1. Introduction
Air pollution with characteristics of high PM2.5 (i.e., particulates with a diameter < 2.5 μm) concentrations has affected northern China in recent years. In addition, extreme rainfall is occurring more frequently in southern China. The mechanisms responsible for this, with consequences for them, have been extensively studied, respectively (e.g., Li and Sun 2015; An et al. 2020; Yin et al. 2021). Air pollution has a detrimental effect on both human health and social activities (Chen et al. 2017; Hughes et al. 2018; Lelieveld et al. 2019). Similarly, heavy rainfall in southern China has significant socioeconomic impacts (Zhang et al. 2015; Li and Sun 2015; Ma et al. 2018), such as the extreme snowstorm event in southeastern China during the winter of 2007/08 (Ding et al. 2008). One important question is whether these two important weather phenomena are related. An et al. (2020, 2022a) found that the co-occurrence of haze (i.e., air pollution) over the North China Plain (NCP; 32°–42°N, 110°–122°E) and rainfall over southern China (i.e., simultaneous southern rainfall and northern pollution, herein called the SR–NP pattern) is modulated by the two westerly jet waveguides and rainfall-induced diabatic heating. Circulation induced by rainfall heating in southern China might worsen air pollution over the NCP (An et al. 2020, 2022a), and thus it is important to understand the interannual variation in the SR–NP pattern over eastern China.
Zhai et al. (2016) reported more rainfall over southern China and higher PM2.5 concentrations over the NCP during the mature phase of the super El Niño event in 2015/16. This was attributed to an anomalous anticyclone in the Philippines and a negative Eurasia–Pacific teleconnection associated with El Niño, respectively. This means that El Niño might have an important role in the SR–NP pattern in its mature phase. However, previous studies have focused either on the influence of El Niño on air pollution over the NCP (Chang et al. 2016; Jeong et al. 2018, 2021; Zhao et al. 2018; G. Zhang et al. 2019) or on rainfall over southern China (e.g., Weng et al. 2009; Ma et al. 2018; Zhang et al. 2019a,b). No previous study has investigated the influence of El Niño on the SR–NP pattern. Moreover, it is thought that El Niño affects East Asian climate via an anomalous anticyclone in the Philippines (or an anticyclonic anomaly over the western North Pacific), which is caused by the Rossby wave response (Gill 1980; Zhang et al. 1996, 1999; Wang et al. 2000; Zhang and Sumi 2002). It is generally accepted that El Niño affects winter rainfall over southern China. For instance, during the mature phase of El Niño in the boreal winter, warm sea surface temperature (SST) anomalies in the central–eastern tropical Pacific affect water vapor transport in East Asia through the anomalous Walker circulation and an associated lower tropospheric anticyclone over the western North Pacific (Zhang et al. 1999; Yuan and Yang 2012; Zhang and Chen 2021). The southwesterly flow of water vapor along the northwest side of the anticyclonic anomaly results in positive rainfall anomalies in southern China. However, it is debated as to whether El Niño leads to poor air quality over the NCP, as this is distant from the anomalous anticyclone in the Philippines. Some previous studies have suggested that El Niño affects air pollution over the NCP (Chang et al. 2016; Yuan et al. 2017; Jeong et al. 2018; G. Zhang et al. 2019). In detail, in the winters of eastern Pacific El Niño years, the occurrence of an anomalous anticyclone over northeast Asia suggests that the East Asian winter monsoon (EAWM) is suppressed and the flow of cold air is weakened, which increases the number of winter haze days in the Jing-Jin-Ji region of China (Chang et al. 2016; Sun et al. 2018; Yu et al. 2020; W. Zhao et al. 2021). However, some studies have suggested that El Niño has no effect on air pollution over the NCP (Li et al. 2017; Zhao et al. 2018; Cheng et al. 2019; He et al. 2019). These studies argue that the response of low-level winds over the NCP to the El Niño–related anticyclone over the western North Pacific is insignificant, and that the reduced southerly wind anomalies do not cause air pollution over the NCP (Li et al. 2017; He et al. 2019).
