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--> --> -->The vertical coupling between the WPA and the SAH is essential for the onset of the summer monsoon in the South China Sea and determines the start of the EASM rainy season in May (e.g., Kueh and Lin, 2010; Liu and Zhu, 2016). The westward movement of the WPA and the eastward movement of the SAH enhance the meridional and vertical circulations of EASM and regulate the northward transport of warm air and the moisture flux, which induces anomalous summer precipitation over inland China from June to August (Dao and Chu, 1964; Zhang et al., 2002, 2005; Ren et al., 2007; Liu and Zhu, 2016). The Mongolian cyclone (MC), a cold vortex in the troposphere over Lake Baikal, is most crucial for the transport of the cold air modulating the EASM (e.g., Lau and Li, 1984; He et al., 2007; Zhu et al., 2012; Chen et al., 2017; Liu and Zhu, 2019). The distinct interactions of the MC with the SAH and the WPA determine the subseasonal variability of the EASM and rainfall over East Asia (e.g., Song et al., 2016).
The interannual variability of the WPA is regarded as the dominant factor determining the seasonal anomalies in the EASM due to its physical linkage with the external tropical forcing of El Ni?o–Southern Oscillation (ENSO) (Huang and Wu, 1989; Zhang et al., 1996; Wang et al., 2000, 2013; Wu and Zhou, 2008; Xie et al., 2009, 2016). However, increasing evidence has shown that the relation between the WPA and ENSO cannot fully explain the anomalous rainfall patterns of the EASM. Recent studies suggest that the EASM is also influenced by an anomalous SAH (Zhang and Wu, 2001; Wei et al., 2015), a meridional shift in the upper-level westerly jet over North Asia (Yang et al., 2002; Zhang et al., 2006; Chiang et al., 2017; Liu et al., 2018), and the Silk Road teleconnection pattern in the mid to high latitudes (Lu et al., 2002; Enomoto et al., 2003; Kosaka et al., 2009; Chen and Huang, 2012; Hong and Lu, 2016). Also, the anomalous year-to-year pattern of summer rainfall in China is determined by a distinct coupling circulation regime (Pang et al., 2019).
In the present study, we seek to understand more comprehensively the effects of the interactions of the WAP, SAH, and MC on the interannual anomalies in the three-dimensional circulation, to investigate the diverse coupling modes of the EASM, and discuss the possible challenges in the seasonal forecasting of rainfall over China based on the dominant modes of SST anomalies.
The rainfall occurs near the subtropical front of the EASM, along with substantial adjustment of the atmospheric static stability. The change in the atmospheric thermal structure follows the latent heat released by the EASM rainfall (Liu et al., 2004). An appropriate indicator of the EASM should include not only the wind fields but also the diabatic heating of the atmosphere. Previous research has shown that the potential vorticity is a good description of the low-level monsoonal flows (Yang and Krishnamurti, 1981), WPA (Rodwell and Hoskins, 2001; Liu et al., 2004), SAH (Wu and Liu, 2003; Ren et al., 2015), and midlatitude cyclone (Hoskins et al., 1985). Here, we apply the vertical component of the isobaric potential vorticity
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The second mode (MV-EOF2) accounts for 10.7% of the total covariance, presenting a typical mei-yu front with above-normal rainfall along the Yangtze River and Southwest China, together with strong interaction between the intensified WPA and the southeastward shift of the MC at 850 hPa. At 200 hPa, the enhanced SAH is coupled with the southeastward shift of the MC over Indochina and the Korean peninsula via the anomalous westerly around 30°N over the Yangtze River to Japan.
These two modes are significantly orthogonal according to the criterion of North et al. (1982). They show two different interactions among the EASM circulation. The first mode is ascribed to the EASM intensity, but the second is associated with the spatial displacement of the EASM. Note that the WPA, SAH and MC anomalies are interconnected and act as a cogwheel comprising the meridional monsoonal flow and the westerly jet between the upper and lower troposphere over East Asia. These two dominant modes represent the diverse coupling wheels of the EASM on an interannual time scale.
The spatial structure of the EASM coupling wheels are well organized in the potential vorticity fields associated with the principal components (PC1 and PC2), with a different vertical shear of the meridional flow along 115°E (Fig. 2). In the first mode, the MC and the WPA are weakened at 850 hPa, presenting negative and positive anomalies of potential vorticity over Northeast China and the western North Pacific, respectively (Fig. 2a). By contrast, the coupling between the MC and the SAH at 200 hPa exhibits a dipole of anomalous potential vorticity over Lake Baikal and central China (Fig. 2b). This coupling wheel accompanies a less stable atmosphere (
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In the second mode, a dipole of potential vorticity anomalies extends northeastward south and north of 25°N, suggesting an interaction between the southward shift of the WPA and the enhanced MC at 850 hPa (Fig. 2d). The enhanced MC manifests westward tilting of the positive potential vorticity anomaly in the vertical direction, along with a southward shift in the SAH over Indochina at 200 hPa (Fig. 2e). In this way, the negative anomaly of the upper-level potential vorticity is above the Yangtze River, where the anomaly of the low-level potential vorticity is negative, leading to a more stable troposphere (
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The correlation of potential vorticity fields with PCs suggest essential roles of the WPA, SAH and MC in regulating the diversity of the coupling wheels of the EASM (Fig. 2). The temporal correlation coefficients (TCCs) of PC1 with the WPAI, SAHI and MCI are +0.20, +0.68 and ?0.74, respectively, suggesting dominant roles of the SAH and MC anomalies in the first coupling wheel of the EASM. The TCC between the SAHI and MCI is ?0.45, passing the significance test at the 0.05 level. Thus, the SAH and MC intensity show an opposite change to the meridional fluctuation of the westerly jet at 200 hPa.
