1. Introduction
Unstable hydrological cycles and water resource instabilities in the “Asian Water Tower” region of the Tibetan Plateau (TP) have been the focus of much concern (Yao et al. 2012, 2017). Phenomena such as permafrost degradations (Cheng and Wu 2007; Guo and Wang 2013), lake expansions (Lei et al. 2013, 2014; Yang et al. 2017; Zhang et al. 2017), accelerated glacier retreats (Kang et al. 2010; Yao et al. 2012), increased precipitation (You et al. 2008), and water storage changes (Zhang et al. 2017) have all been accompanied by a considerable spatial heterogeneity. The TP is the world’s highest, and China’s largest, plateau; hydrological changes in this vast region may therefore exert profound influences on local and regional climate through thermal and dynamic processes (Wu et al. 2007; Xu et al. 2008). Furthermore, the TP provides water to a significant proportion of the Asian population (Immerzeel et al. 2010), meaning that the subsequent socioeconomic consequences of any changes to water sources in TP are likely to be considerable (Pritchard 2019).
Atmospheric circulations (Sun et al. 2020), moisture sources (Guo et al. 2017), and topography (Guo et al. 2016; Li et al. 2017) during airmass transportation (Qi et al. 2016) are closely related to precipitation patterns, and further affect the spatial distributions of water resources and their variabilities (Yang et al. 2011; Kuang and Jiao 2016; Hrudya et al. 2020). Circulation systems and moisture sources (Tian et al. 2001; Yao et al. 2013) have long been studied against the background of the TP’s varied topography. Using water stable isotopes, Yao et al. (2013) divided the TP’s circulation systems into three zones: the Westerlies Zone, the Transition Zone, and the Indian Monsoon Zone. By revealing the geographical distributions of water stable isotopes, Tian et al. (2001) defined the Tanggula Mountains as the northern limits of Indian summer monsoon (ISM) in TP. Moisture transported by the ISM is the dominant source for southern TP; this moisture can also reach the central, or even the northern, TP during the monsoon-dominated months from June to September (Feng and Zhou 2012; Yao et al. 2013; Mohan and Rajeevan 2017). Abundant seasonal precipitation is supplied by ISM, reflected in the high proportions of total annual precipitation accounted for by summer precipitation (Yao et al. 2013).
Being one of the most important suppliers of moisture to the TP, the ISM intensity and its onset date, retreat date, and duration all become critical factors controlling any changes in precipitation (Stolbova et al. 2016), as well as water-related environments. ISM intensity in TP has been based on analysis of satellite datasets (Simon et al. 2006; B. Wang et al. 2009), or other indices (Noska and Misra 2016; Yu et al. 2016; RavindraBabu et al. 2019), while much uncertainty remains. Furthermore, there have been few detailed studies of ISM onset date, retreat date, duration, or strength, meaning that further investigation is necessary, especially set against the background of intense climate change. For the traditional indices of ISM activities, uncertainties or difficulties include data acquisition [precipitation isotopes (Yu et al. 2016), precipitation (Noska and Misra 2016)], only one point but not the grided results [GPS precipitable water (Puviarasan et al. 2015)], partial detections only cover some of the ISM activities [satellites (Simon et al. 2006), radio occultation data (Rao et al. 2013), GPS measurements (Puviarasan et al. 2015), precipitation (Noska and Misra 2016)], and short time series without trends analysis [precipitation isotopes (Yu et al. 2016)]. The outgoing longwave radiation (OLR) datasets have the advantages of easy data access and high-resolution, long time series, as well as working well in capturing all the ISM characteristics; therefore, they are used as new indices of ISM activities in TP in this research. The remarkable TP climate coaffected by ISM and the Westerlies make it the perfect study area for ISM activities detections. This paper presents our first attempts to delineate the ISM activities in TP using OLR datasets during 1975–2020. We identify the ISM onset date, retreat date, duration, intensity, and strength; precipitation isotopes (δ18O) recorded at Lhasa (southern TP) are used to verify the ISM characteristics defined using OLR. We aim to 1) define each ISM characteristic using OLR datasets and 2) fully explore each characteristic’s spatial patterns and temporal variabilities in TP in recent decades. Thereby, this research aims to improve our understanding of hydrological cycles in TP, and provide insights into the ecology and paleoclimate reconstructions in the Asian Water Tower region.
2. Materials and methods
a. Study area
As shown in Fig. 1b, the study area is restricted to regions below the bold gray lines, which is the boundary between ISM and the Westerlies (the M–W boundary). Our study area covers the whole of Indian Monsoon Zone and parts of the Transition Zone, as defined in the work of Yao et al. (2013). It is mainly located within a rectangle defined by 23.875°–33.375°N, 73.875°–106.125°E. Summer climates in our study area are generally influenced by the ISM (Fig. 1a). The TP boundary is downloaded at the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn), referring to the works of Zhang (2019) and Zhang et al. (2002, 2014, 2021). It covers the areas with elevations > 3000 m and in the mainland China.
