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Article

Investigation on the Linkage Between Precipitation Trends and Atmospheric Circulation Factors in the Tianshan Mountains

by
Chen Chen
1,
Yanan Hu
2,
Mengtian Fan
1,*,
Lirui Jia
1,
Wenyan Zhang
1 and
Tianyang Fan
1
1
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(5), 726; https://doi.org/10.3390/w17050726 (registering DOI)
Submission received: 31 December 2024 / Revised: 14 February 2025 / Accepted: 27 February 2025 / Published: 1 March 2025
Browse Figures
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Abstract

:
The Tianshan Mountains are located in the hinterland of the Eurasian continent, spanning east to west across China, Kazakhstan, Kyrgyzstan, and Uzbekistan. As the primary water source for Central Asia’s arid regions, the Tianshan mountain system is pivotal for regional water security and is highly sensitive to the nuances of climate change. Utilizing ERA5 precipitation datasets alongside 24 atmospheric circulation indices, this study delves into the variances in Tianshan’s precipitation patterns and their correlation with large-scale atmospheric circulation within the timeframe of 1981 to 2020. We observe a seasonally driven dichotomy, with the mountains exhibiting increasing moisture during the spring, summer, and autumn months, contrasted by drier conditions in winter. There is a pronounced spatial variability; the western and northern reaches exhibit more pronounced increases in precipitation compared to their eastern and southern counterparts. Influences on Tianshan’s precipitation patterns are multifaceted, with significant factors including the North Pacific Pattern (NP), Trans-Niño Index (TNI), Tropical Northern Atlantic Index (TNA*), Extreme Eastern Tropical Pacific SST (Niño 1+2*), North Tropical Atlantic SST Index (NTA), Central Tropical Pacific SST (Niño 4*), Tripole Index for the Interdecadal Pacific Oscillation [TPI(IPO)], and the Western Hemisphere Warm Pool (WHWP*). Notably, NP and TNI emerge as the predominant factors driving the upsurge in precipitation. The study further reveals a lagged response of precipitation to atmospheric circulatory patterns, underpinning complex correlations and resonance cycles of varying magnitudes. Our findings offer valuable insights for forecasting precipitation trends in mountainous terrains amidst the ongoing shifts in global climate conditions.

1. Introduction

The far-reaching impacts of global climate change on local climates have garnered widespread attention [1,2,3,4]. Nested within the heart of the Eurasian continent, the Tianshan Mountains exhibit a heightened sensitively to these global climate shifts [5,6,7], drawing their primary moisture sources from the Atlantic and Arctic Oceans [8]. The precipitation in Tianshan plays a pivotal role in the ecological preservation and sustainable progress of Central Asia, making the examination of its patterns in relation to global climate change particularly crucial.
The formidable elevation and intricate relief of the Tianshan Range pose significant challenges to the collection of meteorological data. To overcome data scarcity in mountainous regions, various reanalysis precipitation products have been developed, including MERRA [9], ERA-Interim [10], ERA5 [11], and GPCP [12]. ERA5 stands out for its high temporal resolution, enabling detailed atmospheric observations [13,14,15], and boasts a comprehensive historical record [11]. Notably, ERA5 data demonstrates strong agreement with observed precipitation patterns in Northwest China, as evidenced by recent studies [16,17,18]. This research utilizes ERA5 reanalysis data to investigate the precipitation dynamics within the Tianshan region.
Regional precipitation is influenced by myriad factors, including global warming, atmospheric patterns, terrestrial characteristics, and vegetation cover [19,20,21,22]. Atmospheric circulation, in particular, has been the focus of numerous studies due to its considerable effect on precipitation [23,24,25,26]. In the Tianshan area, the westerlies play a crucial role in modulating precipitation patterns [27,28]. Changes in the position and strength of the westerly jet, driven by atmospheric circulatory changes, are instrumental in shaping precipitation trends within the Tianshan Mountains. Prior research has established a positive correlation between the Indian Summer Monsoon (ISM), the El Niño–Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and precipitation trends in the Tianshan Mountains of China [29]. A link between the winter precipitation index and the Arctic Oscillation (AO) index has also been observed in the region [30]. Kononova et al. [31] identified atmospheric circulation as a determinant in the interannual variance of Tianshan’s summer precipitation. Additionally, Yue et al. [32] highlighted a synergistic interplay between atmospheric circulation and the decadal variability of summer precipitation in the range. Notably, dry periods occurring in the western Tianshan have been associated with the winter North Atlantic Oscillation (NAO) [33], and it has been established that the South Asian Summer Monsoon Index (SASMI) exhibits a significant positive correlation with annual precipitation. Concurrently, the PDO, Pacific–North American Oscillation (PNA), and AO show a modest positive correlation [7].
Nevertheless, much of the existing literature focuses on precipitation–circulation dynamics in specific areas of the Tianshan Mountains, leading to a paucity of comprehensive studies covering the entire range. Furthermore, the challenging natural environment poses limitations on meteorological data collection, often leading to insufficient information to fully capture the extent of climatic changes in these mountainous regions [34,35]. Previous research has typically utilized a limited selection of circulation indices, yielding a less holistic view of the prevailing dynamics [7,31,32,36]. Addressing these gaps, our study employs ERA5 reanalysis data alongside 24 atmospheric circulation indices to provide a comprehensive assessment of Tianshan’s precipitation shifts and their relation to atmospheric circulatory factors.
Utilizing ERA5 reanalysis precipitation products and a robust set of atmospheric circulation indices, this study delves into the annual and seasonal precipitation variations in Tianshan from 1981 to 2020. By employing a partial correlation coefficient as well as the cross wavelet method, we examine the nexus between precipitation patterns and 24 distinct atmospheric circulatory elements. This research delineates 40-year precipitation changes in Tianshan, uncovering their intricate dynamic associations with atmospheric circulation. The study identifies the primary circulation factors influencing climate change in the Tianshan Mountains, contributing to a deeper understanding of how arid mountain regions respond to global climate change, and providing important recommendations for water resource management.

