Modern isotope climatology of Russia: A first assessment

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi:10.109/003jd003404, 004 Modern isotope climatology of Russia: A first assessment Naoyuki Kurita, 1 Naohiro Yoshida,,3 Gen Inoue, 4 and Eleonora A. Chayanova 5 Received 11 January 003; revised July 003; accepted 0 October 003; published 5 February 004. [1] The spatial and temporal variation in the stable isotopic composition of precipitation collected at 13 monitoring stations across Russia between 1996 and 000 was determined. The results show that eastward moisture transport over the continent generates a tendency toward more negative isotopic content farther inland throughout the year. This negative isotopic gradient can be explained by the gradual rain-out of moist, oceanic air masses, which are transported inland by westerly winds. In summer, however, the isotopic gradient is less clear, because of additional moisture that is supplied from land surfaces. The isotopic pattern of summer precipitation is less sensitive to moisture content but is largely influenced by the original moisture. In Siberia, more than half of the moisture that forms summer precipitation originates from land surfaces; thus the isotopic content of precipitation in this region is controlled mainly by the contribution of recycled water; e.g., the proportion of water that is recycled (recycling ratio) and its isotopic composition. Comparisons of the observed summer isotopic content of precipitation and the calculated recycling ratio from National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data show that about 0% of the total variability of isotopic content during summer can be linked to the recycling ratio. About 45% of the summer isotopic variability cannot be explained by either temperature, which is used as an indicator of moisture content, or the recycling ratio. This remaining variability may be linked to the isotopic variability of the recycled water that falls as observed precipitation. It may be that the isotopic content of recycled water varies in space and time and the isotopic distribution in summer precipitation reveals details in this feature; thus it might be possible to deduce information about the interaction between land and atmosphere in the hydrologic cycle from the isotopic content of precipitation. INDEX TERMS: 1040 Geochemistry: Isotopic composition/chemistry; 33 Meteorology and Atmospheric Dynamics: Land/atmosphere interactions; 3354 Meteorology and Atmospheric Dynamics: Precipitation (1854); KEYWORDS: precipitation, stable isotope, water recycling Citation: Kurita, N., N. Yoshida, G. Inoue, and E. A. Chayanova (004), Modern isotope climatology of Russia: A first assessment, J. Geophys. Res., 109,, doi:10.109/003jd Introduction 1 Frontier Observational Research System for Global Change, Yokohama, Kanagawa, Japan. Interdisciplinary Graduate School of Science and Engineering and Frontier Collaborative Research Center, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan. 3 Also at SORST Project, Japan Science and Technology Corporation, Kawaguchi, Saitama, Japan. 4 National Institute for Environmental Studies, Tukuba, Ibaraki, Japan. 5 Central Aerological Observatory, Moscow, Russia. Copyright 004 by the American Geophysical Union /04/003JD [] The International Atomic Energy Agency (IAEA), in cooperation with the World Meteorological Organization (WMO), has surveyed the isotopic composition of monthly precipitation at 550 meteorological stations since This global data set reveals general temporal and spatial features of the isotopic composition [Birks et al., 00]. The global distribution of isotopes is related to geographical and meteorological parameters at the observation site (including latitude, altitude, distance from the continent edge, surface temperature, and precipitation amount) [Dansgaard, 1964; Yurtsever and Gat, 1981; Rozanski et al., 1993; Araguás-Araguás et al., 1998]. These parameters can be reproduced by global-scale general circulation models (GCMs) [Joussaume et al., 1983; Jouzel et al., 1987, 000; Hoffmann et al., 1998; Cole et al., 1999; Mathieu et al., 00]. IAEA/WMO observations from the past four decades, which are summarized in the Global Network of Isotopes in Precipitation (GNIP) data set [International Atomic Energy Agency/World Meteorological Organization (IAEA/WMO), 1998] (available at gnipmain.html), have been used to validate global model predictions and to calibrate isotopic indicators of past climates. [3] The distribution of the global isotopic coverage in the IAEA/WMO network is sparse over some regions. For example, data over Siberia (central and eastern Eurasia) consist of information from just a few stations, with coverage of a year or less (Figure 1). Recently, Saurer et al. [00] showed that tree ring data could be used to track d 18 O across Eurasia. They found that it decreased to the east. Tree ring 1of15

2 Figure 1. Geographical distribution of the Siberian Network of Isotopes in Precipitation (SNIP) stations (circles) and IAEA/WMO stations (crosses). Only IAEA/WMO stations with observation periods exceeding 1 year are shown. d 18 O is influenced by the isotopic composition of water absorbed by roots and modulated by evaporation from the leaf surface, which is related to air humidity. Thus isotopic depletion depends on spatial variations in the isotopic composition of precipitation and the residence time of rainwater in the soil. However, a lack of observational isotopic data for precipitation has precluded further quantitative discussion on how the seasonal isotopic variation of precipitation contributes to isotopic variation in tree rings in each region. This lack of isotopic precipitation observations will be rectified by a newly established observation network in Russia. [4] Hydrologic studies of the Eurasian continent have focused on land-surface-atmosphere interactions and the corresponding impact on the changing hydrological cycle. As part of the continental water circulation, external moisture precipitates onto the surface, and some of this moisture re-evaporates from the land surface