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# δ18O records in water vapor and an ice core from the eastern Pamir Plateau: Implications for paleoclimate reconstructions

Wusheng Yu, Lide Tian, Camille Risi, Tandong Yao, Yaoming Ma, Huabiao Zhao, Haifeng Zhu, You He, Baiqing Xu, Hongbo Zhang, et al.

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Wusheng Yu, Lide Tian, Camille Risi, Tandong Yao, Yaoming Ma, et al.. δ18O records in water vapor and an ice core from the eastern Pamir Plateau: Implications for paleoclimate reconstructions. Earth and Planetary Science Letters, 2016, 456, pp.146-156. 10.1016/j.epsl.2016.10.001. insu-03727094

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*$\delta^{18}$O records in water vapor and an ice core from the eastern Pamir Plateau: implications for paleoclimate reconstructions*

1. Wusheng Yu$^{a,b,*}$, Lide Tian$^{a,b}$, Camille Risi$^c$, Tandong Yao$^{a,b}$, Yaoming Ma$^{a,b}$, Huabiao Zhao$^{a,b}$, Haifeng Zhu$^{a,b}$, You He$^a$, Baiqing Xu$^{a,b}$, Hongbo Zhang$^a$, Dongmei Qu$^a$

3. $^a$Key Laboratory of Tibetan Environment Changes and Land Surface Processes,

Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

5. $^b$CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing, China

7. $^c$Laboratoire de Météorologie Dynamique, Institut Pierre Simon LaPlace, Centre

National de la Recherche Scientifique, 75252 Paris, France

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<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: yuws@itpcas.ac.cn (W. Yu)</footnote>
This study is the first to examine δ¹⁸O in daily water vapor at Taxkorgan on the eastern Pamir Plateau. The results show that changes in observed and simulated δ¹⁸O values in water vapor/precipitation at the event scale (using a LMDZ-iso model) were mainly affected by temperature. The influences of humidity, precipitation amount, and different moisture sources, such as the westerlies, local evaporated moisture, and polar air masses, on δ¹⁸O values are comparatively weak. The combination of the δ¹⁸O record from the Muztagata ice core, 58 km away from the study area, and the LMDZ-iso simulated annual δ¹⁸O pattern in precipitation at Taxkorgan also demonstrated that temperature, and particularly the temperature of the regions over which the southern branch of the westerlies flows, is the most important factor controlling δ¹⁸O variations. The results from this study area, which is dominated by the westerlies throughout the year, are markedly different from those derived from parts of the Tibetan Plateau that are dominated by the combined influences of the westerlies in winter and the Indian monsoon in summer. The results suggested that the eastern Pamir Plateau is an ideal location to reconstruct past temperature variations and that the δ¹⁸O records preserved in ice cores from the region are a suitable and robust proxy for temperature.

Keywords: water vapor, ice core, δ¹⁸O, eastern Pamir Plateau

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# 1. Introduction

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The stable isotope ratio of ¹⁸O to ¹⁶O represents a “fingerprint” of source water that provides a helpful tool for reconstructing paleoclimate information preserved in ice cores (Yao et al., 1996; Thompson et al., 2000; Tian et al., 2006), tree rings (Treydte et al., 2006; Shi et al., 2011; Liu et al., 2012), and speleothems from the Tibetan Plateau and adjacent regions. However, to accurately interpret isotopic paleoclimate records obtained from these archives, it is essential to understand δ¹⁸O variations in modern water and the relationships between δ¹⁸O and meteorological conditions in different regions (Dansgaard, 1964; Epstein et al., 1965) in order to establish a clear link between the source water and δ¹⁸O values (Cuthbert et al., 2014; Yu et al., 2014; Amesbury et al., 2015).

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The δ¹⁸O values of liquid water, particularly precipitation, may co-vary with a number of meteorological variables, such as temperature, humidity, pressure, and precipitation intensity (Dansgaard, 1964; Araguás-Araguás et al., 1998; Aggarwal et al., 2004). These relationships are associated with processes such as evaporation and condensation (Dansgaard, 1964; Araguás-Araguás et al., 1998), rain-vapor isotopic exchange (Lee and Fung, 2008; Field et al., 2010), convective processes (Risi et al., 2008a; Rohrmann et al., 2014), or eddy diffusion (Winnick et al., 2014). Recent studies have also demonstrated that different moisture sources and moisture transport processes could strongly modify the δ¹⁸O values of precipitation (Aggarwal et al., 2004; Sengupta and Sarkar, 2006; Breitenbach et al., 2010; Risi et al., 2010a; Zhao et al., 2011; Noone et al., 2013; Aemisegger et al., 2014; Liu et al., 2014a).
In the last few decades, the isotopic composition of precipitation has been widely surveyed to determine the various moisture sources across the Tibetan Plateau (Tian et al., 2001, 2003; Yu et al., 2008; Yao et al., 2013). Previous studies have indicated that different moisture sources produce different effects on δ¹⁸O values of precipitation, with a “temperature effect” in the northern Tibetan Plateau, where moisture is delivered by the westerlies, and an “amount effect” in the south (Tian et al., 2003; Yu et al., 2008; Yao et al., 2013), where moisture is delivered by the Indian monsoon (Yu et al., 2016). In comparison, few measurements have been made for δ¹⁸O values in water vapor over the Tibetan Plateau (Kurita et al., 2008; Yin et al., 2008; Huang and Wen, 2014; Yu et al., 2015).

Compared with the Tibetan Plateau, fewer studies have been conducted on δ¹⁸O values for precipitation over the eastern Pamir Plateau, located northwest of the Tibetan Plateau (Fig. 1) (Li et al., 2006; Yu et al., 2006). Moreover, the current understanding of variations in δ¹⁸O values in water vapor over the eastern Pamir Plateau remains a significant gap. Unlike precipitation, water vapor δ¹⁸O measurements can be made across different seasons and synoptic situations (sunny/rainy) and are not limited to rainy days (Angert et al., 2008). Hence, studying δ¹⁸O values of water vapor represents a new approach to understanding signals from δ¹⁸O records preserved in ice cores and other sedimentary archives that may aid in reconstruction of past climate changes (Okazaki et al., 2015) and make important contributions to the fields of hydrology (Iannone et al., 2010) and ecology (Lai et al., 2006).
This study is the first to survey δ¹⁸O values in daily water vapor from Taxkorgan on the eastern Pamir Plateau. In this paper, we report daily changes in the δ¹⁸O composition of water vapor and discuss the relationships between δ¹⁸O values in water vapor and various meteorological factors, such as temperature, specific humidity, wind speed, and precipitation amount. In addition, based on observed δ¹⁸O values in water vapor and simulated δ¹⁸O values in water vapor and precipitation at Taxkorgan and the δ¹⁸O record from the Muztagata ice core (38°17′N, 75°06′E, 7010 m MSL) (Tian et al., 2006) located close to the study region, we examine the factors controlling changes in δ¹⁸O values (in water vapor, precipitation, and ice cores) on the eastern Pamir Plateau at different temporal and spatial scales and discusses the implications of the water isotopic compositions for paleoclimate reconstructions.

