As an agricultural and economic center, eastern China is a typical monsoon area and is of national importance. The summer climate over this region exhibits complex features, especially for the precipitation. The summer precipitation over eastern China is greatly affected by the East Asian summer monsoon (EASM) (Jiang et al., 2008). The EASM consists of the staged progression of zonally oriented rain belts, which include mixed tropical and baroclinic features. The onset of the EASM occurs in early to mid May and is accompanied by abrupt changes in precipitation, winds, outgoing longwave radiation, and clouds (Tao and Chen, 1987). The EASM shows two abrupt northward jumps and three stationary periods. The three stationary periods correspond to the three latitudinally oriented rain belts in southern China, the Yangtze–Huai River valley, and northern China (Ding, 1992). The surface air temperature (SAT) is influenced by the seasonal development of the EASM through clouds, the atmospheric circulation, and land surface processes (Betts, 2007). Shi and Zhu (1996) showed that a strong EASM is associated with higher than average SATs over large areas of China. Chen and Wu (2017) reported that the atmospheric circulation has a great impact on the SAT through cloud-induced changes in surface radiation.

The western Pacific subtropical high (WPSH), with southerly or southwesterly winds along its western flank in the lower troposphere, is a dominant member of the EASM circulation system and has a significant impact on summer precipitation over eastern China (Huang and Tang, 1987). Moisture transported by the southwesterly winds at the western edge of the WPSH feeds the Meiyu front, which is accompanied by the Meiyu rain belt over the Yangtze River valley from June to mid July (Wu et al., 2012). The WPSH contributes to the distribution of summer precipitation over eastern China (Huang and Chen, 2010). The WPSH also plays an important part in abnormally hot summers over eastern China (Wang W. W. et al., 2016) and the western ridge of the WPSH has extended significantly westward since the late 1970s (Gong and Ho, 2002). Moreover, the establishment and retreat of the Okhotsk high are also closely related to summer precipitation over eastern China (Zhang and Tao, 1998) and a high frequency of the Okhotsk high in July and August leads to abnormally cool summers due to strong northwesterly winds over East Asia (Ninomiya and Mizuno, 1985).

The East Asia/Pacific (EAP) pattern has been recognized as the dominant mode over East Asia in summer and reflects the configuration of the WPSH, the Meiyu trough, and the Okhotsk high (Nitta, 1987; Huang, 1992). The positive EAP pattern intensifies precipitation over the Yangtze River valley and reduces precipitation over the Atlantic coast (Chen and Zhai, 2015). Zhao et al. (2007) suggested that the Meiyu rain belt shifts northward under high Asia/Pacific Oscillation conditions, whereas Sung et al. (2006) suggested the possible delayed impact of the winter North Atlantic Oscillation (NAO) on EASM precipitation in the following summer. Notaro et al. (2006) concluded that a positive Pacific/North American pattern accompanied by a southward-shifted upper-level jet stream leads to dry and cold conditions in northeastern America during early winter. Park et al. (2011) also showed that the Arctic Oscillation (AO) phase is related to the East Asian cold surge. Diao et al. (2015) concluded that winters with frequent warm extremes in Europe are related to a northeast–southwest tilted positive NAO pattern. These teleconnection patterns and their impacts on regional and remote precipitation and SAT have attracted much attention from meteorologists. However, the regional summer atmospheric circulation patterns of East Asia are still unclear and need verifications from more observational data. In particular, most of the variability in atmospheric circulation over East Asia is not captured by the EAP pattern. For exam-ple, Wang J. B. et al. (2016) indicated that the first leading mode of the 30–60-day filtered and normalized summer 500-hPa geopotential height anomalies resembles the EAP pattern with a percentage variance of only 18%. Therefore, it is useful to further probe the summer atmospheric circulation patterns over East Asia and their impacts on regional precipitation and SAT. The combination of socioeconomic and atmospheric circulation characteristics makes the region (10°–70°N, 100°–160°E), a most appropriate study area, to explore the summer atmospheric circulation patterns of East Asia.

