1. Introduction
El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual variation over the tropical Pacific (Trenberth 1997; McPhaden et al. 2006). During El Niño events, warm sea surface temperature (SST) anomalies occur over the tropical central and eastern Pacific and result in an anomalous SST gradient between the tropical eastern and western Pacific, inducing westerly wind anomalies along the equatorial Pacific. These equatorial westerly wind anomalies drive warm surface water eastward and suppress upwelling with a deepened thermocline in the tropical central and eastern Pacific, facilitating local SST warming (Bjerknes 1969; Cane and Zebiak 1985; Neelin et al. 1998). Since ENSO events generally reach their peak during boreal winter, they have tremendous impacts on weather and climate in the wintertime, especial in the Asian–Pacific–American region (Ropelewski and Halpert 1986; Webster and Yang 1992; Wang et al. 2000; Alexander et al. 2002; Chen and Kumar 2004; McPhaden et al. 2006; Zhou et al. 2007; He and Wang 2013; Geng et al. 2017). In recent years under global warming, more extreme temperature events have been occurring more frequently in the Asian–Pacific–American region as well as ENSO events (Zhou et al. 2009; Wang et al. 2010; Lan and Chen 2013; Zhai et al. 2016; Shi et al. 2017; Herring et al. 2020; Hu et al. 2020; Jian et al. 2020). Since ENSO causes huge impacts on climate variation and extreme weather events, it provides potential predictability for temperature variation, suggesting that the relationship between ENSO events and temperature should be of great concern.
Although ENSO-related SST anomalies are located mainly in the tropical Pacific, they can influence temperature variation over the Asian–Pacific–American region via atmospheric teleconnection by changing the large-scale circulation (Rasmusson and Wallace 1983; Halpert and Ropelewski 1992; Leung and Zhou 2016). For example, El Niño–related SST anomalies in the tropical Pacific can induce an anomalous low-level anticyclonic circulation over the western North Pacific, which can weaken the East Asian winter monsoon (EAWM), resulting in a warmer winter over East Asia (Zhang et al. 1996, 1999; Chen et al. 2000; Wang et al. 2000). Li (1990) indicated that both Hadley and Ferrel cells are strengthened due to El Niño forcing, enhancing lower troposphere westerlies in the middle and high latitudes, which is unfavorable for southward cold-surge outbreaks. Moreover, accompanied by a weak Siberian high and a shallow East Asian trough (EAT), fewer cold-surge events occur over East Asia during El Niño years (Zhang et al. 1997; Cheung et al. 2012; Leung and Zhou 2015). On the other hand, based on observations and model simulations, many studies have also discovered that ENSO-related SST anomalies can influence North American temperature variation by inducing a Rossby wave train that propagates from the tropical central and eastern Pacific to North America (Rasmusson and Wallace 1983; Trenberth et al. 1998; Straus and Shukla 2002; Bulić and Branković 2007), which is similar to the Pacific–North American (PNA) teleconnection pattern (Horel and Wallace 1981; Wallace and Gutzler 1981). Therefore, when El Niño occurs, northwestern North America experiences a warmer winter while the southeastern United States is colder (Ropelewski and Halpert 1986; Trenberth et al. 1998; Wu et al. 2005). All of this suggests that the impact of ENSO on winter temperatures over the Asian–Pacific–American region is worth considering due to the possible huge socioeconomic impact in areas of concentrated agricultural production and developed economy due to large population.
Although many previous studies have focused on the impact of ENSO on the magnitude of seasonal or monthly mean temperature anomalies rather than temperature variability during boreal winter, recent studies have discovered that ENSO events are also related to subseasonal temperature variability over the Asian–Pacific–American region (Geng et al. 2017; Martineau et al. 2020). Moreover, Leung and Zhou (2016) indicated that ENSO can influence East Asian and North American winter synoptic temperature variability (STV) in different ways. ENSO directly modulates temperature variability over the tropical eastern Pacific and western North America. On the other hand, ENSO affects the winter STV over East Asia by influencing the location of the EAT. Leung et al. (2017) also found that the influence of ENSO on the location of the EAT occurs via a Rossby wave train induced by anomalous heating in the midtroposphere in the tropical western Pacific. During El Niño (La Niña) years, the EAT shifts northeastward (southwestward), associated with less (more) southward cold-air transport, causing a stronger (weaker) meridional temperature gradient and baroclinicity over East Asia. This results in larger (smaller) winter STV with a stronger (weaker) eddy growth rate in the midlatitudes. Ren et al. (2020) further investigated the relationship between ENSO and winter STV over China, finding that the variability in winter synoptic temperature becomes greater (smaller) during El Niño (La Niña) years. One possible reason they suggested is that El Niño events can enhance the meridional temperature gradient in the mid- to high latitudes over Eurasia, inducing stronger Siberian storm activity and larger STV over China downstream, which is also similar to the results of Ma et al. (2017). In addition, except for tropical SST forcing, Wettstein and Mearns (2002) also discovered the relationship between the North Atlantic/Arctic Oscillation (NAO/AO; Thompson and Wallace 1998; Thompson et al. 2000) and the daily variance in extreme temperature over the northeastern United States and Canada, showing that variances in extreme temperature are stronger (weaker) with a high (low) NAO/AO index. Moreover, based on data from 150 meteorological stations, Gong et al. (2004) discovered that the AO can modulate the winter daily temperature variance over China via the Siberian high. When the AO is in its positive (negative) phase, the winter daily temperature variance over China is smaller (larger).
