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Characteristics and Mechanisms of Persistent Wet–Cold Events with Different Cold-air Paths in South China


doi: 10.1007/s00376-023-3088-4

  • We investigate the characteristics and mechanisms of persistent wet–cold events (PWCEs) with different types of cold-air paths. Results show that the cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part. The pattern of single-station frequency of the PWCEs are “Yangtze River (YR) uniform” and “east–west inverse”. The YR uniform pattern is the dominant mode, so we focus on this pattern. The cold-air paths for PWCEs of the YR uniform pattern are divided into three types—namely, the west, northwest and north types—among which the west type accounts for the largest proportion. The differences in atmospheric circulation of the PWCEs under the three types of paths are obvious. The thermal inversion layer in the lower troposphere is favorable for precipitation during the PWCEs. The positive water vapor budget for the three types of PWCEs mainly appears at the southern boundary.
    摘要: 对中国南方不同类型冷空气路径下的持续性湿冷事件的特征和机理进行研究,结果表明:南方西部地区持续性湿冷事件的单站频次高于东部地区。主要表现为“长江流域一致型”和“东西反向型”两个模态。 “长江流域一致型”为主要模态,因此我们重点研究这一模态。“长江流域一致型”持续性湿冷事件的冷空气路径分为西路、西北路和北路三类,其中西路类型占比最大。三类路径下持续性湿冷事件的大气环流特征明显不同。对流层低层的逆温层有利于降水的形成。三类路径湿冷事件的正水汽收支主要出现在南边界。
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  • Figure 1.  Frequency distribution of single-station PWCEs from 1951 to 2019.

    Figure 2.  Spatial distribution of the EOF eigenvector of the (a) first and (c) second patterns and their (b, d) respective time coefficients for the single-station frequency of PWCEs. The blue and red lines indicate the linear trends.

    Figure 3.  Accumulated frequency (red bars) and mean days (blue bars; units: d) of PWCEs of YR uniform pattern from 1951 to 2019.

    Figure 4.  Distribution of the precipitation anomaly (shaded; units: mm d−1) and temperature anomaly (contours; units: °C) of the PWCEs of YR uniform pattern. The red dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level. The yellow star indicates the border of Hunan Province and Hubei Province.

    Figure 5.  (a) The SSE of the cold-air paths of the PWCEs of YR uniform pattern and their (b) north paths, (c) west paths, and (d) northwest paths.

    Figure 6.  The 500-hPa geopotential height (contours; units: gpm) and its anomalies (shaded areas; units: gpm) for the PWCEs of YR uniform pattern with the (a–d) west, (e–h) northwest, and (i–l) north type of cold-air path, from day −4 to day L. The white dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level.

    Figure 7.  The SLP (contours; units: hPa) and its anomalies (shaded areas; units: hPa) for the PWCEs of YR uniform pattern with the (a–d) west, (e–h) northwest, and (i–l) north type of cold-air path, from day −4 to day L. The black dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level.

    Figure 8.  The 300-hPa geopotential height field anomalies (shaded areas; units: gpm) and wave activity flux (vectors; units: m2 s−2) for the PWCEs of YR uniform pattern with the (a–e) west, (f–j) northwest, and (k–o) north types of cold-air path, from day −10 to day 0. The green dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level.

    Figure 9.  Composite wind anomalies (vectors; units: m s−1) for the PWCEs of YR uniform pattern with the (a, d, g) west, (b, e, h) northwest, and (c, f, i) north types of cold-air path, at (a–c) 700 hPa, (d–f) 850 hPa, and (g–i) 925 hPa. Red vectors show that the composite wind anomalies pass the significance test at the 95% confidence level. The grey shading represents the orography.

    Figure 10.  Pressure–latitude cross sections (averaged over 99°E–122°E) of the composite temperature anomalies (shaded areas; units: °C) and wind anomalies (vectors; units: m s−1) for the PWCEs of YR uniform pattern with the (a) west, (b) northwest, and (c) north types of cold-air path. The white dotted areas indicate that the temperature anomaly values pass the significance test at the 95% confidence level.

    Figure 11.  The vertically integrated water vapor flux anomalies (vectors; units: kg m−1 s−1) and vapor flux divergence anomalies (shaded areas; units: 10−5 kg m−2 s−1) for the PWCEs of YR uniform pattern with the (a) west, (b) northwest, and (c) north types of cold-air path. The black boxed areas denote South China. The grey shading represents the orography. (d–f) Water vapor budget (units: 100 kg hPa−1 s−1) for the PWCEs with the (d) west, (e) northwest, and (f) north types of cold-air path at each boundary of the study area.