It is widely accepted that external forcing—for example, the sea surface temperature (SST), snow cover, and sea ice—could produce a modulating effect on the atmospheric circulation (e.g., Wu et al., 2009; Matsumura et al., 2010; Sun et al., 2013; Wang et al., 2014; Ding et al., 2016). Positive tropical SST anomalies increase precipitation and convection, and anomalous diabatic forcing triggers anomalous divergent circulation that propagates from the tropics to high latitudes (Trenberth and Caron, 2000). The El Niño–Southern Oscillation may modify the variations in atmospheric circulation between the subtropics and high-latitude regions (Li et al., 2012; Feng et al., 2017). Li et al. (2014) showed that the warm pool El Niño induces an abnormal anticyclonic circulation over the Yangtze River valley and a cyclonic circulation over the northern South China Sea. Zhang et al. (2017) demonstrated that a west–east dipole pattern in Eurasian spring snowmelt anomalies, with an increase in snowmelt over eastern Europe and western Russia and a decrease in snowmelt over Baikal Lake, is associated with summer precipitation in East Asia, through triggering an anomalous Eurasian wave train. The earlier spring snowmelt reduces the surface albedo and soil moisture and then produces surface warming, which contributes to the development of stationary Rossby waves in summer (Matsumura and Yamazaki, 2012). Wu et al. (2013) indicated that anomalous winter sea ice in Baffin Bay influences the summer atmospheric circulation anomalies over northern Eurasia via spring atmospheric circulation anomalies south of Newfoundland. We therefore investigate how the summer atmospheric circulation patterns over East Asia are related to the various kinds of external forcing.

Surface weather conditions are directly governed by large-scale atmospheric circulation patterns, which act as a bridge between external forcing and surface weather conditions. We focus here on the summer atmospheric circulation patterns over East Asia and their direct impacts on simultaneous precipitation and SAT over eastern China. We also investigate the relationship between the summer atmospheric circulation patterns and external forcing to obtain a more comprehensive understanding of the atmospheric circulation patterns through correlation analysis. Linear regression analysis is further applied to validate the driving effects of external forcing on the atmospheric circulation patterns.

The paper is organized as follows. Section 2 gives a brief introduction to the data and methods used. Section 3 reports the summer atmospheric circulation patterns over East Asia and their corresponding vertical structures. Section 4 presents the direct impacts of the summer atmospheric circulation patterns on the simultaneous precipitation and SAT over eastern China. Section 5 illustrates the driving effects of the external forcing on the atmospheric circulation patterns, and finally, the conclusions and discussion are given in Section 6.

Monthly atmospheric circulation data for the time period 1961–2013 are provided by the NCEP/NCAR at a horizontal resolution of 2.5° × 2.5° (Kalnay et al., 1996). In addition, monthly data for the 10-m horizontal wind, net latent heat flux, 2-m air temperature, and soil moisture at 0–10-cm depth for 1961–2013 are also obtained from the NCEP/NCAR but with a horizontal resolution of T62. Monthly precipitation and SAT data at 160 stations over China for 1961–2013 are obtained from the China Meteorological Administration. Monthly SST data are obtained from the NOAA extended reconstructed SST V3 dataset for 1960–2013 with a 2.0° × 2.0° horizontal resolution (Smith et al., 2008). Monthly precipitation data from the NOAA’s Precipitation Reconstruction dataset for 1961–2013 (Chen et al., 2002) and NOAA’s interpolated outgoing longwave radiation data for 1979–2013 (Liebmann and Smith, 1996) with a 2.5° × 2.5° horizontal resolution are used to validate the tropical convection.