2. Materials and Methods

2.1. Study Area

Tianshan is located in the hinterland of Eurasia. This research investigated the region between 38° N–45° N and 67° E–95° E (Figure 1). This east–west oriented region spans over 2500 km in length and varies between 250 and 350 km in width. Characterized by a complex topography that encompasses towering peaks, deep canyons, and vast basins, Tianshan’s altitudinal range extends from 255 m to a remarkable 7126 m above sea level. Known as the “Water Tower of Central Asia”, Tianshan is a critical repository of precipitation, fostering glacial and snow formations that serve as vital water sources for Central Asia’s major river networks. Far from the ocean, Tianshan is subject to a temperate/continental arid climate. It records a multi-year mean temperature of 7.88 °C, with a warming trend of 0.306 °C per decade observed [37], alongside an average annual precipitation of approximately 290 mm [33]. Influenced by the region’s topography and the direction of westerly wind flows, precipitation predominantly favors the western and northern areas over the eastern and southern areas [38,39]. Moreover, Tianshan showcases a diverse array of vegetation, arranged in distinct altitudinal zones that reflect the range’s ecological variance.

2.2. Data

In this research, ERA5 reanalysis products were used to reveal the characteristics of precipitation changes over the past 40 years. ERA5, developed by the European Centre for Medium-Range Weather Forecasts (https://cds.climate.copernicus.eu/, accessed on 15 February 2025), integrates observations from meteorological stations and satellite imagery. This dataset is notable for its granular temporal and spatial resolution, rapid data refresh rates, and comprehensive parameter coverage. Validation studies corroborate the ERA5 products’ fidelity to actual observations [17,40], with particular praise for their precision in higher-altitude regions [13,14,41]. The dataset covers the period from 1981 to February 2020, with monthly temporal resolution and a spatial granularity of 0.25° by 0.25°. Precipitation values are presented in millimeters per day, which are then aggregated to monthly totals.
Observed precipitation data from 14 meteorological stations (Table 1) in the study area spanning from March 1981 to February 2021 were used to test the applicability of the ERA5 precipitation products in the Tianshan Mountains, and the test metrics included the Pearson correlation coefficient (r), root mean square error (RMSE), and slope (Table 2). The precipitation data from the meteorological stations were obtained from the National Meteorological Information Centre (https://data.cma.cn, accessed on 15 February 2025). As shown in Table 2, the ERA5 dataset demonstrates strong correlations with station observations, with coefficients consistently exceeding 0.5. RMSE values are acceptably low, and the regression slopes approach unity. Notably, Baluntai exhibits the highest correlation at 0.868, while Aksu has the lowest at 0.516. RMSE peaks at Barkol Kazakh Autonomous County with 13.943 and dips to its lowest at Baicheng at 7.917. The slope is nearest to unity at 0.962 in Aheqi, whereas Qumul shows the greatest deviation with a slope of 0.633. To validate the accuracy of the ERA5 precipitation data, we calculated the bias between the precipitation data from 14 observation stations and the ERA5 precipitation (Figure 2). Overall, the errors are relatively small. Most of the bias values in Figure 2 are positive, indicating that the ERA5 precipitation is higher than the observed precipitation. This discrepancy can be attributed to the lower altitudes of the observation stations, as precipitation tends to be higher at higher altitudes compared to lower altitudes. The altitudes of the observation stations range from 440.5 m to 3504.4 m (Table 1), while the altitudes corresponding to the ERA5 precipitation data in the Tianshan Mountains range from 255 m to 7126 m (Figure 1).
It has been indicated that water vapor in Tianshan primarily originates from sources such as the Atlantic Ocean, Arctic Ocean, etc. [42]. Prior research has established a correlation between the Indian Summer Monsoon (ISM), the El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Arctic Oscillation (AO), and the North Atlantic Oscillation (NAO) and precipitation trends in the Tianshan Mountains [29,30,31,32,33]. This research selected 24 atmospheric circulation indices to further explore the precipitation changes in Tianshan in response to large-scale circulation patterns (Table 3) [43]. Circulation indices were sourced from NOAA Physical Science Experiments (NOAA, Physical Sciences Laboratory, PSL) (https://psl.noaa.gov/, accessed on 15 February 2025). It is important to note that the reported timeframe for the NP and NTA indices spans from March 1981 to December 2019, and that of the TPI (IPO) index covers March 1981 to December 2009, while the timeframe for all other indices extends from March 1981 to February 2021.

