A Climatological Interpretation of Precipitation δ18O across Siberia and Central Asia

Abstract

Siberia and Central Asia are located at middle to high latitudes, encompassing a large landlocked area of the Eurasian continent and vast tracts of permafrost, which are sensitive to global climate change. Here, we investigated the data from 15 Global Network of Isotopes in Precipitation (GNIP) stations to clarify the relationship between precipitation δ¹⁸O (δ¹⁸O_P) and the local temperature and precipitation amount on the monthly, seasonal, and annual timescales. Three main conclusions as following: (1) On the monthly time scale, the variation in δ¹⁸O_P is mainly controlled by the “temperature effect”. (2) The weighted average value of precipitation δ¹⁸O (δ¹⁸O_w) exhibited “temperature effect” over 60° N–70° N. However, δ¹⁸O_w was dominated by multiple factors from 40° N to 60° N (e.g., the North Atlantic Oscillation (NAO) and water vapor source changes). (3) The variations of δ¹⁸O_W can be attributed to the changes in pathway of the westerly dominated by the NAO at annual timescale. Therefore, it is possible to reconstruct the histories of past atmospheric circulations and water vapor sources in this region via δ¹⁸O in geologic archives, e.g., speleothem and ice core records.

1. Introduction

The stable oxygen isotopic compositions (δ¹⁸O) of ice cores, tree rings, ocean sediments, and cave speleothems have been widely used as proxies for paleo-climatic change. However, the climatological interpretation of δ¹⁸O among different paleo-archives remains controversial [1]. Monitoring the δ¹⁸O of modern atmospheric precipitation (δ¹⁸O_P) can promote the understanding of climatic significance of δ¹⁸O in various paleo-archives. The Global Network of Isotopes in Precipitation (GNIP, Boulder, CO, USA) was established by the World Meteorological Organization (WMO, Geneva, Switzerland) and the International Atomic Energy Agency (IAEA, Vienna, Austria) in 1961 [2]. Stable hydrogen and oxygen isotopic compositions of precipitation (δ²H_P and δ¹⁸O_P) are observed constantly by GNIP, providing numerous isotope data sets for researchers in the field of global and local atmospheric circulation [3,4]. Subsequent related studies indicated that factors influencing the changes in δ²H_P and δ¹⁸O_P exhibited distinct characteristics at different latitudes, e.g., the variations in δ¹⁸O_P over middle and high-latitude regions are dominated by temperature, while those at low latitudes are dominated by precipitation amount [5,6,7,8,9,10]. In addition, changes in the source of water vapor and in the atmospheric circulation pattern can also result in variations in δ²H_P and δ¹⁸O_P [11,12].

However, modern meteorological monitoring networks and paleo-climatic research are relatively scarce and the explanation of δ¹⁸O values in different archives is controversial in Siberia and Central Asia. For the present available paleo-climatic studies, the interpretation of δ¹⁸O values is diverse [13,14,15,16]. For example, the variation of stalagmite δ¹⁸O in the south of Ural Mountains [17] and the permafrost ice wedge δ¹⁸O in central Eurasia [18] are considered to be governed by winter temperature, and the authors found this proxy can also indicate the warming trend during the Holocene. The ice core δ¹⁸O record in Belukha plateau [19] and the alpine peat a-cellulose δ¹³C record [20] in Altai Mountains are dominated by the warm season temperature, which also show the trend of Holocene warming. In addition, the stalagmite δ¹⁸O in Central Asia has been considered as an indicator of climate changes, responding to the shift in the proportion of moisture deriving from mid-latitude westerlies during the winter/spring season [21]. Note that the δ¹⁸O records in the Kesang Cave in western China and the Tonnel’naya Cave in Uzbekistan reflect local precipitation, which showed similar signatures to the cave δ¹⁸O records in the Asian monsoon [22]. However, Cai et al. [1] interpreted the variation of the Kesang Cave δ¹⁸O as the change of water vapor source, while Rao et al. [23] proposed that the interpretation of the Kesang Cave δ¹⁸O should be the same as the ice core δ¹⁸O in the surrounding area, which is controlled by temperature change. In addition, stalagmite records in the Altai Mountains of southern Siberia also show that the δ¹⁸O is related to the intensity change of the Siberian high [24,25]. Since the δ¹⁸O in paleo-climate records is mainly inherited from the atmospheric precipitation, it is of great significance to clarify the climatic significance of δ¹⁸O_P for the interpretation of paleo-climate carrier in Central Asia.

The findings of previous research on the stable isotopes of precipitation in this region have been summarized as follows: (1) The stable isotopes of meteoric precipitation are controlled by local temperature with a distinct “temperature effect” [26,27]. (2) The main source of moisture in Eurasia is the Atlantic Ocean [28,29], while in the summer season, the supply of circulating water makes an important contribution for the Eurasia region. The isotopic changes in circulating water also affect the relationship between δ¹⁸O_P and temperature [13,16,21,26,30]. (3) Eurasian zonal circulation index (EZCI) represents the intensity of mid-latitude westerly wind of Eurasia, a common circulation pattern which leads to changes in the transport of water vapor over Siberia and Central Asia. Moreover, variation in δ¹⁸O_P is positively correlated with the frequency of westerly wind [13,27,31,32]. (4) The climate of western Siberia displays obvious inter-annual variations dominated by North Atlantic Oscillation (NAO) during the winter period [16,21,33,34,35]. Regional temperature also affects the decadal winter variations in δ¹⁸O_P, but the inter-annual summer variations in δ¹⁸O_P can be attributed to short-term regional scale processes, such as evaporation and convective precipitation [16,35].

Though it has been proposed in some research that δ¹⁸O_P changes in Siberia can be attributed to temperature effect and variations in the source of water vapor, there are still some pending scientific questions to consider. Notably, how do the abovementioned factors affect the isotopic composition of local atmospheric precipitation at different timescales (e.g., monthly, seasonal, and annual)?

