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王圣杰|西北师范大学分享 http://blog.sciencenet.cn/u/Z2112132 干旱区降水同位素 Stable Water Isotopes in Arid Central Asia

博文

[转载]Atmospheric Research：天山降水同位素年际变化的水汽路径因素

已有 4019 次阅读 2021-6-28 22:11 |个人分类:亚洲中部干旱区同位素|系统分类:论文交流|文章来源:转载

Yudong Shi, Shengjie Wang*, Liwei Wang, Mingjun Zhang, Athanassios A Argiriou, Yang Song, Shijun Lei. Isotopic evidence in modern precipitation for the westerly meridional movement in Central Asia. Atmospheric Research, 2021, 259: 105698. DOI: 10.1016/j.atmosres.2021.105698


文献导读：

新疆天山降水同位素的温度效应已经广为报道，但是近年来水汽来源对亚洲中部干旱区降水同位素的影响越来越受到重视。西北师范大学地理与环境科学学院王圣杰副教授课题组通过对天山南脉乌恰的多年降水同位素监测资料，结合同位素大气环流模式结果，分析了年际尺度上亚洲中部干旱区降水同位素与环流系统的联系，发现西风的南北摆动可以敏感地体现在降水同位素中。论文发表在《Atmospheric Research》杂志上。



https://www.sciencedirect.com/science/article/pii/S0169809521002544


Isotopic evidence in modern precipitation for the westerly meridional movement in Central Asia

Author links open overlay panelYudongShiab ShengjieWangab LiweiWangabc MingjunZhangab Athanassios A. Argirioud YangSongab ShijunLeiab

  • a

  • College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China

  • b

  • Key Laboratory of Resource Environment and Sustainable Development of Oasis, Gansu Province, Lanzhou 730070, China

  • c

  • Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

  • d

  • Laboratory of Atmospheric Physics, Department of Physics, University of Patras, GR-26500 Patras, Greece


Abstract

The seasonal and inter-annual variations of stable isotopes in alpine precipitation in arid Central Asia is of great help to understand the modern hydrological processes and climate proxy in the westerly-dominant region. In this study, we measured the 3-year precipitation isotope values at a site located on the southern Tianshan Mountains in Central Asia. The δ18O values of precipitation show a statistically significant relationship with air temperature (R2 = 0.58). The trajectory diagnostics indicate the strong influence of westerly moisture, although the local trajectories do exist. According to the 3-year isotope records in the sampling site, the δ18O values of precipitation correlated with the meridional circulation index, while the correlation with the zonal circulation index is generally weak. An isotope-enabled GCM is used to analyze the correlation between the δ18O values in summer precipitation and the meridional and zonal wind speeds on a longer time scale; there is a strong correlation between the δ18O values and the meridional wind speed for the upwind direction. The findings indicate the meridional movement of mid-latitude westerly can be sensitively recorded in the modern precipitation in arid Central Asia, and the inter-annual variability of precipitation isotopes in Central Asia can not only be attributed to temperature effect.


Keywords

Precipitationδ18O; Temperature effect; Moisture source; Central Asia

1. Introduction

As an important component of the water cycle, precipitation is the main source of terrestrial water resources. Since Craig (1961) used stable hydrogen and oxygen isotopes as natural tracers for water cycle research, the stable isotope method has been widely used to diagnose hydrological processes related to precipitation from regional to global scales (Bowen et al., 2019; Galewsky et al., 2016), including the contribution estimation of local evapotranspiration to precipitation (Wang et al., 2016a; Zhao et al., 2019), the assessment of below-cloud evaporation on raindrop (Kong and Pang, 2016; Wang et al., 2016b) and the moisture source diagnostics (Krklec et al., 2018; Valdivielso et al., 2020). Especially in the arid and semi-arid regions, the strain between water supply and demand is prominent. A deep understanding of the precipitation process and its influencing factors can provide a basis for the rational use of water resources.

