also investigated the subseasonal evolution of ENSO teleconnections in the North Pacific from early to late winter. The results obtained from this study are consistent with those derived from Kim et al. (2018), although these two studies focused on different regions and different physical process related to the ENSO teleconnections. In particular, Kim et al. (2018) suggested that the atmospheric teleconnection related to ENSO over the North Pacific is mainly induced by the dipolar precipitation anomaly pattern in the tropical western North Pacific (WNP) and the central Pacific (CP). Note that these conclusions are consistent with our analyses. We showed that the cooling anomalies in the WNP can lead to an anomalous anticyclone over the North Pacific east of Japan (Fig. S2b), while warming anomalies in the CP can lead to an anomalous cyclone in the above region (Fig. S2a). In addition, the atmospheric response to the combined heating anomalies over the WNP and CP are mainly located over the North Pacific (Fig. S2d).

Unlike Kim et al. (2018), this study suggests an atmospheric wave train originates in the tropical Indian Ocean and propagates northeastward to East Asia in early winter. This atmospheric wave train exhibits different features compared to those in Figs. S2c and S2d. In addition, the physical process suggested in our paper is different from that of Kim et al. (2018). Kim et al. (2018) put more emphasis on the mechanism responsible for the sudden disappearance of the Kuroshio anticyclone in late winter. They suggested that relative changes in WNP and CP precipitation anomalies played an important role. However, we focused more on the generation mechanisms of the tIO-EA wave train in early winter, which was not examined in Kim et al. (2018). Hence, compared to previous studies, the present study improves our understanding regarding the physical process for the formation of the ENSO–East Asia teleconnection in early winter.

Several questions still remain to be answered. For example, in early winter, in addition to the tIO-EA wave train, some northeastward WAFs from higher latitudes also contribute to the Z200 anomalies over Japan (Figs. 2a,b). Possible mechanisms for these northeastward WAFs are still unclear. Moreover, in late winter, atmospheric circulation anomalies over central and eastern Asia seem to be related to a higher-latitude wave train. This higher-latitude wave train seems to originate from the North American and Atlantic regions. The details for the generation and maintenance of this wave train should be further pursued in the near future.

Previous studies have shown that the atmospheric teleconnections of ENSO in the Euro-Atlantic region are different during early and late winter (e.g., Abid et al. 2021), so it is important to know whether these changes, especially the NAO/AO, can play some role in the different responses in East Asia. To explore the above, we first examined the relationship between ENSO and the NAO/AO during early and late winters. The correlation coefficients between the Niño-3.4 index and the NAO index for the period 1979–2019 are 0.29 and −0.15 in early and late winter, respectively, both of which are statistically insignificant. Then, we calculated the correlation coefficients between the NAO index and the tIO-EA wave train index. Their correlation coefficients are 0.24 and −0.02 in early and late winter, respectively, also both statistically insignificant. In addition, the spatial patterns of Z200 anomalies associated with ENSO and NAO are examined. Partial regression analysis shows that the responses of Z200 anomalies in East Asia associated with the Niño-3.4 index and the NAO index are almost linearly independent with each other (figure not shown). The above evidence indicates that the NAO seems not to play an important role in relaying the impact of ENSO on East Asian climate in winter. However, due to the complexity of the mechanisms by which ENSO affects the NAO/AO and the NAO/AO affects East Asia (e.g., Wu and Wang 2002; Chen et al. 2005; Song and Wu 2018), we are unable to clarify the above question definitively in this study.

For the early winter, the North Atlantic atmospheric response to El Niño (La Niña) exhibits a positive (negative) east Atlantic pattern (e.g., King et al. 2018). Ayarzaguena et al. (2018) suggested that the ENSO-related precipitation anomalies in the Gulf of Mexico and Caribbean Sea seem to be responsible for the ENSO teleconnection to the east Atlantic pattern. These east Atlantic anomalies are suggested to contribute to the tIO-EA wave train in conjunction with the tIOWP precipitation anomaly (Fig. S5). For the late winter, the North Atlantic atmospheric response to El Niño (La Niña) shifts to a negative (positive) NAO pattern (see Figs. 2c,d), again consistent with previous studies (e.g., Ayarzaguena et al. 2018). Figure S6 shows the regression of Z200/WAFs anomalies on the NAO index in late winter. The result indicates that the Z200/WAFs anomalies associated with the NAO are mainly confined to the Europe/North Africa region, but very weak over the central-eastern Eurasian continent. This may demonstrate that the negative (positive) NAO response in El Niño (La Niña) late winter is not likely to be an important factor leading to circulation anomalies over the central-eastern Eurasian continent.

