1. Introduction
Central Asia (CA) occurs in Eurasia’s interior and includes Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and the northwestern part of China. Due to its location, this area is dominated by westerlies of varying strengths; it is characterized by an uneven topography and complex land types, including deserts, glaciers, and lakes; and it is a sensitive region. The central Asian high mountain (CAHM) region mainly encompasses the Tianshan Mountain and Pamir Plateau regions and notably impacts the water distribution by lifting airflows passing through high-elevation areas. The climatic differences in most areas of CA are characterized by precipitation seasonality (Sha et al. 2018; Baldwin and Vecchi 2016). Against the background of global warming, many studies have shown that CA is facing abnormal hydrological recycling and is becoming warmer and wetter; these conditions have been increasingly considered by researchers (Chen et al. 2011; Shi et al. 2007; Wu et al. 2019; Yang et al. 2020; Zhang et al. 2019). Global warming contributes to accelerating the hydrological cycle and enhancing extreme precipitation events. The distribution of precipitation in the mountainous area of CA is highly complex due to the phenomenon of elevation dependence on warming, and the region experiences increased precipitation or extreme precipitation (Chen et al. 2011; Rangwala and Miller 2012; Zhang et al. 2017). These phenomena inevitably affect local areas through unstable water supplies and can cause severe disasters. Glaciers change, and more precipitation eventually leads to anomalous hydrological recycling.
Atmospheric circulation and water supply are crucial for precipitation. Numerous studies have focused on the weather and climate impacts in CA. The North Atlantic Ocean and western Eurasia are primary moisture sources for CA. Yang et al. (2020) found that western CA’s seasonal precipitation predominantly occurs in winter and spring, while eastern CA receives most precipitation in summer. Zhao et al. (2014a) identified that cooling in the mid- and upper troposphere due to a weak South Asian monsoon affects summer precipitation in the Tarim Basin by causing a southward shift in the westerly jet stream, leading to increased precipitation through anomalous cyclones and moisture from lower latitudes. Huang et al. (2015) noted that water vapor from the Arabian Sea is transported by an anomalous pressure gradient. Zhao et al. (2016) reported tropical air masses invasions in the Tarim Basin due to the strong plateau monsoon by southward movement of westerlies. Jiang et al. (2021) identified that anomalous surface sea temperatures in the tropical Pacific and Atlantic Oceans were the main drivers of wetter conditions in CA from 1955 to 2004. Although the physical mechanisms causing increased precipitation in CA vary, a number of researchers agree that the main moisture source for CA is located in high-latitude regions such as the North Atlantic Ocean and Arctic Ocean and is generated by westerlies (Bothe et al. 2012; Ren et al. 2022; Wang et al. 2017). Cyclonic anomalies, including monsoon depressions and troughs in westerlies, enhance CA’s precipitation through increased southerly water supply. While some studies argue that localized evapotranspiration due to warming impacts increased precipitation (Peng et al. 2018; Yao et al. 2020), the effect of local water cycling on enhanced precipitation in most CA regions is very limited (Chen et al. 2021; Wang et al. 2016; Wu et al. 2019). Basically, large-scale circulation changes remain responsible for the increase in CA precipitation.
Although many studies have explored anomalous precipitation in CA, few studies have focused on specific extreme precipitation events in the CAHM due to a lack of observations in this harsh environment. There are many glaciers in the CAHM, which is referred to as the central Asia water tower, crucial for downstream water supplies, are experiencing increased precipitation under warmer, wetter CA conditions (Chen et al. 2016; Hu et al. 2017). However, the mechanisms driving extreme precipitation in these mountainous regions and whether differences exist between CA remain poorly understood. This research investigates extreme precipitation in the CAHM area, specifically the Pamir Plateau and Tianshan Mountains, during monsoon season (May–September) from 1979 to 2022 using the fifth major global reanalysis produced by ECMWF (ERA5) dataset. We examine how atmospheric systems around the CAHM affect extreme precipitation by analyzing water supply and atmospheric circulation under abnormal and normal conditions.
2. Dataset and methods
a. Dataset
We used the ERA5 reanalysis dataset (Hersbach et al. 2006a,b) to analyze precipitation and atmospheric circulation. The ERA5 dataset serves as a valuable resource for studies on synoptic meteorology and climate. The temporal and spatial resolutions of ERA5 dataset are 1 h and 0.25° from 1979 to 2022 (May–September). The daily precipitation is the sum of the hourly precipitation over a whole day. We also employed the Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) dataset (Yatagai et al. 2012) in a comparison to the ERA5 data for ensuring reasonable mean extreme precipitation values for its better representativity of central Asia extreme precipitation (Lai et al. 2020).
