1. Introduction
Extreme precipitation events (EPEs) have exerted severe impacts on human health, the natural environment, and the socio-economy worldwide by inducing disasters such as floods and landslides (Easterling et al. 2000; Duan et al. 2016; Dottori et al. 2018). Regional persistent extreme precipitation events (RPEPEs) with longer durations and higher intensities tend to cause more severe disasters (Chen and Zhai 2013). Southwest China (SWC) is one of the areas that are most vulnerable to natural disasters due to frequent extreme precipitation and its fragile geological conditions (Li et al. 2013; Shi et al. 2016). For instance, a record-breaking persistent heavy rainfall event hit the Sichuan basin (in SWC) in mid-August 2020. The rainfall affected over 8 million people and resulted in a direct economic loss of more than 60 billion RMB (https://www.mem.gov.cn/xw/yjglbgzdt/202101/t20210102_376288.shtml). Therefore, it is essential and pressing to understand EPEs over SWC to predict such events and mitigate their impacts.
The causes of EPEs over SWC have been extensively studied. Atmospheric circulations in both mid- to high latitudes and low latitudes play important roles in generating extreme precipitation over SWC. Many case studies have demonstrated that mid- to high-latitude Rossby waves are key factors in these EPEs (D. Chen et al. 2010; Li et al. 2014; Y. Zhou et al. 2020; Xia et al. 2021). The troughs along the westerly jet favor extreme precipitation over SWC by providing cold air and inducing ascending motion. Moreover, EPEs over SWC may occur under distinct phases of mid- to high-latitude Rossby wave trains (Nie and Sun 2021). On the interannual time scale, the high frequency of summer extreme precipitation over SWC is linked to the Eurasian pattern excited by the anomalous Arctic sea ice concentration in May (Xu et al. 2021) and North Atlantic Oscillation (Zhang et al. 2014). In the low latitudes, the extreme precipitation over SWC is dominated by the anomalous anticyclone in the lower troposphere over the western North Pacific (WNP) and southeastern China. The anomalous anticyclone is usually associated with a strengthened and westward-extending western Pacific subtropical high (WPSH) that steers sufficient moisture and energy to SWC through anomalous southerlies along the west edge (Xiao and Yu 2003; D. Chen et al. 2010; Y. R. Chen et al. 2010; Hu et al. 2021). In addition, the sea surface temperature (SST) anomalies over the Maritime Continent, the WNP, the equatorial central and eastern Pacific, and the tropical Atlantic can also affect the extreme precipitation over the SWC by modulating the atmospheric circulations over East Asia (Jiang et al. 2015, 2017; Xia et al. 2020; Yuan and Yang 2020; Xu et al. 2021; Nie and Sun 2022a).
Intraseasonal oscillations (ISOs) have crucial impacts on the occurrence of extreme precipitation over East Asia. Statistically, the frequency of precipitation extreme occurrence over southern China changes with the phases of boreal summer intraseasonal oscillation (Hsu et al. 2016; Ren et al. 2018). The ISOs also largely contribute RPEPEs over southern China. For example, intraseasonal disturbances originating from the tropics and intraseasonal Rossby wave trains in the mid- to high latitudes can prolong and intensify EPEs over South China during early summer (Miao et al. 2019; Liu et al. 2022). The summer persistent heavy precipitation events over the Yangtze River basin are also associated with ISOs (Li and Mao 2019; Cheng et al. 2021; Li et al. 2021; Liang et al. 2021). In SWC, although some studies have indicated that precipitation is influenced by ISOs (Li et al. 2016; Nie and Sun 2022a), the relationship between ISOs and RPEPEs over SWC remains uncertain. Moreover, our previous study found that the 7–20-day summer precipitation over SWC may occur under diverse configurations of low-latitude atmospheric circulations (Nie and Sun 2022a), implying the complexity of the influence of low-latitude ISOs on RPEPEs over SWC. Several recent studies have also pointed out the diversity of low-latitude ISOs during boreal summer (Chen and Wang 2021; Wang et al. 2021). However, the diverse impacts of low-latitude ISOs on SWC extreme precipitation are rarely focused on. Motivated by this, the present paper intends to investigate whether RPEPEs over SWC are caused by different types of low-latitude ISOs. If so, the differences in the mechanisms and the background atmospheric circulations between the different types of RPEPEs over SWC will be further explored.
The moisture sources for EPEs have been widely studied using Lagrangian simulations (Huang and Cui 2015b; Bohlinger et al. 2017; Huang et al. 2018; Ma et al. 2020; Zhang et al. 2021; Nie and Sun 2022b). A common conclusion is that the tropics and subtropics are the main moisture sources for EPEs over the Asian monsoon region. Previous studies have indicated that the extremity of the precipitation over SWC depends on the intensity of moisture supply dominated by low-latitude atmospheric circulations (D. Chen et al. 2010; Y. R. Chen et al. 2010; Nie and Sun 2021). Thus, we speculate that there may be significant differences in moisture sources between the SWC RPEPEs under different types of low-latitude ISOs. To verify our hypothesis, the flexible particle dispersion model (FLEXPART), a Lagrangian model, is employed to trace the moisture sources of different types of RPEPEs and examine their differences.
The remainder of the paper is organized as follows. The data and methods are introduced in section 2. Section 3 distinguishes different types of RPEPEs over SWC according to low-latitude ISOs. The mechanisms and moisture sources for different types of RPEPEs are examined in section 4. In section 5, the backgrounds of different types of RPEPEs are compared. In the last section, we summarize the results and provide a discussion.
2. Data and methods
a. Data
The SWC is defined as the region within 21°–34.5°N, 97°–110.5°E, similar to previous studies (Nie and Sun 2022a). Daily observed precipitation gauge data at 160 stations in SWC are obtained from the National Meteorological Information Center of the China Meteorological Administration. The stations with missing values greater than 5% during May–September are removed. The period of May–September is considered because it is the rainy season of SWC (Nie and Sun 2020), and only the rainy-season RPEPEs are selected. The missing values in the retained data are filled by the interpolated values of neighboring stations using the method proposed by Cressman (1959). The daily mean atmospheric variables are calculated from the 6-hourly global European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) datasets, with a 1.0° × 1.0° spatial resolution (Hersbach et al. 2020). The variables used in this study include geopotential height, horizontal winds, vertical velocity (ω), air temperature, specific humidity, etc. The daily outgoing longwave radiation (OLR) data at 2.5° × 2.5° grids from the National Oceanic and Atmospheric Administration (NOAA) are used to capture the low-latitude ISOs (Liebmann and Smith 1996). This study also uses monthly SST data from the Met Office Hadley Centre Sea Ice and SST dataset version 1.1 (HadISST1) with a 1.0° × 1.0° grid (Rayner et al. 2003) and daily Optimum Interpolation SST (OISST) version 2.1 provided by NOAA with a 0.25° × 0.25° grid (Reynolds et al. 2007; Huang et al. 2021). The period from 1979 to 2020 is considered in this study. As OISST data only cover the period since 1982, the RPEPEs before 1982 are not included in the results related to daily SST.
b. The intraseasonal time-scale composite analysis
The intraseasonal components of variables are extracted as follows. The daily anomalies are first obtained by removing the daily climatology. Second, a 5-day running mean is performed to remove synoptic-scale fluctuations, based on previous studies (Mao et al. 2010; Yang et al. 2010; Hu et al. 2016; Yang et al. 2017; Cheng et al. 2020; Ren et al. 2022). Next, a sixth-order Butterworth bandpass filter is applied to extract the intraseasonal components (Butterworth 1930). Our previous study indicated that the summer precipitation over SWC is dominated by a 7–20-day period (Nie and Sun 2022a). A similar dominant period is also found in power spectrum analysis and wavelet analysis on rainy-season daily precipitation over SWC (figure not shown). Therefore, the 7–20-day period is concerned in this study, and a 7–20-day filtering is performed. We also applied a 7–90-day Butterworth bandpass filter, instead of 5-day running mean, to extract the intraseasonal components of precipitation variations over SWC, and then performed the same power spectrum analysis and wavelet analysis. The dominant period is basically unchanged (figure not shown). A lagged composite analysis is used for the filtered variables. A two-tailed one-sample Student’s t test is applied to estimate the statistical significance.
c. Definition of RPEPEs
The RPEPEs over the SWC are selected following the method proposed by Lin et al. (2020). A total of 73 RPEPEs are selected. We first identify regional extreme precipitation days (REPDs) according to two conditions. 1) The number of adjacent stations with extreme precipitation exceeds 4% of the total number of stations in SWC. The adjacent stations are required to be within 350 km to ensure the spatial continuity of the rainbands. The extreme precipitation threshold of a station is defined as the 90th percentile of precipitation on rainy days (precipitation ≥ 1.0 mm) during all rainy seasons. 2) The average precipitation among all stations in SWC is required to be 0.5 standard deviations higher than its rainy-season mean value. The thresholds for the number of stations with extreme precipitation and station-average precipitation anomalies are necessary to ensure that the events affect an area instead of individual stations. The threshold for station distance is to ensure that the stations hit by extreme precipitation are concentrated over an area. The threshold values of 4% for station number, 0.5 standard deviations for station-average precipitation anomalies, and 350 km for station distances are chosen following a recent study by Lin et al. (2020).
