1. Introduction
Located in a monsoon domain, East Asia not only experiences a seasonal reversal of the dominant winds between summer and winter but also displays a large difference in seasonal rainfall between summer and winter (e.g., Huang et al. 2003). The East Asian monsoon features strong southerly winds and abundant rainfall in summer and strong northerly winds and little rainfall in winter. Strong southerly flow in summer brings a large amount of water vapor from the tropical western Pacific and the Indian Ocean to eastern China, Japan, and the Korean Peninsula, causing continuous and heavy rainfall in these regions (Tao and Chen 1987; Ding 1994; Chang et al. 2000a; Chen et al. 2009). On the contrary, in winter cold surges move southward along the coast of East Asia to the South China Sea and the Indochinese peninsula, inducing severe cold waves/snowstorms in East Asia (e.g., Lau and Li 1984; Ding 1994; Chen et al. 2000, 2005; Chang et al. 2006). Hence, the East Asian climate is dominated by the East Asian summer and winter monsoons.
Since floods and droughts caused by anomalous East Asian summer monsoon (EASM) are among the most devastating natural disasters, there have been many efforts to understand the variability of the EASM and to predict the variation in the summer monsoon circulation (Lau et al. 1988; Ding 1992; Trenberth et al. 2006; Zhou et al. 2005; Huang et al. 2007). The year-to-year variability of summer rainfall is affected by many factors, such as sea surface temperature (SST), snow cover, and soil moisture (e.g., Charney and Shukla 1981; Lau 1992; Huang et al. 2003). Among these factors, the El Niño–Southern Oscillation (ENSO) is regarded as the most important one (Chen et al. 1992; Shen and Lau 1995; Zhang et al. 1999; Chang et al. 2000a; Wang et al. 2000; Lau and Weng 2001; Huang et al. 2004; Yim et al. 2006; Zhou and Chan 2007; Feng et al. 2011; among others). The influence of ENSO on the EASM is complex and may depend upon the phase of ENSO. As demonstrated by Huang and Wu (1989), summer rainfall anomalies in China are different between the developing and decaying phases of ENSO. In the developing phases of El Niño, southern and northern China experience dry conditions, while the central part of China experiences wet conditions. Roughly opposite rainfall anomalies are observed in the summer after the peak of El Niño. Lau and Weng (2001) showed that the percentage of variance explained by coupled China rainfall–global SST modes for northern China and the Yangtze River region is very different between the summers of 1997 and 1998, which correspond to the developing and decaying phases of El Niño. The different influences of ENSO are related to the different atmospheric responses to SST anomalies in the developing and decaying phases of ENSO (Wang et al. 2000; Wu et al. 2003).
While there is a comprehensive literature concerning the EASM, the East Asian winter monsoon (EAWM) variability has not been well studied. One reason may be that the winter monsoon is not so important for food production. Nevertheless, the EAWM is an essential component of the climate system and may exert a large social and economic impact on many East Asian countries (e.g., Ding 1994; Wang and Chen 2010; Wei et al. 2011). In addition, the EAWM can induce deep convection over the Maritime Continent through the intrusion of cold surges into the tropics, which serves as a major heat source for the atmospheric circulation (Chang et al. 2006; Wang et al. 2012). This heat source gives rise to strong midlatitude–tropical interactions and affects the midlatitude East Asian jet (Lau and Chang 1987), which in turn may influence the climate in remote regions such as North America (Yang et al. 2002).
So far, most previous research on the monsoon has focused on the EASM or the EAWM variability separately. However, several studies found that there is a link between the EASM and the EAWM on interannual time scales. Sun and Sun (1994) suggested that the summertime drought/flood in the Yangtze–Huaihe River valley is related to the previous winter atmospheric circulation anomalies over East Asia. Chen et al. (2000) investigated the possible influence of the EAWM on the following EASM. Their results show that after a weak (strong) EAWM the summer western Pacific subtropical high tends to move northward (southward), which is in favor of flood (drought) in the Yangtze River valley and drought (flood) in northern China. In addition, some previous studies documented the interaction between the EAWM and ENSO. On one hand, a strong EAWM is suggested to be a necessary condition for the occurrence of a warm ENSO event (Li 1989; Chen and Wu 2000; Xu and Chan 2001). On the other hand, a warm (cold) ENSO event can weaken (enhance) the EAWM through teleconnection and remote response (Chen et al. 2000; Wang et al. 2000). Chen (2002) compared the link between the EAWM and EASM in ENSO and non-ENSO years and found that the EAWM and EASM are closely related only in ENSO years. However, the physical mechanism behind this link is not yet clear.