The arid and semi-arid regions of Central Asia are far away from the ocean, and the marine water vapor does not usually reach this area. Analyzing the stable hydrogen and oxygen isotope values in regional precipitation is of great help to assess the source and sink of meteoric water. In Central Asia, a large number of studies have been conducted to reconstruct paleoclimate through ice cores (Thompson et al., 1997), tree rings (Jiao et al., 2019; Xu et al., 2014, Xu et al., 2018), lacustrine sediments (Chen et al., 2010), and loess deposits (Miao et al., 2020; Parviz et al., 2020). For the research in which the stable hydrogen and oxygen isotope values are used as climate proxies, a clear knowledge of modern precipitation isotopes is needed. However, due to the short-term observations in the arid region, the studies focusing on the intra-annual controls are always limited (Wang et al., 2016c, Wang et al., 2019b). Some case studies using multiple year data (Liu et al., 2015; Yu et al., 2016) showed that the controlling factor on an inter-annual scale are not always consistent with that on an intra-annual scale. It is clear that the in-situ observations of modern precipitation isotopes for longer period than 2 years is urgently needed.

For a long time, some local environmental variables have been considered as the main factors controlling the stable isotope composition of precipitation. Therefore, there is a good correlation between precipitation isotope values and environmental factors in some region. Some studies (Tian et al., 2020; Yao et al., 2013) on the southern Qinghai-Tibet Plateau found a significant amount effect on precipitation isotope values, that is, there is a negative correlation between isotope values and precipitation amount. However, in the arid and semi-arid regions of Central Asia, precipitation isotope values show a more obvious temperature effect, and there is a positive correlation between isotope values and temperature (Bowen and Wilkinson, 2002; Liu et al., 2014), rather than amount effect. Based on observation data from 23 stations around the Tianshan Mountains in Central Asia (Wang et al., 2016c), precipitation isotope values at all stations show a clearly marked temperature effect; in many other cases across Central Asia (e.g., Wang et al., 2019b; Sun et al., 2019), the isotope values of precipitation also usually show a positive relationship with air temperature.

In addition to local environmental factors that affect precipitation isotope values, some external factors, such as large-scale circulation, upwind convective activities, and moisture sources, can also cause changes in precipitation isotope values. In the monsoon region of eastern Asia (Cai and Tian, 2016), it was found that there is a significant correlation between precipitation δ18O and cloud-top pressure, which shows a positive correlation in the monsoon region at low latitudes, but in the mid-high latitude monsoon regions, there is a negative correlation or no correlation. In the southern Qinghai-Tibet Plateau (Wang et al., 2020), there is a significant negative correlation between precipitation isotope ratio and high level cloud cover on the seasonal and inter-annual scales, which indicates that high level moisture transport is the main controlling factor of precipitation isotope values. On the southern slopes of the Himalayas (Adhikari et al., 2020) and central Vietnam (Wolf et al., 2020), the seasonal variation of moisture sources is the main factor leading to seasonal differences in precipitation isotope values. It can be seen that in the monsoon controlled region of eastern Asia, there are many external factors that affect the isotope values of precipitation. However, previous studies in the arid Central Asia found that the external factors that affect the change of isotope values of precipitation are mainly the moisture source (Juhlke et al., 2019; Zhang and Wang, 2018; Sun et al., 2019). A study in the western Pamirs (Juhlke et al., 2019) found that the moisture sources in this region are complex; the North Indian Ocean, Persian Gulf, Caspian Sea, Mediterranean and even polar waters, all contribute to precipitation in the region, and there are seasonal differences in moisture sources. Wang et al. (2017) studied the moisture sources at 23 stations in the Tianshan Mountains and found that the terrestrial evaporative vapor in Europe and Central Asia is the direct source of moisture sources in the region; moisture from the Atlantic does not usually reach Central Asia, and the Tarim Basin on the leeward slope may be more affected by local moisture. It should also be noted that the previous assessments on modern precipitation isotopes in the arid Central Asia usually focused on the intra-annual scale (Juhlke et al., 2019; Kong and Pang, 2016; Wang et al., 2017; Zhang and Wang, 2018), and generally ignored the influencing regimes on the inter-annual scale using in-situ observations. In this study, 3-year data from the Wuqia station on the southern slope of the Tianshan Mountains in Central Asia were used to analyze the seasonal and inter-annual changes of the isotope values of precipitation. In addition, we study the relationship between the wind fields and isotope values of precipitation, especially on the inter-annual scale, in order to explore the linkage between the meridional movement of westerlies and isotope values, which provides a reference for modern hydrological processes and paleoclimate studies.