APHRODITE dataset is a comprehensive collection of daily precipitation measurements over Asia. This initiative is one of the most detailed and accurate efforts in the region to quantify and analyze precipitation data. It amalgamates and interpolates data from a multitude of sources including surface observations, rain gauges, and satellite retrievals, offering a high spatial and temporal resolution. The dataset is particularly valuable for hydrological studies, climate research, and in the assessment and management of water resources. By providing a long-term record of precipitation patterns, APHRODITE enables the scientific community to better understand climatic trends, variability, and extremes in Asia. The spatial and temporal resolutions of the APHRODITE dataset are 0.25° and 1 day, respectively, and the time ranges from 1979 to 2015 (May–September).
b. Methods
In this study, climatology refers to the monthly average from 1979 to 2022 (May–September), while anomalies denote deviations from the climatology. The other important definitions in this paper are as follows:
1) Extreme precipitation
Extreme precipitation events can be identified in two steps. First, we sorted the spatially averaged daily precipitation values of the four key study regions in ascending order and then defined extreme precipitation events for each region as those exceeding 99% of the entire sorted sequence. Here, we obtained 67 extreme precipitation events for each key region from 1979 to 2022 (May–September). Extreme precipitation in No. 01 is referred to as C01, with C02, C03, and C04 similarly representing No. 02, No. 03, and No. 04, respectively.
2) Westerlies index
To examine the influence of westerlies in the study area, we defined the westerlies index by the mean zonal wind at geopotential heights from 200 to 300 hPa in the area of 35°–45°N and 70°–90°E regions.
3) Monsoon index
We adopted the definitions of the NOAA Climate Prediction Center based on Wang et al. (Wang and Fan 1999; Wang et al. 2008). The India summer monsoon index was defined as the difference between the longitudinal wind speed at 850 hPa (U850) averaged over 5°–15°N and 40°–80°E and that averaged over 20°–30°N and 70°–90°E, and the East Asia monsoon index was defined as the difference between U850 averaged over 5°–15°N and 90°–130°E and that averaged over 20°–30°N and 110°–140°E.
4) Local meteorological indices
U_local/V_local indicates the mean zonal (meridional) wind averaged over the midtroposphere (i.e., 300–600 hPa) in each key region.
Uq_local/Vq_local indicates the mean zonal/meridional water vapor flux integrated from 300 to 600 hPa across each key region.
3. Results
a. Extreme precipitation events in the CAHM region
The CAHM region, featuring some of the world’s highest peaks like Hindu Kush, Pamir, and the Tianshan Mountain ranges, is crucial for both regional and global climate systems (Fig. 1a). These mountains, acting as natural barriers, significantly influence atmospheric circulation and consequently the weather and climate of surrounding areas. The Tianshan Mountains and Pamir Plateau particularly affect the spatial–temporal distribution of precipitation in CA, with complex terrain enhancing precipitation seasonality through orographic effects (Baldwin and Vecchi 2016). Additionally, the region is a vital freshwater source for local people, with many rivers originating from its glaciers and snowfields, supporting millions downstream. The CAHM spans from 35° to 45°N and from 70° to 90°E, encompassing four key study regions: Pamir (No. 01) and the eastern, central, and western Tianshan Mountain areas (No. 02, No. 03, and No. 04). The climatological average position of the westerly jet stream, adjacent to the Pamir and Tianshan Mountains during the monsoon period (as shown by the black bold line in Fig. 1a), suggests distinct processes for extreme precipitation in these regions.