The monthly sea ice concentration (SIC) data provided by the UK Meteorological Office Hadley Center for 1961–2013 have a 1.0° × 1.0° horizontal resolution (Rayner et al., 2003). The weekly snow cover extent (SCE) data obtained from Rutgers University Climate Laboratory for 1973–2013 are projected onto a grid with a 2.0° × 2.0° horizontal resolution (Robinson et al., 1993). The results in this study are based mainly on the period 1961–2013, except for the outgoing longwave radiation and SCE datasets, which are limited by the availability of data. The monthly AO, NAO, Polar/Eurasia (POL), East Atlantic (EA), Scandinavian (SCAN), East Atlantic/West Russia (EAWR), and West Pacific (WP) teleconnection pattern indices are provided by the NOAA Climate Prediction Center. The conventional Eurasian (EU) and western Atlantic (WA) teleconnection pattern indices are calculated according to the definition of Wallace and Gutzler (1981) as follows,

To remove any influences from the long-term trend, the linear trend is removed from all variables. We use normalized empirical orthogonal function (EOF) analysis, in which the principal components (PCs) are divided by their standard deviation and the spatial EOF patterns are multiplied by the corresponding standard deviation (Zheng et al., 2013). The statistical significance is assessed by using a two-tailed Student’s t-test. The seasonal mean refers to the average of December, January, and February for preceding winter; March, April, and May for spring; June, July, and August for summer; and September, October, and November for autumn.

The normalized EOF analysis is performed for summer mean 500-hPa geopotential height anomalies over 10°–70°N, 100°–160°E. Figure 1a shows that the first leading EOF mode, which explains 30.2% of the total variance, presents a typical tripole pattern with two positive centers over the western subtropical Pacific and northeastern Asia and one negative center over Japan. This mode is identified as the EAP pattern. The second leading EOF mode (Fig. 1b), which explains 17.9% of the total variance, exhibits a latitudinally oriented dipole pattern, with positive anomalies over Baikal Lake and negative anomalies over the Okhotsk Sea. This mode is identified as the Baikal Lake/Okhotsk Sea (BLOS) pattern. The third leading EOF mode explains 12.2% of the total variance and is referred to as the eastern China/northern Okhotsk Sea (ECNOS) pattern. The positive ECNOS pattern shows a large area of positive anomalies over eastern China and negative anomalies over the northern Okhotsk Sea (Fig. 1c). We changed the domain prescribed for the normalized EOF analysis slightly (10°–70°N, 102.5°–157.5°E) and obtained similar results. Therefore, the results are not sensitive to the choice of domain. The first three EOF modes account for 60.3% of the total variance, indicating that they capture well the atmospheric circulation characteristics of East Asia. The PCs of the three leading EOF modes are normalized by their corresponding standard deviations and are characterized by distinct interannual variations (Figs. 1d–f).

Figure 1 Leading modes of the summer 500-hPa geopotential height anomalies over East Asia and their corresponding PCs: (a) EOF1, (b) EOF2, (c) EOF3, (d) PC1, (e) PC2, and (f) PC3. The numbers in blue at the top of the right-hand panels indicate the percentage of variance explained.

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Figure 2 shows the summer atmospheric circulation anomalies associated with the positive EAP pattern from linear regression analysis. In the upper troposphere, large-scale positive geopotential height anomalies emerge at lower latitudes and the EAP-like pattern is obvious and significant (Fig. 2a). In the mid-troposphere, the positive EAP pattern shows two centers of positive anomalies located over the western subtropical Pacific and northeastern Asia and one center of negative anomalies over Japan (Fig. 2b). The magnitude of the EAP-like pattern at 850 hPa is weaker and less significant than that in the mid-troposphere (Fig. 2c). The sea level pressure (SLP) anomalies over East Asia also show a tripole wave-like pattern (Fig. 2d). The EAP pattern shows a quasi-barotropic structure from the surface to the upper troposphere. Although a Pacific/North American-like pattern is also observed over the Pacific/North America sector, it is weaker than the EAP-like pattern, especially in the lower troposphere.

Figure 3 shows the summer atmospheric circulation anomalies associated with the positive BLOS pattern from the surface to the upper troposphere. The geopotential height field in the upper troposphere features a wave train pattern over the mid–high latitudes of Eurasia, with negative anomalies over the northern Urals and the Okhotsk Sea and positive anomalies over western Europe and Baikal Lake (Fig. 3a). The dominant features of the 500-hPa geopotential height are similar to those at 200 hPa (Fig. 3b). This wave train pattern is weaker in the lower troposphere than in the upper troposphere (Fig. 3c). The wave train pattern over the mid–high latitudes of Eurasia is distinct at the surface (Fig. 3d). The BLOS pattern presents an obvious quasi-barotropic structure from the surface to the upper troposphere.