An RPEPE is identified based on two conditions as well. 1) There are three consecutive REPDs. 2) For the spatiotemporal continuity of the rainbands, two consecutive REPDs should share at least one common station with extreme precipitation or share more than 20% of common stations with large precipitation. A station observes large precipitation when the precipitation is 1.0 standard deviation higher than its rainy-season climatological mean value. The thresholds used to identify RPEPEs are also determined with reference to those used by Lin et al. (2020).
Furthermore, the RPEPEs affected by tropical cyclones (TCs) are excluded. The impacts of TCs are not considered because the TCs are a weather phenomenon and are also influenced by tropical ISOs (Liebmann et al. 1994; Li and Zhou 2013). An RPEPE is considered to be affected by a TC if the distance between the center of extreme precipitation and a TC center is less than 1000 km at any time during the RPEPE. The center of extreme precipitation is defined as the average position of the adjacent stations where extreme precipitation occurs. The TC best track data are taken from the International Best Track Archive for Climate Stewardship (IBTrACS) v04r00 provided by the National Climate Data Center (Knapp et al. 2010). To ensure independence between the RPEPEs, the RPEPEs should be separated at least 10 days apart from each other. Otherwise, the weaker one of the two non-independent RPEPEs is excluded. Such a consideration is because the overlap of the atmospheric circulations between two RPEPEs with short intervals could have an adverse impact on the results.
Next, we check whether there are 7–20-day signals in the RPEPEs. For each RPEPE, a daily precipitation series is calculated as the average precipitation at the stations where large precipitation occurs during the RPEPE. Then, the 7–20-day component of the daily precipitation series is extracted. An RPEPE is considered to have a significant >7–20-day signal if the 7–20-day component exceeds its 1.0 standard deviation for 3 consecutive days during the RPEPE. The following analyses are based on the RPEPEs with significant 7–20-day signals. Day 0 of an RPEPE is defined as the day when the 7–20-day signal peaks.
d. Clustering for the low-latitude ISOs
Clustering is widely used in classification problems (Wilks 2011). The k-means clustering has shown good performance in categorizing ISOs in recent studies (Cheng et al. 2020; Chen and Wang 2021; Wang et al. 2021). In this study, k-means clustering is applied to the 7–20-day OLR anomalies on days −8, −6, −4, −2, 0, and 2 of the RPEPEs over the low-latitude Indo-Pacific region (5°S–35°N, 60°E–180°). The OLR anomalies between −5 and 5 W m−2 are set to zero before clustering. The silhouette coefficient is used to measure the clustering results (Rousseeuw 1987). A high silhouette coefficient indicates that the sample is well classified. The RPEPEs with silhouette coefficients lower than 0.01 are not considered in the further analyses because they are not well categorized. The results are insensitive to the region of OLR anomalies used for clustering and the threshold of the silhouette coefficient.
As k-means clustering is appropriate for data with Gaussian distribution, we apply the Kolmogorov–Smirnov test for the 7–20-day OLR anomalies used for the clustering. The result indicates that they satisfy a Gaussian distribution well. It means that k-means clustering is applicable in our study. To verify the results of k-means clustering, another method of k-medoids clustering is also used, according to the study by Ma et al. (2020). Similar results can be also obtained by k-medoids clustering (figure not shown). We eventually choose k-means clustering due to the higher mean silhouette coefficient of its results.
e. Wave activity flux
f. The quasigeostrophic ω equation
g. FLEXPART model description and moisture source attribution
FLEXPART version 10.4 is employed to trace the moisture sources of the RPEPEs. The model is widely used in the studies on moisture sources for many regions over East Asia, e.g., the East Asian summer monsoon region (Baker et al. 2015; Zhang et al. 2021), the semiarid grassland in China (Sun and Wang 2014), Xinjiang in Northwest China (Yao et al. 2021a,b), and the Tibetan Plateau (TP) (Yang et al. 2020). In our simulations, a “domain filling” technique is adopted by which the global atmosphere is divided into approximately 1 million equal-mass air parcels. The trajectory of each particle is tracked during the forward simulations. The FLEXPART model is driven by the 6-hourly ERA5 data at 137 model levels with a horizontal resolution of 1.0° × 1.0°. The three-dimensional positions, specific humidity, temperature, etc., for each air parcel are recorded in the output data at 3-h intervals. More details about FLEXPART are described in Stohl et al. (2005) and Pisso et al. (2019). Only the air parcels with a decrease in specific humidity greater than 0.001 kg kg−1 within SWC during the RPEPEs are selected for further analyses.
3. Categorization for the RPEPEs over SWC
Following the steps in section 2c, there are 73 independent RPEPEs over SWC during rainy seasons without the influence of TCs identified. Most of the RPEPEs last 3–4 days. Among the 73 RPEPEs over SWC, there are only four RPEPEs without significant 7–20-day signals, and the remaining 69 RPEPEs are associated with the 7–20-day signals. Our preliminary analysis indicates that the four abnormal RPEPEs are mainly caused by synoptic-scale disturbances and three of them are also influenced by the 30–60-day ISOs. This result indicates that the SWC RPEPEs could be significantly influenced by the 7–20-day ISOs.
Based on k-means clustering for the 7–20-day OLR anomalies over the low latitudes before and during the RPEPEs, the 69 RPEPEs can be divided into 2 clusters. The cluster number of 2 is chosen considering both silhouette coefficients and physical interpretability. Hereafter, the two clusters are referred to as type 1 and type 2. There are 36 and 33 events in type 1 and type 2, respectively. The RPEPEs over SWC are mainly concentrated in boreal summer, i.e., June, July, and August (Fig. 1), which is in line with that of extreme precipitation events that do not consider persistence (Nie and Sun 2021). The numbers of RPEPEs in the two types exhibit year-to-year variations. However, the trends of the event numbers are not significant.
The temporal distribution of two types of RPEPEs over SWC. The red and blue dots are the RPEPEs in type 1 and type 2, respectively. The gray dots are the RPEPEs without significant 7–20-day signals.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
The spatial distributions of 7–20-day precipitation anomalies have some similarities during the two types of RPEPEs (Fig. 2). The precipitation anomalies are mainly distributed over eastern SWC. A possible reason is that the climatological precipitation amounts and variability are higher in eastern SWC than in western SWC due to the topography (Nie and Sun 2021). On day −4, most stations over SWC have negative precipitation anomalies. On day −2, positive precipitation anomalies appear in northeastern SWC. Then, the areas with positive precipitation anomalies enlarge and the center is in central-eastern SWC, reaching the peaks of precipitation anomalies on day 0. Afterward, the precipitation anomalies weaken and disappear from the southeast of SWC.
Evolution of composite 7–20-day precipitation anomalies (unit: mm day−1) from day −4 to day 4 with an interval of 2 days for the RPEPEs in (left) type 1; (center) type 2; and (right) the differences between the RPEPEs in type 2 and type 1. The triangles in the left and center columns represent the stations with values significant at the 95% confidence level. The triangles in the right column indicate that the differences between the two types of RPEPEs are statistically significant at the 95% confidence level. The rectangles denote SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
In comparison, the positive precipitation anomalies in type 2 are more northeastward than in type 1. In addition, the rainbands over SWC are connected with those over eastern China. However, there are distinctly different characteristics in the precipitation distributions for the two types of RPEPEs. For type 1, the SWC rainbands show a west–east distribution, having a connection with precipitation anomalies over South China. By contrast, for type 2, the rainbands show a southwest–northeast distribution, having a connection with precipitation anomalies over the Yangtze River basin, which resembles the mei-yu precipitation.