2. Materials and methods

2.1. Study site and sample collection

Wuqia (75.25° E, 39.72° N, 2175 m a.s.l.) is located on the southern Tianshan Mountains in the arid Central Asia (Fig. 1). This area has been affected by westerly winds all year round (Juhlke et al., 2019; Wang et al., 2017; Zhang and Wang, 2018). The mean annual temperature and the annual precipitation in Wuqia from 1981 to 2010 were 7.7 °C and 188.7 mm, respectively.

Fig. 1

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Fig. 1. Map showing the Wuqia station in Central Asia. The background is based on Natural Earth (https://www.naturalearthdata.com/).

In this study we used 164 event-based precipitation samples from August 2012 to August 2013, from October 2014 to August 2015 and from May 2018 to March 2019 (Table S1). The samples were collected after each precipitation event; the liquid sample was immediately put into 50 mL HDPE bottles, and then sealed and stored in a refrigerated environment at 4 °C until being tested. While solid (snow or hail) precipitation samples were collected in zip-lock LDPE bags, melted at room temperature, and then sealed into 50 mL HDPE bottles. The data for the first year from August 2012 to August 2013 were acquired from Wang et al. (2016c). See Wang et al. (2016c) for details of sampling in the Wuqia station.

2.2. Isotopic analysis

All samples were measured at the Stable Isotope Laboratory, College of Geography and Environmental Science, Northwest Normal University. The first and second sampling years, August 2012 to August 2013 and October 2014 to August 2015, were analyzed using the liquid water isotope analyzer DLT-100 (Los Gatos Research, Inc.); the experimental process is detailed in Wang et al. (2016c). the samples collected between May 2018 and March 2019 were analyzed using the liquid water isotope analyzer T-LWIA-45-EP (ABB-Los Gatos Research, Inc.). The commercial standard samples LGR-3e, LGR-4e, and LGR-5e produced by ABB-LGR are used as reference standards, and the results are expressed as δ-values relative to V-SMOW (Vienna Standard Mean Ocean Water), calculated as follows:(1)‐‰δ=RsampleRV‐SMOW–1×1000‰

In this formula, R is the isotope ratio, 18O/16O or 2H/1H, in sample or V-SMOW. The test errors of δ2H and δ18O are lower than ± 1.0‰ and ± 0.3‰, respectively, for both analyzers. The results from the analysis of the samples collected in the first year (August 2012 to August 2013) were presented in Wang et al. (2016c).

In addition, the daily precipitation isotope data are calculated as the water vapor isotope value using (2), (3) (Clark and Fritz, 1997; Kendall and Caldwell, 1998):(2)‐δ18OPV=δ18OP–103αw‐v18–1‐δ2HPV=δ2HP–103αw‐v2–1

In the formula, the subscripts P and PV of δ18O represent precipitation and precipitating water vapor, respectively; α18w-v and α2w-v are the equilibrium fractionation coefficients of oxygen and hydrogen isotope, respectively, and the two coefficients can be calculated by temperature (T, in K; Friedman and O'Neil, 1977; Criss, 1999):(4)‐103lnαw‐v18=1.137106/T2–0.4156103/T–2.0667‐103lnαw‐v2=24.844106/T2–76.248103/T+52.612