The NAO has an enormous influence on climate patterns at middle to high latitudes within the Northern Hemisphere [35,36]. In addition, the monitoring data, climate simulation results, and paleoclimate proxies from Europe reflect a strong relationship between δ¹⁸O_P and North Atlantic Oscillation index (NAOI) [37,38,39,40,41]. However, whether the Siberian δ¹⁸O_P respond to the signals of NAO is still an open question. Similarly, the influences of Atlantic water vapor and Arctic Ocean water vapor on Siberia precipitation were not clearly understood. Consequently, addressing the above-mentioned scientific problems will provide more in-depth comprehension on climatic and environmental significance of isotopic composition on atmospheric precipitation throughout the region. Moreover, it is necessary to get a thorough understanding of the physical mechanisms related to regional changes in modern meteoric isotopes, which will build a framework for reconstructing the regional paleoclimate via geological records, such as speleothems and ice core records.

Here, we have analyzed stable isotopic observation data pertaining to atmospheric precipitation at 15 GNIP sites over Siberia and northern Central Asia (40° N–70° N, 55° E–125° E) (Figure 1). By analyzing these observed data in different patterns of atmospheric circulation, the following scientific issues have been clearly investigated: (1) variations in the characteristics of atmospheric precipitation isotopes in Siberia region and its controlling factors on different timescales (i.e., monthly, seasonal, and annual); (2) relationship between the isotopic compositions of precipitation and atmospheric circulations in the Siberia region. These questions were analyzed and discussed to provide reliable support for modern monitoring endeavors, and facilitating paleo-climatic reconstruction efforts via regional δ¹⁸O proxies.

2. Study Area

The study region, which is located in the northern part of Eurasia, includes inland Siberia and the northern part of Central Asia (40° N–70° N, 55° E–125° E) (Figure 1). The region of interest herein is a typical middle to high-latitude continental area extending from Ural Mountains in the west to Stanovoy Range in the east and from the Arctic Ocean in the north to a series of mountain ranges toward the south, namely, mountains in northern Kazakhstan to the southwest, Urumqi in the south, and Qiqihar (northeastern China) to the southeast (Figure 1).

The moisture over Eurasia is transported by the westerly, and water vapor flux decreases as the wind direction shifts from West Eurasia to East Eurasia [26,28,29,42]. Elevations of the sites in the study region range from 53 to 1338 m a.s.l. (above sea level), the annual average air temperature varies from −11.8 to 10.0 °C, and the annual average precipitation reaches 230–706 mm (Table 1). In winter, the moisture in this region originates from the ocean, whereas half of the precipitation in summer is sourced from local evaporation of water vapor due to the high evaporation rate [16,26,29]. In autumn and winter, the climatic conditions in this region are controlled mainly by the Siberian high-pressure system and the surface evaporation within the study area is relatively weak during these seasons due to low temperature and high snow cover, resulting in a wide range of cold and dry conditions throughout Siberia [30].

3. Data and Methods

The meteoric isotopic and meteorological data in this study were derived from the GNIP [2]. We mainly analyzed stable isotopic observation data from 15 stations located in the study region for statistical analysis (Figure 1, red triangles; Table 1, sites in bold). In addition, we have collected synchronous data from 14 sites in Europe outside the study area to explore the similarities and differences in δ¹⁸O_P between Siberia and Europe on an interannual scale (Figure 1, purple triangles; Table 1, sites in non-bold). The δ¹⁸O_p data were downloaded through the IAEA website (https://nucleus.iaea.org/wiser/index.aspx/). The monitoring period ranged from 1970 to 2016 (A.D), however, data for some years were missed within the monitoring period. For example, there are only 36 and 38 monthly samples in a 10-year period (1980–1990) for stations of Pechora and Perm, or only 14 samples instead of 228 for a 19-year period (in Irkutsk) and so on. The missing data should be attributed to an accident in sample collection or the termination of cooperation with IAEA by individual station in specific years (http://www-naweb.iaea.org/napc/ih/IHS_resources_gnip.html). For example, for the data of Irkutsk in the GNIP data center, which were monitored in February, June, October, and November in 1971 (A.D) and January, March to November in 1990 (A.D). But there are no data in the period from 1972 to 1989 (A.D). So, this is the main reason for the disagreement between the months of the monitoring period and the number of available data as presented in Table 1. In addition, for each station, the parameters of air temperature, precipitation amount, δ¹⁸O_P, and δ²H are not always coupled strictly. The data missing resulted in the inconsistency in numbers of samples in different tables for a specific station. In order to be comparable to the majority of the published literature on meteoric water lines, we used an ordinary least square regression (OLSR) to determine the local meteoric water line (LMWL), which is of the form δ²H = a δ¹⁸O + b, though recent publications have suggested alternatives for calculating local meteoric water lines [43,44]. The NAOI data were downloaded from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA) (http://www.cpc.ncep.noaa.gov/data/indices/). The Eurasian zonal circulation index (EZCI) data were downloaded from the National Climate Center of the China Meteorological Administration (http://cmdp.ncc-cma.net/cn/monitoring.htm).

To estimate the moisture transportation paths at different sites, we used the NCEP reanalysis data sets (ftp://arlftp.arlhq.noaa.gov/pub/archives/reanalysis) as the meteorological input for Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory analysis, which is available from the NOAA Air Resources Laboratory at http://ready.arl.noaa.gov/HYSPLIT.php. On the target month, 240-h (10-day) back trajectories were calculated for target sites at variable heights 500, 1000, 1500, 2500 m above ground level (a.g.l.) and statistical clustering of similar trajectories was done through the internal HYSPLIT clustering program [44].

