Abstract

In winter 2018/19, southeastern coastal China experienced extreme warm temperatures that were due to a weak East Asian winter monsoon. On the basis of observations from 10 meteorological stations and reanalysis data, the large-scale circulation patterns associated with this extreme warm winter and the possible driving mechanism of its related sea surface temperature (SST) anomalies are investigated in this study. During this winter, many places in this region reached their highest winter mean temperature record and had more extreme warm days and fewer extreme cold days relative to climatology. According to the circulation patterns during winter 2018/19, several large-scale circulation conditions associated mainly with the weak East Asian winter monsoon are identified: the eastward shift of the Siberian high and a shallower East Asian trough, which is related to the low blocking frequency over the Aleutian region, are both unfavorable for cold-air intrusion southward. Meanwhile, strong low-level southerly wind anomalies over southeastern China are related mainly to the 2018/19 El Niño event. Furthermore, the possible role of SST anomalies over the North Atlantic and tropical western Pacific Oceans is examined by using an atmospheric general circulation model, suggesting that both the “tripole pattern” of North Atlantic SST and tropical western Pacific SST anomalies in winter 2018/19 played a role in influencing the East Asian trough. The combined effect of all of these factors seems to be responsible for this extreme warm winter over southeastern coastal China.

1. Introduction

Southeastern coastal China is a region of concentrated agricultural production and developed economy due to its large population. However, because of its location, it often suffers from climate disasters and extreme weather events, such as heat waves and floods in summer and extreme freezing rain and strong cold surges in winter, which cause huge economic losses and harm to people in this region. Under the global warming trend of recent years, global surface temperatures have increased rapidly and extreme climate events are occurring more frequently, suggesting that extreme temperature events are worthy of study.

Based on 54-yr station data, the annual mean surface temperatures over China have increased at a rate of +0.25°C per 10 years, and winter temperatures have increased even more significantly, at a rate of +0.39°C per 10 years (Ren et al. 2005). Data from 160 stations and a high-resolution reanalysis dataset show a uniform warming pattern in the dominant mode of winter temperature variations over China in recent decades (Kang et al. 2006; Guo et al. 2016). Consistent with the nationwide warming trend, winter temperatures over South China have also increased, with a linear trend of +0.24°C per 10 years, +0.54°C per 10 years, and +0.18°C per 10 years in mean temperature, extreme cold temperature, and extreme warm temperature, respectively (Liang and Wu 1999; Wu and Yang 2014). In addition, more extreme temperature events over South China during winter have occurred in the last decade (Wen et al. 2009; Zhou et al. 2009; C. Wang et al. 2010; Lan and Chen 2013; Ding et al. 2017). For example, in January and early February 2008, a very rare cold event occurred in South China along with a strong East Asian winter monsoon (EAWM), leading to persistent low temperatures, freezing rain, and snow. This extreme cold event gave rise to huge economic losses and personal injury and was cause for great concern. Although extreme cold events can be catastrophic, extreme warm events in winter are also of concern. For example, in winter 2016/17, temperatures over China reached record highs with a much weaker EAWM, and China was nearly +2°C warmer than normal, resulting in the warmest winter since 1961 (Ding et al. 2017). Warmer winters provide unsuitable growing conditions for crops, disturb the phenophase of plants, and allow increases in crop insects and diseases, all of which significantly affect agricultural production (Li et al. 2010). Moreover, warm temperatures in winter also cause more human diseases that usually occur in the warm season (Haines et al. 2006).

During the boreal winter, the southeast coast of China is controlled by the EAWM, with prevailing northeasterly winds in the low-level circulation that play an important role in influencing local winter temperatures (Lau and Li 1984; Ding 1994; Wu and Chan 1995; Chen et al. 2000; Ding 2004; Huang et al. 2007; Wang and Fan 2013). Based on 40-yr meteorological station data over China, Guo (1994) indicated that winter temperature anomalies over China are highly related to EAWM anomalies at both interannual and decadal time scales. For example, when the EAWM is weak, the corresponding winter temperature over China is higher. Wang and Ding (2006) pointed out that the frequency of cold surge events over China has decreased in the last 53 years, which is related to the decreasing trend of EAWM intensity under the background of global warming. Recently, many studies have consistently confirmed that the EAWM was weakened after the mid-1980s due to decadal variations in different EAWM indices (L. Wang et al. 2010; Wang and He 2012; Lee et al. 2013).

On the other hand, the intensity of the EAWM is modulated by many factors in both mid- and high-latitude circulation systems and sea surface temperature (SST). As the source of the EAWM, the intensity of the Siberian high is associated with its upstream blocking high (Rex 1950). The blocking high enhances the meridional flow and transports more cold air from the polar region, which increases cold mass convergence over Siberia and intensifies the Siberian high (Chang 2004; Ding and Krishnamurti 1987; Ding 1990, 2004; Takaya and Nakamura 2005a,b). Therefore, during winters with more (less) blocking, an intensified (weakened) Siberian high tends to strengthen (weaken) the EAWM, with more (fewer) cold surges breaking out toward East Asia (Joung and Hitchman 1982; Wu and Leung 2009; Cheung et al. 2012; Lan and Li 2016).

Moreover, the intensity of the EAWM is also affected by SSTs. El Niño–Southern Oscillation (ENSO) is the dominant interannual variability over the tropical Pacific and can exert a strong impact on the EAWM. When El Niño occurs, the EAWM tends to be weakened and temperatures increase over China (Zhang et al. 1999; Chen et al. 2000; Yang et al. 2002). Accompanied by a weak Siberian high, the cold surge frequency in East Asia is reduced significantly during El Niño (Zhang et al. 1997; Cheung et al. 2012). Li (1990) suggested that both Hadley and Ferrell cells are strengthened during El Niño years, enhancing mid- and high-latitude westerlies in the lower troposphere, which is unfavorable for southward cold surge outbreaks. In addition, some previous studies have indicated that winter North Atlantic SST anomalies can also influence downstream circulation systems (e.g., Ural–Siberian blocking) via a wave train, based on statistical analyses and numerical simulations (Palmer and Sun 2007; Li 2004; Fu et al. 2008; Li and Gu 2010; Han et al. 2011). For example, during January 2008, warm SST anomalies over the northwest Atlantic induced a wave train, causing positive geopotential height anomalies over Ural–Siberia with extremely high blocking frequency, which led to persistent extreme freezing weather over South China (Fu et al. 2008; Liu et al. 2008; Li and Gu 2010; Han et al. 2011). Recently, Qiao and Feng (2016) pointed out that the East Asian Trough (EAT), which is directly related to the EAWM (Chen et al. 2000; Wang et al. 2009), can be influenced by the NAO and its related SST anomalies during winter through wave trains across Eurasia.