(a) Main location and topography (shading) of the CAHM area. Bold blue lines indicate the boundaries of the CA. Black bold lines denote the mean climatological zonal wind speeds greater than 30 m s−1, characterizing the axis of the westerly jet stream. The red, green, purple, and yellow squares denote No. 01, No. 02, No. 03, and No. 04 study areas, respectively. Seasonal precipitation distribution in the CAHM area derived from the (b) APHRODITE and (c) ERA5 datasets during the monsoon period (from May to September) from 1979 to 2022.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
CA is in the interior of Asia, so precipitation is generally low and concentrated in the mountainous areas, which is caused by the high elevation (Figs. 1b,c). Airflows crossing these mountains undergo adiabatic cooling, leading to vapor saturation and precipitation if the atmosphere is unstable, as seen in Tianshan Mountains (Roe 2005). ERA5 and APHRODITE datasets effectively capture the CAHM area’s precipitation characteristics, though ERA5 shows higher monthly precipitation during the monsoon period from 1979 to 2022, reaching up to 5 mm day−1, even 9 mm day−1 at high altitudes. Conversely, APHRODITE records a maximum of 5 mm day−1 in the northwestern Himalayas, with precipitation in CA primarily concentrated in the Tianshan Mountains. Both datasets indicate lower precipitation in Pamir compared to surrounding areas, suggesting minimal rainfall in the usual atmospheric circulation.
1) Precipitation
As Fig. 2 shows, ERA5 dataset shows higher extreme precipitation than APHRODITE, similar to the situation of monthly average precipitation in CA. However, both datasets present similar spatial distributions in each region, despite ERA5 has broader precipitation range. For ERA5, extreme precipitation for each region could reach 15 mm month−1, with some places exceeding 20 mm month−1. Remarkably, for C01 and C02, precipitation distributions (Figs. 2a–d) show unique patterns compared to others, as Pamir experiences less precipitation for C03 and C04. Moreover, the long-term mean monthly precipitation in CA is distinct from that when Pamir experiencing extreme precipitation.
Mean daily precipitation distribution in the CAHM region when extreme precipitation events occur in (a),(b) No. 01, (c),(d) No. 02, (e),(f) No. 03, and (g),(h) No. 04 regions based on the (b),(d),(f),(h) ERA5 and (a),(c),(e),(g) APHRODITE datasets. The dots indicate the correlations between extreme precipitation of key regions and precipitation in the region of 30°–50°N, 60°–100°E are significant at the 95% confidence level.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
The comparative analysis of long-term monthly average and extreme precipitation in four regions showed a consistent pattern between ERA5 and APHRODITE, especially in the Tianshan region, more obvious in the ERA5 than in the APHRODITE dataset. Analyzing extreme precipitation correlations across CAHM derived from ERA5, we found significant correlations between each key region and the south area located the key region such as Pamir and the northwestern part of the Himalayas for C01 (Fig. 2b). Precipitation correlation results vary between the APHRODITE and ERA5 datasets. The parts related to the key study area’s extreme precipitation are more consistent in No. 01 (Figs. 2a,b) and No. 04 (Figs. 2g,h) across the two datasets, while clear differences are observed for C03 and C04 (Figs. 2c–f). This discrepancy may be due to fundamental differences in their underlying data sources and structural composition. APHRODITE is a merged product with observational data integration, whereas ERA5 is dynamic and physical modeling precipitation. Otherwise, APHRODITE dataset’s shorter duration compared to ERA5.
We also analyze the mean water vapor flux and mean water vapor divergence anomalies during extreme precipitation events for the monsoon period in each key region from 850 to 300 hPa (Fig. 3). Notably, CAHM was dominated by anomalous water vapor convergence for C01 and C02. Conversely, for C03 and C04, anomalous convergence was confined there, while No. 01 and No. 02 showed anomalous water vapor divergence.
Mean vertically integrated 300–850-hPa water vapor flux anomalies (arrows) and mean water vapor convergence anomalies (colors), and 500-hPa geopotential height anomaly (black lines) based on the extreme precipitation events in the four key regions during the monsoon periods from 1979 to 2022. Red, green, purple, and yellow squares denote No. 01, No. 02, No. 03, and No. 04, respectively.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
2) Water vapor transport
From a moisture transport view, the CAHM’s primary moisture source for extreme precipitation is the southern region to their regions. For C01 and C02 (Figs. 3a,b), significant water vapor transport occurs via the Bay of Bengal–Arabian Sea/India–Pakistan corridor. For C02, less notable moisture anomalies occur over northern India and the Arabian Sea, notably through western India and Pakistan. Another distinction for C02 compared to C01 is the anomalous water vapor flux from southern China below 30°N. For C02, the water vapor flux from southern China, below 30°N, diverged westward as it moved north. This path took it across northern Mongolia, where it merged with the southern branch of water vapor from the northern Arabian Sea and continued northeastward. For C03 and C04, No. 03 and No. 04 receive moisture mainly from the eastern Bay of Bengal/Burma–South China Sea–central/eastern China–East China Sea–northern China. However, No. 04’s water vapor flux from the north Indian Ocean’s west is more pronounced than in No. 03. Past research on CA water vapor sources confirms tropical Indian Ocean and South China Sea as moisture origins, except for North Atlantic (Chen et al. 2021; Yang et al. 2020), matching our results.