## 2. Sampling sites, materials, and methods

The Taxkorgan station (37°46′N, 75°16′E, 3100 m MSL) lies on the eastern Pamir Plateau (in central Asia) (Fig. 1), which is a mountain range located at the confluence of the Himalayas with the Karakoram, Kunlun, Hindu Kush, and Tian Shan ranges. The study region is characterized by a frigid-arid plateau climate, with an annual mean temperature of 3.6 °C and annual mean relative humidity of 40%. The average annual precipitation amount is approximately 61 mm. The majority of the rainfall at this site occurred during June through August, accounting for approximately 72% of the annual precipitation amount. These data are from the 1957–2007 period.

This study collected water vapor samples at Taxkorgan during the period from May
2006 to June 2007. Air was pumped slowly by a vacuum pump through a glass trap immersed in ethanol, which was continuously maintained at a temperature as low as -80 °C with a set of improved electric cryogenic coolers driven by a compressor. The cold trap method is widely used to capture water vapor (Jacob and Sonntag, 1991; Gat et al., 2003; Lai et al., 2006; Angert et al., 2008; Uemura et al., 2008; Deshpande et al., 2010; Midhun et al., 2013; Purushothaman et al., 2014), although such measurements have previously required laborious techniques, such as cryogenic sampling (Bastrikov et al., 2014). Furthermore, the cold trap was made in a linked-ball shape to increase the surface area for condensation to avoid isotope fractionation during sampling (Gat et al., 2003). The validity of the cold trap operation was confirmed by connecting an additional glass trap to the outlet of the original trap; no visible condensed vapor was found in the additional trap. The flow rate was approximately 5 L min⁻¹ (Gat et al., 2003) for approximately 24 h. One sample of approximately 2–10 ml was collected each day. The detailed sampling procedure has been published elsewhere (Yu et al., 2015). A total of 340 water vapor samples were collected. All the samples were stored below -15 °C until analysis.

The isotope analysis of all samples was performed at the Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Beijing, China), using a Finnigan MAT-253 mass spectrometer. The measured oxygen isotope ratios (δ¹⁸O) are reported as per mil deviations from the Vienna Standard Mean Ocean Water (VSMOW). The precision of the oxygen isotopic analysis was 0.1 ‰. Unfortunately, deuterium data at Taxkorgan
126 were not available for this project due to technical problems at the laboratory at that time.
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128 To identify the moisture transport paths and better interpret δ¹⁸O variability in the time series, this study determined 120 h back trajectories for air parcels in different seasons, i.e., winter (December–February), spring (March–May), summer (June–August), and autumn (September–November), using the NOAA HYSPLIT model (Draxler and Hess, 1998) and NCEP reanalysis data sets (available at ftp://arlftp.arlhq.noaa.gov/pub/archives/reanalysis). The origins of the air parcels, as diagnosed from the back trajectory analysis, appear to approximate the moisture source direction for the water vapor at the study site (Guan et al., 2013). The trajectories originated at 1500 m above ground level (AGL).

137 An isotopic LMDZ general circulation model (i.e., LMDZ-iso GCM, LMDZ-iso) was used to better understand the factors controlling the isotopic composition of water vapor and precipitation (δ¹⁸O_v and δ¹⁸O_p) at Taxkorgan. The LMDZ-iso is a general circulation model (GCM) developed at the Laboratoire de Météorologie Dynamique (LMD) (Risi et al., 2010b). The validity of the global simulations produced by LMDZ-iso at different scales was demonstrated by Risi et al. (2010b). Its stretched grid provides increased horizontal resolution down to a few tens of kilometers, allowing us to focus on a specific region (He et al., 2015). He et al. (2015) published a detailed assessment of the LMDZ-iso simulations in large mountainous regions, such as the Tibetan Plateau. They evaluated its capacity to simulate the temporal variations in δ¹⁸O values in water vapor (δ¹⁸O_v) and precipitation (δ¹⁸O_p) on the Tibetan Plateau
and found that LMDZ-iso reasonably captures the variability in δ¹⁸O_v and δ¹⁸O_p
values at the event scale. Moreover, those studies indicated that the high-resolution
simulation performed with a zoomed LMDZ-iso produces more realistic daily
variations in δ¹⁸O values in water vapor and precipitation than the standard LMDZ-iso
(Yao et al., 2013; He et al., 2015). In this study, the isotopic composition of water
vapor/precipitation was simulated by LMDZ-iso, and the results were compared with
observations on the eastern Pamir Plateau. The daily outputs for the isotopic
composition of water vapor and precipitation at Taxkorgan in 2006–2007 were
derived from a zoomed LMDZ-iso simulation with a horizontal resolution of ~60 km,
nudged by the ECMWF wind field. The monthly (annual) outputs for δ¹⁸O_v and δ¹⁸O_p
values were derived from the longer simulation, nudged by the 20CR reanalysis, with
a relatively coarse resolution and recent model version. The detailed LMDZ-iso
model was published by Risi et al. (2010b).

3. Results

3.1. *Seasonal fluctuations of water vapor δ<sup>18</sup>O values and meteorological factors*

Figure 2 shows the temporal variations in δ¹⁸O values in water vapor in relation to
various meteorological factors including temperature, specific humidity, wind speed,
and precipitation amount at Taxkorgan for the period from May 2006 to June 2007.
The δ¹⁸O values exhibit a clear seasonal fluctuation, with high values in summer and
low values in winter (Fig. 2a). These δ¹⁸O fluctuation trends match those of
temperature, specific humidity, and wind speed (Fig. 2a–d). The δ¹⁸O values of water
vapor range from -34.2 ‰ to -7.1 ‰ during the sampling period, with a mean (± one
170 standard deviation) of -18.8 ‰ ± 6.6 ‰ and a coefficient of variation of 35 %.

