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# Temperature signals of ice core and speleothem isotopic records from Asian monsoon region as indicated by precipitation δ18O

Wusheng Yu, Tandong Yao, Lonnie G. Thompson, Jean Jouzel, Huabiao Zhao, Baiqing Xu, Zhaowei Jing, Ninglian Wang, Guangjian Wu, Yaoming Ma, et al.

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► To cite this version:

Wusheng Yu, Tandong Yao, Lonnie G. Thompson, Jean Jouzel, Huabiao Zhao, et al. Temperature signals of ice core and speleothem isotopic records from Asian monsoon region as indicated by precipitation δ18O. Earth and Planetary Science Letters, 2021, 554, pp.116665. 10.1016/j.epsl.2020.116665. hal-03108156

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Distributed under a Creative Commons Attribution 4.0 International License
Temperature signals of ice core and speleothem isotopic
records from Asian monsoon region as indicated by
precipitation δ¹⁸O

4 Wusheng Yu a,b,*, Tandong Yao a,b, Lonnie G. Thompson c, Jean Jouzel d, Huabiao Zhao

5 a,b, Baiqing Xu a,b, Zhaowei Jing a, Ninglian Wang e, Guangjian Wu a,b, Yaoming Ma a,b,

6 Jing Gao a,b, Xiaoxin Yang a,b, Jingyi Zhang a, Dongmei Qu a

a Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of
Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.

9 b CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China.

10 c Byrd Polar and Climate Research Center, The Ohio State University, Columbus, OH 43210, USA.

11 d Laboratoire des Sciences du Climat et de l'Environnement - IPSL, UMR 8212,
CEA-CNRS-UVSQ, Gif sur Yvette, France.

13 e College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.

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<footnote>* Corresponding author at: Key Laboratory of Tibetan Environment Changes and Land Surface
Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101,
China.</footnote>

<footnote>E-mail address: yuws@itpcas.ac.cn (W. Yu).</footnote>
18 ABSTRACT

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Ice cores and speleothem δ¹⁸O records from Asia have been widely used as a proxy to reconstruct paleoclimate changes. However, whether those δ¹⁸O records are a proxy of temperature or monsoon intensity has remained a great controversy. Generally, ice core δ¹⁸O records from non-monsoon and transition regions indicate temperature, but ice core and speleothem δ¹⁸O records from monsoon regions have been regarded as proxies for monsoon intensity or precipitation. Here, we address the controversy by showing three 20-year long daily precipitation δ¹⁸O (δ¹⁸Oₚ) series and 120 monthly δ¹⁸Oₚ series based on 17461 precipitation samples throughout Asia. We find that the δ¹⁸Oₚ signals preserved in precipitation are consistent with those in ice cores, both in the non-monsoon and monsoon regions. The results confirm previous research that ice core δ¹⁸O records in the non-monsoon region provide reliable histories of surface temperature. However, ice core δ¹⁸O records can still directly indicate surface temperature in the monsoon domain if winter/spring precipitation is heavy. When winter/spring precipitation is sparse, inverted ice core records show good agreement with surface temperature records. This may be due to the effect of cloud-top temperatures (which differ from surface temperatures) on summer δ¹⁸Oₚ values. Similarly, inverted speleothem δ¹⁸O records in the monsoon regions are similar to Greenland/Antarctic ice core δ¹⁸O/δD time series and other paleotemperature records. Our findings provide a reinterpretation of Asian ice core and speleothem δ¹⁸O records, and demonstrate that temperature signals have been preserved in those archives from the Asian monsoon region.

**Keywords:** Inverted δ¹⁸O records, temperature, ice core, speleothem, Asian monsoon region
## 1. Introduction

The ratios of stable isotopes, ¹⁸O to ¹⁶O and ²H to ¹H (expressed as δ¹⁸O and δD), have been defined as “fingerprints” of water. Hence, δ¹⁸O and δD are widely used to identify different moisture sources in various regions (Cobb et al., 2007), to determine various components of the hydrological cycle (Birks and Edwards, 2009), and to reconstruct paleoclimate variations, such as climatic oscillations (Dansgaard et al., 1969), general instability of past climate (Dansgaard et al., 1993), temperature (Jouzel et al., 2007), and monsoon intensity (Cheng et al., 2016) variations.

In paleoclimate studies, several long ice cores have been drilled through the Greenland and the Antarctic ice sheets from which paleotemperature records have been reconstructed (Jouzel et al., 1987; Johnsen et al., 1992; Dansgaard et al., 1993; North Greenland Ice Core Project members, 2004), with the longest continuous record covering 800 kyr (Jouzel et al., 2007). Recently, an Antarctic ice core record dating two million years has been reported, unfortunately it is only a snapshot in time and not continuous (Yan et al., 2019). Over the past 30 years several valuable ice core and speleothem δ¹⁸O records have been recovered from throughout Asia in order to help reconstruct the regional paleoclimate (Thompson et al., 1989, 1997, 2000; Yao et al., 2007; Hou et al., 2003; Zhao et al., 2012; Wang et al., 2008; Sinha et al., 2011; Cheng et al., 2016; Kathayat et al., 2017) (Fig. 1, Supplementary Table 1, and Supplementary Table 2). Compared with the polar regions, the topographic relief and atmospheric circulation systems of Asia are more complex, and Asian ice core and speleothem δ¹⁸O records reflect influences of multiple air masses and atmospheric and oceanic
processes. As a consequence, the use of Asian paleoclimate δ¹⁸O records as
temperature proxies has long been controversial, especially in the monsoon domain. It
is generally believed that ice core δ¹⁸O time series from the non-monsoon region and
the transitional region between the monsoon and non-monsoon regions record
temperature change (Thompson et al., 1989, 1997; Yao et al., 2007; Wang et al., 2003;
Tian et al., 2006; Kang et al., 2010), but within the monsoon domain δ¹⁸O records are
often considered to be a monsoon intensity (precipitation amount) signal (Hou et al.,
2003; Zhao et al., 2012; Yuan et al., 2004; Wang et al., 2008; Sinha et al., 2011;
Cheng et al., 2016; Kathayat et al., 2017) or a temperature signal (Zhao et al., 2017)
(Supplementary Fig. 1). It is evident that the signals preserved in ice cores from the
monsoon region are more difficult to interpret. For example the Noijin Kangsang ice
core δ¹⁸O records from the Lhagoi Kangri mountains (Zhao et al., 2012) (Fig. 1) are
inversely correlated with the Northern Hemisphere temperature (NHT) anomalies
(Supplementary Fig. 1, 2), and the East Rongbuk ice core δ¹⁸O records from Mt.
Everest appear to record Indian monsoon precipitation (Hou et al., 2003). However,
the Dasuopu ice cores from Mt. Xixiabangma (located near Mt. Everest) (Thompson
et al., 2000) and the Zuoqiupu ice core from the southeastern margin of the Himalayas
(Zhao et al., 2017) are considered to be indicators of temperature on interannual time
scales (Supplementary Fig. 2). This controversy is because the factors affecting
variations in stable isotopes in ice cores and speleothems are unclear.

