Abstract

The current study investigates the interdecadal changes in the relationship between the winter precipitation anomalies in southeastern China, El Niño–Southern Oscillation (ENSO), and the East Asian winter monsoon (EAWM) at the end of the twentieth century. It appears that the relationships between the interannual variability of the southeastern China winter precipitation and ENSO as well as EAWM are obviously weakened after 1998/99. The possible mechanisms accounting for this interdecadal change in the relationship have been examined by dividing the data into two subperiods [1980–98 (P1) and 1999–2015 (P2)]. The results indicate that, without the linear contribution of EAWM, ENSO only play a limited role in the variability of winter precipitation in southeastern China in both subperiods. In contrast, in P1, corresponding to an ENSO-independent weaker-than-normal EAWM, anomalous southerlies along coastal southeastern China associated with an anticyclone over the northwestern Pacific transport water vapor to China. However, in P2 the impact of EAWM on winter precipitation in southeastern China is weakened because of the regime shift of EAWM. The EAWM-related positive SLP anomalies over the North Pacific move eastward in P2, causing an eastward migration of the associated anomalous southerlies along its western flank and therefore cannot significantly contribute to the positive winter precipitation anomalies in southeastern China.

1. Introduction

Enormous research attention has been attracted by the interannual variation of the East Asian monsoon because of its large social and economic impacts for Asian countries and regions, including China, India, the Korea Peninsula, Japan, etc. (e.g., Tao and Chen 1987; Webster and Yang 1992; Zhang et al. 1996, 1999; Chang et al. 2000; Wang et al. 2000; Wu and Wang 2000; Yang et al. 2002; Huang et al. 2004; Yang and Lau 2004; B. Wang et al. 2008; Chen et al. 2013). Compared with summer rainfall studies, few studies of winter precipitation over East Asia (EA) have been conducted. Although winter precipitation accounts for a relatively small fraction of total annual rainfall comparing to the summer season, it experiences a large year-to-year variability over some regions, like southern China (e.g., Ge et al. 2016). In fact, severe cold surges and the associated heavy snowfall during wintertime can significantly impact agriculture, stock raising, and water resources and result in serious economic losses (e.g., Zhou and Wu 2010; Huang et al. 2014; Ding et al. 2015).

Previous studies indicate that the precipitation over southern China can be affected by many anomalous states of lower boundary conditions. Among them, El Niño–Southern Oscillation (ENSO), which is the strongest atmosphere–ocean coupled mode on the interannual time scale, is one of the most important factors that can influence winter climate variability in China (e.g., Wang et al. 2000; Wu and Wang 2000; Sung et al. 2010; Zhou and Wu 2010; Jia and Lin 2011; Jia et al. 2015, 2016; Sun et al. 2016; Ge et al. 2016). Wang et al. (2010) point out that the western Pacific subtropical high can act as a bridge for ENSO’s influence on East Asian climate. During El Niño events, this system can enhance the moisture transport to southern China and cause abnormally wet conditions over that and nearby regions. The East Asian winter monsoon (EAWM), which results from the large thermal contrast between the Eurasian continent and the Indo-Pacific region, exhibits large-amplitude year-to-year fluctuations and dominates winter climate variation over Asian–Pacific areas (Nitta 1987; Gong et al. 2001; Wang and Chen 2010; Ding et al. 2015; Sun et al. 2016). Many studies have shown that above-average winter precipitation is often observed over southeastern China during weak EAWM years, whereas below-average precipitation is usually associated with strong EAWM years (Chen et al. 2000; Wang and Chen 2010; Zhou and Wu 2010; Jia et al. 2015; Ge et al. 2016).

In addition, some studies have revealed that EAWM and ENSO are not independent (e.g., Chen et al. 2000; Zhou and Wu 2010; Jia et al. 2015; Ge et al. 2016). A weak EAWM, meaning a warm winter over EA, is usually observed following the development of the mature phase of an El Niño event, while a strong EAWM is often associated with a La Niña year (Wang et al. 2000; Wu et al. 2003; Li et al. 2005; Zhou and Wu 2010; Jia et al. 2015). During a strong EAWM year, the related northerly winds can penetrate the eastern coastal EA and adjacent oceans and reach the subtropical western Pacific and Indo-China. The intrusion of the cold air from high-latitude to equatorial EA may cause intensified deep convection and precipitation over the Maritime Continent. Previous work (e.g., Li 1988) suggested that a strong EAWM may trigger an El Niño event because an abnormally stronger EAWM can cause anomalous westerly winds over the equatorial western Pacific. The relationship between EAWM and ENSO can also be explained by the enhancement of the Hadley and Ferrel cells during El Niño and the reduction of the Hadley and Ferrel cells during La Niña years. A detailed review of EAWM–ENSO interaction can be found in Huang et al. (2004).

However, most previous studies investigating the contributions of EAWM and/or ENSO to the precipitation variation over China only considered a single index for EAWM or ENSO. Results obtained without considering the interdependence of EAWM and ENSO might include the impacts of both factors. In a previous study, Zhou and Wu (2010) investigated the respective impacts of EAWM and ENSO on winter rainfall in China for the period from 1951 to 2003. These authors found that the influence of ENSO on winter rainfall is greatest over southern China, whereas the lower-level southerly winds associated with a weak EAWM can penetrate farther northward and influence winter rainfall in eastern China. Recently, the independent impacts of ENSO and EAWM on the winter rainfall anomalies over China were investigated by Ge et al. (2016). They showed that the anomalous southerlies along coastal southeastern China before the mid-1980s are related to EAWM. However, after the mid-1980s, the impact of EAWM is weakened, and the anomalous southerlies mainly result from an ENSO-related tropical Pacific anticyclone centered over the Philippines.