171 3.2. Simulated $\delta^{18}$O values in Taxkorgan compared with $\delta^{18}$O in Muztagata ice core

172 Figure 3a illustrates the daily changes in the observed $\delta^{18}$O$_v$ and the simulated
173 $\delta^{18}$O$_v$ and $\delta^{18}$O$_p$ values using a zoomed LMDZ-iso model at Taxkorgan during the
174 entire sampling period. The results indicate that the fluctuations in the observed $\delta^{18}$O
175 values in water vapor and simulated $\delta^{18}$O values in water vapor and precipitation are
176 highly similar, with high values in summer and low values in winter, especially
177 between observed and simulated $\delta^{18}$O$_v$ values (Fig. 3a). The correlation between the
178 observed and simulated $\delta^{18}$O$_v$ values at Taxkorgan can be expressed as follows:
179 $\delta^{18}$O$_{v-sim} = 0.60 \ \delta^{18}$O$_{v-obs} - 8.74 ($r = 0.80, n = 340, p < 0.01$) (Fig. 3b). Moreover, a
180 positive correlation between the simulated $\delta^{18}$O$_p$ and observed $\delta^{18}$O$_v$ values ($\delta^{18}$O$_{v-obs}$
181 = 0.75$\delta^{18}$O$_{p-sim} - 10.89, r = 0.75, n = 275, p < 0.01$) exists during the entire sampling
182 period (Fig. 3c). Similar to the zoomed LMDZ-iso simulation for the Tibetan Plateau
183 (Yao et al., 2013; He et al., 2015), our results demonstrate that the zoomed LMDZ-iso
184 model simulation for the eastern Pamir Plateau captures the variability of $\delta^{18}$O values
185 in water vapor reasonably well at the event scale. Furthermore, the simulated water
186 vapor $\delta^{18}$O values ($\delta^{18}$O$_{v-sim}$) are positively correlated with the simulated precipitation
187 $\delta^{18}$O values ($\delta^{18}$O$_{p-sim}$) at Taxkorgan ($\delta^{18}$O$_{v-sim} = 0.71\ \delta^{18}$O$_{p-sim} - 12.94, r = 0.93, n=
188 311, p < 0.01$) (Fig. 3c). The water vapor samples in this study were collected on each
189 day, but precipitation events did not occur every day. Thus, to better compare our data
190 and the $\delta^{18}$O records of the Muztagata ice core, we use the simulated precipitation
191 $\delta^{18}$O values (rather than the simulated water vapor $\delta^{18}$O values) in the following
discussions.

Based on the LMDZ-iso model nudged by the 20CR reanalysis, the annual $\delta^{18}$O values in precipitation ($\delta^{18}$O$_{p-sim}$) at Taxkorgan and the corresponding temperatures were simulated for the period from 1955 to 2007 (Fig. 4). The LMDZ-iso model successfully simulated the increasing $\delta^{18}$O trend, matching the observations well (Fig. 4a). In other words, the tendencies of the $\delta^{18}$O$_{p-sim}$ time series (1955–2002) were similar to those of the $\delta^{18}$O record ($\delta^{18}$O$_{ic}$) from the Muztagata ice core (Fig. 4a), and both $\delta^{18}$O time series increased gradually from 1955 to 2002 (Fig. 4a). A positive correlation exists between $\delta^{18}$O$_{p-sim}$ and $\delta^{18}$O$_{ic}$, with a regression relationship of $\delta^{18}$O$_{ic}$ = 1.57$\delta^{18}$O$_{p-sim}$ – 0.46 ($r$ = 0.52, n= 48, p < 0.01). Consequently, the LMDZ-iso model simulation for the eastern Pamir Plateau also suitably describes the variability of $\delta^{18}$O values in precipitation at the annual scale.

## 4. Discussion

### 4.1. Effects of meteorological factors and atmospheric trajectories on water vapor $\delta^{18}$O values at the site scale

The observed $\delta^{18}$O values of daily water vapor at Taxkorgan are positively correlated with temperature (T) for the entire sampling period, with a correlation coefficient of 0.85 and a confidence limit of 0.01 (Fig. 5a, Table 1). The isotopic records of water vapor from stations in the northern westerly wind belt, such as Heidelberg (Jacob and Sonntag, 1991), southern Greenland (Bonne et al., 2014), and western Siberia (Bastrikov et al., 2014), also exhibit water vapor $\delta^{18}$O values that are linearly correlated with temperature. These results indicate that water vapor $\delta^{18}$O
variations at Taxkorgan over the sampling period were controlled by temperature (Table 1) and resemble those of water vapor and precipitation from other stations in the northern westerly wind belt.

The $\delta^{18}$O values of water vapor are affected by not only temperature but also other meteorological factors, such as specific humidity and water vapor mixing ratios (Wen et al., 2010; Tremoy et al., 2012; Steen-Larsen et al., 2013, 2014; Samuels-Crow et al., 2014; Galewsky and Samuels-Crow, 2015) and wind speed. In this study, linear-log correlations exist between the $\delta^{18}$O values of water vapor and specific humidity ($q$) and wind speed (WS), with correlation coefficients of 0.77 and 0.68, respectively, within a confidence limit of 0.01 (Fig. 5b, c, Table 1). If the $\delta^{18}$O values of water vapor are positively correlated with temperature, the $\delta^{18}$O values can be modeled the Rayleigh distillation model expressed as a function of specific humidity. As discussed above, the $\delta^{18}$O values of water vapor measured here are highly correlated with temperature; therefore, the impact of specific humidity on water vapor $\delta^{18}$O values can be described by the Rayleigh distillation process using the following equation (Dansgaard, 1964):

