1. Introduction
Increasing surface air temperature will bring changes to precipitation characteristics in terms of amount, frequency, intensity, and extremes as atmospheric water vapor holding capacity increases according to the Clausius–Clapeyron (C-C) relationship. Studies have suggested that extreme precipitation events will become more frequent under a warmer climate, even in places where total precipitation may not show significant increases (e.g., Allan and Soden 2008; Groisman et al. 2005; Easterling et al. 2000; Meehl et al. 2007; Pall et al. 2007; Shiu et al. 2012; Zolina et al. 2010). Multimodel ensembles from phases 3 and 5 of the Coupled Model Intercomparison Project suggest that extreme precipitation in general increases faster than total precipitation under a warming climate (Sillmann et al. 2013). Climate models have also suggested that daily precipitation intensity will increase even in places where precipitation totals do not increase as global warming continues (Lau et al. 2013; Meehl et al. 2005; Tebaldi et al. 2006). If this is true, then precipitation frequency should decrease in places where higher-intensity precipitation events become more common, or increases in high-intensity precipitation events should occur at the expense of low-intensity events as the climate warms. The increases in extremes and intensity are especially evident in moist high-latitude regions, as summarized by the IPCC (Collins et al. 2013). This may be explained by the fact that high-latitude rainfall events are largely nonconvective and that all events produce more precipitation, resulting in fewer light events and more moderate and heavy events (Hennessy et al. 1997).
In a study of seasonal precipitation frequency at an interannual time scale, Ye (2008) also suggested that higher winter surface air temperature is associated with more frequent snow and rain events over northern Eurasia. However, Ye also pointed out that precipitation frequency decreases in the warm season when air temperatures are higher. These observations are in general agreement with regional projections of climate models (Khon et al. 2007). In a recent study, Ye et al. (2014) found that summer total precipitation and precipitation efficiency have a very strong negative correlation with air temperature at an interannual time scale as a result of decreases in relative humidity accompanying higher temperatures. The authors concluded that increasing water vapor associated with higher temperature directly contributes to higher precipitation total in winter, but in summer the precipitation total decreases despite the availability of more precipitable water.
Compared to air temperature, precipitation has larger variability at both spatial and temporal scales but a weaker long-term trend (Allen and Ingram 2002). The sign and amplitude of precipitation trends are highly dependent on the period chosen. Therefore, attribution of changes in precipitation to anthropogenic forcing is challenging. Another way to understand the responses of precipitation to changing air temperature is to examine the direct correlation between the two (Berg et al. 2009; Hardwick Jones et al. 2010; Trenberth and Shea 2005; Westra et al. 2013; Ye 2008; Ye and Cohen 2013). The relationship between monthly precipitation and air temperature anomalies is generally wet and cool or dry and hot in most land areas in summer but wet and warm or dry and cold across high-latitude land areas during winter (Trenberth and Shea 2005; Ye et al. 2014). The Berg et al. (2009) study on monthly 99th-percentile precipitation intensity and air temperature over Europe showed positive relationships in winter but negative relationships in summer based on both observational records and climate model output. They argued that the Clausius–Clapeyron relationship determines the increase of large-scale precipitation in winter, while the availability of moisture is the dominant limiting factor in summer. It is clear that winter and summer precipitation have very different processes and that they need to be examined separately, especially in high-latitude regions where seasonality is strongest.
Earlier studies of precipitation and air temperature relationships are confined to interannual, intraseasonal, daily, hourly, and even shorter time scales (Berg et al. 2009; Bulygina et al. 2007; Hardwick Jones et al. 2010; Trenberth and Shea 2005; Utsumi et al. 2011; Westra et al. 2013; Ye 2008, 2009; Ye and Cohen 2013), and thus results are not easily translated into implications under a warming climate reflective of decadal and longer time scales. To understand potential changes in precipitation characteristics associated with a warming climate, it is necessary to examine relationships at decadal and longer time scales in order to filter out shorter-term noisy variability in precipitation characteristics.