During winter 2018/19, many places in southeastern coastal China experienced extreme warm temperatures compared to the same period in the historical record (Table 1), and it is worth investigating the possible cause of these temperature extremes. Many studies have discussed the synoptic features and large-scale factors contributing to extreme weather (Ding et al. 2008; Fu et al. 2008; Han et al. 2011; Wen et al. 2009; Zhou et al. 2009). For example, Ding et al. (2017) suggested that a weak Siberian high, a positive phase of the Arctic Oscillation (AO), and less autumn Arctic sea ice cover, which were affected by Arctic atmospheric circulations in summer 2016, contributed to the weaker EAWM and the warmer temperature anomalies over China in winter 2016/17. All of this suggests that extreme cases are likely influenced by more than one factor. The patterns of temperature anomalies over China in winter 2016/17 and winter 2018/19 are entirely different, so the factors that may have played a role in the extreme warm winter of 2018/19 are of great concern.

Table 1.

Monthly mean temperatures (T¯; °C) and mean temperature deviations (Dev; °C) at 10 meteorological stations in winter 2018/19. Stations are ordered from north to south. Values in parentheses are their ranking among 40 years.

Monthly mean temperatures (T¯; °C) and mean temperature deviations (Dev; °C) at 10 meteorological stations in winter 2018/19. Stations are ordered from north to south. Values in parentheses are their ranking among 40 years.
Monthly mean temperatures (T¯; °C) and mean temperature deviations (Dev; °C) at 10 meteorological stations in winter 2018/19. Stations are ordered from north to south. Values in parentheses are their ranking among 40 years.

Since many previous studies have focused mainly on synoptic features and the causes of extreme cold events, in this study we focus on the characteristics of temperature evolution during winter 2018/19, the warmest winter over southeastern coastal China since 1979, enriching our knowledge of extreme climate events under global warming. We will also discuss the possible influence of large-scale conditions in accounting for this extreme warm winter, aiming to provide implications for climate prediction. The rest of this paper is organized as follows: Section 2 describes the data, methods, and model used in this study. Section 3 describes the evolution and statistical characteristics of the record-high temperatures over southeastern coastal China during winter 2018/19. Section 4 investigates the large-scale conditions associated with this extreme warm winter. Section 5 examines the possible influence of SST anomalies over the North Atlantic and tropical western Pacific on the East Asian trough, and a summary and discussion are provided in section 6.

2. Data and methods

a. Data

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 air temperature, geopotential height, vertical velocity, sea level pressure, and horizontal winds. We also use the fifth major global reanalysis produced by ECMWF (ERA5) for the same variables with a horizontal resolution of 0.25° latitude × 0.25° longitude (Hersbach and Dee 2016), which can be downloaded freely from the Copernicus Climate Change Service (C3S, available online at https://climate.copernicus.eu/climate-reanalysis). SST is obtained from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature, version 4, with a horizontal resolution of 2° latitude × 2° longitude (Huang et al. 2015). 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 data from 10 meteorological stations over southeastern coastal China are obtained from NOAA’s National Centers for Environmental Information (NCEI) website (https://www.ncei.noaa.gov/). Here we select these 10 stations based on the second EOF mode of winter surface air temperature of the ERA5 reanalysis over South China (106°–122°E, 18°–36°N) from 1980 to 2019 as well as the extreme high value of principal component 2 (PC2) in winter 2018/19 (Figs. 1c,d). The EOF2 mode shows warm surface temperature anomalies over southeastern coastal China and the adjacent South China Sea, but cold for inland areas. These 10 stations match well with the area with warm temperature anomalies, and their names and locations are shown in Fig. 2a. Our research period is from December 1979 to February 2019, and the climatology is obtained from winter data for the 40 years from 1980 to 2019. Here, winter 1980 is defined as the period from December 1979 to February 1980, and so on.

Fig. 1.

(left) Spatial patterns and (right) PCs of EOF modes of winter surface air temperature (°C) over South China for 1980–2019 for (a), (b) the first mode; (c), (d) the second mode; (e), (f) the third mode. Their explained variance is shown at the top right of (a), (c), and (e).

Fig. 1.

(left) Spatial patterns and (right) PCs of EOF modes of winter surface air temperature (°C) over South China for 1980–2019 for (a), (b) the first mode; (c), (d) the second mode; (e), (f) the third mode. Their explained variance is shown at the top right of (a), (c), and (e).

Fig. 2.

(a) Distribution of the 10 meteorological stations over southeastern China. (b) Time series of winter temperature anomalies (°C) at the 10 stations and the reanalysis data for 1980–2019. The dashed black line represents the average of the 10 stations.

Fig. 2.

(a) Distribution of the 10 meteorological stations over southeastern China. (b) Time series of winter temperature anomalies (°C) at the 10 stations and the reanalysis data for 1980–2019. The dashed black line represents the average of the 10 stations.

b. Method

To estimate the winter blocking frequency in the Northern Hemisphere, we here detect blocking events mainly following the methodology used by Barriopedro et al. (2006, hereinafter B06), which is a modified version based on Trigo et al. (2004, hereinafter T04). Previous studies have identified blocking using different parameters, such as 500-hPa geopotential height (Lejanäs and Økland 1983; Tibaldi and Molteni 1990), potential temperature on the 2 PVU (1 PVU = 10−6 K kg−1 m2 s−1) potential vorticity surface (Pelly and Hoskins 2003), and 500-hPa zonal wind (Scaife et al. 2010). Since the most common definition of blocking is geopotential height, which is widely used by many studies of weather and climate monitoring [e.g., CPC; National Climate Center (NCC)], we also detect blocking events by geopotential height in this study. The geopotential height blocking index is based on the work of Lejenäs and Økland (1983) and is characterized by a positive 500-hPa geopotential height gradient in the midlatitudes from 40° to 60°N. To ensure a minimum geostrophic westerly wind poleward of the block, the adapted version of this index was proposed by Tibaldi and Molteni (1990, hereinafter TM90) to detect blocking with an additional negative geopotential height gradient from 60° to 80°N. Also, since the location of blocking varies with the season, reference latitudes with a fluctuation of 4° are considered. Due to the higher resolution of the NCEP–NCAR reanalysis data, a new version of the TM90 index was modified by T04, which can provide more opportunities for blocking detection by using five values of fluctuation for reference latitudes rather than the three proposed by TM90. Similar to B06, we detect blocking with a detailed procedure as follows:

We calculate the geopotential height gradient at both midlatitudes (GHGS) and high latitudes (GHGN) for each longitude point of the grid:

 
GHGS =Z(λ,ϕ0)Z(λ,ϕS)ϕ0ϕS and
(1)
 
GHGN =Z(λ,ϕn)Z(λ,ϕ0)ϕnϕ0,
(2)

where ϕn = 80°N + Δ, ϕ0 = 60°N + Δ, ϕs = 40°N + Δ, and Δ = −5°, −2.5°, 0°, +2.5°, and +5°; Z(λ, ϕ) is the 500-hPa geopotential height at latitude ϕ and longitude λ.

These three reference latitudes were identified by previous studies of the climatology blocking pattern. The central point ϕ0 is chosen as 60°N (Treidl et al. 1981), and the southern point ϕs and northern point ϕn are selected as 40° and 80°N, respectively (Austin 1980; TM90). If the grid point satisfies the following conditions at a specific time with at least one value of Δ, then this grid is defined as “blocked”:

  1. GHGS > 0,

  2. GHGN < −10 gpm per degree of latitude, and

  3. Z(λ,ϕ0)Z(λ,ϕ0)¯>0.

To minimize the problem of identifying the cutoff low as blocking, B06 used another requirement to constrain the blocking selection, as shown in condition 3, which demands that the geopotential height anomaly at ϕ0 is positive. Here, Z(λ,ϕ0)¯ is the daily climatology of 500-hPa geopotential height at latitude ϕ and longitude λ.

c. Model experiments

To examine the atmospheric responses to SST anomalies over the North Atlantic and tropical western Pacific during winter 2018/19, we employ a model called Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY; Molteni 2003; Kucharski et al. 2006, 2013) to perform the 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 (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), this model can well capture atmospheric circulation responses to SST forcing, suggesting that it can appropriately be used to investigate the North Atlantic and tropical western Pacific SST anomaly forcings. In this study, we use four types of SST fields as forcing: SST0, SST1, SST2, and SST3. SST0 is the climatological SST with an annual cycle during 1979–2018 from observations (Kennedy et al. 2011). SST1 is the seasonal mean (December, January, and February) SST anomalies over the North Atlantic Ocean (80°–20°W, 0°–60°N; Fig. 10a) in winter 2018/19. SST2 is also the seasonal mean SST anomalies in winter 2018/19, but for the tropical western Pacific Ocean (120°–150°E, 10°S–15°N; Fig. 11a), and SST3 includes SST1 and SST2 together (Fig. 12a). We design four experiments in this study: Exp_CTRL, which is a control run forced by SST0 with a running period of 100 years; Exp_NATL, a sensitivity run forced by (SST0 + SST1), which restarts each December based on Exp_CTRL and runs for three months; and Exp_WP and Exp_NATL+WP, which are the same as Exp_NATL but forced by (SST0 + SST2) and (SST0 + SST1 + SST2), respectively. To remove the model spinup period and make sure the initial conditions are the same in each experiment, here we use the last 60 years of Exp_CTRL to restart Exp_NATL, Exp_WP, and Exp_NATL+WP and then calculate the average of 60 members for analysis. The differences among Exp_NATL, Exp_WP, and Exp_CTRL represent the atmospheric response to the North Atlantic and tropical western Pacific SST anomalies separately, and Exp_NATL+WP − Exp_CTRL represents the combined effect of SST forcings over these two different regions during winter 2018/19.

3. Record-high warm winter over southeastern coastal China in 2018/19

Table 1 shows the basic statistical results for temperature at 10 meteorological stations over southeastern coastal China during winter 2018/19, including values of winter and monthly mean temperatures and their deviations. The winter mean temperatures of these 10 stations are much higher than the 40-yr climatology. Six of the 10 stations even have record-breaking temperatures, and 2 other stations (Guangzhou and Nanping) also reach their second highest temperatures. However, the monthly mean temperatures during this winter do not consistently reach their highest record at these stations, except for Xiamen. Many stations have relatively low temperatures in December 2018 when compared with January and February 2019 based on historical records. Similar to the temperature ranking, the averaged deviations of monthly mean temperature in December, January, and February at the 10 stations are +1.4°, +1.9°, and +2.8°C, respectively, suggesting that the mean temperatures over southeastern coastal China are relatively warm during the whole winter, especially in February. Here we also estimate the relative contribution of monthly temperature anomalies to seasonal temperature anomalies in winter 2018/19 for these 10 stations, on average, with the results of 23% in December, 32% in January, and 45% in February. Furthermore, the mean temperature anomalies in February are much higher than in December and January except for Fuzhou, and the rest of the stations show that both the mean temperature and temperature deviation in February increase generally from north to south, especially at Dongfang, the southernmost of the 10 stations, with a mean temperature deviation reaching above +4°C.

Besides the seasonal and monthly time scale, we also consider detailed temperature evolution during winter 2018/19. The daily mean temperature variations and number of extreme days at the 10 stations from December 2018 to February 2019 are shown in Fig. 3. Although the locations of the 10 stations are different, their temperature evolution is quite similar, with correlation coefficients between each all above +0.8, exceeding the 99.9% confidence level based on the Student’s t test. In fact, the temperature variations for all stations do not show persistent warming during the whole winter. Significant cold surges occur in December 2018, when the temperature rapidly drops below the 10th percentile value of daily mean temperature for 1980–2018 (blue line). But for January and February 2019, the temperature at many stations starts to rise and remains above the climatology for 1980–2018 (purple line), even exceeding the 90th percentile value of daily mean temperature for 1980–2018 (red line). Furthermore, the 40-yr mean values of the average number of extreme warm days and extreme cold days in winter at the 10 stations are around 11 and 10 days, respectively (Table 2). But in winter 2018/19, the number of extreme warm days at most stations is much higher than normal, with an increasing trend from north to south. Meanwhile, 8 of the 10 stations have fewer extreme cold days in winter 2018/19 (Fig. 3). As Table 2 shows, many stations have the largest number of extreme warm days during winter 2018/19 on a 40-yr record, such as Xiamen (34 days), Guangzhou (25 days), Yangjiang (35 days), Shantou (35 days), and Dongfang (48 days). On the other hand, there are no extreme cold days at Shantou, Xiamen, or Yongan in winter 2018/19. Therefore, the statistical features of winter temperature at seasonal, monthly, and daily time scales consistently show that extreme warm temperatures occur over southeastern China in winter 2018/19.