$$ \delta_v = \delta_0 + (\alpha(T) - 1)\ln(q/q_0) \quad (1) $$

where $\delta_0$ and $q_0$ are the initial isotopic ratio of water vapor and the initial specific humidity, respectively; $\delta$ and $q$ denote the final isotopic ratio of the water vapor and the final specific humidity, respectively; $T$ is the temperature; and $\alpha$ is the temperature-dependent equilibrium fractionation factor between vapor and condensate (Majoube, 1971). It is easy to find that, the most data points of observed daily water
236 vapor $\delta^{18}$O values versus specific humidity lie between the Rayleigh distillation line
237 (the solid blue curve in Fig. 6c) and the mixing line calculated for two isotopically
238 distinctive air masses (the dashed blue curve in Fig. 6c). The results demonstrated that
239 the Rayleigh distillation process can explain the influence of specific humidity on
240 water vapor $\delta^{18}$O values. During the Rayleigh distillation process, continuous
241 condensation of an air mass occurs as the air mass migrates away from its source
242 region. As a result, $\delta^{18}$O values of the residual water vapor decrease gradually (Fig.
243 6c). Moreover, as specific humidity reaches high values and the humidity approaches
244 saturation, changes in the water vapor $\delta^{18}$O values become weaker and the $\delta^{18}$O
245 values approach a constant value (Fig. 6c). Using SCIAMACHY satellite
246 measurements, Liu et al. (2014b) found that $\delta D$ values in water vapor in a
247 non-monsoon domain in China (35°N-45°N and 80°E-100°E, which is near
248 Taxkorgan) followed the Rayleigh distillation process. Unlike the results from this
249 extremely arid region, a previous study demonstrated that water vapor $\delta^{18}$O values at
250 Nagqu (31°29′N, 92°04′E, 4,508 m MSL) over the central Tibetan Plateau (Fig. 1)
251 deviate from the Rayleigh model (Yu et al., 2015). Liu et al. (2014b) also found that
252 water vapor over the monsoon domain in China (25°N-35°N and 80°E-120°E,
253 including Nagqu) does not follow a Rayleigh distillation based on SCIAMACHY
254 satellite measurements. The significant negative log-correlation between $\delta D$ values
255 and specific humidity over the monsoon domain in China is consistent with an
256 “amount effect” (Liu et al., 2014b).

257 Wind speed intensity affects the $\delta^{18}$O values in water vapor at Taxkorgan in the
same manner as specific humidity: the water vapor δ¹⁸O values increased gradually with increases in wind speed. For wind speeds > 3.7 m/s, the changes in water vapor δ¹⁸O are extremely weak and approach a constant value (Fig. 5c). Consequently, the influences of specific humidity and wind speed are limited and can be ignored when these variables reach some critical value. Nevertheless, the corresponding temperature continues to influence changes in δ¹⁸O values at Taxkorgan.

Clearly, the infrequent precipitation events at Taxkorgan (Fig. 2e) can have a significant impact on the δ¹⁸O values of the corresponding water vapor, and give rise to relatively depleted δ¹⁸O values there (Fig. 2a), as well as in the Sahel (Risi et al., 2008b). A previous study from Nagqu on the central Tibetan Plateau (Fig. 1) also indicated that precipitation intensities are significantly correlated with relatively depleted water vapor δ¹⁸O values (Yu et al., 2015). This pattern results when water vapor reaches equilibrium with falling raindrops during precipitation events (Lee et al., 2006), so that evaporation of the falling raindrops generally produces relatively depleted δ¹⁸O values of the air near the ground. In this study, however, the δ¹⁸O values are rapidly restored after cessation of the precipitation event. The study of Deshpande et al. (2010) from Ahmedabad (semi-arid western India) also reported this interesting phenomenon. These results are different from those at Nagqu (semi-humid central Tibetan Plateau, with a high frequency of precipitation events), where precipitation events affected the δ¹⁸O values of water vapor not only on the same day but also for the following several days (Yu et al., 2015). Consequently, the precipitation amount in this study is weakly and negatively correlated with water vapor δ¹⁸O values over the
entire sampling period (Table 1). In other words, the influence of precipitation on the δ¹⁸O values of water vapor at Taxkorgan for the full year and at Ahmedabad is far weaker than at Nagqu due to the much lower amount of precipitation and the much lower frequency of precipitation events occurring at arid and semi-arid stations.

A multiple linear regression between stable isotopes in water vapor and various meteorological factors can help explain the presence or absence of effects from temperature, humidity, wind speed, and precipitation amount on the δ¹⁸O values in water vapor. We modeled multiple linear relationships for δ¹⁸O as $δ¹⁸O = a×T + b×\ln(q) + c×\ln(WS) + d×P + e$ using a simple multiple regression model. Surprisingly, the specific humidity, wind speed, and precipitation amount are excluded based on the backward method and the output of this model. Note that the “adjusted R-squared” values for Model 1 (all variables were included), Model 2 (specific humidity was excluded), Model 3 (specific humidity and wind speed were excluded), and Model 4 (only temperature included) changed from 0.724, to 0.725, to 0.726, and to 0.726. In other words, only temperature is ultimately used in the model. This result demonstrates once more that temperature is the dominant and directly controlling factor for changes in δ¹⁸O values in water vapor at Taxkorgan, and suggests that other variables only affect δ¹⁸O to a second order. The first-order effect of temperature can be quantified with a Rayleigh distillation. We calculated water vapor δ¹⁸O as a function of temperature based on a Rayleigh distillation (Fig. 6a) and found that the Rayleigh-calculated δ¹⁸O correlates well with observed δ¹⁸O with a slope of 0.96 (approach to 1) (Fig. 6b). The result indicates that the vast majority of variations in
water vapor $\delta^{18}$O can be explained by temperature variations and that only 27.4% of the variance in $\delta^{18}$O values cannot be explained by temperature. We examined the ratios of the unexplained variances contributed by analytical and model uncertainties, respectively. The results demonstrated that uncertainties in the measurement of $\delta^{18}$O and the multiple regression model can be neglected, although a polynomial regression fit the $\delta^{18}$O values slightly better ($Y = 0.0071X^2 + 0.4521X - 22.387$, $R^2 = 0.758$, n = 340, p < 0.01). This indicates that factors other than local temperature likely explain the variances in the $\delta^{18}$O values, such as moisture sources and processes affecting vapor along its trajectories (precipitation events, continental recycling, etc.). These factors are the most likely explanations of the unexplained variance in the $\delta^{18}$O values.

In general, different atmospheric trajectories contribute to variations in water vapor $\delta^{18}$O (or $\delta$D) values (Strong et al., 2007; Pfahl and Wernli, 2008; Deshpande et al., 2010; Steen-Larsen et al., 2013). This study used the NOAA HYSPLIT model to calculate 120 h of back trajectories of air parcels for each day of each season (winter, spring, summer, and autumn) for the entire sampling period (Fig. 7). With k-means clustering algorithm (Alabti et al., 1997), we produced four clusters from clustering items because the cluster number of four is an appropriate choice after several tries. Based on the clustering analysis for the model output, four possible categories of moisture source regions can be identified from the atmospheric trajectories (Fig. 7). These are (1) the northern trajectories of the westerlies (long and short trajectories), (2) the southern trajectories of the westerlies (long and short trajectories), (3) the local
region, and (4) the polar region. Then we counted the proportion of each cluster and averaged in-situ temperature and averaged δ¹⁸O in one cluster (Table 2). The results show that the majority of trajectories over Taxkorgan are similar in all four seasons. In the spring, moisture is clearly controlled by the combined effects of the northern (6%) and southern (20% long and 35% short) trajectories of the westerlies and local moisture recycling (39%) (Fig. 7b, Table 2). However in summer, the regions over which the southern trajectories flow are influenced by the onset and advance of the Indian monsoon, with weakening of the southern trajectories as a result. Consequently, the moisture over Taxkorgan is mainly dominated by northern trajectories (13% long and 16% short) and the local region (41%) (Fig. 7c, Table 2). In autumn, the southern trajectories (21%) strengthen with the gradual withdrawal of the Indian monsoon (Fig. 7d, Table 2). Until winter, the northern (10%) and southern (15% long and 37% short) trajectories of the westerlies and local moisture recycling (38%) jointly prevail at Taxkorgan (Fig. 7a, Table 2). Therefore, the main moisture sources over Taxkorgan in different seasons are consistently dominated by the westerlies. Consequently, the fluctuations in water vapor δ¹⁸O values at Taxkorgan throughout the year are generally consistent with the fluctuations in wind speed of the westerlies (Fig. 2a, d).

