1. Introduction
As the largest arid and semiarid region in the Northern Hemisphere, Central Asia (CA) lacks water vapor sources. Therefore, fresh and agricultural water supply depend mainly on precipitation. However, most regions receive little precipitation; therefore, water shortages are a serious issue. Under global warming and anthropogenic activities, CA has faced a series of disaster threats, including increased droughts (Hu et al. 2018a; Yu et al. 2021), decreased water storage (Tangdamrongsub et al. 2011; Deng and Chen 2017; Hu et al. 2019, 2021), and an increased risk of extreme temperature and precipitation events (Zhang et al. 2017; Ma et al. 2020; Yao et al. 2021; Zou et al. 2021). Therefore, understanding the spatiotemporal characteristics of precipitation variations in CA and its mechanisms are important for societal survival and sustainable social and economic development in the region.
Summer precipitation in southern CA has displayed an increasing trend in recent decades (Chen et al. 2021), and a decreasing rainfall trend was observed over northern CA (Jiang and Zhou 2021). Previous studies have highlighted that the wetting trend in whole CA is mainly due to human contributions (Peng et al. 2018). While the decadal-scale warming of the North Atlantic is favorable for the increase of summer precipitation in eastern CA (Ma et al. 2021) and even the annual precipitation in whole CA (Jiang et al. 2021) by adjusting the quasi-stationary wave train. Furthermore, El Niño–Southern Oscillation (ENSO) is also closely related to the precipitation in CA, which is evident in the seasons from autumn to spring (Mariotti 2007; Kamil et al. 2019; Rana et al. 2018; Chen et al. 2018). Increased winter and spring rainfall caused by El Niño events will enhance soil moisture of CA in the following summer, favoring more summer precipitation through modulated evaporation (Chen et al. 2022). Summer precipitation over CA is also modulated by various large-scale circulation systems, including the east Atlantic/western Russia (EA/WR) pattern (Ma et al. 2020), “Silk Road” teleconnection pattern (Huang et al. 2015; de Beurs et al. 2018), and Scandinavian teleconnection patterns (Guan et al. 2021), the subtropical westerly jet (SWJ), and South Asian high (SAH), as well as the Indian, South Asian, East Asian summer monsoons (Jiang et al. 2021; W. Wei et al. 2017; Zhao et al. 2014, 2019; Chen et al. 2021). Therefore, the spatiotemporal variation of summer precipitation in CA is quite different (Chen et al. 2011; Hu et al. 2017; Chen et al. 2018) and its effect factors and mechanisms are relatively complex, and it is important to understand and clarify how the dominant factors affect precipitation in CA.
In recent years, the Indian Ocean has exhibited a significant warming trend with an increased impact on regional and even global climate variability (Li et al. 2008; Yang et al. 2007; Chu et al. 2018; Sun et al. 2019; Hu and Fedorov 2019). The strong relationship between Indian Ocean Basin Mode (IOBM) and summer precipitation in CA has been confirmed by some studies, especially in southeastern CA (Zhao et al. 2014; Meng et al. 2021). Previous studies have discussed the potential role of the SWJ and the middle- and upper-tropospheric cooling in the Indian Ocean SST effect on the summer rainfall changes in southeastern CA. It was established that when the equatorial Indian Ocean is warmer, anticyclonic anomalies at lower levels over the Indian subcontinent regions and the cyclonic flow in the midtroposphere correspond to the weakened SASM and moist air from the tropics can be easily transported to the southeastern CA (Zhao et al. 2014; Meng et al. 2021). The Indian Ocean SST anomalies may also affect the condensation latent heat release in the Asian monsoon region, adjust the position and intensity of the summer subtropical high and SWJ, consequently affecting the summer precipitation in southeastern CA (Meng et al. 2021; Wu and Liu 1999; W. Wei et al. 2017). However, it is unclear how the South Asian atmospheric heat source changes associated with Indian Ocean warming affect the SWJ by altering the midlatitude circulation and temperature gradients. Moreover, the vertical motion of the southeastern CA and the variation in water vapor flux related to the SWJ are also unclear. Therefore, in this study, we mainly analyzed the mechanism by which specific changes in atmospheric heat sources associated with Indian Ocean warming influence summer precipitation in southeastern CA. We not only identify the major contribution of temperature anomalies and vertical motion over southeastern CA but also quantitatively analyze the differences in water vapor delivery associated with precipitation change in southeastern CA from the perspective of water vapor transport and explain and verify the circulation responses related to atmospheric heat source changes from the perspective of thermal adaption.
The remainder of this paper is organized as follows. In section 2, the observational data, model, and definitions of the various indexes used in this study are introduced. In section 3, we investigate the close relationship between summer rainfall over southeastern CA and SWJ at an interannual scale and analyze the potential impact of the NI heat source and SWJ using reanalysis data and model experiments. Finally, section 4 summarizes the main conclusions.