During extreme precipitation in the CAHM region, both meridional and zonal moisture fluxes deviate from the climatological mean. We analyzed correlations between 300- and 600-hPa layer moisture transport and regional precipitation to understand their impact (Table 1). Results indicated correlation coefficients ranging from 0.31 to 0.63 between 300- and 600-hPa meridional moisture transport and precipitation across each key region. However, only No. 03 showed a significant correlation with zonal moisture transport. This suggests meridional moisture transport exerts a substantial influence on the occurrence of extreme precipitation. Previous studies have shown increased precipitation in CA is often accompanied by anomalous southerly winds, which agrees with our results (Bothe et al. 2012; Chen et al. 2021; Li and Ma 2018; Zhao and Zhang 2016; Huang et al. 2017).
Mean meridional and zonal moisture fluxes in the 300–600-hPa layer passing through the study area during extreme precipitation events in the four regions, along with the correlation coefficients with precipitation. The values in parentheses are the correlation coefficients, where one asterisk indicates significance at the 95% level and two asterisks denote significance at the 99% level.
As Fig. 4 shows, meridional winds are positively correlated with precipitation in areas such as the northern Arabian Sea and Pakistan for C01, northeastern China and parts of the East China Sea for C02, northwestern Tibetan Plateau for C03, and parts of the Indochina Peninsula, a small part of the eastern Tibetan Plateau and lower-latitude areas at approximately 115°E and 10°N for C04. These locations correspond with the anomalous water vapor fluxes shown in Fig. 3, indicating that these regions could be key in influencing the moisture supply for the CAHM area.
Meridional wind anomaly at 300 hPa (black contour; m s−1) regressed against extreme precipitation in (a) No. 01, (b) No. 02, (c) No. 03, and (d) No. 04 regions during the monsoon period from 1979 to 2022. Yellow (dark blue) shading indicates that the positive (negative) correlations between 300-hPa meridional wind in each region and meridional wind are significant at the 95% confidence level. Red, green, purple and yellow squares indicate No. 01, No. 02, No. 03, and No. 04, respectively.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
Slight differences exist in how meridional winds correlate with extreme precipitation in CAHM, especially below 500 hPa (Fig. 5). For C01 and C02, a positive correlation was found between extreme precipitation and lower-atmospheric meridional winds, more pronounced in No. 01, but this correlation was absent for C03 and C04. Accompanying with Fig. 3, it suggests that both lower- and upper–middle-atmospheric water vapor flux anomalies may influence extreme precipitation in No. 01 and No. 02, while only upper–middle-atmospheric water vapor plays a role in No. 03 and No. 04. Research (Chen et al. 2021; Huang et al. 2015) indicates lower levels have moisture transport from the equatorial Indian Ocean and South China Sea, entering via the Tibetan Plateau’s (TP) western and eastern sides, while the midlevel and upper level transport moisture across the TP to CA.
(a)–(d) Pressure–latitude cross sections of the meridional wind anomaly (contour; m s−1) and meridional wind and vertical velocity (−ω, 10−2 Pa s−1) vectors (gray vectors) averaged across the longitudinal range (light gray box) of the four key regions for extreme precipitation events. (e)–(h) Pressure–longitude cross section of the meridional wind anomaly (contour; m s−1) and zonal wind and vertical velocity (−ω, 10−2 Pa s−1) vectors (gray vectors) averaged across the latitudinal range (light gray box) of the 4 key regions for extreme precipitation events. The black shading indicates the terrain. The yellow (dark blue) shading indicates that the positive (negative) correlations between precipitation and meridional wind in each region are significant at the 95% confidence level.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
3) Large-scale circulation
The CAHM area has a wide latitudinal range and distinct physical mechanisms when extreme precipitation happens due to its geographic position. Figures 3a and 3b show mostly negative potential height anomalies north of 20°N at 500 hPa, except for positive anomalies over the northern Caspian and Black Seas. A notable negative anomaly over Kazakhstan positions No. 01 and No. 02 ahead of a deep trough. The omega equation indicates that areas ahead of a trough in a shortwave system are prone to experience upward motion. This, along with the pressure gradient force from the southern deep trough, facilitates northward moisture transport from low-latitude equatorial regions, causing precipitation. However, the negative geopotential height anomaly occurs slightly further north for C02. While still favoring northward moisture movement, northward moisture transport does not readily occur south of 30°N (vectors in Fig. 3b).