2.3. Methodology

This study aims to analyze the precipitation change and its response to the circulation index in the Tianshan Mountains. Firstly, we applied Sen’s slope to the trend of precipitation change in the Tianshan Mountains and tested the significance of the trend change using the Mann–Kendall approach. Then, we used partial correlation analysis to calculate partial correlation coefficients between the precipitation and atmospheric circulation indices. Finally, we used the wavelet analysis method to analyze the response of the precipitation to atmospheric circulation indices at different scales.

2.3.1. Sen’s Slope Analysis

Sen’s slope is a robust model for fitting straight lines in non-parametric statistics [44,45]. It effectively reduces the impact of outliers on the data and is suitable for trend analysis in long time series. For the formula of Sen’s slope, please refer to Tabari [45].

2.3.2. Mann–Kendall Trend Test

The Mann–Kendall trend test is a non-parametric test based on the rank of the data rather than the data itself [46,47,48]. It is frequently utilized to analyze time series with an unstable central tendency, such as a continuous increasing or decreasing tendency (monotonic tendency), and to evaluate the statistical significance of such trends. For the formula of the Mann–Kendall trend test, please refer to Khosravi [48].

2.3.3. Partial Correlation Coefficient

With multiple variables, a simple correlation may not adequately capture the true correlation due to the interference from other variables. In the case of multiple random variables, denoted as X , Y , Z 1 Z 2 , ⋯ , Z n , investigating the degree of correlation between two specific variables requires higher-order partial correlation analyses to eliminate the interactions between the variables [49]. To exclude ocean–atmospheric oscillation patterns’ influence on a single climate element, Yang et al. [50] applies partial correlation analysis in order to discern exclusively the relationship between climate indices and the SPEI-removed precipitation or temperature variations from it. Partial correlation analysis can more accurately reveal the intrinsic relationships between variables by excluding the effects of confounding factors. Therefore, in this study, the partial correlation coefficient is used to quantify the strength of the correlation between precipitation and one circulation factor. For the formula of partial correlation analysis, please refer to Yang et al. [50].

2.3.4. Wavelet Analysis

Wavelet coherence analysis is an advanced signal processing technique that melds wavelet transform with cross-spectral methods. This approach effectively captures and visualizes both global and local fluctuations within meteorological data sequences, providing a robust framework to ascertain correlations between two temporal series across diverse time–frequency domains [51]. Different from the well-known cross wavelet power spectrum, which is adept at highlighting zones of shared high-energy values and the phase relationships between two variables, the wavelet coherence spectrum offers a nuanced lens to gauge the local correlation within regions of lower energy values between two time series within the time–frequency continuum [52]. The application of wavelet analysis in this study facilitates a detailed exploration of how precipitation patterns respond to various circulation indices across multiple scales. For the formula of wavelet analysis, please refer to Fan et al. [5].

3. Precipitation Change Analysis

3.1. Temporal Changes in Precipitation

Utilizing the ERA5 reanalysis products, this study examined the variations in precipitation across the Tianshan region over the last four decades (Figure 3). The records indicate notably high annual precipitation in 1993 and 1998, surpassing the threshold of 800 mm, in contrast to the particularly low precipitation in 1996 and 2012, falling below 590 mm—with 1996 witnessing a particular modest 545 mm. Overall, precipitation shows a fluctuating decreasing trend, with a rate of 0.29 mm/10a, punctuated by considerable interannual variability. Distinct periods of decline occurred during 1981–1985, 1988–1991, 1994–1996, 1999–2008, 2011–2014, and 2017–2020, while incremental increases were observed during 1986–1987, 1992–1993, 1997–1998, 2009–2010, and 2015–2016.
An analysis of the seasonal precipitation patterns (Figure 4) reveals that the Tianshan Mountains receive an average of 67.99 mm in spring, 82.78 mm in summer, 46.39 mm in autumn, and 30.40 mm in winter. Peak summer precipitation can reach up to 104.90 mm, demonstrating significant seasonal fluctuations. A decreasing trend is discernible in spring, summer, and autumn, with decreases of 0.48, 0.91, and 0.33 mm/10a, respectively. In contrast, winter precipitation exhibits an increasing trend of 0.80 mm/10a. The years 1997–1999 marked a period where summer, autumn, and winter all contributed to an upsurge in precipitation, with respective seasonal increases of 29.25 mm, 26.19 mm, and 12.34 mm.