Previous studies have emphasized that systematic variabilities, such as in
temperature, humidity, and pressure, strongly affect δ¹⁸O values in precipitation
(Dansgaard et al., 1964). Moreover, the seasonal shifts of moisture sources can play a key role in the seasonal cycle of precipitation δ¹⁸O (Cobb et al., 2007). In addition, intensified meridional and zonal circulation can modify δ¹⁸O values in precipitation (Birks and Edwards, 2009). Recent studies have demonstrated that precipitation δ¹⁸O can reflect proportions of convective and stratiform precipitation and is negatively correlated with stratiform fractions, especially in the tropical regions (Aggarwal et al., 2016). Because the signals of ice cores and speleothems are related to those of ancient precipitation, understanding the key influences that dominate precipitation δ¹⁸O variations is very important for the interpretation of these paleoclimatic records.

Here, we attempt to address the controversy mentioned above from the perspective of δ¹⁸O records from daily precipitation samples collected over 20 years at three meteorological stations located at Delingha, Tuotuohe, and Lhasa of the Tibetan Plateau Network for Isotopes in Precipitation (TNIP) (Fig. 1). To achieve this goal, we first prove that low-altitude δ¹⁸O values from precipitation are consistent with those from high-altitude ice cores, then identify the consistent relationship between δ¹⁸O and different meteorological parameters such as temperature, precipitation amount, and relatively humidity in the different regions (monsoon, transition, and non-monsoon regions), and finally, demonstrate that the temperature signals were preserved in the δ¹⁸O records (especially inverted δ¹⁸O records) from Asian ice cores and speleothems.

## 2. Materials and methods
## 2.1. Observed δ¹⁸O database

In the TNIP project, samples from each precipitation event were collected immediately after the event from a rain gauge. Rainfall samples were collected immediately after each event from a rain gauge with a collection funnel. Note a plastic table tennis ball was placed in the collection funnel to seal the collector bottle against evaporation and debris. The samples were transferred into 15 mL plastic bottles, sealed with screw caps, and wrapped with parafilm to prevent sample loss and evaporation prior to analysis. Solid precipitation samples were collected on clean porcelain plates, put into clean plastic bags, and sealed. The solid samples were processed using the same methodology as rainfall samples after they melted at room temperature. All samples were stored below freezing until being analyzed. Additionally, the duration of each precipitation event, surface temperature (T), relative humidity (RH), and precipitation amount (P) were recorded as samples were collected (Yu et al., 2017). In this study, we collected and measured 11095 precipitation event samples from 21 stations, including the three 20-year long observation stations of Lhasa, Tuotuohe, and Delingha (1540, 1869, and 2040 samples, respectively) (Supplementary Table 3).

Precipitation samples collected prior to 2004 were analyzed at the State Key Laboratory of Cryosphere (Lanzhou), Chinese Academy of Sciences, using a MAT-252 mass spectrometer with a precision of ±0.2‰ for δ¹⁸O values. Precipitation samples collected in 2005–2008 and in 2009–2014 were measured at the Key Laboratory of Tibetan Environmental Changes and Land Surface Processes (Beijing),
129 Chinese Academy of Sciences, using a MAT-253 mass spectrometer and a
130 Picarro-L2130i Cavity Ring-down Spectroscopy with precisions of ±0.1‰ and ±0.1‰
131 for δ¹⁸O values, respectively (Yao et al., 2013). All the data of the measured
132 precipitation are expressed as parts per thousand of their deviation relative to the
133 Vienna Standard Mean Ocean Water (VSMOW or VSMOW2). The detailed sampling
134 and measurement procedures were published by Yao et al. (2013).

135 We also selected some monthly precipitation δ¹⁸O data from 69 observation stations
136 of the International Atomic Energy Agency/World Meteorological Organization
137 (IAEA/WMO) Global Network of Isotopes in Precipitation (GNIP) (available at
138 https://www.iaea.org/services/networks/gnip). Similar to the TNIP, rainfall samples
139 were collected from a rain gauge. Large collection bottles were needed to collect
140 monthly samples. The rain gauge was read and emptied as soon as possible in the
141 morning following each precipitation event. After reading and recording the volume,
142 the collected water was poured into the accumulation bottle, which was tightly sealed
143 and stored in a refrigerator. The samples were analyzed mainly in the IAEA's Isotope
144 Hydrology Laboratory in Vienna, but were also measured in cooperating laboratories.
145 The measurements reported in GNIP have a long term precision of about ±0.1‰ for
146 δ¹⁸O values at one standard deviation (IAEA/WMO, 2018). In addition, some
147 precipitation δ¹⁸O data at 30 observation stations collected by previous studies (Liu et
148 al., 2007; Pang et al., 2011; Duan et al., 2016; Li et al., 2016; Guo et al., 2017) were
149 used. The data from the 120 stations present a good representation of precipitation
150 δ¹⁸O (δ¹⁸Oₚ) changes in the non-monsoon, transition, and monsoon regions (Yao et al.,
151 2013) (Fig. 1, Supplementary Fig. 3, Supplementary Table 3).

152 2.2. Retrieved δ¹⁸O database

153 In addition to the observed precipitation δ¹⁸O dataset, we also used water vapor
154 isotopic data (δD) derived from Tropospheric Emission Spectrometer (TES). The TES
155 instrument is a Fourier transform spectrometer onboard the National Aeronautics and
156 Space Administration (NASA) Aura satellite, which was launched in 2004 and ended
157 surveillance in 2018. It captured the global vertical profiles of water vapor and its
158 isotopes by measuring radiances in the range of 1200–1350 cm⁻¹ at a spectral
159 resolution of 0.1 cm⁻¹ (Worden et al., 2011). The detailed retrieval procedures have
160 been described elsewhere (Worden et al., 2011). The water vapor δD data were
161 provided by NASA Langley Research Center Atmospheric Science Data Center
162 (available at https://tes.jpl.nasa.gov/data). We used the retrieved H₂O and HDO of
163 version 6 Lite level 2 H₂O and HDO nadir retrievals from three stations (Lhasa,
164 Tuotuohe and Delingha) and a region covering 10°N to 25°N and 65°E to 130°E
165 during 2006–2009 because of good measuring continuities. To ensure data quality, we
166 omitted data whose species retrieval quality was 0 and the degrees of freedom for
167 signals were less than 0.5. To better compare with the observed precipitation δ¹⁸O data,
168 the δD data were used to calculate δ¹⁸O using the equation δD = 8.0δ¹⁸O + 10 (Craig,
169 1961).