The wintertime climate over China experienced a new regime shift at the end of the twentieth century. For example, in a recent study, Huang et al. (2014) showed that the winter surface air temperature (SAT) in China underwent a change from a “similar pattern in the whole China” to a “south–north oscillation pattern” in the late 1990s, which was accompanied by the more frequent occurrence of low-temperature disasters in the northern part of China after the end of the 1990s. Xiao et al. (2016) also suggested an interdecadal change in the wintertime SAT over East Asia around the late 1990s. They examined the possible external forcing factors accounting for this regime shift and noted that the link between EAWM and some external forcing increased at the end of the 1990s.

With the new regime shift of the winter climate over China at the end of the twentieth century, it is of interest to determine whether an interdecadal change occurred in the relationship between winter precipitation in southeastern China, ENSO, and EAWM at this time. What are the different roles played by ENSO and EAWM in the variation of winter precipitation before and after the regime shift compared to the mid-1980s regime shift. We are also interested in comparing the relative contributions to the winter precipitation anomalies over China when the interdependence between EAWM and ENSO is removed from a single index. These questions will be investigated by the current study.

This paper is organized as follows. The data and methods used in this study are described in section 2. The characteristics of the winter precipitation over China and the associated climate anomalies of the two subperiods of 1980–98 (P1) and 1999–2015 (P2) are examined in section 3. The interdecadal changes of the relationships between ENSO, EAWM, and the wintertime precipitation over southeastern China at the end of the twentieth century are investigated in section 4, followed by a summary in section 5 and a discussion of the results in section 6.

2. Data and methods

Reanalysis of sea level pressure (SLP), geopotential height at 500 (Z500) and 200 hPa (Z200), wind, and specific humidity are obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data (Dee et al. 2011). Sea surface temperature (SST) is obtained from the Met Office Hadley Centre’s SST dataset (Rayner et al. 2003), which is a unique combination of monthly globally complete fields of SST and sea ice with a horizontal resolution of 1.0° × 1.0°. The precipitation data are obtained from the Global Precipitation Climatology Project (GPCP) monthly mean precipitation data and have a horizontal resolution of 2.5° × 2.5° (Adler et al. 2003). Another set of observational precipitation data from 160 meteorological stations in China (the 160 station distributions are represented by dots in Fig. 1a) is obtained from the Chinese Meteorological Data Center. The data were collected and edited by the China Meteorological Administration and were relatively homogeneously distributed, especially the data from eastern China (Wu et al. 2003). The data used in this study cover the period from January 1980 to March 2015. The data for the winter are 3-month means of the data from January to March (JFM). Following previous studies (e.g., Jia et al. 2015; Ge et al. 2016), to focuses on the interannual variations, the variations with periods longer than 10 years have been removed using a bandpass filtering in order to avoid plausible contamination of interannual relationship by interdecadal changes.

Fig. 1.

(a) Standard deviation of the JFM rainfall over China. The contour interval is 5 mm. The red dots indicate stations in southeastern China defined in this study. (b) The normalized index of the area-averaged precipitation in southeastern China (PI; bars). The black solid line depicts its variation longer than 10 years.

Fig. 1.

(a) Standard deviation of the JFM rainfall over China. The contour interval is 5 mm. The red dots indicate stations in southeastern China defined in this study. (b) The normalized index of the area-averaged precipitation in southeastern China (PI; bars). The black solid line depicts its variation longer than 10 years.

In this study, the ENSO variability is represented by the oceanic Niño index (ONI), which is defined as the SST anomalies (SSTAs) over the Niño-3.4 region (5°S–5°N, 170°–120°W). The EAWM index (EAWMI) is defined as the regionally averaged meridional winds at a height of 10 m over the area of the East China Sea (25°–40°N, 120°–140°E) and South China Sea (10°–25°N, 110°–130°E) (Chen et al. 2000). To examine the effect of ENSO-independent EAWM, the coherent variability of the ENSO index is removed by means of a linear regression from the time series of the ONI as follows:

 
formula

Similarly, the EAWM-independent influence of ENSO is obtained by

 
formula

where r1 and r2 are the regression coefficients of the EAWMI and ONI with respect to the ONI and EAWMI, respectively. For the period from 1980 to 2015, the correlation coefficient between the EAWMI and EAWMIres is 0.73, and the correlation coefficient between the ONI and ONIres is 0.62. Both are significant at the 99% confidence level according to a Student’s t test.

3. The winter precipitation over China and its related climate anomalies

Figure 1a depicts the standard deviation of winter precipitation over China. The variation of the winter precipitation has the largest values over southeastern China while it clearly increases in magnitude from northwestward to southwestward with a maximum reaching 100 mm. Based on the standard deviation in Fig. 1a, the winter precipitation over China is analyzed by using observations from stations over southeastern China, which are presented by red dots in Fig. 1a. The normalized time series of the winter precipitation anomaly averaged for these selected stations are represented by bars in Fig. 1b [precipitation index (PI)]. The component of the PI containing Fourier harmonics that have periods longer than 10 years is depicted by the solid thick curve in Fig. 1b. An obvious regime shift of the winter precipitation around the late 1990s can be observed. The PI shows that winter precipitation over southeastern China is above normal during most years before 1998/99, whereas large and negative rainfall anomalies are dominant throughout the period after the late 1990s.