We have compared the results for the identified moisture sources at four different elevations (500, 1000, 1500, and 2000 m a.g.l.), for four monitoring sites, namely Bagdarin, Qiqihar, Ulaanbaatar, and Wulumuqi. Most of the moisture in atmosphere is believed to reside up to 2000 m a.g.l. [12,27,45,46] therefore, we did not consider higher levels over 2000 m a.g.l. The results revealed little variation in the sources of moisture at different elevations (Figures S1–S3, Supplementary Materials) [47]. Hence, the air mass history was present for a single elevation of 1000 m a.g.l. during the previous 240-h (10-day) period, which is considered the average residence time of water vapor in the atmosphere [12,29,47].

4. Results 4.1. Seasonal Variations of δ¹⁸O_P

The average monthly temperature and δ¹⁸O_P exhibited seasonal variations, and δ¹⁸O_P displayed positive excursion in summer and negative trend in winter (Figure 2). The maximum average monthly temperature was observed in July, and the minimum average monthly temperature was observed in January. The δ¹⁸O_P also followed a similar trend, that is, the maximum values were observed in July, and the minimum values were observed between December and February (Figure 2). It is worth noting that the precipitation amount at all sites in the study area also presented similar seasonal variations with temperature and δ¹⁸O_P.

4.2. Relationship between δ²H_P and δ¹⁸O_P

Craig [48] established the first global meteoric water line (GMWL): δ²H = 8.0 δ¹⁸O + 10.0 [48] using the relationship between δ²H_P and δ¹⁸O_P. As shown in Figure 3, a correlation analysis was performed on the relationship between δ²H_P and δ¹⁸O_P with data from 15 stations in Siberia and Central Asia to obtain the local meteoric water line (LMWL): δ²H = 7.8 δ¹⁸O + 5.0 (r = 0.98). We used δ²H_P and δ¹⁸O_P data from monthly precipitation observations collected at 9 GNIP site (the numbers of available monthly paired δ¹⁸O_P and δ²H_P must ≥ 30) to obtain the LMWL equations shown in Table 2. The slope of GMWL can reflect the fractionation or ratio between δ²H_P and δ¹⁸O_P [48], however, the LMWL slopes were generally lower than the GMWL slopes at most stations (8/9) (Table 2). Since the deuterium component is much less sensitive to kinetic effects than the ¹⁸O component, evaporation during the precipitation period in the inland region led to equations for atmospheric precipitation with a lower slope in most monitoring sites [5,9,49,50,51,52].

5. Discussion 5.1. Relationship between Temperature and δ¹⁸O_p

According to the patterns of changes in multi-year temperature and precipitation amount records, a year is divided into four seasons: spring (March–April–May, MAM), summer (June–July–August, JJA), autumn (September–October–November, SON), and winter (December–January–February of the following year, DJF) [26,53]. We have analyzed the correlation between δ¹⁸O_P and monthly average temperature, monthly precipitation, EZCI in all seasons and monitoring sites with δ¹⁸O_P data covering at least two years (Table 3, Table 4 and Table 5). Figure 4 also shows the correlation coefficients of the above stations in different seasons and all year round.

There is a significant positive correlation between δ¹⁸O_P and monthly average temperature (Figure 4A, Table 3), coincident with the so-called “temperature effect” [5]. On the other hand, the correlation between δ¹⁸O_P and monthly average temperature in spring and autumn was more significant than that in winter and summer (Figure 4A), demonstrating the dominant of “temperature effect” in the spring and autumn (Table 3). The correlation between δ¹⁸O_P and monthly average temperature was weaker in summer, indicating that the effect of temperature on δ¹⁸O_P is relatively imperceptible during this season. This result may be attributed to the strong surface water evaporation in summer, during which more than 80% of precipitation in Siberia originates from snowmelt and subsequent evaporation of soil moisture, resulting in a weak correlation between δ¹⁸O_P and temperature in the summer season [26,31]. Previous research has suggested that the recycled water from land surfaces carrying the isotopic imprint of winter precipitation exerted significant influence on the isotopic composition of regional precipitation [26,29,30,31]. In addition, Kurita [31] noted that the isotope composition of meteoric precipitation in eastern Siberia was controlled by water vapor transportation from the westerly with relatively high δ¹⁸O_P values in summer, and the water vapor may also be mixed with snowmelt water characterized by the lower δ¹⁸O_P value. Because of the influence of changes in the source of water vapor, the temperature effect of δ¹⁸O_P was not obvious in summer. The intense winter inversion disrupts the normal response of moisture content variations to the temperature anomaly. In fact, addition of water vapor and heat from the land surface, and the mixing of air masses of different origin, result in poor winter correlation [14,31].

5.2. Relationship between Precipitation Amount and δ¹⁸O_p

A significant positive correlation was observed between δ¹⁸O_P and monthly precipitation amount at 8 of 13 sites (Table 4, Figure 4B), furthermore, the correlation coefficient between δ¹⁸O_P and monthly precipitation amount (R_P) at every station (Table 4) was less than that between δ¹⁸O_p and monthly average temperature (R_T) (Table 3). A significant correlation exists between monthly average temperature and monthly precipitation amount at each monitoring site (Appendix Table A1). The positive correlation between precipitation amount and δ¹⁸O_P is due to the similar seasonal variation in temperature, precipitation amount, and δ¹⁸O_P (Figure 2), which is statistically positive. While, at seasonal timescale, there is no significant correlation between precipitation amount and δ¹⁸O_P (Table 4). At annual timescale, the positive correlation between δ¹⁸O_P and monthly precipitation amount (Table 4), is contrary to the “amount effect”. So, the “amount effect” is not the dominate reason for the change of δ¹⁸O_P.