170 2.3. *Simulated* δ<sup>18</sup>O *Database*

171 In this study, the simulations of the δ¹⁸O database including the stable oxygen
isotopic ratios in water vapor (δ¹⁸O_v) at 500 hPa and the δ¹⁸O_p were performed by the ECHAM5-wiso model (Mutz et al., 2016). The ECHAM5-wiso is an atmospheric general circulation model enhanced by stable water isotope diagnostics (Werner et al., 2011). The model is run for modern conditions with a horizontal spatial resolution of 1.8° × 1.8° and 19 vertical levels (up to 10 hPa). Subgrid scale orography parameterization has been implemented in ECHAM5-wiso to better quantitatively understand the local climatic and topographic controls on precipitation δ¹⁸O (Mutz et al., 2016). Recent studies have demonstrated that the simulation with ECHAM5-wiso on the Tibetan Plateau captures the corresponding observations very well (Li et al., 2016). The detailed ECHAM5-wiso simulation procedures have been described elsewhere (Werner et al., 2011; Mutz et al., 2016). The standard climatological reference period (1979–1999) modeling outputs are used in this study.

## 2.4. Meteorological and other related data

During the precipitation sampling, we recorded the duration of each precipitation event, surface temperature, relative humidity, and precipitation amount. To compare the seasonal variations and annual trends of precipitation amounts at the three sampling stations with those at the ice core drilling sites, we used the precipitation data observed by the Tropical Rainfall Measuring Mission (TRMM) (available at: https://pmm.nasa.gov/data-access/downloads/trmm). In addition, in order to discuss the relationships between cloud-top temperature (CTT) and the precipitation δ¹⁸O, we used the International Satellite Cloud Climatology Project (ISCCP) D1 and D2
products (cloud-top temperature), which were obtained from NASA Goddard Institute
for Space Studies (available at:
https://eosweb.larc.nasa.gov/project/isccp/isccp_table).

## 2.5. The iLOVECLIM Earth system model

As a three-dimensional Earth system model, the LOch-Vecode-Ecbilt-CLio-agIsM (LOVECLIM) model has a simpler representation of physical processes, although its resolution is coarser than state-of-the-art climate General Circulation Models (GCMs).
Hence, LOVECLIM is much faster than GCMs (Goosse et al., 2010). The iLOVECLIM model is a code fork of the LOVECLIM-1.2 climate model, with the atmosphere, ocean, sea ice and vegetation components interactively coupled (Roche, 2013).

In our study, the annual mean atmospheric temperature and annual mean precipitation δ¹⁸O data over the regions (~17°–33° N and 87°–116° E) during the past 150,000 years, simulated by the iLOVECLIM1.2 model, were obtained from Caley et al. (2014). The orbital configuration is calculated from Berger (1978) with constant year 1950. The run is performed using present day land-sea mask, freshwater routing and interactive vegetation (Roche, 2013). The temperature is driven by both natural (insolation) and anthropogenic (greenhouse gas) forcings (Yin and Berger, 2010).
With regards to the precipitation δ¹⁸O data, the main development lies in the atmospheric component in which evaporation, condensation and existence of different phases (liquid and solid) all affect the isotopic conditions of the precipitation δ¹⁸O data (Roche, 2013). The precipitation δ¹⁸O data were corrected from the ice-sheet
contribution to the global seawater $\delta^{18}$O data over the last 150,000 years (Caley et al., 2014).

## 2.6. Statistical analysis

All statistical analyses were performed on IBM SPSS Statistics version 19.0 (IBM Corp. Armonk, NY, USA). Least squares linear regression was used to determine if there was a linear correlation between independent variables and dependent variables.

Relationships between variables were assessed using standard bivariate procedures. A value of *p* < 0.05 is considered significant.

## 3. Results

### 3.1. Two hypotheses were tested

Before the controversy can be resolved, it is first necessary to prove that low-altitude $\delta^{18}$O$_p$ values are consistent with those from high-altitude ice cores. Here we proposed two hypotheses: i) the signal consistency (i.e., the temporal trends in ice core $\delta^{18}$O records at high altitude should be similar to those in $\delta^{18}$O$_p$ records at low altitude); and ii) the consistency of precipitation amount patterns (i.e., the seasonal variations and annual trends of precipitation amounts at the three sampling stations should be similar to those at the ice core drilling sites). To test the first hypothesis, we compared the $\delta^{18}$O records from the three precipitation sampling stations of Delingha, Tuotuohe, and Lhasa with those from the adjacent three ice cores (from north to south, they were Dunde, Puruogangri, and Noijin Kangsang ice cores, the distances between the ice core drilling sites and adjacent sampling stations are 116.3 km, 311.5 km,
111.7 km in order). In addition, it is important to note that the absolute values of the δ¹⁸Oₚ records and ice core δ¹⁸O records are different due to the elevation and precipitation phase differences. If we exclude the influence of the ice core dating error by moving one year backward for the ice core δ¹⁸O record, the trends of the three-site δ¹⁸Oₚ records will be similar as those of the adjacent ice core δ¹⁸O records (Fig. 2a-2f).

The adjusted δ¹⁸O records in the Puruogangri (Yao et al., 2007) (Fig. 2d) and Noijin Kangsang (Zhao et al., 2012) (Fig. 2f) ice cores, and the raw δ¹⁸O data in the Dasuopu ice core (Fig. 2g) are positively correlated with those in precipitation, with a sample size (n) of 9, 14, and 9, a correlation coefficient (r) of 0.93, 0.60, and 0.76, within a 0.01, 0.05, and 0.05 confidence level (p), respectively. Note the positive relationship between the adjusted δ¹⁸O record in the Dunde ice core (Takeuchi et al., 2009) and δ¹⁸O in the Delingha precipitation (Fig. 2b) is weak (r = 0.2) due to the uncompleted precipitation sampling in 1995. This indicates that the signals of the low-altitude δ¹⁸Oₚ records are consistent with those captured by the high-altitude ice core δ¹⁸O records, and the low-altitude δ¹⁸Oₚ records can represent the adjacent high-altitude ice core δ¹⁸O records. The same is true for the low-altitude δ¹⁸Oₚ records at Nyalam, located on the south slope of the Himalayas and the nearby Dasuopu ice core δ¹⁸O record (Thompson et al., 2000; Wang et al., 2008) (the distance between the ice core-station is 35.6 km) (Fig. 2g). Note that the result of δ¹⁸Oₚ at Delingha in 1995 was not consistent with the corresponding Dunde ice core records, possibly because some of the precipitation samples at Delingha in that year were not obtained.