In the study area, precipitation events produce only small amounts of rainfall (Fig. 2e). As a result, the land surface dries due to evapotranspiration and moisture in soils and plants evaporates into the atmosphere. Hence, moisture from evaporation and transpiration is another (secondary) source at Taxkorgan. Trajectories from the local region can be easily observed in Fig. 7. In general, the rate of evaporation and
transpiration depends on certain ambient factors, including wind speed, temperature, and humidity. In the study area, the water vapor of soils and plants is characterized by relatively depleted (low) δ¹⁸O values that evaporate into the atmosphere at the beginning of evapotranspiration. Thus, the isotopic composition of the residual water in the soils and plants is relatively enriched. Increasing wind speed and/or temperature (Fig. 2b, d) causes more intense evaporation and transpiration. The “residual water” characterized by relatively enriched isotopic compositions can also evaporate into the atmosphere (Yu et al., 2015). This process results in relatively higher water vapor δ¹⁸O values at Taxkorgan (Fig. 2a). For example, the trajectories at Taxkorgan on July 19, 2006 originate in local region (Fig. 7c) and feature a relatively enriched δ¹⁸O value of -12.3 ‰ (Fig. 2a). Hence, the δ¹⁸O–T relationship becomes even more significant. Correspondingly, wind speed is also positively correlated with δ¹⁸O values in water vapor (Fig. 5c) and lower wind speeds (Fig. 2d) and lower ambient temperatures (Fig. 2b) conversely result in lower rates of evaporation and relatively depleted water vapor δ¹⁸O values (Fig. 2a). Previous studies also indicate that land surface characteristics and processes such as evaporation and transpiration influence δ¹⁸O changes in water vapor (Kurita et al., 2008; Deshpande et al., 2010; Yu et al., 2015), especially during dry periods.

Furthermore, polar air masses are occasionally present at Taxkorgan in summer (Fig. 7c) or autumn (Fig. 7d). We checked the effect of polar air by calculating the mean δ¹⁸O for trajectories originating in polar regions and compared this value with mean δ¹⁸O for trajectories from other regions (Table 2). It can be found that, for
example, in autumn, 7% of total trajectories over Taxkorgan came from polar regions (Fig. 7d), and the corresponding mean $\delta^{18}$O value of the water vapor was relatively low (-19.1 ‰) compared to the mean $\delta^{18}$O values of water vapor (-16.9 ‰) from the local region (Table 2). Because temperatures in the polar region are lower than that at Taxkorgan, the water vapor $\delta^{18}$O value from the polar region was relatively depleted.
As the air mass traveled across the continent to Taxkorgan, moisture became increasingly depleted and the resulting $\delta^{18}$O value of the residual water vapor that reached Taxkorgan was relatively low. However, moisture sources at Taxkorgan on the eastern Pamir Plateau are relatively few in number compared to those at Nagqu on the central Tibetan Plateau, which is dominated by the westerlies in the dry season and by the Indian monsoon in the wet season (Yu et al., 2008, 2015). The occasional presence of polar air masses cannot significantly modify the entire $\delta^{18}$O pattern in water vapor, although they do contribute to changes in water vapor $\delta^{18}$O values during portions of the sampling period.

However, temperature differences can still explain $\delta^{18}$O differences between different categories of trajectories. In other words, low (or high) temperatures over the study region will result in relatively depleted (or enriched) $\delta^{18}$O in water vapor. For example in autumn, the mean temperature is relatively high (9.2 °C) for the 41% of total trajectories coming from the local region, compared to a lower mean temperature (4.6 °C) for the 7% of total trajectories coming from the polar region (Table 2). This is why $\delta^{18}$O values in water vapor from the local region are higher than those from the polar region in the same season. The results demonstrate that the trajectories
themselves have indirect effects on changes in water vapor δ¹⁸O at Taxkorgan.

## 4.2. Controlling factors on the δ¹⁸O changes over larger spatial scale

The δ¹⁸O values of water vapor/precipitation and in ice cores are affected by metrological factors not only in the local region but also over larger spatial scales (Zhao et al., 2012; Pang et al., 2014). In the areas to the south of Taxkorgan, marine moisture transported by the Indian monsoon mainly falls as precipitation on the southern slope of the Himalayan mountains due to the blocking effect of the high elevations. Residual moisture continues to travel across the west Kunlun mountains to reach the eastern Pamir Plateau. The extremely low moisture content in residual monsoon-derived air masses makes it difficult for precipitation to form, so that the westerlies contribute most of the precipitated moisture at Taxkorgan. Unlike moisture, the spatial pattern of temperatures over the areas to the south is less affected by the high mountains. Because temperatures in the areas to the south are higher than in the north, the simulated δ¹⁸O values in precipitation and ice core records on the eastern Pamir Plateau exhibit a highly positive correlation with temperature over the areas to the south of Taxkorgan (Fig. 8a, b).

As on the event scale, the wind speed of the westerlies at long time scales over the broad region also positively correlates with simulated precipitation δ¹⁸O values at Taxkorgan and the δ¹⁸O record in the Muztagata ice core (Fig. 8e, f). Compared to the areas to the south of Taxkorgan, which feature the confluence of several massive mountain ranges, including the Himalayas and Karakoram and Kunlun mountains, the blocking effect of the mountains to the north of Taxkorgan is relatively weak. Hence,
the northern branch of the westerlies contributes significantly more to the δ¹⁸O
changes in precipitation and ice cores (Fig. 8e, f) than the southern branch of the
westerlies.