On the other hand, behavior of the relation of the EAWM to the EASM resembles the in-phase tropospheric biennial oscillation (TBO) process (Ropelewski et al. 1992; Meehl 1997; Meehl and Arblaster 2002). The in-phase TBO shows that a strong Indian summer monsoon is often followed by a strong Australian summer monsoon and vice versa in the weak monsoon annual cycles (Meehl and Arblaster 2002). Moreover, the TBO usually encompasses most ENSO years that have a biennial tendency cycle as well. Thus, the TBO is a fundamental feature of the air–sea coupling mode over the entire Indo-Pacific Ocean region (Loschnigg et al. 2003). An ENSO event plays an important role in this process and is a main factor responsible for the in-phase TBO transition via a shift of the large-scale east–west circulation across the equatorial Indo-Pacific Oceans (Yu et al. 2003; Li et al. 2006). It is an interesting issue whether the role of ENSO in the EAWM–EASM link resembles that in the TBO phenomenon and how the EAWM–EASM persistence fits in the TBO-like behavior over the broad Asian monsoon region.
In recent decades, there is a growing body of evidence that the East Asian monsoon features interdecadal variations, and most studies attribute the interdecadal variations to the SST anomalies (e.g., Chang et al. 2000a,b; Wu and Wang 2002; Zhou et al. 2006). In particular, the relations of ENSO to both EAWM and EASM are shown to be modulated by the Pacific decadal oscillation (PDO; Chan and Zhou 2005; Wang et al. 2008). Whether the link between the EAWM and EASM is influenced by the PDO has not been studied. Besides, it is not clear whether the active role of the EAWM in the development of ENSO events can be extended to the EASM–ENSO relationship. Given the importance of the East Asian monsoon, it is therefore necessary to investigate the roles of ENSO and PDO in the link of EAWM to EASM and the associated atmospheric processes.
The organization of the text is as follows: The datasets and analysis methods used in this study are described in section 2. In section 3, we investigate the general relationship between the EAWM and the following EASM and the role of ENSO by separating the EAWM index into ENSO-related and ENSO-unrelated parts. Section 4 then presents the modulation of the relationship between the EAWM and the following EASM by the PDO. Finally, the conclusions and discussions are given in section 5.
2. Datasets and methods
The monthly-mean atmospheric data used in this study are derived from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) (Uppala et al. 2005), which spans the period from September 1957 to August 2002. This dataset has a horizontal resolution of 2.5° × 2.5° and extends from 1000 to 1 hPa with 17 vertical pressure levels. The SST used in this study is the monthly-mean Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST). It is a unique combination of monthly-mean fields of SST and sea ice concentrations with a global coverage on a 1° latitude–longitude grid from 1870 to the present (Rayner et al. 2003). In addition, we employ the accumulated monthly station rainfall data at 160 stations of China, which are obtained from the Chinese Meteorological Data Center. The station rainfall data are available starting from January 1951. To avoid the possible influence from global warming, all data are detrended using the linear regression method before analyses.
The intensity of the EAWM is estimated with an EAWM index (EAWMI), which is defined by the meridional winds at 850 hPa averaged over the region 20°–40°N, 100°–140°E (Yang et al. 2002). Figure 1a describes the time series of the EAWM measured by the normalized EAWMI averaged for December–February (DJF). Here, the winter of 1957 refers to the boreal 1957/58 winter. Positive index indicates a weak EAWM event and vice versa. ENSO events are identified by the normalized Niño-3 index, which is defined by the SST anomalies averaged over the region (5°S–5°N, 150°–90°W). We separate the EAWMI into two parts: ENSO related and ENSO unrelated, as shown in Figs. 1b,c, respectively. The ENSO-related part is calculated by a linear regression of the EAWMI with respect to the winter Niño-3 index, which is called EAWMIEN. Then the ENSO-unrelated part is computed as the difference between the total index and the ENSO-related part, which is called as the EAWMIres.