2. Data and methods
a. Observational data
The monthly reanalysis with a 1.25° × 1.25° horizontal resolution for 1958–2019 from the Japanese 55-year Reanalysis (JRA55) is used to investigate the impact of Indian Ocean warming on the summer precipitation over CA, including geopotential height, wind, specific humidity, surface pressure, vertical velocity, air temperature, and diabatic heating rate (Kobayashi et al. 2015). The diabatic heating rate refers to the sum of the large-scale condensation heating rate (lrghr), convective heating rate (cnvhr), vertical diffusion heating rate (vdfhr), solar radiative heating rate (swhr), and longwave radiative heating rate (lwhr) from the JRA55 product. A monthly global observed sea surface temperature (SST) dataset from the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5 was adapted; this is a global monthly SST analysis from 1854 to the present with a 2.0° × 2.0° horizontal spatial resolution (Huang et al. 2017). The monthly precipitation dataset constructed by the Global Precipitation Climatology Centre (GPCC; Schneider et al. 2014) with a spatial resolution of 1° × 1° from 1891 to 2019 is used. The CRU TS v4.04 (Harris et al. 2020) precipitation dataset with a 0.5° latitude × 0.5° longitude spatial horizontal resolution and the gridded Precipitation Reconstruction over Land (PREC/L) (Chen et al. 2002) are also used to comparison.
b. Model introduction
The AGCM interferometric model at Princeton University is a three-dimensional background field linearization model that can be used to verify the response and development of the atmospheric circulation after a given initial heat source perturbation under ideal conditions. Therefore, we use it to carry out numerical experiments to verify the influence of the North Indian continental heat source anomaly on the circulation over CA.
The horizontal resolution of this model is T42, the atmosphere is divided into five layers in the vertical direction, and the (δ = p/ps) coordinate system is used. The top of the atmosphere is δ = 0, the bottom layer is δ = 1, and the interval between each layer is 0.2. Since we designed a sensitivity test for heat source anomalies at the tropics–midlatitudes (North Indian continent), Newtonian cooling was set to 1 K day−1.
In the numerical experiments, positive and negative anomaly tests of the heat source are carried out with the average background field from June to August in summer. The model is integrated for 60 days, and the average results of 31 to 60 days are used for analysis.
c. Definition of indexes used in this study
In this study, we used various indexes, and their detailed definitions are shown in Table 1. The IOBM is the mean SST in the Indian Ocean basin (20°S–20°N, 30°–120°E) (Yang et al. 2015). The South Asian summer monsoon index (SASMI) (Webster and Yang 1992) is defined based on the mean zonal wind shear between 850 and 200 hPa in the South Asia region (0°–20°N, 40°–110°E). The location index of the central subtropical westerly jet (SWJI) is defined as the first dominant component of 200 hPa u over (20°–60°N, 50°–90°E) (Jiang and Zhou 2021). To analyze the horizontal temperature gradient changes associated with SWJ position changes over southeastern CA, we also defined a southeastern Central Asian troposphere temperature index (TCAI), which is specifically defined as the average air temperature from 500 to 200 hPa over southeastern CA (35°–48°N, 64°–84°E).
Detailed definitions and references for indexes used in this study.
d. Method
3. Results
a. The spatiotemporal variation of summer precipitation in CA
Most of the CA receives precipitation less than 100 mm, with more precipitation concentrated in the southeast and a few areas in the northern part of CA (Figs. 1a–c). Summer precipitation in CA shows great interannual variation, with a standard difference of more than one-third of the average summer precipitation (Fig. 1). In particular, the southeastern region of CA has an average summer precipitation of about 160 mm at most, with a standard deviation of precipitation of about 50 mm at most, implying significant interannual variation in summer precipitation in this region. Similarly, the large mean summer precipitation and standard deviation of precipitation are also concentrated in parts of northern CA (Fig. 1), which means that southeastern and northern CA are more vulnerable to severe extreme droughts or floods. In this study, we mainly focus on the interannual variation of summer precipitation in the southeastern region of CA, and the study area is situated at 35°–48°N, 64°–84°E.
Spatial distribution of summer precipitation (mm) and its standard deviation during the period of 1960–2019 from three observational datasets. The regions marked in black line represents CA, and the red box represents the region of CA studied in this paper (35°–48°N, 64°–84°E).
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
To explore temporal variation characteristics of summer precipitation in southeastern CA, time series and wavelet analysis for 1960–2019 was conducted from three observations (PREC/L, CRU, and GPCC), shown in Fig. 2. It can be observed that there is a weak increasing trend of summer precipitation in southeastern CA during 1960–2019. During 1960–80, the three datasets all show a weak decreasing trend before 1975, and then an increase until 1980. After 1980, there was almost no increasing trend and mainly interannual variation with a large variation range. The average summer precipitation in southeastern CA during 1960–2019 from the three observations (PREC/L, CRU, and GPCC) were 48.7, 46.1, and 48.3 mm, with standard deviations of up to 11.8, 10.4, and 12.0 mm, respectively, characterized by interannual variability (Fig. 2a). A period analysis also found that the summer precipitation in southeastern CA from GPCC operates on a 3–6-yr cycle, which is consistent with the results from PREC/L (Fig. 2b). The summer precipitation in CA from the CRU only operates on a 6-yr cycle. Combined with previous studies (Hu et al. 2018b), multiple observational data were compared with the observational data of the stations, and it was highlighted that the GPCC had the best performance in CA, and the number of stations showed a significant increase after the 1950s. Therefore, the subsequent work of this study mainly analyzed the interannual variation in precipitation during 1960–2019 using GPCC data.