To verify the diverse regime of the EASM circulation (Figs. 3b–d), we conduct principal components analysis on the WPAI, SAHI and MCI indicated by the potential vorticity. The three leading modes account for 58%, 24% and 18% of the total variance, respectively, corresponding to the different EASM circulation regimes. The first mode (mode 1) shows an out-of-phase variation between the SAHI and MCI (Fig. 3b). The second mode (mode 2) shows a unified in-phase variation of the three circulation indices. In contrast, the third mode (mode 3) shows an inverse change in the WPAI with the SAHI and MCI. PC1 (PC2) in the MV-EOF is significantly correlated with the time series of mode 1 (mode 2), showing a TCC of +0.84 (+0.55). No significant correlation is observed between the time series of mode 3 and PC3. Therefore, the first coupling wheel of the EASM (Fig. 1a) primarily depends on the MC with its more considerable variance and its interaction with the SAH (Fig. 3b). However, the mutual interactions among the WPA, MC and SAH constitute the second coupling wheel of the EASM (Fig. 1b), in which the WPA is most important due to its significant correlation with either mode 2 or PC2.
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The diversity of the coupling wheels of the EASM indicates the complex interannual variability of the EASM. We use the k-means cluster method (Hartigan and Wong, 1979) to validate the statistical results of the EOF analysis and show the existence of diverse regimes of EASM circulation in the period 1979–2015. Since k-means clustering is a method of vector quantization, which can partition the data space into Voronoi cells (Hartigan and Wong, 1979), this method has been applied in the atmospheric and ocean sciences for decades, and the categories are obtained by grouping the cases for their similarities.
Figure 4 presents the first four clusters of the circulation indices and their associated summer rainfall anomalies in mainland China during 1979–2015. Categories 1 and 3 represent the positive and negative phases of the first mode of the coupling wheel of the EASM, respectively (Figs. 1a and b). In this case, the larger changes in the MCI and SAHI are opposite, with a relatively weaker variation in the WPAI (Figs. 4a and e). The composite anomalous rainfall is also reversed between the two categories, corresponding to the positive and negative phases of this circulation regime of the EASM (Figs. 1a, 4b and 4f). This mode occurs in 24 of 37 samples, suggesting its dominant role in the year-to-year variation of the EASM.
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Cluster 2 and its related rainfall anomalies (Figs. 4c and d) reflect the positive phase of the second coupling wheel of the EASM (Fig. 1b), featured by a tight interaction between the SAH and the WPA and with the anomalous rainfall centered along the Yangtze River (Figs. 4c and d). There are seven samples in cluster 2, suggesting a lower frequency of the second mode of the coupling wheel of the EASM. The residual six samples are classified as cluster 4, with the lowest frequency during 1979–2015, showing the reverse change in the SAHI and WPAI and the less summer rainfall over South China. Therefore, the coupling between the MC and the SAH is the leading target of seasonal forecasts of the EASM and shows the importance of the MC and its related cold air activity in regulating the summer rainfall pattern of the EASM.
ENSO is regarded as a crucial source for seasonal forecasts of the EASM, and the related SST anomalies in the northern tropical Indian Ocean (NTIO) can affect the EASM through either the WPA in the mid-to-lower troposphere (Zhang et al., 1996, 2016; Wang et al., 2000; Xie et al., 2009, 2016) or the SAH in the upper troposphere (Yang et al., 2007; Huang et al., 2011; Liu et al., 2017). The tripole and dipole modes (NATM and NADM) of the North Atlantic SST anomalies are also responsible for changes in the EASM (e.g., Lau et al., 2004; Wu et al., 2009; Zuo et al., 2013; Cui et al., 2015). However, the first mode of the coupling wheel of the EASM shows a weak correlation with the SST anomaly and index of the Ni?o3.4, NTIO, NATM and NADM in the regions of tropical Pacific, North Atlantic and Indian Oceans in the preceding months (Fig. 5a). By contrast, the second mode is closely associated with the Ni?o3.4, NTIO and NADM indices, with a maximum positive correlation in April, May and July, respectively (Fig. 5b). Such correlation suggests that the El Ni?o in winter, the NTIO SST anomaly in spring, and the Atlantic SST anomalies in summer may affect this mode via a relay process, suggesting its higher potential predictability on interannual time scales. However, the first mode shows very low predictability due to its weak correlation with the SST anomalies, particularly for the MC (figure not shown). The prediction of the first mode of the coupling wheel of the EASM is, therefore, a great challenge in forecasting the seasonal climate over China based only on the SST anomalies.
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Acknowledgements. The authors acknowledge the anonymous reviewers’ helpful suggestions. This study was jointly supported by the National Natural Science Foundation of China (Grant Nos. 41830969, 41775052, 42005011, 41776023 and 42076020), the National Key R&D Program (Grant No. 2018YFC1505904), the Scientific Development Foundation of the Chinese Academy of Meteorological Sciences (CAMS) (Grant No. 2020KJ012 and 2020KJ009), the Basic Scientific Research and Operation Foundation of CAMS (Grant Nos. 2018Z006), and Youth Innovation Promotion Association CAS (Grant No. 2020340). This study was also supported by the Jiangsu Collaborative Innovation Center for Climate Change. The authors declare that they have no conflicts of interest. The NCEP–DOE AMIP-II reanalysis dataset and ERSST.v4 data were provided by NOAA/OAR/ESRL PSD, Boulder, Colorado, USA (