Although these previous studies have investigated the effects of El Niño on rainfall in southern China or air pollution over the NCP, respectively, the physical mechanisms as to how El Niño modulates the SR–NP pattern remain unclear. If El Niño affects the SR–NP pattern, the controversy over whether El Niño leads to poor air quality over the NCP might be resolved and result in a better understanding of air pollution in the region. In this study, we investigated interannual variations in the SR–NP pattern and how they are modulated by the strong eastern Pacific El Niño.
The remainder of this paper is organized as follows. Section 2 describes the datasets and methods, and section 3 provides details of the spatiotemporal variability in the SR–NP pattern and associated weather patterns. The influence of the strong eastern Pacific El Niño on the SR–NP pattern over eastern China is considered in section 4. Finally, a brief summary and discussion of the results are provided in section 5.
2. Data and methods
a. Data
The monthly global gridded precipitation dataset (CMAP) for 1979–2018 was obtained from the National Oceanic and Atmospheric Administration (NOAA) website, which was enhanced by the National Centers for Environmental Prediction (NCEP) reanalysis. The data have a spatial resolution of 2.5° latitude × 2.5° longitude (Xie and Arkin 1997).
The daily PM2.5 dataset (9.5°–60.5°N, 100.5°–140.5°E) for “constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980–2019 using a machine learning approach” was produced by Yang (2020), which has a spatial resolution of 1° latitude × 1° longitude and covers the period 1980–2019. The PM2.5 concentration dataset for China was constructed using a space–time random forest model with atmospheric visibility observations and other auxiliary data (i.e., meteorological variables, anthropogenic emissions, land use, national population, topography, and geographic and time variables of observations) (Li et al. 2021). The daily PM2.5 concentrations are in excellent agreement with ground-based measurements, with a coefficient of determination of 0.95 and mean relative error of 12% (Li et al. 2021). A continuous increase in the mean PM2.5 concentrations from 1985 to 2014 in this dataset is similar to the trend of PM2.5 data simulated with the GEOS-Chem model by Yang et al. (2016) (Li et al. 2021).
Two monthly SST datasets were used in this study: the Hadley Centre SST (HadISST) dataset version 3, which covers the period 1979–2018 and has a spatial resolution of 1.0° × 1.0° (Kennedy et al. 2011a,b), and the NCEP Extended Reconstructed Sea Surface Temperature (ERSST) dataset version 5, which covers the period 1979–2018 and has a spatial resolution of 2.5° × 2.5° (Huang et al. 2017). The other reanalysis data (i.e., zonal and meridional wind and geopotential height) were taken from the NCEP–DOE Reanalysis 2 dataset, which covers the period 1979–2018 and has a spatial resolution of 2.5° × 2.5° and 12 vertical levels (1000–10 hPa).
b. Methods
We undertook an empirical orthogonal function (EOF) analysis of the normalized PM2.5 concentration and rainfall anomalies to constrain the spatiotemporal variability of winter PM2.5 concentrations and rainfall in eastern China (i.e., the region east of 100°E). A 9-yr running mean was used to examine the interdecadal variability from the raw SR–NP data. Pearson correlation and linear regression were used to examine the statistical link between the SR–NP pattern and El Niño. Hereafter, anomalies based on our regression analyses are referred to as “regressed anomalies.” The statistical significance of all the analyses was determined by the standard two-tailed Student’s t test. Caution is needed when assessing regressions between two variables where one or both variables have pronounced trends. To avoid this, we conducted the regressions on detrended data.
The EAWMI can quantify changes in the intensity of the midtropospheric East Asian trough and lower tropospheric northerly winds. If the EAWMI is more than 0.5, then the EAWM is strong. If the EAWMI is less than −0.5, then the EAWM is weak (Wang and Chen 2014; S. Zhao et al. 2021).