To verify the consistency of mass changes, we compared the precipitation amounts
from the three sampling stations (Delingha, Tuotuohe, and Lhasa) to those from the corresponding adjacent ice core drilling sites (Dunde, Puruogangri, and Noijin Kangsang ice cores). It was expected that the overall trend of precipitation amount between the low-altitude stations and the high-altitude drilling sites would be similar, whether on monthly or annual time scales. First, we compared the precipitation amounts observed at the sampling stations ($P_{station}$) with those observed by Tropical Rainfall Measuring Mission (TRMM) at the sampling stations ($P_{TRMM}$). The results showed that at the low-altitude sampling stations, the trends of the TRMM and surface observations were highly similar, whether for the monthly changes (with a correlation coefficient of 0.51–0.89, within a 0.05 and 0.01 confidence level) (Supplementary Fig. 4, Supplementary Table 5) or for the annual changes (with a correlation coefficient of 0.80–0.97, within a 0.01 confidence level) (Supplementary Fig. 5, Supplementary Table 6). Thus, we believe that the TRMM results can capture the monthly and annual patterns of precipitation in Asia. Second, we compared the TRMM precipitation records at the sampling stations with those at the corresponding adjacent ice core drilling sites. On the whole, whether considering the monthly patterns (Supplementary Fig. 4) or the annual changes (Supplementary Fig. 5), the fluctuations of precipitation amounts at low-altitude TRMM were similar to those at the corresponding adjacent ice core drilling sites. Therefore, we believe that the change in trends of precipitation amount at low altitude can indicate those at the adjacent high altitude.

The consistencies between the temporal variations of the δ¹⁸O records and
precipitation amounts at low and high altitudes demonstrate that the low-altitude $\delta^{18}$O$_p$ time series can record the corresponding adjacent ice core $\delta^{18}$O signals at high altitude. This results from the fact that water isotope fractionation at different altitudes is subject to the same isotope fractionation mechanism. As an air mass follows a trajectory from its vapor source region to higher latitudes, it cools and loses its water vapor along the way as precipitation, a process called ‘rainout’ (Dansgaard, 1964; Clark and Fritz, 1997), which can be expressed by the Rayleigh distillation:

$$R = R_0 f^{(\alpha-1)}$$

where $R_0$ and $R$ are the initial isotope ratio of the vapor and the isotope ratio after rainout, respectively. The quantity $f$ is the residual fraction of vapor in the air mass, and $\alpha$ is the equilibrium isotopic fractionation factor between vapor and liquid water at the in-cloud temperature. For $\delta^{18}$O, $\ln\alpha = 1.137/(T + 273.16)^2 \times 10^3 - 0.4156/(T + 273.16) - 2.0667 \times 10^{-3}$ (Majoube, 1971). It is apparent that temperature is the primary driver of rainout. Hence, the $\delta^{18}$O values of rain or snow are mainly related to the condensation temperature (Dansgaard, 1964). Both rain and snow is subject to Rayleigh distillation with slightly different isotopic fractionation factors.

## 3.2. Consistent relationships between $\delta^{18}$O$_p$ and temperature

If $\delta^{18}$O$_p$ can be considered as a good indicator of temperature, humidity, or precipitation amount within each region, it should meet the criterion of a significant relationship between the isotopic values and these parameters across different time scales (daily, monthly, annual). If the relationships between the isotopic values and any parameter across different time scales in any region can not maintain consistent
positive or inverse correlations, they do not meet this criterion. Our findings show that the δ¹⁸Oₚ values are positively correlated with surface temperature (i.e., temperature effect) in the non-monsoon and transition regions on different time scales, with a correlation coefficient of 0.11 – 0.80, within a 0.05 or 0.01 confidence level (Note the positive correlation for the Tuotuohe station on annual time scale is weak, it may be because the sample number is very small). In the monsoon region a consistent but inverse correlation between δ¹⁸Oₚ values and surface temperature exists on different time scales (defined as “inverse temperature effect” in this text), with a correlation coefficient of -0.16 – -0.22, within a 0.01 confidence level (Note the inverse correlation for the Lhasa station on annual time scale is also weak, it may be because the sample number is as small as 22) (Fig. 3, Supplementary Table 4). Thus, we argue that although δ¹⁸Oₚ measurements and ice core δ¹⁸O records from the non-monsoon and transition regions can directly reflect temperature, they can “inversely” reflect the temperature in the monsoon region. However, there is not a consistent relationship between δ¹⁸Oₚ and precipitation amount (or humidity) within or between the regions (Fig. 3, Supplementary Table 4). Note that although the δ¹⁸O in the monsoon region is significantly and positively correlated with precipitation amount on daily and monthly time scales, the δ¹⁸O – P annual time scale correlation in the monsoon region is not significant (Fig. 3, Supplementary Table 4). Similarly, although there is a consistent relationship between δ¹⁸O and RH in the monsoon region, the δ¹⁸O – RH correlations in the transition and non-monsoon regions on different time scales are not consistent. Hence, there is a degree of uncertainty over whether δ¹⁸O in either precipitation or ice
cores or other sediment archives (such as speleothems) can be used as a proxy for
precipitation amounts or relative humidity.