(a) Time series of summer precipitation in southeastern CA during 1960–2019 from GPCC, CRU, and PREC/L datasets. (b) Markov spectrum of detrended southeastern CA precipitation from GPCC, CRU, and PREC/L datasets.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
b. The impact of the subtropical westerly jet on summer rainfall in CA
Located in the hinterland of Eurasia and the midlatitude westerly wind belt, the rainfall in CA is mainly controlled by the subtropical westerly jet stream (SWJ). Various studies have indicated that the summer precipitation changes in CA are closely related to the SWJ (Jiang and Zhou 2021; W. Wei et al. 2017; Zhao et al. 2019). Following Jiang and Zhou (2021), we applied an empirical orthogonal function (EOF) analysis to the 200 hPa zonal wind and reflected the meridional shift of the SWJ with the first EOF mode (Fig. 3a). The time series of SWJ and southeastern CA summer rainfall are shown in Fig. 3c. During 1960–2019, a southward shift of the SWJ brought more precipitation over southeastern CA, and the correlation coefficients between these two reached 0.65 (passing the 99% significant level). Especially in 1972, 1973, 1981, 1984, 1987, 1988, 1994, and 2010, the south (north) shift of the SWJ location coincides well with the more (less) summer precipitation in southeastern CA (Fig. 3c). Following the southward shift of the SWJ, an anomalous cyclonic circulation appears over southeastern CA (Fig. 3b) with an anomalous ascending movement (Figs. 3d,e). Here, we further applied the quasigeostrophic pressure vertical velocity (ω) equation to determine the main causes of vertical flow anomalies over southeastern CA.
Relationship between the SWJI and southeastern Central Asian precipitation in summer. (a) The first leading modes (EOF1) derived from EOF analysis of the 200 hPa zonal wind over CA. (b) Regressed JJA mean rainfall (colors) and 500 hPa wind (vectors) against the SWJI. (c) Time series of corresponding PC1 (defined as the SWJI, black line) for (a) and southeastern Central Asian rainfall (blue line) from 1960 to 2019. (d),(e) Regressed JJA geopotential height (colors) and vertical wind and horizontal wind (vectors) against the SWJI. (f) The 500 hPa w tendency in positive and negative SWJ years based on Eq. (2) averaged over southeastern CA. The southeastern CA region is indicated by the red box in (b). In (b), (d), and (e), only values reaching the 95% significance level are shown.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
Each term on the right side of Eq. (2) in the domain of 35°–48°N, 64°–84°E was calculated to determine the main contributors to the vertical flow anomalies over southeastern CA related to the SWJ change. The results revealed that the anomalous relative vorticity advection by the basic zonal flow (V2) is the primary contributor to anomalous vertical motion over southeastern CA (Fig. 3f). The basic zonal winds over the southeastern CA region are positive. Southeastern CA is located on the eastern side of the anomalous cyclone (Fig. 3b); thus, the zonal gradients of the relative vorticity anomalies are negative. In addition, the north–south position movement of the SWJ is also closely related to the terms V7, T1, T2, and T5 (all passed the 99% significance test), but their contributions were much less than that of V2. Therefore, the positive anomalous relative vorticity advection by the basic westerly winds produces ascending anomalies over southeastern CA. In contrast, when the SWJ is located to the north, the abnormal subsidence over southeastern CA is mainly attributed to anomalous relative vorticity advection by the basic zonal flow (V2 in Fig. 3f). The abnormal ascending motion must be combined with water vapor to produce precipitation. The following section discusses the impact of the SWJ from the perspective of water vapor transport.
Figures 4a and 4b show the spatial distribution of water vapor transport and divergence in the lower and upper layers associated with more precipitation in southeastern CA. From a low-level perspective, the north side of southeastern CA is dominated by water vapor input, and the south and east directions are dominated by water vapor output, while from a high-level perspective, the north and west are water vapor input surfaces, and the south and east are water vapor output surfaces (Figs. 4e,f). There are three main water vapor transmission channels in CA (Fig. S1 in the online supplemental material). The first channel is the water vapor transported from the west and north sides of the upper level along the westerly belt. The second channel is the lower-level water vapor from the Arabian Sea that passes through northern India to reach CA. The third channel is high-level water vapor from the Bay of Bengal that is transported to CA along the southern side of the Qinghai–Tibet Plateau. The specific values of the average water vapor flux in all directions in southeastern CA from 1960 to 2019 are shown in Figs. 4e and 4f. The water vapor input in southeastern CA is mainly from the north (2.2 × 107 kg s−1) and west (2.4 × 107 kg s−1), while the water vapor output is mainly from the south (−0.9 × 107 kg s−1) and east (−6.3 × 107 kg s−1) (Figs. 4e,f). The north input is mainly concentrated in the lower level (2.0 × 107 kg s−1), the water vapor input from the west side is mainly concentrated in the upper level (2.4 × 107 kg s−1), the water vapor output from the south side is concentrated in the low level (−0.7 × 107 kg s−1), and the water vapor output from the east side is concentrated in the upper levels (−4.6 × 107 kg s−1) (Figs. 4e,f).