The contributions of 7–20-day precipitation anomalies to total precipitation anomalies are further investigated (Fig. 3). Only the stations with large precipitation are selected to calculate the average precipitation (see section 2). The results of the two types of RPEPEs are similar. The large values of total precipitation anomalies persist from day −1 to day 1 and peak on day 0. The maxima of the total precipitation anomalies are approximately 13–14 mm day−1 on average. The 7–20-day components on day 0 are slightly higher than 6 mm day−1. In other words, the 7–20-day components contribute nearly half of the total anomalies. The contributions of synoptic components (<7 days) and the components with a lower-than-20-day period are not negligible but are less than those of 7–20-day components during the RPEPEs. Thus, the 7–20-day precipitation contributes the most to the total precipitation anomalies.
Evolution of composite total precipitation anomalies (unit: mm day−1) and their 7-day high-pass-filtered components, 7–20-day filtered components, and 20-day low-pass-filtered components averaged at the stations with large precipitation over SWC during the RPEPEs in (a) type 1 and (b) type 2. Note that the 5-day running mean is not conducted here.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
4. Mechanisms for the two types of RPEPEs
a. Atmospheric circulation and convection anomalies
The distinct 7–20-day convections and atmospheric circulations responsible for the RPEPEs are analyzed. A northwestward propagation of the 7–20-day convection-circulation-coupled system over the WNP is essential for the two types of RPEPEs (Fig. 4). However, the two types of 7–20-day oscillations exhibit almost opposite phases before the RPEPEs, which is the major distinction between them. The convection-circulation-coupled systems in both types are typical quasi-biweekly oscillations over the WNP during the boreal summer, which are Rossby waves in essence (Kikuchi and Wang 2009; Chen and Sui 2010). For type 1, a suppressed convection anomaly accompanied by an anomalous anticyclone in the lower troposphere appears over the tropical WNP more than one week before the RPEPEs. The suppressed convection and anomalous anticyclone propagate northwestward and reach the South China Sea (SCS) on day −4. The anomalous anticyclone transports moisture to SWC through the anomalous southwesterly along its northwest edge. The coupled suppressed convection and anomalous anticyclone then propagate westward and occupy the Indochina Peninsula and the Bay of Bengal, continuing to transport moisture to SWC through the anomalous westerly to the south of the TP. In addition, along with the propagation of the suppressed convection and anomalous anticyclone, an anomalous cyclone is formed over SWC and South China from day −2 to day 0. More moisture and the cyclonic anomaly favor the occurrence of precipitation over the region. Finally, the positive precipitation anomalies vanish when the anomalous cyclone behind the anticyclone approaches the SCS.
Evolution of composite 7–20-day OLR (shading; unit: W m−2) and horizontal winds anomalies at 850 hPa (vectors; unit: m s−1) from day −8 to day 2 with an interval of 2 days for the RPEPEs in (left) type 1 and (right) type 2. Only the anomalies significant at the 95% confidence level are displayed. The 1510 gpm contours (green) at 850 hPa and the 5880 gpm contours (orange) at 500 hPa represent the WPSH. The 12 520 gpm contours (cyan) at 200 hPa represent the SAH. The brown contours are isolines of 2500 m altitude. The rectangles denote SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
A distinct convection-circulation evolution is observed in type 2. An enhanced convection anomaly accompanied by an anomalous cyclone originates from the tropical WNP more than one week before the RPEPEs in type 2. The coupled enhanced convection and anomalous cyclone then propagate northwestward and reach the SCS on day −4. Influenced by the enhanced convection over the SCS, the WPSH to its northeast side in the lower troposphere extends westward, and the South Asian high (SAH) to its northwest side in the upper troposphere shifts eastward. The variations in the WPSH and SAH are responses to the nonuniformity of convection-induced diabatic heating in vertical and meridional directions over the WNP (Liu et al. 1999, 2001; Lin et al. 2021). The coupled enhanced convection and anomalous cyclone decay after day −4. However, the westward-extending WPSH, reflected by a lower-level anomalous anticyclone over the Philippine Sea, transports moisture to SWC and the Yangtze River basin in the following several days. Meanwhile, the eastward-shifting SAH results in anomalous upper-level divergence. The variations in both the WPSH and the SAH favor SWC precipitation. Many previous studies have indicated that quasi-biweekly zonal oscillations can be observed in both the WPSH (Qian and Yu 1991; Zhang and Yu 1992) and the SAH (Krishnamurti et al. 1973; Liu et al. 2007). Their zonal oscillations exert significant impacts on the precipitation over the Yangtze River basin (Ren et al. 2013; Chen and Zhai 2016; Wei et al. 2019a; Cheng et al. 2021). Given that the rainband over the SWC in type 2 is contiguous with that over the Yangtze River basin (Fig. 2), the same mechanism can also explain the type of RPEPE over SWC. On day 2, the atmospheric circulations are weakened and the precipitation anomalies are also weakened.
In brief, the coupled suppressed convection and anomalous anticyclone directly induce anomalous precipitation over SWC in type 1. In contrast, the coupled enhanced convection and anomalous cyclone play an indirect role in inducing anomalous precipitation over SWC in type 2. Instead, the zonal approach of the WPSH and the SAH associated with the coupled enhanced convection and anomalous cyclone plays a direct role.
The intraseasonal zonal variations in the WPSH and the SAH are related to the ISOs over the WNP (Yang and Li 2020; Lin et al. 2021; Zi et al. 2022). There is also a close relationship between the zonal variations in the WPSH and the SAH (Tao and Zhu 1964). How these two systems evolve throughout the two types of RPEPEs is further examined. Following Chen and Zhai (2016), a WPSH index (WPSHI) is defined as the normalized geopotential height anomaly at 850 hPa averaged over the region of 15°–22.5°N, 115°–130°E and a SAH index (SAHI) is defined as the normalized geopotential height anomaly at 200 hPa averaged over the region of 22.5°–30°N, 110°–122.5°E. A positive WPSHI indicates the westward extension of the WPSH. A positive SAHI indicates the eastward shift of the SAH. Moreover, an SCS convection index (SCSCI) is introduced to measure the variation in the coupled convection-circulation system which is a vital trigger of both types of RPEPEs. The SCSCI is defined as the normalized OLR anomaly averaged over the region of 10°–20°N, 105°–120°E (multiplied by −1). A positive and negative SCSCI indicates the SCS convection is enhanced and suppressed, respectively. All the indices are 7–20-day filtered.
For type 1 (Fig. 5a), the WPSHI shows significantly positive and negative anomalies before and after day 0 of the RPEPEs, respectively. The WPSH reaches its westernmost phase when the anticyclonic anomaly superimposes its main body and then retreats eastward when the anticyclonic anomaly moves away. In other words, the WPSH is in its transitional phase from the westernmost phase to the easternmost phase during the RPEPEs. The variation in SAH during type-1 RPEPEs is in phase with WPSH but not significant. Significant negative SCSCI appears on day −4, representing the suppressed convection over the SCS (Fig. 4e). The anomalous low-level anticyclone coupled with the suppressed convection is accompanied by the southwestward shift of the WPSH (Figs. 4e and 5a). Subsequently, the anomalies in the WPSH disappear as the coupled suppressed convection and low-level anticyclone move farther to the northwest. The SCSCI reaches its minimum 2 days before the RPEPE peaks. The low-level anticyclone starts to directly trigger the onset of the RPEPEs over SWC. Afterward, negative phases of WPSH and SAH occur. Thus, the variations in WPSH during type-1 RPEPEs may also be related to SCS convection anomalies but have little effect on type-1 RPEPEs. For type 2, the enhanced convection reaches its maximum on day −3, followed by the peaks of WPSHI and SAHI almost simultaneously (Fig. 5b). The WPSH and SAH respectively reaches its westernmost and easternmost phase after day 0. The SCSCI shifts from a positive phase to a negative phase at the same time. Interestingly, all three indices show no significant variations if we composite all the events (Fig. 5c), highlighting the necessity of categorization analysis. Similar results can be obtained using the WPSHI proposed by Zi et al. (2022) and the SAHI proposed by Wei et al. (2014) and Ren et al. (2015) (figure not shown). Therefore, our results do not depend on the selection of the WPSHI and the SAHI. The amplitude of the SCSCI in type 1 is greater than that in type 2, suggesting that the convection anomalies over the SCS for RPEPEs in type 1 are stronger than those in type 2, which can also be seen in Fig. 4. The amplitudes of the SCSCIs are greater than those of the WPSHI and SAHI during both types of RPEPEs. The results imply that the SCSCI may serve as a better index than the WPSHI and SAHI in terms of predicting the RPEPEs over SWC on the intraseasonal time scale.