In the summers with the positive phase of the ECNOS pattern, two significant negative anomaly centers at 200-hPa geopotential height are located over the northern Urals and the Far East and one significant positive anomaly center is observed near Baikal Lake (Fig. 4a). In the mid-troposphere, the two negative anomaly centers at 500 hPa are similar to those at 200 hPa, whereas the positive anomalies over East Asia become more significant than those in the upper troposphere (Fig. 4b). The two negative anomaly centers are clearly weaker in the lower troposphere, but the positive geopotential height anomalies over East Asia are obvious (Fig. 4c). The SLP anomaly pattern is similar to that at 850 hPa (Fig. 4d). The ECNOS pattern presents a quasi-barotropic structure from the surface to the upper troposphere.

Figure 2 Summer mean anomalies regressed onto PC1 for 1961–2013: (a) 200-, (b) 500-, and (c) 850-hPa geopotential height (gpm), and (d) SLP (hPa). The dotted areas exceed the 95% confidence level with a two-tailed Student’s t-test.

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Figure 3 As in Fig. 2, but for PC2.

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Figure 4 As in Fig. 2, but for PC3.

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We further investigate the connections between the BLOS and ECNOS patterns and the conventional atmospheric circulation teleconnection patterns in the Northern Hemisphere (Table 1). The BLOS pattern index is significantly correlated with the NAO, POL, and EAWR pattern indices, but the spatial distribution of the BLOS pattern is distinctly different from that of the above three patterns (figures omitted). In particular, the BLOS pattern index is significantly correlated with the SCAN pattern index. The spatial distribution of the BLOS pattern resembles that of the SCAN pattern, although the anomaly center over the Okhotsk Sea for the BLOS pattern is dramatically stronger. Therefore, the BLOS pattern is also referred to as an SCAN-like teleconnection pattern. The correlation coefficients between the ECNOS pattern and the EA and WP patterns are 0.236 and 0.234, respectively, which are significant only at the 90% confidence level. The spatial features of the ECNOS pattern are obviously different from those of the EA and WP patterns (figures omitted). Hence, the ECNOS pattern is probably distinct from the conventional atmospheric circulation teleconnection patterns in the Northern Hemisphere.

We focus now on the effects of the three different summer atmospheric circulation patterns on precipitation and SAT over eastern China. The positive EAP pattern is associated with above-average precipitation over the Yangtze River valley and northeastern China and below-average precipitation over the Yellow River valley and southeastern China (20°–25°N, 115°–120°E) in the summer (Fig. 5a). The variance of summer precipitation associated with the EAP pattern is about 20%–40% of the total variance over the Yangtze River valley, but it is less than 10% of the total variance over most of northeastern China (Fig. 5b). The summer precipitation associated with the positive BLOS pattern is below average south of 30°N, especially over southwestern China, and above average over northern China (Fig. 5c). The BLOS pattern explains 10%–20% of the total variance in summer precipitation over most of eastern China and 30%–40% of the total variance over southwestern China (Fig. 5d). The summer precipitation associated with the positive ECNOS pattern is more than the average over the Yangtze River valley and less than the average over northeastern China (Fig. 5e). The ECNOS pattern explains about 30% and 40% of the total variance of summer precipitation over the Yangtze River valley and northeastern China, respectively (Fig. 5f).

The positive EAP pattern produces cooling north of the Yangtze River and warming south of the Yangtze River (Fig. 6a). This pattern explains 10%–20% of the total variance in the summer SAT over most of eastern China (Fig. 6b). In association with the positive BLOS pattern, significant cooling is observed over northeastern China, whereas warming occurs south of the Yangtze River (Fig. 6c). The explained variance of the summer SATs associated with the BLOS pattern exceeds 30% of the total variance north of the Yellow River, whereas it is only 10%–20% of the total variance south of the Yangtze River (Fig. 6d). Robust warming is present over northeastern China in the positive ECNOS phase and significant cooling is seen over the Yangtze River valley (Fig. 6e). The explained variance exceeds 40% of the total variance over northeastern China and reaches 30% of the total variance south of the Yangtze River (Fig. 6f).