(a),(b) Regressed water vapor flux (vectors) and divergence (colors) against southeastern Central Asian rainfall in summer during 1960–2019 at low (surface–600 hPa) and high (600–300 hPa) levels. (c),(d) The low- and high-level water vapor flux (vectors) and divergence (colors) anomalies in positive SWJI years. (e),(f) The low- and high-level water vapor fluxes for climatology (orange bars), positive SWJI years (green bars) and their difference (gray bars) during 1960–2019.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
From the perspective of water vapor transport, the increase in summer precipitation in southeastern CA is mainly related to the decrease in water vapor output on the south side, which is closely related to the southerly position of the SWJ (Table 2). When the SWJ is located to the south, the abnormal cyclone in southern CA is more suitable for transporting water vapor from NI to southeastern CA, leading to water vapor convergence in the area (Fig. 4d). Simultaneously, the water vapor input over the NI continent is increased, partly because of the strengthening westerly winds on the south side of the Central Asian cyclone (Fig. 4d) and partly because the water vapor from the Arabian Sea passes more easily through northern India along the north by the weakened SASM (Fig. 4c). The increase in water vapor input to the North Indian continent would result in an increase in water vapor transport from the North Indian continent region to southeastern CA. At the whole level, when the SWJ is located to the south, the increase in water vapor transport from the north side caused by the anticyclone on the northwest side of CA is basically offset by the decrease in water vapor input from the west side along the westerly belt (Figs. 4e,f). At a high level, the water vapor from the Bay of Bengal on the south side is transported along the north road to the southern Tibetan Plateau, and with the southwesterly wind on the southern side of southeastern CA, the output (−1.9 × 106 kg s−1) of water vapor on the southern side is transformed into input (0.6 × 106 kg s−1) (Fig. 4f). There is an abnormal convergence of water vapor over southeastern CA (Fig. 4d), which is the main reason for the increased precipitation in southeastern CA (Table 2). The reduction in output water vapor (Fig. 4) combined with the abnormal cyclones that occurred in southeastern CA (Fig. 3b) and the vertical upward movement (Figs. 3d,e) caused by the southward shift of the SWJ have led to more summer rainfall in this area. How the North Indian atmospheric diabatic heating anomalies affect the north–south position of the SWJ will be discussed in the next section.
Correlation coefficients between southeastern CA summer precipitation (P) and subtropical westerly jet (SWJ) and water vapor flux. Note that positive values of water vapor flux refer to the input; a positive r means that the input water vapor increases or the output water vapor decreases. One, two, and three asterisks after the values indicate significance tests passing at the 90%, 95%, and 99% levels, respectively.
c. The relationship between SST in the Indian Ocean and summer rainfall in CA
The summer precipitation over CA is closely related to the SST change in the Indian Ocean, especially in the first mode, the IOBM (Meng et al. 2021). In the past few decades, the SST of the Indian Ocean and even the entire ocean has experienced rapid warming, which has made the IOBM also show a significant increasing trend (Y. Wei et al. 2017; Hu and Fedorov 2019). Since the IOBM show a significant upward trend (Fig. S2), and the interannual variability in summer precipitation over southeastern CA is dominant in 1960–2019. We mainly analyze the relationship between the IOBM index and summer precipitation in southeastern CA after unified detrend processing. The spatial distribution of the correlation coefficients between the detrended IOBM index and summer precipitation during 1960–2019 is given in Fig. 5c, which shows that the positive correlation between the summer precipitation in southeastern CA and IOBM is significant and the correlation coefficient can reach above 0.4 in local areas (passing the 95% significance test). At the same time, the negative correlations between the summer precipitation in North India and IOBM is also significant. Therefore, the IOBM affects summer precipitation in southeastern CA and North India on an interannual scale. When the SST in the Indian Ocean basin is warmer, the rainfall in Mongolia and northern CA has a significant decreasing trend, while the summer rainfall in southeastern CA increases significantly (Fig. 5c). During 1960–2019, after detrending, the correlation coefficient between the IOBM and summer rainfall in southeastern CA reached 0.4 (passing the 95% significance test) (Fig. 5a). For example, in 1969, 1970, 1972, 1987, 1988, 1998, 2010 and 2016, the SST of the Indian Ocean was warmer, and the summer precipitation in southeastern CA was greater, while in 1971, 1978, 1984, 1985, 1994, and 2008, the colder SST of the Indian Ocean coincides with less summer precipitation in southeastern CA (Fig. 5a). A period analysis found that the IOBM operates on a 4–5-yr cycle, while the summer precipitation cycle in southeastern CA is also approximately 5 years (Fig. 2b). Thus, the SST in the Indian Ocean has a significant impact on summer precipitation in southeastern CA on an interannual scale. In the following sections, how the IOBM affects the changes in precipitation in southeastern CA are discussed.