(a) Location of the Tibetan Plateau (TP, black curves), with arrows showing the usual moisture transportation pathways surrounding the TP and with shading showing the general topography as delineated by the Shuttle Radar Topography Mission. (b) ΔOLR values [color shading, defined by Eq. (2)] averaged for 1975–2020. The bold gray lines (or dashed lines) show the boundary between ISM and the Westerlies (the M–W boundary) [or the boundary among the Indian Monsoon Zone, the Transition Zone, and the Westerlies Zone, in Yao et al. (2013)]. Colored circles are the typical grids in each climate zone, with their monthly OLR values averaged during 1975–2020 for (c) the Indian Monsoon Zone, (d) the Transition Zone, and (e) the Westerlies Zone.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
(a) Location of the Tibetan Plateau (TP, black curves), with arrows showing the usual moisture transportation pathways surrounding the TP and with shading showing the general topography as delineated by the Shuttle Radar Topography Mission. (b) ΔOLR values [color shading, defined by Eq. (2)] averaged for 1975–2020. The bold gray lines (or dashed lines) show the boundary between ISM and the Westerlies (the M–W boundary) [or the boundary among the Indian Monsoon Zone, the Transition Zone, and the Westerlies Zone, in Yao et al. (2013)]. Colored circles are the typical grids in each climate zone, with their monthly OLR values averaged during 1975–2020 for (c) the Indian Monsoon Zone, (d) the Transition Zone, and (e) the Westerlies Zone.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
(a) Location of the Tibetan Plateau (TP, black curves), with arrows showing the usual moisture transportation pathways surrounding the TP and with shading showing the general topography as delineated by the Shuttle Radar Topography Mission. (b) ΔOLR values [color shading, defined by Eq. (2)] averaged for 1975–2020. The bold gray lines (or dashed lines) show the boundary between ISM and the Westerlies (the M–W boundary) [or the boundary among the Indian Monsoon Zone, the Transition Zone, and the Westerlies Zone, in Yao et al. (2013)]. Colored circles are the typical grids in each climate zone, with their monthly OLR values averaged during 1975–2020 for (c) the Indian Monsoon Zone, (d) the Transition Zone, and (e) the Westerlies Zone.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
b. Hypothesis
We use two sets of data in this study. The OLR datasets play a major part in calculations of ISM activities; precipitation isotopes at Lhasa are used for verifications of ISM activities. In seasons with convective processes or in monsoon regions, OLR values are negatively correlated with the intensities of convection (Wang and Xu 1997). The higher (lower) OLR values relate to weaker (stronger) convective activities. OLR is therefore specifically used to distinguish the cloudiness and rainfall caused by deep convections in tropics (Risi et al. 2008a,b). Besides that, OLR also positively correlates with the land surface temperatures, and is also developed for monitoring the Earth–atmospheric radiation balance or estimating Earth’s radiation budgets (Gruber and Winston 1978). In nonmonsoon seasons, the higher (lower) the OLR values, the higher (lower) the temperatures. Based on the representativeness of OLR on both the convection and temperatures, the spatial patterns of OLR are widely taken to illustrate large-scale circulations (Guo et al. 2017); results perform well in both the monsoon and nonmonsoon seasons (Guo and Tian 2022).
The ISM generally breaks out in June, then prevails in July–August, and gradually retreats during September–October (Prasad and Hayashi 2005; Guimberteau et al. 2012; Puviarasan et al. 2015; Noska and Misra 2016). The OLR values in May–October are the twofold results from the intensities of convection and temperatures. In Monsoon Zone (the Westerlies Zone), convection (temperature) plays a more important role on the OLR variabilities. With the grids changing from the Monsoon Zone to the Westerlies Zone, monthly OLR patterns are changing from the “summer valley” to “summer peak” patterns (Fig. 1). Those in the Transition Zone exhibit as medium. Under the inversely twofold affections from convective activities and temperatures, the OLR is ideal indices to distinguish ISM from the Westerlies. Based on daily OLR, when we compute their cumulative anomalies (C-ΔOLR′) in ISM months of May–October, the C-ΔOLR′ patterns show many discrepancies in the three climate zones (Fig. 2). In the Monsoon Zone, with the temperatures rising in spring (OLR and temperatures, positive), the C-ΔOLR′ gradually increases and reaches its maxima; when the ISM breaks out (OLR and convection, negative), the OLR values are largely controlled by convection intensity, resulting in declining C-ΔOLR′. Finally, the trends of C-ΔOLR′ turn from negative to positive at ISM retreat date (Fig. 2b3). While the inverse patterns of C-ΔOLR′ in the Westerlies Zone are controlled by temperatures with a certain threshold (Fig. 2b1), C-ΔOLR′ in the Transition Zone shows medium variabilities. We further define the ISM onset (retreat) date as the point of C-ΔOLR′ maxima (minima) (Fig. 2c); those periods between the ISM onset and retreat date are defined as the ISM duration (days). The ISM intensity (ISM strength) is the mean ISM activities per day (the total monsoon activities in the whole ISM periods), which is indicated with the mean OLR values in ISM duration (the differences of C-ΔOLR′ maxima and minima).