3.2. Spatial Changes in Precipitation

Figure 5 presents the spatial distribution and variability of annual precipitation across Tianshan from 1981 to 2020. The annual precipitation across the region ranges from 10 mm to 784 mm. The examination of spatial trends [Figure 5a] reveals a discernible gradient in precipitation levels, with the western and northern sectors receiving more precipitation than their eastern and southern counterparts. The western Tianshan typically record precipitation levels over 500 mm. In contrast, the eastern Tianshan experience precipitation levels below 400 mm. It is also found that the northern slopes of the Tianshan benefit from moisture-laden westerly winds, resulting in higher precipitation, while the southern slopes, lying in the rain shadow, receive considerably less. The maximum annual precipitation is observed in the western regions, often exceeding 700 mm. Conversely, the eastern regions register the minimum, sometimes falling below 50 mm. The contour plot of the variation of annual precipitation [Figure 5b] indicates that regions such as the Tekes Basin, Yuldus Basin, and Yilenkhabir Ga Mountains are undergoing a statistically significant increasing trend in precipitation at a rate exceeding 2 mm/10a. Conversely, the Issyk-Kul Lake region is witnessing a pronounced decreasing trend, with rates of decrease surpassing −4 mm/10a. This is because the moisture transport system in the Tianshan Mountains is primarily controlled by the westerly circulation and monsoon intensity, with the majority of moisture originating from the Atlantic Ocean and a smaller portion from the Arctic Ocean [43,53,54]. Additionally, the Tianshan Mountains are characterized by a narrow structure on both sides and a wider middle section, combined with their high elevation, which effectively blocks the moisture carried by the westerly circulation from the Atlantic and Arctic Oceans. This results in significant precipitation on the windward slopes in the northwest of the Tianshan Mountains. Furthermore, circulation factors such as the Pacific Decadal Oscillation also influence climate change in the Tianshan region [43].
The seasonal analysis of precipitation in Tianshan (Figure 6) provides a comprehensive overview of its distribution across different mountain ranges and basins. During the spring season, the highest recorded precipitation reaches 257 mm. The Alay, Talas, and Kyrgyz ranges frequently experience more than 100 mm of precipitation, while the eastern regions typically receive less, with amounts dropping below 80 mm [Figure 6a]. Summer, the season of the highest precipitation levels in the Tianshan, witnesses a peak of 405 mm. The majority of the mountain ranges register precipitation upwards of 200 mm. Conversely, the eastern and southwestern parts of the Tianshan exhibit lower precipitation, not exceeding 80 mm, and display pronounced spatial variations [Figure 6b]. Autumn precipitation presents a maximum of 198 mm. The western ranges, notably the Alay, Talas, and Kyrgyz, receive more than 100 mm. In stark contrast, the eastern parts of the Tianshan see less than 40 mm [Figure 6c]. Winter precipitation patterns show a maximum of 185 mm, with the western Alay and Kungey Alatau mountains receiving over 80 mm. The central and eastern regions, however, experience significantly lower precipitation, often less than 30 mm [Figure 6d].
Figure 7 examines the nuances of seasonal precipitation changes across the Tianshan region over recent decades. In spring, an increasing precipitation trend is observed in the Bogda and Uken mountains, surpassing the 95% significance threshold with a rate exceeding 1.5 mm/10a. However, central Lake Issyk-Kul and the southern Arai mountains are experiencing a decline in precipitation, with rates dropping by more than −4.1 mm/10a [Figure 7a]. In summer, the Narathi Mountains, Yurdus Basin, Erbin Mountains, and Halkhtau Mountains exhibit a pronounced rising trend in precipitation, with increases exceeding 3.2 mm/10a. In contrast, precipitation rates in the southern regions of Issyk-Kul and the Alay Mountains are declining by more than −1.0 mm/10a [Figure 7b]. Autumn sees a significant upturn in precipitation at Khan Tengri and the Tomur Peaks, with rates escalating beyond 1.5 mm/10a. In stark contrast, the vicinity south of Bogda Mountain and the Alay Mountains registers substantial decreases, with trends surpassing −1.8 mm/10a [Figure 7c]. Winter reveals a general decreasing trend across most of Tianshan, with a rate of decrease exceeding −0.1 mm/10a. Yet, regions such as the Ferghana and Alay Mountains are experiencing notable increases in precipitation, with rates climbing by more than 2.3 mm/10a [Figure 7d].