For C03 and C04, a distinct positive–negative–positive potential height anomaly pattern occurs between 40° and 60°N at 500 hPa, characteristic of the quasi-zonal distribution influenced by Rossby wave propagation (Ding and Wang 2005; Wang et al. 2017), which known as the Silk Road pattern (SRP), coincides with anomalous meridional wind distribution in the mid- and upper troposphere. No. 03 or No. 04 positioned ahead of the trough, experienced upward motion conducive to precipitation, similar to No. 01 and No. 02 for C01 and C02, and exhibited anomalous meridional water vapor flux. Additionally, to the south of the positive geopotential height anomaly over Mongolia, anomalous easterly winds correspond to anomalous water vapor flux eastward (Figs. 3c,d). For C04, a negative geopotential height anomaly over the Tibetan Plateau and to the west forms a wide trough, creating an anomalous southeasterly water vapor flux, less evident for C03.
The CAHM region’s moisture supply predominantly comes from lower latitudes. Geographical factors enable moisture from the northern Arabian Sea and Bay of Bengal to directly affect extreme precipitation in No. 01 and No. 02. Conversely, moisture primarily comes from the Tibetan Plateau in No. 03 and No. 04.
b. Role of monsoons and westerlies in extreme precipitation events in the CAHM region
In the monsoon season, Eurasian weather is influenced mainly by monsoons and westerlies. Therefore, we also calculated the westerlies index over the CAHM, as well as ISM index and EASM index and their climatological averages from 1979 to 2022 (May–September), as summarized in Table 2. Results show that during extreme precipitation, the westerlies intensity is consistently high, while monsoon indices differ. ISM and EASM have weaker average indices for C01 and C02, especially EASM for C01. ISM is slightly above the average, but EASM remains below for C03 and C04.
Mean westerlies, ISM, and EASM indices for extreme precipitation in each study area and their monthly climatological states.
To understand how westerlies and monsoons influence CAHM precipitation, we normalized daily indices during the monsoon season, defining extreme values as greater than 2 for strong westerlies and less than −2 for weak ones. Figure 6 displays strong westerlies correlate with significant precipitation, especially in the northern Pamir Plateau and Tianshan, while weak westerlies lead to minimal precipitation. Strong westerlies in CAHM are associated with negative geopotential height anomalies north of the jet stream, promoting upward motion and an anomalous southern moisture supply from the tropical western Pacific to southwestern CAHM, enhancing precipitation. For weak westerlies (Fig. 7b), positive geopotential height anomalies over CAHM, drier moisture from higher latitudes are less favorable for precipitation.
Mean precipitation distribution under strong and weak westerlies and monsoon indices.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
Mean vertically integrated 300–850-hPa water vapor flux anomalies (arrows) and mean water vapor convergence anomalies (colors) and 500-hPa geopotential height anomaly (black lines) based on strong and weak westerlies and monsoons. Red, green, purple, and yellow squares indicate No. 01, No. 02, No. 03, and No. 04, respectively.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
However, monsoons exert less influence on the CAHM than westerlies, as precipitation occurs in some parts of the Tianshan Mountains regardless of the monsoon strength. High ISM or EASM values show positive geopotential height anomalies in CAHM without significant moisture flux (Figs. 7c,e), not favoring precipitation. Conversely, it is a different situation under low ISM or EASM, especially noticeable with a low ISM. Low ISM conditions bring considerable rainfall to the western Pamir Plateau and Tianshan due to lower geopotential heights at midlatitude (Fig. 7d) and increased northward moisture flux from tropical regions. With low EASM, these areas see a slight increase in precipitation and its moisture flux anomaly is similar to weak ISM conditions, but a positive geopotential height anomaly emerges to the north of the CAHM and weaker moisture transport in southwestern CAHM result in less pronounced precipitation (Fig. 7f). Basically, westerlies play a pivotal role in determining whether the CAHM experiences widespread and obvious precipitation, while monsoons may influence moisture transport from lower latitudes. This result might explain why both the ISM and EASM are relatively weak during extreme precipitation for C01 and C02.