before falling again as precipitation over the surrounding continental area. This precipitation recycling process is important in the hydrological cycle and may regulate the balance between precipitation and evaporation over the continent. The precipitation recycling ratio R is defined as the relative contribution to the precipitation from the water evaporated from the land surface. Atmospheric data analyses [Budyko, 1974; Shiklomanov, 1989; Brubaker et al., 1993] and GCM experiments [Numaguti, 1999] have led to a better understanding of the hydrological circulation over Eurasia. [5] The stable isotopes (dd and d 18 O) are additional tools that may be used to evaluate water recycling. Although isotopes in water are usually used to indicate temperature, such isotopes are also influenced by the history of the water, which includes evaporation from ocean, precipitation amount, and moisture input into the atmosphere from the land surface (so-called recycled water). Generally, the heavier isotopes in precipitation are depleted with each condensation, because the vapor pressures of heavy water isotopes (HDO, H 18 O) are lower than that of H 16 O. The progressive removal of atmospheric moisture through condensation depletes the heavy-isotope content as the air travels from ocean to inland regions. This depletion, however, is affected by mixing of atmospheric moisture that originates from the land surface. Salati et al. [1979] suggested that the very small d 18 O gradient observed in the Amazon Basin is a result of the large contribution of evapotranspiration to precipitation. Rozanski et al. [198] examined isotopic depletion of dd in Europe and concluded that the isotopic depletion observed in summer, which is relatively smaller than implied by the degree of rain-out of moisture with fractionation, occurs because 35% of the evaporation in the summer is of water that fell on the continent in winter. In GCM studies, Cole et al. [1993] found a negative correlation between d 18 O and the recycling ratio in tropical South America. Furthermore, Koster et al. [1993] showed that global summer spatial isotopic variations not explained by temperature can be interpreted as an effect of precipitation recycling. Although the relationship between water recycling and isotopic ratio are not definitively discussed in these studies, the results suggest that isotopic variation over the continent during summer is influenced by isotopic composition and the water recycling ratio. [6] This study focuses on the spatial and temporal characteristics of isotopic variation over Russia and how the recycling process contributes to summertime spatial isotopic variation over the region. A new recycling model has been designed to achieve this goal. The model estimates the recycling ratio for observed monthly precipitation at each station by using atmospheric data sets (see Appendix A), and compares the estimated recycling ratios with the observed isotopic contents of precipitation. From the comparison, factors controlling the observed isotopic composition in summer can be clarified and the advantages of using isotopes in studies of the continental hydrologic cycle can be discussed.. Atmospheric Circulation and Climate [7] In this section, reanalysis data sets are used to describe seasonal variations in moisture transport and di- of15

3 vergence, evaporation from the land surface, and moisture content (as measured by precipitable water PW)..1. Meteorological Data Set [8] Moisture flux, moisture flux divergence, and moisture content were calculated using reanalysis data sets from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR), which are produced four times daily. These data sets have a T6 horizontal spectral resolution that is equivalent to a Gaussian grid with 19 longitude 94 latitude points, and 8 vertical s levels [Kalnay et al., 1996]. Moisture flux and moisture content are integrated vertically from the surface to the top of the atmosphere. The calculated divergence fields were converted to.5 longitude-latitude grids using a spherical harmonic expansion [Masuda et al., 001]. Divergence fields were smoothed because they contained a wavy structure associated with the spherical harmonics. [9] The evaporation field is estimated by the residual technique, based on the moisture budget of the atmospheric column. Thus the evaporation flux is equal to the difference between moisture divergence and precipitation. Precipitation data from surface gauges and satellite-based estimation that was presented in the Xie-Arkin Center- Merged Analysis of Precipitation (CMAP) [Arkin and Xie, 1994, Xie and Arkin, 1996, 1997] was used to calculate the moisture budget. Changes in the total moisture stored in the atmospheric column, which are simply the changes in precipitable water, were also considered in calculating the moisture budget, but such changes were small as compared to the above fluxes. Although errors that arise from the difference between the above data sets and reality yield uncertainties in the estimated evaporation data, this residual technique is often used in hydrologic budget studies [e.g., Serreze et al., 00; Fukutomi et al., 003]... Moisture Transport and Its Distribution [10] Figure shows average moisture flux and moisture flux divergence in December, January, and February (DJF), March, April, and May (MAM), June, July, and August (JJA), and September, October, and November (SON), for The dominant features are strong westerly moisture transport over the continent throughout the year, and large moisture divergence over the western part of the continent in summer. In each season, moisture flux decreases because of precipitation as distance from the coast increases. However, moisture flux increases throughout the divergence region in the summer because moisture enters the atmosphere by evaporation from the land surface and by transpiration from plants. [11] Figure 3 shows estimated evaporation fields over the continent for each season from 1996 to 000. Strong seasonal variability is evident. Continental evaporation rates are small (<0.5 mm d 1 ) in winter; however, they exceed mmd 1 