Time series of detrended IOBM (red line) and southeastern CA summer rainfall (blue line) from 1960 to 2019. The r values in (a) represent the correlation coefficients between the detrended IOBM and P. (b) Wavelet analysis of detrended IOBM. (c) Spatial distribution of the correlation coefficient between the detrended IOBM and precipitation in summer. The southeastern CA region is represented by a black box. The shaded area in (c) indicates that the correlation coefficient passed the 95% significance test.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
d. The impact of diabatic heating in northern India
The relationship between the detrended IOBM index, the SASMI, and NI–Q1 is given in Fig. 6a. There is a significant negative correlation between the IOBM and the South Asian summer winds, with a correlation coefficient as high as −0.41 (which passes the 99% significance test) (Fig. 6a). The weakening of the thermal difference between the sea and land caused by the warmer Indian Ocean will weaken the SASM, which would lead to less summer precipitation and negative atmospheric diabatic heating in northern India (Fig. 6a). As shown in Fig. 6b, when the SST of the Indian Ocean basin is unusually warm, easterly anomalies appear in the lower layers from the Arabian Sea to the Bay of Bengal, and westerly/northwesterly anomalies appear in the upper layers. This finding means that the SASM is weakened, making it difficult for the water vapor from the Arabian Sea to reach northern India, resulting in less summer precipitation here (Fig. 6b). The atmospheric diabatic heating over northern India is mainly convective heating (figure not shown). Therefore, the decrease in precipitation in northern India reduces the atmospheric condensation latent heat here, and a negative anomaly of the atmospheric diabatic heating appears (Fig. 6c).
(a) Time series of IOBM (red line), South Asian summer monsoon index (SASMI, black line), and North Indian Q1 (NI–Q1, blue line) from 1960 to 2019. (b) Regressed JJA mean rainfall (colors) and 850 hPa wind (vectors) against the IOBM. (c) Regressed JJA mean NI–Q1 (colors) and 200 hPa wind (vectors) against the IOBM. The southeastern CA and North Indian regions are indicated by red boxes in (b) and (c).
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
(a) Time series of the standardized diabatic heating rate in North India (NI–Q1, black line), the southeastern CA upper-troposphere temperature (TCAI, blue line) and SWJI (blue line) in summer from 1960 to 2019. (b) The difference in the vertical profile of diabatic heating between years with positive and negative NI–Q1. (c) Regressed JJA mean 500–200 hPa temperature (colors) and 200 hPa wind (vectors) against NI–Q1 in (a). (d) Latitude–height sections of the regression of air temperature on NI–Q1 in (a); only values reaching the 95% significance level are shown.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
The north–south position of the SWJ is closely related to the horizontal gradient of temperature. Therefore, in this study, we mainly discuss how the Indian Ocean SST affects the SWJ by changing the horizontal gradient of temperature in the middle and high latitudes. When negative temperature anomalies occur in the middle and upper levels of southern CA, the position of the westerly jet tends to the south. As shown in Figs. 7a–d, the relationship between the air temperature gradients in the middle and upper levels of southern CA and diabatic heating rate in NI (NI–Q1) are discussed. The temperature of the middle and upper troposphere in southern CA is significantly positively correlated with the diabatic heating rate in NI. This shows that when a positive (negative) anomalous diabatic heating rate appears in NI, the positive (negative) air temperature anomalies of the entire troposphere occur (Fig. 7c), which corresponds to the northward (southward) SWJ well (Fig. 7a). According to the formation principle of thermogenic winds, the strength of the SWJ is proportional to the horizontal gradient of temperature at middle and high latitudes. The positive temperature anomalies in southern CA will increase the north–south temperature gradient, which will enhance the SWJ to the north and the anti-Central Asian SWJ to the south (Fig. 7).
Figure 8a shows the vertical profile comparison of temperature anomalies over southeastern CA for positive/negative anomalies (anomalies exceeding one standard deviation) of the North Indian continental heat source, and when the North Indian continental heat source is positive, positive temperature anomalies appear in the upper-to-middle troposphere of southeastern CA, with the maximum value of positive anomalies occurring between 300 and 200 hPa and anomalies up to 1°C. Conversely, when the North Indian continental heat source is negative, the opposite negative temperature anomalies appear in the upper-to-middle troposphere of southeastern CA (Fig. 8a). To investigate what thermal processes affect the upper southeastern Central Asian temperature anomaly when the North Indian continental heat source is anomalous, we decompose the contributions to the upper southeastern Central Asian temperature anomaly using the thermodynamic energy equation [Eq. (3)] in the p-coordinate system. The T variation with time over southeastern CA associated with the North Indian continental heat source anomalies can be expressed as Eq. (5).