To determine the impact of monsoon of varying intensities on precipitation and moisture transport in the context of strong westerlies, we used a criterion defining a high index for values over 1 and a weak index for values below −1. We found that significant precipitation occurred regardless of monsoon strength during strong westerlies in the Tianshan (Fig. 8). However, in the western Pamir Plateau and western Tianshan, precipitation was notable only under weak ISM and weak EASM conditions. This is characterized by a substantial anomalous moisture flux from the low-latitude tropical sea and negative geopotential height anomalies over the northern midlatitudes (Fig. 9b), favoring precipitation in southwestern CAHM. In contrast, under weak ISM and strong EASM, the northward moisture flux from the Arabian Sea was greatly reduced and despite a negative geopotential height anomaly over CAHM is observed (Fig. 9d), there is less precipitation than the conditions shown in Fig. 9b.
Mean precipitation distribution for different monsoon strengths under strong westerlies passing through the CAHM. The cyan line indicates the boundary of CA. The blue, red, green, and yellow squares indicate No. 01, No. 02, No. 03, and No. 04, respectively.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
Mean vertically integrated 300–850-hPa water vapor flux anomalies (arrows) and mean water vapor convergence (colors) and 500-hPa geopotential height anomaly (black lines) for the different monsoon synergy configurations. Red, green, purple, and yellow squares indicate No. 01, No. 02, No. 03, and No. 04, respectively.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
During strong ISM (Figs. 9a,c), moisture flux anomalously moves northward from eastern India, along the southern Himalayan foothills to reach CAHM. The difference lies in the moisture origin: with strong ISM and EASM, it starts near 120°E and 20°N in the sea south of China, moving through southern China, northern Myanmar, and northern India, but with strong ISM and weak EASM, it moves from the Philippine Islands through southern Myanmar. Their atmospheric circulation patterns are similar, showing positive geopotential height anomalies over CAHM, hindering upward air movement. That is why there was no significant precipitation over Pamir Plateau under strong ISM. Thus, in No. 01 and No. 02, strong westerlies combined with weaker ISM enhance northward moisture transport, leading to precipitation.
However, EASM does not promote northward moisture movement from the Arabian Sea or negative geopotential height anomaly in north of CAHM. This suggests that precipitation in southwestern CAHM depends on the combined effects of westerlies, ISM, and EASM. Figure 9b compared with Figs. 3a and 3b show related atmospheric patterns, implying a similar situation between mechanisms for extreme and regular precipitation. While westerlies bring precipitation to No. 03 and No. 04, the negative phase of SRP is responsible for their extreme precipitation. This suggests the mechanisms driving extreme precipitation events are not entirely identical to those responsible for regular precipitation events. Moreover, SRP is influenced by equatorial sea temperatures and monsoons (Ding and Wang 2005; Chen and Huang 2012), highlighting the monsoon’s significant impact on CAHM’s extreme precipitation events.
c. Establishment of atmospheric circulation relative to extreme precipitation in the CAHM
Wind fields at 850 and 300 hpa related to the monsoon index and extreme precipitation were analyzed for the CAHM’s extreme precipitation mechanisms (Figs. 10 and 11). At lower atmosphere, wind fields related to precipitation in the CAHM are predominantly concentrated around the Arabian Sea, except for C02, suggesting extreme precipitation in the CAHM are closely linked to ISM. Nevertheless, ISM’s impact on extreme precipitation varies across CAHM, as shown in Fig. 3 and Table 2.
Temperature anomaly and wind field (vector; m s−1) at 850 hPa. For (a)–(d), light blue vectors (dark blue vectors) indicate that the wind field relative to precipitation (ISM) in the study region is significant at the 95% confidence level. For (e)–(h), it is the same meaning for EASM. Dotted regions show the wind field relative to ISM or EASM and precipitation in the study regions is significant at the 95% significance level.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
As in Fig. 10, but for 300 hPa.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
For C01 and C02, 850-hPa warm anomaly over the Indian subcontinent (Figs. 10a,b) implies a weaker ISM’s insufficient latent heat transport, which is reflected as negative geopotential height anomalies coincide with negative temperature anomalies in mid- and upper troposphere, inducing an anomalous cyclonic circulation according to the thermal wind principle. Low temperatures correspond to anomalous cyclonic circulation, forming positive feedback and constituting one of the key circulation systems affecting precipitation in central Asia. Southerly winds on the western side of this anomalous cyclone intensify, while those on the northern side weaken, resulting in a southward shift in westerlies in this region. The moisture transport paths for C01 and C02 differ, with No. 01’s extreme precipitation linked to Arabian Sea wind fields and No. 02’s largely independent of them. ISM’s strength affects extreme precipitation by influencing westerlies, while EASM impacts moisture transport from the north Indian Ocean, determining the location of extreme events.