(a) Daily OLR values during May–October (2015 as an example) and (b) corresponding cumulative ΔOLR′ (C-ΔOLR′) values calculated using Eq. (4) for each of the four typical grids in the three climate zones (Fig. 1). (c) Diagrams showing the ISM characteristics delineated using OLR datasets. Max and min are short for maxima and minima.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
(a) Daily OLR values during May–October (2015 as an example) and (b) corresponding cumulative ΔOLR′ (C-ΔOLR′) values calculated using Eq. (4) for each of the four typical grids in the three climate zones (Fig. 1). (c) Diagrams showing the ISM characteristics delineated using OLR datasets. Max and min are short for maxima and minima.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
(a) Daily OLR values during May–October (2015 as an example) and (b) corresponding cumulative ΔOLR′ (C-ΔOLR′) values calculated using Eq. (4) for each of the four typical grids in the three climate zones (Fig. 1). (c) Diagrams showing the ISM characteristics delineated using OLR datasets. Max and min are short for maxima and minima.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
In TP with harsh climate, precipitation isotopes are especially precious ground-based measurements. We therefore use precipitation isotopes in the verification processes, and Lhasa in Monsoon Zone with long-time (1993–2012) isotope records are used. Precipitation δ18O correlates positively with temperatures for inland regions, in the nonmonsoon seasons (Bowen 2008; Yao et al. 2013), or in long-scale sediments such as ice cores (Jouzel et al. 1997; Yu et al. 2021), which is called “temperature effects.” While in ISM seasons or at the coastal sites, inverse variabilities are found between precipitation δ18O and the intensities of convection (Rozanski et al. 1993; Kurita et al. 2009; Eastoe and Dettman 2016). Precipitation isotopes are ideal indices for convection (negative, in monsoon seasons or the coastal sites) or temperatures (positive, in inland areas). Based on the consistent or opposite variabilities between precipitation δ18O and the ISM intensity or its strength, the ISM activities from OLR can be verified. In this study, the ISM activities are between the ISM onset and retreat date; since the ISM generally breaks out in June and withdraws between late September and early October (Noska and Misra 2016), we therefore use the isotope records in June–September. Precipitation records only cover the day with precipitation events; we do not screen the precipitation isotopes strictly to the date right after the ISM starts, and the same for the ISM retreat date.
c. Datasets
1) OLR datasets
The daily and monthly OLR records for the period 1975–2020 are issued by the NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded/data.interp_OLR.html) (Liebmann and Smith 1996). These records have a spatial resolution of 2.5° × 2.5°.
2) Precipitation isotopes
Precipitation isotopes (δ18O) at Lhasa (29.70°N, 91.13°E; 3650.10 m) in the months of June–September during 1993–2012 are used to confirm the ISM activities identified by OLR datasets. Precipitation samples are collected using a specifically designed container (Gröning et al. 2012). We sample the rainfall at 2000 local time each day it occurred; snowfall is collected immediately, then melted in sealed plastic bags at room temperature. All samples are kept frozen in a storage freezer and then analyzed in the laboratory (State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research) using a Picarro-2130i Liquid Water Isotope Analyzer. The analytical precision is ±0.1‰ for δ18O.
d. Methods
Below we outline the general procedures used to delineate the ISM activities in TP using OLR reanalysis and precipitation δ18O.
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Calculations of ΔOLR and definitions of ISM domains in TP. The ΔOLR values [Eq. (2)] are calculated for each grid based on the marked differences of the monthly OLR patterns for grids in different climate zones. The boundary between the positive and negative ΔOLR values form the M–W boundary in TP. We define the grids with positive ΔOLR values as the ISM domains (Fig. 1, below the bold gray lines).
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Cumulative anomalies of OLR (C-ΔOLR′). Using daily OLR values (Fig. 2a), we compute their C-ΔOLR′ [Eqs. (3) and (4)] for each grid during May–October (covering the whole ISM period) in each year (Fig. 2b).
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Definitions of the ISM activities. On the basis of these C-ΔOLR′ variabilities, we define the particular ISM characteristics, i.e., ISM onset date (the date with maximum C-ΔOLR′), ISM retreat date (the date with minimum C-ΔOLR′), ISM duration (period between the maximum and minimum C-ΔOLR′), ISM intensity (negatively correlates with the mean OLR values between ISM onset and retreat date), and ISM strength (the differences of the maximum and minimum C-ΔOLR′) (Fig. 2c).
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Spatial distributions of each ISM activity. We present the mean ISM onset date, retreat date, and duration for each grid in the ISM domains (Fig. 3, Fig. S1 in the online supplemental material).
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Temporal variabilities for each ISM activity. We calculate the temporal variabilities for each ISM activity. The linear trends are computed for the mean values of all grids in our study area (Fig. 4), in each grid (Fig. 5, Fig. S2), and the mean values in each column (Fig. 7, Fig. S3), respectively.
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ISM activities in relation to precipitation isotopes. Ground-based measurements of precipitation δ18O values at Lhasa of southern TP are used. By analyzing the temporal variabilities in precipitation δ18O values and ISM intensity/strength, we confirm the applicability of OLR reanalysis to ISM activities. As remarkable changes in circulation systems are reported in the year ∼1998 (Bell et al. 1999; Fox-Rabinovitz et al. 2002; Ding et al. 2013), we use 1998 as timing nodes and detect the ISM activities for periods 1975–98, 1999–2020, and 1975–2020. In Table 1, we list the abbreviations for items used in this study.