in summer. [1] Lydolph [1977] summarized this seasonal variation by considering the radiation balance. Almost all of Russia has a net radiation loss during winter because of weak insolation, short daylight hours and high reflectivity from snow. Thus there is little evaporation from the land surface. Figure. Seasonal mean moisture transport from the NCEP reanalysis for Areas of moisture flux divergence are shaded. As winter ends, insolation intensifies and daylight hours lengthen. The radiation balance becomes positive as temperatures increase. During early spring, excess radiation melts snow. Once the snow melts, excess radiation drives evaporation. Maximum latent heat flux occurs in early summer, when plant growth is most vigorous and soil moisture is widely available. [13] The maximum evaporation flux and moisture divergence over Russia underscore the importance of surface evaporation to summer precipitation. This is especially true in western Russia (Figures and 3) [Serreze et al., 00; Fukutomi et al., 003]. GCM experiments also reveal that the water for summer pre- 3of15

4 Figure 3. Seasonal mean evaporation from 1996 to 000 using residuals from the divergence of total moisture transport and precipitation. The precipitation is evaluated using the CMAP estimation. Evaporation exceeding mmd 1 is shaded. Contour interval of 1 mm d 1. Figure 4. Seasonal mean atmospheric moisture content (precipitable water) from 1996 to 000 from the NCEP reanalysis data. Values over 16 mm are shaded. Counter interval is 8 mm. cipitation over Russia is supplied mostly by evaporation from the land surface: recycled moisture contributes 80% of the water vapor for summer precipitation [Koster et al., 1986, 1993; Numaguti, 1999]. [14] Moisture is transported from west to east. It is removed by precipitation en route, and supplemented by evaporation from underlying land surfaces and by transpiration from surface vegetation. The distribution of moisture content (Figure 4) therefore depends on the balance of these factors. For most of the year, moisture content decreases from west to east, as water rains out of the atmosphere. However, in the summer, evaporation from land replenishes the moisture in the atmosphere, resulting in nearly constant moisture content over the continent. 3. Isotopic Data 3.1. Sampling and Analysis [15] The Siberian Network of Isotopes in Precipitation (SNIP) observation program gathered monthly precipitation samples at 13 meteorological stations over Russia from January 1996 to December 000. Collection sites are shown in Figure 1. At each station, after routine measurements of 4of15

5 Figure 5. The dd d 18 O relationship in precipitation, based on all observed monthly samples for SNIP stations. Crosses, monthly precipitation less than 10 mm; circles, monthly precipitation exceeding 10 mm. under the GMWL, even though monthly precipitation exceeded 10 mm. Except for the light precipitation samples, the local dd d 18 O relationship is dd =7.9d 18 O+.9%. The intercept difference in the present study is about 7% less than the GMWL. This smaller intercept is consistent with the inclusion of errors caused by evaporation after sampling, and implies an increase in dd of about 5%. [19] Present monthly results are compared with observations from individual rainfall events at Yakutsk in May August 000 [Kurita et al., 003] to assess the evaporation effect quantitatively. The monthly SNIP results were 0.3.0% for d 18 O and 0 8% for dd heavier than results from the daily rainfall computations of isotopic composition. This difference yields a 5 8% decrease in deuterium excess. The error magnitude is similar to that predicted by the observed small intercept and agrees with the range of the spatial or temporal distribution of deuterium excess from the observations. Hence we concluded that it is difficult to speculate on the spatial pattern or seasonal variation of deuterium excess from these data, and we used deuterium excess only as a check for data quality. In this study, the focus is on the characteristics of the dd variation, which is less sensitive to evaporation effects than d 18 O. precipitation amounts, the collected monthly precipitation was stored in a large bucket covered by a lid to prevent evaporation. In the case of snow samples, melted samples were stored in the covered bucket. Each month s precipitation was transferred to 500-ml plastic bottles, and shipped to the Tokyo Institute of Technology (TITech). [16] Precipitation samples were analyzed for d 18 O and dd using a mass spectrometer (Thermoquest-MAT5) at TITech. The H O-CO equilibrium method for d 18 O analysis, and H O-H equilibrium with a Pt catalyst for dd analysis were used; analytical precision was consistently within 0.1% for d 18 O and 1% for dd. 3.. Sample Quality [17] Samples at stations with scant monthly precipitation may have been affected by evaporation before the isotopic analysis, because of the large head space in the buckets that were used to store the monthly precipitation, and the long storage period prior to analysis. Figure 5 shows the relationship between monthly d 18 O and dd in precipitation, derived for all SNIP stations, which can be used to estimate the effect of evaporation on samples. The global relationship between d 18 O and dd in meteoric waters, the Global Meteoric Water Line (GMWL), was established by Craig [1961] as dd =8d 18 O+10% from GNIP/WMO observations. Evaporation shifts the isotopic values under the GMWL, and lowers deuterium excess values (d = dd 8 d 18 O). Deuterium excess in present-day precipitation typically is positive, on the basis of IAEA studies and other precipitation samples. However, in the SNIP observations, negative deuterium excess occurred if monthly precipitation was less than 10 mm, which was not enough to fill the 500-ml plastic bottles sufficiently (see Figure 5). Therefore samples with little precipitation were considered contaminated by evaporation and were ignored. [18] Errors associated with evaporation after precipitation remain in the data. Figure 5 shows many samples plotted 4. Results 4.1. Isotopic Relationships [0] SNIP