Figure 2 shows the sliding correlation of the PI and ONIres as well as EAWMIres with an 11-yr window. The sliding correlations for the ONI and EAWMI are also plotted in Fig. 2 for the purpose of comparison. Figure 2 highlights that the correlation between the EAWMI and PI is generally higher than that between the ONI and PI. The correlation between the ONI and PI is obviously decreased and not significant after removing the impact of EAWM. The sliding correlation between the EAWMI and PI remains significant during the 1980s and 1990s, whereas an obvious decline in the correlation is observed in the 2000s. The correlation between EAWMIres and PI displays a similar interdecadal change at the end of the 1990s. The correlation between the ONI and PI also shows an interdecadal change at the end of the 1990s; however, this change is not as significant as that of EAWMI.

Fig. 2.

The sliding correlation coefficients of the JFM PI and EAWMI (solid line with circles), EAWMIres(solid line with dots), ONI (dashed line), and ONIres (dotted line) with a 11-yr window.

Fig. 2.

The sliding correlation coefficients of the JFM PI and EAWMI (solid line with circles), EAWMIres(solid line with dots), ONI (dashed line), and ONIres (dotted line) with a 11-yr window.

Based on the above analysis, it is clear that both the wintertime precipitation itself and the relationships between winter precipitation and EAWM as well as ENSO experience an obvious change around the end of the 1990s. Figure 2 shows that the 11-yr sliding correlation has the least significant value in year 2004; thus, we divide the dataset into two subperiods for the subsequent correlation and regression analyses, with the first subperiod covering from 1980 to 1998 and the second subperiod covering from 1999 to 2015. The large-scale atmospheric circulation anomalies associated with winter precipitation in southeastern China in P1 (Prec-P1) and P2 (Prec-P2) are presented in Fig. 3. Obvious differences between the climate anomalies associated with Prec-P1 and Prec-P2 are observed. It appears that the atmospheric circulation anomalies associated with Prec-P1 are closely related to the tropics. At the surface, associated with a positive Prec-P1, negative SLP anomalies are observed over the central-eastern tropical Pacific, whereas pronounced positive SLP anomalies dominate the western Pacific. Positive anomalies over the northwestern Pacific can also be observed at Z500 (Fig. 3c), indicating an anomalous weak East Asian trough at this level. Over the North Pacific–North America sector, the spatial distributions of the atmospheric anomalies from the surface to Z200 share many similarities to a positive Pacific–North American (PNA) teleconnection pattern (Figs. 3c,e). In contrast, the atmospheric circulation anomalies associated with Prec-P2 are confined to the mid-to-high latitudes of the Northern Hemisphere (Figs. 3b,d,f). Positive anomalies associated with Prec-P2 over the western Pacific are observed, whereas the anomalies are quite weak with significance only occurring over very limited areas (Fig. 3b).

Fig. 3.

The spatial distributions of (a),(b) SLP; (c),(d) Z500; and (e),(f) Z200 anomalies obtained by regression onto PI for (left) P1 and (right) P2. The contour interval is 0.6 hPa for (a),(b) and 8 m for (c)–(f). Light and dark red (blue) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

Fig. 3.

The spatial distributions of (a),(b) SLP; (c),(d) Z500; and (e),(f) Z200 anomalies obtained by regression onto PI for (left) P1 and (right) P2. The contour interval is 0.6 hPa for (a),(b) and 8 m for (c)–(f). Light and dark red (blue) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

The moisture transport and its divergence anomalies associated with Prec-P1 and Prec-P2 are presented in Fig. 4 to understand the different transportation of water vapor. In P1, two anomalous anticyclonic systems are observed over the western Pacific, with one anticyclonic system centered over the Philippine Sea and the other centered over the midlatitudes of the North Pacific at approximately 25°N, 150°E. Anomalous northeastward transport of moisture along eastern costal China is observed converging in southeastern China and the subtropical northwestern Pacific, causing anomalous positive precipitation over there. The flow that impacts the southeastern China winter precipitation anomalies in P2 is obviously different from that in P1. No anticyclone over the western tropical Pacific is observed in P2. Instead, southerly wind occurs along an anomalous anticyclone centered over the midlatitudes of the North Pacific at approximately 22°N, 145°E. Compared to P1, the northward transport of the moisture in P2 can reach farther northward to the Huai area of China.

Fig. 4.

Anomalies of moisture transport represented as a vector [kg (m s)−1] and its divergence represented as shading (mm day−1) obtained by regression onto PI for (a) P1 and (b) P2.

Fig. 4.

Anomalies of moisture transport represented as a vector [kg (m s)−1] and its divergence represented as shading (mm day−1) obtained by regression onto PI for (a) P1 and (b) P2.

To understand the relationship between the tropical Pacific SST forcing and the winter precipitation variation in southeastern China in P1 and P2, the regression maps of the SST associated with Prec-P1 and Prec-P2 are presented in Fig. 5. It shows that the SSTAs associated with Prec-P1 are dominated by significant positive anomalies over the central-eastern tropical Pacific, mimicking an El Niño–like SSTAs structure (Fig. 5a), consistent with the Prec-P1 associated PNA-like atmospheric circulation anomalies as shown in Fig. 3. Significant negative and positive SSTAs can also be observed over the western tropical Pacific and Indian Ocean, respectively. In contrast, the Prec-P2-related SSTAs are relatively weak over the eastern tropical Pacific, as are the cases for the SSTAs over the western tropical Pacific and the Indian Oceans. The above SSTAs analysis indicates that the winter precipitation variability in southeastern China is related to ENSO-like SSTAs in P1. However, this relationship obviously weakens in P2, indicating that the predictability of winter precipitation in southeastern China might decrease after the end of the twentieth century due to the weaker correlation of winter precipitation with the tropical Pacific SST variability.