Previous studies have found a significant influence of atmospheric circulation on Arctic climate variability, represented by the Arctic Oscillation (AO; Thompson and Wallace 1998), which describes the fluctuation of atmospheric pressure differences between the central Arctic and the two weaker centers at about 45°N over the Atlantic and Pacific basins. A regional study over western Siberia suggested a large influence of the AO on air temperature and a lesser influence on precipitation during winter (Frey and Smith 2003). Thus, to understand the association between precipitation characteristics and air temperature, especially the component of anthropogenic-related long-term temperature trends, the dependence on the phase and amplitude of the major atmospheric circulation patterns also needs to be assessed.
Studies using historical records have generally been focused either on a small geographical area or on global scales that emphasize tropical and subtropical regions, in part owing to the limited number of station records available for high-latitude regions. Furthermore, most studies have focused only on either precipitation totals or extremes. The lack of observational data analyses on precipitation sensitivity to changes in air temperature hinders our ability to assess the accuracy of climate models’ future projections for high-latitude regions. Given that northern Eurasia is the largest high-latitude landmass experiencing amplified warming in recent decades, it is an ideal area in which to study changes in precipitation under a warming climate. Relatively dense and long-term historical precipitation observation records have recently become available for northern Eurasia, making in-depth analyses at multiple time scales for high-latitude regions possible.
In this study, the relationships of seasonal precipitation total, frequency, and daily intensity to seasonal air temperatures are carefully examined at the local station level on interannual and longer time scales for the four seasons. In addition, the influence of major atmospheric circulation patterns on precipitation intensity is assessed. The paper is organized as follows: section 2 includes data and methodology, section 3a illustrates the climatology of precipitation characteristics, section 3b examines the trends of all variables considered, section 3c examines the relationships between precipitation characteristics and the major atmospheric circulation patterns, section 3d examines the relationships of precipitation characteristics to air temperature, section 3e presents quantitative assessment for changing precipitation characteristics, and section 4 presents the summary and conclusions.
2. Data and methodology
We used the daily temperature and precipitation data from 518 Russian meteorological stations from the Carbon Dioxide Information Analysis Center (CDIAC) (Bulygina and Razuvaev 2012) for our station dataset. Observations were made at each station for varying time periods from 1881 to 2010. Variables include daily mean, minimum, and maximum surface air temperature, as well as daily total precipitation (liquid equivalent). The data have been quality controlled and adjusted for wetting, evaporation, and condensation due to instrumentation changes for the time period starting in 1966 (Bogdanova et al. 2002; Groisman et al. 1991, 2014). One station listed in the inventory does not have records in the data file, while another station in the data file is not listed in the inventory. There are no matching station codes between these two, so neither station is used in this study. As a result, a total of 517 stations are retained for analyses. The time period chosen for this study is 1966–2010, during which the same types of precipitation gauges were used and consistent correction methods were implemented (Groisman et al. 2014).
Seasonal precipitation totals are summed from daily values for all three months of the corresponding season. If a day is missing, this seasonal value is considered as missing to ensure that no data uncertainty is introduced. The frequency of seasonal precipitation is the total number of days precipitation is recorded above a ⅝1/10-mm minimum. We have also performed similar analyses using a daily precipitation cutoff of 1 mm or higher. Results do not affect our general conclusion, although the number of wet days decreased by almost 50% in winter, 42% in spring, 29% in summer, and 37% in fall. Given the fact that 0.1 mm is the standard record for precipitation events and also for models’ outputs (Sun et al. 2007; Chou et al. 2012), we only show results using 0.1 mm as the minimum for daily precipitation records.
The seasonal mean daily precipitation intensity is the seasonal precipitation total divided by seasonal frequency for each year, which translates to millimeters per day (Meehl et al. 2005; Tebaldi et al. 2006). The seasonal mean air temperature is the seasonal average of mean daily temperature. If more than 10% of air temperature records are missing, the seasonal air temperature is considered missing.