3.3. Precipitation Response to Atmospheric Circulation

3.3.1. Partial Correlation Analysis

The partial correlation coefficients between precipitation in the Tianshan Mountains and 24 circulation factors, including EP/NP and NAO*, are calculated with the remaining 23 circulation factors under control. The results are summarized in Table 4. The maximum magnitude of partial correlation between circulation factor and precipitation in the Tianshan are found with the NP (0.347) and TNI (−0.333), passing the α = 0.01 significance level test. Following closely are the TNA* (0.261), Niño 1+2* (0.259), NTA (−0.252), Niño 4*(−0.198), TPI(IPO) (−0.160), and the WHWP*(0.143). These findings suggest that precipitation in the Tianshan Mountains is influenced by the NP, TNI, TNA*, Niño 1+2*, NTA, Niño 4*, TPI (IPO), and the WHWP*, with the NP and TNI exerting the strongest influence.

3.3.2. Multi-Scale Correlations

Recent studies have highlighted the scale-dependent influence of atmospheric circulation on local precipitation patterns [55,56]. Therefore, our research employs wavelet analysis to investigate the Tianshans’ precipitation response to various circulation factors, including the NP, TNI, TNA*, Niño 1+2*, NTA, Niño 4*, TPI, and the WHWP*. The results of computed XWT and WTC are presented in contour plots in Figure 7 and Figure 8. Resonant cycles observed with different circulation factors are summarized as follows.
  • Niño 1+2*
During the period from 2009 to 2012, we identified a significant 1–8 month resonant cycle correlating precipitation with Niño 1+2*. This cycle is characterized by a positive correlation, with precipitation lagging Niño 1+2* by approximately 15 days. Additionally, during the periods between 1980 and 1982, 1984 and 1988, and 2010 and 2012, a notable 8–16 month resonant cycle was observed, with the phase relationship transitioning from negative to positive. Furthermore, the period from 1985 to 1992 revealed a significant 24–40 month cycle, while an extended resonant cycle of 32–96 months was discerned from 1989 to 2016. These cycles demonstrated precipitation lag times of approximately 120 days and 240 days, respectively [Figure 8b].
  • Niño 4*
The period from 1985 to 1990 and 2002 to 2006 witnessed significant 8–16 month resonant cycles, predominantly characterized by a negative phase relationship, which was particularly strong during 2000–2004 and 2017–2020. Additionally, the interval from 2005 to 2010 presented significant 24–48 month cycles, maintaining a predominantly negative phase relationship [Figure 8d].
  • TNI
During the years 1984–1987, 2000–2007, and 2017–2020, precipitation showed a resonant cycle of 4–16 months, mainly exhibiting a negative phase. Between 1985 and 1987, 1990 and 2020, and 2012 and 2020, an 8–16 month cycle was found, with a positive correlation dominant during 1990–2020. Notably, precipitation lagged behind the TNI by approximately 45 days during the period from 2012 to 2020. A significant resonant cycle of 26–32 months was also observed from 1987 to 1991, where precipitation lagged TNI by about 109 days [Figure 8f].
  • NP
The study further reveals a consistent resonant cycle of 6–16 months across the entire study area between precipitation and the NP. A strong positive correlation was predominant before 2000. However, a lag of about 60 days was evident after 2000, underscoring the substantial impact of the NP on precipitation within the Tianshan region [Figure 8h].
  • TPI
Distinct resonant cycles between Tianshan precipitation and TPI were uncovered by this study. During 1988–1990, 1993, 1997, and 2008, a significant 1–8 month resonant cycle was observed, characterized by a phase relationship evolving from negative to positive. Additionally, a longer 4–16 month resonant cycle from 1988 to 2003 also exhibited a mainly negative phase relationship. Furthermore, extended cycles of 32–64 months during 1980–1990 and 64–128 months in 1989–2004 were significant and predominantly negative, indicating a precipitation lag behind TPI by approximately 144 days [Figure 9b].
  • NTA
The relationship between precipitation and NTA was marked by significant 4–16 months cycles in 1986, 2000–2005, and 2014, though the correlation demonstrated instability. Longer cycles of 16–48 months in 1985–1992 and 32–64 months from 2010 to 2018 presented predominantly positive phase relationships, with the latter period showing precipitation advancing the NTA by roughly 180 days [Figure 9d].
  • WHWP*
From 1995 to 2020, an 8–16 months resonance cycle was significantly correlated with the WHWP*, primarily characterized by a positive and strong phase relationship. Additionally, a substantial 32–64 months cycle was detected from 1995 to 2015. Initially, precipitation was in phase with the WHWP* from 1995 to 2005, but it began lagging by about 180 days from 2006 to 2015 [Figure 9f].
  • TNA
Significant resonant cycles of 6–16 months between precipitation and TNA were noted in 1986–1989, 2001–2004, and 2010–2018, although the correlation exhibited fluctuations. Notable 24–40 and 32–64 month cycles were observed from 1984 to 1993 and 2009 to 2018, respectively, both demonstrating a generally positive phase relationship [Figure 9h].
The aforementioned analysis underscores the considerable influence exerted by the TNI, Niño 1+2*, WHWP, and Niño 4* on the precipitation patterns in the Tianshan Mountains. We have further visualized the monthly fluctuations of these four key indices, as depicted in Figure S1. Notably, between 1980 and 2020, these indices exhibited varying degrees of oscillation.