2.3. HYSPLIT model and PSCF analysis

We determined the moisture source for each precipitation sample using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by the National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory (ARL). The HYSPLIT model can simulate the air mass trajectory to the target location in a certain time, and has been used widely to trace the moisture sources (Bershaw et al., 2012; Zhao et al., 2019; Wu et al., 2019). In the calculation, the HYSPLIT model needs as input the starting position, height, time and the backward duration. In this study, the starting point is the location of the sampling station and the initial moment of the precipitation event is taken as the starting time. The average residence time of atmospheric water is approximately 10 days (Gat, 2000; Trenberth, 1998); however, specific humidity-adjusted Lagrangian method (Wang et al., 2017, 2019) indicated the effective backward duration is much less than 10 days in Central Asia, so here we set the backward duration to be 4 days. The Laplace pressure formula (Barnes, 1968; Berberan-Santos et al., 1997) is used to calculate the lifting condensation level as the precipitation height.

The PSCF (Potential source contribution factor, Hopke et al., 1993) analysis is a method that identifies potential remote source areas, which is a conditional probability function that calculates the location of the potential source area. It combines the air mass trajectory and the threshold of a certain parameter in order to give the possible evaporation source location. This method is often used in air pollution research (Kant et al., 2020; Sano et al., 2012; Wang et al., 2019a). In this study, the PSCF analysis, implemented in the TrajStat software (Wang et al., 2009), was applied to assess potential moisture source areas, using the water vapor d value (deuterium excess, d = δ2H − 8 × δ18O) as the threshold parameter. When the element value corresponding to the air mass trajectory is higher than the threshold value (monthly average water vapor d value), the moisture underwent evaporation and recycling during transport, and the corresponding grid is regarded as a potential evaporation source area. The larger the PSCF value, the greater the contribution of the grid to the water vapor concentration of the observation point.

2.4. Other data

In order to calculate the backward trajectory to the target site, the Global Data Assimilation System (GDAS, available at ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1) operated by the National Centers for Environmental Prediction (NCEP) is used (Kleist et al., 2009). The spatial resolution is 1° × 1°, and the data include air pressure, temperature, precipitation amount, relative humidity and specific humidity.

To assess the relationship between the large-scale circulation system and the isotope ratio of precipitation, here we use the Zonal Index over Asia (ZIA) and Meridional index over Asia (MIA) released by the National Climate Center of China (http://cmdp.ncc-cma.net/cn/index.htm) as the reference factor.

We also applied the long-term precipitation isotope data simulated by an isotope-enabled GCM, LMDZiso, which was compiled and released by the Stable Water Isotopes Intercomparison Group 2 (SWING2; Sturm et al., 2010; available at https://data.giss.nasa.gov/swing2). This simulation developed by the Laboratoire de Météorologie Dynamique is based on the AMIP (Atmospheric Model Inter-comparison Project) protocol (Risi et al., 2010). Some studies (Wang et al., 2015; Yang et al., 2017) show that this model is suitable in describing the precipitation isotopes for the study region. In this study, the monthly surface precipitation isotope data and meridional and zonal wind speed at different pressure layers (663, 546, 425 and 318 hPa) during 1979–2007 were used. The horizontal resolution of the simulation is 2.54°(Latitude) × 3.75°(Longitude).

3. Results and discussion

3.1. Seasonal and inter-annual variations of precipitation isotopes

The precipitation isotope values obtained for the three sampling years in Wuqia presented obvious seasonal variations (Fig. 2). Based on the precipitation amount, the complete year can be divided into the summer half-year (April–October) and winter half-year (November–March; as marked in shadow in Fig. 2). Generally, the δ18O value are high in the summer half-year and low in the winter half-year. The monthly precipitation-weighted δ18O values fluctuate from 0.48‰ (August 2012) to −28.38‰ (December 2012).