(a) ISM onset date, (b) ISM retreat date, and (c) ISM duration (days) averaged for the period 1975–2020.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
(a) ISM onset date, (b) ISM retreat date, and (c) ISM duration (days) averaged for the period 1975–2020.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
(a) ISM onset date, (b) ISM retreat date, and (c) ISM duration (days) averaged for the period 1975–2020.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Temporal variabilities in (a) ISM onset date (solid step plots), retreat date (dashed step plots), and duration (vertical colored lines with arrows, showing the lengthening or shortening trends); (b) ISM intensity (W m−2); and (c) ISM strength (W m−2). Statistics are based on the mean values of all grids in our study area. Increasing (+, solid lines) or decreasing (−, dashed lines) linear trends [days (10 years)−1 for (a), W m−2 (10 years)−1 for (b), W m−2 yr−1 for (c)] for the periods 1975–98 (light blue), 1999–2020 (pink), and 1975–2020 (black) are regressed, with the corresponding regression coefficients (trends) provided. An asterisk represents those trends significant at p < 0.1 confidence level.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Temporal variabilities in (a) ISM onset date (solid step plots), retreat date (dashed step plots), and duration (vertical colored lines with arrows, showing the lengthening or shortening trends); (b) ISM intensity (W m−2); and (c) ISM strength (W m−2). Statistics are based on the mean values of all grids in our study area. Increasing (+, solid lines) or decreasing (−, dashed lines) linear trends [days (10 years)−1 for (a), W m−2 (10 years)−1 for (b), W m−2 yr−1 for (c)] for the periods 1975–98 (light blue), 1999–2020 (pink), and 1975–2020 (black) are regressed, with the corresponding regression coefficients (trends) provided. An asterisk represents those trends significant at p < 0.1 confidence level.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Temporal variabilities in (a) ISM onset date (solid step plots), retreat date (dashed step plots), and duration (vertical colored lines with arrows, showing the lengthening or shortening trends); (b) ISM intensity (W m−2); and (c) ISM strength (W m−2). Statistics are based on the mean values of all grids in our study area. Increasing (+, solid lines) or decreasing (−, dashed lines) linear trends [days (10 years)−1 for (a), W m−2 (10 years)−1 for (b), W m−2 yr−1 for (c)] for the periods 1975–98 (light blue), 1999–2020 (pink), and 1975–2020 (black) are regressed, with the corresponding regression coefficients (trends) provided. An asterisk represents those trends significant at p < 0.1 confidence level.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Linear trends in (a) ISM onset date, (b) retreat date, (c) duration [days (10 years)−1], (d) ISM intensity [W m−2 (10 years)−1], and (e) ISM strength (W m−2 yr−1) during 1975–2020. An asterisk represents those trends significant at the p < 0.1 confidence level. “+” and “−”indicate the general patterns in our study area: increasing (+) or decreasing (−). The blue dashed frames outline the ISM characteristics in southwestern and southeastern TP.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Linear trends in (a) ISM onset date, (b) retreat date, (c) duration [days (10 years)−1], (d) ISM intensity [W m−2 (10 years)−1], and (e) ISM strength (W m−2 yr−1) during 1975–2020. An asterisk represents those trends significant at the p < 0.1 confidence level. “+” and “−”indicate the general patterns in our study area: increasing (+) or decreasing (−). The blue dashed frames outline the ISM characteristics in southwestern and southeastern TP.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Linear trends in (a) ISM onset date, (b) retreat date, (c) duration [days (10 years)−1], (d) ISM intensity [W m−2 (10 years)−1], and (e) ISM strength (W m−2 yr−1) during 1975–2020. An asterisk represents those trends significant at the p < 0.1 confidence level. “+” and “−”indicate the general patterns in our study area: increasing (+) or decreasing (−). The blue dashed frames outline the ISM characteristics in southwestern and southeastern TP.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Abbreviations used in this analysis and their corresponding meanings.
3. Results
a. Spatial patterns in ISM activities
In Fig. 3, we present the spatial patterns in different ISM activities during 1975–2020. The ISM onset date falls between ∼31 May and 19 July, with the earliest onset occurring in southeastern TP, gradually progressing through the calendar from the southeastern to the northwestern sectors of our study area. The ISM retreat date falls between ∼8 August and 27 September. The ISM retreat date is earliest at the margins of our study area (the northwestern or the eastern sectors), becoming later toward the central-southern TP. The ISM duration varies between ∼40 and 110 days. For grids near the M–W boundary, the OLR values are coaffected by ISM and the Westerlies, resulting in unordered C-ΔOLR′ variabilities (Fig. 2b2) and the further ISM onset/retreat date. This phenomenon occurs only in some grids in the Transition Zone. We mark the ISM duration in grids with retreat date ≤ onset date as blanks in Fig. 3c and Figs. S1c and S1f. Monsoon duration in the central-southern TP is the longest, generally shortening toward the margins of our study area. Although different ISM activities such as the ISM onset date, retreat date, and duration vary in different study periods (Fig. 3, Fig. S1), their spatial patterns appear consistent. Similar to the results in the entire 1975–2020 period, the spatial patterns of different ISM characteristics during 1975–98 and 1999–2020 show that the ISM onset date is earliest in the southeastern sector of our study area, growing gradually later toward the northwestern sector, whereas the ISM retreat date gradually grows later from northern to the southern TP. The ISM duration lengthens from the northern margins of our study area toward the central-southern TP.