(a) Differences in vertical profiles of summer temperature anomalies over southeastern CA in years with positive (red line) and negative (blue line) NI–Q1 anomalies. (b) Contributions of various processes to the temperature heating rate anomalies over southeastern CA in years with negative NI–Q1 anomalies using the decomposition of the thermodynamic equation. In (b), “a” and “m” represent the anomalies and multiyear averages of a variable. For example, Sm and Sa stand for
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
Figure 8b gives the contributions of the temperature anomalies over southeastern CA during negative heat source anomalies (anomalies exceeding one standard deviation) of the North Indian continent. The main contribution to the negative temperature anomaly over southeastern CA during the negative heat source anomaly of the North Indian continent is the horizontal transport term of temperature (orange line in Fig. 8b). From the spatial distribution of each contribution (Fig. 9), the westerly wind belt over CA transports cold air from the west to CA during the negative heat source anomaly of the North Indian continent, and the cold temperature vertical advection causes cooling over southeastern CA (Fig. 9). In conclusion, the reduction in the heat source in northern India will lead to anomalous cyclone responses over southern CA, and the cold advection of temperature will cause cold temperature anomalies over southeastern CA. At this time, the reduction in the temperature gradient at middle and high latitudes will lead to a weakening and southward movement of the SWJ. This relationship is also verified by the significance tests below.
Spatial distribution of the contributions of various processes to the 500–200 hPa mean temperature anomalies over southeastern CA after decomposition of the thermodynamic equation in the years with negative NI–Q1 anomalies. (a) 500–200 hPa mean temperature anomalies over southeastern CA in years with negative NI–Q1 anomalies. (b) Temperature heating rate anomalies over southeastern CA calculated from the heat source equation in years with negative NI–Q1 anomalies. (c),(d) Temperature heating rate anomalies caused by (c) diabatic heating and (d) other processes over southeastern CA in years with negative NI–Q1 anomalies. (e)–(l) Temperature heating rate anomalies over southeastern CA caused by eight processes other than diabatic heating in years with negative NI–Q1 anomalies.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
e. Validation by model sensitivity testing
When negative heating rates and cold anomalies appear in northern India, anticyclone anomalies appear in the upper layers of southern CA, and negative temperature anomalies appear in areas below 100 hPa, which will weaken the southern temperature gradient and cause the SWJ position to move south. Figure 10c shows the difference in the profile of the positive and negative anomalies of the heating rate in northern India and the difference in the spatial distribution of heat sources. The heat source difference in northern India is mainly concentrated in 400–500 hPa (Fig. 10c). In exceptionally few years, less diabatic heating centers are concentrated in northern India, with an amplitude of up to 40 W m−2 (Fig. 10a). In an unusually large number of years, the more diabatic heating centers are also concentrated in northern India, and the amplitude can also reach 40 W m−2 (Fig. 10b). Therefore, this article mainly studies the influence of the central area heat source in northern India.
Composite difference in Q1 in summer between years with (a) positive and normal NI–Q1 and (b) negative and normal NI–Q1. The dots in (a) and (b) represent the difference that passed the 95% significance test. (c) The difference in the vertical profile of diabatic heating anomalies between years with positive and negative NI–Q1.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
Based on the location centers and heating intensities of the positive and negative heat source anomalies observed in Fig. 10, we added the corresponding heat source anomalies to the AGCM anomaly tests, as shown in Fig. 11. We designed two sets of tests, a positive heat source test (AGCM-P) and a negative heat source test (AGCM-N), and the vertical profile of the anomalous heat source for atmospheric heating is shown in Fig. 11c. The horizontal spatial distribution of the anomalous heat source at the δ = 0.5 level is shown in Figs. 11a and 11b. The differences in the high and low air circulation responses for the positive and negative heat source tests are given in Fig. 12. When a positive heat source anomaly occurs in the North Indian continent, northerly winds occur in the upper layers, anticyclonic anomalies occur in the northwest side of the North Indian continent, easterly anomalies occur in the south side of CA, and temperature anomalies in southern CA are positive (Fig. 12a); anticyclonic anomalies occur in the lower layers of the North Indian continent, and summer winds in South Asia are enhanced, corresponding to the high precipitation in the North Indian continent (Fig. 12c). In contrast, when the North Indian continent has a negative heat source anomaly, the circulation response is the opposite, with southerly winds in the upper layers, cyclonic anomalies on the northwest side of the North Indian continent, westerly anomalies on the south side of CA, and negative temperature anomalies in southern CA (Fig. 12b); cyclonic anomalies in the lower layers of the North Indian continent and weakened South Asian summer winds correspond to the low precipitation in the North Indian continent (Fig. 12d). This finding is consistent with our conclusion in the previous subsection that positive (negative) temperature anomalies occur in the upper layers over southern CA when there is a strong (weak) heat source anomaly over the North Indian continent. According to the previous subsection, when positive (negative) temperature anomalies appear in the upper layers over southern CA, the horizontal temperature gradient increases (decreases) at middle and high latitudes, corresponding to the strengthening and northward movement of the SWJ (weakening and southward movement of the SWJ). Therefore, the north–south position shift of the SWJ linked to the summer precipitation in southeastern CA is associated with the heat source anomaly of the North Indian continent due to the warmer SST of the Indian Ocean.