5.3. Relationship between EZCI and δ¹⁸O_P

EZCI was used to represent the variation of westerly intensity. When the zonal circulation was strengthened (weakened) in the winter season, the westerly was also strengthened (weakened) as the intensity of warm oceanic air mass from the Atlantic increased (decreased), and the intensity of cold continental air mass decreased (increased), resulting in rising (falling) winter temperatures over Europe and northern Asia. In contrast, when the zonal circulation was strengthened (weakened) as the westerly winds strengthened (weakened) during the summer period, the intensity of cool air mass from the Atlantic Ocean increased (decreased), and the intensity of warm air mass from the continent decreased (increased), resulting in a decrease (increase) in the summer temperatures over Europe and northern Asia [32,54,55]. Our results indicated that the monthly δ¹⁸O_P values at most stations (9 out of 13) were of significant negative correlation with the EZCI (Table 5, Figure 4C). At seasonal timescale, for most of the sites, there is no significant correlation between the δ18OP and EZCI (Table 5).

While, at annual and seasonal timescales, the different correlations between δ¹⁸O_P and EZCI were the result of seasonal variations in temperature and zonal wind. At an annual timescale, EZCI presented seasonal variations opposite to those of the temperature (the EZCI is high in winter and low in summer) (Figure S4, Supplementary Materials). Additionally, δ¹⁸O_P and monthly average temperature were positively correlated at an annual timescale (Table 3, Figure 4A), resulting in a significant negative correlation between δ¹⁸O_P and the EZCI. In winter, when the zonal circulation was stronger (weaker), the westerly wind was strengthened (weakened), and the temperatures over Europe and northern Asia increased (decreased). Therefore, there is a positive correlation between δ¹⁸O_P and EZCI in winter. By contrast, in spring and autumn, a negative correlation was found between δ¹⁸O_P and EZCI due to the increase (decrease) of the westerly wind intensity, and this correlation led to a decrease (increase) in the temperature and δ¹⁸O_P value [54]. In summer, as the EZCI weakened (Figure S4, Supplementary), the Arctic vortex extended toward the south [54], and water vapor from the Arctic and Pacific Oceans mixed with inland local evaporative water, which exerted potential influence on the relationship among the EZCI, δ¹⁸O_P, and temperature. Therefore, the correlation coefficient (R) between δ¹⁸O_P and EZCI exhibited a different trend of variation at each site. For example, in July 1996 and July 1998 (A.D), the temperatures at Bagdarin were similar (16.9 and 16.8 °C, respectively), whereas the δ¹⁸O_P values were quite different (−10.3 and −15.8 (‰, V-SMOW), respectively). This difference can be attributed to an increase in moisture originating from the Pacific Ocean in July 1996 (A.D), resulting in a positive δ¹⁸O_P value (Figure 5A,B). Furthermore, the temperatures at Qiqihar were both 6.2 °C in April 1990 and April 1991 (A.D). The increase in water vapor from the Arctic Ocean led to a lower δ¹⁸O_P value in April 1990 (−18.0 (‰, V-SMOW)) than in April 1991 (−16.2 (‰, V-SMOW)) (Figure 5C,D). In both July 1994 and August 2000 (A.D), the temperature at Ulaanbaatar was 17.1 °C, the δ¹⁸O_P values were −4.0 and −10.4 (‰, V-SMOW), respectively (Figure 5E,F), and the EZCI values were 6.1 and 10.9, respectively. In July 1994 (A.D), the EZCI decreased as the westerly wind weakened, in which inland evaporation played a leading role (Figure 5E). Moreover, warm water vapor from South China also affected the precipitation in the region and led to the enrichment of δ¹⁸O_P in July 1994 (A.D) (Figure 5E). Conversely, in August, 2000 (A.D), almost all water vapor originated from high-latitude areas (i.e., the high latitudes of the North Atlantic and near the Ural Mountains), and the δ¹⁸O_P values were lower (Figure 5F). In July and August 2001 (A.D), the temperatures at Wulumuqi were both 23.4 °C, and the EZCI (6.8) in July was lower than that in August (10.3). The polar air mass invaded Wulumuqi in July, resulting in a lower δ¹⁸O_P value (−7.4 (‰, V-SMOW)) than that in August (−3.9 (‰, V-SMOW)) (Figure 5G,H).

The above results suggested that changes in moisture sources and their relative proportions caused by variations in zonal circulation can be taken as the reason for the complex correlation between EZCI and δ¹⁸O_P in summer, and less obvious temperature effect in summer than that in spring and autumn. 5.4. Inter-Annual Variations of δ¹⁸O_P

The North Atlantic Oscillation (NAO) is named from the apparent dipolar nature of the weather between Greenland and Europe, and the zonal index (ZI) is the name given to the changes in zonal-averaged zonal wind. In fact, they are just different aspects of the same phenomenon [56]. However, the zonal circulation index ignores the effect in north-south oscillation of the westerly. Here, we mainly considered the relationship between δ¹⁸O_P and NAO rather than the Eurasian zonal circulation index (EZCI) on the inter-annual scale. A correlation analysis was conducted between the weighted mean δ¹⁸O in annual precipitation amount (δ¹⁸O_W) and the annual average temperature, annual precipitation, NAO at 20 stations that recorded over 5 years of δ¹⁸O_P data (Table 6).

The δ¹⁸O_W values at Espoo, Krakow, Kuopio, and Salekhard exhibited a significantly positive correlation (p < 0.05) with the average annual temperature, however, no significant correlation was observed at the other stations (Figure 6A, Table 6). The stations north of 60° N exhibited a positive correlation between the annual average temperature and δ¹⁸O_P. In contrast, the correlation coefficient (R_T) was irregular in the region to the south of 60° N (Figure 6A). Therefore, we proposed that the distribution of δ¹⁸O_W at high latitudes over Eurasia reflected the changes of annual average temperature, while the δ¹⁸O_W distribution in the range of 40° N–60° N did not follow the temperature trend at the inter-annual scale. For example, the correlation coefficient (R_T) between δ¹⁸O_W and annual average temperature at Qiqihar was negative, which was attributed to the Pacific Ocean and the effect of the Asian summer monsoon [57]. Moreover, Wulumuqi exhibited a negative correlation coefficient (R_T) between δ¹⁸O_W and annual average temperature due to the north-south swinging of the westerly wind at the inter-annual scale [58]. Therefore, annual mean temperature and δ¹⁸O_W exhibited “temperature effect” at high latitudes (60° N–70° N). However, at an annual scale, the sites located within 40° N–60° N may be situated in a transition zone of “amount effect” or “circulation effect” and “temperature effect”. Therefore, different monitoring sites were controlled by different factors, resulting in complex relationships between δ¹⁸O_W and the annual average temperature [6,42,47,53,59,60,61].