Fig. 3.

(left) Time evolution of daily mean temperature (°C) and (right) the number of extreme temperature days at the 10 stations during winter 2018/19. The lines in the left panels represent the daily mean temperature of winter 2018/19 (black), 10th percentile of winter climatology (blue), 90th percentile of winter climatology (red), and winter climatology (purple). (right) The bars in the right panels represent the number of extreme warm days (above 90th percentile; red) and extreme cold days (below 10th percentile; purple) in winter 2018/19 and the number of extreme warm days (above 90th percentile; orange) and extreme cold days (below 10th percentile; blue) in the 40-yr winter climatology.

Fig. 3.

(left) Time evolution of daily mean temperature (°C) and (right) the number of extreme temperature days at the 10 stations during winter 2018/19. The lines in the left panels represent the daily mean temperature of winter 2018/19 (black), 10th percentile of winter climatology (blue), 90th percentile of winter climatology (red), and winter climatology (purple). (right) The bars in the right panels represent the number of extreme warm days (above 90th percentile; red) and extreme cold days (below 10th percentile; purple) in winter 2018/19 and the number of extreme warm days (above 90th percentile; orange) and extreme cold days (below 10th percentile; blue) in the 40-yr winter climatology.

Table 2.

Number of days with extreme daily mean temperature at 10 meteorological stations in winter 2018/19 and 40-yr climatology. Stations are ordered from north to south. Values in parentheses are their ranking among 40 years. T10 days and T90 days represent the daily temperature below the 10th percentile and above the 90th percentile of temperature during the climatological period, respectively.

Number of days with extreme daily mean temperature at 10 meteorological stations in winter 2018/19 and 40-yr climatology. Stations are ordered from north to south. Values in parentheses are their ranking among 40 years. T10 days and T90 days represent the daily temperature below the 10th percentile and above the 90th percentile of temperature during the climatological period, respectively.
Number of days with extreme daily mean temperature at 10 meteorological stations in winter 2018/19 and 40-yr climatology. Stations are ordered from north to south. Values in parentheses are their ranking among 40 years. T10 days and T90 days represent the daily temperature below the 10th percentile and above the 90th percentile of temperature during the climatological period, respectively.

Under global warming, variations in winter temperature anomalies over southeastern coastal China have increased in the past 40 years based on the average of the 10 stations and reanalysis data (Fig. 2b). Consistent with the results shown in Table 1, the winter temperature anomalies for most stations in winter 2018/19 reach their highest level in recent decades. Before the following discussion, we first examine the reliability of reanalysis data over our research region. Here we adopt bilinear interpolation to interpolate the reanalysis data of surface air temperature over this region (108°–120°E, 18°–28°N) to the location of the 10 stations, and then calculate the average of the 10 stations. In Fig. 2b, we find that the interpolated reanalysis data (solid black line) can well capture the variation in winter temperature compared with the observed average at the 10 stations (dashed black line), with a high correlation coefficient of +0.99, exceeding the 99.9% confidence level based on the Student’s t test. Since the higher winter temperature in recent decades may be related to global warming, we estimate the relative contribution of the linear trend portion and nontrend portion to winter temperature anomalies. To estimate their relative contribution, we first detrend the 40-yr raw anomaly time series, and then we can calculate the linear trend portion of the winter temperature anomaly in 2018/19 by subtracting the detrended anomaly from the raw anomaly. After that, we calculate the ratios of the linear trend portion and the detrended anomaly to the raw anomaly in winter 2018/19, respectively, representing the relative contribution of the linear trend portion and nontrend portion to winter temperature anomalies in 2018/19. The contribution of the linear trend portion and nontrend portion to winter temperature anomalies in 2018/19 is 32% and 68%, respectively, suggesting that the nontrend portion is more important. Therefore, we will focus on the climate anomalies with their linear trend removed in the following discussion. Similar results can also be obtained with NCEP reanalysis data (figure not shown). For convenience, we show only the results based on the ERA5 reanalysis data in the following due to their higher resolution. Since the winter temperature over southeastern China is closely associated with the EAWM, the spatial distribution of surface temperature and low-level circulation anomalies in winter 2018/19 is shown in Fig. 4a. Warm surface temperature anomalies are observed over southeastern coastal China, accompanied by low-level southwesterly wind anomalies. Meanwhile, strong positive geopotential height anomalies in the midlevel exist over East Asia, corresponding to a shallower EAT (Fig. 4b). All these circulation patterns indicate that the EAWM is relatively weak in winter 2018/19 compared with the climatology.

Fig. 4.

Spatial patterns of the climate anomalies in winter 2018/19: (a) surface air temperature (shaded; °C) and 850-hPa horizontal winds (vectors; m s−1; wind speeds less than 0.5 m s−1 have been masked out), and (b) 500-hPa geopotential height (shaded; gpm) and its climatology (contours; interval: 100 gpm).

Fig. 4.

Spatial patterns of the climate anomalies in winter 2018/19: (a) surface air temperature (shaded; °C) and 850-hPa horizontal winds (vectors; m s−1; wind speeds less than 0.5 m s−1 have been masked out), and (b) 500-hPa geopotential height (shaded; gpm) and its climatology (contours; interval: 100 gpm).