In comparison, δ¹⁸O values of precipitation and ice cores from the eastern Pamir
Plateau are weakly related to humidity and precipitation amount over the larger spatial
scale (Fig. 8c, d, g, h). These results indicate that changes in water δ¹⁸O values on the
eastern Pamir Plateau depend significantly on the joint contributions of temperature
and wind speed over a large area. Moreover, the contribution of temperature to
changes in water δ¹⁸O values is far larger than that of wind speed (Fig. 8a, b, e, f).

Previous studies have indicated that δ¹⁸O records from ice cores are a good proxy
for temperature (Johnsen et al., 1972; Jouzel et al., 1987; Rozanski et al., 1992). As
discussed above, in this study, the δ¹⁸O values in water vapor/precipitation and an ice
core on the eastern Pamir Plateau exhibit a significant positive correlation with
temperature on both short and long time scales and at both the site scale and the larger
spatial scale. Therefore, the results imply that water δ¹⁸O values at Taxkorgan from
the eastern Pamir Plateau may be a good proxy for temperature at the regional scale.
We compared both the simulated precipitation δ¹⁸O values at Taxkorgan and the
Muztagata ice core δ¹⁸O record with the temperature record and the simulated
temperature series from the local meteorological station (Taxkorgan) and found that
both the δ¹⁸Oₚ and δ¹⁸Oᵢc trends coincide with that of temperature (Fig. 4). The
temperature record and the simulated temperature series from Taxkorgan positively
correlate with the Muztagata ice core δ¹⁸O record and the simulated precipitation δ¹⁸O
time series, with regression equations of $\delta^{18}$O$_{ic}$ = 1.50T – 22.00 (r = 0.67, n = 46, p < 0.01) and $\delta^{18}$O$_{p}$ = 0.57T – 8.54 (r = 0.69, n = 53, p < 0.01), respectively. To verify whether the correlations are due to the observed trend or to inter-annual variability, or both, we calculated the correlations for a detrended time series, and found that the regression equations change to $\delta^{18}$O$_{ic}$ = 0.98T – 0.15 (r = 0.57, n = 46, p < 0.01) and $\delta^{18}$O$_{p}$ = 0.53T + 0.002 (r = 0.44, n = 53, p < 0.01), respectively. Therefore, the above correlations are neither due to the trend, nor to inter-annual temperature variability. In particular, the $\delta^{18}$O record from the Muztagata ice core shows a rapidly increasing trend during the period from 1986 to 2001, which is consistent with the temperature trend from Taxkorgan over the same time period. These good agreements indicate that the $\delta^{18}$O variations in the Muztagata ice core are related to both the local temperature and to northern hemisphere temperature changes (Tian et al., 2006). Consequently, our findings demonstrate that water $\delta^{18}$O values from the eastern Pamir Plateau as a good proxy for temperature at Taxkorgan is also representative of temperature signals at the regional scale.

In addition, the tendencies of the anomalies in the simulated precipitation $\delta^{18}$O values at Taxkorgan and in the Muztagata ice core $\delta^{18}$O record are highly similar to those in the zonal index (Fig. 9a, b, c). A positive correlation exists between the trends in the anomalies in $\delta^{18}$O ($\delta^{18}$O$_{p}$ and $\delta^{18}$O$_{ic}$) and the zonal index anomaly, with correlation coefficients of 0.55 and 0.36 within confidence limits of 0.01 and 0.05, respectively (Table 3). However, the tendency of the anomalies in dynamical Indian monsoon index is not related to that of the anomalies in $\delta^{18}$O (Fig. 9a, b, d, Table 3).
Hence, in addition to the contribution of temperature, the water $\delta^{18}$O trends on the eastern Pamir Plateau over nearly the last six decades likely reflect strengthened zonal winds in the westerlies (Li and Wang, 2003) (Fig. 9c) rather than weakening of the Indian monsoon (Zhou et al., 2010) (Fig. 9d). These results indicate that the water $\delta^{18}$O records from the eastern Pamir Plateau can be used for paleoclimate reconstructions, particularly of past variations in temperature.

## 5. Conclusions

This study compared the observed and simulated $\delta^{18}$O in water vapor/precipitation at the event scale and also compared the simulated annual $\delta^{18}$O in water vapor/precipitation with the $\delta^{18}$O record from the Muztagata ice core. The results indicate that the simulation for the eastern Pamir Plateau suitably captures the $\delta^{18}$O variability in water vapor/precipitation at both the event scale and long time scales.

Our data reveal that the observed and simulated water $\delta^{18}$O values from the eastern Pamir Plateau are mainly controlled by temperature on both short and long time scales and at both the site scale and the larger spatial scale. In particular, temperature in the regions underlying the southern branch of the westerlies significantly contributes to changes in water $\delta^{18}$O values from the eastern Pamir Plateau. In addition, the wind speed in the northern branch of the westerlies also partly contributes to the water $\delta^{18}$O changes. In particular, water $\delta^{18}$O values from the Pamir Plateau are positively correlated with the zonal index but not with the Indian monsoon index. In comparison, the relatively weak influences of humidity, precipitation amount and different
moisture sources, such as the occasional presence of polar air masses, do not significantly modify the water $\delta^{18}$O pattern, although they contribute to changes in water vapor $\delta^{18}$O values during portions of the sampling period.

The results of this study on the eastern Pamir Plateau, which was dominated by a relatively simple atmospheric circulation system (the westerlies) throughout the sampling period, differ remarkably from results on the central and southern Tibetan Plateau, which is dominated by the combined influences of the westerlies in winter and the Indian monsoon in summer. Compared to the semi-humid region of the central Tibetan Plateau, changes in water $\delta^{18}$O values in the extremely arid region of the eastern Pamir Plateau are affected by relatively few factors. Our findings suggest that the eastern Pamir Plateau is an ideal location for paleoclimate reconstructions and that the water $\delta^{18}$O record from the study region is a good proxy for temperature.

## Acknowledgments

This work is supported by CAS (XDB03030100) and NSFC (91437110, 91337212, 41671054, 41371089, and 41571201). LMDZ simulations were performed on the Ada supercomputer at IDRIS under GENCI project 0292. The authors gratefully acknowledge Guangjian Wu and Zhen Li for their important contributions to the project of Muztagata ice core, and Zhongfang Liu for help preparing Figure 6c. Special thanks are given to the anonymous reviewers for their constructive comments. The NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport model (http://ready.arl.noaa.gov/HYSPLIT.php) was used in this publication.
498 NCEP and 20th Century Reanalysis-derived data were provided by the
499 NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, via their website:
500 ftp://arlftp.arlhq.noaa.gov/pub/archives/reanalysis. Some meteorological data were
501 provided by the Climatic Data Center, National Meteorological Information Center,
502 China Meteorological Administration.