Figure 5 Observed summer precipitation anomalies (mm) regressed onto (a) PC1, (c) PC2, and (e) PC3 for 1961–2013 and the spatial distribution of the explained variance of the (b) EAP, (d) BLOS and (f) ECNOS patterns. The black dots in (a, c, e) indicate areas exceeding the 95% confidence level with a two-tailed Student’s t-test.

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Figure 6 As in Fig. 5, but for the summer SAT (℃).

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To give a more complete understanding of the atmospheric circulation patterns, we investigate the relationship between the three summer circulation patterns in East Asia and the various types of external forcing. Figure 7 shows the correlation between PC1 and the seasonal mean SST anomalies from the preceding winter to autumn. Significant positive correlation is found over the tropical Indian Ocean (TIO; 10°S–20°N, 60°–110°E), accompanied by the decaying El Niño from the preceding winter to autumn, indicating a possible driving effect of the TIO SST anomalies on the EAP pattern. We define the TIO index by averaging the SST anomalies over the region (10°S–20°N, 60°–110°E).

The 27.5℃ SST isotherm is the convection threshold for the climatological state (Graham and Barnett, 1987). During the El Niño decaying summer, basin-wide warming over the TIO (i.e., total SST > 27.5℃) enhances the anomalous convective heating and produces an anomalous upper level divergence ( Figs. 8a, b). The anomalous convection over the TIO contributes to tropospheric warming and forces a Kelvin wave wedge, which penetrates into the western equatorial Pacific (Xie et al., 2009). The Ekman divergence induced by the Kelvin wave weakens the convection and triggers an anomalous anticyclone over the Philippine Sea (Fig. 8c), leading to the generation and persistence of the EAP pattern (Qu and Huang, 2012). In particular, there is a negative Indo-Pacific warm pool and North Pacific dipole (IPOD) pattern, with warm SST anomalies over the Indo-Pacific warm pool and cold SST anomalies over North Pacific (Fig. 8a). According to Zheng et al. (2014), this negative IPOD pattern contributes to the strengthened WPSH and then intensifies the Philippine anticyclone in the lower troposphere, leading to anomalous convection over the nothwestern subtropical Pacific that contributes to the excitation of the EAP pattern. Therefore, both the anomalous TIO SST and the IPOD pattern are responsible for excitation of the EAP pattern through anomalous convection over the northwestern subtropical Pacific. The atmospheric circulation and SST fields associated with the warm TIO SST anomalies resemble those associated with the positive EAP pattern (Figs. 8d–f), suggesting that the anomalous TIO SST triggers the EAP pattern. The relationship between the TIO SST and the EASM shows a decadal shift in the late 1970s. The Indian Ocean dipole SST anomaly pattern was closely related to the EASM before the mid 1970s, whereas the relationship between the EASM and the SST anomalies in the northern Indian Ocean strengthened after the late 1970s (Ding et al., 2010).

In this process, the warm summer SST anomalies in the TIO decrease the land–sea thermal contrast and the north–south SLP gradient, which, in turn, leads to anomalous northeasterly winds over the TIO and the South China Sea. The anomalous northeasterly winds decrease the 10-m wind speed and hence decrease the net latent heat flux (Fig. 9a). This change in the net latent heat flux induces warm SST anomalies and provides a positive feedback consistent with the wind–evaporation–SST feedback theory (Xie and Philander, 1994). The warm TIO SST anomalies produce obvious convection, intensified ascending motion, and increased precipitation over the TIO, and suppress convection, ascending motion, and precipitation over the Philippine Sea and northwestern subtropical Pacific (Figs. 9b–d). These results further validate the view that the anomalous TIO SSTs contribute to the generation of the EAP pattern via anomalous convection over the northwestern subtropical Pacific associated with the Ekman divergence induced by the Kelvin wave.

Figure 7 Correlations between PC1 and seasonal mean SST in (a) the preceding winter, (b) spring, (c) summer, and (d) autumn. The dotted areas indicate the 95% confidence level with a two-tailed Student’s t-test.