4. Large-scale conditions for record-high winter temperatures over southeastern coastal China in 2018/19

a. Siberian high

Since the Siberian high can influence the Asian winter climate directly (Ding and Krishnamurti 1987; Ding 1990; Wang and Ding 2006; Lan and Li 2016), we also examine its possible effect during winter 2018/19. Similar to Gong et al. (2001), we define the Siberian high intensity index as the averaged winter sea level pressure (SLP) anomalies over the Siberian region (70°–120°E, 40°–60°N), which is mainly consistent with the high pressure center of the climatological winter SLP over Eurasia in Fig. 5b. Figure 5a shows the variation of the normalized Siberian high intensity index during 1980–2019. The Siberian high index in winter 2018/19 is +0.76, indicating that the Siberian high is stronger than normal. As previous studies have shown (Ding 1990, 2004; Gong et al. 2002), a strong winter Siberian high is more likely associated with cold temperatures over East Asia, which seems unfavorable for extreme warm temperatures over southeastern China during winter 2018/19. However, during winter 2018/19, the spatial pattern of winter surface temperature anomalies over South China (Fig. 4a) is more similar to the EOF2 mode of winter surface temperature (Fig. 1c) but not to EOF1, suggesting that the relationships between the Siberian high and the two different modes of winter surface temperature are different. To examine this hypothesis, we investigate the relationships between the two EOF modes of winter surface temperature over South China and the Siberian high. The spatial distributions of correlation coefficients between winter SLP anomalies and the time series of PC1 and PC2 are shown in Figs. 5b and 5c, respectively. By comparing these two correlation maps, we find that the relationships between the Siberian high and the two EOF modes of winter surface temperature over South China are quite different. The EOF1 mode and the winter SLP anomalies have a large significant negative correlation area over Siberia (Fig. 5b), indicating that when the Siberian high is weak the surface temperature anomalies over South China tend to be warm and when it is strong the anomalies tend to be cold. The EOF2 mode is well related to the SLP anomalies over east-central Siberia (Fig. 5c), however, suggesting that the eastward shift of the Siberian high center is related to the EOF2 mode. In other words, the EOF1 mode is closely associated with the intensity of the Siberian high in its climatological location, while the EOF2 mode is related more to the location of the Siberian high. During winter 2018/19, the spatial distribution of positive SLP anomalies shows a “northwest–southeast” tilting belt over eastern Siberia, indicating the eastward shift of the Siberian high center (Fig. 5d), which well matches the result associated with the EOF2 mode (Fig. 5c). In addition, associated with the eastward shift of the Siberian high, low-level anticyclonic circulation over northern China seems unfavorable for cold-air intrusion toward the south (Fig. 4a). All these results indicate that it is not the abnormal intensity of the Siberian high but its abnormal spatial pattern that exerts an obvious influence on warm temperatures over northeastern Asia and a certain degree of influence on extreme warm temperatures over southeastern China during winter 2018/19. Here we also find that the cold temperature anomalies over inland South China cannot be elucidated with the aforementioned abnormality of the Siberian high in winter 2018/19 (Fig. 1b); we will discuss the possible reason for this phenomenon in a later section.

Fig. 5.

(a) Normalized winter Siberian high index for 1980–2019. Also shown is the distribution of the climatology of winter SLP (contours) and the correlation coefficients (shaded) between the winter SLP anomalies and (b) PC1 and (c) PC2 for the period 1980–2019 and (d) seasonal mean SLP (contours, with interval of 4 hPa) and anomalies (shaded; hPa) in winter 2018/19. The stippling in (b) and (c) indicates that the correlation coefficients exceed the 0.05 significance level.

Fig. 5.

(a) Normalized winter Siberian high index for 1980–2019. Also shown is the distribution of the climatology of winter SLP (contours) and the correlation coefficients (shaded) between the winter SLP anomalies and (b) PC1 and (c) PC2 for the period 1980–2019 and (d) seasonal mean SLP (contours, with interval of 4 hPa) and anomalies (shaded; hPa) in winter 2018/19. The stippling in (b) and (c) indicates that the correlation coefficients exceed the 0.05 significance level.

b. East Asian trough

As we mentioned in section 3, accompanied by strong positive geopotential height anomalies over the climatological location of the EAT, winter mean circulation over southeastern coastal China is controlled by low-level southwesterly anomalies (Figs. 4a,b). To quantitatively represent the abnormality of the EAT associated with EOF2 in Fig. 1c, here we define an EAT morphological index (EATI) as follows:

 
EATI=GPH500(A)0.5×GPH500(B)0.5×GPH500(C).

The EATI is characterized by the difference between the averaged winter 500-hPa geopotential anomalies over regions A (120°–150°E, 25°–40°N), B (90°–120°E, 40°–55°N), and C (150°–180°E, 40°–55°N), which are based on the correlation map of winter 500-hPa geopotential height anomalies with the PC2 time series over East Asia (Fig. 9c), representing the opposite variation between the trough bottom and its adjacent upstream and downstream areas. Figure 6a shows the time series of the normalized EATI for 1980–2019, which is well related to PC2 with a correlation coefficient of +0.65, exceeding the 99.9% confidence level based on the Student’s t test. In winter 2018/19, the EATI is +2.3, while the shape of the EAT is shallow, especially in the bottom of the trough, with stronger zonal circulation compared with climatology (Fig. 6b), suggesting that this shallow EAT seems unfavorable for cold air to move southward in East Asia. To further demonstrate the relationship between EAT morphology and temperature over southeastern China during this winter, the vertical structure of EAT anomalies is shown in Fig. 6c. Positive geopotential height anomalies occupy a deep layer from the surface to the upper level along the East Asian coast, corresponding to the shallower EAT. This weak EAT is directly related to the weak EAWM, partly contributing to the low-level southerly anomalies around 120°E (Fig. 6d), which causes warm temperature anomalies over the East Asian coast with less cold air (Fig. 6c). Meanwhile, such low-level southerly anomalies are also closely associated with ENSO-related low-level anomalous circulation, which we will discuss later. Actually, the EAT may be directly influenced by an anomalous upstream wave train (Fig. 4b); we will discuss the possible influence from the upstream factor on the EAT in a later section. In addition, we consider that the low-level cold temperature anomalies along 105°–110°E (Fig. 6c) are related to the inland cold anomalies over South China, as shown in Fig. 4a. A possible reason for this cooling may be directly related to the low-level cyclonic circulation (Fig. 4b) and anomalous rising motion (Figs. 8c,d) in this region, and physically associated with positive precipitation anomalies (figure not shown) and cloudy weather over inland South China, which cause surface cooling. Furthermore, based on the vertical motion distribution (figure not shown), we find that the upstream wave train may also play a role in affecting this anomalous rising motion, which is similar to the results in Hu et al. (2018) and Chowdary et al. (2019). They suggest that the wave train along the Asian jet in wintertime can influence the East Asian climate. We will discuss the possible source of the wave train in a later section.