For C03 and C04, SRP shows negative phase in the mid- and upper troposphere during weak heating anomalies in north Indian Ocean, while strong anomalies tend to induce a positive phase (Huang et al. 2015; Chen and Huang 2012). The weak heating anomaly over the northern Arabian Sea consist with SRP’s negative phase in these regions, indirectly affecting extreme precipitation in northwestern CAHM through the ISM and tropical oceanic heat anomalies. The black lines in Figs. 3c and 3d show the slight variation in negative and positive geopotential height anomalies in the western and eastern parts of these regions, influencing anomalous southerly wind position and moisture supply, thus causing variability in extreme precipitation locations. The correlation between monsoon indices and wind fields at 300 hPa suggests that these discrepancies are due to differing monsoonal impacts on wind fields.
The monsoon’s effect on the westerlies jet is more pronounced in the upper troposphere. For C03 (Figs. 11c,g), ISM shows no significant correlation with wind fields near geopotential height anomalies. Conversely, EASM correlates strongly with the northeastern wind field, potentially causing warm advection and positive geopotential height anomalies, which may shift southerly winds westward. For C04 (Figs. 11d,h), extreme precipitation is associated with a correlation between ISM and wind fields on both sides of the geopotential height anomalies, while EASM shows a partial correlation with western wind fields. This leads to cold and warm advection on the western and eastern sides, respectively, and corresponding cold and warm anomalies. The western monsoon wind fields may induce negative geopotential heights, while eastern ones could lead to positive anomalies, intensifying the negative and positive geopotential heights in No. 04’s negative SRP phase and slightly altering the direction of the anomalous southerly winds compared to No. 03.
Therefore, the negative phase of SRP creates conditions for precipitation, with anomalous southerly winds supplying moisture in No. 03 and No. 04. Variations in the ISM and EASM’s impacts on the wind field along the westerlies may subtly alter SRP’s negative phase and the east–west positioning of moisture transport in the CAHM’s northeast, affecting precipitation locations.
d. Possible period of extreme precipitation in the different CAHM areas during the monsoon season
We also examined monthly distributions and changes in westerly and monsoon indices during the monsoon (Figs. 12c,d). In southwestern CAHM, extreme events peak in May especially No. 01, while in northeastern CAHM, they are spread out and often occurring in midmonsoon (June–July), influenced by different temporal dynamics of westerlies and monsoons. Westerlies gradually strengthen during the monsoon, with ISM peaking in July and EASM peaking in August.
(a),(b) Schematic and water vapor flux anomalies when the different regions in the CAHM experience extreme precipitation. Red, green, purple, and yellow squares (curving lines) denote No. 01, No. 02, No. 03, and No. 04 areas (water vapor anomalies), respectively. W (C) indicates warming (cooling). The clockwise (anticlockwise) arrows denote positive (negative) geopotential height anomalies. (c) Temporal distribution of extreme precipitation events in the four key regions in the CAHM, and number in different color means the count of extreme precipitation for each key region. (d) Trends of the average month westerlies index and the ISM and EASM indices for the CAHM during the monsoon period from 1979 to 2022.
Citation: Journal of Climate 38, 1; 10.1175/JCLI-D-24-0041.1
Southwestern CAHM’s extreme events often coincide with a weaker ISM and stronger westerlies, indicating unique conditions for such precipitation since the stronger westerlies typically do not occur in May. In No. 02, extreme precipitation is also in May, but No. 02 experiences more extreme events from June to September when extreme precipitation happens compared to No. 01, attributed to stronger EASM during this period.
In northeastern CAHM, extreme precipitation aligns with stronger westerlies and ISM. Despite lower-than-average EASM values in No. 03 and No. 04, their similarity suggests a relationship with SRP, which occurs in boreal summer, consistent with average westerlies and monsoon indices of June and July (Fig. 12d). July peak of extreme events in No. 03 and the June peak in No. 04 may result from slight differences in ISM and EASM’s impact on SRP.
4. Conclusions and discussion
In recent decades, CA has experienced a trend of warming and humidification, and even Tianshan area seeing more extreme precipitation events, significantly affecting the environment. Using ERA5 data from May to September (1979–2022), we focused on four key regions in CAHM: Pamir Plateau, western, central, and eastern Tianshan.