(a) The time series of the normalized EAWMI averaged for DJF from 1957/58 to 2001/02. (b) The ENSO-related part and (c) the ENSO-unrelated part of EAWMI.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
(a) The time series of the normalized EAWMI averaged for DJF from 1957/58 to 2001/02. (b) The ENSO-related part and (c) the ENSO-unrelated part of EAWMI.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
(a) The time series of the normalized EAWMI averaged for DJF from 1957/58 to 2001/02. (b) The ENSO-related part and (c) the ENSO-unrelated part of EAWMI.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
The EAWMIEN and EAWMIres contribute 35% and 65% of the total EAWMI variance, respectively. Note that the variance of the EAWM explained by ENSO is subject to interdecadal change due to the modulation of the PDO on the relationship between ENSO and the EAWM. Figure 2 presents the running correlations between the EAWMI and the Niño-3 index with a window of 11 yr. The robust positive correlation coefficients are observed spanning the period of 1962–78, which corresponds to the negative PDO phases. However, these positive correlations become weak and insignificant during 1979–93 when the PDO is roughly in its positive phases. This result is consistent with Wang et al. (2008). In fact, the percentage of the variance of the EAWMIEN for the total EAWMI reaches up to 48% during the negative PDO phases, while it reduces to 27% during the PDO positive phases. In addition, the running correlations between the EAWMIres and the Niño-3 index are further calculated. The interdecadal change of these correlations has an in-phase relation to that for the EAWMI and the Niño-3 index. This similarity indicates that the PDO can also modulate the temporal behavior of EAWMIres. In other words, the residual part of EAWMI is mainly associated with the North Pacific Ocean SST variability, which will be discussed further in next section.
The time series of winter-mean (DJF) PDO index (solid line) and the running correlation coefficients between the winter-mean Niño-3 index with the EAWMI (red dashed line) and the EAWMIres (purple dashed line) with a window of 11 yr, respectively. The coefficients of 0.6 or −0.6 denote the 95% confidence level. Here, the year 1957 denotes the 1957/58 winter.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
The time series of winter-mean (DJF) PDO index (solid line) and the running correlation coefficients between the winter-mean Niño-3 index with the EAWMI (red dashed line) and the EAWMIres (purple dashed line) with a window of 11 yr, respectively. The coefficients of 0.6 or −0.6 denote the 95% confidence level. Here, the year 1957 denotes the 1957/58 winter.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
The time series of winter-mean (DJF) PDO index (solid line) and the running correlation coefficients between the winter-mean Niño-3 index with the EAWMI (red dashed line) and the EAWMIres (purple dashed line) with a window of 11 yr, respectively. The coefficients of 0.6 or −0.6 denote the 95% confidence level. Here, the year 1957 denotes the 1957/58 winter.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
The linear regression method is adopted in this paper. It should be noted that the effective number of degrees of freedom is considered according to Davis (1976) when examining the statistical significance of the linear regression.
3. Link of the EAWM with the following EASM
a. The general relationship between the EAWM and EASM
To elucidate the general relationship of the EAWM to the following EASM, Fig. 3 presents the evolutions of 850-hPa wind anomalies obtained by regression upon the EAWMI from the simultaneous winter to the following summer. Climatologically the wintertime low-level winds over East Asia feature strong northwesterlies along the east flank of the Siberian high. These winds split into two branches south of Japan with one branch turning eastward toward the subtropical western and central Pacific and the other flowing southward along the coast of East Asia into the South China Sea (see Fig. 1 of Chen et al. 2000). As shown in Fig. 3a, both of these two branches of winds tend to decrease in a weak EAWM case. This induces an anomalous western North Pacific (WNP) anticyclone over the tropics and a weakened Aleutian low over the North Pacific. An interesting feature is that this anomalous WNP anticyclone persists through the following spring to the summer (Figs. 3b,c). In addition, the anticyclone over the North Pacific appears to shift southward with the advance of the season. Hence, a link between the EAWM and the following EASM is established. Generally, after a weak EAWM there are anomalously strong southwesterlies over subtropical East Asia in the following summer, which corresponds to a strong EASM. The key system in this link is the anomalous WNP anticyclone. In addition, there is an anomalous cyclone over northern China. These anticyclonic and cyclonic anomalies correspond to a dominant teleconnection pattern in summer over East Asia addressed by many studies (Huang et al. 2003, and references therein).
Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) +MAM, and (c) +JJA with respect to EAWMI. The plus symbol, +, stands for the season following winter. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) +MAM, and (c) +JJA with respect to EAWMI. The plus symbol, +, stands for the season following winter. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) +MAM, and (c) +JJA with respect to EAWMI. The plus symbol, +, stands for the season following winter. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
The relationship between the EAWM and the following EASM is manifested not only in the anomalous atmospheric circulation but also in the rainfall anomalies. Figure 4 shows the pattern of summer rainfall anomalies in China derived through regression upon the EAWMI. The rainfall anomalies display alternative positive and negative distribution in eastern China. After a weak EAWM, significant positive rainfall anomalies tend to occur in the middle reaches of the Yangtze River (at about 30°N) and northern China, which are consistent with anomalous atmospheric circulations as shown in Fig. 3c. To the northwest side of the anomalous WNP anticyclone, the southwesterlies bring more water vapor from the South China Sea and there is a convergence around 30°N in central China. An anomalous cyclone lies over North China and Northeast China. The above-normal rainfall there corresponds to southerly winds.
As in Fig. 3, but for the summertime rainfall anomalies. The contour interval is 5 mm month−1. The light and dark shading indicate the 90% and 95% confidence level, respectively, according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 3, but for the summertime rainfall anomalies. The contour interval is 5 mm month−1. The light and dark shading indicate the 90% and 95% confidence level, respectively, according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 3, but for the summertime rainfall anomalies. The contour interval is 5 mm month−1. The light and dark shading indicate the 90% and 95% confidence level, respectively, according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
b. Role of ENSO in the relationship between EAWM and the following EASM
To illustrate the respective influence of the two decomposed parts of EAWMI on the subsequent EASM, Fig. 5 shows the regression patterns of seasonal-mean 850-hPa winds with respect to the EAWMIEN and the EAWMIres. It is clear that the anomalous WNP anticyclone associated with the EAWMIEN persists from winter to the following summer. The intensity of this anticyclone is stronger compared to that in Fig. 3, especially in the following spring and summer. In contrast, the anomalous WNP anticyclone associated with the EAWMIres appears only in winter and disappears in the following seasons. This result implies that the anomalous anticyclonic flow over the WNP in winter may be forced by ENSO or other factors. If there is an ENSO event, the WNP anticyclone tends to persist into the following summer. In this case, a close relationship is established between the EAWMEN and the subsequent EASM, whereas in a winter without ENSO the EAWMres has no relation to the following EASM. Hence, we may conclude that the link of the EAWM with the following EASM is predominantly induced by the variability of EAWM associated with ENSO. Chen (2002) showed that the EAWM–EASM relationship tends to be established via the ENSO event, which is consistent with our results. How the ENSO events link the EAWM to the following EASM was not discussed in Chen (2002). Here, we suggest that the persistence of the anomalous WNP anticyclone related to ENSO is the key reason for the EAWM–EASM link.
Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) +MAM, and (c) +JJA with respect to EAWMIEN. (d)–(f) As in (a)–(c), but with respect to EAWMIres. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) +MAM, and (c) +JJA with respect to EAWMIEN. (d)–(f) As in (a)–(c), but with respect to EAWMIres. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) +MAM, and (c) +JJA with respect to EAWMIEN. (d)–(f) As in (a)–(c), but with respect to EAWMIres. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
To further reveal the winter-to-summer monsoon link, the correlation coefficients between several EAWM and EASM indices are calculated (Table 1). Here, three EASM indices are selected in order to reflect different types of circulation features of the EASM. The first EASM index (EASMIWF) is defined by the 850-hPa zonal wind difference as the average over the region of 22.5°–32.5°N and 110°–140°E minus that over the region of 5°–15°N and 90°–130°E reflecting the shear vorticity at the low level over the WNP (Wang and Fan 1999). The second one, named EASMIZHW, displays the north–south thermal contrast by using the vertical shear of zonal winds, which is defined by the differences of the 850- and 200-hPa zonal winds over the regions (0°–10°N, 100°–130°E) (Zhu et al. 2000). The third one, named EASMIWN, is constructed by the meridional winds averaged over the regions (20°–30°N, 110°–130°E) (Wu and Ni 1997). From Table 1, it can be found that the total EAWMI has a significant correlation with both the EASMIZHW and the EASMIWN but not the EASMIWF. However, when we only consider the ENSO-related part of EAWMI, the correlation coefficients of EAWMIEN with all the three EASMI become much more significant, far beyond the 99.9% confidence level. The result indicates that after a weak EAWMEN there tends to be a robust anomalous WNP anticyclone, strong north–south thermal contrast in East Asia, and strong southerly winds along the east coast of China in the following summer, indicating a strong EASM, which is consistent with the previous results. In contrast, the correlations of the EAWMIres with the three EASMI are weak and insignificant. The result implies that the ENSO-related EAWM is not related to the following EASM.