observations from 1996 to 000 yielded the weighted mean annual dd and d 18 O in precipitation and the annual means for meteorological data that are summarized in Table 1. Weighting is with respect to the monthly precipitation amount. At Ayon, where there was little meteorological data, the isotopes are shown only as arithmetic mean values. Observations were recorded at most stations throughout the 5 years from 1996 to 000. For a few stations, however, only limited monthly isotopic data were available, either because the samples were affected by strong evaporation of the sample prior to analysis, in which case the samples were ignored, or because the samples did not arrive at the laboratory. [1] Table summarizes the isotopic relationships in winter (DJF) and summer (JJA). These relationships are derived from linear regressions of the seasonal mean isotopic data from 1996 to 000 at each observation site, except at Ayon, which lacked meteorological data. The relationships describe the seasonal and spatial distribution of isotopes over the continent. [] The dd-d 18 O relationship in winter in this study closely agrees with the MWL, but the relationship in summer does not. The intercept for summer data in this study is about 5% lower than in the MWL. As described in the above section, partial evaporation of the collected samples would lower the intercept value, so we can conclude that post-sampling evaporation affected summer data more than winter data. [3] Precipitation isotopic variability in midlatitudes and high latitudes is correlated strongly with temperature, as shown in the multidecadal GNIP network. The relationship between dd of precipitation and the surface temperature T was first noted by Dansgaard [1964], and occurs because the degree of rain-out from the atmosphere through condensation is highly correlated with the condensation tem- 5of15

6 Table 1. Summary of Annual Weighted Mean Isotope and Meteorological Data for SNIP Stations a Station Location Altitude, m asl Rainfall Amount, mm Mean Air Temperature, C dd w, % d 18 O w, % n Kandalaksa 3.35 E, N Salekhard E, N Olenek E, N Yakutsk E, 6.08 N Ayon E, 67.5 N b 1.09 b 0 Kursk E, N Astrakhan E, 46.7 N Kirov E, N Khanty-Mansiysk E, N Barabinsk E, N Bagdarin E, 54.6 N Terney E, N P.-Kamchatskiy E, 5.97 N a Here, m asl, meters above sea level; n, number of samples. b Arithmetic mean value. perature (see, e.g., Jouzel et al. [1997] for a review). Table shows the dd-t relationship from the SNIP data set for Russian stations and from the GNIP data set for European stations. Corresponding regression lines for each region have different slopes although the tendency for a decrease in correlation coefficient and slope from winter to summer is common. Siegenthaler and Matter [1983] examined the spatial relationship between dd and T for European stations and explained the linear relationship by rain-out effects from source moisture advected from the Atlantic Ocean, except for summer. Hence the difference in the winter slopes of the SNIP data and the European GNIP data may reflect the different origins of the air masses precipitating in each region. The agreement of the winter slope in the SNIP data with northern European GNIP stations located between 50N and 60N supports the above explanation. The smaller slope and correlation of the spatial dd-t relationship in summer results from weak horizontal temperature gradients and a decreased effect of rain-out because of evaporation from the land surface. The SNIP network extends from midlatitudes to high latitudes, and there is a significant temperature distribution in the summer. This temperature difference between SNIP stations may result in higher summer slopes than at European stations, although other effects may also influence the difference. 4.. Spatial Isotopic Distribution [4] Figure 6 shows the spatial distributions of weighted mean dd and d 18 O for precipitation throughout Russia in winter (DJF) and summer (JJA). In both seasons, isotopic values are more negative for precipitation inland and to the north. This inland isotopic gradient is known as the continental effect, and has been observed in the Amazon [Salati et al., 1979; Gat and Matsui, 1991] and in Europe [Rozanski et al., 198]. This continental effect is caused by gradual rain-out as air masses move over continents [Salati et al., 1979, Rozanski et al., 198; Ingraham and Taylor, 1986]. A continental effect is notable in this study, despite the small number of stations, from eastern Europe (Kursk and Kandalaksa) to eastern Siberia (Bogdarin and Yakutsk). At coastal stations near the Pacific (P.-Kamchatskily and Terney) and Arctic oceans (Ayon), marine moisture is a local source for precipitation. Isotopes at these stations are heavier than at stations inland at the same latitude. [5] Continental gradients for winter (DJF) and summer (JJA) at inland stations in Russia and Europe are listed in Table. Results are presented for each latitude band. The gradient amplitude is controlled by rain-out and the contribution of recycled moisture. Thus the gradient is significantly smaller in summer than in winter. Gradients are similar in both latitude bands, although a larger intercept occurs at higher latitudes. The summer gradient has similar values over Europe and Russia. In contrast, the winter gradient in Europe is slightly larger than in Russia. Both SNIP and GNIP data sets reveal a continental isotopic gradient from the Atlantic coast to eastern Siberia between 50 N and 60 N of about 3.0% for dd per 100 km in winter, and 0.9% for dd per 100 km in summer. [6] The spatial distribution of dd is described here in more detail. The observed continental effect during winter was modeled using Rayleigh condensation to determine quantitatively the distribution. The Rayleigh condensation model preferentially removes heavier isotopes in the air mass according to a temperature-dependent fractionation Table. Least Squares Regression of Seasonal Weighted Average of Isotopic Relationship Slope Intercept R s n dd-d 18 O Winter (DJF) >99% 1 Summer (JJA) dd-temperature, % K 1 Winter (DJF) >99% 1 Summer (JJA) Europe (DJF) a >99% 40 Europe (JJA) a dd-continentality, %(100 km) 1 Winter (DJF) 60 N >99% 4 50 N to60 N >95% 4 Summer (JJA) 60 N >90% 4 50 N to60 N >95% 4 Europe (DJF) a 50 N to60 N >99% 3 Europe (JJA) a 50 N to60 N a Calculated from European stations that are located between 40 N and 55 N and included in the Global Network of Isotopes in Precipitation (GNIP) data set. 6of15