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719 Yu, W., Yao, T., Tian, L., Ma, Y., Ichiyanagi, K., Wang, Y., Sun, W., 2008.
720 Relationships between δ¹⁸O in precipitation and air temperature and moisture
721 origin on a south-north transect of the Tibetan Plateau. Atmos. Res. 87, 158-169.

722 Yu, W., Yao, T., Tian, L., Ma, Y., Wen, R., Devkota, L., Wang, W., Qu, D., Chhetri,
723 T.B., 2016. Short-term variability in the dates of the Indian monsoon onset and
724 retreat on the southern and northern slopes of the central Himalayas as
725 determined by precipitation stable isotopes. Clim. Dyn. 47, 159-172.

726 Zhao, H., Xu, B., Yao, T., Wu, G., Lin, S., Gao, J., Wang, M., 2012. Deuterium excess
727 record in a southern Tibetan ice core and its potential climatic implications. Clim.
728 Dyn. 38, 1791-1803.

729 Zhao, L., Yin, L., Xiao, H., Cheng, G., Zhou, M., Yang, Y., Li, C., Zhou, J., 2011.
730 Isotopic evidence for the moisture origin and composition of surface runoff in
731 the headwaters of the Heihe River basin. Chinese Sci. Bull. 56, 406-416.

732 Zhou, T., Brönnimann, S., Griesser, T., Fischer, A.M., Zou, L., 2010. A reconstructed
733 dynamic Indian monsoon index extended back to 1880. Clim. Dyn. 34, 573-585.

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738
741 Linear and linear-log correlations between daily stable oxygen isotopes (δ¹⁸Oobs-v, 742 δ¹⁸Osim-v, and δ¹⁸Osim-p) and meteorological factors (temperature, specific humidity, 743 wind speed, and precipitation amount) at Taxkorgan during the period from May 2006 744 to June 2007. Note that the subscripts “obv-v”, “sim-v”, and “sim-p” indicate 745 observations for water vapor and simulations for water vapor and precipitation, and 746 “r”, “n”, and “p” indicate the correlation coefficient, the number of precipitation 747 samples, and the p-value (probability), respectively.

<table><thead><tr><th></th><th>Fitting curve function</th><th>r</th><th>n</th><th>p</th></tr></thead><tbody><tr><td>δ¹⁸Oobs-v-T</td><td>y = 0.43x - 20.93</td><td>0.85</td><td>340</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Osim-v-T</td><td>y = 0.54x - 17.94</td><td>0.87</td><td>390</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Osim-p-T</td><td>y = 0.67x - 7.58</td><td>0.82</td><td>311</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Oobs-v-q</td><td>y = 7.28ln(x) - 26.34</td><td>0.77</td><td>340</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Osim-v-q</td><td>y = 4.85ln(x) - 25.09</td><td>0.68</td><td>390</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Osim-p-q</td><td>y = 6.25ln(x) - 17.19</td><td>0.65</td><td>311</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Oobs-v-WS</td><td>y = 7.67ln(x) - 24.03</td><td>0.68</td><td>340</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Osim-v-WS</td><td>y = 5.33ln(x) - 23.95</td><td>0.62</td><td>390</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Osim-p-WS</td><td>y = 6.85ln(x) - 15.48</td><td>0.59</td><td>311</td><td>&lt; 0.01</td></tr><tr><td>δ¹⁸Oobs-v-P</td><td>y = -0.70x - 16.05</td><td>-0.22</td><td>50</td><td>&gt; 0.1</td></tr><tr><td>δ¹⁸Osim-v-P</td><td>y = -1.07x - 18.30</td><td>-0.40</td><td>50</td><td>&lt; 0.01*</td></tr><tr><td>δ¹⁸Osim-p-P</td><td>y = -1.25x - 7.84</td><td>-0.38</td><td>50</td><td>&lt; 0.01*</td></tr></tbody></table>
748 * The precipitation amount on December 5, 2005, was abnormally high (10 mm, far higher than the average value of 1.3 mm). If this high value is discarded, the fitting curve functions change to $y = -0.75x - 18.60$ ($r = -0.22, n = 49, p > 0.1$) for $\delta^{18}\text{O}_{\text{sim-v}}$ and $y = -0.80x - 8.25$ ($r = -0.19, n = 49, p > 0.1$) for $\delta^{18}\text{O}_{\text{sim-p}}$.

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769
770 **Table 2**

Results of NOAA HYSPLIT model for percent of moisture sources over different seasons (Winter: December–February, Spring: March–May, Summer: June–August, and Autumn: September–November) and the corresponding mean temperature and mean δ¹⁸O in water vapor.

<table><thead><tr><th>Season</th><th>Category</th><th>Ratio</th><th>T</th><th>δ¹⁸O</th></tr></thead><tbody><tr><td rowspan="4">Winter</td><td>Category 1</td><td>10%</td><td>-19.97</td><td>-32.39</td></tr><tr><td>Category 2-L</td><td>15%</td><td>-17.99</td><td>-28.28</td></tr><tr><td>Category 2-S</td><td>37%</td><td>-17.56</td><td>-27.41</td></tr><tr><td>Category 3</td><td>38%</td><td>-14.95</td><td>-28.26</td></tr><tr><td rowspan="4">Spring</td><td>Category 1</td><td>6%</td><td>8.9</td><td>-18.25</td></tr><tr><td>Category 2-L</td><td>20%</td><td>9.44</td><td>-18.30</td></tr><tr><td>Category 2-S</td><td>35%</td><td>6.20</td><td>-17.58</td></tr><tr><td>Category 3</td><td>39%</td><td>7.39</td><td>-17.90</td></tr><tr><td rowspan="4">Summer</td><td>Category 1-L</td><td>13%</td><td>14.88</td><td>-15.01</td></tr><tr><td>Category 1-S</td><td>26%</td><td>16.66</td><td>-13.03</td></tr><tr><td>Category 3</td><td>41%</td><td>16.20</td><td>-13.23</td></tr><tr><td>Category 4</td><td>20%</td><td>15.04</td><td>-13.68</td></tr><tr><td rowspan="3">Autumn</td><td>Category 1</td><td>31%</td><td>6.87</td><td>-18.78</td></tr><tr><td>Category 2</td><td>21%</td><td>3.02</td><td>-20.63</td></tr><tr><td>Category 3</td><td>41%</td><td>9.25</td><td>-16.94</td></tr></tbody></table>
Note that Category 1, Category 2, Category 3, and Category 4, respectively, indicate the northern trajectories of the westerlies, the southern trajectories of the westerlies, the local region, and the polar region, where “L” and “S” indicate long and short trajectories, respectively.