Fig. 2

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Fig. 2. Monthly variations of precipitation δ18O, d value, air temperature (T), precipitation amount (P) and relative humidity (h) in the sampling period (August 2012 to August 2013, October 2014 to August 2015 and May 2018 to March 2019). The shades denote the winter half-year from November to March.

Regarding the monthly d value in precipitation, the minimum is −3.3‰ observed in April 2013 and the maximum is 24.1‰ observed in August 2018. The annual average d values (12.9‰) during the sampling years are higher than the global average (10‰). This result is similar or slightly higher compared to the results in the surrounding areas (Wang et al., 2016c; Sun et al., 2019; Juhlke et al., 2019). For example, Sun et al. (2019) analyzed the precipitation isotopes at five stations in the northwestern part of the Qinghai-Tibet Plateau and found that the average d value was 10.1‰; on the western Pamirs, the multi-year average of d value at two stations is 7.6‰ (Juhlke et al., 2019); the 23 stations around the Tianshan Mountains showed a wide range of annual d value between −8.6‰ to 16.6‰, and the finding in our study is generally consistent with previous stations for the western part of southern Tianshan Mountains.

3.2. Local meteorological controls on precipitation isotopes

For Central Asia, the local air temperature is considered to be the dominant meteorological factor controlling stable isotope ratios in precipitation, which is confirmed by the observations (Liu et al., 2014; Sun et al., 2019; Wang et al., 2016c) and simulations (Yang et al., 2017; Yu et al., 2016). This temperature effect is also found in this study using the three-year precipitation isotope data, and the precipitation δ18O value shows a positive correlation with air temperature (Fig. 3a). The analysis of our data led to the following relation: δ18O = 0.61 T − 12.27 (R2 = 0.58, n = 164). The regression coefficient calculated in this study (0.61‰/°C) is lower than that observed in most stations of Central Asia (Liu et al., 2014; Sun et al., 2019; Wang et al., 2016c; Wang et al., 2019b).

Fig. 3

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Fig. 3. Correlations between δ18O and air temperature (T), precipitation amount (P), relative humidity (h) and vapor pressure (e).

The different physical state of precipitation samples may affect the regression coefficients of δ18O ~ T in Central Asia; however, due to the limited samples, the findings in previous studies are not consistent. For example, Pang et al. (2011) indicated the snow samples may have larger regression coefficients at two alpine sites in the middle part of the Tianshan Mountains; however, this conclusion is not applicable to the whole mountain range when more stations are sampled (Wang et al., 2016c). In this study, the regression equations are δ18O = 0.58 T − 11.70 (R2 = 0.27, n = 137) for liquid samples, and δ18O = 0.58 T − 12.76 (R2 = 0.20, n = 27) for solid samples, respectively; the solid samples have slightly lower regression coefficient than the total sample, but the coefficients for solid and liquid samples are quite similar.

In addition, there is a positive relationship between water vapor pressure and δ18O value (Fig. 3d) (R2 = 0.44), and a significant negative relationship between relative humidity and precipitation δ18O value (R2 = 0.24, Fig. 3c). There is no amount effect for precipitation isotopes, and the R2 value is less than 0.01 (Fig. 3b). The average precipitation amount of all 164 events in the sampling period is only 4.3 mm; when the precipitation intensity is small, the raindrop sub-cloud evaporation is strong (Wang et al., 2016b, Wang et al., 2021), which may weaken the relationship between precipitation amount and isotope values to some degree.