Evolution of composite 7–20-day filtered WPSHI, SAHI, and SCSCI during the RPEPEs in (a) type 1, (b) type 2, and (c) both types. The solid lines represent the values significant at the 95% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
In addition to the low-latitude atmospheric anomalies, the fluctuations in the mid- to high latitudes are also indispensable for both types of RPEPEs over SWC (Fig. 6). For type 1, an anomalous barotropic cyclone over Northeast Asia is maintained from day −4 to day 0, shifting from northwest–southeast tilting to northeast–southwest tilting. The anomalous northerly along the west edge of the cyclone steers cold air to SWC and favors precipitation there. The wave train pattern along the westerly jet is insignificant except for the anomalous cyclone and anticyclone downstream of SWC, suggesting that the upstream atmospheric circulations have few common features among the type-1 RPEPEs. The anomalous cyclone over Northeast Asia may be induced by the suppressed convection over the SCS or the upstream wave trains. The formation of the anomalous cyclone is beyond the scope of this paper. For type 2, an upstream wave pattern clearer than that in type 1 is observed along the westerly jet. An anomalous anticyclone develops over Central Asia before day 0 and disperses its energy downstream. It induces a cyclonic anomaly over the northeast of the TP and a variation in the SAH during the later stage of the RPEPEs. Such a result suggests that the eastward-shifting SAH responsible for the type-2 RPEPEs is associated with not only the low-latitude ISOs but also the mid- to high-latitude Rossby wave trains, which agrees with previous studies (Ren et al. 2015; Yang and Li 2016; Lin et al. 2021). Additionally, an anomalous cyclone and anticyclone are observed downstream of SWC (Figs. 6d,f), which could be excited by the variation in SAH and latent heating release over SWC (F. Zhou et al. 2020).
Evolution of the composite 7–20-day geopotential height anomalies at 200 hPa (shading; unit: gpm) and 850 hPa (red and blue contours with an interval of 2 gpm for positive and negative values, respectively; the zero contours are omitted) and horizontal wave activity fluxes at 200 hPa (vectors; unit: m2 s−2; the values less than 1.0 m2 s−2 are omitted) from day −4 to day 2 with an interval of 2 days for the RPEPEs in (left) type 1 and (right) type 2. Stippled areas and solid contours denote the geopotential height anomalies significant at the 95% confidence level at 200 and 850 hPa, respectively. The brown contours are isolines of 2500 m altitude. The rectangles denote SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
The anomalous atmospheric circulations throughout the two types of RPEPEs have been preliminarily investigated. The ascending motion and moisture, two critical ingredients for precipitation, will be analyzed in-depth in the next section to compare the differences between the two types of RPEPEs.
b. Quasigeostrophic ω equation diagnosis
Next, the differences in dynamic conditions are examined between the two types of RPEPEs. We focus on the average ω in the middle troposphere (400–500 hPa) over SWC. The contributions of ω anomalies with different time scales to the total ω anomalies are first displayed (Fig. 7). Similar to the results in Fig. 3, the 7–20-day ω anomalies contribute the most to the total ω anomalies during the RPEPEs.
As in Fig. 3, but for the ω anomalies (unit: Pa s−1) averaged between 400 and 500 hPa over SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
The quasigeostrophic ω equation [Eq. (3)] is diagnosed during the RPEPEs to analyze the formation of 7–20-day ω anomalies. There are three notable terms with positive contributions to 7–20-day ω anomalies: terms A-x, A-y1, and B-y (Fig. 8). Term B-y reaches its maximum before the RPEPE peaks, suggesting that this term favors the onset of the RPEPEs. Term A-x shows the largest amplitude among the five terms. It changes from negative to positive around day −2, peaks during the RPEPEs, and decreases afterward. Similar to term A-x, term A-y1 also plays a role during the middle and later stages of the RPEPEs (mainly for type 2) but with a smaller amplitude. Thus, the middle-level 7–20-day ω anomalies over SWC are mainly contributed by the vertical variation in zonal and meridional geostrophic relative vorticity advection (term A-x and term A-y1), and the Laplacian of geostrophic meridional temperature advection (term B-y).
Evolution of composite five terms (unit: 10−18 m s−1 kg−1) on the right-hand side in Eq. (3) averaged between 400 and 500 hPa over SWC from day −5 to day 5 of the RPEPEs in (a) type 1 and (b) type 2.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
Furthermore, the variables associated with the abovementioned three terms are decomposed into four components as Eq. (4) to estimate the contributions of the variables at different time scales to the 7–20-day ω. As all three terms consist of two variables, each of them can be decomposed into 16 terms. It should be pointed out that the decomposed terms are all 7–20-day filtered. The variations in the 48 terms averaged over SWC are shown in Fig. 9.
Evolution of composite 16 decomposed components (unit: 10−18 m s−1 kg−1) of terms (a),(b) A-x; (c),(d) A-y1; and (e),(f) B-y on the right-hand side in Eq. (3) averaged between 400 and 500 hPa over SWC from day −5 to day 5 of the RPEPEs in (left) type 1 and (right) type 2.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
The vertical variations in 7–20-day relative vorticity advected by basic-state zonal winds (term A-x-03), the vertical variations in 7–20-day relative vorticity advected by basic-state meridional winds (term A-y1-03, only for type 2), and the Laplacian of basic-state temperature advection by 7–20-day meridional winds (term B-y-09) are the major contributors to the terms A-x, A-y1, and B-y, respectively (Fig. 9). The variations in terms A-x-03, A-y1-03, and B-y-09 also resemble those in terms A-x, A-y1, and B-y. Term B-y-09 first increases before the RPEPE occurrences and peaks on day −3 and day −1 of type-1 and type-2 RPEPEs, respectively. Thus, the basic-state temperature advected by 7–20-day meridional wind is critical for the onset of both types of RPEPEs, despite their differences in the peak time (Figs. 9e,f). For type 2, the 7–20-day temperature advected by basic-state meridional wind (term B-y-03) also contributes to the RPEPE onsets but in a secondary position (Fig. 9f). Next, the ascending motion over SWC is maintained and intensified mainly by the vertical variations in relative vorticity advected by basic-state zonal winds (Figs. 9a,b). This term peaks on day 0 for both types of RPEPEs. For type-2 RPEPEs, the 7–20-day ascending motion is further maintained by vertical variations in 7–20-day relative vorticity advection by basic-state meridional winds after the precipitation peak (Fig. 9d). However, this term is small for type-1 RPEPEs (Fig. 9c). The other terms are too small and will not be concerned in the rest of the analyses.
The mechanisms for the formation of the terms A-x-03, A-y1-03, and B-y-09 are different between the two types of RPEPEs. The two types of 7–20-day atmospheric circulation anomalies trigger ascending motion over SWC in different ways. The basic-state temperature at 400–500 hPa over SWC is warmer than the areas to its north and south sides. For type 1, the 7–20-day anticyclone over the SCS induces cold advection to the south side of SWC. Meanwhile, the northerlies associated with the 7–20-day cyclone over Northeast Asia induce cold advection to the north of SWC (Fig. 10a). Such a field of temperature advection corresponds to positive term B-y-03 over SWC that contribute ascending motion. For type 2, 7–20-day midlevel southerlies associated with the westward-extending WPSH dominate eastern SWC (Fig. 10b), leading to warm advection, i.e., positive term B-y-03 over northern SWC. Moreover, a 7–20-day warm core is observed on the north of SWC on day −2 (Fig. 10d), which is related to the eastward-shift SAH. The basic-state northerlies can also induce warm advection over northern SWC. Such a warm core is weak for type-1 RPEPEs (Fig. 10c). Thus, 7–20-day ascending motion over northern SWC is incurred by the warm advection and corresponding positive Laplacian during the early stage of type-2 RPEPEs.