Fig. 5.

Anomalies of SST obtained by regression onto PI for (a) P1 and (b) P2. The contour interval is 0.2°C. Light and dark red (blue) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

Fig. 5.

Anomalies of SST obtained by regression onto PI for (a) P1 and (b) P2. The contour interval is 0.2°C. Light and dark red (blue) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

4. Possible mechanisms for the interdecadal changes of the relationships between winter precipitation, ENSO, and EAWM

In this section, the interdecadal change of the relationships between winter precipitation in southeastern China and ENSO and EAWM at the end of the twentieth century is investigated.

a. Interdecadal changes in the relationship between ENSO and winter precipitation

As shown in Fig. 5, Prec-P1 and Prec-P2 are associated with different tropical Pacific SSTAs. We start by examining the impact of the changes of ENSO on the interdecadal change of the relationship. Consistent with the regime shift of the winter precipitation in southeastern China that we have investigated in this study, the ONI also shows a clear interdecadal change at the end of the twentieth century (not shown). The ONI is greater than normal during most years in P1 but is dominated by the negative phase in P2 (not shown). Previous studies showed that the changes in the tropical Pacific SSTAs can affect the tropical–extratropical teleconnections as well as their related climate anomalies (e.g., Gu and Philander 1997; Zhang et al. 2016). Recently, Jo et al. (2015) suggested that the correlation between the SST variability over the central-eastern tropical Pacific and the North Pacific significantly increased after 1998/99, indicating an enhancement of the predictability of the North Pacific SST. The anomalous Aleutian low pressure associated with the tropical SST forcing intensified, and its center shifted to the south and west after 1998/99.

The ENSO-related ocean forcing is represented by regression maps of the SST and precipitation fields onto the ONI for P1 and P2 (Figs. 6a–d). The El Niño–related SSTAs reveal obviously different structures between the two subperiods. The most notable change of the ENSO-related eastern tropical Pacific SSTAs is a westward shift from P1 to P2. With this westward SSTAs migration, the SSTAs in P2 resemble more of a central Pacific El Niño–type anomalies rather than the typical ENSO distribution as observed in P1 (Yeo et al. 2012). Corresponding to the El Niño episodes, negative SSTAs can be noticed over the subtropical Pacific in both hemispheres as well as over the western tropical Pacific for both subperiods. However, the negative SSTAs over the midlatitudes of the North Pacific are stronger in P2 than those in P1, consistent with the results of Jo et al. (2015), which indicate that the relationship between the SST variability over the tropical Pacific and the North Pacific is enhanced after 1998/99.

Fig. 6.

Anomalies of (a),(b) SST, (c),(d) precipitation, and (e),(f) 850-hPa divergent winds (vector; m s−1) and velocity potential (contour; m2 s−1) obtained by regression onto ONI for (left) P1 and (right) P2. The contour interval is 0.2°C for (a) and (b); 1 mm day−1 for (c) and (d); and 2 × 105 m2 s−1 for (e) and (f). Light and dark red (blue) in (a),(b),(e),(f) and green (brown) in (c),(d) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

Fig. 6.

Anomalies of (a),(b) SST, (c),(d) precipitation, and (e),(f) 850-hPa divergent winds (vector; m s−1) and velocity potential (contour; m2 s−1) obtained by regression onto ONI for (left) P1 and (right) P2. The contour interval is 0.2°C for (a) and (b); 1 mm day−1 for (c) and (d); and 2 × 105 m2 s−1 for (e) and (f). Light and dark red (blue) in (a),(b),(e),(f) and green (brown) in (c),(d) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

The precipitation anomalies associated with El Niño show that the peak value of the equatorial positive precipitation anomalies in P1 is near 160°W, while its corresponding partner in P2 is centered at 180° longitude; that is, the anomalous precipitation center migrates westward by approximately 20° longitude from P1 to P2. The El Niño–related changes of the Walker circulation are further examined and displayed in Figs. 6e and 6f, represented by regression maps of the divergence wind and velocity potential at 850 hPa onto the ONI. The anomalous velocity potential at this level features a dipole structure with anomalous centers of opposite signs in the east–west direction. In P1, significant divergence and decent motion over the western tropical Pacific are observed centered over the eastern Maritime Continent (Fig. 6e). Also, it can be noticed that the western tropical Pacific divergence center, however, moves westward to the South China Sea from P1 to P2, consistent with the westward shift of the SST and precipitation fields.

The interdecadal change of the tropical Pacific forcing from P1 to P2 can impact the characteristics of the western Pacific subtropical high, which is the key system that bridges the ENSO forcing with the EA winter climate variations (Wang et al. 2000; Wu et al. 2003, 2010; Zhou and Wu 2010; Feng et al. 2011). This can be confirmed by Figs. 7a and 7b, which depict the El Niño–related moisture transport in P1 and P2, respectively. The anticyclone around the Philippines is centered at 125°E in P1 (Fig. 7a), whereas it moves westward by approximately 10° longitude in P2 (Fig. 7b), causing a weakened moisture transport to southeastern China and therefore a weakened impact on the winter precipitation. Notably, an additional anticyclone over the Indian Ocean is observed in P2 (Fig. 7b). The westerlies along the northern flank of this Indian Ocean anticyclone combine with the southerlies along the western flank of the Philippine anticyclone over the South China Sea, and together they contribute to the moisture transport to southeastern China. Compared to P1 over the western Pacific, the ENSO-related water vapor transport in P2 penetrates farther northward, suggesting a northward movement of the El Niño impact on the winter precipitation in southeastern China after 1998.