326 **4. Discussion**

327 *4.1 Temperature signals preserved in Asian ice cores*

328 The positive correlations are significant between surface temperature and 10
329 representative ice core δ¹⁸O records (Supplementary Fig. 1) in the non-monsoon and
330 transition regions (with a correlation coefficient of 0.16–0.78, within a 0.05 or 0.01
331 confidence level); however, correlations in the monsoon region are not consistent.
332 Significant positive correlations occur for Dasuopu (Thompson et al., 2000) and
333 Zuoqiupu (Zhao et al., 2017) ice cores, while there is a significant negative correlation
334 between temperature and the Noijin Kangsang ice core (with a correlation coefficient
335 of -0.24, within a 0.01 confidence level) (Zhao et al., 2012). The correlation with the
336 East Rongbuk ice core is not significant; however, the negative correlations between
337 the 5-year running means of δ¹⁸O values from the East Rongbuk ice core and the NHT
338 during 1850-1898 and 1899-1997 are significant, with a correlation coefficient of
339 -0.51 and -0.23, within a 0.05 and 0.01 confidence level, respectively (Hou et al.,
340 2003) (Supplementary Fig. 2, Supplementary Table 1). Because the strong Asian
341 summer monsoon and convection results in relatively low δ¹⁸Oₚ values in summer (Yu
342 et al., 2017), the trends of low summer δ¹⁸Oₚ values (Fig. 4g) are opposite to those of
343 surface temperatures (Fig. 4c), although the pre-monsoon δ¹⁸Oₚ values and surface
344 temperatures gradually increase synchronously (Fig. 4g, 4c). Therefore, in the
345 monsoon region it is necessary to consider two cases involving either heavy or sparse
precipitation in winter/spring (Supplementary Fig. 6). When the precipitation in winter/spring is heavy, temperature and δ¹⁸O are more closely linked both in precipitation and in the ice core records. For example, the meteorological stations at Purang and Nyalam record heavy winter/spring precipitation (Supplementary Fig. 6A) due to the favorable local topographic conditions that interact with the western disturbances (Pang et al., 2014). Hence, the nearby Dasuopu ice core δ¹⁸O record may be used as a proxy for temperature (Thompson et al., 1997). Another good example is the Zuoqiupu ice core (Zhao et al., 2014), which is located in the monsoon region and is also characterized by heavy winter/spring precipitation as shown by the precipitation records from Bomi and Zayu (Supplementary Fig. 6D). Moreover, the positive correlation between δ¹⁸Oₚ values and surface temperature before the onset of the monsoon in this region (Yu et al., 2016) supports the conclusion that the temperature signal is preserved in the nearby Zuoqiupu ice core (Zhao et al., 2017).

In regions where the winter/spring precipitation is sparse, glaciers receive proportionately more snow in the summer and thus show more of an “inverse” temperature signal that results from the negative correlation between summer δ¹⁸Oₚ values and surface temperature (Fig. 4c, 4g). The precipitation at the meteorological stations at Lhasa and Nagarze, near the Noijin Kangsang ice core site, falls mainly in summer (Supplementary Fig. 6C), which results in a negative correlation between the Noijin Kangsang ice core δ¹⁸O record (Zhao et al., 2012) and the NHT anomalies back to 1850 (Supplementary Fig. 2). This is also observed for the correlation between the East Rongbuk δ¹⁸O record (Hou et al., 2003) from a glacier that also
receives less winter/spring than summer precipitation as indicated by the Tingri and
368
369 Pagri meteorological stations (Supplementary Fig. 6B).

Since these ice core δ¹⁸O records in the monsoon region with sparse winter/spring
precipitation are inversely correlated with the NHT anomalies (Supplementary Fig. 2),
we inverted their time series and compared them with the relevant instrumental
records (Supplementary Fig. 7). The results are mixed. On the one hand, the inverted
whole East Rongbuk record (Hou et al., 2003) and the new Noijin Kangsang record
(Zhao et al., 2012) (moved one year backward and then inverted) before 1989 are
similar to the NHT since 1850. The 5-year running δ¹⁸O record of East Rongbuk ice
core is negatively correlated with the NHT over the periods of 1850-1898 and
1899-1997, with a correlation coefficient of -0.51 and -0.23, with a confidence level
of 0.01 and 0.05, respectively. The new Noijin Kangsang record before 1989 is
significantly correlated with the NHT, with a correlation coefficient of -0.27 and a
0.01 confidence level. However, part of the new the Noijin Kangsang ice core δ¹⁸O
record after 1989 was still abnormal (marked by a green box). The reason for the
discrepancy remains unclear. It may be related to the abnormal local circulation (Gao
et al., 2016), but further study is required to determine why the trend seems to reverse
in the most recent part of the record. Interestingly, the higher frequency fluctuations of
the inverted δ¹⁸O record from the Noijin Kangsang ice core over the period
1993-2004 are still similar to those of the NHT, with a correlation coefficient of -0.80,
within a 0.05 confidence level (Supplementary Fig. 8). Hence, we argue that the ice
core δ¹⁸O records in the monsoon region with sparse winter/spring precipitation can
390 "inversely" indicate the surface temperature changes.

391 4.2 *Temperature signals preserved in Asian speleothems*

392 The assumption that the speleothem δ¹⁸O records (δ¹⁸Oₛ) can be used as a
393 paleoclimate proxy requires that the speleothem δ¹⁸O records inherit the signals of
394 δ¹⁸O in precipitation (δ¹⁸Oₚ) outside the caves. Therefore, we can determine that the
395 speleothem δ¹⁸O records indicate temperature (or precipitation), if the precipitation
396 δ¹⁸O records outside the caves capture the temperature (or precipitation) signal. In
397 Section 3.2, we have already demonstrated that there is not a consistent relationship
398 between δ¹⁸Oₚ and precipitation amount within or between the regions of the monsoon,
399 transitional, and non-monsoon regions. The result suggests speleothem δ¹⁸O records
400 do not consistently reflect changes in precipitation amount. Previous studies also
401 argued that speleothem δ¹⁸O records in East Asia can not indicate the changes in
402 annual precipitation (or summer precipitation) based on the relationship between
403 precipitation amount and speleothem δ¹⁸O records on annual, decadal and orbital time
404 scales (Chen et al., 2016; Rao et al., 2016; Liu et al., 2020). In this study, we found
405 precipitation δ¹⁸O records outside the caves in the Asian monsoon region are
406 negatively correlated with surface temperature. Hence, the inverted speleothem δ¹⁸O
407 records from the Asian monsoon region may also indicate temperature.

408 To demonstrate this conclusion, we firstly compared the inverted speleothem δ¹⁸O
409 curves of Jhumar-Wah caves in the South Asian monsoon region (Sinha et al., 2011),
410 and of Zhijin (Kuo et al., 2011), Dongge (Cheng et al., 2016), and Dayu (Tan et al.,
411 2009) caves in the East Asian monsoon region with sparse winter/spring precipitation
412 with the NHT curve during 1850-2010 (Supplementary Fig. 9). The results show that
413 the composite δ¹⁸O₃ record from Jhumar and Wah Shikar caves is significantly
414 correlated with the NHT over the periods of 1850-2007, with a correlation coefficient
415 of -0.40, within a 0.01 confidence level. The speleothem δ¹⁸O record from Zhijin cave
416 is negatively correlated with the NHT over the periods of 1850-1942 and 1943-1997,
417 with a correlation coefficient of -0.43 and -0.3, within a 0.01 and 0.05 confidence
418 level, respectively. For Dongge and Dayu caves, the trends of the inverted speleothem
419 δ¹⁸O curves are very similar to those of the NHT curve, with a correlation coefficient
420 of -0.48 and -0.24, within a 0.05 and 0.05 confidence level, respectively, except part
421 of the speleothem δ¹⁸O records from Dongge cave after 1954 and from Dayu cave
422 after 1976 are still abnormal (marked by a green box). The results indicate that the
423 inverted speleothem δ¹⁸O records from the Asian monsoon region may reflect changes
424 in surface temperature.