Fig. 6.

(a) Normalized winter EAT intensity index for 1980–2019. (b) The 500-hPa geopotential height (red contours, with interval of 100 gpm) and zonal wind anomalies (shaded; m s−1) in winter 2018/19, and the winter climatology of 500-hPa geopotential height (black contours, with interval of 100 gpm). Also shown are longitude–height cross section of climate anomalies averaged over 25°–35° for (c) geopotential height (contours; gpm) and air temperature (shaded; °C) and (d) meridional wind (contours; m s−1). The solid and dashed lines in (d) represent southerly and northerly wind anomalies, respectively.

Fig. 6.

(a) Normalized winter EAT intensity index for 1980–2019. (b) The 500-hPa geopotential height (red contours, with interval of 100 gpm) and zonal wind anomalies (shaded; m s−1) in winter 2018/19, and the winter climatology of 500-hPa geopotential height (black contours, with interval of 100 gpm). Also shown are longitude–height cross section of climate anomalies averaged over 25°–35° for (c) geopotential height (contours; gpm) and air temperature (shaded; °C) and (d) meridional wind (contours; m s−1). The solid and dashed lines in (d) represent southerly and northerly wind anomalies, respectively.

c. Blocking

Many previous studies have suggested that winter temperatures over southeastern China are directly related to cold-air outbreaks to the south from Siberia via the EAWM, and the frequency of its upstream blocking high is closely related to the frequency of cold surges (Joung and Hitchman 1982; Ding 1990; Zhou et al. 2009; Wu and Leung 2009; Cheung et al. 2012). Due to this relationship between the blocking high and cold surges, we estimate the frequency of blocking highs over the Northern Hemisphere. Figure 7a shows the blocking frequency over the Northern Hemisphere in winter 2018/19 and its climatology for 1980–2019. As this figure shows, except for Europe and the North Pacific coast, blocking highs occur less frequently in winter 2018/19 over the Northern Hemisphere, especially in the longitudinal band of 30°–90°E, which is the key region that influences cold surges in China (Ji et al. 2008; Wu and Leung 2009; Zhou et al. 2009; C. Wang et al. 2010; Cheung et al. 2012), consistent with the significant correlation region between PC1 in Fig. 1b and blocking frequency (Fig. 7b). But in winter 2018/19, the temperature anomalies over South China dominate with the EOF2 mode, which is related to the blocking frequency in the longitudinal band of 130°E–160°W (Fig. 7b). During winter 2018/19, the lower blocking frequency when compared with climatology over the Aleutian region (130°E–160°W) tends to contribute to the negative geopotential height anomalies in the eastern part of the EAT (Fig. 7c), making the EAT flatter (Fig. 6b), which is unfavorable for cold-air intrusions from the north. Although blocking activity occurs on a synoptic time scale, we consider that it can partly influence seasonal mean temperature via an accumulation effect. Therefore, the lower frequency of blocking highs over the Aleutian region during winter 2018/19 is related to the warmer temperatures over southeastern China by affecting the EAT.

Fig. 7.

(a) Blocking frequency for the Northern Hemisphere midlatitudes in winter 2018/19 (red line) and the climatology for 1980–2019 (black line). The blocking frequency is defined as the percentage of blocking days per season. The error bars represent the standard deviation of the blocking frequency. (b) Distribution of correlation coefficients of blocking frequency for the Northern Hemisphere with PC1 (black line) and PC2 (red line). The dashed lines represent the 0.05 significance level. (c) The 500-hPa geopotential height along 60°N in winter 2018/19 (red line) and the winter climatology for 1980–2019 (black line). The vertical orange belt highlights the longitudinal range from 130°E to 160°W.

Fig. 7.

(a) Blocking frequency for the Northern Hemisphere midlatitudes in winter 2018/19 (red line) and the climatology for 1980–2019 (black line). The blocking frequency is defined as the percentage of blocking days per season. The error bars represent the standard deviation of the blocking frequency. (b) Distribution of correlation coefficients of blocking frequency for the Northern Hemisphere with PC1 (black line) and PC2 (red line). The dashed lines represent the 0.05 significance level. (c) The 500-hPa geopotential height along 60°N in winter 2018/19 (red line) and the winter climatology for 1980–2019 (black line). The vertical orange belt highlights the longitudinal range from 130°E to 160°W.

d. 2018/19 El Niño

During winter 2018/19, the ONI intensity is +0.8, indicating a moderate El Niño event. In general, when El Niño events occur, the EAT tends to weaken, resulting in a weak EAWM (Chen et al. 2000), which is also consistent with our results in the previous sections. In this section, we will discuss the possible link between the circulation patterns associated with southeastern China and the 2018/19 El Niño. Figure 8a shows the streamfunction and rotational wind anomalies at 850 hPa and SST anomalies in winter 2018/19. As in previous studies (Chen et al. 2000; Wang et al. 2000), the equatorial central and eastern Pacific warming produces anomalous convective heating, inducing an anomalous low-level cyclonic circulation over the tropical central Pacific on its northwest (southwest) side as a “Gill-type” Rossby wave response (Fig. 8a). Therefore, the anomalous cyclone enhances the trade winds over the western Pacific, thus resulting in cooling SST anomalies in the western Pacific. Furthermore, the tropical western Pacific cooling SST anomalies tend to induce anticyclonic circulation anomalies over the northwest Pacific as a Rossby wave response, which may also contribute to the southwesterly anomalies over South China. The anomalous upward motion associated with warm SST anomalies occurs over the tropical central Pacific, while the associated low-level (upper level) convergence (divergence) center can be found in the same region (Figs. 8b,c). The opposite vertical structure in the troposphere can also be found over the western North Pacific associated with the cooling SST anomalies. To show this possible remote connection clearly, the vertical profile of circulation in the troposphere along these two vertical motion centers is shown in Fig. 8d. There is an overturning cell across the tropical Pacific, connecting the rising motion over the equatorial central Pacific and the sinking motion over the tropical western Pacific, which is consistent with the low-level circulation pattern (Fig. 8a). All these results suggest that El Niño may play a role in influencing the circulations over South China via teleconnection during winter 2018/19, which weakens the EAWM.

Fig. 8.