Fig. 7.

Anomalies of (a),(b) moisture transport [vector; kg (m s)−1] and its divergence (shading; mm day−1) and (c),(d) winter precipitation obtained by regression onto ONI for (left) P1 and (right) P2. In (c) and (d), the contour interval is 5 mm, and light and dark green (brown) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

Fig. 7.

Anomalies of (a),(b) moisture transport [vector; kg (m s)−1] and its divergence (shading; mm day−1) and (c),(d) winter precipitation obtained by regression onto ONI for (left) P1 and (right) P2. In (c) and (d), the contour interval is 5 mm, and light and dark green (brown) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

The changes in the relationship between El Niño and winter precipitation over China from P1 to P2 is further demonstrated by regressing the observational station precipitation data in China onto the ONI (Figs. 7c,d). In P1, corresponding to El Niño episodes, abnormally positive precipitation is observed in southeastern China (Fig. 7c). In contrast, during P2, the significance of the positive correlation is decreased, and the significant correlation area moves northeastward to eastern China, centered at 30°N (Fig. 7d), consistent with the farther northward water vapor transport shown in Fig. 7b. The weaker correlation between ENSO and winter precipitation in southeastern China from P1 to P2 is also consistent with previous studies. In previous work, Feng et al. (2011) showed that the rainfall over southeastern China exhibited different spatial distributions associated with a conventional ENSO pattern and ENSO Modoki. The winter rainfall anomalies over southeastern China associated with ENSO Modoki were obviously weaker in magnitude compared to those associated with a conventional ENSO pattern.

b. Interdecadal changes in the relationship between independent ENSO and winter precipitation

The last section reveals that the regime shift of ENSO at the end of the twentieth century impacts its relationship with winter precipitation in southeastern China through the changing characteristics of the anticyclone around the Philippines. However, as previously mentioned, ENSO and EAWM are not independent of each other (Li 1988; Wang et al. 2000; Wu et al. 2003; Li et al. 2005; Zhou and Wu 2010; Ge et al. 2016). This conclusion is also confirmed by the high correlation coefficients between the EAWMI and ONI used in our current study, which are 0.78 and 0.65 for P1 and P2, respectively, both exceeding the 99% confidence level according to a Student’s t test. The correlation result also suggests that the interaction between EAWM and ENSO is more significant in P1 than in P2. Because of the interdependent relationship between ENSO and EAWM, the results obtained in section 4a may also include the impacts of EAWM. Therefore, in this section, the respective impact of ENSO without the impact of EAWM is further investigated using the method described in section 2. The results obtained in this subsection will also be used to compare with those in section 4a to show the necessary steps to exclude the EAWM factor when exploring the contributions of ENSO to the climate variability over China.

The EAWM-independent El Niño–related SSTAs for P1 and P2 are examined. It is found that, without the linear impact of EAWM, the general spatial characteristics of the El Niño–related SSTAs and the westward shift character of the eastern tropical Pacific SSTAs from P1 to P2 are also observed, while the significance and magnitude of the SSTAs are weakened (not shown). Figures 8a and 8b show the moisture transport and its divergence associated with the ONIres. The anticyclonic system around the western tropical Pacific can still be observed in both subperiods; however, it is notably different from that shown in Figs. 7a and 7b. Without EAWM, the northward moisture transport along eastern coastal China is obviously weakened. In P1, the water vapor transport and the convergence area are only confined to a very limited area of southeastern China. Over the Huai River basin of China, the southwesterly wind that appears in Fig. 7a is replaced by northerly wind (Fig. 8a). In P2, no significant moisture convergence is observed in southeastern China (Fig. 8b). The regression map of the station precipitation onto the ONIres is depicted in Figs. 8c and 8d. Positive correlations can be observed over some areas of southeastern China in P1; however, they cannot pass the 95% significance test. In P2, the spatial distribution of the precipitation anomalies is similar to Fig. 7d; however, no significant changes of winter precipitation are observed in southeastern China. The above results suggest that the impact of El Niño on the winter precipitation variation in southeastern China significantly decreases without the contribution of EAWM. The EAWM-independent El Niño plays a limited role in the winter precipitation variation over southeastern China for both subperiods.

Fig. 8.

As in Fig. 7, but for the anomalies regressed onto ONIres.

Fig. 8.

As in Fig. 7, but for the anomalies regressed onto ONIres.

c. Interdecadal changes in the relationship between EAWM and winter precipitation

EAWM experienced an interdecadal change during the mid-1980s, and the mechanisms accounting for this regime shift have been extensively investigated (e.g., Wang et al. 2009; Ding et al. 2015; Jia et al. 2015; Ge et al. 2016). Recently, some researchers noted that EAWM experienced another regime shift near the late 1990s, with EAWM in a weak epoch since the 1980s, and then reamplified at the end of the twentieth century (e.g., Huang et al. 2014; Wang and Chen 2014; Ding et al. 2015; Xiao et al. 2016). The new regime shift of EAWM has many differences from the regime shift of the mid-1980s (e.g., Huang et al. 2014; Xiao et al. 2016). How does this change impact the relationship between EAWM and the wintertime precipitation in southeastern China? This issue will be examined in this section.