The correlation coefficient between NAO and annual weighted δ¹⁸O_P (δ¹⁸O_W) was not completely consistent with that between temperature and δ¹⁸O_W (Figure 6), which might change with longitude, indicating that the influence of NAO on precipitation isotopes in different regions was not exactly the same (Figure 6C). The reason is that NAO affects the δ¹⁸O_P indirectly by affecting temperature mainly in Europe [37,38,39,41]. From the Ural Mountains (60° E) to the east, the NAO models are different from those in Europe (Figure 6C), which we will discuss in Section 5.5.

5.5. Relationship between NAO and δ¹⁸O_W

The North Atlantic Oscillation (NAO) is one of the most important modes for the variation of atmospheric circulation in the northern hemisphere. In positive NAO state, the surface pressures to the south of 55° N was higher than normal, while a broad region was with low surface pressure throughout the Arctic and Subarctic. It resulted in a strengthening sub-polar westerly [56], bringing about moisture with lower δ¹⁸O_W from high-latitude ocean inland through the British Isles [62]. In negative state (NAO), both the Icelandic low and the Azores high-pressure were weaker than normal, which led to the weakening of the mid-latitude westerly [35]. The weakening of Azores anticyclone made a contribution to the change in the route of westerlies rich of moisture, which moved across the Iberian Peninsula into the Mediterranean and eventually to eastern Europe and Siberia, resulting in higher δ¹⁸O_W values.

Isotopic composition of precipitation is changing in space and time. Therefore, it is important to compare only parallel sets of data. In order to explore the influence of atmosphere circulation on the moisture source and δ¹⁸O_P in Europe and Siberia, we firstly analyzed the data for the period of 1980–1990 (A.D), when the largest number of parallel data was available for all stations (Figure S6, Supplementary Materials, modified by Hatvani et al., [63]. But even during this period, there is still a lot of data missed during 1984–1990 (A.D) (Figure S6, Supplementary Materials, modified by Hatvani et al., [63]. While, the relative complete data during the period of 1980–1983 (A.D) provided an opportunity to evaluate the correlation between the change of atmosphere circulation and δ¹⁸O_P (Figure 7 and Figure 8). From 1980 to 1983, the sites with continuous monitoring data, namely, Arkhangelsk, Kalinin, Pechora, Perm, and St. Petersburg, were selected to investigate the δ¹⁸O_P trends at different sites during this period. Negative δ¹⁸O_P trends were found for all the stations, contrary to the trends for the NAO and EZCI (Figure S5). We arithmetically averaged and combined the δ¹⁸O_P time series from Arkhangelsk, Kalinin, Pechora, Perm, and St. Petersburg (Figure 7A) into a single δ¹⁸O_P sequence (Figure 7B). Additionally, NAOI and EZCI were compared with the δ¹⁸O_P sequence (Figure 7C,D). Evident fluctuations were observed in the δ¹⁸O_P, NAOI, and EZCI curves, which exhibited opposite trends (Figure 7B–D). Taking 1981 (NAOI: −0.21) and 1982 (NAOI: 0.43) as examples for comparison, when the NAO was negative (1981), the westerly wind (from ~45° N–55° N) passing by western Europe turned northeast at 30° E (Figure 8A), brought warmer water vapor from the southwest to the northeast, resulting in higher δ¹⁸O_W values. When the NAO was positive (1982), the westerly strengthened, migrating toward the north (~50° N–60° N) and generally moved in the zonal direction. Therefore, the δ¹⁸O_W values exhibited a relatively low trend (Figure 8B). Moreover, the NAO influenced the changes of δ¹⁸O_W by changing the intensity and pathway of the westerly [38,56,64]. Therefore, we speculate that over middle to high-latitude regions throughout Eurasia δ¹⁸O_W was affected by both pathways of the westerly and the temperature. This joint influence was the main reason for the absence of “temperature effect” in the variations of δ¹⁸O_W at the inter-annual time scale. It is worthy to point out that all the deductions in this work were based on limited available data from GNIP. More continuous and long tern monitoring data throughout the vast Eurasia continent are essential to improve the understanding about the climatic significance of δ¹⁸O_P in this region.

6. Conclusions In this study, 29 GNIP stations situated throughout middle and high latitudes of Eurasia were selected for analysis in combination with the changes in atmospheric circulation patterns at middle to high latitudes, and the conclusions are as follows: (1) At monthly and seasonal time scales, the summer δ¹⁸O_P values were higher, and the winter δ¹⁸O_P exhibited lower values, which was in coincidence with “temperature effect”. At annual time scale, average temperature and δ¹⁸O_W exhibited “temperature effect” at high latitudes (60° N–70° N), but the “temperature effect” was not observed in the region over 40° N–60° N. (2) The δ¹⁸O_P values were significantly positive-correlated with monthly precipitation at monthly time scale, which was contrary to “amount effect”. This phenomenon was attributed to the similar seasonal change between monthly average temperature and monthly precipitation amount in the study area. In comparison, no significant correlation was observed between δ¹⁸O_W and local precipitation amount. (3) The δ¹⁸O_P values were negatively correlated with the EZCI at monthly time scale. The zonal circulation resulted in changes in δ¹⁸O_P throughout Eurasia by exerting impact on local temperature and water vapor source. The relationship among δ¹⁸O_P, the temperature, and the EZCI presented seasonal variations. (4) The δ¹⁸O_P values in the study region and the NAOI exhibited opposite trends on inter-annual timescale. The NAO can change the source of water vapor transported by the westerly, leading to changes in δ¹⁸O_P and δ¹⁸O_W. (5) Based on above-mentioned findings, we proposed that reconstructing patterns of past atmospheric circulation and changes in water moisture sources via δ¹⁸O proxy in geologic archives (e.g., cave speleothems) within this region may be informative.