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795
796 Table 3

797 Correlations between anomalies in water $\delta^{18}$O values (simulated $\delta^{18}$O$_p$ for Taxkorgan
798 precipitation during 1955–2007 and $\delta^{18}$O$_{ic}$ for the Muztagata ice core during 1955–
799 2002) and anomalies in the zonal wind index (ZWI, 1955–2007) and the dynamical
800 Indian monsoon index (DIMI, 1955–2007). Note that the subscripts “sim-p” and “ic”
801 indicate simulations for precipitation and the ice core record, respectively, and “r”,
802 “n”, and “p” are the same as in Table 1.

<table><thead><tr><th></th><th>Fitting curve function</th><th>r</th><th>n</th><th>p</th></tr></thead><tbody><tr><td>δ<sup>18</sup>O<sub>sim-p</sub>–ZWI</td><td>y = 0.49x + 9E-15</td><td>0.55</td><td>53</td><td>0.000</td></tr><tr><td>δ<sup>18</sup>O<sub>ic</sub>–ZWI</td><td>y = 0.94x + 0.04</td><td>0.36</td><td>48</td><td>0.012</td></tr><tr><td>δ<sup>18</sup>O<sub>sim-p</sub>–DIMI</td><td>y = 0.04x + 9E-15</td><td>0.05</td><td>53</td><td>0.715</td></tr><tr><td>δ<sup>18</sup>O<sub>ic</sub>–DIMI</td><td>y = 0.34x + 0.01</td><td>0.15</td><td>48</td><td>0.318</td></tr></tbody></table>

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812
813 **Figure Captions**

<figure><img src="image_4.png" /></figure>

814

815 **Fig. 1.** Location of the water vapor sampling station of Taxkorgan (black dot) and the drill site of the Muztagata ice core (white dot) on the eastern Pamir Plateau. Inset shows the location of the Pamir Plateau (blue rectangle) and the comparison sampling site of Nagqu (red dot) on the Tibetan Plateau.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Daily changes in water vapor δ<sup>18</sup>O (a), temperature (T) (b), specific humidity (q) (c), wind speed (WS) (d), and precipitation amount (P) (f) at Taxkorgan during May 2006–June 2007. The high precipitation amounts (the two highest values of 10.0 and 5.9) are marked by dashed lines and correspond to relatively high specific humidity and relatively low δ<sup>18</sup>O values in water vapor.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparison of the observed daily $\delta^{18}$O$_v$ (blue dashed line) and the simulated daily $\delta^{18}$O$_v$ values (yellow solid line) in water vapor and $\delta^{18}$O$_p$ values (pink solid line) in precipitation based on the LMDZ-iso model at Taxkorgan for the entire sampling period (a). The relationships $\delta^{18}$O$_{v-sim}$-$\delta^{18}$O$_{v-obs}$ (b), $\delta^{18}$O$_{v-sim}$-$\delta^{18}$O$_{p-sim}$ (c), and $\delta^{18}$O$_{v-obs}$-$\delta^{18}$O$_{p-sim}$ (d) at Taxkorgan during May 2006–June 2007 are also shown.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Comparison of the changes in the simulated annual δ<sup>18</sup>O values in precipitation (δ<sup>18</sup>O<sub>p</sub>) at Taxkorgan during 1955–2007 (pink solid line) and the δ<sup>18</sup>O record from the Muztagata ice core (δ<sup>18</sup>O<sub>ic</sub>) during 1955–2002 (blue solid line) (a). The changes in the observed (green solid line) and simulated (red solid line) annual temperature at Taxkorgan during 1955(1957)–2007 are also shown (b). Dashed lines indicate the linear trends.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Correlations between observed water vapor δ<sup>18</sup>O and temperature (T) (a), specific humidity (*q*) (b), wind speed (WS) (c), and precipitation amount (P) (d) at Taxkorgan for the entire sampling period.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Comparison of the observed daily $\delta^{18}$O$_v$ (blue solid line) and the simulated daily $\delta^{18}$O$_v$ values (pink solid line) in water vapor based on the Rayleigh distillation model (a) and the correlation (b) between the observations and the Rayleigh simulation at Taxkorgan for the entire sampling period. A scatterplot of observed daily water vapor $\delta^{18}$O values versus specific humidity ($q$) at Taxkorgan is also shown (c), and the fitting curve for a linear-log correlation is shown as a solid green line. The solid blue and dashed blue curves represent the Rayleigh distillation line calculated for initial conditions of $\delta^{18}$O$_0$ = -8 ‰ (a value for vapor in equilibrium with ocean</figcaption></figure>
887 water; black dashed line) at T = 25°C and the mixing line calculated for two isotopically distinctive air masses ($\delta^{18}$O = -35‰ and $\delta^{18}$O = -8‰) initializing at a water vapor mixing ratio of 0.5 g/kg, respectively.

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<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Back trajectories calculated by NOAA HYSPLIT at 1500 m AGL over Taxkorgan for winter (December–February) (a), spring (March–May) (b), summer (June–August) (c), and autumn (September–November) (d) during the entire sampling period. Note that four possible categories of moisture source regions can be identified: (1) the northern trajectories of the westerlies (long (pink lines) and short (purple lines) trajectories), (2) the southern trajectories of the westerlies (long (red lines) and short (dark yellow) trajectories), (3) the local region (green lines), and (4) the polar region (blue lines). The numbers indicate the ratio for each category of the total trajectories.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Spatial correlation patterns ($p < 0.05$) between the simulated annual precipitation $\delta^{18}$O$_p$ record at Taxkorgan (solid dots) and 20C reanalysis temperatures (a), specific humidity (c), zonal wind (e), and precipitation amount (g) at 500 hPa during 1955–2007. The spatial correlation patterns ($p < 0.05$) between the $\delta^{18}$O$_{ic}$ record from the Muztagata ice core (open circles) and 20C reanalysis temperatures (b) and humidity (d), zonal wind (f), and precipitation amount (h) at 500 hPa during 1955–2002 are also shown. The color scale indicates the correlation coefficient within a confidence limit of 0.05.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 9.** Time series of anomalies in the simulated annual δ<sup>18</sup>O<sub>p</sub> values in precipitation at Taxkorgan (1955–2007) (a), δ<sup>18</sup>O<sub>ic</sub> record from the Muztagata ice core (1955–2002) (b), zonal index for the Northern Hemisphere (1955–2007) (Li and Wang, 2003) (c), and the dynamical Indian monsoon index (1955–2007) (Zhou et al., 2010) (d).</figcaption></figure>