Spatial patterns of winter 2018/19 climate anomalies: (a) SST (shaded; °C), 850-hPa streamfunction (contour interval of 0.5 × 106 s−1), and rotational wind (vectors; m s−1), (b) SST (shaded; °C), 850-hPa velocity potential (contour interval of 0.5 × 106 m2 s−1), and divergent wind (vectors; m s−1), and (c) 500-hPa vertical velocity (shaded; 10−2 Pa s−1), 200-hPa velocity potential (contour interval of 0.5 × 106 m2 s−1), and divergent wind (vectors; m s−1). (d) Vertical cross section of anomalous airflow streamline along the solid blue line from point A (110°E, 30°N) to point B (180°, 0°) in (c). The horizontal and vertical components in (d) are divergent wind and vertical velocity anomalies, respectively. The vertical velocity anomalies in (c) and (d) are multiplied by −1.

Fig. 8.

Spatial patterns of winter 2018/19 climate anomalies: (a) SST (shaded; °C), 850-hPa streamfunction (contour interval of 0.5 × 106 s−1), and rotational wind (vectors; m s−1), (b) SST (shaded; °C), 850-hPa velocity potential (contour interval of 0.5 × 106 m2 s−1), and divergent wind (vectors; m s−1), and (c) 500-hPa vertical velocity (shaded; 10−2 Pa s−1), 200-hPa velocity potential (contour interval of 0.5 × 106 m2 s−1), and divergent wind (vectors; m s−1). (d) Vertical cross section of anomalous airflow streamline along the solid blue line from point A (110°E, 30°N) to point B (180°, 0°) in (c). The horizontal and vertical components in (d) are divergent wind and vertical velocity anomalies, respectively. The vertical velocity anomalies in (c) and (d) are multiplied by −1.

5. Influence of North Atlantic and tropical western Pacific SST anomalies on the East Asian trough in winter 2018/19

During winter 2018/19, the geopotential height anomalies at 500 hPa show a wave train–like pattern over Eurasia (Fig. 4b), which is directly related to the EAT abnormality. Since the EAT can be influenced by an upstream wave train (Qiao and Feng 2016), the possible upstream influencing factors accounting for the wave train during this winter need to be investigated. Figure 9a shows the horizontal wave activity flux (WAF) and its divergence (Plumb 1985) at 300 hPa during winter 2018/19. As this figure shows, there is a divergence center of WAF over the North Atlantic, suggesting that the wave train seems to originate from the same region and propagate downstream via the upper-level jet over Eurasia, which is consistent with the pattern of geopotential height anomalies at 300 hPa (Fig. 9b). In addition, we also find an analogous link between the EOF2 mode and the wave train across Eurasia (Fig. 9c), suggesting that such a wave train circulation pattern in winter 2018/19 is closely related to the EOF2 mode. Meanwhile, the North Atlantic SST anomalies show a “negative–positive–negative” distribution from south to north during winter 2018/19. Some studies have pointed out that northwest Atlantic SST anomalies can influence the downstream climate via a wave train (Li 2004; Li and Gu 2010; Han et al. 2011). Here we speculate whether this “tripole pattern” of SST anomalies (Czaja and Frankignoul 2002) may have impacted downstream circulation by forcing out a wave train during winter 2018/19. To examine this hypothesis, we perform a numerical experiment (Exp_NATL) using the SPEEDY model as described in detail in section 2c. The atmospheric circulation response to the North Atlantic SST anomaly forcing (Fig. 10a) during winter is shown in Fig. 10b. As this result indicates, the North Atlantic SST anomalies can induce a wave train that propagates from the North Atlantic to East Asia (Fig. 10b). Meanwhile, there is a low-level anticyclone over East Asia, corresponding to the southerly wind in southeastern China. These results are consistent with the observed circulation patterns during winter 2018/19 (Fig. 9b), highlighting that the tripole pattern of the North Atlantic SST anomalies can influence the EAT via a wave train, which makes the EAT shallow and flat, weakening the intensity of the EAWM. On the other hand, we also examine the possible role of the western Pacific SST anomalies (Fig. 11a) during winter 2018/19 in Exp_WP as a local forcing. Similarly, the low-level southerly wind anomalies over southeastern China and the upper-level positive geopotential height anomalies over East Asia can also be found in Exp_WP (Fig. 11b), suggesting that both the tripole pattern of North Atlantic SST anomalies and the tropical western Pacific SST cooling anomalies may affect the EAT. Meanwhile, the combined effect of these two SST forcings (Fig. 12b) is also quite similar to the observations (Fig. 4). Moreover, although both of these SST forcings can affect the EAT, the tripole pattern North Atlantic SST anomalies influence the downstream circulation pattern as a remote forcing, while the tropical western Pacific SST cooling anomalies can influence the low-level circulation pattern over southeastern China more locally, suggesting that we need to consider their relative contribution to the EAT and the climate anomalies over southeastern China. Here, we compare the EATI and the response intensity of the low-level southerly wind anomalies and low-level air temperature over southeastern China in Exp_NATL and Exp_WP, respectively. The ratio for the EATI, which is defined in section 4b, for the temperature and southerly wind anomalies in Exp_NATL and Exp_WP is about 2.7 to 1, 1 to 2.2, and 1 to 3, respectively. These results indicate that the North Atlantic SST anomalies influence the EAT more than the tropical western Pacific SST anomalies, but the western Pacific SST anomalies play a more important role in southeastern China climate anomalies compared with the tripole pattern North Atlantic SST anomalies in winter 2018/19.

Fig. 9.

(a) SST anomalies (shaded; °C), 300-hPa wave activity flux (vectors; m2 s−2), and its divergence (contours of interval 2 × 10−6 m s−2) in winter 2018/19. (b) The 300-hPa geopotential height anomalies (shaded; gpm) and wave activity flux (vectors; m2 s−2) in winter 2018/19. (c) Distribution of the correlation coefficients between the winter geopotential height anomalies at 300 hPa and PC2 for the period 1980–2019. The brown and black contours in (c) indicate the area exceeding the 0.05 and 0.1 significance levels, respectively.

Fig. 9.