Figure 1. Locations of the GNIP (Global Network of Isotopes in Precipitation) stations in Siberia and Central Asia (red triangles) and Europe (purple triangles) in this study.

Figure 2. Basic climate background for the 15 GNIP meteorological stations in the Siberia and Central Asia region referred in this study, including monthly mean precipitation amount (blue curve and axis in mm/month), monthly mean air temperature (red curve and axis in °C), and δ18O of monthly precipitation (black curve and axis in , V-SMOW). X-axes (1-12) denote months (January-December). Although the elevation, geographic location, moisture sources, and precipitation amount seasonality may be quite different, the seasonal variation of temperature and δ18OP are broadly similar in the Siberia and Central Asia region.

Figure 3. LMWL (local meteoric water line; red line) based on the δ18O and δ2H values (red dots) of precipitation from 15 stations in Central Asia and Siberia. The GMWL (global meteoric water line; blue line; [48]) is plotted for comparison.

Figure 4. Linear correlation coefficient between air temperature (RT)(A), precipitation amount (RP)(B), EZCI (RZ)(C), and δ18OP (calculated for every two consecutive years for GNIP stations with records longer than 2 years) plotted in different seasons and all year round (DJF, MAM, JJA, SON, and ALL). The diamonds represent correlation coefficients that are significantly lower than 0.05 (i.e., higher significance) and circles represent insignificant correlations greater than 0.05. Red means positive correlation and blue means negative correlation. (For details see Table 3, Table 4 and Table 5).

Figure 5. Months with similar average monthly temperatures and with significantly different δ18OP values were compared to present the influence of the water vapor source on the change of precipitation δ18O. The cluster of back trajectories presented for Bagdarin (A) and (B), Qiqihar (C) and (D), Ulaanbaatar (E) and (F), and Wulumuqi (G) and (H) in different months. The bold black numbers in the figure are the monthly δ18OP values (, V-SMOW). The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used in the backward tracking of air parcels for the above sites. The air mass history was calculated at 1000 m a.g.l. (above ground level) over the previous 240 h. Each dot represents the location of the air parcel at 12-h intervals. The model is available from the NOAA Air Resources Laboratory at http://ready.arl.noaa.gov/HYSPLIT.php.

Figure 6. Distribution of the correlation coefficients between annual average temperature (A), annual precipitation amount (B), NAOI (North Atlantic Oscillation index) (C), and δ18OW, respectively. Pink means positive correlation and cyan means negative correlation. The size of the circle indicates the absolute value of the correlation coefficient. Values significant at 0.05 levels are marked by a plus/minus sign. (Details refer to Table 6).

Figure 7. Time series (1980-1983) of (A) δ18OP from Arkhangelsk (green dot), Kalinin (brown dot), Pechora (purple triangle), Perm (yellow triangle), and St. Petersburg (blue square). (B) Mean values of δ18OP from Arkhangelsk, Kalinin, Pechora, Perm, and St. Petersburg. (C) NAOI [56] and (D) EZCI [32]. The dashed lines in (B), (C), and (D) indicate the long-term trends.

Figure 8. Spatial distribution of δ18OW values in the precipitation at the GNIP stations in different NAO phases. The circled numbers (1-5) indicate ① St. Petersburg, ② Kalinin, ③ Arkhangelsk, ④ Perm, and ⑤ Pechora. The average atmospheric moisture transport fluxes (vectors; unit: kg/(m × s)) in different NAO phases are indicated: (A) 1981 (A.D) (NAOI = -0.21) and (B) 1982 (A.D) (NAOI = 0.43). The δ18OW values change due to different water vapor transport sources in different NAO phases.

Site	Latitude (°N)	Longitude (°E)	Altitude (m)	Annual Precipitation (mm)	δ18Ow (‰, V-SMOW)	Annual Average Temperature (°C)	Monitoring Period (A.D)	n
Amderma	69.77	61.68	53	443	−15.5	−7	1980–1990	74
Murmansk	68.97	33.05	46	489	−12.5	0.2	1980–1990	71
Olenek	68.5	112.43	220	300	−18.7	−11.8	1996–2000	36
Salekhard	66.53	66.67	16	446	−16.5	−6.2	1996–2000	58
Rovaniemi	66.5	25.76	107	517	−13.2	2.3	2004–2014	128
Pechora	65.12	57.1	56	578	−15	−1.8	1980–1990	79
Arkhangelsk	64.58	40.5	13	548	−12.9	1	1980–1986	78
Kuopio	62.89	27.63	116	670	−12.8	7.8	2005–2015	132
Khanty-Mansiysk	60.97	69.07	40	563	−11.6	−1.3	1996–1997	13
Espoo	60.18	24.83	30	706	−11.4	6.6	2001–2015	182
St. Petersburg	59.97	30.3	4	664	−11.5	5.6	1980–1989	107
Kirov	58.65	49.62	164	653	−13.4	7.1	1980–2000	102
Enisejsk	58.45	92.15	78	491	−13.3	−1.5	1990	12
Perm	58.01	56.18	161	616	−12.5	2.1	1980–1991	79
Riga	56.97	24.07	3	661	−9.7	5.6	1980–1988	98
Kalinin	56.9	35.9	31	649	−11.8	8	1881–1988	94
Moscow	55.75	37.57	157	695	−11.1	5.4	1970–1979	64
Barabinsk	55.33	78.37	120	371	−12.4	1.1	1996–2000	28
Novosibirsk	55.03	82.9	162	422	−14.6	0.9	1990	12
Omsk	55.01	73.38	94	401	−13.5	1.6	1990	8
Arkona	54.68	13.43	42	564	−8.9	9	1998–2007	134
Bagdarin	54.47	113.58	903	438	−13.7	−5.3	1996–2000	34
Greifswald	54.1	13.41	2	635	−8.6	9.2	2003–2013	132
Berlin	52.47	13.4	48	574	−8	10	1978–2012	419
Irkutsk	52.27	104.35	485	445	−12.4	0	1971–1990	14
Krakow	50.06	19.85	205	647	−9.1	8.4	1975–2016	489
Ulaanbaatar	47.93	106.98	1338	249	−8.5	−0.3	1998–2001	44
Qiqihar	47.38	123.92	147	581	−10.6	4.3	1988–1992	50
Wulumuqi	43.78	87.62	918	303	−10.6	7.5	1986–2003	131