Composite variables associated with the main contributors of the terms A-x, A-y1, and B-y on the right-hand side of Eq. (3) during the two types of RPEPEs. (a),(b) Basic-state temperature (shading; unit: K) and 7–20-day geostrophic winds (vectors; unit: m s−1) averaged between 400 and 500 hPa on day −3 (−1) of type-1 (type-2) RPEPEs. (c),(d) The 7–20-day temperature (shading; unit: K) and basic-state geostrophic meridional winds (cyan contours; unit: m s−1) averaged between 400 and 500 hPa on day −2. The 7–20-day geostrophic relative vorticity (shading; unit: 10−6 s−1) and basic-state geostrophic zonal winds (cyan contours; unit: m s−1) at (e),(f) 200 and (g),(h) 700 hPa on day 0. The 7–20-day geostrophic relative vorticity (shading; unit: 10−6 s−1) and basic-state geostrophic meridional winds (cyan contours; unit: m s−1) at (i),(j) 200 and (k),(l) 700 hPa on day 0. Stippled areas and displayed vectors are statistically significant at the 95% confidence level. The brown contours are isolines of 2500 m altitude. The rectangles denote SWC. The dates are selected according to the peaks of corresponding terms in Fig. 9.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
During the peaks of RPEPEs, the vertical variations in 7–20-day relative vorticity advected by basic-state zonal winds (term A-x-03) play a prominent role in maintaining and enhancing ascending motion over SWC. For type 1, the 7–20-day low-level anticyclone originating from the tropical WNP covers the Bay of Bengal and the Indochina Peninsula on day 0 (Fig. 4i). The region to the southwest of SWC is dominated by anticyclonic vorticity in the lower troposphere. Meanwhile, cyclonic vorticity has already existed in the center of SWC. Consequently, a positive 7–20-day zonal vorticity gradient is established to the southwest of SWC. The basic-state westerlies advect negative relative vorticity to SWC in the lower troposphere (Fig. 10g). The relative vorticity advection increases with height, although the relative vorticity advection is insignificant in the upper troposphere (Fig. 10e). For type 2, with the variation in SAH, a 7–20-day upper-level cyclonic vorticity center is formed to the northwest of SWC (Fig. 6f). The basic-state westerlies advect positive relative vorticity to SWC in the upper troposphere (Fig. 10f). The relative vorticity advection in the lower troposphere is weak (Fig. 10h). Consequently, the 7–20-day relative vorticity advected by basic-state zonal winds increases with height, resulting in a positive term A-x-03. Simply, low-level negative vorticity advection induces local convergence and consequent ascending motion for type-1 RPEPEs. The positive relative vorticity advection results in upper-level divergence over SWC and consequent low-level convergence and ascending motion in situ for type-2 RPEPEs.
On day 2, for type-2 RPEPEs, the upper-level 7–20-day cyclonic relative vorticity center moves to the north of SWC. The basic-state northerlies advect positive relative vorticity to SWC (Fig. 10j), forming the positive term A-y1-03 and favoring the maintenance of ascending motion over SWC, despite the relative vorticity advection is weak in the lower troposphere (Fig. 10l). In contrast, for type 1, the relative vorticity advection by basic-state meridional winds at upper and lower troposphere are too weak to have effects on 7–20-day ω (Figs. 10i,k).
Overall, the terms A-x-03, A-y1-03, and B-y-09 are closely related to the distinctive 7–20-day atmospheric circulation anomalies. The formations of 7–20-day ω and precipitation over SWC during the type-1 RPEPEs reflect the direct effects of the coupled suppressed convection and lower-level anticyclone. In contrast, the direct effects of the eastward-shifting SAH and the westward-extending WPSH are crucial for the 7–20-day ω and precipitation over SWC during the type-2 RPEPEs.
c. Moisture sources
Sufficient moisture is another vital condition of extreme precipitation. How the two types of low-latitude ISOs influence the moisture sources for the RPEPEs over SWC is investigated here using the FLEXPART model in the Lagrangian view. The FLEXPART model and the algorithm used to calculate moisture sources are described in section 2. Each RPEPE is simulated from 0000 UTC 12 days before its first day to 0000 UTC on the day after its last day. As the average residence time of moisture in the atmosphere is approximately 10 days (Trenberth 1998, 1999), we intercept the 12-day trajectory of an air parcel ending at the time when the air parcel first precipitates over SWC. Our results indicate that more than 90% of moisture can be attributed within 12 days.
The trajectories of the air parcels for each RPEPE are divided into 10 clusters using k-means clustering. Hence, 10 cluster-average trajectories for each RPEPE can be obtained. The cluster-average trajectories for the two types of events bear great resemblances (Figs. 11a,b). The air parcels are transported to SWC mainly through three pathways. The first one is related to the Indian summer monsoon in the lower troposphere. The air parcels originate from the south Indian Ocean, cross the equator, travel through the Arabian Sea and the Bay of Bengal, and arrive in SWC. The second pathway is related to upper-level westerlies in the midlatitudes. The air parcels travel along the north and northeast edges of the TP before arriving in SWC. The third pathway is related to the southeasterlies along the southwest edge of the WPSH. However, the number of trajectories through this pathway is less than those through the other two pathways. We calculate the trajectory density on a 1.0° × 1.0° grid to measure the differences in air-parcel pathways between the two types of RPEPEs. For each RPEPE, the trajectory density on a grid is defined as the number of trajectories passing through the grid divided by the total number of trajectories for the RPEPE. The trajectory density is calculated based on raw trajectories, instead of clustered average ones. The composite difference in trajectory density between the two types of RPEPEs is shown in Fig. 11c. A positive (negative) value means that the number of air parcels passing through that location is greater in type-2 (type-1) RPEPEs than in the other type. The result indicates that more (fewer) air parcels travel through India, South China, and the subtropical WNP before arriving in SWC for type-1 (type-2) RPEPs. More (fewer) air parcels travel through the tropical WNP, the Maritime Continent, and the northeast edge of the TP before arriving in SWC for type-2 (type-1) RPEPEs, respectively. The air-parcel trajectories have significant differences between the two types of RPEPEs.
Cluster-average 12-day trajectories of air parcels colored by the height above sea level (unit: km) for the RPEPEs in (a) type 1 and (b) type 2. (c) Composite difference in trajectory density between the two types of RPEPEs (unit: %; type 2 minus type 1). Stippled areas are statistically significant at the 90% confidence level. The brown contours are isolines of 2500 m altitude.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
Different air-parcel trajectories determine differences in the moisture sources. The moisture sources for the two types of RPEPEs are displayed in Figs. 12a and 12b. The two types of RPEPEs also share similar features with major moisture contributions from the Indian Ocean and the surroundings of SWC. These results resemble the moisture sources for climatological summer precipitation and summer extreme precipitation over SWC (Huang and Cui 2015a; Yuan and Yang 2020; Zhang and Wu 2021). Nevertheless, moisture sources also vary between the two types of RPEPEs (Fig. 12c), which is consistent with the results of the trajectory density (Fig. 11c). The moisture sources over southeastern China, India, and subtropical WNP contribute more to the type-1 RPEPEs than to the type-2 RPEPEs. The moisture sources over the tropical WNP, the Maritime Continent, and the northeast edge of the TP contribute more to the type-2 RPEPEs than to the type-1 RPEPEs. The differences in moisture sources over several regions, such as southeastern China and the Maritime Continent, are greater than one-tenth of the basic state, indicating the necessity to distinguish the moisture sources of the two-type RPEPEs.
Composite moisture sources (unit: mm day−1) for the RPEPEs in (a) type 1, (b) type 2, and (c) their differences (type 2 minus type 1). Stippled areas in (c) are statistically significant at the 90% confidence level. The brown contours are isolines of 2500 m altitude.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
The differences in the moisture sources are mainly attributed to different low-latitude 7–20-day atmospheric circulations. For type 1, a 7–20-day anticyclonic anomaly covers the SCS, the Indochina Peninsula, and the Bay of Bengal around day 0 (Fig. 4i). The 7–20-day southwesterly anomalies along the northwest edge of the anomalous anticyclone transport more moisture from India to SWC. For type 2, the RPEPEs are related to the zonal approaching WPSH and SAH. On the one hand, the WPSH-related 7–20-day anticyclonic anomaly over the Philippine Sea is conducive to transporting moisture from the tropical WNP and the Maritime Continent to SWC (Fig. 4j). Concurrently, the southwesterly along the 7–20-day anticyclonic anomaly hinders the westward moisture transport from southeastern China to SWC. As a result, positive moisture source anomalies over the tropical WNP and the Maritime Continent and negative moisture source anomalies over southeastern China are observed. On the other hand, the eastward-shifting SAH induces upper-level anomalous divergence and compensatory 7–20-day low-level northerlies north of SWC (Fig. 4j), favoring southward moisture transport from northeast of the TP to SWC.