7 Figure 6. Spatial distribution of seasonal weighted mean (a) dd and (b) d 18 O values in precipitation at the SNIP stations. The weighted mean used the precipitation amount. dd of winter (DJF) and summer (JJA) are indicated for each station. At Ayon, data are available for only one winter month (January 1998), and the summer isotopic value shown is an arithmetic mean. coefficient a. [e.g., Dansgaard, 1964; Siegenthaler and Matter, 1983; Van der Straaten and Mook, 1983; Jouzel and Merlivat, 1984; Johnsen et al., 1989]. The model assumes that an air mass travels in isolation inland from the coast and that moisture is removed from the fully mixed air mass via precipitation. This scenario is plausible for winter precipitation over Eurasia, because snow cover limits evaporation from the land surface and most moisture enters the continent from the Atlantic coast (Figures and 3). The isotopic ratio of precipitation is calculated in the Rayleigh model by stepwise development as shown in equation (1), d i ¼ a i ð1 þ d i 1 ÞF ai 1 i 1 ð1þ where d i 1 is the isotope content of vapor at the previous step, F is the rain-out factor characterizing the removal of moisture from the air parcel during each step, a i is the fractionation factor for the observed stations, and a i is its mean value based on condensation at the previous step and observed precipitation. The subscript i refers to each model step. Sonntag et al. [1983] used the ratio of monthly mean total precipitable water over the continent to precipitable water over the source region as an indicator of the average degree of rain-out of air masses moving inland. Thus precipitable water over the continent (see Figure 4) is used in this study as a proxy for the water vapor content of air parcels forming precipitation. Because westerly moisture flux dominates for flow over the continent during winter, the westernmost stations at midlatitudes and high latitude, Kursk and Kandalaksa, are used as initial conditions in the model. The rain-out factor, F, is the ratio of precipitable water over the continent to the precipitable water at the western boundary stations in Russia. Continental isotopic depletion of dd for trajectories at high latitudes (Kandalaksa) and midlatitudes (Kursk) was calculated using a stepwise Rayleigh model at every 1mm of precipitable water. The condensation temperature at 850-hPa was estimated from precipitable water, PW, using a best fit 7of15

8 Figure 7. Comparison of the observed dd in winter precipitation (DJF) with Rayleigh model results. F is the rain-out factor that characterizes the removal of moisture from the reservoir of atmospheric water. The lines indicate model results from different stations (Kursk and Kandalaksa), and symbols denote observation location (diamonds, stations between 50 N and 60 N; crosses, stations poleward of 60 N). curve: T 850 = 13.4 ln PW -33.9(R = 0.964). This relationship was computed using winter NCEP/NCAR reanalysis data in the study region from 1996 to 000. Condensation of vapor directly to ice was assumed to begin once temperatures fell below 5 C. At temperatures colder than 5 C, only vapor-ice transitions with a kinetic fractionation occur [Jouzel and Merlivat, 1984]. To simplify calculations, the change from the vapor-liquid transitions ( 5 C) to vaporice transitions ( 5 C) assumed a moderate and uniform increase in the fractionation factor. We do not consider the Bergeron-Findeisen process, during which condensation forms both liquid and ice, as described by Ciais and Jouzel [1994]. [7] Figure 7 shows comparisons between the stepwise Rayleigh model and the winter mean of dd at each station as a function of the rain-out factor. Recall that rain-out factor at each SNIP station is the ratio of precipitable water at that station to precipitable water at the western boundary stations (Kursk or Kandalaksa). The precipitable water used is the winter mean value as shown in Figure 4. The model reproduces the general features of continental isotopic depletion, although the dd at Kirov and at stations near the Pacific (Terney and P.-Kamchatskiy) deviate significantly from predicted values. dd at Kirov is between predictions for high latitudes and midlatitudes and may be influenced by moisture from higher latitudes. The heavier dd at Pacific stations reflects precipitation formed from moisture imported from the Pacific. Astrakhan, where the amount of atmospheric moisture exceeded that at Kursk, is not shown in Figure 7. Ayon was also far from the model curve and is not shown in Figure 7. Except for stations located near the ocean, the observed continental dd depletion of precipitation during winter can be explained by the rain-out factor. [8] As noted above, contributions from recycled (i.e., evaporated) water during summer reduce the inland depletion of water vapor content, and the extent of isotopic depletion of precipitation [Salati et al., 1979; Rozanski et al., 198, 1993]. Quantitative explanations of this effect are beyond the scope of this paper, but factors that contribute to the isotopic composition of precipitation in summer are discussed below Seasonal Variations [9] Figure 8 illustrates the annual variability of dd and surface temperature. A distinct seasonality is normally evident in both temperature and the dd of precipitation, with maxima during summer. Some coastal stations, however, (Terney and P.