Since the winter STV over Eastern China is closely associated with the EAWM, ENSO plays an important role in East Asian climate prediction, and it is important to consider whether there is a stable relationship between ENSO and the EAWM. We need to confirm whether ENSO can be a reliable predictor for East Asian climate over a longer period. Recent research indicates that the relationship between ENSO and the EAWM is not stable, and their linkage has oscillated during past decades. Based on data from 1871 to 2009, He and Wang (2013) found a decadal change in the ENSO–EAWM relationship in different periods; the relationship was highly correlated during 1902–26 and 1952–76 but appeared to break down during 1927–51 and 1977–2001, which is also supported by several recent studies (He et al. 2013; Wang and He 2012). They suggested that both the Pacific decadal oscillation (PDO; Mantua et al. 1997) and the Atlantic multidecadal oscillation (AMO; Delworth and Mann 2000) may have a combined modulation effect on the North Pacific Oscillation (NPO; Wallace and Gutzler 1981) teleconnection, which can influence the ENSO–EAWM relationship via the Pacific–East Asian (PEA) teleconnection (Wang et al. 2000). In addition, a recent study (Sung et al. 2019) also found that mean-state change in the La Niña–like pattern over the tropical Pacific on an interdecadal time scale can induce zonal displacement of the NPO via a Rossby wave train, which modulates the atmospheric mean baroclinicity in the extratropical North Pacific, thus causing interdecadal variation in the relationship between North American winter temperature and the NPO. All these results suggest that decadal or multidecadal change in SST can be an important factor in modulating the intensity of the connection between climate variations with long-term variability. Therefore, whether a shift in the relationship between ENSO and STV over the Asian–Pacific–American region has occurred in recent decades is worth investigating, as it is important for Asian–Pacific–American climate prediction.
In this study, we aim to investigate the long-term variability of ENSO and winter synoptic temperature over the Asian–Pacific–American region during 1951–2018, and to find a possible reason for the interdecadal shift in this relationship over Eastern China, which will provide implications for our selection of climate predictors. The rest of this paper is organized as follows. Section 2 describes the data and methodologies used in this study. Section 3 compares the relationship between ENSO and winter STV over North America and Eastern China during 1951–87 (P1) and 1988–2018 (P2). Section 4 investigates a possible reason for the interdecadal shift in the ENSO–STV relationship over Eastern China in the 1980s via a numerical simulation. Finally, a summary and discussion are provided in section 5.
2. Models, datasets, and methodologies
a. Models and datasets
In this study, we use the daily mean and monthly output datasets from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis 1 with a horizontal resolution of 2.5° latitude × 2.5° longitude (Kalnay et al. 1996), including near-surface air temperature, geopotential height, sea level pressure, and horizontal winds. Sea surface temperature is obtained from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature (version 5) with a horizontal resolution of 2° latitude × 2° longitude (Huang et al. 2017), and HadISST1 from the Met Office Hadley Centre (Rayner et al. 2003). In addition, we use the ocean Niño index (ONI), which represents the intensity of ENSO events and can be downloaded from the Climate Prediction Center (CPC) website (https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php). The daily mean temperature from 33 meteorological stations over Eastern China is obtained from NOAA’s National Centers for Environmental Information (NCEI) website (https://www.ncei.noaa.gov/). We selected these 33 stations based on the significant area of the correlation coefficient distribution between the ONI and winter STV over Eastern China (Fig. 1b) from 1951 to 2018. Our research period is from December 1950 to February 2018 for reanalysis data, and for the daily mean temperature from meteorological stations it is from 1958 to 2018 due to limited data availability. Here winter 1951 is defined as the period from December 1950 to February 1951, and so on.
(a) Correlation map between winter synoptic temperature variability (STV) and ONI during 1951–2018. Stippling indicates correlation coefficients exceeding the 0.05 significance level. (b) Time series for 23-yr sliding correlation between ONI and winter STV over Eastern China; the short and long dashes represent the 0.01 and 0.05 significance levels, respectively. (c) As in (b), but for North America. The winter STV over Eastern China (20°–40°N, 100°–125°E) and North America (30°–60°N, 130°–80°W) is calculated by the area-average over the significant area in (a).
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
b. Methodologies
To investigate the synoptic temperature variability, we first apply the Lanczos filter to the daily mean near-surface temperature to remove low-frequency variations longer than 10 days from November to the following March (Duchon 1979; Leung et al. 2019). Here we extend one month before and after the data period we aim to use, ensuring data length after filtering. Then we calculate the standard deviation for synoptic temperature from December to the following February, representing the winter synoptic surface temperature variability. All data used in this study have been detrended. The Student’s t test is applied to examine the significance of the correlation coefficients and composite analysis.
3. Comparison of the relationship between ENSO and winter STV over North America and Eastern China during 1951–2018
Figure 1a shows a correlation map between the ONI and the winter STV over the Northern Hemisphere. As the figure shows, the winter STV in both Eastern China and North America is significantly related to ENSO events. According to the correlation map (Fig. 1a), Eastern China winter STV becomes larger (smaller) and North American winter STV becomes smaller (larger) during El Niño (La Niña) years, which is also consistent with previous studies (Leung and Zhou 2016; Ren et al. 2020). We then check the stability of this relationship between ENSO and winter STV over Eastern China and North America during 1951–2018. We find that the relationship between ENSO and Eastern China winter STV has an interdecadal shift in the 1980s (Fig. 1b), such that the significant relationship becomes insignificant after the 1980s. Although the ENSO–STV relationship shows some fluctuations over North America it is significantly stable for the whole period (Fig. 1c). Following Wang et al. (2013), we here use a 23-yr sliding window to examine the evolution of the relationship. But similar results for the relationship between ENSO and STV over Eastern China and North America can also be found by using different sliding windows (from 11- to 21-yr windows). Since the evolutions of the relationship between ENSO and winter STV are different over Eastern China and North America, we analyze the detailed features of each separately in the following discussion.