425 During the last 2000 years, we also found that the inverted speleothem δ¹⁸O record
426 from Sahiya cave (Kathayat et al., 2017) and the composite δ¹⁸O record from Jhumar
427 and Wah Shikar caves (Sinha et al., 2011) from the South Asian monsoon region,
428 agree with the Northern Hemisphere and Northern Hemisphere extratropical
429 temperature anomalies (Moberg et al., 2005; Christiansen and Ljungqvist, 2012), with
430 a correlation coefficient of -0.22 and -0.15, within a 0.01 and 0.01 confidence level,
431 respectively (Supplementary Fig. 10) (Note these r values are lower than the
432 correlation observed from Jhumar-Wah, Zhijin, and Dongge Caves). In the East Asian
433 monsoon region, during the last 2000 years, the trends of the inverted speleothem
δ¹⁸O record from Lianhua (Cosford et al., 2008) and Heshang (Hu et al., 2008) caves are also similar to those of the NHT (Supplementary Fig. 11). The Heshang speleothem δ¹⁸O record is negatively correlated with the NHT during 0-1980, with a correlation coefficient of -0.10, and a confidence level of 0.01. The negative relationship between the Lianhua speleothem δ¹⁸O record and the NHT during 0-1997 is also significant, with a correlation coefficient of -0.14, within a 0.05 confidence level. Clearly, those r values are close to zero indicating that the relationships only account for a small amount of the variance. We do not rule out that the Asian summer monsoon (ASM) intensity can be contributing to the variance. It is necessary to discuss the relationships between the ASM intensity and speleothem δ¹⁸O records in our future studies.

In addition, we extrapolated further our findings over the longer timescales. One example is in the East Asian monsoon region, we found that an inverted speleothem δ¹⁸O record (Hulu, Dongge and Sanbao caves) (Cheng et al., 2016) is similar to surface temperature over the region in which the caves are located (~17°–33° N and 87°–116° E) as simulated by the iLOVECLIM model during periods of 8-73 and 124-150 kyr BP, with a correlation coefficient of -0.69 and -0.70, within a 0.01 and 0.01 confidence level (Caley et al., 2014) (Supplementary Fig. 12). Those results indicate that the inverted speleothem δ¹⁸O records from the Asian monsoon region can capture temperature signal. Note there are still some periods where the temperature to δ¹⁸O records do not agree (Supplementary Figs. 10 and 12). It may be because the precipitation patterns during those periods do not conform with the above assumption
that the speleothems receive heavy summer precipitation and sparse winter/spring precipitation.

As mentioned in Cheng et al. (2019), before the data processing and comparison of the Asian monsoon (Chinese speleothem δ¹⁸O) and Antarctic temperature (ice core δD) records, it was difficult to imagine that there exists a close internal relationship between the Antarctic temperature and Chinese speleothem δ¹⁸O records, because the two records are so distant. The relationship can be described as: δ¹⁸OChinese speleothem – InsolationJuly 21@65°N ≈ ΔδDAntarctica ≈ δDAntarctica – δ¹⁸Obenthic. This indicates that there is a “simple” relationship between climate changes in the high latitude region of the Southern Hemisphere and the mid-low latitude monsoon region of the Northern Hemisphere (Cheng et al., 2019). Thus, it is in fact possible to compare Asian speleothem δ¹⁸O records to both Greenland and Antarctic ice core records.

It is widely accepted that ice core δ¹⁸O records from Greenland and the Antarctic can be used as proxies of temperature. Hence, we directly compared the speleothem δ¹⁸O records from the Asian monsoon region with those ice core δ¹⁸O records in order to reveal the signal of the speleothem δ¹⁸O records. Here, we compared the inverted speleothem δ¹⁸O record (Hulu, Dongge and Sanbao caves) from the Asian monsoon region (Cheng et al., 2016) with the Greenland NGRIP δ¹⁸O and the Antarctic EDC3 δD ice core records (North Greenland Ice Core Project members, 2004; Jouzel et al., 2007) during some periods. The results show that the inverted speleothem δ¹⁸O composite record from the East Asian monsoon region, which receives sparse winter/spring precipitation, is similar to the Greenland NGRIP δ¹⁸O and the Antarctic
EDC3 δD ice core records from 0 to 130 kyr BP (Supplementary Fig. 13). Note the inverted curve of millennial mean δ¹⁸Oₛ records are correlated with the Antarctic EDC3 ice core δD records during periods of 0-130 kyr BP, with a correlation coefficient of -0.22, within a 0.05 confidence level; the inverted curve of centurial mean δ¹⁸Oₛ records are correlated with Greenland NGRIP δ¹⁸O records during periods of 0-130 kyr BP (Yuan et al., 2004), with a correlation coefficient of -0.35, within a 0.01 confidence level. Moreover, the variations in the inverted speleothem δ¹⁸O record (Cheng et al., 2016) are highly similar to those in the Antarctic EDC3 δD record (Jouzel et al., 2007) during 240 to 334, 343 to 386, 394 to 462, and 603 to 640 kyr BP, with a correlation coefficient of -0.30, -0.65, -0.38, and -0.63, within a 0.05, 0.01, 0.05, and 0.05 confidence level (Supplementary Fig. 14). The results show that our reinterpretation of ice core δ¹⁸O records in the monsoon domain can be applied to speleothem δ¹⁸O records from the Asian monsoon region (Wang et al., 2008; Sinha et al., 2011; Cheng et al., 2016; Kathayat et al., 2017; Yuan et al., 2004). Hence, we argued that the inverted speleothem δ¹⁸O records from the Asian monsoon region can also reflect temperature on the centennial as well as the orbital time scales.