The atmospheric circulation anomalies associated with EAWM are presented in Fig. 9. It should be noted that in this study a positive EAWMI represents a weaker-than-normal EAWM, while a negative EAWMI indicates a stronger-than-normal EAWM to provide a better comparison with the other figures in the current study. Quite different characteristics of the EAWM-related atmospheric circulation anomalies in P1 and P2 are observed. In P1, corresponding to a positive EAWMI, the spatial structure of the circulation anomalies is composed of a Rossby wave train–like pattern with positive SLP anomalies appearing in the western tropical Pacific, pronounced negative SLP anomalies in the northeastern North Pacific, and positive SLP anomalies over northeastern Canada. Another zonal wave train–like anomaly pattern can be observed originating from the mid-to-high-latitudes of the North Atlantic and propagating to the Eurasian continent, suggesting a possible influence of the upstream North Atlantic Ocean on EAWM during this period. The wave train–like structure of the EAWM-related anomalies can also be noticed at Z500, indicating a quasi-barotropic structure. Over the eastern Eurasian continent, no significant anomalies are observed.

Fig. 9.

Anomalies of (a),(b) SLP and (c),(d) Z500 obtained by regression onto EAWMI for (left) P1 and (right) P2. The contour interval is 0.6 hPa for (a),(b) and 5 m for (c),(d). Light and dark red (blue) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

Fig. 9.

Anomalies of (a),(b) SLP and (c),(d) Z500 obtained by regression onto EAWMI for (left) P1 and (right) P2. The contour interval is 0.6 hPa for (a),(b) and 5 m for (c),(d). Light and dark red (blue) shadings denote the positive (negative) correlation coefficients significant at the 95% and 99% confidence levels, respectively.

In contrast to P1, the SLP anomalies associated with a positive EAWMI are represented by significant negative anomalies over the eastern Eurasian continent and positive anomalies over the northwestern Pacific in P2. Additionally, at Z500 it can be noticed that the atmospheric anomalies are dominated by positive anomalies over the polar area, while negative anomalies dominate over the midlatitudes of the Northern Hemisphere, which is reminiscent of a negative phase of the North Atlantic Oscillation (NAO) pattern. However, previous studies suggest that a weak EAWM is usually related to positive NAO-like anomalies (e.g., Wu and Huang 1999; Chen et al. 2005), which is in contrast to the result of Fig. 9c. To better understand the EAWM-related circulation anomalies, the running correlation coefficient between the EAWMI and the Arctic Oscillation (AO) index with an 11-yr window is calculated (not shown). The results indicate that the correlation between the EAWMI and the AO index experiences an obvious interdecadal change during the period under examination. A weak but positive correlation between the EAWMI and the AO index is observed before the mid-1990s; however, it becomes negative and significant after the mid-1990s. The increased correlation may suggest an enhanced influence of the Arctic Ocean on the wintertime climate variability over East Asia. The cause for this interdecadal change is an interesting question; however, this is beyond the scope of this study.

In addition, a comparison between Fig. 9 and Fig. 3 indicates that the atmospheric circulation anomalies associated with the PI and those associated with the EAWMI share many similarities in P1, whereas their structures are obviously different in P2, suggesting that EAWM has a closer relationship with winter precipitation in southeastern China in P1 compared to in P2. More important, the different EAWM-related surface circulation anomalies over the Asian–Pacific sector between P1 and P2 can impact the anomalous southerly over coastal eastern China, which is the main flow that transports water vapor to China. Figures 10a and 10b show that, corresponding to a weak EAWM, the southerly winds in P1 includes two branches of wind anomalies: that is, one associated with an anticyclone over the tropical western Pacific and the other associated with an anticyclone centered over the midlatitudes of the northwestern Pacific. The anticyclone over the western tropical Pacific is relatively weak in P2 and moves westward compared to P1. The anticyclone over the northwestern Pacific migrates northeastward from P1 to P2. The changes in these systems result in a decreased moisture transport to southeastern China in P2 compared to P1.

Fig. 10.

As in Fig. 7, but for the anomalies regressed onto EAWMI.

Fig. 10.

As in Fig. 7, but for the anomalies regressed onto EAWMI.

The interdecadal change of the impact of EAWM on the winter precipitation over China is also confirmed using the station precipitation data (Figs. 10c,d). The significance of the positive correlation between the EAWMI and winter precipitation over southeastern China obviously decreased in the second subperiod, with the significant correlation area only confined to the downstream area of the Yangtze River basin in P2. The temporal correlation coefficient between the PI and EAWMI is 0.78 during P1, significant at the 99% confidence level according to a Student’s t test, but declines to 0.37 during P2. This result also suggests that the impact of EAWM on the winter precipitation in southeastern China has decreased since the end of the twentieth century.

d. Interdecadal changes in the relationship between independent EAWM and winter precipitation

The ENSO-independent changes of EAWM-related atmospheric anomalies are displayed in Fig. 11. Without the linear impact of ENSO, the atmospheric circulation anomalies associated with EAWM are obviously weakened in magnitudes and mainly constrained to the mid-to-high latitudes of the Northern Hemisphere for both subperiods. In P1, the PNA-like anomalies are no longer observed over the Pacific–North American sector. The most prominent characteristics of the anomalies associated with positive EAWMIres over the Asia–Pacific sector are dominated by a pronounced meridional dipole structure with anomaly centers of opposite signs. Negative anomalies are noticeable in the high-latitudes of the North Pacific centered over the Bering Strait, whereas significant positive anomalies dominate the midlatitudes of the North Pacific. The spatial distributions of the anomalies are similar at the surface and at Z500. The positive anomalies of the dipole over the North Pacific extend westward to the Asian continent at Z500, suggesting a weakened East Asian trough. In P2, the atmospheric circulation associated with the EAWMIres is similar to that associated with the EAWMI, also indicating that the impact of ENSO on EAWM is more significant in P1 than in P2. Over the Asian–Pacific sector, both the negative anomalies over the eastern Eurasian continent and the positive anomalies over the North Pacific are obviously enhanced in P2. Also of note is an eastward movement of the positive anomalies over the North Pacific from P1 to P2.