The correlation coefficients between EAWM indices and EASM indices. The bold and bold italic values exceed the 99% and 99.9% confidence level based on the Student’s t test, respectively.
To understand why the anomalous WNP anticyclone has different behavior, we examine the evolution of the SST anomalies from the simultaneous winter to the following summer associated with the EAWMEN and EAWMres, respectively (Fig. 6). For the ENSO-related part, the SST anomalies present an El Niño pattern from winter to the following summer, whereas there are weak SST anomalies in the extratropical and western tropical Pacific Ocean and little anomalies in the eastern tropical Pacific Ocean for the ENSO-unrelated part. The results demonstrate that a linear separation generally works for the ENSO’s influence on the EAWM. As we know, there exist two explanations for the persistence of the anomalous WNP anticyclone associated with El Niño. One is the local air–sea interaction proposed by Wang et al. (2000). This mechanism suggests that, to the east of the anomalous WNP anticyclone, the anomalous northeasterly winds strengthen the local SST cooling through wind-evaporation processes. In turn, this SST cooling favors the WNP anticyclone by exciting Rossby waves. Therefore, the positive feedback between the ocean and atmosphere sustains the anomalous WNP anticyclonic winds. The other is the Indian Ocean capacitor mechanism proposed by Xie et al. (2009). They suggested that the persistence of the El Niño–induced warm SST anomalies in the northern tropical Indian Ocean may increase the tropospheric temperature, thereby emanating a Kelvin wave propagating into the WNP. The WNP anticyclone can persist until summer through the Kelvin wave–induced Ekman divergence mechanism. To understand the roles of local SST cooling and northern Indian Ocean warming in the maintenance of the WNP anticyclone, we show in Fig. 6 the regressed SST anomalies upon the EAWMIEN. There is a robust warming of SST in the northern Indian Ocean (Figs. 6b,c), which can contribute to the maintenance of the WNP anticyclone through the following summer according to the Indian Ocean capacitor mechanism (Xie et al. 2009). The WNP SST cooling is strong in the following spring (Fig. 6b), but the cooling becomes weak in the following summer (Fig. 6c). This suggests that the local air–sea interaction mechanism may contribute to the persistence of the anomalous WNP anticyclone through spring, but this contribution weakens once summer is approaching. With respect to the EAWMIres (Figs. 6e,f), there are no obvious signals of the SST anomalies in the following spring and summer, which possibly explains why the anomalous WNP anticyclone disappears in the following spring and summer. The result is not surprising given that the residual from ENSO may consist of a number of different drivers. The lack of coherent SST signals in the SST and atmospheric field is self-consistent, indicating that our method can effectively remove the ENSO signal.
As in Fig. 5, but for the SST anomalies. The contour interval is 0.2°C. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 5, but for the SST anomalies. The contour interval is 0.2°C. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 5, but for the SST anomalies. The contour interval is 0.2°C. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Chen et al. (2000) indicated that the persistence of SST anomalies in the South China Sea (SCS) from winter to the following summer may be induced by the EAWM, which possibly plays an important role in the EAWM–EASM relationship. According to Fig. 6, the warm SST anomalies in the SCS associated with the EAWMEN can persist from winter to the following summer, while those associated with the EAWMres are weak in winter and spring and then disappear in summer. Therefore, the warm SST anomalies in the SCS are mainly associated with the ENSO-related part of EAWM.