    Table 1.  The selected PWCEs.

    Year Start day
    (YY-MM-DD)
    End day
    (YY-MM-DD)
    Day with the strongest
    intensity
    Duration (days) Path type
    1954 1954−12−01 1954−12−14 1954−12−09 14 west
    1954 1954−12−23 1955−01−09 1954−12−29 18 north
    1963 1964−01−11 1964−02−06 1964−01−23 27 northwest
    1979 1980−01−18 1980−02−16 1980−02−11 30 west
    1980 1981−01−20 1981−02−02 1981−01−26 14 west
    1981 1982−02−02 1982−02−16 1982−02−05 15 west
    1982 1983−01−06 1983−01−15 1983−01−09 10 west
    1983 1984−01−15 1984−02−11 1984−01−20 28 northwest
    1988 1989−02−15 1989−02−25 1989−02−23 11 northwest
    1989 1990−01−25 1990−02−07 1990−02−01 14 west
    1991 1991−12−22 1991−12−29 1991−12−27 8 north
    1992 1993−01−08 1993−01−23 1993−01−17 16 west
    1995 1996−02−17 1996−02−29 1996−02−19 13 northwest
    1995 1996−01−11 1996−01−22 1996−01−18 12 west
    1996 1997-02−01 1997-02−21 1997-02−07 21 northwest
    1997 1998−01−11 1998−01−26 1998−01−23 16 west
    1999 2000−01−19 2000−02−04 2000−01−28 17 north
    2004 2004−12−21 2005−01−01 2004−12−27 12 west
    2007 2008−01−02 2008−02−03 2008−01−03 33 west
    2004 2005−01−07 2005−01−14 2005−01−10 8 west
    2010 2010−12−30 2011−01−15 2011−01−03 17 northwest
    2010 2011−01−17 2011−01−22 2011−01−18 6 west
    2012 2013−01−02 2013−01−12 2013−01−04 11 northwest
    2014 2014−02−05 2014−02−15 2014−02−07 11 west
    2015 2016−01−19 2016−01−26 2016−01−24 8 north
    2017 2018−01−24 2018−02−10 2018−01−28 18 northwest
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Manuscript received: 15 May 2023
Manuscript revised: 27 September 2023
Manuscript accepted: 24 October 2023
通讯作者: 陈斌, bchen63@163.com
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Characteristics and Mechanisms of Persistent Wet–Cold Events with Different Cold-air Paths in South China

    Corresponding author: Xiaojuan SUN, sxjzy709@nuist.edu.cn
  • 1. Key Laboratory of Meteorological Disaster, Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2. School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 3. Qianxinan Bouyei and Miao Autonomous Prefecture Meteorological Bureau of Guizhou Province, Xingyi 562400, China

Abstract: We investigate the characteristics and mechanisms of persistent wet–cold events (PWCEs) with different types of cold-air paths. Results show that the cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part. The pattern of single-station frequency of the PWCEs are “Yangtze River (YR) uniform” and “east–west inverse”. The YR uniform pattern is the dominant mode, so we focus on this pattern. The cold-air paths for PWCEs of the YR uniform pattern are divided into three types—namely, the west, northwest and north types—among which the west type accounts for the largest proportion. The differences in atmospheric circulation of the PWCEs under the three types of paths are obvious. The thermal inversion layer in the lower troposphere is favorable for precipitation during the PWCEs. The positive water vapor budget for the three types of PWCEs mainly appears at the southern boundary.

摘要: 对中国南方不同类型冷空气路径下的持续性湿冷事件的特征和机理进行研究,结果表明:南方西部地区持续性湿冷事件的单站频次高于东部地区。主要表现为“长江流域一致型”和“东西反向型”两个模态。 “长江流域一致型”为主要模态,因此我们重点研究这一模态。“长江流域一致型”持续性湿冷事件的冷空气路径分为西路、西北路和北路三类,其中西路类型占比最大。三类路径下持续性湿冷事件的大气环流特征明显不同。对流层低层的逆温层有利于降水的形成。三类路径湿冷事件的正水汽收支主要出现在南边界。

    • Densely populated South China is vulnerable to cold events during the winter. Persistent wet–cold events (PWCEs) often occur with precipitation and low temperatures severely affecting power infrastructure, agriculture, transportation, and human health. For example, in January 2008, a freezing rain and snow disaster weather process lasting for more than 20 days caused socioeconomic losses of up to 151.65 billion yuan in South China (Bueh et al., 2008; Tao and Wei, 2008; Hu et al., 2017). In addition, severe PWCEs also occurred in South China in 2016 and 2018 (Liao et al., 2018; Sun et al., 2019; Qin and Li, 2020). Therefore, the study of PWCEs is of great significance for disaster prevention and mitigation.