Our findings show westerlies are the main factors in CAHM’s extreme precipitation, with the meridional water vapor flux being a direct contributor during such events. This is linked to ISM and EASM’s effects on vapor transport and atmospheric circulation. Our analysis divides CAHM at approximately 79°E into southwestern and northeastern regions, each with unique water vapor anomalies and controls over extreme events (Figs. 12a,b). In southwestern CAHM, a weaker ISM leads to a negative geopotential height anomaly in the west and an anomalous cold center, shifting the westerly jet southward, favoring upward motion and extreme precipitation due to additional water vapor from the Bay of Bengal and Arabian Sea. In northeastern CAHM, a weak warm north Indian Ocean may trigger negative SRP phase, creating a positive–negative–positive geopotential height anomaly pattern along westerlies jet stream, with anomalous southerly winds causing extreme precipitation. Both situations get involve with westerlies and monsoon synergy, but the southwestern CAHM is affected by stronger westerlies and a weaker ISM, while the northeastern by stronger westerlies and ISM.
While EASM appears less critical for extreme precipitation in CAHM, it affects the meridional positioning of such events in southwestern CAHM. A stronger EASM hinders northward vapor movement from the north Indian Ocean, shifting extreme precipitation eastward in the western Tianshan, but prompts it on the western Pamir Plateau when EASM is weak. In northeastern CAHM, ISM and EASM’s varying effects on circulation fields alter southerly wind anomalies’ positions, changing precipitation locations. When extreme precipitation occurs in the central part of Tianshan (No. 03), EASM is correlated with southwesterly winds to the east of the anomalous southerly winds, where the positive potential height is located. When extreme precipitation events occur in eastern Tianshan (No. 04), northwesterly and southwesterly winds associated with the ISM are present on both sides of the anomalous southerly winds, while northwesterly winds related to the EASM are confined to the western side. They may enhance the negative geopotential height anomaly and shift the southerly winds eastward compared to C03. Therefore, EASM’s influence on atmospheric circulation and vapor flux modifies the placement of extreme precipitation in CAHM.
Statistical analysis shows May is the peak month for extreme precipitation in the southwestern CAHM, with a concentrated distribution due to early season weak monsoons favoring such events. In contrast, SRP governs the northeastern CAHM, spreading extreme precipitation across June and July. Recent studies have shown that most simulation results tend to produce a weaker ISM (Zhao et al. 2014a,b) and may possibly lead to southwestern events, while the northeastern region’s future is less clear due to complex SRP–monsoon interactions with each other. Moreover, research simulations also predict increased extreme precipitation in Tianshan under warming (Zhang et al. 2022).
This paper identifies key mechanisms impacting extreme precipitation events in the CAHM region, but the precise physical processes, particularly in its northeastern part, remain unclear. SRP is an expression of the internal atmospheric variability in the boreal summer and can influence and be influenced by monsoons (EASM and ISM) (Wang et al. 2021).
Notably, the study does not consider local water cycling. Whether local precipitation significantly impacts central Asia has spatial differences. Additionally, since this research does not include comparisons with observational data, there may be some discrepancies with actual conditions. However, ERA5 has demonstrated good performance in detecting precipitation and is therefore considered valuable for use in this paper. Addressing these issues requires extensive observational data over a sufficiently large range to determine the spatiotemporal characteristics of extreme precipitation in CAHM and to use model simulations to reproduce more realistic and reliable physical processes. Furthermore, the extreme precipitation in northeastern CAHM is closely related to the SRP, but ISM is not the only factor that can trigger SRP pattern. Besides, EASM-ISM linkage may also change under global warming, thereby affecting the SRP, which means climate assessments of CAHM should also consider these aspects.
Acknowledgments.
This research was funded by the National Natural Science Foundation of China (42230610) and the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (2019QZKK0103). Data solving progress in this paper was supported by the National Key Scientific and Technological Infrastructure project “Earth System Numerical Simulation Facility” (EarthLab). Thank you for everyone who provided anonymous advice and feedback for this paper.
Data availability statement.
ERA5 dataset is available at https://cds.climate.copernicus.eu/ and has a resolution of 0.25° × 0.5° horizontally and 37 levels in the vertical direction. APHRODITE dataset is available at https://www.chikyu.ac.jp/.
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