4. Discussion

This paper identifies a fluctuating decline in precipitation across Tianshan from 1981 to 2020. Moreover, the results show an increasing trend of precipitation in spring, summer, and autumn, but a decreasing trend in winter. This finding contrasts with prior research suggesting a shift towards a warmer and more humid climate [57,58]. Additionally, Guan et al. [54] reported a slight increasing trend in Tianshan’s annual precipitation at a rate of 0.90 mm/10a from 1950 to 2016, marking a positive moisture period commencing around 1980. Similarly, Xu et al. [59] indicated a period of rapid warming and increasing humidity, with an annual precipitation rise of 5.82 mm/10a and a net gain of 33.2 mm in average annual precipitation from 1960 to 2016. This article uses the ERA5 precipitation dataset, which differs from other research datasets in terms of spatial resolution, data coverage, and climate statistical attributes. These different features may explain the differences between the results of this study and previous research. In addition, the period of this study was from 1981 to 2020, but some previous surveys used different time periods. It is reasonable for the climate system to exhibit different trends over different time frames, which provides an additional explanation for the differences between our research findings and earlier ones. The comparison of precipitation data from meteorological stations with ERA5 precipitation data in this paper (Table 2) shows a high degree of agreement. This study indicates that the bias between the precipitation data from the 14 observation stations and the ERA5 precipitation (Figure 2) is relatively small. The ERA5 precipitation is higher than the observed precipitation, and this discrepancy can be attributed to the lower altitudes of the observation stations, as precipitation tends to be greater at higher altitudes compared to lower altitudes. The altitudes of the observation stations range from 440.5 m to 3504.4 m (Table 1), while the altitudes corresponding to the ERA5 precipitation data range from 255 m to 7126 m (Figure 1).
This study shows that precipitation in the Tianshan Mountains is influenced by the NP, TNI, TNA*, Niño 1+2*, NTA, Niño 4*, TPI (IPO), and the WHWP*. Among them, the NP and TNI exerted the strongest influence. This phenomenon can be attributed to possible reasons, as discussed below.
NP inter-decadal projections are significantly influenced by the North Atlantic [60]. A positively phased, eastward-moving wave train originating from the North Atlantic induces a unique atmospheric setup. When the NP is at the apex of a positive potential, this setup results in a dipolar negative potential over the North Atlantic and Central Asia. Such conditions facilitate upward motion in northern Central Asia, allowing westerly winds to convey moisture from the North Atlantic and Northern Europe, culminating in increased precipitation within the region [41].
The TNI is associated with “central Pacific El Niño” events, during which the Western Pacific Subtropical High (WPSH) intensifies, and its ridge shifts northward. This results in significant anticyclonic circulation over the South China Sea and the Philippines, promoting heavy precipitation that extends to Central Asia [61]. The interannual variability of the Tropical North Atlantic Module (NTAM) has been identified as the primary source of springtime variability in the Tropical North Atlantic [62], with the region’s intrinsic ocean–atmospheric dynamics influencing SST development [63]. SST anomalies in the Atlantic can generate atmospheric waves that propagate along the westerly jet stream, impacting Eurasian atmospheric circulation [64]. These atmospheric vorticity anomalies, dispersing as Rossby waves, alter large-scale circulation patterns and vertical motions across Central Asia, thereby affecting precipitation patterns in the region [41].
Additionally, the influence of the Western Hemisphere Warm Pool (WHWP*) has been observed on the winter climate of the Northwest Pacific (WNP), as noted by Park et al. [65]. Rossby waves associated with the WHWP* extend westward, inducing wind anomalies across the North Pacific that subsequently influence climatic conditions in East Asia.
Against the backdrop of global warming, understanding regional climate variations becomes increasingly crucial. This paper delineates the underlying mechanisms driving precipitation variability in the Tianshan Mountains over the period from 1981 to 2020. Through wavelet analysis, we have examined how precipitation responds to different atmospheric circulation indices across various scales, uncovering a complex dynamic interplay. This relationship not only aids in forecasting future climatic trends in Eurasia but also underpins the strategic management and sustainable utilization of water resources in the Tianshan region, contributing to a foundational dataset for broader climate change attribution studies.
It is imperative to acknowledge the inherent level of uncertainty associated with this study. To begin with, the ERA5 dataset carries the potential for bias. This research analyzed the precipitation patterns within the Tianshan Mountains using ERA5-generated products. While meteorological station observations confirm the high accuracy of the dataset, the reanalysis of products, especially in data-sparse high-altitude mountainous regions, can only partially address the gap in observational data. However, it must be noted that any product’s estimation of regional precipitation is subject to bias, necessitating additional research to validate these estimates. Furthermore, the research area is characterized by a limited number of ground stations, all of which are situated below the 4000-m altitude threshold. The absence of high-altitude meteorological stations introduces a degree of uncertainty into the precipitation verification outcomes. Additionally, the wavelet analysis employed in this study is not without its own level of uncertainty. Moving forward, it is crucial to give due attention to the aforementioned issues in future research endeavors.
In future work, we aim to extend the temporal scope of our research to deepen our understanding of the drivers behind these climatic changes. Furthermore, while this paper has focused on 24 atmospheric circulation factors, subsequent research will incorporate additional influences such as EAP and EA/WR. These factors, identified by Guan et al. [42], are significant contributors to the interdecadal precipitation variability in Tianshan. By expanding our consideration to these and other potential atmospheric contributors, we hope to enhance the granularity and precision of our climate model projections for the region.