      Unlike traditional cold weather, PWCEs are accompanied by both precipitation and low temperatures. Studies have shown that the average duration of ordinary cold-wave weather processes is 4.6 days (Bueh et al., 2018), while PWCEs last longer, with precipitation and cooling co-occurring, causing a greater impact on local populations. More mature theories have been developed for research on cold waves, including cold-air sources, paths, and circulation causes (Tao, 1959; Ding and Krishnamurti, 1987). Cai et al. (2019) used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to track and cluster the paths of autumn cold waves affecting North China. They classified the cold-air paths of cold waves into northwest, west, and north types, and analyzed the circulation characteristics of the different types. By tracking the 24-h temperature change of eight persistent low-temperature events in southern China, Peng and Sun (2017) indicated that the cold air of persistent low-temperature processes affecting South China is dominated by the west type. However, it is necessary to depict the cold-air paths of the PWCEs affecting South China.

      In the context of global warming and the weakening of the East Asian winter monsoon, many previous studies have focused on extreme high temperatures (Lu et al., 2020; Wang et al., 2021) or summer heavy precipitation (Li et al., 2016a, b), while less research has been conducted on extreme cold events. Research on persistent disastrous weather in winter was not emphasized until the severe freezing rain and snow disaster in South China in 2008. Studies have indicated that integrated circulation anomalies directly caused the freezing rain and snow disaster in 2008, including the anomalously strong blocking high over the Ural Mountains to Lake Baikal, the northward position of the western Pacific subtropical high, the anomalously strong south branch trough, and the anomalously deepened East Asian trough (Ding et al., 2008; Gao et al., 2008; Gu et al., 2008; Li et al., 2008). Liao et al. (2020) compared the differences between the events in 2016 and 2008 from the perspective of different circulation dynamics. In addition, studies have shown that persistent freezing rain and snow events affecting China are associated with the Arctic Oscillation (Liao et al., 2018). The large-scale tilted ridge and trough in Eurasia are key circulation features for the formation and maintenance of extensive and persistent low-temperature events in China (Fu and Bueh, 2013; Bueh et al., 2018). Also, persistent freezing rain and snow processes in South China show regional characteristics (Sun et al., 2022). Considering that research on PWCEs has mostly been based on individual cases, it is necessary to comprehensively investigate the circulation characteristics of PWCEs with long-term data, with emphasis on their different cold-air paths.

      In this research, we analyze the characteristics of PWCEs in South China in winter from 1951 to 2019 and discuss the differences in the high-latitude circulations and the configuration of the subtropical circulation system for the PWCEs with different cold-air paths. The remainder of the paper is organized as follows. The data and methods are described in section 2. Section 3 presents the characteristics, circulation characteristics, and water vapor conditions of the PWCEs with different types of cold-air paths. Section 4 provides the main conclusions.

    2.   Data and methods
    • The data used in this study include the daily air temperature and precipitation data from 753 stations during 1951–2019 provided by the National Meteorological Center of the China Meteorological Administration. The daily reanalysis dataset used in this research is from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR), with a horizontal resolution of 2.5° × 2.5°, including daily air temperature, geopotential height, sea level pressure (SLP), wind, and specific humidity fields. The six-hour NCEP–NCAR reanalysis data are used for the cold-air path tracking, including three-dimensional wind fields, temperature, geopotential height, vertical velocity, and surface pressure, with a horizontal resolution of 2.5° × 2.5°. The climatology of physical quantities is obtained for the winters of 1980–2010.

    • With reference to studies on extreme low temperature (Cai et al., 2020), widespread and persistent low temperature (Peng and Bueh, 2011), and widespread freezing rain events (Peng et al., 2021), extremity and persistence are considered for the definition of PWCEs in South China, and both low temperature and precipitation are emphasized. The single-station and regional PWCEs in South China are defined as follows. First, we calculate the 5th percentile values of the daily average temperature at a station in South China in December, January and February of 1951–2020, and the three numbers are averaged as the low-temperature threshold at this station. The low-temperature threshold in South China is the regional average of single-station low-temperature thresholds. Second, if the average temperature at a station on a certain day is less than or equal to the low-temperature threshold in South China, precipitation (including snowfall) is observed at this station, and both of these conditions last for more than 3 days, this weather event is regarded as a single-station PWCE. Finally, when single-station PWCEs occur simultaneously at more than 20% of stations in a region, it is considered a regional PWCE.