3. Spatiotemporal variability in the south rainfall–north pollution pattern
Winter air pollution and rainfall in eastern China show complex spatiotemporal variability. In El Niño years since 1979, winter air pollution has commonly occurred over the NCP (Chang et al. 2016; Zhao et al. 2018). At the same time, southern China has typically experienced heavy rainfall (Li and Sun 2015; An et al. 2020, 2022a). We undertook an EOF analysis to extract the dominant mode of NDJ PM2.5 concentrations and precipitation in eastern China (i.e., 10°–60°N, 100°–140°E) for 1979–2019. The EOF modes described below are all statistically significant and independent according to the test criteria of North et al. (1982).
The first mode (EOF1) of PM2.5 concentrations exhibits no obvious regional characteristics (not shown), even though it explains 46.75% of the total variance. The EOF1 of PM2.5 concentrations might be related to emissions (not shown). This was not a focus of this study. The second mode (EOF2) explains 15.5% of the total variance and reveals a marked north–south dipole structure, with a seesaw pattern of PM2.5 concentration anomalies over the NCP (maximum values in Shandong, Henan, and Hebei provinces) and southern China (mainly Guangdong province) (Fig. 1a). A similar pattern was also documented by An et al. (2020, 2022a), based on observed visibility data. The positive phase of EOF2 reflects air pollution over the NCP and clear air over southern China (i.e., northern pollution pattern). The corresponding principal component (PC2) of EOF2 shows that this pattern of PM2.5 concentrations in eastern China exhibits obvious interannual variability (Fig. 1c). Significant air pollution (i.e., higher PM2.5 concentrations) occurred over the NCP in winter 2015/16 (Fig. 1c), which is consistent with previous studies (e.g., Chang et al. 2016; G. Zhang et al. 2019; An et al. 2020; W. H. Zhang et al. 2021). The air pollution index (PM2.5I) is defined as the standardized PC2 associated with the EOF2 mode for PM2.5 concentrations, which is used to identify air pollution events.
Spatial distribution of EOF modes for NDJ in 1979–2018. (a) PM2.5 concentrations (EOF2) and (b) precipitation (EOF1) in 10°–60°N, 100°–140°E. (c) Standardized principal component of EOF2 for the PM2.5 concentration (PC2) and EOF1 for the precipitation (PC1). The variance explained by EOF2 for the PM2.5 concentration and EOF1 for the precipitation are 15.5% and 56.6%, respectively. These two EOF modes yield a true result according to North’s test. The gray-shaded region is the Tibetan Plateau.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
The EOF1 of precipitation accounts for 56.6% of the total variance, and is characterized by extreme rainfall to the south of the middle–lower reaches of the Yangtze River (including Guangdong, Guangxi, Fujian, Jiangxi, and Hunan provinces) (Fig. 1b). This is consistent with the results of Li and Sun (2015) and Ma et al. (2018). The positive phases of EOF1 for precipitation represent higher than normal rainfall over southern China. The corresponding PC1 of EOF1 is also shown in Fig. 1c. The winter rainfall over southern China also exhibits marked interannual variability. The precipitation index (PrecipI) is defined as the standardized PC1 associated with the EOF1 mode for precipitation. The correlation coefficient between PrecipI and PM2.5I is 0.64 (statistically significant at the 99% level, Student’s t test), which means that rainfall in southern China and air pollution in the NCP tend to occur at the same time.