5. Conclusions

This research investigated the precipitation changes in Tianshan in response to large-scale circulation patterns from 1981 to 2020. The findings can be summarized as follows:
(1) The Tianshan Mountains experience increasing precipitation in spring, summer, and autumn, while winter precipitation shows a decreasing trend.
(2) The precipitation in Tianshan is affected by the NP, TNI, TNA*, Niño 1+2*, NTA, Niño 4*, TPI (IPO), and the WHWP*. Among these, it is the NP and TNI that are the most influential, contributing substantially to the observed decreases in precipitation.
(3) Precipitation in Tianshan demonstrates a lagged response to atmospheric circulation patterns, with observed correlations and resonance cycles between these variables exhibiting variation across multiple scales.
This research provides valuable insights for predicting precipitation in high-altitude mountainous areas against the background of global change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w17050726/s1, Figure S1. Changes in major circulation indices related to precipitation in the Tianshan Mountains.

Author Contributions

Conceptualization, methodology, validation, writing—original draft preparation and editing, C.C. and M.F.; writing—review and supervision, Y.H. and L.J.; verification of results, W.Z. and T.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Opening Foundation of State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences (Grant No. G2024-02-02), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 23KJB170017), the Third Xinjiang Scientific Expedition Program (2022xjkk0100), and by the Natural Science Foundation of Jiangsu Province (Grant No. BK20240710).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the Tianshan Mountains.
Figure 1. Location of the Tianshan Mountains.
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Figure 2. Bias: The difference between ERA5 precipitation and observed station precipitation during the period 1981–2020.
Figure 2. Bias: The difference between ERA5 precipitation and observed station precipitation during the period 1981–2020.
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Figure 3. Regional annual average ERA5 precipitation and anomaly curves for the period 1981–2020.
Figure 3. Regional annual average ERA5 precipitation and anomaly curves for the period 1981–2020.
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Figure 4. Changes in the seasonal precipitation in Tianshan: (a) spring (b) summer (c) autumn (d) winter.
Figure 4. Changes in the seasonal precipitation in Tianshan: (a) spring (b) summer (c) autumn (d) winter.
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Figure 5. Spatial distribution (a) and spatial variation (b) of annual precipitation.
Figure 5. Spatial distribution (a) and spatial variation (b) of annual precipitation.
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Figure 6. Spatial distribution of precipitation by season in the Tianshan: (a) spring (b) summer (c) autumn (d) winter.
Figure 6. Spatial distribution of precipitation by season in the Tianshan: (a) spring (b) summer (c) autumn (d) winter.
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Figure 7. Seasonal changes in precipitation: (a) spring (b) summer (c) autumn (d) winter.
Figure 7. Seasonal changes in precipitation: (a) spring (b) summer (c) autumn (d) winter.
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Figure 8. The Cross Wavelet Transformation (XWT) and Wavelet Transform Coherence (WTC) of precipitation and Niño 1+2*, Niño 4*, TNI and NP.
Figure 8. The Cross Wavelet Transformation (XWT) and Wavelet Transform Coherence (WTC) of precipitation and Niño 1+2*, Niño 4*, TNI and NP.
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Figure 9. The Cross Wavelet Transformation (XWT) and Wavelet Transform Coherence (WTC) of precipitation and TPI, NTA, WHWP* and TNA.
Figure 9. The Cross Wavelet Transformation (XWT) and Wavelet Transform Coherence (WTC) of precipitation and TPI, NTA, WHWP* and TNA.
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Table 1. Latitude, longitude, and altitude of the 14 meteorological stations.
Table 1. Latitude, longitude, and altitude of the 14 meteorological stations.