The present study implies that the evolution of the IOD during ENSO events may play an important role in the formation of the dipolar precipitation anomalies in the tIOWP region. Figure S3 shows that during ENSO events, the evolution of the tIOWP precipitation index shows great consistency with that of the DMI index. However, not all ENSO events are accompanied by an IOD event in the preceding boreal autumn. In this regard, the combined effect of ENSO and IOD should be further analyzed. On the other hand, the study of Wu et al. (2012) showed that the IOD preceding the ENSO event is associated with two distinct precipitation anomaly patterns in the Indian Ocean during winter, the zonal dipole mode and the monopole mode. So, it is difficult for us to clarify the combined effect of ENSO and IOD on the convection in the tropical Indian Ocean definitively in this study. It is crucial to understand the above question because a recent study (Ham et al. 2017) reported that the ENSO–IOD coupling has weakened in the recent decades (the coupling is weaker in the period of the 2000s–2010s than that in the 1980s–1990s). The response of convection anomalies to the combined effect of ENSO and IOD, especially for the recent two decades, should be examined carefully in a further study.

Acknowledgments.

We thank the editor Dr. Agus Santoso and three anonymous reviewers for their constructive suggestions, which helped to significantly improve the paper. This work was supported jointly by the National Natural Science Foundation of China (Grants 41721004, 41961144025, and 42005032), the National Key Research and Development Program of China (2018YFC1506003), and the Jiangsu Collaborative Innovation Center for Climate Change. CIG is supported by the ISF-NSFC joint research program (3259/19). We are grateful to Dr. Sen Zhao, Prof. Jianping Li, and Dr. Yanjie Li for making the Rossby wave tracing code readily available. The authors declare no potential conflict of interest.

Data availability statement.

The monthly HadISST dataset (Rayner et al. 2003) is provided by the Met Office Hadley Centre, and can be downloaded from their website at https://www.metoffice.gov.uk/hadobs/hadisst/. The NCEP–DOE reanalysis (Kanamitsu et al. 2002) and the GPCP dataset (Adler et al. 2018) are provided by the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory Physical Sciences Division and can be downloaded from their website at https://www.esrl.noaa.gov/psd. The ERA5 reanalysis is available at https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. The list of historical El Niño and La Niña events is available at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php.

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    • Export Citation
  • Feng, J., W. Chen, and Y. Li, 2017: Asymmetry of the winter extra-tropical teleconnections in the Northern Hemisphere associated with two types of ENSO. Climate Dyn., 48, 21352151, https://doi.org/10.1007/s00382-016-3196-2.

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  • Ferrett, S., M. Collins, H.-L. Ren, B. Wu, and T. Zhou, 2020: The role of tropical mean-state biases in modeled winter Northern Hemisphere El Niño teleconnections. J. Climate, 33, 47514768, https://doi.org/10.1175/JCLI-D-19-0668.1.

    • Search Google Scholar
    • Export Citation
  • Garfinkel, C. I., A. H. Butler, D. W. Waugh, M. M. Hurwitz, and L. M. Polvani, 2012: Why might stratospheric sudden warmings occur with similar frequency in El Niño and La Niña winters? J. Geophys. Res., 117, D19106, https://doi.org/10.1029/2012JD017777.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., and J.-S. Kug, 2011: How well do current climate models simulate two types of El Niño? Climate Dyn., 39, 383398, https://doi.org/10.1007/s00382-011-1157-3.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., and J.-S. Kug, 2015a: Improvement of ENSO simulation based on intermodel diversity. J. Climate, 28, 9981015, https://doi.org/10.1175/JCLI-D-14-00376.1.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., and J.-S. Kug, 2015b: Role of north tropical Atlantic SST on the ENSO simulated using CMIP3 and CMIP5 models. Climate Dyn., 45, 31033117, https://doi.org/10.1007/s00382-015-2527-z.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., J.-Y. Choi, and J.-S. Kug, 2017: The weakening of the ENSO–Indian Ocean Dipole (IOD) coupling strength in recent decades. Climate Dyn., 49, 249261, https://doi.org/10.1007/s00382-016-3339-5.

    • Search Google Scholar
    • Export Citation
  • Herold, N., and A. Santoso, 2018: Indian Ocean warming during peak El Niño cools surrounding land masses. Climate Dyn., 51, 20972112, https://doi.org/10.1007/s00382-017-4001-6.