    • The HYSPLIT model (Draxler and Hess, 1998) is used to track the cold-air paths of the PWCEs. The simulation method of the model is performed by integrating the spatial and temporal position vector of a mass point on its moving path. The moving position of a mass point takes the average velocity of the initial position and the first guessed position. Assuming that the cold air mass can be replaced by a mass point, the moving path of the cold air mass can be replaced by the trajectory of the mass point (Park et al., 2011; Wang et al., 2017). The HYSPLIT method has been successfully used to track the cold-air paths of cold waves affecting North China (Cai et al., 2019; Cai, 2019). In this study, the PWCEs are tracked backward for 7 days from the day with the strongest PWCE intensity, and the station with the lowest daily average temperature in the region is chosen as the starting point. The tracked trajectories are then classified.

    • The tracked cold-air paths are classified by using the k-means clustering method, wherein the k-means algorithm divides a given sample set into k clusters based on the distance between samples. First, the centers of the k clusters are randomly selected, and all samples are assigned to the nearest centers. The distance between the sample points and the center is calculated by using the Euclidean distance, and the positions of cluster centers are recalculated by the average distance of the cluster sample points. In this way, sample points are redistributed, and cluster centers are re-established. Iterations are performed until the centers of k clusters are fixed. The number (k) of clusters needs to be given before clustering, which is determined by the test of the intra-cluster sum of squared error (SSE).

    • The whole layer water vapor transport flux is calculated by Eq. (1):

      where $ q $ indicates the specific humidity (g kg−1), $ V $ the horizontal wind velocity (m s−1), and $ g $ the acceleration of gravity (9.8 m s−2).

      The water vapor budget at each boundary can be obtained by Eq. (2):

      where $ l $ represents the perimeter of the calculated area, and $ {Q}_{n} $ denotes the water vapor transport flux at each boundary.

    • The 2D wave activity flux (Takaya and Nakamura, 2001) can be expressed as

      where W denotes the horizontal wave activity flux; U = (U, V) is the base flow; a, φ and λ are the Earth’s radius, Earth’s rotation rate, and latitude and longitude, respectively; and $\psi $ indicates the stream function.

    3.   Results
    • According to the definition, the single-station PWCEs in South China during the winters of 1951–2019 are selected. From Fig. 1 (the black boxed area is the key area where the events occurred, from 25°–35°N and 99°–122°E), it can be seen that the western part of South China is the high-frequency area of PWCEs, which range from 50 to 200, and the eastern part has a low frequency, which are all less than 50 times. The cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part. This result is consistent with the findings of Sun et al. (2022).

      Figure 1.  Frequency distribution of single-station PWCEs from 1951 to 2019.

      In order to recognize the main spatial distribution of the PWCEs, we perform an EOF analysis on their single-station occurrence frequency. The first modal variance contribution is 28.60%, and the second is 14.68%. Because the cumulative variance contribution of the first two modes is 43.3%, which already explains a larger part of the variance, only the first two modes are discussed here. Figure 2a shows that the first mode is “Yangtze River (YR) uniform pattern”. From the perspective of the entire study period, decreasing trends are perceptible in the YR uniform pattern (Fig. 2b). However, the separation of the study period by years with an abrupt transition reveals an increasing trend in the occurrence number of the PWCEs since 1980 (Fig. 2b). The second mode is the “east–west reverse pattern” (Fig. 2c), which also shows an overall an increasing trend (Fig. 2d). Since the YR uniform pattern accounts for the largest variance contribution and shows an increasing trend after 1980, we focus on for this pattern for in-depth study and analysis below. The east–west reverse pattern of the events will be analyzed in detail later.

      Figure 2.  Spatial distribution of the EOF eigenvector of the (a) first and (c) second patterns and their (b, d) respective time coefficients for the single-station frequency of PWCEs. The blue and red lines indicate the linear trends.

      Based on the definition of a regional event, we count the PWCEs of the YR uniform pattern from 1951 to 2019. The year-by-year frequency shows that such PWCEs occurred 26 times, with an average duration of 15.7 days (Fig. 3). The events occurred only 3 times before 1980, while 23 occurred after 1980, which may be related to sudden changes in global warming.

      Figure 3.  Accumulated frequency (red bars) and mean days (blue bars; units: d) of PWCEs of YR uniform pattern from 1951 to 2019.

    • Precipitation and temperature anomalies are analyzed for the PWCEs of YR uniform pattern. As shown in Fig. 4, the center of negative temperature values for the PWCEs is at the border of Hunan Province and Hubei Province (the yellow star), with a temperature change of −4.9°C. In addition, the area with large positive anomaly values of precipitation is in the Jianghuai River Basin, with a center value of about 2.5 mm. The precipitation and temperature anomalies for the PWCEs are centered in the east.