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Figure 8 Summer mean (a) SST (shading; ℃), (b) 300-hPa potential velocity (contours; m2 s–1) and divergent wind (vectors; m s–1), and (c) 300-hPa geopotential height (shading; gpm) and 850-hPa horizontal wind (vectors; m s–1) anomalies associated with the simultaneous TIO index for 1961–2013. (d)–(f) Same as (a)–(c), respectively, but associated with PC1. In (a, d), the green solid lines indicate the 27.5℃ of the total SST associated with the TIO index and PC1, respectively. The total SST is defined as the sum of the TIO index/PC1 regression pattern and climatological mean SST. In (b, e), the contour interval is 0.1 × 105 m2 s–1; the zero contour lines are black, whereas the negative (positive) values are blue (red).

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Figure 9 Summer mean (a) net latent heat flux (shading; W m–2) and 10-m wind (vectors; m s–1), (b) outgoing longwave radiation (W m–2), (c) 500-hPa vertical velocity (Pa s–1), and (d) precipitation (mm day–1) anomalies associated with the simultaneous TIO index. The dotted areas exceed the 95% confidence level with a two-tailed Student’s t-test.

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Figure 10 shows the correlation between PC2 and SIC anomalies in the preceding winter and spring. The PC2 and SIC anomalies in the preceding winter are significantly and negatively correlated over the northern Barents Sea (Fig. 10a). This significant negative correlation over the northern Barents Sea is also observed in spring, although it covers a relatively smaller region (Fig. 10b). Li and Leung (2013) indicated that the anomalous Arctic sea ice changes the local surface turbulent heat flux from spring to summer, triggering a quasi-stationary Rossby wave train over the Eurasian continent. Therefore, the anomalous spring SIC over the northern Barents Sea may contribute to the generation and persistence of the BLOS pattern in the summer. The spring SIC (SSIC) index is defined as the average SIC over 82°–90°N, 20°W–80°E.

To compare these results, the SSIC index is multiplied by –1 in Figs. 11a, b. The 300-hPa geopotential height anomalies associated with the decreased SSIC over the northern Barents Sea show an obvious wave train pattern over Eurasia, with two positive anomaly centers over western Europe and northern Baikal Lake and two negative anomaly centers over the northern Urals and the Okhotsk Sea (Figs. 11a, b). The wave activity flux is used to explore the propagation of the extra-tropical Rossby wave (Takaya and Nakamura, 2001). The Rossby wave activity flux associated with the decreased SSIC over the northern Barents Sea originates from the Greenland Sea and propagates eastward, penetrating into North Pacific. The atmospheric circulation pattern and the Arctic SIC associated with the decreased SSIC over the northern Barents Sea are similar to those associated with the positive BLOS pattern, despite differences in some regions (Figs. 11c, d), identifying that the anomalous SSIC over the northern Barents Sea is responsible for the excitation and persistence of the BLOS pattern. This wave activity flux is reinforced over the Okhotsk Sea, implying that other factors may contribute to the Okhotsk Sea anomaly center in the BLOS pattern.

The spring SLP anomalies associated with the positive BLOS pattern are characterized by a pattern similar to the North Pacific Oscillation (Fig. 12a). The surface wind anomaly pattern over North Pacific, which is similar to the North Pacific Oscillation, contributes to the development of the Victoria mode, which is defined as the second EOF mode of the SST anomalies in the North Pacific poleward of 20°N (Bond et al., 2003). The anomalous westerly winds over the central tropical Pacific decrease the northeasterly trade winds and produce warm SST anomalies in the spring (Fig. 12b). In association with the positive spring Victoria mode (Fig. 12c), anomalous southwesterly winds occur over the western tropical Pacific and an obvious convergence is observed over the central–eastern tropical Pacific, leading to the development of the warm central tropical Pacific SST anomalies in the summer, which may persist into the following autumn and winter (Ding et al., 2015a, b). This process is accompanied by anomalous upper level divergent winds and intensified precipitation over the central tropical Pacific. These anomalous divergent winds contribute to the development of the Okhotsk Sea anomaly center in the BLOS pattern (Fig. 12d).