StationsLongitude (°E) Latitude (°N)Elevation (m)Landform Type
Urumqi87.6243.78917.9Slope
Tianchi88.1243.881942.5Valley
Caijiahu87.5344.20440.5Basin
Qitai89.5744.02793.5Slope
Balikun93.0543.601679.4Valley
Yining81.3343.95662.5Basin
Dabancheng88.3243.351103.5Valley
Baluntai86.3342.671752.5Valley
Aheqi78.4540.931984.9Valley
Kumishi88.2242.23922.4Slope
Baicheng81.9041.781229.2Slope
Kuqa82.9541.721099.0Slope
Aksu80.2341.171103.8Slope
Turgat75.4040.523504.4Ridge
Table 2. Results of the ERA5 accuracy assessment.
Table 2. Results of the ERA5 accuracy assessment.
StationsRRMSESlope
Urumqi0.8619.660 0.893
Tianchi0.80513.224 1.271
Caijiahu0.744 9.894 0.956
Qitai0.7779.6730.758
Balikun0.754 13.9431.277
Yining0.8189.2770.763
Dabancheng0.588 9.907 0.741
Baluntai0.8689.449 0.753
Aheqi0.73313.849 0.962
Kumishi0.5738.1470.633
Baicheng0.7967.9170.685
Kuqa0.6129.8030.731
Aksu0.5169.8280.649
Turgat0.77712.8760.860
Table 3. Atmospheric circulation indices.
Table 3. Atmospheric circulation indices.
IndexTimeIndex Abbreviations
East Pacific/North Pacific OscillationMarch 1981 to February 2021EP/NP
North Atlantic OscillationMarch 1981 to February 2021NAO*
Pacific North American IndexMarch 1981 to February 2021PNA*
Tropical Southern Atlantic IndexMarch 1981 to February 2021TSA*
Western Pacific IndexMarch 1981 to February 2021WP*
Multivariate ENSO IndexMarch 1981 to February 2021MEI V2
Quasi-Biennial OscillationMarch 1981 to February 2021QBO*
Atlantic Meridional ModeMarch 1981 to February 2021AMM
Central Tropical Pacific SSTMarch 1981 to February 2021Niño 4*
East Central Tropical Pacific SSTMarch 1981 to February 2021NINO 3.4*
North Pacific PatternMarch 1981 to December 2019NP
Indices of El Niño EvolutionMarch 1981 to February 2021TNI
Tropical Northern Atlantic IndexMarch 1981 to February 2021TNA*
Western Hemisphere Warm PoolMarch 1981 to February 2021WHWP*
Antarctic OscillationMarch 1981 to February 2021AAO
Atlantic Multidecadal OscillationMarch 1981 to February 2021AMO
Eastern Atlantic/Western RussiaMarch 1981 to February 2021EA/WR
North Tropical Atlantic SST IndexMarch 1981 to December 2019NTA
Oceanic Niño IndexMarch 1981 to February 2021ONI
Arctic OscillationMarch 1981 to February 2021AO
Extreme Eastern Tropical Pacific SSTMarch 1981 to February 2021Niño 1+2*
Pacific Decadal OscillationMarch 1981 to February 2021PDO
Tripole Index for the Interdecadal Pacific OscillationMarch 1981 to December 2009TPI(IPO)
Southern Oscillation IndexMarch 1981 to February 2021SOI*
Table 4. Correlation coefficient between circulation factor and precipitation.
Table 4. Correlation coefficient between circulation factor and precipitation.
Circulation FactorPartial CorrelationCirculation FactorPartial Correlation
EP/NP0.071 *TNA*0.261 ***
NAO*0.066WHWP*0.143 ***
PNA*−0.025AAO−0.007
TSA*−0.040AMO−0.012
WP*0.041EA/WR−0.029
MEI V20.067NTA−0.252 ***
QBO*−0.074 *ONI−0.018
AMM−0.098 **AO0.026
Niño 4*−0.198 ***Niño 1+2*0.259 ***
NINO 3.4*−0.019PDO0.074 *
NP0.347 ***TPI(IPO)−0.160 ***
TNI−0.333 ***SOI*0.056
Note: *** passes 0.01 significance level test (one-tailed); ** passes 0.05 significance level test; * passes 0.1 significance level test.
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Chen, C.; Hu, Y.; Fan, M.; Jia, L.; Zhang, W.; Fan, T. Investigation on the Linkage Between Precipitation Trends and Atmospheric Circulation Factors in the Tianshan Mountains. Water 2025, 17, 726. https://doi.org/10.3390/w17050726

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Chen C, Hu Y, Fan M, Jia L, Zhang W, Fan T. Investigation on the Linkage Between Precipitation Trends and Atmospheric Circulation Factors in the Tianshan Mountains. Water. 2025; 17(5):726. https://doi.org/10.3390/w17050726

Chicago/Turabian Style

Chen, Chen, Yanan Hu, Mengtian Fan, Lirui Jia, Wenyan Zhang, and Tianyang Fan. 2025. "Investigation on the Linkage Between Precipitation Trends and Atmospheric Circulation Factors in the Tianshan Mountains" Water 17, no. 5: 726. https://doi.org/10.3390/w17050726

APA Style

Chen, C., Hu, Y., Fan, M., Jia, L., Zhang, W., & Fan, T. (2025). Investigation on the Linkage Between Precipitation Trends and Atmospheric Circulation Factors in the Tianshan Mountains. Water, 17(5), 726. https://doi.org/10.3390/w17050726

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