a. North America
Figure 2a shows the normalized variation of winter STV over North America during 1951–2018, which is calculated by the averaged winter STV anomalies in the significant region of the ENSO–STV relationship over North America (30°–60°N, 130°–80°W) based on Fig. 1a. According to our definition, STV is related to the day-to-day temperature fluctuation during winter. However, the relative contribution of the intensity or frequency of temperature change to the magnitude of STV is still unclear. To examine this hypothesis, we here use temperature change to investigate the detailed features of STV. Similar to Leung et al. (2019), we define a case of temperature change as a temperature increase from day −1 to day 0 and a decrease from day 0 to day 1, representing temperature evolution due to the passage of an extratropical cyclone. Although the influence of extratropical cyclones and extratropical anticyclones on the evolution of temperature change in two adjacent days is opposite, the results for the following discussion are quite similar. For convenience, we here show only the results relating to the influence of extratropical cyclones. We then perform composite analysis for temperature change cases by choosing 12 strong STV years and 13 weak STV years with STV anomalies exceeding ±1 standard deviation, respectively. The composite number and intensity of temperature change cases in strong and weak STV years over North America during 1951–2018 are shown in Figs. 2b and 2c. The average number of temperature change cases in strong STV years is less than in weak STV years (Fig. 2b). But the average magnitude of temperature change, here represented by temperature drop intensity, is larger in strong STV years than in weak STV years, while the result for temperature increase is similar (figure not shown). This suggests that the intensity of STV, which represents day-to-day temperature fluctuation during winter, depends mainly on the magnitude of temperature change rather than temperature change frequency.
(a) Normalized winter STV anomalies over North America for 1951–2018. (b) The average number of temperature change cases during winter in STV(+) years and STV(−) years. (c) As in (b), but for the average magnitude of temperature drop (°C). Blue and red bars represent the weak and strong STV years that exceed ±1 standard deviation, respectively.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
Since the relationship between ENSO and STV undergoes an interdecadal shift in the 1980s over Eastern China but remains significant over North America (Figs. 1b,c), we divide the study period into period 1 (P1: 1951–87) and period 2 (P2: 1988–2018), based on the turning point when the correlation coefficients change from significant to insignificant for the ENSO–STV relationship over Eastern China. Figure 3 shows the composite patterns of STV anomalies over North America in the different periods for El Niño and La Niña events, respectively. Consistent with the previous result (Fig. 1a), STV tends to be smaller (larger) during El Niño (La Niña) events for the whole period (Figs. 3a,b). Meanwhile, the results for P1 and P2 are also quite similar (Figs. 3c–f), indicating a significant relationship between STV and ENSO for the whole period in North America.
Composite pattern of winter STV anomalies over North America for (left) El Niño events and (right) La Niña events during (a),(b) the whole period, (c),(d) P1, and (e),(f) P2. Stippling indicates values exceeding the 0.05 significance level.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
b. Eastern China
To further compare the inconsistent evolution of the ENSO–STV relationship between North America and Eastern China, we also examine the detailed features of winter STV over Eastern China in the same way. Figure 4a shows the normalized series of winter STV over Eastern China during 1951–2018, which is calculated by the averaged winter STV anomalies in the significant region of the ENSO–STV relationship over Eastern China (ECSTV; 20°–40°N, 100°–125°E), shown in Fig. 1a. Based on ±1 standard deviation of the normalized STV anomalies, there are 10 strong STV years and 8 weak STV years over Eastern China during 1951–2018. Similar to the composite results for North America, fewer (more) temperature change cases as well as temperature changes of larger (smaller) magnitude during strong (weak) STV years can also be found over Eastern China (Figs. 4b,c), suggesting that the intensity of daily temperature fluctuation during winter in both North America and Eastern China is associated mainly with temperature change magnitude. Moreover, the composite patterns for the whole period (Figs. 5a,b) are similar to Fig. 1a, showing that STV is likely to be larger (smaller) over Eastern China during El Niño (La Niña) events, and the same features are also displayed in P1 (Figs. 5c,d). However, in P2, the contrast of STV anomalies in different ENSO phases is not obvious.
As in Fig. 2, but for Eastern China.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
As in Fig. 3, but for Eastern China.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
To validate the interdecadal shift in the ENSO–STV relationship over Eastern China that occurred in the 1980s, we use temperature data from 33 meteorological stations that match well with the significant area of the ENSO–STV relationship; station names and locations are shown in Fig. 6a. As Fig. 6b shows, the station data also indicate an interdecadal shift in the ENSO–STV relationship in the 1980s. All these results confirm that inconsistent evolution of the ENSO–STV relationship occurred over the Asian–Pacific–American region during 1951–2018.