(a) SST anomalies (shaded; °C), 300-hPa wave activity flux (vectors; m2 s−2), and its divergence (contours of interval 2 × 10−6 m s−2) in winter 2018/19. (b) The 300-hPa geopotential height anomalies (shaded; gpm) and wave activity flux (vectors; m2 s−2) in winter 2018/19. (c) Distribution of the correlation coefficients between the winter geopotential height anomalies at 300 hPa and PC2 for the period 1980–2019. The brown and black contours in (c) indicate the area exceeding the 0.05 and 0.1 significance levels, respectively.

Fig. 10.

(a) SST anomaly forcing pattern over the North Atlantic for Exp_NATL. (b) Differences between Exp_NATL and Exp_CTRL in 300-hPa geopotential height (shaded; gpm) and 850-hPa wind (vectors; m s−1).

Fig. 10.

(a) SST anomaly forcing pattern over the North Atlantic for Exp_NATL. (b) Differences between Exp_NATL and Exp_CTRL in 300-hPa geopotential height (shaded; gpm) and 850-hPa wind (vectors; m s−1).

Fig. 11.

(a) SST anomaly forcing pattern over the western Pacific for Exp_WP. (b) Differences between Exp_WP and Exp_CTRL in 500-hPa geopotential height (shaded; gpm) and 850-hPa wind (vectors; m s−1).

Fig. 11.

(a) SST anomaly forcing pattern over the western Pacific for Exp_WP. (b) Differences between Exp_WP and Exp_CTRL in 500-hPa geopotential height (shaded; gpm) and 850-hPa wind (vectors; m s−1).

Fig. 12.

As in Fig. 10, but for Exp_NATL+WP.

Fig. 12.

As in Fig. 10, but for Exp_NATL+WP.

6. Summary and discussion

In this paper, we investigate the characteristics of the extreme warm temperatures over southeastern coastal China during winter 2018/19, and the associated large-scale atmospheric and oceanic background conditions. Under global warming, the mean temperatures over southeastern China during this winter reach record highs at most of the stations we studied as well as in the reanalysis data for 1980–2019 (Fig. 2b). Many stations have a positive temperature deviation of more than +2°C in winter 2018/19, compared to the climatology. Besides the higher mean temperatures, the numbers of extreme warm and cold days for daily mean temperature at many stations also reach record-breaking highs. The climatology value for both extreme warm and cold days at the 10 stations during winter is nearly 11 days. But during winter 2018/19, 5 stations have more than 30 extreme warm days, and Dongfang has 48 days. On the other hand, there are fewer extreme cold days at most stations, and at Xiamen, Shantou, and Yongan, no extreme cold days occur during winter 2018/19. All these statistical results confirm that a rare climatic extreme warm event occurred over southeastern China in winter 2018/19.

Furthermore, the large-scale background conditions related to warm temperatures during winter 2018/19 are also investigated. First, an eastward shift of the Siberian high center occurs in winter 2018/19 (Fig. 5d), resulting in less cold-air intrusion toward the south due to low-level anticyclonic circulation over northern China (Fig. 4a), which is favorable for the extreme warm winter over southeastern China. Meanwhile, the weak EAWM in winter 2018/19 is also related to the shallower EAT (Fig. 6), which seems to be influenced by an upstream wave train, with positive geopotential height anomalies over East Asia and strong low-level southerly wind anomalies over southeastern China. In addition, the lower blocking frequency over the Aleutian region compared with climatology during winter 2018/19 (Fig. 7a) partly contributes to the negative geopotential height anomalies in the eastern part of the EAT, making the EAT flatter (Figs. 7c and 6b). Last but not least, the 2018/19 El Niño event also plays a role in influencing the EAWM, inducing an anomalous anticyclone over the western North Pacific with strong southerly wind anomalies over southeastern China, which weakens the EAWM (Fig. 8). Overall, these factors provide favorable conditions for the extreme warm event in southeastern China in winter 2018/19.

In addition, the influence of both the SST anomalies of the tripole pattern over the North Atlantic and the cooling over the tropical western Pacific on southeastern China climate anomalies during winter 2018/19 is examined by AGCM experiments. On the one hand, the “negative–positive–negative” SST anomalies in the North Atlantic can induce a wave train to propagate eastward across Eurasia, making the downstream EAT shallower, which weakens the intensity of the EAWM over southeastern China (Fig. 10b). On the other hand, the tropical western Pacific cooling anomalies not only directly cause low-level southerly wind anomalies over southeastern China via local forcing but also induce a wave train to propagate toward mid- and high latitudes, thus flattening the EAT, which is unfavorable for a cold-air outbreak (Fig. 11b). Moreover, the SST anomalies over the North Atlantic have a stronger influence on the EAT compared with the tropical western Pacific. But the tropical western Pacific SST cooling anomalies play a more important role in the extreme warm winter in 2018/19 over southeastern China when compared with the upstream tripole pattern SST anomalies over the North Atlantic.

In this study, we focus on the impact of North Atlantic and tropical western Pacific SST anomaly forcing on the circulation response patterns, which are directly related to the EAWM. However, other factors may also play a role in this extreme warm winter. First, both the AO and NAO are in their positive phase during winter 2018/19 (the AO/NAO index we used can be downloaded from the CPC, available online at https://www.cpc.ncep.noaa.gov/), which causes a low frequency of Ural–Siberian blocking via a teleconnection pattern, corresponding to a weak Siberian high and a weak EAWM (Wu and Huang 1999; Jeong and Ho 2005; Chen and Zhou 2012; Li et al. 2012). Meanwhile, the tripole-pattern SST anomalies over the North Atlantic may also be related to the NAO due to air–sea interaction (Czaja and Frankignoul 2002). In addition, the anomalous low-level cyclonic circulation and cold temperature anomalies over the Arctic in summer 2018, which weaken the negative lagged feedback of Arctic sea ice loss to the atmosphere, may also contribute to the weakening of the EAWM (Francis et al. 2009; Wu et al. 2015). Whether the relationship or the driving mechanism of EAWM-influencing factors will change under global warming is worth consideration. As more and more extreme events have occurred in recent decades, this extreme case study provides implications for climate prediction, suggesting that more detailed analysis is required regarding the possible mechanisms accounting for winter climate over East Asia. For example, the relative contribution of these factors to extreme warm winters over southeastern China needs to be investigated in future work.

Acknowledgments

This work is supported by the National Natural Science Foundation of China Grant (41675062), the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU 11305715 and 11335316), the National Natural Science Foundation of China Grant (41530530), 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).

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