PM2.5I (PrecipI) reveals both interannual and interdecadal variability in air pollution (precipitation). The interdecadal variability of PM2.5I and PrecipI was examined with a 9-yr running mean of the raw PM2.5I and PrecipI variation. The interannual variability of PM2.5I and PrecipI was assessed by removing the interdecadal variability from the raw PM2.5I and PrecipI. Figure 2 shows the interannual and interdecadal components for PM2.5I and PrecipI. The interannual component accounts for 76.46% (93.30%) of the total variance of the year-to-year PM2.5I (PrecipI) variability, while the interdecadal component accounts for 23.54% (6.70%) of the total variance of the year-to-year PM2.5I (PrecipI) variability. The correlation coefficient between the interannual component of PM2.5I and PrecipI is 0.70 (statistically significant at the 99% level, Student’s t test), which is more significant than the correlation between the raw PM2.5I and PrecipI (0.64), indicating that variations in these two indexes are synchronous in some years (Figs. 1c and 2). Therefore, in our study we only focused on the interannual variability of the SR–NP pattern. In particular, in three super El Niño (eastern Pacific–type) years (1982, 1997, and 2015), PM2.5 concentrations and precipitation exhibit obvious anomalies (Fig. 2). We propose a new index (
Time series of the interannual and interdecadal components of the year-to-year PM2.5I and PrecipI. The brown solid and dashed lines are the interannual component of PM2.5I (IAPM2.5I) and interdecadal component of PM2.5I (IDPM2.5I), respectively. The blue solid and dashed lines are the interannual component of PrecipI (IAPrecipI) and interdecadal component of PrecipI (IDPrecipI), respectively. A linear trend in the raw data was removed by linear regression.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
To investigate the local atmospheric circulation associated with the SR–NP pattern, anomalous geopotential height, and wind vector at 850 hPa over East Asia were regressed onto the interannual variability of PM2.5 concentrations and rainfall (Fig. 3). Notably, an anomalous horizontal trough is captured over eastern China at ∼30°N. South of the trough, an anomalous southwesterly wind transports abundant moisture from the South China Sea and Bay of Bengal, which then converges in front of the trough (Fig. 4). This convergence results in an anomalous ascending motion and moisture convergence, which ultimately produce heavy rainfall in southern China (Fig. 4). To the north of the trough, there is a prominent anomalous southeasterly airflow that leads to cyclonic wind shear and enhanced rainfall over southern China. However, this airflow also weakens the northerly winds, which are responsible for the accumulation of particulates and subsequent air pollution. From the above analysis, we conclude that the SR–NP pattern is closely related to the anomalous horizontal trough, which in turn is related to the cyclonic anomaly in southern China, anticyclonic anomaly in the Philippine Sea, and the northeast Asian anomalous anticyclone (Fig. 3). These circulations may be due to El Niño–related air–sea interactions in the eastern Pacific and northern Indian Ocean (Zheng et al. 2019; Yu and Sun 2018, 2020). In addition, the anomalous horizontal trough might be related to local air–sea interactions through wind–evaporation–SST feedback and the Matsuno–Gill-type atmospheric response to asymmetric atmospheric heating in the Philippines Sea and northwest Pacific (Zhang et al. 1996; Park et al. 2017).
The sum of the regression coefficients of the horizontal velocity (shading; unit: m s−1) at 850 hPa regressed onto the interannual variability of PM2.5I and PrecipI. The sum of the regression coefficients of the geopotential height anomalies (contours; unit: gpm) at 850 hPa regressed onto the interannual variability of PM2.5I and PrecipI. The black and purple arrows are the horizontal wind vectors at 850 hPa regressed onto the interannual variability of PM2.5I and PrecipI, respectively. The thick white contours represent a regression coefficient of zero for the geopotential height anomaly. Stippled areas indicate a statistical significance of 95% for regression coefficients for the geopotential height anomaly. Only wind vectors and velocity that exceed 95% significance are shown. The gray-shaded area is the Tibetan Plateau.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
Regression of PrecipI onto the vertically integrated water vapor flux from the surface to 300 hPa (vectors; unit: kg s−1 m−1), moisture divergence (shading; 2 × 10−1 kg s−1 m−2), and omega at 500 hPa (contours; unit: 10−2 Pa s−1). Only those areas and contours that are significant at the 95% confidence level are shaded (moisture divergence) and shown (omega), respectively. The gray-shaded region is the Tibetan Plateau.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
4. Atmospheric teleconnections leading to the south rainfall–north pollution pattern