In addition to the role of the tropical SST anomalies on the persistence of the WNP anticyclone, the midlatitude SST anomalies may account for its persistence through seasonal footprinting mechanism (SFM) proposed by Vimont et al. (2001, 2003a,b) as well. The SFM suggests that the wintertime intrinsic atmospheric variability in the midlatitudes imparts an SST “footprint” onto the ocean via changes in the net surface heat flux. This SST footprint displays a triple pattern with warm (cold) SST anomaly band in the North Pacific straddled by the cold (warm) SST anomaly on its northern and southern sides. It can persist into the late spring and summer. In turn, this SST footprint forces an anomalous anticyclone (cyclone) in the tropical and subtropical regions. The midlatitude SST anomalies related to EAWMIEN in the following spring and summer display a triple pattern similar to that in the SFM (Figs. 6b,c), thereby possibly benefiting the persistence of the WNP anticyclonic anomalies. In contrast, with respect to the EAWMIres, this triple SST anomaly pattern is seen in the flowing spring but disappears in summer, which may explain the disappearance of the WNP anticyclonic anomalies.
The corresponding summer rainfall anomalies in China are presented in Fig. 7. The distribution of rainfall anomalies related to the EAWMIEN displays a pattern very similar to that seen in Fig. 4 (Fig. 7a). The positive rainfall anomalies in the Yangtze River valley and northern China are enhanced and more significant compared to those related to the EAWMI. On the contrary, there are no significant summer rainfall anomalies over China related to the EAWMIres (Fig. 7b). These results are consistent with the differences in atmospheric circulation (Figs. 5c,f) and further demonstrate the role of ENSO in the link of the EAWM with the following EASM.
As in Fig. 5, but for the summertime rainfall anomalies. The contour interval is 10 mm month−1. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 5, but for the summertime rainfall anomalies. The contour interval is 10 mm month−1. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 5, but for the summertime rainfall anomalies. The contour interval is 10 mm month−1. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Lee et al. (2005) separated the EASM into two components: that is, the ENSO-related part and the extratropics-related part. The correlation map of the summer Global Precipitation Climatology Project (GPCP) precipitation with the EAWMIEN shows a pattern similar to that correlated with the ENSO-related EASM index (figure not shown), which suggests a close relation of the summer rainfall to the previous ENSO-related EAWM. However, the EAWMIres has very weak correlations with the summer GPCP precipitation, suggesting that the summertime rainfall anomalies associated with the extratropics-related EASM have no relation to the previous EAWMres. Again, the results confirm the persistence of the EAWMIEN, but not the EAWMres.
4. PDO modulation of the relationship between the EAWMEN and the EASM
Recently, many studies have suggested that the PDO can modulate the impact of ENSO on the global climate (e.g., Gershunov and Barnett 1998; Power et al. 1999, 2006; Wang et al. 2008). Here, we examine whether the PDO can modulate the relations of EAWM to the following EASM. The PDO index is defined by the time series of the leading mode in empirical orthogonal function (EOF) analysis of monthly SST anomalies in the North Pacific Ocean (Zhang et al. 1997; Mantua et al. 1997). The time series of winter-mean PDO index from 1957 to 2001 are shown in Fig. 2 (obtained from http://jisao.washington.edu/pdo). To demonstrate the influence of PDO, we separate the whole data into the positive and negative PDO phase winters based on the values of index. Here, the positive (negative) PDO phase is simply defined as the index > 0 (< 0). Hence, 22 negative PDO phase winters and 23 positive PDO phase winters are obtained as shown in Table 2. Figure 8 presents the patterns of seasonal-mean winds at 850 hPa from the winter to the following summer as obtained by regression upon the EAWMIEN during the negative and positive PDO phases, respectively. During the negative PDO phases, the anomalous atmospheric circulation is characterized by a WNP anticyclone and a weakened Aleutian low in the winter (Fig. 8a). The anomalous WNP anticyclone persists into the following spring and summer, while the anomalous Aleutian low disappears in the following seasons (Figs. 8b,c). During the positive PDO phases, an anomalous anticyclone occupies a large domain including not only the western Pacific but also the northern Pacific in the winter (Fig. 8d). Therefore, the difference is seen in the midlatitude North Pacific between the different PDO phases (Figs. 8a,d). In the subsequent spring, the anticyclone maintains a large areal coverage although its intensity is somewhat weakened (Fig. 8e) during the positive PDO phases. In the following summer, this anticyclone shrinks back to the western Pacific but continues to be strong (Fig. 8f). It should be noted that there are obvious differences in the summer atmospheric circulation between the negative and positive PDO phases (Figs. 8c,f). The anomalous WNP anticyclone is much stronger during the positive PDO phases than the negative PDO phases. In addition, the anomalous WNP anticyclone has a meridional elongated structure from the tropics to the middle latitudes at 50°N during the negative PDO phases, while it has a zonal band structure spanning southern China–western Pacific–North Pacific during the positive PDO phases. At the same time, an anomalous cyclone lies over northern China during the negative PDO phases, whereas an anomalous cyclone lies over Japan during the positive PDO phases.