b. Temporal variabilities in ISM activities
The scatterplots and temporal trends in the ISM onset date, retreat date, duration, intensity and strength are shown in Fig. 4. Calculations are based on the mean values for all grids in our study area. We observe generally decreasing trends in ISM onset date, indicating that the ISM starts progressively earlier during the observational periods. This is the case for all the three study periods, though the amplitudes of the negative trends during 1975–98, 1999–2020, and 1975–2020 vary. The inversely positive trends exhibited by the ISM retreat date suggest generally delayed ISM retreats over the recent decades. This is especially true for 1975–98, and 1975–2020 as a whole. Insignificant advances (negative), i.e., an earlier ISM retreat date, occur during 1999–2020. Analysis of the ISM onset and retreat date identifies longer ISM duration in 1975–98 and 1975–2020; there is an insignificant shortening (negative) of ISM duration in 1999–2020. For the whole study period 1975–2020, the ISM intensity and strength increased (Figs. 4b,c). When comparing the subperiods 1975–98 and 1999–2020, consistently positive trends are observed in ISM strength; discrepancies are found in ISM intensity (i.e., significantly increasing in 1975–98, insignificantly decreasing in 1999–2020). With all grids counted, it is apparent that, for the whole study periods, the ISM onset date has arrived earlier and the retreat date occurs later, that the monsoon period has become longer, and that the intensity and strength of the ISM have both increased. The different ISM characteristics vary throughout the subperiods: in 1975–98, temporal variabilities in ISM onset and retreat date, ISM duration, and ISM intensity and strength appear consistent with those for the entire 1975–2020 period; during the 1999–2020 subperiod, however, there is a statistically insignificant reversal of the overall changes in ISM retreat date and ISM intensity or strength experienced during the 1975–2020 period.
c. Spatial distributions of the trends in ISM activities
We analyze the spatial patterns exhibited by the ISM onset date, retreat date, duration, intensity, and strength during the whole 1975–2020 period; “+” and “−” indicates the overall positive and negative variabilities of these different ISM characteristics, respectively (Fig. 5).
Significant spatially inhomogeneous patterns are found in the different ISM activities. Grids in the western sector of our study region experience earlier ISM onset, delayed ISM retreat, longer ISM duration, increases in ISM intensity, and enhanced ISM strength. These grids are mainly distributed in the left 9 or 10 columns of our study area. Grids in the 3 or 4 right-hand columns exhibit inverse trends compared with the left-hand columns; this is true for the ISM onset date, retreat date, duration, and intensity. The trends apparent in the different ISM activities and their spatial distributions throughout the 1975–98 subperiod resemble those observed over the entire 1975–2020 period (Fig. S2), with minor differences found in ISM intensity in eastern TP. The trends evident in different ISM activities in 1999–2020 appear different from those observed in the 1975–98 and 1975–2020 periods, namely, delayed ISM onset, earlier retreat, shorter ISM duration, and decreased ISM intensity or strength. Spatially, the trends in ISM activities during 1999–2020 appear irregularly distributed. When comparing the subperiods 1975–98 and 1999–2020, they seem to exhibit inverse trends and spatial distributions for ISM onset date and ISM intensity.
The spatial patterns of different ISM activities are qualitatively and quantitatively evaluated, as summarized in Fig. 6. The ISM activities in the left 10 columns and during the 1975–98 period resemble the patterns apparent in the whole study region, and during the whole 1975–2020 study period. With all the grids and the three study periods counted (Fig. 6, rows 2–4), it is clear that, overall, the ISM begins earlier, retreats later, lasts longer, and is both more intense and stronger. When comparing the 10 left-hand columns with the 3 right-hand ones, and considering all the three study periods (Fig. 6, rows 5–10), we detect significant spatial patterns. The trends in the different ISM activities in the right-hand columns are generally inverse in relation to those observed in the left-hand columns. We also observe significant spatial patterns (for both the left- and the right-hand columns) in trends of ISM activities during 1975–2020 for each of the 13 columns (Fig. 7): the “left–right” pattern for the subperiods 1975–98 and 1999–2020 is not that significant (Fig. S3). It can therefore be concluded that 1) during 1975–2020, and in most grids of the study area, the ISM starts earlier, retreats later, lasts longer, and is both more intense and stronger; 2) temporally, trends of the different ISM activities in the 1975–98 subperiod determine the ISM characteristics for the entire 1975–2020 study period as a whole; and 3) spatially, the trends evident in the different ISM activities in the 3 right-hand columns are generally inverse to those observed in the 10 left-hand columns.
The general trends in ISM onset date, retreat date, duration, intensity, and strength for the periods 1975–98, 1999–2020, and 1975–2020 in our study area (below the gray lines in Fig. 1b). Qualitative and quantitative statistics are computed for all grids, the 10 left-hand columns, and the 3 right-hand columns. “Q” and “q” are short for qualitative and quantitative. Qualitative analysis represents the overall trends, shown as + (increasing) or − (decreasing). In terms of quantitative analysis, we calculate the numbers of grids with positive trends vs the total numbers of grids, with the results of >50% labeled as “1”, <50% as “0”, and =50% as “–”. The orange or blue backgrounds show the positive or negative “Q”, and “q” = 1 and + “Q” (orange) or “q” = 0 and – “Q” (blue).
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
The general trends in ISM onset date, retreat date, duration, intensity, and strength for the periods 1975–98, 1999–2020, and 1975–2020 in our study area (below the gray lines in Fig. 1b). Qualitative and quantitative statistics are computed for all grids, the 10 left-hand columns, and the 3 right-hand columns. “Q” and “q” are short for qualitative and quantitative. Qualitative analysis represents the overall trends, shown as + (increasing) or − (decreasing). In terms of quantitative analysis, we calculate the numbers of grids with positive trends vs the total numbers of grids, with the results of >50% labeled as “1”, <50% as “0”, and =50% as “–”. The orange or blue backgrounds show the positive or negative “Q”, and “q” = 1 and + “Q” (orange) or “q” = 0 and – “Q” (blue).