5. Backgrounds of the two types of RPEPEs
The background anomalies of the two types of RPEPEs are further examined (Fig. 13). The background anomalies are defined as the 20-day low-pass-filtered components of anomalies. Few significant background anomalies can be observed during the type-1 RPEPEs. This result implies that the type-1 RPEPEs do not depend much on any specific background. In contrast, the type-2 RPEPEs prefer to occur under the background with warmer SST over the north Indian Ocean and the WNP and an anomalous anticyclone over the WNP in the lower troposphere. The anomalous background anticyclone over the WNP transports moisture from the tropical Indo-Pacific to SWC, contributing to the positive moisture source anomalies over the tropical WNP and Maritime Continent and negative moisture anomalies over southeastern China and the subtropical WNP for type-2 RPEPEs (Fig. 12c). As analyzed in the above sections, the 7–20-day enhanced convection over the SCS induces the westward extension of the WPSH that directly causes RPEPEs over SWC. Therefore, a strong WPSH is indispensable to make the mechanism work. Such an anomalous background anticyclone over the WNP can strengthen the WPSH. The phase locking between the 7–20-day and anomalous background WNP anticyclones is conducive to generating more intense precipitation over SWC. The warmer SST over the north Indian Ocean can emanate a Kelvin wave (low-level tropical easterlies) into the Pacific as a Matsuno–Gill-type response, causing Ekman divergence over the subtropical WNP and the resultant anticyclone (Matsuno 1966; Gill 1980; Xie et al. 2009). The warming over the WNP may be caused by the suppressed convection associated with the anomalous anticyclone.
Evolution of composite anomalous background SST (shading; unit: °C), horizontal winds at 850 hPa (vectors; unit: m s−1), and OLR (red and blue contours with an interval of 3 W m−2 for positive and negative values, respectively; the zero contours are omitted) from day −9 to day 3 with an interval of 3 days for the RPEPEs in (left) type 1 and (right) type 2. Stippled areas, displayed vectors, and solid contours denote the SST, 850 hPa wind, and OLR anomalies significant at the 95% confidence level, respectively. The rectangles denote SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
Are the background anomalies during type-2 RPEPEs contributed by 20–90-day ISOs or by anomalies in the basic states? To answer this question, the 20–90-day and above-90-day components (basic-state anomalies) are separated during the type-2 RPEPEs. The basic-state anomalies can also be considered anomalies in the annual cycle. Results indicate that both components have significant contributions to the background anomalies, but from the atmospheric circulation perspective, the 20–90-day ISOs contribute more to the WNP anticyclone than the basic-state anomalies (Fig. 14). In contrast, the SST anomalies are mainly contributed by basic-state anomalies. We further define a WNP anticyclone index (WNPACI) as the normalized relative vorticity anomaly (multiplied by −1) at 850 hPa averaged over the region of 10°–25°N, 110°–135°E, where the anomalous WNP anticyclone is mainly located. The 20–90-day and basic-state components of WNPACI are separated. The 20–90-day and basic-state WNPACIs remain positive throughout most type-2 RPEPEs (Fig. 15). The magnitudes of the 20–90-day component are greater than those of the basic-state component before the RPEPE peaks. These results suggest that the 20–90-day ISOs may play a more important role in forming a favorable atmospheric background for type-2 RPEPEs over SWC than basic-state anomalies.
As in Fig. 13, but for (left) 20–90-day anomalies and (right) basic-state anomalies.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
Evolution of the 20–90-day and anomalous basic-state WNPACIs during the type-2 RPEPEs. The bottom and top of the box represent the lower and upper quartiles and the bar in the box is the median. The whiskers extend from the 5th percentile to the 95th percentile. The dots in the boxes are the mean values exceeding the 90% confidence level.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
Finally, the summer-mean SST and atmospheric circulation anomalies are examined. We calculate summer-mean anomalies between the years with at least two RPEPEs in the same type and the years without any RPEPEs in this type. Summer, instead of May–September, is chosen because most of the RPEPEs occur during summer (Fig. 1). There are few significant anomalies in both atmospheric circulation and SST during the years with two or more type-1 RPEPEs (Figs. 16a,c), confirming the results that the type-1 RPEPEs do not rely on any specific backgrounds. In contrast, an anomalous WNP anticyclone and warm SST anomalies over the north Indian Ocean and the northeastern Maritime Continent can be observed in the years with two or more type-2 RPEPEs (Figs. 16b,d), resembling the results in Fig. 13 except for the warm SST anomalies over the northeastern Maritime Continent. The SST warming over the northeastern Maritime Continent may be caused by the low-level easterly anomalies that transport and accumulate sea surface warm water there. The WNP anticyclone reflects the variation in basic states. Thus, the interannual and interdecadal variations in SST over the north Indian Ocean may have a potential impact on the type-2 RPEPEs over SWC.
Composite differences in summer-mean (a),(b) geopotential heights (shading; unit: gpm) and horizontal winds (vectors; unit: m s−1) at 850 hPa and (c),(d) SST (unit: °C) between the years with two or more (a),(c) type-1 and (b),(d) type-2 RPEPEs and the years with no type-1 and type-2 RPEPEs. Stippled areas and displayed vectors are significant at the 90% confidence level. The rectangles denote SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
6. Summary and discussion
Previous studies have found that the rainy-season precipitation over SWC is influenced by different types of low-latitude 7–20-day ISOs. However, the specific effects of low-latitude 7–20-day ISOs on precipitation, especially persistent extreme precipitation over SWC, are still not clear. In this study, we first objectively identify 69 independent RPEPEs with significant 7–20-day components over SWC from May to September during 1979–2020. The precipitation anomalies of the RPEPEs are dominated by 7–20-day components. These RPEPEs are further divided into two types according to the evolution of low-latitude 7–20-day ISOs using k-means clustering. The atmospheric dynamical processes, moisture sources, and background conditions of the two types of RPEPEs are analyzed. The results are summarized in Fig. 17. Day −4 is used as the stage before the RPEPEs because it is the day when the 7–20-day convection anomalies start to appear over SCS, reflected by significant SCSCI (Figs. 5a,b).
Schematic diagram of the formation mechanisms of (left) type-1 and (right) type-2 RPEPEs over SWC. Day −4 and day 0 represent the stages before and during the RPEPEs, respectively. The cyclones and anticyclones with thick lines directly contribute to the RPEPEs. The brown contours denote the Tibetan Plateau. The rectangles denote SWC.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0310.1
For type 1, a coupled 7–20-day low-level anticyclone and suppressed convection originate from the tropical WNP more than 1 week ahead of the RPEPEs. The anticyclone first propagates northwestward to the SCS and then westward to the Bay of Bengal. Influenced by the 7–20-day anticyclone, the WPSH is in its transitional phase from the westernmost phase to the easternmost phase during the RPEPEs. When occupying the region from the SCS to the Bay of Bengal, the 7–20-day anticyclone can directly cause RPEPEs over SWC in cooperation with an anomalous cyclone over Northeast Asia. The diagnosis of the quasigeostrophic ω equation indicates that the 7–20-day atmospheric circulation anomalies can induce anomalous ascending motion over SWC through different processes during different stages of RPEPEs. The cold advection to the north and south sides of SWC related to the 7–20-day meridional winds and the resultant ascending motion over SWC is conducive to the onset of the RPEPEs. Afterward, the ascending motion and precipitation over SWC are maintained and intensified by negative 7–20-day geostrophic relative vorticity advected to SWC by basic-state zonal geostrophic winds in the lower troposphere, which leads to increases in vorticity advection with height. In addition, the 7–20-day anticyclone transports more moisture to SWC through the anomalous southwesterly along its northwest edge. The results of moisture source attribution indicate that the above-normal moisture from India is transported to SWC by the 7–20-day anticyclone. The mechanisms for type-1 RPEPEs do not depend much on the background conditions.
For type 2, a coupled 7–20-day low-level cyclone and enhanced convection also originate from the tropical WNP over 1 week before the RPEPEs. It propagates northwestward to the SCS and then decays before day 0. Different from the low-latitude 7–20-day anticyclone for type-1 RPEPEs, the enhanced convection over the SCS has an indirect effect on the RPEPEs over SWC. When arriving in the SCS, the enhanced convection causes an anomalous low-level anticyclone to the northeast and an anomalous upper-level anticyclone to the northwest due to the vertical and meridional nonuniformity of diabatic heating. Therefore, the enhanced convection over the SCS serves as a trigger for the westward extension of the WPSH and the eastward shift of the SAH. The WPSH- and SAH-related 7–20-day atmospheric circulation anomalies are crucial for the formation of the ascending motion over SWC during the RPEPEs. The warm advection linked to the WPSH-related southerlies and SAH-related temperature anomalies favor the onset of the RPEPEs. The relative vorticity anomalies associated with the eastward-shifting SAH result in the increase of vorticity advection with height, which intensifies the ascending motion over SWC. Type-2 RPEPEs prefer to occur under the background with an anomalous low-level anticyclone over the WNP. This implies that the WPSH, the direct contributor to the type-2 RPEPEs, should be stronger than normal to make the mechanisms work. Such an anomalous WNP anticyclone can be induced by warmer SST over the north Indian Ocean that excites a Kelvin wave over the tropical WNP. The 7–20-day and background anomalous WNP anticyclone and the eastward-shifting SAH jointly result in above-normal moisture contributions from the tropical WNP, the Maritime Continent, and the northeast TP, and below-normal moisture contributions from southeastern China and the subtropical WNP for the type-2 RPEPEs.