The winters selected based on the winter (DJF) PDO index.
Regression patterns of seasonal-mean 850-hPa winds in (a),(d) DJF; (b),(e) +MAM; and (c),(f) +JJA with respect to EAWMIEN in the negative and positive PDO phases. Here, (a)–(c) are for negative PDO phases and (d)–(f) are for positive PDO phases. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Regression patterns of seasonal-mean 850-hPa winds in (a),(d) DJF; (b),(e) +MAM; and (c),(f) +JJA with respect to EAWMIEN in the negative and positive PDO phases. Here, (a)–(c) are for negative PDO phases and (d)–(f) are for positive PDO phases. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Regression patterns of seasonal-mean 850-hPa winds in (a),(d) DJF; (b),(e) +MAM; and (c),(f) +JJA with respect to EAWMIEN in the negative and positive PDO phases. Here, (a)–(c) are for negative PDO phases and (d)–(f) are for positive PDO phases. Shading indicates the 90% confidence level according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
An important question is why the PDO influences the behavior of the anomalous WNP anticyclone. In the previous section, we discussed there are three possible mechanisms responsible for the persistence of the anomalous WNP anticyclone. To illustrate how the three mechanisms behave in the different PDO phases, Fig. 9 gives the regressions of the SST anomalies upon the EAWMIEN for the negative and positive PDO phases, respectively. It can be seen that the positive SST anomalies in the Indian Ocean as well as the negative SST anomalies in the tropical western Pacific become much weaker during the negative PDO phases than positive PDO phases in the following spring and summer. At the same time, the midlatitude triple SST anomaly pattern is only seen in spring (Fig. 9a) and then impairs in summer for the negative PDO phases (Fig. 9b), while this triple pattern is robust and significant for the positive PDO phases (Figs. 9c,d). Therefore, all the three mechanisms play a much weaker role in the persistence of the anomalous WNP anticyclone during the PDO negative phases than positive phases, perhaps explaining why the anomalous WNP anticyclone has a much weaker signal when the PDO is in its negative phases as compared to its positive phases.
As in Fig. 8, but for the SST anomalies in the following spring and summer. The contour interval is 0.2°C. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 8, but for the SST anomalies in the following spring and summer. The contour interval is 0.2°C. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 8, but for the SST anomalies in the following spring and summer. The contour interval is 0.2°C. The light and dark shading indicate the 90% and 95% confidence level, respectively, from a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
Figure 10 shows the regression patterns of summertime rainfall over China with EAWMIEN in the different PDO phases. During the negative PDO phases, the most significant positive rainfall anomalies appear over northern China (Fig. 10a). These rainfall anomalies are caused by the southerlies between the anomalous cyclone and anticyclone that bring more moisture to northern China (Fig. 8c). In contrast, during the positive PDO phases, robust positive rainfall anomalies cover a large domain in central China (Fig. 10b), which is consistent with the strong anomalous anticyclone (Fig. 8f). Particularly, to the northwestern side of the anomalous WNP anticyclone, the southwesterlies bring water vapor to about 30°N, contributing to above-normal rainfall in central China. Comparing Fig. 10 with Fig. 7a, we may conclude that above-normal rainfall tends to be in northern China during the negative PDO phases, while above-normal rainfall occurs in central China during the positive PDO phases. Hence, the PDO strongly modulates the relationship between the EAWMEN and the following EASM.
As in Fig. 8, but for the summertime rainfall. The contour interval is 10 mm month−1. The light and dark shading indicates the 90% and 95% confidence level, respectively, according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 8, but for the summertime rainfall. The contour interval is 10 mm month−1. The light and dark shading indicates the 90% and 95% confidence level, respectively, according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
As in Fig. 8, but for the summertime rainfall. The contour interval is 10 mm month−1. The light and dark shading indicates the 90% and 95% confidence level, respectively, according to a two-tailed Student’s t test.