### 4.3 Implications of inverted δ¹⁸O records

How can the inverted ice core and speleothem δ¹⁸O records from the Asian monsoon region indicate surface temperature changes? First, it should be noted that it is the in-cloud temperatures (or condensation temperatures) that control condensation and isotope fractionation. These cannot be routinely measured, and so correlation with surface air temperatures are made in many studies (Clark and Fritz, 1997). This can be
done since the trends of surface temperatures in most regions are similar to those of
the in-cloud temperatures. In this study, in the tropical regions, intense summer
monsoon onset involves the abrupt increase of convection (Risi et al., 2008). This
involves the uplift of warm and humid marine moisture from the tropical ocean to the
high troposphere where the relatively low summer cloud-top temperatures (Fig. 4l)
(Cai and Tian, 2016) produce an enhanced Rayleigh fractionation, resulting in low
water vapor δ¹⁸O (δ¹⁸O_v) (Fig. 4p, and Fig. 5) values and subsequently low δ¹⁸O_p
values in monsoon precipitation (Fig. 4h) despite the high summer surface
temperatures (Fig. 4d, and Fig. 5). The residual moisture is transported to higher
latitudes by the summer monsoon circulation, and the uplift of the subsequent
convection over the mountains causes the δ¹⁸O_v values of the residual moisture to
decrease further (Fig. 4o). The subsequent precipitation is also characterized by lower
δ¹⁸O_p values (Fig. 4g) (Yao et al., 2013; Yu et al., 2017). In addition, we find that the
summer cloud-top temperatures over the region where uplifted precipitation occurs is
also relatively low, such as in Lhasa (Fig. 4k), which also contributes to the lower
δ¹⁸O values in the rain produced by the residual moisture (Fig. 4o; Fig. 5). Thus, the
relatively lower δ¹⁸O values of summer precipitation in the monsoon region are
controlled by the cloud-top temperatures, or strictly speaking, they are controlled by
the in-cloud (condensation) temperatures, which is a key parameter of the Rayleigh
fractionation equation. That the corresponding surface temperatures in summer are
high (Fig. 4c, 4d) and thus inversely correlated with precipitation δ¹⁸O in the monsoon
region (Fig. 4g, 4h) are a secondary consideration. We further find that the cloud-top
temperatures over Lhasa (Fig. 4k), Tuotuohe (Fig. 4j), and Delingha (Fig. 4i) in
summer gradually increase as the moisture moves from the monsoon region to the
non-monsoon region, which results in the summer water vapor δ¹⁸O_v values
increasing from south to north (Fig. 4g, 4f, 4e; Fig. 5). In addition, the monsoon
circulation gradually weakens from south to north, where the westerlies and local
moisture recycling gradually strengthen. The result is that the magnitude of the
decrease in summer precipitation δ¹⁸O_v values from the monsoon region to the
non-monsoon region becomes smaller (Fig. 5). The ECHAM5-wiso (Mutz et al., 2016)
simulations also demonstrate that the north-south (cross-sections at 90.000° E –
95.625° E) long-term mean δ¹⁸O_v and δ¹⁸O_p value profiles over the monsoon and
transition regions of the Tibetan Plateau for July (monsoon season) are lower than
those for May (pre-monsoon season), respectively (Fig. 6). Moreover, the δ¹⁸O_v and
δ¹⁸O_p value shifts between the pre-monsoon and monsoon seasons from south to north
are highly consistent with changes in cloud-top temperatures, respectively (Figs. 8 and
9). Thus, the implications of δ¹⁸O, whether in ice cores, speleothems or in
precipitation in the monsoon region and the non-monsoon region and at high or low
altitude, can be explained by isotope fractionation.

The spatial correlation patterns between δ¹⁸O_p and surface temperatures are shown
in Fig. 7, based on the data from 120 stations in Asia (10°–50° N, 65°–130° E) (see
Supplementary Table 3 for details). The ice core and speleothem δ¹⁸O records from
the regions north of the white line (that is affected by the westerlies and a weak
monsoon) reflect surface temperature changes. Moreover, the temperature proxies of
ice core and speleothem δ¹⁸O records from the regions north of the black line, which
are affected by intense westerlies, are defined by significant positive correlations of
δ¹⁸Oₚ – T (Supplementary Table 3). In the regions south of the white line, which are
intensely affected by the monsoon, the δ¹⁸Oₚ values are significantly negatively
correlated with surface temperatures. In this case, ice core and speleothem δ¹⁸O
records may suggest the surface temperature changes in an inverse and indirect way,
when the nearby meteorological stations are characterized by sparse precipitation in
winter/spring.

552 **5. Conclusions**

This study diagnosed the consistency of signals preserved in low-altitude
precipitation δ¹⁸O and high-altitude ice core δ¹⁸O. The results demonstrate that the
trend changes in the δ¹⁸O record at low altitude are consistent with those at high
altitude. Clearly, the low-altitude δ¹⁸Oₚ can capture the corresponding adjacent ice
core δ¹⁸O signals at high altitudes.

Our data show that the δ¹⁸Oₚ values are consistently positively correlated with
surface temperature in the non-monsoon and transition regions but are consistently
inversely correlated with surface temperature in the monsoon region on different time
scales. This implies that the δ¹⁸Oₚ measurements and ice core δ¹⁸O records from the
non-monsoon and transition regions can directly indicate temperature, but they can
"inversely" reflect temperature in the monsoon region. It indicates that ice core δ¹⁸O
records in the non-monsoon region can be used as a temperature proxy. In the
monsoon region, if winter/spring precipitation is heavy, the ice core δ¹⁸O records can still directly reflect temperature because temperature and δ¹⁸O are more closely linked both in precipitation and in the ice core records. However, in the monsoon region, if winter/spring precipitation is sparse, the inverted ice core δ¹⁸O records that agree with temperature will also reflect temperature changes. This results from the fact that trends of surface temperatures in the monsoon region are in contrast to the corresponding cloud-top temperatures, which controlled summer precipitation δ¹⁸O. Our findings also indicate that inverted curves of speleothem δ¹⁸O records in the monsoon region are in accordance with the Greenland/Antarctic ice core δ¹⁸O/δD and other paleotemperature records. This suggest that the inverted δ¹⁸O records from Asian speleothems may also reflect temperature.

Here we demonstrate how temperature signals may have been preserved in stable isotope records from ice cores and speleothems from the Asian monsoon region. This study very clearly indicates that researchers need to be cautious when interpreting the ice core and speleothem δ¹⁸O records from this region in the reconstruction of paleoclimate. Our findings suggest that in order to reconstruct temperature changes in the Asian monsoon region, ice core climate records from the regions where winter/spring precipitation is heavy may be more useful and their interpretation may depend in part on comparisons with inverted speleothem δ¹⁸O records.

## Acknowledgments

This research is supported by the CAS (XDA20060200), the National Key R&D Program of China (2017YFA0603303), and the NSFC (41671054, 41771085). Thanks
go to Dr. Mary E. Davis (The Ohio State University, USA) for her valuable editing and comments. The authors acknowledge Dr. Sebastian G. Mutz (University of Tuebingen, Germany) for providing ECHAM5-wiso simulated isotopic data. Special thanks are given to two anonymous reviewers for their constructive comments. Some observed isotopic data used in this paper are from the IAEA/WMO Global Network of Isotopes in Precipitation (GNIP). Water vapor isotopic data derived from Tropospheric Emission Spectrometer (TES), onboard the NASA Aura satellite, were provided by NASA Langley Research Center Atmospheric Science Data Center. Some meteorological data were obtained from the Climatic Data Center, National Meteorological Information Center, China Meteorological Administration. NCEP reanalysis-derived data were obtained from the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. The TMPA data were provided by the NASA/Goddard Space Flight Center's Mesoscale Atmospheric Processes Laboratory and PPS, which develop and compute the TMPA as a contribution to TRMM. The ISCCP D1 and D2 products (cloud-top temperature, CTT) were obtained from NASA Goddard Institute for Space Studies. The data of the northern hemisphere temperature (CRUTEM4) were obtained from the Climatic Research Unit (University of East Anglia) and the Hadley Centre (UK Met Office).