(a) Distribution of the 33 meteorological stations over Eastern China. The purple dashed lines indicate the correlation coefficients between ONI and winter STV at the 0.05 significance level. (b) Time series for 23-yr sliding correlation between ONI and winter STV; the short and long dashes represent the 0.01 and 0.05 significance levels, respectively. The dashed red line and solid gray lines represent the results for the average of the 33 stations and for each station, respectively.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
4. Possible reason for the interdecadal shift of the ENSO–STV relationship over Eastern China in the 1980s and numerical simulation
The intensity of winter STV is closely associated with the magnitude of day-to-day temperature change, as shown in Fig. 4c. This is possibly related to extratropical eddies, which lead to temperature fluctuation along their path. Therefore, we first investigate the relationship between extratropical eddies and ECSTV in P1 and P2. Regression maps of the Eady growth rate with ECSTV in P1 and P2 are shown in Figs. 7a and 7b, respectively. In both P1 and P2, ECSTV is related to the midlatitude Eady growth rate over East Asia, which represents the intensification of extratropical eddies (Figs. 7a,b). Although the pattern for the ECSTV-related Eady growth rate during P1 extends more eastward compared to P2, we consider that the area near Northeast Asia is more important due to the radius of influence of extratropical eddies. When a high (low) Eady growth rate occurs in the midlatitudes, ECSTV tends to be larger (smaller), which is physically consistent with the stronger (weaker) variation of meridional wind in the lower troposphere over Eastern China (Figs. 7c,d). In other words, the eddy propagates eastward along the midlatitudes and strengthens more (less) near Northeast Asia with a high (low) Eady growth rate. The intensified eddy brings southerly wind and raises the temperature over Eastern China when it approaches Eastern China, followed by a resulting temperature drop with northerly wind when it moves out. Hence, a stronger eddy causes stronger wind variation and larger temperature fluctuation over Eastern China. Furthermore, although the relationship between ENSO and ECSTV has an interdecadal shift in the 1980s, the relationship between the intensification of extratropical eddies and ECSTV is stable for the whole period. To examine whether the relationship between ENSO and extratropical eddy development may have an interdecadal shift, regression maps of the Eady growth rate with ONI during P1 and P2 are shown in Figs. 8a and 8b, respectively. During P1, ENSO is well related to extratropical eddy development over Northeast Asia (Fig. 8a) and is also consistent with low-level meridional wind variation over Eastern China (Fig. 8c), which overlaps with the ECSTV-related region (Figs. 7a,b). This means that when El Niño occurs, extratropical eddies strengthen more and result in more variation of low-level meridional wind, associated with larger STV over Eastern China, while the opposite occurs during La Niña events. But during P2, ENSO is not significantly related to the Eady growth rate over East Asia or to low-level circulation variation (Figs. 8b,d), suggesting that the relationship between extratropical eddy development and ENSO also has an interdecadal shift in the 1980s.
Regressions of the Eady growth rate (EGR) at 850 hPa onto ECSTV (day−1) during (a) P1 and (b) P2. Regressions of synoptic meridional wind variability at 925 hPa onto ECSTV during (c) P1 and (d) P2. Stippling indicates the regression coefficients exceeding the 0.05 significance level.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
In addition, the intensification of extratropical eddies is closely related to the instability of atmospheric baroclinicity, which is associated with the horizontal temperature gradient. As mentioned above, we here hypothesize that whether ENSO-related temperature over East Asia has a pattern change in the 1980s may be modulated by ENSO-related SST forcing via a Rossby wave train. Since the ONI magnitude represents only the intensity rather than the spatial pattern of ENSO events, we employ empirical orthogonal function (EOF) analysis to extract the dominant spatial pattern of SST anomalies over the tropical Pacific in order to describe the ENSO patterns in the two periods separately. We then check the consistency between the ONI and PC1 in both P1 and P2, with correlation coefficients of +0.98 and +0.99, respectively, exceeding the 0.01 significance level based on the Student’s t test. As the results of the two SST datasets are quite similar, we show only the results from ERSST (V5) in the following discussion. The EOF1 modes of SST anomalies over the tropical Pacific during P1 and P2 are shown in Figs. 9a and 9b, respectively. Compared to P1, an El Niño–like warming pattern occurs over the tropical Pacific during P2, with the warming center over the central Pacific (Fig. 9c), which may be attributed to more central Pacific El Niño events occurring after 1990 (Yeh et al. 2009). Since the ENSO-related SST anomaly pattern shows a change in P2 compared to P1, we examine the possible change of ENSO influence on air temperature over East Asia, including vertical variation of potential temperature (N) and temperature gradient, which are the two main factors that account for the Eady growth rate. On the one hand, ENSO-related SST patterns are not related to N over Northeast Asia in both P1 and P2 (Figs. 10a,b). On the other hand, as shown in Fig. 10c, the EOF1 mode of SST anomalies is significantly correlated with the air temperature gradient over Northeast Asia during P1, where it is consistent with the key region that influences the development of extratropical eddies over Northeast Asia (Figs. 7a,b). Similar to previous studies (Leung et al. 2017; Hu et al. 2018), the anomalous anticyclonic (cyclonic) circulation over the western North Pacific (Northeast Asia) brings warm (cold) air northward (southward) with southerly (northerly) winds via the advection effect, which plays a dominant role in temperature change, resulting in the anomalous temperature pattern over Northeast Asia. During P2, however, the relation between ENSO and temperature over Northeast Asia is not significant (Fig. 10d), while the ENSO-related circulation shifts far eastward compared to P1, which causes limited influence on the temperature over East Asia. All these results suggest that the ENSO-related SST pattern over the tropical Pacific changes from P1 to P2, resulting in a change in the temperature gradient over Northeast Asia, which is associated with the magnitude of extratropical eddy intensification and ECSTV. Therefore, the change in the ENSO pattern from P1 to P2 may cause the interdecadal shift in the ENSO–STV relationship over Eastern China.