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
The general trends in ISM onset date, retreat date, duration, intensity, and strength for the periods 1975–98, 1999–2020, and 1975–2020 in our study area (below the gray lines in Fig. 1b). Qualitative and quantitative statistics are computed for all grids, the 10 left-hand columns, and the 3 right-hand columns. “Q” and “q” are short for qualitative and quantitative. Qualitative analysis represents the overall trends, shown as + (increasing) or − (decreasing). In terms of quantitative analysis, we calculate the numbers of grids with positive trends vs the total numbers of grids, with the results of >50% labeled as “1”, <50% as “0”, and =50% as “–”. The orange or blue backgrounds show the positive or negative “Q”, and “q” = 1 and + “Q” (orange) or “q” = 0 and – “Q” (blue).
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Linear trends in (a) ISM onset date, (b) retreat date, (c) duration, (d) ISM intensity, and (e) ISM strength during 1975–2020. Calculations are based on the mean values in each of the 13 columns, and all grids counted in our study area (below the bold gray lines in Fig. 1). Colored arrows with varying lengths indicate the positive (red) or negative (blue) trends. An asterisk represents those trends significant at the p < 0.1 confidence level.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Linear trends in (a) ISM onset date, (b) retreat date, (c) duration, (d) ISM intensity, and (e) ISM strength during 1975–2020. Calculations are based on the mean values in each of the 13 columns, and all grids counted in our study area (below the bold gray lines in Fig. 1). Colored arrows with varying lengths indicate the positive (red) or negative (blue) trends. An asterisk represents those trends significant at the p < 0.1 confidence level.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Linear trends in (a) ISM onset date, (b) retreat date, (c) duration, (d) ISM intensity, and (e) ISM strength during 1975–2020. Calculations are based on the mean values in each of the 13 columns, and all grids counted in our study area (below the bold gray lines in Fig. 1). Colored arrows with varying lengths indicate the positive (red) or negative (blue) trends. An asterisk represents those trends significant at the p < 0.1 confidence level.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
4. Discussion
Precipitation δ18O (June–September) values recorded at Lhasa, in the southern TP, show exactly inverse temporal variabilities with ISM intensity and ISM strength (Fig. 8). In the years with increased ISM intensity or strength, precipitation δ18O values appear more depleted. In Table 2, negative linear correlations are observed between precipitation δ18O values and the ISM intensity or strength, except for the insignificant positive relations found between ISM strength and precipitation δ18O in June. Precipitation isotopes are ideal indicators for intensities of convection, and the general negative correlations between precipitation δ18O and ISM activities (Table 2) are reasonable. Convection has been defined as the principal factor controlling precipitation isotopes in the tropics, or in seasons that experience convective activities (Bony et al. 2008; Risi et al. 2008a,b), using OLR as the indices. Dong et al. (2016) insisted that hydrometeors and moisture transported to the southwestern TP were lifted by convective storms over central-eastern India or the Himalayan foothills. The dominant controls exerted by integrated convective activities on precipitation δ18O at Lhasa have also been studied during the ISM season (Gao et al. 2013). In Fig. 8 and Table 2, precipitation δ18O values recorded at Lhasa indicate the convection intensities in ISM domains, as detailed in previous studies (Risi et al. 2008a; Guo et al. 2017); ISM intensity and strength are defined using OLR datasets in this study. The increased ISM intensity or strength relates to more intense convective activities, and more depleted precipitation isotopes, exactly as shown in Fig. 8 and Table 2. In Table 2, the abnormal positive correlations in June may result from the not entirely consistent study periods in precipitation isotopes (June) and the ISM activities (between the ISM onset and retreat date). Considering the cumulative effect of climate factors on precipitation isotopes, the values of precipitation isotopes at the early ISM starts (June) may largely be affected by temperatures before the ISM onset. It is noticeable that the positive correlation in June is insignificant. With study periods consistently in June–September (Table 2, last row), their negative relations are enhanced. This suggests that using OLR datasets to delineate different ISM characteristics provides a reasonable approach.
Temporal variabilities in precipitation δ18O values (squares) and (a) ISM intensity (gray circles) or (b) ISM strength (blank circles) during 1993–2012 at Lhasa (Fig. 1b, pink circles). “+” and “−” represent the higher and lower values for indices on the y axis, respectively.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Temporal variabilities in precipitation δ18O values (squares) and (a) ISM intensity (gray circles) or (b) ISM strength (blank circles) during 1993–2012 at Lhasa (Fig. 1b, pink circles). “+” and “−” represent the higher and lower values for indices on the y axis, respectively.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Temporal variabilities in precipitation δ18O values (squares) and (a) ISM intensity (gray circles) or (b) ISM strength (blank circles) during 1993–2012 at Lhasa (Fig. 1b, pink circles). “+” and “−” represent the higher and lower values for indices on the y axis, respectively.
Citation: Journal of Climate 36, 12; 10.1175/JCLI-D-22-0518.1
Correlation coefficients (r) between precipitation δ18O values and the ISM intensity or strength at Lhasa, for ISM periods in 1993–2012. An asterisk indicates that r significant at the p < 0.1 confidence level.