      Figure 4.  Distribution of the precipitation anomaly (shaded; units: mm d−1) and temperature anomaly (contours; units: °C) of the PWCEs of YR uniform pattern. The red dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level. The yellow star indicates the border of Hunan Province and Hubei Province.

    • The HYSPLIT model is used to track the cold-air paths of the 26 PWCEs (Table 1), and the k-means clustering algorithm is used to classify these paths. The optimal number of clusters is selected by the intra-cluster SSE. From Fig. 5a, it can be seen that the decreasing trend of the SSE becomes slower when the number of clusters is 3. Therefore, the cold-air paths of the PWCEs are divided into three categories. According to the main three directions of cold air invading the eastern part of South China, the paths are classified as north, west, and northwest paths. In the north path, the cold air moves southeastward over Lake Baikal, passes by northern China, and subsequently invades southern China (Fig. 5b); in the west path, after passing by Xinjiang, the cold air invades Qinghai and Gansu, before finally reaching southern China (Fig. 5c); and in the northwest path, the cold air passes by the northern part of Xinjiang, the central part of Inner Mongolia, as well as Shanxi, and then reaches southern China (Fig. 5d). The percentages of the north, west and northwest paths are 15.3%, 53.8% and 30.9%, respectively, among which the west path accounts for the largest proportion.

      Year Start day
      (YY-MM-DD)
      End day
      (YY-MM-DD)
      Day with the strongest
      intensity
      Duration (days) Path type
      1954 1954−12−01 1954−12−14 1954−12−09 14 west
      1954 1954−12−23 1955−01−09 1954−12−29 18 north
      1963 1964−01−11 1964−02−06 1964−01−23 27 northwest
      1979 1980−01−18 1980−02−16 1980−02−11 30 west
      1980 1981−01−20 1981−02−02 1981−01−26 14 west
      1981 1982−02−02 1982−02−16 1982−02−05 15 west
      1982 1983−01−06 1983−01−15 1983−01−09 10 west
      1983 1984−01−15 1984−02−11 1984−01−20 28 northwest
      1988 1989−02−15 1989−02−25 1989−02−23 11 northwest
      1989 1990−01−25 1990−02−07 1990−02−01 14 west
      1991 1991−12−22 1991−12−29 1991−12−27 8 north
      1992 1993−01−08 1993−01−23 1993−01−17 16 west
      1995 1996−02−17 1996−02−29 1996−02−19 13 northwest
      1995 1996−01−11 1996−01−22 1996−01−18 12 west
      1996 1997-02−01 1997-02−21 1997-02−07 21 northwest
      1997 1998−01−11 1998−01−26 1998−01−23 16 west
      1999 2000−01−19 2000−02−04 2000−01−28 17 north
      2004 2004−12−21 2005−01−01 2004−12−27 12 west
      2007 2008−01−02 2008−02−03 2008−01−03 33 west
      2004 2005−01−07 2005−01−14 2005−01−10 8 west
      2010 2010−12−30 2011−01−15 2011−01−03 17 northwest
      2010 2011−01−17 2011−01−22 2011−01−18 6 west
      2012 2013−01−02 2013−01−12 2013−01−04 11 northwest
      2014 2014−02−05 2014−02−15 2014−02−07 11 west
      2015 2016−01−19 2016−01−26 2016−01−24 8 north
      2017 2018−01−24 2018−02−10 2018−01−28 18 northwest

      Table 1.  The selected PWCEs.

      Figure 5.  (a) The SSE of the cold-air paths of the PWCEs of YR uniform pattern and their (b) north paths, (c) west paths, and (d) northwest paths.

    • Different cold-air paths correspond to different circulation characteristics. In the following, we study the evolution of the atmospheric circulation in PWCEs under different cold-air paths. Figure 6 shows the 500-hPa geopotential height field and its anomalies from day −4 to the strongest day (day L). For the western cold-air path, there is positive geopotential height anomaly west of the Ural Mountains (Fig. 6a). Subsequently, the ridge intensifies eastward into an oblique ridge, and in front of the oblique ridge a low trough extends from Lake Baikal to Lake Balkhash. From day 0 to day L (Fig. 6c, d), the tilted ridge and trough is maintained steadily and intensifies. Such a circulation configuration of the large-scale tilted ridge and trough facilitates a more sustained invasion of cold air into South China, resulting in extreme low temperature (Bueh et al., 2011a, b).