3.3. Possible moisture source and precipitation isotopes

Previous observations in the arid Central Asia (Wang et al., 2017, Wang et al., 2019c) showed that atmospheric moisture in this region is mainly transported by westerlies. The Wuqia station is located on the leeward slope of the Pamir and Tibetan plateaus, and the rain shadow effect may lead to a dry condition for this region; the moisture delivery for the southern slope of the Tianshan Mountains is usually slower than that for the northern slope (Wang et al., 2017). Here the HYSPLIT backward trajectory model was applied to trace the moisture sources at the Wuqia station for each precipitation event (Fig. 4). According to the cluster analysis, two main paths can be identified. During the first sampling year (August 2012 to August 2013; Fig. 4a), 50.77% of the water vapor came from the western direction of the station, and 49.23% of the water vapor came from the local source. During the second sampling year (October 2014 to August 2015; Fig. 4b), the contribution of water vapor from the western part of the station increased, accounting for 67.39%; in addition, 32.61% of the water vapor originated from the local source. In the third sampling year (May 2018 to March 2019; Fig. 4c), both cluster trajectories show that water vapor comes from the west of the station. Generally, the advected moisture at the Wuqia station is transported by the westerlies; local evaporation also contributes to the regional moisture but there are inter-annual differences (Fig. 4d). There is a connection between the inter-annual variability of the moisture sources and the precipitation isotope values.

Fig. 4

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Fig. 4. Cluster analysis of moisture source at the Wuqia station in the sampling period. The Wuqia station is marked as the red star.

Using the PSCF method (Fig. 5), we found that the potential moisture sources are almost distributed in the inland regions of Central Asia. In the first sampling year (Fig. 5a), the shading to the west of the Wuqia station darkens from west to east, indicating that the evaporation along the westerly path is the main moisture source; there is also a potential moisture source in the region to the east of the station, which is mainly due to the contribution of local recycled moisture. Wang et al. (2017) found that regional precipitation in the Tarim Basin is more affected by local recycled moisture. In the second sampling year (Fig. 5b), the potential moisture sources were more distributed to the west of the research station. In the third sampling year, although the cluster trajectories (Fig. 4c) highlight the western region, the potential contribution sources (Fig. 5c) show that the eastern area is also appreciable. The results using the two methods are slightly different for each year, but the main finding (Figs. 4d and 5d) is that the regional moisture sources are greatly affected by the westerlies, and that local evaporation is also a main moisture source. The inter-annual differences in moisture sources are associated to the inter-annual changes in precipitation isotope values.

Fig. 5

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Fig. 5. Potential source contribution factor (PSCF) analysis of the Wuqia station in the sampling period. The Wuqia station is marked as the red star.

3.4. Wind field and precipitation isotopes

Traditionally, the regional precipitation in Central Asia was mainly attributed to the westerly moisture, instead of monsoon moisture (Yao et al., 2013), although more details are necessary to understand the impact of the westerlies in this region. Based on the precipitation isotope data of the three sampling periods at the Wuqia station, we analyzed the relationship between the precipitation isotope values and the meridional (MIA) and zonal (ZIA) circulations over Asia (Fig. 6). On a monthly scale, there is a negative correlation between the precipitation δ18O value and the MIA. When the meridional circulation strengthens, the δ18O in precipitation would gradually depleted, and the correlation coefficient of δ18O vs. MIA calculated on the monthly scale is −0.47 (p < 0.01). The δ18O precipitation and the ZIA also show a negative correlation, with a correlation coefficient of −0.12 (p < 0.01). The correlation between the meridional circulation and precipitation isotope is greater than that for the zonal circulation. The d-value does not correlate with either the MIA or the ZIA, and the correlation coefficients are both less than 0.1.

Fig. 6

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Fig. 6. Monthly variations of the precipitation δ18O value, d value, Meridional Index over Asia (MIA) and Zonal Index over Asia (ZIA). The shades denote the winter half-year from November to March.