-Kamchatskily on the Pacific coast (solid red lines) and Astrakhan near the Caspian Sea (solid green line)) experience little dd variation during the year, because precipitation originating from adjacent waters affects these stations throughout the year. Condensation with marine origins results in little variability of dd independent of temperature. Ayon is also on the coast, but because the Arctic Ocean is covered with evaporationinhibiting sea ice during winter, dd shows a clear seasonal variation there. [30] The difference between the maximum and minimum dd of precipitation and temperature progressively increases for stations farther inland. The greatest seasonal differences in the 5-year average data occur at Yakutsk (solid blue line), where the average dd range was 188% ( 86% in December and 98% in July), and the temperature range was 56 C ( 35.5 C in December and 0.1 C in July). [31] During transition seasons, inland stations experience rapid changes in the dd of precipitation. In addition, the autumnal decrease in dd is faster than the vernal increase in dd. These characteristics are consistent with seasonal temperature variations. [3] The distribution of seasonal dd variations is influenced mostly by winter conditions. The maximum spatial isotopic difference occurs in January, when the range of dd reaches 16% between Terney ( 70%) and Yakutsk ( 86%). This large variation mirrors the large-scale temperature variation, from about 0 C along the coasts to less than 30 C in eastern Siberia, where radiational cooling Figure 8. Annual variation of (a) surface temperature and (b) dd for the SNIP stations. 8of15

9 Figure 9. Relationship between (a) dd of annual mean precipitation and weighted mean surface temperature and (b) the monthly mean dd in precipitation and weighted mean surface temperature for nine inland stations in SNIP observations. Coastal stations (Ayon, Astrakhan, Terney, and P. Kamchatskiy) are not included. under the Asiatic High dominates. The minimum spatial difference occurs in July, when the range of dd is87% between Astrakhan ( 35%) and Ayon ( 1%). Over the continent, the minimum spatial temperature difference also occurs in summer, even though temperatures reach maxima in July. This suggests that dd is less sensitive to temperature changes in the summer. Jacob and Sonntag [1991] observed atmospheric water vapor at the surface in Heidelberg, and concluded that the lack of a strong relationship between d 18 O and temperature in summer is a result of water vapor released by plant transpiration. High temperatures over Russia in the summer drive evaporation and transpiration from the land surface. Isotope content in summer may therefore be controlled more by the source of the water than by the rain-out effect that occurs in winter. 5. Relationship Between Isotopes and Surface Temperature 5.1. Seasonal Change [33] The relationship between the annual mean of dd and surface temperature T shows a good linear correlation (Figure 9a), but the spatial relationship between observed monthly mean dd and temperature differs in each season (Figure 9b). For example, in winter (DJF), the dd varies linearly as the temperature varies, which is different from the annual correlation line. The dd values, however, do not increase along this line as temperatures increase. They become greater when the gradient is less than the slope of the line. The spatial dd distribution in winter is strongly affected by rain-out, which can be calculated with a Rayleigh model using precipitable water. We can therefore examine the relationship between dd and precipitable water (Figure 10) to discuss factors controlling the seasonal relationship. [34] Figure 10b shows the seasonal variation of dd from DJF to MAM against precipitable water curves from the Rayleigh model. As in Figure 6, the seasonal mean values at the westernmost stations at midlatitudes and high latitudes, Kursk and Kandalaksa, are used as initial conditions in the model. Model curves at midlatitude and high-latitude bands in MAM are similar to observations, and the curves reproduce the monthly mean dd variation in MAM and DJF. In the model, dd is a function of the initial isotope and water vapor masses within the air mass, and of the moisture content remaining when the precipitation forms. Because the dd distribution is close to the predicted curve for both MAM and DJF, we conclude that the precipitation over the continent has almost the same initial isotopic content and moisture content, and that these values depend only on the rain-out factor. Figure 10b shows that the dd at Yakutsk (YK) in May differs from that at other stations with the same amount of precipitable water. In western Siberia, maximum evapotranspiration from plants occurs in May [Shimoyama et al., 001], although evapotranspiration flux is very small in eastern Siberia [Ohta et al., 001]. Evaporated moisture over western Siberia condenses as precipitation in eastern Siberia, causing a deviation from model predictions. [35] The seasonal variation from SON to DJF, however, shows that model curves initialized with the seasonal mean values from the westernmost stations are slightly separated in each latitude band and season (Figure 10c). The monthly dd variation agrees approximately with the model curves, but there is large deviation, especially at Olenek (OL) and Yakutsk (YK) in eastern Siberia, where dd is significantly lower than the model curve (Figure 10c). Considering the former discussion, the deviation might result from the contribution of other moisture sources with different isotopic contents. One possible moisture source is the Arctic Ocean. Parkinson et al. [1987] showed that the minimum extent of Northern Hemisphere sea-ice cover is in September and the maximum is in March, which accords with the explanation in that the isotopic variation is evident only in the autumn precipitation. Furthermore, the fact that dd is smaller at Ayon than at stations in eastern Siberia during autumn (see Figure 8) suggests that evaporation from the Arctic Ocean has lower dd in SON. The negative isotopic deviation from the model curve in eastern Siberia is therefore consistent with the effect of adding moisture originating from the Arctic Ocean. [36] The dd in summer is not sensitive to precipitable water: dd decreases slightly with decreasing precipitable water (Figure 10a). Contributions from recycled water are most active in summer, and this will be the focus of the next section. 9of15