The analysis in this study indicates the influence of the low-latitude ISOs in the zonal shifts of WPSH and SAH. Apart from low-latitude ISOs, previous studies also suggested that the intraseasonal west–east shift of SAH can also be induced by midlatitude Rossby wave trains (Ren et al. 2015) and TP heat sources (Ren et al. 2019). In addition, the eastward shift of SAH can also induce a westward extension of WPSH (Wei et al. 2019b). Therefore, the zonal shifts of the WPSH and SAH are influenced by a number of factors.
During type-2 RPEPEs, the WPSH and the SAH show in-phase variations (Fig. 5b). Their variations lead to anomalous southerlies over southeastern China, indicating a stronger East Asian summer monsoon (Fig. 4j). Consequently, above-normal precipitation is observed over the region from SWC to Yangtze–Huai River basin (Fig. 2h). Such results are consistent with the results at the interannual time scale (e.g., Ren et al. 2013; Wei et al. 2014). However, the mechanism for type-1 RPEPEs is different, which could result in the absence of the influence of WPSH and SAH on SWC RPEPEs on the whole (Fig. 5c). Such a result indicates the complexity of the SWC summer precipitation.
Previous studies have reported the modulations of 30–60-day, interannual and interdecadal variations in climate systems on SWC precipitation during summer or rainy seasons (Li et al. 2016; Jiang et al. 2017; Xia et al. 2020; Xu et al. 2021). For example, phase 4 of the Madden–Julian oscillation (with enhanced convection over the Maritime Continent and suppressed convection over the tropical Pacific) is conducive to the precipitation over SWC (Li et al. 2016). However, our results show that only one type of RPEPE relies on a background. A specific climate background may be favorable for predicting the RPEPEs. So, whether the two types of RPEPEs show different predictability in the subseasonal-to-seasonal models? We will investigate this issue in the future.
Acknowledgments.
This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA23090102). We are grateful to three anonymous reviewers for their valuable comments that helped to improve the quality of this paper.
Data availability statement.
The observational station precipitation data are downloaded from http://data.cma.cn. The ERA5 data are available from https://climate.copernicus.eu/climate-reanalysis. The OLR data are from https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html. The HadISST1 datasets are derived from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The OISST data are from https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html.
REFERENCES
Baker, A. J., H. Sodemann, J. U. L. Baldini, S. F. M. Breitenbach, K. R. Johnson, J. van Hunen, and P. Zhang, 2015: Seasonality of westerly moisture transport in the East Asian summer monsoon and its implications for interpreting precipitation δ18O. J. Geophys. Res. Atmos., 120, 5850–5862, https://doi.org/10.1002/2014JD022919.
Bohlinger, P., A. Sorteberg, and H. Sodemann, 2017: Synoptic conditions and moisture sources actuating extreme precipitation in Nepal. J. Geophys. Res. Atmos., 122, 12 653–12 671, https://doi.org/10.1002/2017JD027543.
Butterworth, S., 1930: On the theory of filter amplifiers. Exp. Wireless Wireless Eng., 7, 536–541.
Chen, D., L. Gu, and X. Jiang, 2010: Characteristics of large-scale circulation background of summer heavy rainfall in Sichuan during 1981–2000 (in Chinese). Daqi Kexue Xuebao, 17, 443–450.
Chen, G., and C.-H. Sui, 2010: Characteristics and origin of quasi-biweekly oscillation over the western North Pacific during boreal summer. J. Geophys. Res., 115, D14113, https://doi.org/10.1029/2009JD013389.
Chen, G., and B. Wang, 2021: Diversity of the boreal summer intraseasonal oscillation. J. Geophys. Res. Atmos., 126, e2020JD034137, https://doi.org/10.1029/2020JD034137.
Chen, Y., and P. Zhai, 2013: Persistent extreme precipitation events in China during 1951-2010. Climate Res., 57, 143–155, https://doi.org/10.3354/cr01171.
Chen, Y., and P. Zhai, 2016: Mechanisms for concurrent low-latitude circulation anomalies responsible for persistent extreme precipitation in the Yangtze River Valley. Climate Dyn., 47, 989–1006, https://doi.org/10.1007/s00382-015-2885-6.
Chen, Y. R., R. Shi, Y. Q. Li, and C. G. Wang, 2010: The study of a kind of circulations characters on sustaining heavy rain in Sichuan Basin (in Chinese). Plateau Mt. Meteor. Res., 30, 29–34.
Cheng, Y., L. Wang, and T. Li, 2020: Causes of interdecadal increase in the intraseasonal rainfall variability over southern China around the early 1990s. J. Climate, 33, 9481–9496, https://doi.org/10.1175/JCLI-D-20-0047.1.
Cheng, Y., L. Wang, and T. Li, 2021: Two distinct types of 10–30-day persistent heavy rainfall events over the Yangtze River Valley. J. Climate, 34, 9571–9584, https://doi.org/10.1175/JCLI-D-20-0741.1.
Cressman, G. P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, 367–374, https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2.
Dottori, F., and Coauthors, 2018: Increased human and economic losses from river flooding with anthropogenic warming. Nat. Climate Change, 8, 781–786, https://doi.org/10.1038/s41558-018-0257-z.
Duan, W., and Coauthors, 2016: Floods and associated socioeconomic damages in China over the last century. Nat. Hazards, 82, 401–413, https://doi.org/10.1007/s11069-016-2207-2.
Easterling, D. R., G. A. Meehl, C. Parmesan, S. A. Changnon, T. R. Karl, and L. O. Mearns, 2000: Climate extremes: Observations, modeling, and impacts. Science, 289, 2068–2074, https://doi.org/10.1126/science.289.5487.2068.
Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447–462, https://doi.org/10.1002/qj.49710644905.
Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.
Holton, J. R., 2004: An Introduction to Dynamic Meteorology. 4th ed. Elsevier Academic Press, 553 pp.
Hsu, P.-C., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 4914–4931, https://doi.org/10.1175/JCLI-D-11-00310.1.
Hsu, P.-C., J.-Y. Lee, and K.-J. Ha, 2016: Influence of boreal summer intraseasonal oscillation on rainfall extremes in southern China. Int. J. Climatol., 36, 1403–1412, https://doi.org/10.1002/joc.4433.
Hu, W., A. Duan, Y. Li, and B. He, 2016: The intraseasonal oscillation of eastern Tibetan Plateau precipitation in response to the summer Eurasian wave train. J. Climate, 29, 7215–7230, https://doi.org/10.1175/JCLI-D-15-0620.1.
Hu, X., W. Yuan, and R. Yu, 2021: The extraordinary rainfall over the eastern periphery of the Tibetan Plateau in August 2020. Adv. Atmos. Sci., 38, 2097–2107, https://doi.org/10.1007/s00376-021-1134-7.
Huang, B., and Coauthors, 2021: Improvements of the daily optimum interpolation sea surface temperature (DOISST) version 2.1. J. Climate, 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1.
Huang, W., X. He, Z. Yang, T. Qiu, J. S. Wright, B. Wang, and D. Lin, 2018: Moisture sources for wintertime extreme precipitation events over South China during 1979–2013. J. Geophys. Res. Atmos., 123, 6690–6712, https://doi.org/10.1029/2018JD028485.
Huang, Y., and X. Cui, 2015a: Moisture sources of torrential rainfall events in the Sichuan basin of China during summers of 2009–13. J. Hydrometeor., 16, 1906–1917, https://doi.org/10.1175/JHM-D-14-0220.1.
Huang, Y., and X. Cui, 2015b: Moisture sources of an extreme precipitation event in Sichuan, China, based on the Lagrangian method. Atmos. Sci. Lett., 16, 177–183, https://doi.org/10.1002/asl2.562.
Jiang, X., Y. Li, S. Yang, J. Shu, and G. He, 2015: Interannual variation of mid-summer heavy rainfall in the eastern edge of the Tibetan Plateau. Climate Dyn., 45, 3091–3102, https://doi.org/10.1007/s00382-015-2526-0.
Jiang, X., J. Shu, X. Wang, X. Huang, and Q. Wu, 2017: The roles of convection over the western Maritime Continent and the Philippine Sea in interannual variability of summer rainfall over southwest China. J. Hydrometeor., 18, 2043–2056, https://doi.org/10.1175/JHM-D-16-0292.1.