Spatial patterns of the first EOF mode of winter SST anomalies (°C) over the tropical Pacific (25°S–25°N, 120°E–80°W) for (a) P1 and (b) P2. Their explained variance is shown at the top right of (a) and (b). (c) Difference between (a) and (b). The black contours in (a) and (b) represent the 0.8°C isotherm of SST anomalies.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
(a) Distribution of the regression coefficients between PC1 and winter Brunt–Väisälä frequency (N; day−1) at 700 hPa for P1. (b) As in (a), but for P2. (c),(d) As in (a) and (b), but for temperature anomalies (shading; °C) and horizontal wind anomalies (vectors; m s−1) at 700 hPa. The purple contours in (c) and (d) represent the winter climatology of the 700-hPa temperature (contour interval: 4°C). Only the regression coefficients exceeding the 0.05 significance level are plotted.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
To examine the atmospheric responses to different SST anomaly patterns over the tropical Pacific in P1 and P2, we employ the Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY) model (Molteni 2003; Kucharski et al. 2006; Kucharski et al. 2013) to perform numerical simulations. SPEEDY is an atmospheric general circulation model (AGCM) developed by the International Centre for Theoretical Physics (ITCP), with eight vertical levels and T30 horizontal resolution. More detailed information about the SPEEDY model can be found online at http://users.ictp.it/~kucharsk/speedy-net.html. As many previous studies have shown (Bracco et al. 2005; Kucharski et al. 2007; King et al. 2010; Dogar et al. 2017; Sun et al. 2017; King et al. 2018; Leung et al. 2020; Jian et al. 2020), this model is widely used to examine atmospheric circulation responses to SST forcing, suggesting that it can appropriately be used to investigate the influence of different tropical Pacific SST anomaly patterns in P1 and P2. In this study, we use three types of SST fields as forcing: SST0, SST1, and SST2. SST0 is the climatological SST with an annual cycle during 1979–2018 from observations (Kennedy et al. 2011). SST1 is the first EOF mode of winter SST anomalies over the tropical Pacific (25°S–25°N, 120°E–80°W; Fig. 9a) during P1. SST2 is also the first EOF mode of winter SST anomalies over the tropical Pacific, but for P2 (Fig. 9b). We design three experiments in this study: Exp_CTRL, which is a control run forced by SST0 with a running period of 145 years; Exp_P1, a sensitivity run forced by (SST0 + SST1), which restarts each December based on Exp_CTRL and runs for 3 months; and Exp_P2, which is the same as Exp_P1 but forced by (SST0 + SST2). To avoid the effect of model spinup and make sure the same initial conditions apply in each experiment, here we use the last 115 years of Exp_CTRL to restart Exp_P1 and Exp_P2 and then calculate the average of 115 members for analysis. The differences between Exp_P1, Exp_P2, and Exp_CTRL represent the atmospheric response to the SST anomalies over the tropical Pacific in P1 and P2, separately.
The temperature responses to the EOF1 modes of SST anomalies over the tropical Pacific during P1 (Fig. 9a) and P2 (Fig. 9b) are shown in Fig. 11. As the figure shows, the temperature pattern induced by EOF1 SST anomalies has a stronger impact on the temperature gradient over Northeast Asia in P1 than in P2 (Fig. 11c), which is quite similar to observations (Fig. 11d). These model results highlight that the ENSO patterns in P1 and P2 can cause different impacts on temperature spatial distribution, resulting in the significant change in the temperature gradient in P1, but not in P2, over Northeast Asia, which contributes to the interdecadal shift in the ENSO–ECSTV relationship in the 1980s.
Spatial patterns of temperature (°C) and horizontal wind (m s−1) anomalies at 700 hPa for (a) Exp_P1-Exp_CRTL, (b) Exp_P2-Exp_CRTL, and (c) the difference in 700-hPa temperature between Exp_P2 and Exp_P1. Wind speed in (a) and (b) less than 0.3 m s−1 has been masked out. The blue and black dashes in (c) indicate the area exceeding the 0.05 and 0.1 significance level, respectively. (d) The difference in the regression pattern of PC1 and 700-hPa temperature anomalies between P2 and P1.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
5. Summary and discussion
a. Summary
In this study, we examine the long-term variability of the relationship between ENSO and winter STV over the Asian–Pacific–American region, and investigate the possible cause for the inconsistent evolution of the ENSO–STV relationship over this region. Based on reanalysis and meteorological station data, the evolution of the sliding correlation shows an interdecadal shift in the relationship between ENSO and winter STV in the 1980s over Eastern China (Figs. 1b and 6b), changing from significant in P1 (1951–87) to insignificant in P2 (1988–2018). But the relationship of ENSO–STV is significant over North America for the whole period (Fig. 1c). Since the relationship between ENSO and ECSTV in different periods is unstable, it causes us to consider the prediction reliability of factors influencing ECSTV. On the other hand, a possible cause of the interdecadal shift in the relationship between ENSO and ECSTV in the 1980s is also investigated. Based on composite analysis, we noted that the intensity of STV in Eastern China is related to the magnitude of day-to-day temperature change rather than the frequency (Fig. 4), which is associated with the intensification of extratropical eddies over Northeast Asia for the whole period (Fig. 7). Meanwhile, ENSO-related SST anomalies during P1 and P2 can induce different temperature gradient patterns over Northeast Asia via a Rossby wave train (Fig. 10), resulting in the difference in the relationship between ENSO and the intensification of extratropical eddies over Northeast Asia, which is directly related to the temperature gradient. Therefore, the change in the ENSO pattern from P1 to P2 contributes to the interdecadal shift in the ENSO–ECSTV relationship in the 1980s by influencing the temperature gradient over Northeast Asia.
Furthermore, the influence of ENSO-related SST anomalies on temperature during P1 and P2 is examined by AGCM experiments, confirming that the ENSO pattern has a stronger influence on the temperature gradient over Northeast Asia in P1 than in P2, which plays a role in the interdecadal shift of the ENSO–STV relationship over Eastern China in the 1980s. Apart from Eastern China, the model simulation also proves that the ENSO-related temperature gradient pattern over North America is stable for the whole period (Fig. 11), which is consistent with observations (Fig. 10), highlighting that the pattern of SST variation is a determining factor in regulating STV in different regions.
b. Discussion
Additionally, the relationship between ENSO and ECSTV in P2 is not significant (Fig. 1b). This suggests that other factors may play a role in controlling ECSTV during P2. For example, Gong et al. (2004) found that daily winter temperature variance over China can be influenced by the AO, by modulating the high-frequency fluctuations of the Siberian high, which is associated with cold-surge events in China. Since the study period in Gong et al. (2004) covered only until 2001, we also checked the relationship between the AO, Siberian high, and ECSTV with an extended period from 1958 to 2018 (Fig. 12). Although the AO (AO index is downloaded from the CPC; https://www.cpc.ncep.noaa.gov/) is not well related to ECSTV during P1, the relationship between the AO and ECSTV is significant during P2, especially after 2000 (Fig. 12a), with a correlation coefficient of −0.35, exceeding the 0.05 significance level. Meanwhile, the evolution of AO–ECSTV relationship (Fig. 12a) is more associated with the evolution of the AO–Siberian relationship (Fig. 12b) rather than the Siberian–ECSTV relationship (Fig. 12c), with a high correlation coefficient of 0.83, exceeding the 0.01 significance level. Therefore, the enhanced relationship between AO and ECSTV during P2 (Fig. 12a) is consistent with the recovery of the AO–Siberian relationship after 2000 (Fig. 12b). This suggests that the influence of the AO on ECSTV is possibly enhanced since 2000, providing a potential predictor for ECSTV in the coming future.