Differences in the spatial distribution and vertical profiles of positive and negative anomalous forcing of heat sources in the AGCM model.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
Differences in circulation and temperature responses in the upper and lower layers in the positive and negative heat source anomaly experiments in the AGCM. (left) Heat source positive abnormal test; (right) heat source negative abnormal test. (top) High level (δ = 0.1); (bottom) low level (δ = 0.9).
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
4. Conclusions and discussion
The summer precipitation in CA in the last 60 years is mainly dominated by interannual variability. Through correlation analysis, we find that the interannual variation in summer precipitation in southeastern CA during 1960–2019 is closely related to the SST in the Indian Ocean, with a positive correlation coefficient of 0.4, passing the 95% significance test. This finding implies that on the interannual scale, when the Indian Ocean SST is warmer, there is a tendency toward more summer precipitation in southeastern CA.
To investigate how the warmer SST in the Indian Ocean affects summer precipitation in southeastern CA, this paper first uses the JRA55 to conduct an observational analysis. The results obtained through correlation and synthetic analyses indicate that when the Indian Ocean SST is warmer, the decreased sea–land thermal contrast leads to weakening of the South Asian summer monsoon, which in turn leads to a decrease in water vapor transport from the Arabian Sea to the North Indian continent and thus less summer precipitation and decreased atmospheric condensation latent heat release, thus stimulating cyclonic anomalies and negative temperature anomalies in the upper layers over southern CA, northwest of the North Indian continent, according to the thermal vorticity adaptation.
An in-depth analysis of the response of the upper Central Asian temperature anomaly to the North Indian continental heat source is provided by the thermodynamic equation diagnosis. When the North Indian continental heat source is negative, the cold temperature advection from the west side of southern CA causes negative temperature anomalies, which weaken the mid- and high-latitude temperature gradients and lead to weakened and southward-shifted SWJs. This finding is verified by the significant correlation between the North Indian continental heat source anomaly, the Central Asian upper-level temperature anomaly and the north–south position of the SWJ. In addition, we also verify the conclusion that the negative anomalous heat source of the North Indian continent causes the negative upper-level temperature anomaly in southern CA based on numerical experiments of the heat source anomaly.
Many previous studies have demonstrated that summer precipitation variability in CA is closely related to the subtropical westerly jet stream (SWJ) (Jiang and Zhou 2021; W. Wei et al. 2017; Zhao et al. 2019). In this aspect, we analyze how the north–south position shift of the SWJ affects precipitation in southeastern CA from two perspectives: water vapor transport and circulation anomalies. The above analyses have demonstrated that when the SST in the Indian Ocean is warmer, a cyclonic anomaly is excited over southeastern CA, and the position of the SWJ shifts southward, the positive anomalous relative vorticity advections by the basic westerly winds causing convergent upward motion over southeastern CA. From the perspective of water vapor transport, the increase in summer precipitation in southeastern CA when the SWJ position is southward is mainly related to the decrease in water vapor output from the south boundary, and it is mainly concentrated in the upper layers. When the SWJ position is southward, the net water vapor transport on the south side is converted to input (0.6 × 106 kg s−1) from output (−1.9 × 106 kg s−1) with southwesterly winds on the south side of southeastern CA. The reduction in the output water vapor combined with the anomalous cyclone occurring over southeastern CA and the anomalous vertical upward motion leads to an increase in summer precipitation in the region.
In conclusion, the mechanism of heat source changes in North India related to Indian Ocean warming affecting summer precipitation in southeastern CA is shown in Fig. 13. When the Indian Ocean SST is warmer, the thermal difference between sea and land decreases, resulting in weaker summer winds in South Asia, and less water vapor is delivered to the North Indian continent, resulting in less precipitation and less latent heat of condensation on the North Indian continent, which further leads to anomalous cyclones over southeastern CA to the northwest according to the thermal vorticity balance. The cold temperature advection makes the upper troposphere in southern CA unusually colder, which weakens the horizontal temperature gradient at middle and high latitudes, thus leading to the southward SWJ. Accompanied by the anomalous cyclone over southeastern CA, the increase in water vapor transport from the north side of CA is basically offset by the decrease in water vapor input from the west side along the westerly belt, while the net water vapor output from the south side is significant decreased with southwesterly winds. And the positive anomalous relative vorticity advections by the basic westerly winds associated with the cyclone anomaly contribute to the ascent motion over southeastern CA. Finally, both the vertical ascending motion in southeastern CA and the reduction in water vapor export from the south side leads to more summer precipitation in southeastern CA.