Here we focus on the influence of the westerlies path on precipitation isotopes on a longer time scale using an isotope-enabled GCM, LMDZiso model, and the long-term simulations from 1979 to 2007 may be helpful to understand the role of meridional movement of westerlies on an annual scale. We use the δ18O value and the meridional (southern) and zonal (westerly) wind speed data to explore the correlation between the δ18O value and the wind speed in the entire upwind region. The precipitation at the study station is mainly concentrated in the summer half-year, so July is selected as a representative month (Fig. 7 and Fig. S1). At different heights, there is always a strong positive correlation between the δ18O values and the meridional wind speed in the upwind regions. As distance increases, the correlation weakens, and when the distance increases within a certain range (approximately 55°E), the correlation between the δ18O values and the meridional wind speed become negative. Affected by the stronger southerly wind speed, the low-latitude water vapor can be transported further north, and then result in a relatively enriched heavy isotopes in precipitation in Central Asia, which is generally consistent with previous hypothesis in Liu et al. (2015).

Fig. 7

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Fig. 7. Maps showing the correlation coefficients between the precipitation δ18O value in July at the Wuqia station and the meridional (southern) wind speed field for each height based on the LMDZiso model from 1979 to 2007.

We also analyzed the correlation between the annual average precipitation δ18O value in July and the zonal (westerly) wind speed in the upwind region (Fig. 8 and Fig. S2). Compared with the meridional wind speed, the correlation between the zonal wind speed and δ18O value is weaker. At different heights, the zonal wind speed near the study site has a weak negative correlation with δ18O, while the wind speed of westerlies near 30°N has a strong positive correlation with the δ18O value in precipitation at the study station. The increase of westerly wind speed in the upwind area at approximately 30°N brings more low-latitude water vapor to the study region. At the same time, under the influence of the southerlies (Fig. 7), the meridional shifts finally lead to the isotopically enriched precipitation at the Wuqia station.

Fig. 8

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Fig. 8. Maps showing the correlation coefficients between the precipitation δ18O value in July at the Wuqia station and the zonal (westerly) wind speed field for each height based on the LMDZiso model from 1979 to 2007.

4. Conclusion

In this study, we analyzed the seasonal and inter-annual variations of the precipitation isotope values at the Wuqia station in Central Asia and highlighted the impact of westerly meridional movement on the δ18O values in precipitation. The main local meteorological factor affecting the isotopic signature of precipitation is air temperature, which is consistent with previous findings about temperature effect in this region. The HYSPLIT backward trajectory model was used to analyze the moisture paths of precipitation at the Wuqia station; the moisture comes mainly from the western area of the station but the local source cannot be ignored. Analyzing the correlation between the meridional/zonal circulation indices over Asia and the precipitation isotope values measured at the Wuqia station, there is a strong correlation between the precipitation δ18O and the circulation factors. The δ18O value in precipitation and the meridional and zonal wind speed simulated by the LMDZiso model are then used to explore the correlation between the δ18O values in precipitation and the wind field over the upwind area on a longer time scale. In the westerly-dominated Central Asia, even on the leeward slope of high plateaus and mountains in this study, the δ18O value in precipitation is still strongly correlated to the westerlies wind speed in the upwind region. In addition, around 30°N, the westerly wind speed and δ18O values also show a significant positive correlation, indicating the connection between the strong low-latitude moisture delivery and isotopically enriched precipitation. The findings indicate the meridional movement of the westerlies can be sensitively recorded in the modern precipitation in arid Central Asia, and the inter-annual variability of precipitation isotopes in Central Asia can not only be attributed to temperature effect. This provides an up-to-date reference for the explanation of stable oxygen isotopes in climate proxies across westerlies-dominated Central Asia.

Author statement

This article has been submitted with the consent of all authors, and the research content has not been published in other journals.

Declaration of Competing Interest

The authors have declared no conflict of interest. The work described here has not been submitted elsewhere for publication, in whole or in part, and all the authors listed have approved the manuscript that is enclosed.

Acknowledgements

The research is supported by the National Natural Science Foundation of China (41971034 and 41701028), the Foundation for Distinguished Young Scholars of Gansu Province (20JR10RA112) and the Scientific Research Program of Higher Education Institutions of Gansu Province (2018C-02).

Appendix A. Supplementary data

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Supplementary material


References




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