Fig. 11.

As in Fig. 9, but for the anomalies regressed onto EAWMIres.

Fig. 11.

As in Fig. 9, but for the anomalies regressed onto EAWMIres.

Without the ENSO signal, the EAWMIres-related moisture transportation is displayed in Figs. 12a and 12b. No obvious anticyclonic system around the western tropical Pacific is observed for both P1 and P2. Instead, the anomalous southerlies along the coastal area of eastern China are associated with an anticyclone over the northwestern Pacific centered at 30°N. However, the anomalous southerlies are obviously weakened in P2 and migrates eastward for approximately 20° longitude from P1 to P2 because of the eastward shift of the positive SLP anomalies over the North Pacific, as shown in Fig. 11b. The interdecadal change of the EAWMIres-related atmospheric circulation over the Asian–Pacific sector between P1 and P2 leads to a reduced contribution of EAWM to the winter precipitation variation in southeastern China in P2.

Fig. 12.

As in Fig. 7, but for the anomalies regressed onto EAWMIres.

Fig. 12.

As in Fig. 7, but for the anomalies regressed onto EAWMIres.

The result of the EAWMIres-related station precipitation anomalies is then presented in Figs. 12c and 12d. In P1, a large area of significant positive correlation occurs over southeastern China, whereas in P2 the significance of the correlation clearly decreases, and the positive correlation area is only limited to the downstream regions of the Yangtze River basin. A comparison between Fig. 12c and Fig. 10c shows that, without the impact of ENSO, the impact area of EAWM becomes less significant over southeastern China, which is consistent with the results of Zhou and Wu (2010). They showed that EAWM influences winter precipitation over eastern China, in contrast with ENSO, which only impacts a limited area over southern China.

The above regression analysis indicates that, without considering the impact of ENSO, the EAWM-related system responsible for the winter precipitation anomalies in southeastern China is confined to the midlatitudes of the North Pacific. The eastward migration of the EAWMIres-related positive anomalies over the North Pacific from P1 to P2 decreases the contribution of EAWM to the winter precipitation anomalies over southeastern China.

5. Summary

The current study investigates the interdecadal changes in the relationships between winter precipitation over southeastern China, ENSO, and EAWM before and after the new regime shift at the end of the twentieth century. In this study, we choose 1980–98 and 1999–2014 as the two subperiods for analysis. It should be noted that the data have been divided into two subperiods according to the variability of winter precipitation in southeastern China (using PI). In addition, the 11-yr sliding correlation of the PI and ONI as well as EAWMI also displays an interdecadal change at the end of the 1990s. However, previous studies reveal that there is a regime shift of the wintertime SAT over East Asia (e.g., Wang and Chen 2014). The specific time of the regime shift may somehow be different if different meteorological variables or different regions are chosen to be the object of a study.

Different characteristics of the winter rainfall anomalies in southeastern China between P1 and P2 are observed. Positive winter precipitation anomalies often occur in southeastern China during the periods of the 1980s and 1990s, whereas large and negative rainfall anomalies are frequently observed throughout the period following the late 1990s (Fig. 1b). The relationship of the winter precipitation anomalies in southeastern China to EAWM and ENSO also experiences interdecadal changes, with some differences compared to the regime shift occurring during the mid-1980s (e.g., Ge et al. 2016). EAWM plays a more important role in the variation of the winter precipitation in southeastern China compared to ENSO during the period under examination. The relative contributions of ENSO and EAWM to the winter precipitation variation in southeastern China during P1 and P2 are examined and compared to each other.

Significant different atmospheric circulation anomalies associated with winter precipitation in southeastern China are identified between P1 and P2 (Fig. 3). During P1, the positive phase of Prec-P1 is associated with significant El Niño–type SST anomalies over the tropical Pacific. In contrast, the SST anomalies associated with Prec-P2 are weak and not significant (Fig. 5). The moisture flux anomalies associated with Prec-P1 and Prec-P2 also display different characteristics (Fig. 4). In P1, the anomalous southwesterlies, which can transport water vapor northward along coastal southern China, are related to two anticyclonic systems over the western Pacific. One anticyclone is located around the low-latitudes of the western Pacific centered on the Philippines, and the other is centered over the midlatitudes of the western North Pacific. In P2, the northward transport of the moisture to southeastern China is relatively weak and is only related to the southerly winds along an anticyclone over the midlatitudes of the North Pacific.