Schematic diagram of the mechanism by which warmer SSTs in the Indian Ocean affect precipitation in southeastern CA. In the figure, SST stands for sea surface temperature, SASM stands for South Asian summer monsoon, Q1 stands for diabatic heating rate, SWJ stands for subtropical westerly jet stream, SCA stands for southeastern CA, Pr stands for precipitation, C stands for the abnormal cyclone in high level and low level. (a) NI–Q1 and southeastern CA–Pr anomalies associated with the warmer IO SST. Layers (a) and (b) represent the low level and high level, respectively.
Citation: Journal of Climate 36, 13; 10.1175/JCLI-D-22-0928.1
In this study, we analytically obtained the North Indian continental heat source anomaly bridging the Indian Ocean SST to affect the summer precipitation in southeastern CA. The influence of the North Indian continental heat source anomaly on the circulation as well as the atmospheric temperature over southern CA is verified in a simple AGCM model. However, there is a positive interaction between the anomalous high over CA and strong convection over the NI region (Ding and Wang 2007). The strong convection over the NI region can be initially triggered by the anomalous central Asian high with the westerly wave train extending from the northeastern Atlantic to East Asia. Conversely, convection is likely to excite a Rossby wave response, which reinforces the central Asian high (Ding and Wang 2007). Likewise, the southward SWJ could also reduce the rainfall over northern Indian regions by weakening the upper-level tropical easterly jet over the Indian subcontinent and reinforcing the low-level anticyclone (Chowdary et al. 2022). In addition, many studies have indicated that following an El Niño event, the basinwide warming over the tropical Indian Ocean can last from spring to summer under the influence of air–sea interactions (Klein et al. 1999; Du et al. 2009). The Indian Ocean warming can act as a discharging capacitor to exert its climatic influence on beyond the lifetime of an ENSO (Yang et al. 2007; Xie et al. 2016). For example, the two most severe droughts in southwestern Asia during 1999–2001 and 2007/08 were attributed to the prolonged duration of La Niña events and unusually warm SST in the western Pacific and eastern Indian Ocean (Barlow et al. 2002, 2016). Therefore, the North Indian continental heat source anomaly related to the Indian Ocean warming can also act as an important intermediary participating in the teleconnections between ENSO and Central Asian summer precipitation.
Furthermore, the differences in the spatial pattern of SST warming in the Indian Ocean (Sun et al. 2020) and the phase of the Pacific decadal oscillation (Wang et al. 2022) both contribute to the interdecadal shift in this relationship. Moreover, this study only discussed the influence of tropical Indian Ocean SSTs and atmospheric heat sources on the North Indian continent, where midlatitude circulation is equally important for precipitation in CA. Many studies have pointed to the existence of Rossby wave trains from the North Atlantic to East Asia in the midlatitudes of the Northern Hemisphere and have indicated that they have an important impact on the climate of Eurasia (Ding and Wang 2007; Zhou et al. 2019; Sun et al. 2020; Ren et al. 2022). The warm Atlantic multidecadal variability can excite a circumglobal teleconnection (CGT) pattern, and the trough node of the CGT to the west of CA drives an anomalous ascending motion and increased precipitation over this region (Hua et al. 2017; Jiang et al. 2021; Ma et al. 2021). However, little is known about how they, together with the Indian Ocean SST, influence summer precipitation in CA. Wu et al. (2016) indicated that the interdecadal variability of the Northern Hemisphere extratropical circulation during summer is associated with Atlantic multidecadal oscillation, whereas its interannual variability is associated with Indian summer monsoon precipitation anomalies. Therefore, further research is required to elucidate the mechanism by which multiple factors synergistically affect summer precipitation in CA.
Acknowledgments.
This work was jointly supported by the National Natural Science Foundation of China (42075043, 42122034), Science and Technology Program in Gansu (21JR7RA067), the Youth Innovation Promotion Association CAS and the “CUG Scholar” Scientific Research Funds at China University of Geosciences (Wuhan) (Project No. 2022121). The authors declare no competing financial interests.
Data availability statement.
The NOAA_ERSST_V5 data are provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, and can be obtained from their website at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. The Japanese 55-year Reanalysis derived data provided by the Japan Meteorological Agency (JMA) can be downloaded from their website at https://rda.ucar.edu/datasets/ds628.0/. The gridded CRU Time-Series (TS) data version 4.04 data are available at https://catalogue.ceda.ac.uk/uuid/89e1e34ec3554dc98594a5732622bce9, which is provided by the Climatic Research Unit (CRU) at the University of East Anglia and funded by the U.K. National Centre for Atmospheric Science (NCAS). The Global Precipitation Climatology Centre (GPCC) data and the NOAA’s Precipitation Reconstruction over Land (PREC/L) provided by the NOAA PSL, Boulder, Colorado, can be downloaded from their website at https://psl.noaa.gov.
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