To understand the physical processes underlying the SR–NP pattern, the NDJ geopotential height anomaly at 250 hPa was regressed onto the interannual variability of PM2.5I and PrecipI, and the linear superposition of PM2.5I and PrecipI. An anticyclonic anomaly occurs at 250 hPa over Northeast Asia (Fig. 5a). This anomaly is part of two Rossby wave trains at 250 hPa (Figs. 5a and 6), which are responsible for the heavy haze over the NCP (An et al. 2020; Yin et al. 2021). One wave train is characterized by anticyclonic anomalies centered over the tropical eastern Pacific, subtropical northwestern Atlantic, western Europe, and northeastern Asia, and cyclonic anomalies centered over western North America, south of Iceland, and the Ural Mountains [i.e., circulation of global teleconnection (CGT)]. The CGT-like circulation has a robust quasi-barotropic structure (Fig. 6b). In particular, the center of the anticyclonic anomaly at 250 hPa over the tropical eastern Pacific is a typical Gill–Matsuno response in higher levels of the troposphere (Gill 1980; Matsuno 1966), which is related to El Niño heating (Zheng et al. 2019). The other wave train is a spherical Rossby wave, has two anticyclonic anomalies centered over the northern Indian Ocean and Northeast Asia, and two cyclonic anomalies centered over southern China and the North Pacific. These are identical to the wave train pattern from the tropical Indo–western Pacific to East Asia in winter (i.e., the IWP–EA pattern) identified by Zheng et al. (2013, 2019), which is also a Gill–Matsuno response to diabatic heating anomalies over the tropical Indo–western Pacific. The IWP–EA pattern also has a marked quasi-barotropic structure (Fig. 6a). The IWP–EA pattern has SST anomalies that exhibit El Niño–like patterns in the tropical Pacific and Indian Ocean dipole–like patterns in the tropical Indian Ocean in autumn, indicating that the wave train reflects the combined effects of El Niño and the Indian Ocean dipole (Zheng et al. 2019). The distribution of geopotential height anomalies at 250 hPa regressed onto the interannual variability of PrecipI is similar to that of PM2.5I (Figs. 5a,b). The cyclonic anomaly over southern China is mainly part of the IWP–EA pattern, which is responsible for winter rainfall over southern China (Zheng et al. 2019). The IWP–EA pattern might also be related to the Antarctic Oscillation (Z. Zhang et al. 2019).
Geopotential height anomaly (contours; unit: gpm) fields at 250 hPa regressed onto the interannual variability of (a) PM2.5I, (b) PrecipI, and (c) PM2.5I + PrecipI. (d)–(f) As in (a)–(c), but for the PM2.5I + PrecipI index after removing the El Niño signal, respectively. Contour intervals are irregular and are defined by color shading. Stippled areas indicate the regression coefficients for the geopotential height are significant at the 95% level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
Vertical section of regression coefficients for the geopotential height anomaly against
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
The geopotential height anomalies at 250 hPa regressed against the linear superposition of the interannual variability of PrecipI and PM2.5I are similar to the regressed map for the interannual variability of PrecipI and PM2.5I, respectively. This indicates that the IWP–EA and CGT-like patterns are important teleconnections that modulate winter SR–NP pattern in eastern China. The rainfall over southern China is mainly modulated by the IWP–EA pattern, whereas high PM2.5 concentrations over the NCP represent the combined effects of the IWP–EA and CGT-like patterns (Fig. 5). Yu and Sun (2018, 2020) found that the eastern Pacific El Niño can affect the climate of East Asia in the form of eastward-propagating, quasi-barotropic Rossby waves along the middle and high latitudes in the boreal winter. W. J. Zhang et al. (2021) suggested that the SST anomalies associated with the eastern Pacific El Niño can be represented by the Niño-3 index. To confirm the effect of the Niño-3 SST anomalies on the IWP-EA and CGT-like patterns, we removed the Niño-3 signal by regression (Figs. 5d–f). Interestingly, no significant correlation was found between the IWP–EA pattern and SST anomalies. The cyclonic anomaly over southern China that is favorable for rainfall almost disappeared. The anticyclonic anomaly over Northeast Asia that enhances air pollution over the NCP also decreased markedly or even disappeared (Figs. 5d–f). Therefore, the eastern Pacific El Niño is a key factor that influences the SR–NP pattern. Notably, some centers of CGT-like circulation remain at the middle and high latitudes, suggesting that this circulation might be related to other factors. An et al. (2020) found that two westerly jet waveguides could explain the formation of heavy haze over the NCP in the winter of 2015. In addition, the North Atlantic Oscillation, Arctic sea ice, and Arctic Oscillation also affect haze over the NCP through the mid- to high-latitude teleconnection (Wang et al. 2019; Yin and Zhang 2020; Yin et al. 2021). A cyclonic anomaly that is unrelated to El Niño in the Ural region (Figs. 5d–f) leads to a weak EAWM, which suggests that the EAWM is not only related to El Niño (Chen et al. 2013; Ma et al. 2018).