10 Figure 10. Monthly means of dd in SNIP observations plotted as a function of the evaluated precipitable water at nine inland stations (a) during a year, (b) from DJF to MAM, and (c) from SON to DJF. The estimated dd values from the Rayleigh model, starting from the westernmost stations (Kandalaksa (high latitude) and Kursk (midlatitude)), are shown as a dashed line in DJF and a long dashed line in MAM and SON. Strongly deviating values observed at Yakutsk (YK) and Olenek (OL) are labeled. [37] Temperature approximates moisture content in midlatitudes and high latitudes because moisture content is proportional to saturation vapor pressure, which is a function of temperature. The different dd-t relationships show, however, that temperature variation includes effects unrelated to moisture variation. The intense temperature inversion and the sensible heat flux from the land surface, which does not add moisture, probably contribute to the differences in the dd-t relationships between DJF and MAM. In contrast, addition of water vapor and heat from the land surface, and the mixing of air masses of different origin, result in a different dd-t relationship between DJF and SON. Many factors complicate the seasonal relationship between moisture content and surface temperature in Russia. It is therefore difficult to interpret the slope and intercept of the dd-t relationship over Russia. 5.. Anomalies in Each Month [38] It is difficult to explain the seasonal isotope-temperature relationship, but temperature anomalies from long term monthly means reveal isotopic anomalies that are caused by the rain-out effect. Temperature anomalies respond linearly to the isotopic anomalies from the moisture variation of air masses. Figures 11a, 11b, 11c, and 11d show the monthly anomalies of dd, plotted against temperature anomalies at the SNIP stations for DJF, MAM, JJA, and SON, respectively. There is a positive correlation between the anomalies, albeit not significant (R = 0.07) in the summer when the isotopic content is less sensitive to moisture variation. [39] The plots show considerable scatter for DJF data, but the anomaly correlation is significant (the standard error of estimates is about 15% with a maximum variation in December). The intense winter inversion disrupts the normal response of moisture content variations to the temperature anomaly. MAM and SON show similar temperature ranges, and the relationship between dd anomalies and temperature anomalies for both seasons have a similar significant linear correlation. However, the standard error is large: 1.4% in MAM and 16.0% in SON. Temperature anomalies in these months explain about 40% of the dd anomaly; the remaining variability is controlled by other factors such as variability in the origin of the moisture and its isotopic content, addition of water from the land surface, etc. 6. Contribution of Water Recycling to Isotopes in Summer Precipitation [40] As noted in the previous section, because isotopes in summer precipitation are not very sensitive to variability in the precipitable water content (see Figure 10a), dd in observed summer precipitation is controlled mainly by the mixing of moisture originating in various regions. Hydrologic studies of the Eurasian continent have shown that recycled water is a major source of inland precipitation [e.g., Koster et al., 1986; Numaguti, 1999; Serreze et al., 00]. Therefore the mixing ratio of recycled water (recycling ratio) and its isotopic composition impact the observed isotopic characteristics. However, the difficulty in estimating the isotopic composition of recycled water precludes a detailed discussion of the effect from direct comparisons. Hence, in this study, the focus is on the effect of the recycling ratio and an examination of the relationship between isotopes and water recycling Recycling Ratio [41] Figure 1 shows a simulated distribution of the recycling ratio in JJA. The recycling ratio R is defined here as the contribution of evaporation from all land surfaces, local or remote, to precipitation at each point. The recycling ratio at an observation site was computed using backward trajectories of air masses that contained observed precipitation. Appendix A lists assumptions made about the atmospheric data sets. The recycling ratio exceeded 0.4 at most continental stations and increased eastward. The maximum recycling ratio (greater than 0.8) was at Bogdarin in eastern Siberia. Features computed in the recycling ratio distribu- 10 of 15

11 Figure 11. Relationship between monthly dd anomalies and surface temperature anomalies for nine inland stations in SNIP observations during (a) DJF, (b) MAM, (c) JJA, and (d) SON. The differences between monthly and monthly mean from 1996 to 000 are plotted. The confidence region that contains 99% of normally distributed data is dashed. tion are consistent with GCM results [Koster et al., 1993; Numaguti, 1999], although magnitudes in this study are slightly smaller than GCM results. Precipitation is assumed to be well-mixed vertically from the surface to the top of the atmosphere. Vertical variations of locally evaporated water in the column suggest that the well-mixed assumption results in a smaller contribution to the recycling ratio. Evaporated moisture concentrates in the lower atmosphere, and moisture that moves from the planetary boundary layer into clouds forms precipitation [Bosilovich, 00]. Uncertainties in the recycling ratio have not been quantified. However, comparisons with recycling ratios estimated from different data sets show similar values (see Figure A1). Thus we can conclude that the spatial distribution and the temporal variation of the estimated recycling ratio reflect actual conditions. 6.. Relationship Between Isotopes and Recycling Ratios [4] Multiple regression analysis on the observed data was used to examine the spatial relationship between isotopes and the recycling ratio, in a manner similar to Koster et al. [1993]. Table 3 contains results for each summer month. The correlation coefficient R dd/t between dd and T increases by about one-half, or twice that for the multiple regression R dd/t,r. As expected from Koster et al. [1993], there is a negative relationship between dd and R, which suggests that recycling explains much of the spatial dd distribution. Comparison of R dd/t with R dd/r shows that recycling contributes to the half degree of dd variation Figure 1. Distribution of the recycling ratio at the SNIP observation sites. 11 of 15