    • Search Google Scholar
    • Export Citation
  • Hoerling, M. P., A. Kumar, and M. Zhong, 1997: El Niño, La Niña, and the nonlinearity of their teleconnections. J. Climate, 10, 17691786, https://doi.org/10.1175/1520-0442(1997)010<1769:ENOLNA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813829, https://doi.org/10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and D. J. Karoly, 1981: The steady linear response of a spherical atmosphere to thermal and orographic forcing. J. Atmos. Sci., 38, 11791196, https://doi.org/10.1175/1520-0469(1981)038<1179:TSLROA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jia, X., H. Lin, and X. Yao, 2014: The influence of tropical Pacific SST anomaly on surface air temperature in China. J. Climate, 27, 14251444, https://doi.org/10.1175/JCLI-D-13-00176.1.

    • Search Google Scholar
    • Export Citation
  • Jia, X., S. Wang, H. Lin, and Q. Bao, 2015: A connection between the tropical Pacific Ocean and the winter climate in the Asian-Pacific region. J. Geophys. Res. Atmos., 120, 430448, https://doi.org/10.1002/2014JD022324.

    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311644, https://doi.org/10.1175/BAMS-83-11-1631.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-W., and S.-I. An, 2019: Western North Pacific anticyclone change associated with the El Niño–Indian Ocean Dipole coupling. Int. J. Climatol., 39, 25052521, https://doi.org/10.1002/joc.5967.

    • Search Google Scholar
    • Export Citation
  • Kim, S., H.-Y. Son, and J.-S. Kug, 2018: Relative roles of equatorial central Pacific and western North Pacific precipitation anomalies in ENSO teleconnection over the North Pacific. Climate Dyn., 51, 43454355, https://doi.org/10.1007/s00382-017-3779-6.

    • Search Google Scholar
    • Export Citation
  • King, M. P., and Coauthors, 2018: Importance of late fall ENSO teleconnection in the Euro-Atlantic sector. Bull. Amer. Meteor. Soc., 99, 13371344, https://doi.org/10.1175/BAMS-D-17-0020.1.

    • Search Google Scholar
    • Export Citation
  • Li, Y., J. Li, F. Jin, and S. Zhao, 2015: Interhemispheric propagation of the stationary Rossby waves in a horizontally non-uniform basic flow. J. Atmos. Sci., 72, 32333256, https://doi.org/10.1175/JAS-D-14-0239.1.

    • Search Google Scholar
    • Export Citation
  • Ma, T., W. Chen, J. Feng, and R. Wu, 2018: Modulation effects of the East Asian winter monsoon on El Niño-related rainfall anomalies in southeastern China. Sci. Rep., 8, 14 107–14 107, https://doi.org/10.1038/s41598-018-32492-1.

    • Search Google Scholar
    • Export Citation
  • Paldor, N., 2015 : Shallow Water Waves on the Rotating Earth. Springer, 83 pp.

  • Paldor, N., 2019: Recent advances in linear wave theory on the spherical Earth. Deep-Sea Res. II, 160, 6367, https://doi.org/10.1016/j.dsr2.2018.10.009.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects of the El Niño/Southern Oscillation. Science, 222, 11951202, https://doi.org/10.1126/science.222.4629.1195.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., and Coauthors, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115, 16061626, https://doi.org/10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole mode in the tropical Indian Ocean. Nature, 401, 360363, https://doi.org/10.1038/43854.

    • Search Google Scholar
    • Export Citation
  • Shiozaki, M., T. Enomoto, and K. Takaya, 2021: Disparate midlatitude responses to the eastern Pacific El Niño. J. Climate, 34, 773786, https://doi.org/10.1175/JCLI-D-20-0246.1.

    • Search Google Scholar
    • Export Citation
  • Son, H.-Y., J.-Y. Park, J.-S. Kug, J. Yoo, and C.-H. Kim, 2014: Winter precipitation variability over Korean Peninsula associated with ENSO. Climate Dyn., 42, 31713186, https://doi.org/10.1007/s00382-013-2008-1.

    • Search Google Scholar
    • Export Citation
  • Song, L., and R. Wu, 2018: Comparison of intraseasonal East Asian winter cold temperature anomalies in positive and negative phases of the Arctic Oscillation. J. Geophys. Res., 123, 85188537, https://doi.org/10.1029/2018JD028343.

    • Search Google Scholar
    • Export Citation
  • Takaya, K., and H. Nakamura, 2001: A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. J. Atmos. Sci., 58, 608627, https://doi.org/10.1175/1520-0469(2001)058<0608:AFOAPI>2.0.CO;2.

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