The circulation over the Ural Mountains (i.e., the Ural ridge and Siberian high) has an important effect on the EAWM (e.g., An et al. 2020). When the Ural ridge and Siberian high are weak, the EAWM is weak (Zhao et al. 2018; Sun et al. 2018; An et al. 2020). Therefore, we examined the EAWMI defined by Wang and Chen (2014) during the SR–NP events, and found that the EAWM was indeed weak, particularly in 1982, 1997, 2006, and 2015 (Fig. 7), which was conducive to the development of haze over the NCP (An et al. 2020; Zhao et al. 2018; W. Zhao et al. 2021). Chen et al. (2013) found that a weak EAWM, with a lower tropospheric anomalous anticyclone over the northwest Pacific, can be caused by an El Niño event. In addition, Ma et al. (2018) found that a weak EAWM unrelated to El Niño also enhances positive rainfall anomalies in southeastern China induced by El Niño. These results further suggest that an eastward-propagating Rossby wave related to El Niño modulates the SR–NP pattern by affecting the Ural ridge and Siberian high.
The East Asian winter monsoon. (a) Method for defining the East Asian winter monsoon index. Shading represents the mean surface level pressure in winter from 1980 to 2019. Mean surface level pressure in the three box-outlined regions is used to calculate the East Asian winter monsoon index. (b) Time series of the East Asian winter monsoon index anomalies relative to 1980–2019.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
5. Dynamic effects of the strong eastern Pacific El Niño on the SR–NP pattern
Given that the eastern Pacific El Niño, in particular the strong eastern Pacific El Niño, may have a key role in the SR–NP pattern, we examined NDJ SST anomalies in the tropical Indian Ocean and Pacific Ocean. Two different SST datasets (ERSST and HadISST) were used to assess the SST variability. The distribution of the regression coefficients of the NDJ SST anomalies onto the interannual variability of PM2.5I and PrecipI is shown in Fig. 8. The interannual variability in PM2.5I exhibits a significantly positive correlation with the SST anomalies in the tropical central–eastern Pacific and the tropical Indian Ocean, with a maximum in the Niño-3 region (Figs. 8a,c). The positive SST anomalies in the tropical Indian Ocean are termed the Indian Ocean basin mode, which might be induced by El Niño through an atmospheric bridge (Klein et al. 1999; Lau and Nath 2000), the Indonesian Throughflow (Meyers 1996), or the Indian Ocean capacitor effect (Xie et al. 2009). This implies that the SST anomalies in the tropical eastern Pacific and the SST anomalies related to eastern Pacific El Niño in the tropical Indian Ocean are two important factors associated with air pollution over the NCP. The distribution of the regression coefficients of the NDJ SST anomalies onto the interannual variability of PrecipI are similar to that of those onto the interannual variability of PM2.5I. This suggests that the SST anomalies affect both air pollution over the NCP and rainfall over southern China (i.e., the SR–NP pattern).