Figure 10 Correlation between PC2 and seasonal mean SIC in (a) the preceding winter and (b) spring. The dotted areas exceed the 95% confidence level with a two-tailed Student’s t-test.

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Figure 11 (a) Spring mean SIC and (b) summer mean 300-hPa geopotentail height (shading; gpm) and wave activity flux (vectors; m–2 s–2) anomalies associated with the inverted SSIC index. (c, d) Same as (a, b), but are associated with PC2. The dotted areas indicate the 95% confidence level with a two-tailed Student’s t-test.

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Figure 12 Spring mean (a) SLP, (b) SST (shading; °C) and 10-m wind (vectors; m s–1) anomalies regressed onto PC2, summer mean (c) SST (shading) and 10-m wind (vectors), and (d) precipitation (shading; mm day–1) and divergent wind anomalies (vectors; m s–1) regressed onto the spring Victoria mode index. The Victoria mode index is defined as the normalized PC of the second EOF mode of the North Pacific SST anomalies in spring. The dotted areas exceed the 95% confidence level with a two-tailed Student’s t-test.

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For the ECNOS pattern, a significant positive correlation of PC3 and the spring SCE is observed over the midlatitudes of Asia; this signal is obvious in April and disappears in May and June (Fig. 13). A significant positive correlation of PC3 and the SCE over the high latitudes of Asia is observed in May and June (Figs. 13c, d). These results indicate that the earlier snowmelt over the midlatitudes of Asia (40°–60°N, 60°–140°E) and later spring snowmelt over the high latitudes of Asia (60°–74°N, 60°–140°E) may be responsible for the formation of the ECNOS pattern. The June SCE is used to represent the spring snow cover because it can more effectively depict earlier or later snowmelt (Matsumura and Yamazaki, 2012). We define the spring snowmelt index as the difference in the area-averaged June SCE between the high and midlatitudes of Asia.

Figure 14 presents the summer 2-m air temperature and soil moisture anomalies associated with the spring snowmelt index. The earlier spring snowmelt reduces the summer soil moisture over the midlatitudes of Asia, especially over Mongolia and northeastern China (Fig. 14a). In addition, the earlier spring snowmelt decreases the surface albedo over the midlatitudes of Asia and favors surface warming in spring. The reduced soil moisture associated with the earlier spring snowmelt enhances surface warming over Mongolia and northeastern China and the later spring snowmelt over the Far East increases the soil moisture and reduces the near-surface air temperature in summer (Fig. 14b), both of which favor the formation of the ECNOS pattern.

Figure 13 Correlation between PC3 and SCE in (a) spring, (b) April, (c) May, and (d) June during 1973–2013.The dotted areas exceed the 90% confidence level with a two-tailed Student’s t-test.

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Figure 14 Summer mean (a) 0–10-cm soil moisture (m3 m–3) and (b) 2-m air temperature anomalies (℃) regressed onto the spring snowmelt index. The dotted areas indicate the 95% confidence level with a two-tailed Student’s t-test.

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This study focused on the summer atmospheric circulation patterns over East Asia and their impacts on simultaneous precipitation and SAT over eastern China. The relationship between the three summer atmospheric circulation patterns and external forcing such as SST, sea ice, and so on was also investigated to give a more complete picture of the circulation patterns. The atmospheric circulation of East Asia features three patterns in the summer mean 500-hPa geopotential height—namely, a typical tripole pattern with two positive centers over the Okhotsk Sea and western subtropical Pacific and one negative center over Japan (i.e., the EAP pattern), a latitudinally oriented dipole pattern with positive anomalies over Baikal Lake and negative anomalies over the Okhotsk Sea (i.e., the BLOS pattern), and an eastern China and northern Okhotsk Sea dipole pattern with positive anomalies over eastern China and negative anomalies over the northern Okhotsk Sea (i.e., the ECNOS pattern).