The ISM characteristics have been defined using various methods or parameters, namely, the delineation of large-scale monsoon currents using various satellite-derived parameters (Simon et al. 2006); analysis of ISM onset, advance, and retreat based on OLR datasets (Khole 2010); precipitation records (Noska and Misra 2016; India Meteorological Department); wind vector fields (Taniguchi and Koike 2006; B. Wang et al. 2009); GPS precipitable water (Puviarasan et al. 2015) or radio occultation data (Rao et al. 2013); the intertropical convergence zone (Saha and Saha 1980); vertically integrated moisture transport (Fasullo and Webster 2003); tropopause parameters (RavindraBabu et al. 2019); or water stable isotopes (Yu et al. 2016). In this study, we use the OLR datasets to identify the ISM onset and retreat date, as well as ISM duration, intensity and strength for the ISM domains in TP. Stable precipitation isotopes at Lhasa are selected to confirm the OLR results. The OLR indices have the advantage of being simple, clear, and able to capture all the different ISM characteristics in our study region.
We identify the ISM onset date as falling between ∼31 May and 19 July, with the ISM retreating between ∼8 August and 27 September. The ISM duration is between ∼40 and 110 days (Fig. 3). Regions in the central and southern TP experience the earliest ISM onset, latest retreat, and longest ISM duration; these are in accordance with the moisture transportation pathways along with ISM to the TP (Fig. 1). The trends of the different ISM activities show large spatial discrepancies in our study area. In the left-hand column regions, the ISM arrives earlier, retreats later, lasts longer, and exhibits increased intensity and strength (Fig. 5); the reverse phenomenon is found in the right-hand columns of our study area. When calculating the ISM duration, grids in the northwestern sector of our study area are missing (Fig. 3c). These grids are mainly located in latitudes northern of 31.25°N, and near the M–W boundary. The ISM duration for these grids may be inaccurate, and therefore are blank. A large number of grids in the southwestern sector of our study area fall outside the TP margins. Taking into consideration the widely divergent spatial patterns observed in TP’s precipitation changes, we especially outline the southwestern and southeastern TP with blue dashed frames and compare their ISM characteristics (Fig. 5). In 1975–2020, trends of the ISM characteristics in southeastern TP appear inverse in relation to those experienced in southwestern TP, except for the ISM strength (Fig. 5). In previous studies, generally “opposite” patterns are also found in precipitation trends (Yao et al. 2012; Zhang et al. 2019; Meng et al. 2020; Yan et al. 2020), and in water vapor contents or water vapor flux trends (Yan et al. 2020), for these two subregions; these are similar to the spatial patterns evident in ISM characteristics obtained in this study. For example, Zhang et al. (2019) identified the predominance of Indian monsoon moisture in southern TP and revealed similarly opposite patterns in the trends of these sources (increases in southwestern TP, decreases in southeastern TP). The consistencies between water-related source changes and their spatial patterns in southern TP and the ISM characteristics defined using OLR datasets confirm that OLR-based analysis performs well in capturing the different ISM characteristics in TP. OLR datasets have been primarily used to distinguish convection; higher contributions of convective precipitation to total annual precipitation (>70%) have also been reported in southern TP (Wang et al. 2018). Yang et al. (2011) reviewed the spatial patterns in precipitation, evaporation, runoff, and soil moisture changes; the most significant decreases were found in southeastern TP (the regions influenced by a weakening ISM). This is also the case for trends in total water storage of the TP (Zhang et al. 2020). All these previous findings corroborate the accuracy of delineating ISM activities using OLR.
Generally earlier trends in ISM onset date are also identified in previous research in India (Nair and Mahajan 2010; Bollasina et al. 2013; Ghanekar et al. 2019; Liu et al. 2019), with minor exceptions (Sahana et al. 2015; Flatau et al. 2003). Our research results for ISM onset date are in accordance with results from other ISM regions, and the variabilities observed in ISM retreat date, duration, ISM intensity, and strength can effectively supplement other ISM studies in the southern TP. These ISM characteristics observed in our study area are spatially inhomogeneous. The ISM characteristics in the western sector of our study area are enhanced, but they are weakening in the eastern sector. In Fig. 1, it can be seen that the ISM affects the TP via two clustered routes: the southwestern TP’s boundary, directly entering the TP from a southwesterly direction, and transport from the southwest, which then turns in a southeastern direction and enters the TP from TP’s southeastern boundary. We notice that the latter ISM routes turn directionally during the airmass transportation. During this process, the ISM weakens, a process that may be further enhanced by the longer moisture transportation pathways and by the topographically rough Hengduan Mountains in eastern TP.