Site	LMWL	n
Amderma	δ2H = 7.62 δ18O + 6.86	31
Bagdarin	δ2H = 7.84 δ18O − 0.18	34
Olenek	δDδ2H = 7.77 δ18O − 2.94	36
Pechora	δDδ2H = 7.89 δ18O + 8.14	36
Perm	δDδ2H = 8.00 δ18O + 13.43	38
Qiqihar	δDδ2H = 7.59 δ18O − 0.14	50
Salekhard	δDδ2H = 7.86 δ18O + 1.21	58
Ulaanbaatar	δDδ2H = 7.82 δ18O + 1.52	44
Wulumuqi	δDδ2H = 6.98 δ18O + 0.43	131

Site	DJF	MAM	JJA	SON	ALL	n
Amderma	0.32	0.49 *	0.08	0.62 **	0.75 **	72
Bagdarin	——	0.82 *	0.17	0.92 **	0.90 **	34
Barabinsk	0.49	0.96 **	0.70	0.97 **	0.96 **	28
Enisejsk	——	——	——	——	0.88 **	12
Khanty-Mansiysk	——	——	——	——	0.94 **	13
Novosibirsk	——	——	——	——	0.88 **	12
Olenek	0.69 *	0.91 **	0.12	0.77	0.94 **	35
Pechora	0.50 *	0.57 **	0.55 *	0.64 **	0.85 **	79
Perm	0.49 *	0.79 **	0.31	0.42	0.79 **	79
Qiqihar	0.51	0.75 **	0.17	0.46	0.76 **	50
Salekhard	0.04	0.72 **	0.54 *	0.90 **	0.88 **	58
Ulaanbaatar	0.41	0.89 *	0.10	0.93 **	0.84 **	44
Wulumuqi	——	0.71 **	0.08	0.67 **	0.86 **	123

* Denotes a statistically significant relationship at p < 0.05. ** Denotes a statistically significant relationship at p < 0.01. DJF: December–January–February; MAM: March–April–May; JJA: June–July–August; SON: September–October–November; All: the entire year.

Site	DJF	MAM	JJA	SON	ALL	n
Amderma	0.13	−0.30	−0.16	−0.05	−0.01	72
Bagdarin	——	0.42	−0.33	0.38	0.48 **	34
Barabinsk	−0.20	0.23	0.02	0.41	0.35	28
Enisejsk	——	——	——	——	0.59 *	12
Khanty-Mansiysk	——	——	——	——	0.88 **	13
Novosibirsk	——	——	——	——	0.40	12
Olenek	−0.19	0.54	0.02	0.14	0.60 **	36
Pechora	−0.21	0.13	−0.13	−0.37	0.19	79
Perm	−0.09	−0.15	−0.35	0.55 *	0.15	79
Qiqihar	−0.25	0.16	−0.11	0.48	0.47 **	50
Salekhard	−0.32	−0.11	−0.29	0.05	0.30 *	58
Ulaanbaatar	0.49	0.46	−0.21	0.70 *	0.63 **	25
Wulumuqi	−0.02	0.03	−0.30	0.10	0.34 **	123

* Denotes a statistically significant relationship at p < 0.05. ** Denotes a statistically significant relationship at p < 0.01. DJF: December–January–February; MAM: March–April–May; JJA: June–July–August; SON: September–October–November; All: the entire year.

Site	DJF	MAM	JJA	SON	ALL	n
Amderma	0.09	−0.55 *	0.27	−0.43	−0.37 **	74
Bagdarin	——	0.07	0.19	−0.04	−0.50 **	34
Barabinsk	0.74 *	−0.33	−0.41	0.87 *	−0.50 **	28
Enisejsk	——	——	——	——	−0.53	12
Khanty-Mansiysk	——	——	——	——	−0.41	13
Novosibirsk	——	——	——	——	−0.56	12
Olenek	0.20	−0.46	−0.13	−0.74	−0.70 **	36
Pechora	−0.52 *	−0.03	−0.55 *	−0.41	−0.59 **	79
Perm	0.18	−0.43	0.15	−0.40	−0.37 **	79
Qiqihar	0.17	−0.13	−0.10	−0.62 *	−0.44 **	50
Salekhard	0.11	−0.36	−0.45	−0.34	−0.54 **	58
Ulaanbaatar	−0.42	0.39	0.12	0.36	−0.19	26
Wulumuqi	0.06	0.11	0.12	−0.42 *	−0.51 **	131

* Denotes a statistically significant relationship at p < 0.05. ** Denotes a statistically significant relationship at p < 0.01. DJF: December–January–February; MAM: March–April–May; JJA: June–July–August; SON: September–October–November; All: the entire year.