Some previous studies have shown that more central Pacific warming events occurred after the end of the twentieth century, which is also confirmed by our results. As shown in Figs. 6a and 6b, the positive tropical Pacific SSTAs associated with El Niño migrate westward to the central tropical Pacific from P1 to P2. Compared with the conventional El Niño counterpart in P1, the central Pacific warming that occurs in P2 results in a weakened descending branch of the Walker circulation over the western tropical Pacific region and a westward shift of the anticyclone near the Philippines. The central Pacific warming results in weakened moisture transport to southeastern China compared to the canonical El Niño events. However, additional studies have revealed that the impact of El Niño on both Prec-P1 and Prec-P2 significantly decreases when the linear contribution of EAWM is removed (Fig. 8). This study suggests that it is necessary to consider the interdecadal evolution of the relationship between the predicted climate element and its predictors in the climate seasonal forecast research.

EAWM also displays a significant regime shift at the end of the twentieth century. Previous studies indicated that EAWM is reamplified because of the strengthening of the Siberian high and the Aleutian low after 1998. In our study, over the mid-to-high latitudes, a weaker-than-normal EAWM is related to atmospheric circulation anomalies reminiscent of a positive PNA-like pattern in P1. However, the circulation anomalies in P2 bear the structure of a negative phase of the NAO-like pattern. At surface, the anomalous southerlies over the coastal region of southeastern China are obviously stronger in P1 than in P2 and therefore can transport more moisture to southeastern China. Without the influence of ENSO, the EAWM-related atmospheric circulation anomalies are weakened and confined to the mid-to-high latitudes in the Northern Hemisphere. The key system that enhances the water vapor transportation to southeastern China is a midlatitude anticyclonic system over the western Pacific, whereas no anticyclone over the Philippines is observed. The southerlies along the western flank of the North Pacific midlatitude anticyclone transport water vapor to southeastern China, resulting in anomalous positive precipitation anomalies over the region. In P2, at the surface the ENSO-independent EAWM-related positive SLP anomalies over the North Pacific move eastward. This change causes the associated anomalous southerlies migrating eastward to the North Pacific Ocean and prevents them from impacting winter precipitation in southeastern China.

In this study, the EAWMI is defined using surface wind (Chen et al. 2000). As mentioned by Wang and Chen (2010), the ENSO–EAWM relationship on the interannual time scale can be best expressed by the low-level wind indices. To test whether or not our results are sensitive to the selection of the EAWM index, several other EAWM indices have also been assessed. It appears that the general characteristics of the circulation anomalies associated with EAWM in P1 and P2 share many similarities to the results in Fig. 6. However, some EAWM indices that are designed to more strongly consider the wintertime temperature variation over the mid-to-high latitudes of East Asia are not very suitable to represent the wintertime precipitation anomalies in southeastern China. The moisture transport to southeastern China appears to be sensitive to the anomalous southerlies along the coastal area of China, which is, however, not well represented by these EAWM indices designed for SAT. The results presented in the study are more appropriate for those EAWM indices defined using a wind variable obtained by area averaging the low-level wind in a typical region over subtropical East Asia, as they emphasize the importance of EAWM along the coasts of East Asia and its influence on the tropics.

6. Discussion

In a previous study, Chen et al. (2014) showed that there was an interdecadal change in the relationship between the south Indian Ocean SSTAs and southern China winter–spring rainfall variability. The Indian Ocean SST plays a more vital role in modulating the variability of the winter–spring rainfall during the period 1974–94 than during 1953–73, which is likely caused by the increased SST variability in the eastern south Indian Ocean. In the current study, positive SSTAs are also observed over the Indian Ocean, which are related to Prec-P1, whereas no obvious SSTAs can be observed in P2 (Fig. 5). Figure 4a also indicates that in P1 the water vapor transport from the tropical Indian Ocean can also contribute to the winter precipitation anomalies in southeastern China. Previous studies indicated that the variation of the Indian Ocean SST can be described as a response to ENSO (e.g., Venzke et al. 2000). The water vapor transport from the tropical Indian Ocean to southeastern China can be observed in the regression map of the moisture transport related to ENSO in P1 (Fig. 7a). However, it is not observed anymore after EAWM is removed (Fig. 8a), suggesting that some parts of the tropical Indian Ocean contributions to winter precipitation anomalies in southeastern China are independent of ENSO but may have a close relationship with EAWM.

Changes in the mean flow resulting from atmospheric internal variability or global warming may also contribute to the interdecadal change in the relationship between tropical and extratropical regions. In a previous study, Jia et al. (2015) point out that the interdecadal change of the mean flow can cause significantly different atmospheric responses to ENSO forcing over the EA region, which may contribute to the interdecadal change in the relationships between the SAT over EA and ENSO before and after the mid-1980s. Another possible factor that can possibly contribute to the interdecadal change of the relationship between EAWM, ENSO, and wintertime precipitation over China is the Pacific decadal oscillation (PDO). The PDO index (not shown) appears to have been in the high phase from the mid-1970s to the 1990s but transitions to a negative phase after the 2000s, which is consistent with the phase change of the wintertime precipitation over southeastern China, which has been the focus here. L. Wang et al. (2008) proposed that the PDO can modulate the relationship between ENSO and EAWM through change in the Pacific–East Asian teleconnection and/or in the atmospheric response to ENSO over the northwestern Pacific. Other mechanisms may also influence the interdecadal changes of winter precipitation over China, such as snow cover over the Eurasian continent, the Walker circulation (Zeng et al. 2011), and the Atlantic multidecadal oscillation (AMO). The details of how these factors account for the decadal modulation of winter precipitation over China remain unclear and require further examination.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grants 41475065 and 41530425). We are grateful to the three anonymous referees for their helpful suggestions on improving our work. The authors would like to acknowledge the support from the training center of atmospheric sciences of Zhejiang University.

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Footnotes

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