Kikuchi, K., and B. Wang, 2009: Global perspective of the quasi-biweekly oscillation. J. Climate, 22, 1340–1359, https://doi.org/10.1175/2008JCLI2368.1.
Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS): Unifying tropical cyclone best track data. Bull. Amer. Meteor. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1.
Krishnamurti, T. N., S. M. Daggupaty, J. Fein, M. Kanamitsu, and J. D. Lee, 1973: Tibetan high and upper tropospheric tropical circulations during northern summer. Bull. Amer. Meteor. Soc., 54, 1234–1250, https://doi.org/10.1175/1520-0477-54.12.1234.
Li, J., and J. Mao, 2019: Impact of the boreal summer 30–60-day intraseasonal oscillation over the Asian summer monsoon region on persistent extreme rainfall over eastern China (in Chinese). Chin. J. Atmos. Sci., 43, 796–812, https://doi.org/10.3878/j.issn.1006-9895.1809.18145.
Li, J., P. Zhai, J. Mao, L. Song, and Q. Xiao, 2021: Synergistic effect of the 25–60-day tropical and midlatitude intraseasonal oscillations on the persistently severe Yangtze floods. Geophys. Res. Lett., 48, e2021GL095129, https://doi.org/10.1029/2021GL095129.
Li, Q., X. P. Cui, and J. Cao, 2014: Observational analysis and numerical simulation of a heavy rainfall event in Sichuan Province (in Chinese). Chin. J. Atmos. Sci., 38, 1095–1108, https://doi.org/10.3878/j.issn.1006-9895.1401.13255.
Li, R. C. Y., and W. Zhou, 2013: Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: Genesis and intensity. J. Climate, 26, 2904–2918, https://doi.org/10.1175/JCLI-D-12-00210.1.
Li, Y., X. Y. Qu, X. D. Yang, H. Fang, and C. R. Yin, 2013: The spatial and temporal distribution of China geo-hazard and key prevention area (in Chinese). Chin. J. Geol. Hazard Control, 24, 71–78, https://doi.org/10.16031/j.cnki.issn.1003-8035.2013.04.023.
Li, Y. H., B. Xiang, C. H. Lu, J. H. Ju, and N. Wang, 2016: Impact of Madden-Julian oscillation activities on precipitation in summer over the east of southwest China and its possible mechanism (in Chinese). Chin. J. Atmos. Sci., 40, 437–450, https://doi.org/10.3878/j.issn.1006-9895.1506.15134.
Liang, P., Z.-Z. Hu, Y. Ding, and Q. Qian, 2021: The extreme Mei-yu season in 2020: Role of the Madden-Julian oscillation and the cooperative influence of the Pacific and Indian oceans. Adv. Atmos. Sci., 38, 2040–2054, https://doi.org/10.1007/s00376-021-1078-y.
Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 1275–1277, https://doi.org/10.1175/1520-0477-77.6.1274.
Liebmann, B., H. H. Hendon, and J. D. Glick, 1994: The relationship between tropical cyclones of the western Pacific and Indian Oceans and the Madden–Julian oscillation. J. Meteor. Soc. Japan, 72, 401–412, https://doi.org/10.2151/jmsj1965.72.3_401.
Lin, A. L., D. J. Gu, D. D. Peng, B. Zheng, and C. H. Li, 2020: A definition index reflecting large-scale characteristics of regional persistent heavy rainfall events (in Chinese). J. Trop. Meteor., 36, 289–298 .
Lin, A. L., T. Li, L. Wang, and C. Li, 2021: The relationship between intraseasonal variation of the South Asian high and tropical intraseasonal oscillation (in Chinese). Chin. J. Atmos. Sci., 45, 633–650.
Liu, H., R. Yan, B. Wang, G. Chen, J. Ling, and S. Fu, 2022: Multiscale combined action and disturbance characteristics of pre-summer extreme precipitation events over South China. Adv. Atmos. Sci, https://doi.org/10.1007/s00376-021-1172-1, in press.
Liu, Y., G. Wu, H. Liu, and P. Liu, 1999: Impacts of spatial differential heating on the formation and variation of the subtropical anticyclone, III. Condensation latent heating and South Asian high and the subtropical anticyclone over western Pacific (in Chinese). Acta Meteor. Sin., 57, 525–538.
Liu, Y., G. Wu, R. Yu, and X. Liu, 2001: Thermal adaptation, overshooting, dispersion, and subtropical anticyclone Part II: Horizontal inhomogeneous heating and energy dispersion (in Chinese). Chin. J. Atmos. Sci., 25, 317–328.
Liu, Y., B. Hoskins, and M. Blackburn, 2007: Impact of Tibetan orography and heating on the summer flow over Asia. J. Meteor. Soc. Japan, 85B, 1–19, https://doi.org/10.2151/jmsj.85B.1.
Ma, Y., M. Lu, C. Bracken, and H. Chen, 2020: Spatially coherent clusters of summer precipitation extremes in the Tibetan Plateau: Where is the moisture from? Atmos. Res., 237, 104841, https://doi.org/10.1016/j.atmosres.2020.104841.
Mao, J., Z. Sun, and G. Wu, 2010: 20-50-day oscillation of summer Yangtze rainfall in response to intraseasonal variations in the subtropical high over the western North Pacific and South China Sea. Climate Dyn., 34, 747–761, https://doi.org/10.1007/s00382-009-0628-2.
Matsuno, T., 1966: Quasi-geostrophic motions in the equatorial area. J. Meteor. Soc. Japan, 44, 25–43, https://doi.org/10.2151/jmsj1965.44.1_25.
Miao, R., M. Wen, R. Zhang, and L. Li, 2019: The influence of wave trains in mid-high latitudes on persistent heavy rain during the first rainy season over South China. Climate Dyn., 53, 2949–2968, https://doi.org/10.1007/s00382-019-04670-y.
Nie, Y., and J. Sun, 2020: Evaluation of high-resolution precipitation products over southwest China. J. Hydrometeor., 21, 2691–2712, https://doi.org/10.1175/JHM-D-20-0045.1.
Nie, Y., and J. Sun, 2021: Synoptic-scale circulation precursors of extreme precipitation events over southwest China during the rainy season. J. Geophys. Res. Atmos., 126, e2021JD035134, https://doi.org/10.1029/2021JD035134.
Nie, Y., and J. Sun, 2022a: Causes of interannual variability of summer precipitation intraseasonal oscillation intensity over southwest China. J. Climate, 35, 3705–3723, https://doi.org/10.1175/JCLI-D-21-0627.1.
Nie, Y., and J. Sun, 2022b: Moisture sources and transport for extreme precipitation over Henan in July 2021. Geophys. Res. Lett., 49, e2021GL097446, https://doi.org/10.1029/2021GL097446.
Pisso, I., and Coauthors, 2019: The Lagrangian particle dispersion model FLEXPART version 10.4. Geosci. Model Dev., 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019.
Qian, Z., and S. Yu, 1991: The relationship between medium-range fluctuation of condensation heating in East Asia and quasi-2 weeks oscillation of the West Pacific subtropical high (in Chinese). J. Trop. Meteor., 7, 259–267.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Ren, P., H.-L. Ren, J.-X. Fu, J. Wu, and L. Du, 2018: Impact of boreal summer intraseasonal oscillation on rainfall extremes in southeastern China and its predictability in CFSv2. J. Geophys. Res. Atmos., 123, 4423–4442, https://doi.org/10.1029/2017JD028043.
Ren, Q., F. Liu, B. Wang, S. Yang, H. Wang, and W. Dong, 2022: Origins of the intraseasonal variability of East Asian summer precipitation. Geophys. Res. Lett., 49, e2021GL096574, https://doi.org/10.1029/2021GL096574.
Ren, R., C. Zhu, and M. Cai, 2019: Linking quasi-biweekly variability of the South Asian high to atmospheric heating over Tibetan Plateau in summer. Climate Dyn., 53, 3419–3429, https://doi.org/10.1007/s00382-019-04713-4.
Ren, X., X.-Q. Yang, and X. Sun, 2013: Zonal oscillation of western Pacific subtropical high and subseasonal SST variations during Yangtze persistent heavy rainfall events. J. Climate, 26, 8929–8946, https://doi.org/10.1175/JCLI-D-12-00861.1.
Ren, X., D. Yang, and X.-Q. Yang, 2015: Characteristics and mechanisms of the subseasonal eastward extension of the South Asian high. J. Climate, 28, 6799–6822, https://doi.org/10.1175/JCLI-D-14-00682.1.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.