Possible ISM controls and their forcing mechanisms have elicited increasing interests, especially set against the background of global climate changes. Factors such as El Niño–Southern Oscillation (ENSO) (Chen et al. 2020; Qadimi et al. 2021), Atlantic multidecadal oscillation (Luo et al. 2018), Arctic Oscillation (Buermann et al. 2005), Eurasian snow cover anomalies (Bamzai and Shukla 1999; Vernekar et al. 1995), equatorial Indian Ocean convection (Murali et al. 2021), and regional rainfall (Zheng et al. 2016) may impact the ISM duration and strength. Close relationships were observed between the ISM onset/retreat date or the ISM intensity and factors such as sea surface pressure fields (Khole 2010), the Madden–Julian oscillation (Taraphdar et al. 2018), intraseasonal oscillation (Qi et al. 2008, 2009), snow cover in the Himalayas (Dey et al. 1985; Senan et al. 2016; Lau and Kim 2018), the TP’s land surface albedo (Cao et al. 2019), sensible heating (Zhang et al. 2015), human activities such as land-use changes (Yamashima et al. 2015) or irrigation (Lee et al. 2009; Guimberteau et al. 2012), different types of Indian Ocean dipole (Anil et al. 2016), and heat contrast between the Asian landmass and the tropical Indian Ocean (Kajikawa et al. 2012). Aerosols are likely responsible for the observed earlier ISM onset, resulting in enhanced precipitation in most of India (Nigam and Bollasina 2010; Sajani et al. 2012; Bollasina et al. 2013; Sanap and Pandithurai 2015; Maharana et al. 2019). The impact of anthropogenic aerosols on ISM northward extents and the strength of ISM convection was documented (C. Wang et al. 2009). The snow-darkening effect of mineral dust aerosols weaken the ISM precipitation significantly during May–June (Shi et al. 2019); these effects are opposite to black carbon. The aerosol-induced cooling will lead to increased surface pressure over the local hotspots in Indian landmass, which further reduces the land–sea pressure contrast and results in weakening ISM circulations (Das et al. 2016). Das et al. (2015) also imply positive feedback of aerosol direct radiative forcing on ISM circulations over India. In the ENSO–ISM coupling models, large volcanic aerosols may increase the angular frequency of ENSO and promote significantly enhanced phase synchronization of the ENSO and ISM oscillations (Singh et al. 2020). In the CMIP5 climate models, strong relationships were revealed between interdecadal Pacific oscillation (IPO) patterns and the IPO–ISM rainfall teleconnection (Joshi and Kucharski 2017). Different decaying speeds of ENSO associated with the IPO phase, largely controlled by both the zonal advective and thermocline feedbacks, were suggested to be mainly responsible for Indian Ocean basin warming and further variabilities in the ISM activities (Liu et al. 2021). For the impacts of intraseasonal oscillation on ISM onset and interannual variations, Qi et al. (2009) showed that the former significantly contributed to the establishment of low-level Westerlies during the ISM onset and developing periods. Using OLR reanalysis and ground-based precipitation isotopes, we observe overall strengthening ISM activities in TP during the 1975–2020 period, although the different ISM characteristics are spatially inhomogeneous. The effects of changes in long-term, large-scale circulations and human activities on ISM activities suggest that such trends in ISM activities and the spatial characteristics of its different elements will continue in the near future, as will the spatial patterns evident in water-related climate changes. However, the low spatial resolutions of OLR datasets may limit the quality of our results to some extent. Other ISM indices with higher spatial resolutions and longer temporal series will be used in our future research. The generally opposite patterns for changes of ISM activities in southeastern and southwestern TP are very exciting. Results may indicate the changes in water-related climate and their spatial patterns in southern TP, such as the spatial–heterogeneity precipitation changes and the widely concerned accelerating precipitation reductions in southeastern TP in the recent decades. Thorough investigations concerning the aforementioned projects, including the ground-based observations, reanalysis, and modeling, are going well or to be conducted in our future research.
5. Conclusions
The TP’s climate is complex. It is affected by variations in different atmospheric circulations and moisture sources and a complex topography. The region is characterized by unstable hydrological cycles and water resource instabilities. The ISM is an important moisture source for the TP, and a thorough knowledge of the different ISM activities is therefore critical for any water-related studies. This study presents our initial attempts to delineate different ISM activities in TP. These include the ISM onset date, retreat date, duration, intensity, and strength. In this study, these characteristics are identified using OLR datasets covering 1975–2020; precipitation isotopes at Lhasa are used to confirm these results from OLR.
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The ISM onset (retreat) date in TP is between ∼31 May and 19 July (∼8 August–27 September). It lasts ∼40–110 days, with the longest duration observed in the central-southern TP.
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In general, the ISM starts earlier, retreats later, lasts longer, and is stronger and more intense over the entire study period, with some discrepancies found in the subperiods.
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Significant spatially inhomogeneous patterns are observed in the trends of the different ISM activities. These trends in the western sector of our study area (the 9 or 10 left-hand columns) are exhibited as: earlier onset, later retreat, longer duration, and increased intensity or strength; trends for the eastern sector of our study area (the 3 or 4 right-hand columns) are largely inverse to those noted for the western sector.
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The trends in ISM activities in 1975–98 and 1975–2020 show considerable consistency, with the 1975–98 trends determining the general patterns for the whole 1975–2020 study period.
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The generally negative relationships between precipitation δ18O values and ISM intensity/strength recorded at Lhasa (the Monsoon Zone) confirm the high reliability of the ISM characteristics we define using OLR datasets.
After reanalysis of OLR datasets and ground-based precipitation isotopes, our research both confirms and effectively complements the knowledge of ISM characteristics reported in previous studies. To refine the gaps arising from the use of low-spatial-resolution OLR datasets, more precise calculations of ISM characteristics in TP will be investigated in our future research.
Acknowledgments.
This research is funded by the National Natural Science Foundation of China (Grants 41988101, 42071090, 42271143, 41701080). We thank NOAA Physical Sciences Laboratory for access to the OLR datasets used in this analysis. We would also like to express our sincere thanks to Edward A. Derbyshire, who helps us improve the English expressions.
Data availability statement.
The outgoing longwave radiation (OLR) data that support the findings of this study are available from NOAA Physical Sciences Laboratory (https://psl.noaa.gov/data/gridded/data.uninterp_OLR.html). Datasets of precipitation isotopes that support this study can be accessed from the corresponding author (Xiaoyu Guo: xiaoyuguo@itpcas.ac.cn), upon reasonable request.
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