      Figure 6.  The 500-hPa geopotential height (contours; units: gpm) and its anomalies (shaded areas; units: gpm) for the PWCEs of YR uniform pattern with the (a–d) west, (e–h) northwest, and (i–l) north type of cold-air path, from day −4 to day L. The white dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level.

      In terms of the northwest type of cold-air path, the ridge extends from the Novaya Zemlya to the polar region on day −4 (Fig. 6e). Then, it develops and moves eastward to the east of Lake Baikal. In front of the ridge, a transverse trough extends from Northeast China to Lake Balkhash, indicating the accumulation of cold air near Lake Baikal (Fig. 6f). On day 0 (Fig. 6g), the tilted ridge and trough is intensified. On day L, the transverse trough turns vertically into the East Asian trough, and the ridges are steadily maintained and cold air continues to affect southern China (Fig. 6h). This trough–ridge configuration makes the cold air invade into southern China.

      For the north type of cold-air path, the circulation over East Asia is mainly characterized by a dipole pattern and blocking circulation. From day −4 to day −2 (Fig. 6i, j), the high-pressure ridge extends from the Caspian Sea to the polar region, connecting with the Okhotsk ridge in the polar region, with the polar region as the warm center (Fig. 6i). There is a transverse trough from Lake Baikal to Lake Balkhash, and the cold air accumulates in Lake Baikal. Accompanied by the transverse trough turning vertically from day 0 to day L, the cold air invades southward to decrease the air temperature in the eastern part of South China (Fig. 6k6l). From day −4 to day L, the ridge from the Caspian Sea to the polar region is stable and maintained. Such a circulation situation is favorable for the cold air to continue to invade southward, causing a decrease in air temperature in South China.

      Figure 7 shows the distribution of SLP for the three types of PWCEs. For the west type of cold-air path, the positive anomaly center of the SLP field is in the west of the Ural Mountains on day −4, corresponding to the 500-hPa positive geopotential height anomaly (Fig. 7a). From day −4 to day −2 (Fig. 7b), the positive anomaly center of the Siberian high strengthens and moves eastward to the east of Lake Baikal, indicating the intensification of cold air accumulation. Then, the Siberian high continues to move southward and strengthen, with the center reaching 1045 hPa and the positive anomaly center reaching 13.1 hPa. The edge of the positive SLP anomaly areas moves to northern South China, resulting in a decrease in temperature in the region (Fig. 7c). On day L (Fig. 7d), the Siberian high continues to move southward, resulting in a widespread temperature decrease in South China.

      Figure 7.  The SLP (contours; units: hPa) and its anomalies (shaded areas; units: hPa) for the PWCEs of YR uniform pattern with the (a–d) west, (e–h) northwest, and (i–l) north type of cold-air path, from day −4 to day L. The black dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level.

      For the northwest type of cold-air path, the significant positive anomaly center of the Siberian high is located in the Novaya Zemlya on day −4 (Fig. 7e), more eastward than the west type. Then, the Siberian high intensifies and moves eastward and southward, and the positive anomaly center of the SLP is in the east of Novaya Zemlya (Fig. 7f). On day 0, the edge of the positive anomaly area of the SLP reaches eastern South China, with a value of 11.0 hPa, leading to a decrease in temperature in the region (Fig. 7g). On day L, the Siberian high continues to press southward, causing a widespread temperature decrease in South China (Fig. 7h).

      The north type of cold-air path is different from the other two types. The whole Siberia region is controlled by a significant positive anomaly center. In addition, the Siberian high is more extensive than the other two types, and the intensity of the positive anomaly center is stronger, reaching 20.9 hPa. Similar to the other two types, the cold air transported by the Siberian high moves southward to decrease the air temperature in South China (Figs. 7il).

    • To explore the precursor dynamical circulation signals of the PWCEs, we analyze the 300-hPa wave activity flux before the PWCEs. For the west type, our focus is on how the positive geopotential height anomalies is formed west of the Ural Mountains. Figure 8a–e shows that this type of event is influenced by a wave train from the mid to high latitudes. From Fig. 8a, it can be seen that the wave activity flux from Greenland propagates eastward on day −10, forming a wave train with a “negative–positive–negative” distribution in the area of Greenland–Nordic–East Asia. Then, with the eastward propagation of the wave energy, the positive geopotential height anomaly forms on day −4 (Fig. 8c). As the wave energy propagates eastward, the Ural ridge forms (Figs. 8d, e). The trough and ridge cooperate to make cold air affect South China.