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<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Location of the ice core drilling sites (white dots), precipitation sampling stations (gray dots), and caves (black dots) in Asia. Location of the modern transition region between non-monsoon and monsoon regions is shown by a black dashed line. The numbers 1–10 in the white dots mark the locations of the ice cores listed in Supplementary Table 1. The numbers 1–10 in the black dots mark the locations of the 10 speleothems listed in Supplementary Table 2. The three gray dots marked with white letters ("D", "T", and "L") mark the locations of the stations at Delingha, Tuotuohe, and Lhasa. The other 117 precipitation sampling stations are also shown (gray dots) (see Supplementary Table 3 for details). Arrows indicate the trajectories of the westerlies (brown), the Indian monsoon (dark green, via the Arabian Sea and the Bay of Bengal) and the East Asian monsoon (light green).</figcaption></figure>
<figure><img src="image_5.png" /></figure>

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**Fig. 2.** Comparisons of δ¹⁸O records from four ice cores (solid blue lines) and nearby precipitation sampling stations (dashed green lines). The blue and green lines indicate δ¹⁸O records in ice cores of Dunde (Takeuchi et al., 2009), Puruogangri (Yao et al., 2007), and Noijin Kangsang (Zhao et al., 2012) and precipitation of Delingha, Tuotuohe, and Lhasa, which represent, in order, the non-monsoon, transition and monsoon regions, respectively. Note that (a), (c), and (e) show the raw data, and ice core δ¹⁸O records were moved one year backward in (b), (d), and (f). In addition, in the monsoon region, the δ¹⁸O changes in Dasuopu ice core (Thompson et al., 2000; Wang et al., 2008) and Nyalam precipitation are also shown (g). Their similar changes indicate no dating error for the Dasuopu ice core records. The station/ice core elevations are also shown in brackets. δ¹⁸O is reported as per mil deviations from
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<figure><img src="image_6.png" /></figure>

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Fig. 3. The relationships δ<sup>18</sup>O<sub>p</sub>-T, δ<sup>18</sup>O<sub>p</sub>-RH, and δ<sup>18</sup>O<sub>p</sub>-P at Delingha (a, d, and g), Tuotuohe (b, e, and h) and Lhasa (c, f, and i) on the different time scales in 1991-2014. The gray, green, and pink legends indicate the relationships on the daily, monthly and annual time scales, respectively. On the different time scales, the correlation δ<sup>18</sup>O<sub>p</sub>-T is consistent in each region (the positive correlation is consistent in the non-monsoon and transition regions, and the inverse correlation is consistent in the monsoon region), but the relationships δ<sup>18</sup>O<sub>p</sub>-RH and δ<sup>18</sup>O<sub>p</sub>-P are not consistent in either the non-monsoon or transition region (see Supplementary Table 4 for details).

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**Fig. 4.** Seasonal changes in surface temperature (ST, pink) and δ¹⁸Oₚ (blue) at Delingha (a, e), Tuotuohe (b, f), and Lhasa (c, g), from 1991–2014, and the moisture source area (10°–25° N and 65°–130° E) (d, h) from 1961-2016. Seasonal changes in the cloud-top temperature (CTT, yellow) and δ¹⁸Oᵥ (green) over Delingha (i, m), Tuotuohe (j, n), and Lhasa (k, o) in the target area and the moisture source area (l, p) from 2006–2009. The shading in each panel indicates standard deviation. The red arrows mark the relatively low values in the summer. STs at the three stations were derived from instrumental data, but surface temperatures (including sea surface temperatures) over the moisture source area are from NCEP reanalysis-derived data.
Note that the δ¹⁸Oᵥ variations of near surface water vapor over the moisture source area (p, light green line) are very slight. δ¹⁸O is reported as per mil deviations from
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<figure><img src="image_8.png" /></figure>

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888 **Fig. 5.** Scheme illustrating how moisture is transported by the Asian monsoon (green arrow). Convection (dashed line with arrow), cloud-top temperature (CTT, yellow dot), water vapor $\delta_v$ (green dot), and surface temperature (ST, pink dot) all affect summer $\delta^{18}O_p$ (blue dot) from the ocean/coastal regions (moisture source area) to the Asian continent (target area). Note the minus (plus) signs within the circles that indicate decreased (increased) summer values, and the sizes of the circles show the magnitudes of those decreases (increases). The sizes of the green arrows and dashed lines indicate the relative strengths of the monsoon and convection.

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Fig. 6. North-south long-term mean simulated δ<sup>18</sup>O<sub>v</sub> (a, b) and δ<sup>18</sup>O<sub>p</sub> (d, e) profiles during 1979-1999 for May (pre-monsoon season) (a, d) and July (monsoon season) (b, e). Shadings show the topography cross-section profiles of the Tibetan Plateau (c, f). Note that the δ<sup>18</sup>O<sub>v</sub> at 500 hPa and the δ<sup>18</sup>O<sub>p</sub> are simulated by the ECHAM5-wiso model (Mutz et al., 2016). The δ<sup>18</sup>O cross-sections at 90.000° E, 91.875° E, 93.750° E, and 95.625° E are marked by the green, red, blue, and pink lines, respectively. The black dashed lines indicate the reference values of -27.5‰ and -10‰ and are used to compare the δ<sup>18</sup>O<sub>v</sub> and δ<sup>18</sup>O<sub>p</sub> values in the monsoon season with those in the pre-monsoon season. The black arrows show the locations of the sampling stations of Lhasa (monsoon region), Tuotuohe (transition region, marked by Tr), and Delingha (non-monsoon region, marked by Non-m) from south to north, respectively. δ<sup>18</sup>O is reported as per mil deviations from VSMOW or VSMOW2.

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<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Distribution of the correlation coefficients of δ<sup>18</sup>O<sub>p</sub>-T in Asia. The distribution was calculated by the Kriging interpolation at 120 stations (open circles) in the region bounded by 10° to 50° N and 65° to 130° E. The area to the south of the white line is affected by intense summer monsoon, the region north of the black line is under the influence of intense westerlies, and the region between is transitional. Note the δ<sup>18</sup>O-T correlations are still significant in the transitional region between the white and black lines (see Supplementary Table 3 for details).</figcaption></figure>