Time series for the 23-yr sliding correlation between (a) the AO index (AOI) and ECSTV, (b) AOI and Siberian high synoptic variability (SHSV; 40°–60°N, 70°–120°E), and (c) SHSV and ECSTV; the short and long dashes represent the 0.01 and 0.05 significance levels, respectively.
Citation: Journal of Climate 34, 13; 10.1175/JCLI-D-20-0931.1
In addition, since the unstable ENSO–EAWM relationship has been pointed out in previous studies (He and Wang 2013) and the relationship between ENSO and ECSTV seems to recover after 2002 (Fig. 1b), investigation into the stability of the ENSO–ECSTV relationship is needed. Owing to the limited length of observational data, we will examine the possible interdecadal variation of the ENSO–ECSTV relationship based on phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6; Taylor et al. 2012; Eyring et al. 2016) model outputs in our future work, aiming to provide some insight for climate prediction.
Acknowledgments
This work is supported by the Center for Ocean Research in Hong Kong and Macau (CORE), the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11300920), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies (Grant 2020B1212060025), and the Jiangsu Collaborative Innovation Center for Climate Change. The first author is a recipient of a research studentship provided by the City University of Hong Kong (CityU).
REFERENCES
Alexander, M. A., and Coauthors, 2002: The atmospheric bridge: The influence of ENSO teleconnections on air–sea interaction over the global oceans. J. Climate, 15, 2205–2231, https://doi.org/10.1175/1520-0442(2002)015<2205:TABTIO>2.0.CO;2.
Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163–172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.
Bracco, A., F. Kucharski, F. Molteni, W. Hazeleger, and C. Severijns, 2005: Internal and forced modes of variability in the Indian Ocean. Geophys. Res. Lett., 32, L12707, https://doi.org/10.1029/2005GL023154.
Bulić, I. H., and Č. Branković, 2007: ENSO forcing of the Northern Hemisphere climate in a large ensemble of model simulations based on a very long SST record. Climate Dyn., 28, 231–254, https://doi.org/10.1007/s00382-006-0181-1.
Cane, M. A., and S. E. Zebiak, 1985: A theory for El Niño and the Southern Oscillation. Science, 228, 1085–1087, https://doi.org/10.1126/science.228.4703.1085.
Chen, J., and P. Kumar, 2004: A modeling study of the ENSO influence on the terrestrial energy profile in North America. J. Climate, 17, 1657–1670, https://doi.org/10.1175/1520-0442(2004)017<1657:AMSOTE>2.0.CO;2.
Chen, W., H. Graf, and R. Huang, 2000: The interannual variability of East Asian winter monsoon and its relation to the summer monsoon. Adv. Atmos. Sci., 17, 48–60, https://doi.org/10.1007/s00376-000-0042-5.
Cheung, H. N., W. Zhou, H. Y. Mok, and M. C. Wu, 2012: Relationship between Ural–Siberian blocking and the East Asian winter monsoon in relation to the Arctic Oscillation and the El Niño–Southern Oscillation. J. Climate, 25, 4242–4257, https://doi.org/10.1175/JCLI-D-11-00225.1.
Delworth, T. L., and M. E. Mann, 2000: Observed and simulated multidecadal variability in the Northern Hemisphere. Climate Dyn., 16, 661–676, https://doi.org/10.1007/s003820000075.
Dogar, M. M., F. Kucharski, and S. Azharuddin, 2017: Study of the global and regional climatic impacts of ENSO magnitude using SPEEDY AGCM. J. Earth Syst. Sci., 126, 30, https://doi.org/10.1007/s12040-017-0804-4.
Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 1016–1022, https://doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.
Eady, E., 1949: Long waves and cyclone waves. Tellus, 1, 33–52, https://doi.org/10.3402/tellusa.v1i3.8507.
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016.
Geng, X., W. Zhang, M. F. Stuecker, and F.-F. Jin, 2017: Strong sub-seasonal wintertime cooling over East Asia and northern Europe associated with super El Niño events. Sci. Rep., 7, 3770, https://doi.org/10.1038/s41598-017-03977-2.
Gong, D., S. Wang, and J. Zhu, 2004: Arctic Oscillation influence on daily temperature variance in winter over China. Chin. Sci. Bull., 49, 637–642, https://doi.org/10.1360/03wd0481.
Halpert, M. S., and C. F. Ropelewski, 1992: Surface temperature patterns associated with the Southern Oscillation. J. Climate, 5, 577–593, https://doi.org/10.1175/1520-0442(1992)005<0577:STPAWT>2.0.CO;2.
He, S., and H. Wang, 2013: Oscillating relationship between the East Asian winter monsoon and ENSO. J. Climate, 26, 9819–9838, https://doi.org/10.1175/JCLI-D-13-00174.1.
He, S., H. Wang, and J. Liu, 2013: Changes in the relationship between ENSO and Asia–Pacific midlatitude winter atmospheric circulation. J. Climate, 26, 3377–3393, https://doi.org/10.1175/JCLI-D-12-00355.1.
Herring, S. C., N. Christidis, A. Hoell, M. P. Hoerling, and P. A. Stott, 2020: Explaining extreme events of 2018 from a climate perspective. Bull. Amer. Meteor. Soc., 101, S1–S140, https://doi.org/10.1175/BAMS-ExplainingExtremeEvents2018.1.
Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813–829, https://doi.org/10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.
Hu, K., G. Huang, R. Wu, and L. Wang, 2018: Structure and dynamics of a wave train along the wintertime Asian jet and its impact on East Asian climate. Climate Dyn., 51, 4123–4137, https://doi.org/10.1007/s00382-017-3674-1.
Hu, Z., A. Kumar, B. Huang, J. Zhu, M. L’Heureux, M. J. McPhaden, and J. Yu, 2020: The interdecadal shift of ENSO properties in 1999/2000: A review. J. Climate, 33, 4441–4462, https://doi.org/10.1175/JCLI-D-19-0316.1.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
Jian, Y., X. Lin, W. Zhou, M. Jian, M. Y. T. Leung, and P. K. Y. Cheung, 2020: Analysis of record-high temperature over southeast coastal China in winter 2018/19: The combined effect of mid- to high-latitude circulation systems and SST forcing over the North Atlantic and tropical western Pacific. J. Climate, 33, 8813–8831, https://doi.org/10.1175/JCLI-D-19-0732.1.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Kennedy, J. J., N. A. Rayner, R. O. Smith, M. Saunby, and D. E. Parker, 2011: Reassessing biases and other uncertainties in sea-surface temperature observations since 1850. Part I: Measurement and sampling errors. J. Geophys. Res., 116, D14103, https://doi.org/10.1029/2010JD015218.
King, M. P., F. Kucharski, and F. Molteni, 2010: The roles of external forcings and internal variabilities in the Northern Hemisphere atmospheric circulation change from the 1960s to the 1990s. J. Climate, 23, 6200–6220, https://doi.org/10.1175/2010JCLI3239.1.
King, M. P., I. Herceg-Bulić, F. Kucharski, and N. Keenlyside, 2018: Interannual tropical Pacific sea surface temperature anomalies teleconnection to Northern Hemisphere atmosphere in November. Climate Dyn., 50, 1881–1899, https://doi.org/10.1007/s00382-017-3727-5.
Kucharski, F., F. Molteni, and A. Bracco, 2006: Decadal interactions between the western tropical Pacific and the North Atlantic Oscillation. Climate Dyn., 26, 79–91, https://doi.org/10.1007/s00382-005-0085-5.
Kucharski, F., A. Bracco, J. H. Yoo, and F. Molteni, 2007: Low-frequency variability of the Indian monsoon–ENSO relationship and the tropical Atlantic: The “weakening” of the 1980s and 1990s. J. Climate, 20, 4255–4266, https://doi.org/10.1175/JCLI4254.1.
Kucharski, F., F. Molteni, M. P. King, R. Farneti, I. S. Kang, and L. Feudale, 2013: On the need of intermediate complexity general circulation models: A “SPEEDY” example. Bull. Amer. Meteor. Soc., 94, 25–30, https://doi.org/10.1175/BAMS-D-11-00238.1.
Lan, X., and W. Chen, 2013: Strong cold weather event over Eurasia during the winter of 2011/2012 and a downward Arctic Oscillation signal from the stratosphere (in Chinese). Chin. J. Atmos. Sci., 37, 863–872.
Leung, M. Y. T., and W. Zhou, 2015: Variation of circulation and East Asian climate associated with anomalous strength and displacement of the East Asian trough. Climate Dyn., 45, 2713–2732, https://doi.org/10.1007/s00382-015-2504-6.
Leung, M. Y. T., and W. Zhou, 2016: Direct and indirect ENSO modulation of winter temperature over the Asian–Pacific–American region. Sci. Rep., 6, 36356, https://doi.org/10.1038/srep36356.
Leung, M. Y. T., H. N. Cheung, and W. Zhou, 2017: Meridional displacement of the East Asian trough and its response to the ENSO forcing. Climate Dyn., 48, 335–352, https://doi.org/10.1007/s00382-016-3077-8.
Leung, M. Y. T., W. Zhou, K. Y. Cheung, H. N. Gong, and Y. Zhang, 2019: Enhancement of lower tropospheric winter synoptic temperature variations in Southwest China and the northern Indochina Peninsula after 2010. Climate Dyn., 53, 2281–2294, https://doi.org/10.1007/s00382-019-04841-x.
Leung, M. Y. T., W. Zhou, D. Wang, P. W. Chan, S. M. Lee, and H. W. Tong, 2020: Remote tropical western Indian Ocean forcing on changes in June precipitation in South China and the Indochina Peninsula. J. Climate, 33, 7553–7566, https://doi.org/10.1175/JCLI-D-19-0626.1.
Li, C., 1990: Interaction between anomalous winter monsoon in East Asia and El Niño events. Adv. Atmos. Sci., 7, 36–46, https://doi.org/10.1007/BF02919166.
Lindzen, R. S., and B. Farrell, 1980: A simple approximate result for the maximum growth rate of baroclinic instabilities. J. Atmos. Sci., 37, 1648–1654, https://doi.org/10.1175/1520-0469(1980)037<1648:ASARFT>2.0.CO;2.
Ma, Y., and Coauthors, 2017: The climatic characteristics of Siberian storm track and its possible maintenance mechanism (in Chinese). J. Meteor. Sci., 37, 587–597.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1079, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
Martineau, P., H. Nakamura, and Y. Kosaka, 2020: Influence of ENSO on North American subseasonal surface air temperature variability. Wea. Climate Dyn. Dis., 1–21, https://doi.org/10.5194/wcd-2020-22.
McPhaden, M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in Earth science. Science, 314, 1740–1745, https://doi.org/10.1126/science.1132588.
Molteni, F., 2003: Atmospheric simulations using a GCM with simplified physical parameterizations. I: Model climatology and variability in multi-decadal experiments. Climate Dyn., 20, 175–191, https://doi.org/10.1007/s00382-002-0268-2.