      Figure 8.  The 300-hPa geopotential height field anomalies (shaded areas; units: gpm) and wave activity flux (vectors; units: m2 s−2) for the PWCEs of YR uniform pattern with the (a–e) west, (f–j) northwest, and (k–o) north types of cold-air path, from day −10 to day 0. The green dotted areas indicate that the anomaly values pass the significance test at the 95% confidence level.

      For the northwest type of PWCEs, the focus is on the ridge from Novaya Zemlya to the polar region. Figure 8f suggests that there is wave energy from the North Atlantic propagating eastward. On day −6, one wave train propagates along the mid and high latitudes, with a “positive–negative–positive–negative” distribution in the area of the North Atlantic–Faroe Islands–Norwegian Sea–East Asia. The other wave train propagates along the subtropics, with a “positive–negative–positive–negative” in the area of the North Atlantic–northern Africa–Arabian Plateau–Caspian Sea (Fig. 8f). As the wave energy propagates, the ridge from the Novaya Zemlya to the polar region is established on day −4, combining with the trough to decrease the air temperature in South China (Fig. 8h). The southern trough deepens in the Bay of Bengal on day 0, providing water vapor for precipitation (Fig. 8j).

      The north type of PWCEs differs greatly from the other two types, and is mainly controlled by the blocking circulation. From day −10 to day −6, there is a warm ridge extending from the Caspian Sea to the polar region, with wave energy from the Barents Sea propagating eastward (Figs. 8k, l). With the eastward propagation of the wave energy, the ridge in the Ural Mountains–polar region is formed on day −4 (Fig. 8m). Subsequently, the ridge extends to the polar region and connects with the ridge in the Okhotsk Sea of the polar region, and Lake Baikal has negative geopotential height anomalies. Then, the trough cooperates with the ridge to cause temperatures to decrease in South China (Figs. 8n, o).

      Comparing the mid- and high-latitude circulation characteristics for the three types of PWCEs, we find that, for the west type, the ridge area in the early stage is located west of the Ural Mountains, which is associated with wave energy propagating eastward from Greenland. For the northwest type, the ridge area in the early stage is located more eastward (from Novaya Zemlya Island to the polar region), and the wave energy comes from the North Atlantic. The north type is controlled by the blocking circulation, and the wave energy comes from the Barents Sea. All three types of PWCEs are influenced by wave energy propagating eastward from mid and high latitudes, which is related to the intraseasonal oscillations of the atmosphere (Li and Gu, 2010; Song and Wu, 2019; Gao et al., 2022). The same circulation characteristics of all three types are that the Siberian high enhances and moves southward, which makes the temperature decrease in South China. Moreover, the center of the positive SLP anomaly corresponds to the 500-hPa ridge area. The comparison reveals that the Siberian high is stronger and more extensive for the north type of PWCEs.

    • Winter precipitation in South China is closely associated with lower-tropospheric circulation. In addition to cold air, the influences of subtropical circulations on the PWCEs cannot be ignored. For the three types of cold-air paths of PWCEs, the composite wind anomalies at 700 hPa (Figs. 9ac) indicate significant southwesterly wind anomalies in the eastern part of South China, and the transport of warm and wet airflow makes a warm layer form at 700 hPa. At 850 hPa (Figs. 9df), there is significant convergence of southwesterly and northeasterly wind anomalies in South China, which is favorable for the occurrence of precipitation. At 925 hPa (Figs. 9gi), northerly anomalies prevail over the South China, indicating that the near surface is mainly influenced by cold air. Thus, the layer at 700 hPa is warm, while the layers at 850 hPa and 925 hPa are relatively colder, i.e., the thermal inversion layer forms in the lower troposphere. This atmospheric stratification contributes to the occurrence of precipitation. Moreover, for the north type of PWCEs, there are significant anticyclonic wind field anomalies in the western Pacific Ocean (Fig. 9c), indicating that the water vapor is influenced by the western Pacific subtropical high in addition to the South branch trough (SBT). However, the water vapor for the west and northwest types of events is mainly affected by the SBT.

      Figure 9.  Composite wind anomalies (vectors; units: m s−1) for the PWCEs of YR uniform pattern with the (a, d, g) west, (b, e, h) northwest, and (c, f, i) north types of cold-air path, at (a–c) 700 hPa, (d–f) 850 hPa, and (g–i) 925 hPa. Red vectors show that the composite wind anomalies pass the significance test at the 95% confidence level. The grey shading represents the orography.

      The pressure–latitude distributions of the temperature, temperature anomalies, and wind field anomalies in the region of the PWCEs (averaged over 99°–120°E) indicate that there is warm advection in the lower troposphere within 15°–25°N (Figs. 10ac), which leads to the formation of a thermal inversion layer and is conducive to precipitation occurrence.