Regression map of NDJ ERSST anomaly (unit: °C) during 1979–2018 against the interannual variability of (a) PM2.5I and (b) PrecipI. (c),(d) As in (a) and (b), but for the HadISST anomaly (unit: °C). The white-dotted region indicates statistical significance at the 95% level. The gray regions denote land.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
The following section further examines the association of rainfall and air pollution with eastern Pacific El Niño events, to understand the connection between the SR–NP pattern in eastern China and tropical SST forcing. The regression coefficient of the NDJ PM2.5 concentrations against the Niño-3 index is positive over the NCP at the 95% confidence level, which suggests that eastern Pacific El Niño tends to increase the probability of air pollution in this region (Fig. 9). Similarly, positive rainfall anomalies obtained by regression against the Niño-3 index occur over southern China (Fig. 9). This indicates that an eastern Pacific El Niño event generally leads to excess winter rainfall over southern China, which is consistent with the finding that rainfall in southern China is related to El Niño (Ma et al. 2018). In addition, positive rainfall anomalies occur in the tropical central–eastern Pacific and the tropical Indian Ocean, whereas a negative rainfall anomaly is mainly located in the Indian–Pacific warm pool, which is a rainfall pattern related to the eastern Pacific El Niño and the positive Indian Ocean dipole phase (Fig. 9). In summary, the evidence suggests that the eastern Pacific El Niño played a key role in the winter SR–NP events in eastern China from 1979 to 2018.
Regression of NDJ PM2.5 concentrations over 32°–42°N, 110°–122°E and precipitation over 30°S–30°N, 30°E–70°W from 1979 to 2018 onto the Niño-3 index. Stippled regions indicate regression coefficients are statistically significant at the 90% level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
To explore the underlying physical mechanisms, we regressed the wave activity flux onto
Regression maps of NDJ wave activity flux (vectors; unit: m2 s−2) anomalies and vorticity anomalies (shading; unit: 10−6 s−1) at 250 hPa onto (a)
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
The wave activity flux along the middle and high latitudes is weak in East Asia (Fig. 10). To further assess whether the Rossby wave energy along the middle and high latitudes propagates to East Asia and finally leads to the SR–NP pattern, we compared the wave activity flux and geopotential height anomalies at 250 hPa for strong SR–NP events. Table 1 provides the combinations of different phases of interannual PM2.5I and PrecipI based on a ±0.4 standard deviation during 1979–2018. A standard deviation of ±0.4 was chosen as the threshold to obtain more samples. Figure 11 shows that two wave trains finally reach Northeast Asia. One originates in the tropical eastern Pacific and reaches Northeast Asia along the middle and high latitudes of the Northern Hemisphere. The other originates in the northern Indian Ocean and reaches Northeast Asia along a great circle path, which is consistent with our regression analysis (Figs. 5 and 10). Yu and Sun (2018, 2020) proposed that the eastern Pacific El Niño–related wave train propagates from the North Pacific Ocean eastward into the North Atlantic, and then bifurcates into two branches farther eastward, which leads to the anomalous Siberian high and East Asian trough. Therefore, we further checked the types of El Niño events and found that except for the 2000 El Niño, the SR–NP events were all related to the eastern Pacific El Niño events, in particular the strong events (i.e., Oceanic Niño Index exceeding 1.5). Composite maps for the strong eastern Pacific El Niño events since 1950 also support the conclusions above (not shown). In addition, the IWP–EA pattern was also strong in 1982, 1997, 2002, and 2006 (Zheng et al. 2013). Using the regression coefficients of geopotential height anomalies at 250 hPa onto
Combinations of different phases of interannual PM2.5I and PrecipI based on a standard deviation of ±0.4 from 1979–2018. Asterisks (*) and hash marks (#) denote El Niño and La Niña events, respectively.
Composite map of the wave activity flux (arrows; unit: m2 s−2) and geopotential height anomaly (shading; unit: gpm) fields at 250 hPa for the SR–NP events. Stippled regions indicate regression coefficients for the geopotential height anomaly are statistically significant at the 90% level.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
Simultaneous one-point correlation of the regression coefficients of the geopotential height anomaly at 250 hPa onto
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
In general, the strong eastern Pacific El Niño modulates the horizontal trough in eastern China through the CGT-like circulation and the IWP–EA pattern, which is conducive to the occurrence of SR–NP events. In particular, the Northeast Asian anomalous anticyclone related to the CGT-like circulation and the IWP–EA pattern leads to conditions favorable to air pollution, such as stagnation and weak ventilation over the NCP. The cyclonic anomaly in southern China associated with the IWP–EA pattern and the southern branch of the wave train provide favorable conditions for rainfall, such as ascending motion and delivery of water vapor.