Neelin, J. D., D. S. Battisti, A. C. Hirst, F.-F. Jin, Y. Wakata, T. Yamagata, and S. E. Zebiak, 1998: ENSO theory. J. Geophys. Res., 103, 14 261–14 290, https://doi.org/10.1029/97JC03424.
Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects of the El Niño/Southern Oscillation. Science, 222, 1195–1202, https://doi.org/10.1126/science.222.4629.1195.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Ren, M., W. Zhang, X. Geng, and C. Liu, 2020: ENSO impact on the variability of wintertime synoptic-scale air temperature over China and possible mechanisms behind (in Chinese). Acta Meteor. Sin., 78, 199–209.
Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114, 2352–2362, https://doi.org/10.1175/1520-0493(1986)114<2352:NAPATP>2.0.CO;2.
Shi, H., Z. Zhang, and H. Ren, 2017: Observed ENSO intensity changes during 1900–2015 (in Chinese). Climate Change Res., 13, 1–10.
Simmonds, I., and E. P. Lim, 2009: Biases in the calculation of Southern Hemisphere mean baroclinic eddy growth rate. Geophys. Res. Lett., 36, L01707, https://doi.org/10.1029/2008GL036320.
Straus, D. M., and J. Shukla, 2002: Does ENSO force the PNA? J. Climate, 15, 2340–2358, https://doi.org/10.1175/1520-0442(2002)015<2340:DEFTP>2.0.CO;2.
Sun, C., F. Kucharski, J. Li, F.-F. Jin, I.-S. Kang, and R. Ding, 2017: Western tropical Pacific multidecadal variability forced by the Atlantic multidecadal oscillation. Nat. Commun., 8, 15998, https://doi.org/10.1038/ncomms15998.
Sung, M., H.-Y. Jang, B.-M. Kim, S.-W. Yeh, Y.-S. Choi, and C. Yoo, 2019: Tropical influence on the North Pacific Oscillation drives winter extremes in North America. Nat. Climate Change, 9, 413–418, https://doi.org/10.1038/s41558-019-0461-5.
Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1.
Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297–1300, https://doi.org/10.1029/98GL00950.
Thompson, D. W. J., J. M. Wallace, and G. C. Hegerl, 2000: Annular modes in the extratropical circulation. Part II: Trends. J. Climate, 13, 1018–1036, https://doi.org/10.1175/1520-0442(2000)013<1018:AMITEC>2.0.CO;2.
Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 2771–2777, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.
Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. Res., 103, 14 291–14 324, https://doi.org/10.1029/97JC01444.
Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784–812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.
Wang, B., R. Wu, and X. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 1517–1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.
Wang, C., H. Liu, and S. K. Lee, 2010: The record-breaking cold temperatures during the winter of 2009/2010 in the Northern Hemisphere. Atmos. Sci. Lett., 11, 161–168, https://doi.org/10.1002/asl.278.
Wang, H., and S. He, 2012: Weakening relationship between East Asian winter monsoon and ENSO after mid-1970s. Chin. Sci. Bull., 57, 3535–3540, https://doi.org/10.1007/s11434-012-5285-x.
Wang, H., S. He, and J. Liu, 2013: Present and future relationship between the East Asian winter monsoon and ENSO: Results of CMIP5. J. Geophys. Res. Oceans, 118, 5222–5237, https://doi.org/10.1002/jgrc.20332.
Webster, P. J., and S. Yang, 1992: Monsoon and ENSO: Selectively interactive systems. Quart. J. Roy. Meteor. Soc., 118, 877–926, https://doi.org/10.1002/qj.49711850705.
Wettstein, J. J., and L. O. Mearns, 2002: The influence of the North Atlantic–Arctic Oscillation on mean, variance, and extremes of temperature in the northeastern United States and Canada. J. Climate, 15, 3586–3600, https://doi.org/10.1175/1520-0442(2002)015<3586:TIOTNA>2.0.CO;2.
Wu, A., W. W. Hsieh, and A. Shabbar, 2005: The nonlinear patterns of North American winter temperature and precipitation associated with ENSO. J. Climate, 18, 1736–1752, https://doi.org/10.1175/JCLI3372.1.
Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511–514, https://doi.org/10.1038/nature08316.
Zhai, P., and Coauthors, 2016: The strong El Niño in 2015/2016 and its dominant impacts on global and China’s climate. Acta Meteor. Sin., 74, 309–321.
Zhang, R., A. Sumi, and M. Kimoto, 1996: Impact of El Niño on the East Asian monsoon. J. Meteor. Soc. Japan., 74, 49–62, https://doi.org/10.2151/jmsj1965.74.1_49.
Zhang, R., A. Sumi, and M. Kimoto, 1999: A diagnostic study of the impact of El Niño on the precipitation in China. Adv. Atmos. Sci., 16, 229–241, https://doi.org/10.1007/BF02973084.
Zhang, Y., K. R. Sperber, and J. S. Boyle, 1997: Climatology and interannual variation of the East Asian winter monsoon: Results from the 1979–95 NCEP/NCAR reanalysis. Mon. Wea. Rev., 125, 2605–2619, https://doi.org/10.1175/1520-0493(1997)125<2605:CAIVOT>2.0.CO;2.
Zhou, W., X. Wang, T. Zhou, C. Li, and J. C. L. Chan, 2007: Interdecadal variability of the relationship between the East Asian winter monsoon and ENSO. Meteor. Atmos. Phys., 98, 283–293, https://doi.org/10.1007/s00703-007-0263-6.
Zhou, W., J. C. L. Chan, W. Chen, J. Ling, J. G. Pinto, and Y. Shao, 2009: Synoptic-scale controls of persistent low temperature and icy weather over southern China in January 2008. Mon. Wea. Rev., 137, 3978–3991, https://doi.org/10.1175/2009MWR2952.1.