      Figure 10.  Pressure–latitude cross sections (averaged over 99°E–122°E) of the composite temperature anomalies (shaded areas; units: °C) and wind anomalies (vectors; units: m s−1) for the PWCEs of YR uniform pattern with the (a) west, (b) northwest, and (c) north types of cold-air path. The white dotted areas indicate that the temperature anomaly values pass the significance test at the 95% confidence level.

    • Besides the cold air from the mid and high latitudes, the water vapor from the south is essential for precipitation. Figure 11 shows the vertically integrated water vapor for the PWCEs. The results suggest that there is water vapor flux convergence and water vapor flux transport in southern China during the PWCEs, which provides favorable water vapor conditions for precipitation. The vertically integrated water vapor for the west (Fig. 11a) and northwest (Fig. 11b) type events is mainly from the Bay of Bengal, and that for the north type comes from the Bay of Bengal and the South China Sea (Fig. 11c). As presented in Fig. 11, the variation of the water vapor budget with height for the three types of PWCEs at each boundary of the study area suggests that the positive water vapor budget mainly appears at the southern boundary above 850 hPa and is concentrated within 850–700 hPa. However, the negative budget mainly appears at the eastern boundary. The sum of the positive water vapor budget is greater than that of the negative budget at each boundary within 850–700 hPa (Figs. 11df).

      Figure 11.  The vertically integrated water vapor flux anomalies (vectors; units: kg m−1 s−1) and vapor flux divergence anomalies (shaded areas; units: 10−5 kg m−2 s−1) for the PWCEs of YR uniform pattern with the (a) west, (b) northwest, and (c) north types of cold-air path. The black boxed areas denote South China. The grey shading represents the orography. (d–f) Water vapor budget (units: 100 kg hPa−1 s−1) for the PWCEs with the (d) west, (e) northwest, and (f) north types of cold-air path at each boundary of the study area.

    4.   Conclusions
    • In this study, we investigate the basic characteristics of PWCEs in South China during winter and their mechanisms with different types of cold-air paths. The main conclusions are as follows:

      (1) The single-station cumulative frequency during 1951–2019 of the western part is higher than that of the eastern part in South China. There are two modes—the YR uniform pattern and the east–west reverse pattern, with the YR uniform pattern being the dominant mode. The precipitation and negative temperature anomalies for the PWCEs of YR uniform pattern are centered in the southeast.

      (2) The cold-air paths of the PWCEs of YR uniform pattern can be divided into west, northwest, and north types, among which the west type accounts for the largest proportion. The high-latitude circulation characteristics for the PWCEs with the three different paths show that eastward propagating waves from Greenland can cause the ridge west of the Ural Mountains to be established for the west type. For the northwestern type, the wave propagating eastward from the North Atlantic can establish a ridge located from Novaya Zemlya Island to the polar region. The north type is controlled by the blocking circulation, with wave energy from the Barents Sea. The ridge and trough cooperate to make cold air invade into South China. The three types of PWCEs are accompanied by intensification of the southward Siberian high.

      (3) The formation of the thermal inversion layer in the lower troposphere is conducive to precipitation during the PWCEs. Comparing the 700-hPa wind field anomalies for the three types of PWCEs, we find that the water vapor for the west and northwest types is influenced by the SBT, while that for the north type is affected by the SBT and subtropical high.

      (4) The positive water vapor budget for the three types of PWCEs mainly appears at the southern boundary and is concentrated from 850 hPa to 700 hPa. There is water vapor convergence and water vapor flux transport during the events, which provides water vapor for precipitation.

      In this research, we analyze the characteristics of PWCEs affecting South China. The results demonstrate that there are differences in the high-latitude circulation characteristics for the PWCEs with the three types of cold-air paths, and the circulations cooperate with the water vapor from the south to result in persistent low temperatures and precipitation. At present, we have only analyzed the circulation characteristics of PWCEs affecting South China. The intraseasonal and extra-seasonal precursor signals associated with PWCEs need to be further studied. On the other hand, whether there is interdecadal variability in the cold-air paths of PWCEs also needs to be further investigated. In addition, we have shown that the second mode of PWCEs is an east–west inversion type, and such events also need to be further investigated.

      Acknowledgements. This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFC1505602), the National Natural Science Foundation of China (Grant No. 41705055), the Graduate Innovation Project of Jiangsu Province (Grant No. CXZZ11_0485), the Creative Teams of Jiangsu Qinglan Project, and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). In addition, we are grateful for the constructive comments of the two reviewers.

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