12 Table 3. Monthly Regression Analysis Statistics Month explained by temperature. This corresponds to 0% of the total variability of dd during summer. However, about 45% of the spatial isotopic variance cannot be explained by temperature and the recycling ratio. [43] Next, multiple regression analysis was used to explore the interannual relationship between isotopic anomalies and recycling ratio anomalies at each observation station. The interannual dd anomalies of dd in summer (see Figure 11c) were regressed against the corresponding temperature anomalies T and recycling ratio anomalies R at each station (Table 4). Except at Kursk in eastern Europe, there was no significant correlation between R dd/r Number of Samples R dd/t R dd/r Optimal Coefficients a a b c R dd/t,r June July August a Optimal coeffients a, b, and c for multiple regression dd = a + bt + cr. and R dd/t,r. At Barabinsk in western Siberia and Olenek in eastern Siberia, the correlation coefficient between dd and R was positive. This suggests that the interannual anomalies of isotopic content depend not on the temperature and recycling ratio anomalies, but on variations in the isotopic composition of source water. [44] Careful examination of results at Yakutsk in eastern Siberia reveals an isotopic content in summer precipitation that varies according to the direction from which air masses are advected into the region. Each air mass has a different isotopic content [Kurita et al., 003]. At Yakutsk, most precipitation has a high recycling ratio during the summer, so the isotopic content of recycled water in the advected air mass may vary greatly as the air mass transport varies. Thus, if the moisture transport patterns change across the continent, the isotopic content of moisture that forms precipitation at Yakutsk changes, even though the recycling ratio might not. This may be the cause of interannual isotopic anomalies. Furthermore, interannual variability in the isotopic content of recycled water would force interannual isotopic anomalies that are independent of changes in the recycling ratio. Observations at Yakutsk, Sugimoto et al. [00, 003] do show interannual variations in the isotopic content of surface soil water. Soil water enters the atmosphere via evapotranspiration in summer, and the isotopic content of this water will depend on the relative concentrations of snow melt in the soil water, as snow has a lower isotopic content than summer precipitation. [45] Both of the above processes yield isotopic anomalies over the continent that are not explained by the recycling ratio. Thus the isotopic content of summer rainfall largely reflects the isotopic composition of the original moisture. Information about the original moisture can therefore be determined if its history can be correctly determined. 7. Conclusion [46] The monthly isotopic content of precipitation over Russia, where isotope data coverage has historically been unsatisfactory, was observed from 1996 to 000. Except in summer, the spatial variability of the isotopic content of precipitation in each season resembled that of the surface temperature distribution and was roughly reproduced by a simple Rayleigh model that depended on the moisture content of the air masses. The spatial isotope-temperature d-t relationship varied with season, which suggests that temperature variation does not control the variability of the rain-out effect, which depends on moisture content. Many factors, including a strong temperature inversion and sensible heat flux from the land surface, make the moisture content insensitive to temperature. Furthermore, water sources contributing to precipitation in spring and autumn may differ on the basis of observations of dd of precipitation in autumn that are lower than that in spring for the same moisture content. Thus there are differences between spatial and temporal isotope-temperature relationships as have been reported already in other regions. [47] In summer, when temperatures are high and temperature gradients are weak, the isotopic content is less sensitive to temperature and moisture content. It depends instead on recycled water, which is a major source of summer precipitation. However, the recycling ratio explains a scant 0% of the spatial variability of isotopic content in summer precipitation. From multiple regression analysis, the temperature and recycling ratio together account for about 55% of the variability of the isotopic content in summer precipitation. A probable reason for this feature is variability in the isotopic content of the moisture that leads to the observed precipitation. Two possibilities for this isotopic variability are proposed. There may be interannual and seasonal variability in the isotopic content of transpired and/or evaporated water from the land surface. Soil water that is absorbed into plants and transpired into the atmosphere has a long residence time over Eurasia and is composed of both snow melt and summer precipitation [Saurer et al., 00]. Isotopic variability may be affected by the relative contributions of snow melt. Snow melt varies from year to year, and snow has a lower isotopic content than summer precipitation. Variability in the contribution of snow melt to total soil water could change the isotopic content of recycled water. Furthermore, surface water bodies (lakes, rivers, swamps, etc.,) all include snow melt. Thus the variability of snow melt causes variability in the isotopic content of the evaporation from these water sources. It is also possible that moisture transport patterns across the Table 4. Statistics From Anomalies Regression Analysis a Station n b R dd/t R dd/t R dd/t,r Astrakhan Kursk Kirov Barabinsk Bagdarin Kandalaksa Salekhard Olenek Yakutsk Terney P. Kamchatskiy a Bold, significance >99%. b Number of samples. 1 of 15