The EAP pattern features a quasi-barotropic structure from the surface to the upper troposphere. In the summer, the positive EAP pattern leads to above-average precipitation over the Yangtze River valley and northeastern China, with below-average precipitation over southeastern China. This pattern explains up to 40% of the total variance in summer precipitation over the Yangtze River valley. The positive EAP pattern favors cooling north of the Yangtze River and warming south of the Yangtze River. During the El Niño decaying summer, the warm TIO SST anomalies contribute to the formation and maintenance of the positive EAP pattern. The warm TIO SST anomalies produce anomalous northwesterly winds, which, in turn, decrease the background southerly winds and the net latent heat flux over the TIO. This change in the net latent heat flux induces warm SST anomalies and produces a positive feedback. The warm TIO anomalies also force an eastward propagating Kelvin wave wedge and the Ekman divergence induced by this Kelvin wave suppresses convection over the northwestern subtropical Pacific. Consequently, the anomalous convection over the northwestern subtropical Pacific associated with the anomalous TIO SST is responsible for the generation of the EAP pattern, supporting previous interpretations (Huang, 1992).

In the BLOS pattern, a wave train pattern in the upper troposphere is present over the mid–high latitudes of Eurasia, but is weaker in the lower troposphere. In the summer, the positive BLOS pattern leads to below-average precipitation south of the Yangtze River and produces significant cooling over northeastern China. This pattern explains about 40%–50% of the total variance in the summer SAT over northeastern China. The Rossby wave activity flux associated with the decreased spring sea ice over the northern Barents Sea originates from the Greenland Sea and propagates eastward, penetrating into North Pacific. Hence the anomalous spring sea ice over the northern Barents Sea contributes to the excitation and maintenance of the BLOS pattern. The spring surface wind anomaly pattern, similar to the North Pacific Oscillation, over North Pacific associated with the positive BLOS pattern contributes to the formation of the Victoria mode. The spring Victoria mode then leads to the development of warm central tropical Pacific SST anomalies in the summer, which produce anomalous divergent winds in the upper troposphere that intensify the stationary Rossby wave over mid–high latitudes.

The ECNOS pattern presents a quasi-barotropic structure from the surface to the upper troposphere. In the summer, the positive ECNOS pattern leads to below-average precipitation over northeastern China with above-average precipitation over the Yangtze River valley, producing robust warming over northeastern China and significant cooling over the Yangtze River valley. It explains up to 50% and 40% of the total variance in the summer SAT over northeastern China and the Yangtze River valley, respectively. Earlier spring snowmelt reduces the soil moisture and leads to surface warming over Mongolia and northeastern China in the following summer. Later spring snowmelt increases the soil moisture and decreases the near-surface air temperature over the high latitudes of Asia, especially over the Far East. Hence, the formation of the ECNOS pattern is partly attributed to earlier spring snowmelt over Mongolia and northeastern China and later spring snowmelt over the Far East. Other external factors also need to be investigated, although they are outside of the scope of this study.

Summer Arctic sea ice has decreased rapidly since the 1980s (Comiso et al., 2008; Zhang and Li, 2017; Luo and Yao, 2018). The anomalies in the extent of Arctic sea ice change the local energy balance, which, in turn, induces feedback to the large-scale atmospheric circulations (Honda et al., 1996). Sea ice and snow cover are the two factors that greatly affect the Arctic and subarctic atmospheric circulation. The winter–spring Eurasian snow cover is closely associated with the East Asian atmospheric circulation and precipitation in the following summer (Wu and Kirtman, 2007). Groisman et al. (2006) showed that the persistence of snow cover in northern Eurasia has decreased during the past few decades. Eurasia becomes snow-free in the early summer, whereas the Arctic Ocean is covered by sea ice until late summer. This lagged seasonal cycle intensifies the surface thermal contrast across the Arctic coastline (Derksen and Brown, 2012). The two significant negative anomaly centers associated with the positive ECNOS pattern are located over the northern Urals and Far East. Therefore, the ECNOS pattern is probably related to the Arctic sea ice and the combined effects of Arctic sea ice and Eurasian snow cover on the ECNOS pattern deserve further investigation.

Acknowledgments. We thank the reviewers for their constructive comments and suggestions that led to significant improvements in this paper. We also thank Dr. Yanjun Guo for her constructive suggestions about the snow cover extent data analysis.