Citation: Journal of Climate 26, 2; 10.1175/JCLI-D-12-00021.1
5. Conclusions and discussion
With the ERA-40 data from the European Centre for Medium-Range Weather Forecasts (ECMWF), the roles of ENSO and PDO in the relations of the EAWM to the following EASM are investigated through the linear regression method. To explore the roles of ENSO in this process, the EAWMI is divided into two parts using the linear regression method: an ENSO-related part and an ENSO-unrelated part. Evolution of the SST anomalies associated with these two parts demonstrates that the method can effectively remove the ENSO signal from the EAWM variability. The results show that when a weak EAWM is mainly caused by an El Niño (EAWMEN), a lower-tropospheric anomalous anticyclone over the WNP persists from winter to the following summer. To the western side of this anticyclone, the anomalous southerlies along the coast of East Asia enhance the climatological southerlies. Hence, a strong EASM follows, which establishes a link between the EAWMEN and the following EASM. This strong EASM has a corresponding anomalous rainfall pattern with above-normal rainfall over the middle reach of the Yangtze River and northern China. The correlation coefficients between the EAWMI and three EASM indices confirm that a robust relationship exists between the ENSO-related EAWM part and the subsequent EASM but not the ENSO-unrelated EAWM part.
The anomalous SST distribution associated with EAWMEN indicates that the persistence of this WNP anticyclone may be mainly attributed to the northern Indian Ocean warming via the Indian Ocean capacitor mechanism proposed by Xie et al. (2009) and the North Pacific triple SST anomaly pattern via the SFM proposed by Vimont et al. (2001). The local air–sea interaction mechanism may contribute to the persistence of the anomalous WNP anticyclone through spring, but this contribution weakens once summer is approaching. When a weak EAWMres happens, the low-level anomalous anticyclone only appears in winter and vanishes in the following seasons. Its disappearance may be due to extremely weak SST anomalies in the tropical Pacific and Indian Ocean and the midlatitude North Pacific. Therefore, there is no relationship between the EAWMres and the following EASM and there is no significant summer rainfall anomalies in China related to the EAWMIres. Such results suggest that ENSO plays a necessary role in the maintenance of the anomalous WNP anticyclone through the following summer.
Further studies demonstrate that the relationship between the EAWMEN and the subsequent EASM is not stable and can be modulated by the PDO. During the different phases of the PDO, the anomalous WNP anticyclone associated with the EAWMEN, acting as a bridge in the relation of the EAWMEN to the EASM, is distinct in both intensity and domain. The anomalous anticyclonic flow in the following summer has a meridional elongated structure during the negative PDO phases, while during the positive PDO phases it has a zonal elongated structure and its intensity is much stronger than in the negative PDO phases. Hence, a weak EAWMEN tends to induce a much stronger EASM during the positive phases of the PDO compared with the negative phases. Evolution of the SST anomalies associated with the EAWMIEN suggests that all the three mechanisms responsible for the persistence of the anomalous WNP anticyclone play a much weaker role during the PDO negative phases than positive phases. This result may explain why the anomalous WNP anticyclone tends to be much weaker when the PDO is in its negative phases as compared to its positive phases. However, the mechanism for the influence of the PDO on the atmospheric circulation related to ENSO is complex. Some studies attribute it to the modulation of the PDO on the background climatic fields (e.g., Power et al. 1999). It needs to be investigated in the future.
A feature to note in Figs. 1a,c is that the EAWM displays an interdecadal change around 1993. This change indicates an intensification of northerly winds along the coast of eastern China. This wind change may be associated with an interdecadal variability of the winter snow cover over the Eurasian regions, which is found to be closely related with the following summer rainfall over the southern China (Yang and Xu 1994). Whether the change in winter monsoon is connected to the sudden shift of summer precipitation over the southern China around 1990s (Wu et al. 2010) remains to be investigated.
Acknowledgments
The comments of three reviewers have led to a significant improvement of this paper. This study is supported by the National Natural Science Foundation of China Grant 41025017 and the National Basic Research Program of China Grant 2009CB421405. RW acknowledges the support of a Direct Grant of the Chinese University of Hong Kong (2021090).
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