Site	RT	RP	RN	Years (A.D)	n
Amderma	0.31	−0.37	−0.64	1980–1990	5
Arkhangelsk	0.70	0.35	0.00	1980–1986	7
Arkona	−0.02	−0.62	−0.26	1998–2007	10
Berlin	0.33	−0.16	−0.05	1978–2012	35
Espoo	0.67 **	0.14	0.52 *	2001–2015	15
Greifswald	0.25	0.01	0.15	2003–2013	11
Kalinin	−0.03	−0.02	0.56	1881/1988	7
Kirov	0.50	−0.52	0.05	1980/2000	9
Krakow	0.43 **	−0.12	0.25	1975–2016	42
Kuopio	0.73 *	0.45	0.62 *	2005–2015	11
Moscow	0.43	−0.38	−0.07	1970/1979	6
Murmansk	0.23	0.47	0.73 *	1980/1990	8
Pechora	0.61	−0.43	−0.89 *	1980–1990	6
Perm	−0.56	−0.02	−0.46	1980–1990	5
Qiqihar	−0.63	−0.47	0.50	1988–1992	5
Riga	−0.05	−0.10	0.06	1980–1988	8
Rovaniemi	0.14	0.60	0.44	2004–2014	11
Salekhard	0.95 *	−0.21	0.51	1996–2000	5
St. Petersburg	0.27	−0.04	0.13	1980–1989	9
Wulumuqi	−0.29	0.27	−0.36	1986–2003	12

* Denotes a statistically significant relationship at p < 0.05. ** Denotes a statistically significant relationship at p < 0.01. Most of the monitoring stations (14 out of 20) with more than 5 years’ data exhibited an insignificant negative relationship between δ18OW and annual precipitation amount (Figure 6B, Table 6). This lack of significance may be due to the limited amount of observation data. Nevertheless, based on the limited observational data available, we can make a preliminary judgment that the precipitation amount at the inter-annual scale may not act as the principal influencing factor for the δ18OW values of the middle to high-latitude regions of Eurasia.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4441/12/8/2132/s1, Figure S1: The backward trajectories at Bagdarin (A) and (B), Qiqihar (C) and (D), Ulaanbaatar (E) and (F) and Wulumuqi (E) and (F) in different months. The bold black numbers in the figure are the monthly δ18OP values (, V-SMOW). The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for the backward tracking of air parcels at the above sites. The air mass history was calculated at 500 m a.g.l. (above ground level) over the previous 240 h. Each dot represents the location of the air parcel at 12-h intervals. The model is available from the NOAA Air Resources Laboratory at http://ready.arl.noaa.gov/HYSPLIT.php; Figure S2: The backward trajectories at Bagdarin (A) and (B), Qiqihar (C) and (D), Ulaanbaatar (E) and (F) and Wulumuqi (E) and (F) in different months. The bold black numbers in the figure are the monthly δ18OP values (, V-SMOW). The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for the backward tracking of air parcels at the above sites. The air mass history was calculated at 1500 m a.g.l. (above ground level) over the previous 240 h. Each dot represents the location of the air parcel at 12-h intervals. The model is available from the NOAA Air Resources Laboratory at http://ready.arl.noaa.gov/HYSPLIT.php; Figure S3: The backward trajectories at Bagdarin (A) and (B), Qiqihar (C) and (D), Ulaanbaatar (E) and (F) and Wulumuqi (E) and (F) in different months. The bold black numbers in the figure are the monthly δ18OP values (, V-SMOW). The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for the backward tracking of air parcels at the above sites. The historical air mass was calculated at 2000 m a.g.l. (above ground level) over the previous 240 h. Each dot represents the location of the air parcel at 12-h intervals. The model is available from the NOAA Air Resources Laboratory at http://ready.arl.noaa.gov/HYSPLIT.php; Figure S4: Annual variations in the Eurasian Zonal Circulation Index (EZCI, 1951-2017). The boxes represent the 25th-75th percentiles, and the line through each box represents the median. The whiskers indicate the 90th and 10th percentiles, and the points above and below the whiskers indicate the 95th and 5th percentiles, respectively; Figure S5: Time series (1980-1983) of the δ18OP at (A) Arkhangelsk, (B) Kalinin, (C) Pechora, (D) Perm, and (E) St. Petersburg and time series (1980-1983) of (F) the NAOI; Figure S6: Number of δ18OP records obtained from the GNIP stations (A). Time series of the δ18OP (1980-1990) (B).

Author Contributions

T.W. and T.-Y.L. designed the research. T.W. wrote the original draft, and T.-Y.L. revised it. Data curation, J.Z. and Y.W.; Investigation, C.-J.C.; Methodology, S.-Y.X.; Project administration, R.H.; Software, Y.W.; Supervision, J.-Y.L. and T.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (NSFC, No. 41772170; 42011530078), and the Fundamental Research Funds for the Central Universities, China (No. XDJK2020D005), and the Russian Foundation for Basic Research (RFBR, No. 20-55-53015/20) to T.A.B.

Acknowledgments

The authors wish to express their gratitude for mentorship to T.-Y.L. who made a great contribution to this article. Special thanks to Tatiana Artemevna Blyakharchuk. The authors thank her for the useful discussions. The authors wish to thank anonymous reviewers and guest editors for their very useful comments which have helped to improve the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table

Table A1.Correlation coefficients between the temperature and rainfall amount based on monthly data (RTP).

Table A1.Correlation coefficients between the temperature and rainfall amount based on monthly data (RTP).

Site	R(TP)	n
Amderma	0.20 *	122
Bagdarin	0.67 **	55
Barabinsk	0.34 **	60
Enisejsk	0.47 **	128
Khanty-Mansiysk	0.49 **	60
Novosibirsk	0.67 *	12
Olenek	0.62 **	59
Pechora	0.38 **	129
Perm	0.30 **	209
Qiqihar	0.66 **	52
Salekhard	0.58 **	191
Ulaanbaatar	0.59 **	114
Wulumuqi	0.41 **	152

* Denotes a statistically significant relationship at p <0.05. ** Denotes a statistically significant relationship at p <0.01.

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