6. Discussion and conclusions
We have shown that rainfall in southern China and air pollution over the NCP tend to occur at the same time in the mature phase of strong eastern Pacific El Niño events, which we term the SR–NP (southern rainfall–northern pollution) pattern. The correlation coefficient between the interannual variability of PrecipI and PM2.5I is 0.7 at the 95% confidence level. Further results show that an anomalous horizontal trough in eastern China at ∼30°N modulates the SR–NP events. The southwesterly flow located to the south of the anomalous horizontal trough transports abundant moisture to southern China, which combines with anomalous ascending airflow related to a cyclonic anomaly, and leads to excess rainfall in southern China. Southeasterly flow to the north of the trough is associated with a weak EAWM due to the weak Ural ridge, which is accompanied by anomalous descending airflow related to the northeast Asian anomalous anticyclone, and leads to poor air quality over the NCP. The anomalous horizontal trough is controlled by a cyclonic anomaly in southern China, the Philippine anomalous anticyclone, and Northeast Asian anomalous anticyclone. The cyclonic anomaly in southern China and Northeast Asian anomalous anticyclone are mainly regulated by the IWP–EA pattern, which is modulated by warm SST anomalies in the northern Indian Ocean during strong eastern Pacific El Niño events (Fig. 13). The Philippine anomalous anticyclone might be caused by cool SST anomalies in the western Pacific related to El Niño, which is consistent with the study of Zhang et al. (1996). In addition to the IWP–EA pattern, the northeast Asian anomalous anticyclone is also affected by an eastward-propagating Rossby wave related to the strong eastern Pacific El Niño (Fig. 13). A weak EAWM is conducive to air pollution over the NCP and positive precipitation anomalies in southern China during eastern Pacific El Niño events (Ma et al. 2018). These two weather phenomena constitute the SR–NP pattern. There was less rainfall in southern China and less pollution over the NCP in 1983, 1995, 1999, 2008, 2010, and 2017. Of these years, 1983, 1995, 1999, 2010, and 2017 were La Niña events, which suggests that La Niña events might have an opposite effect to eastern Pacific El Niño events. The effects of La Niña on the SR–NP pattern need further investigation.
Schematic diagram of the causes of interannual variability in the SR–NP pattern. The red shading indicates positive SST anomalies. Blue and red dashed lines with arrows are the two pathways of the Rossby wave trains caused by the SST anomalies over the tropical eastern Pacific and northern Indian Ocean. Letters C and A represent cyclonic and anticyclonic circulation, respectively. AAC is the Philippine anomalous anticyclone.
Citation: Journal of Climate 35, 22; 10.1175/JCLI-D-21-0662.1
In addition, the Philippine anomalous anticyclone is related to El Niño by air–sea interactions, and may also have an important role in winter rainfall in southern China (e.g., Zhang et al. 1996; Wang et al. 2000; Xie et al. 2009). However, our results highlight the role of the cyclonic anomaly related to the strong eastern Pacific El Niño on rainfall in southern China. Nevertheless, the effect of the Philippine anomalous anticyclone on air pollution in eastern China cannot be ignored (e.g., Feng et al. 2016; Yu et al. 2019). However, our results highlight the effect of the CGT-like circulation and the IWP–EA pattern on poor air quality over the NCP. The southern branch of the Rossby wave along the subtropical westerly jet also plays an important role in the SR–NP pattern (An et al. 2020, 2022a). Further research based on more strong eastern Pacific El Niño events in the future is required to understand the multisystem synergy mechanisms for the SR–NP pattern.
Acknowledgments.
This research was supported by the National Natural Science Foundation of China (Grants 41675146 and 42275191) and the National Key R&D Program of China (Grant 2019YFA0607002). We are grateful for NOAA, NCEP, the Met Office’s Hadley Centre, and Yang (2020) for their dataset.
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