The major atmospheric circulation patterns, in the order of explained variance, are the Arctic Oscillation, the east Atlantic pattern (EA), the east Atlantic–western Russian pattern (EAWR), the Scandinavian pattern (SCA), the polar–Eurasian pattern (POL), the east Pacific–North Pacific pattern (EPNP), the Pacific–North American (PNA) pattern, the west Pacific pattern (WP), the tropical–Northern Hemisphere pattern (TNH; winter only), and the Pacific transition pattern (PT; summer only). These are downloaded from the Climate Prediction Center in the United States (
The relationships between precipitation characteristics, air temperature, and atmospheric indices are examined using both simple linear correlation analyses and Spearman’s ranked correlation analyses. There is very little difference in results using these two different correlation methods because of the large sampling sizes of seasonal values; thus, results from the simple linear regression analysis are presented in the rest of the paper. The rate of change in precipitation characteristics is estimated using simple linear regression with air temperature as an independent variable.
To remove the influence of the atmospheric circulation pattern from air temperature or precipitation characteristics, for each station, a residual time series is constructed by subtracting projected dependent variables using simple linear regression from the original variables. To adjust for different magnitudes of original values, the percentage rate of change is also calculated.
To further examine the relationships at large spatial and temporal scales, all 517 station values are gridded using Shepard’s local-search interpolation on a spherical surface (Willmott et al. 1985) and equal-area-weighted (cosine of the latitude) averages are applied to construct time series representing the entire region. In addition, 11-yr moving averages are applied to emphasize decadal and longer time-scale relationships.
3. Results
a. Climatology of seasonal precipitation characteristics
The geographical distribution of 45-yr mean seasonal precipitation total and frequency are shown in Fig. 1. In general, winter precipitation decreases gradually from the west to the east and then decreases rapidly approaching the Pacific Ocean. Western European Russia receives about 150 mm; this decreases to about 100 mm in western Siberia and 50 mm or lower over northeastern Siberia and then increases again along the coast of eastern Siberia (Fig. 1a). The area-adjusted winter precipitation average based on these 517 stations is 84 mm. The highest precipitation of up to 600 mm occurs locally along the east shore of the Black Sea, possibly related to lake-effect snow and orographic lifting (Korzun 1978; Lydolph 1977; Ye 2001). Spring shows higher precipitation over most regions, especially over central and eastern Siberia (Fig. 1b). Its geographical pattern resembles that of winter over the western part of the study region. In central and eastern Siberia, precipitation shows separation between latitude zones. Local spring maximum precipitation along the east coast of the Black Sea is around 460 mm. The mean area-averaged spring precipitation of the study region is about 98 mm. Summer precipitation almost doubles between European Russia and western Siberia and more than triples into eastern Siberia compared to spring (Fig. 1c). There is a local maximum at the southern edge of central Siberia. The summer area-averaged precipitation reaches 205 mm. The fall precipitation pattern lies between those of spring and summer with an average of 143 mm (Fig. 1d). More precipitation occurs over European Russia and western Siberia and decreases toward the east with the minimum occurring over Arctic coastal regions of central and eastern Siberia during fall. Maximum fall precipitation is found at island stations on the southern east coast and over the eastern shore of the Black Sea, where it reaches around 590 mm.
Geographical distribution of seasonal precipitation total (mm) and frequency (days).
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
Precipitation frequency in winter is around 50–60 days (or 54%–65%) for the majority of stations but decreases to 30–40 days (33%–43%) over far-eastern Siberia (Fig. 1e). Higher frequencies are centered over European Russia and central Siberia, with decreases toward the northern coast and to the south. The area-averaged value is 40.6 days (44%) in winter. The spring shows decreased precipitation frequency everywhere, and the averaged value is reduced to 32.2 days (36%; Fig. 1f). During spring, the highest frequency of around 40–50 days (45%–56%) occurs over northern European Russia and western Siberia and some island stations over the east coast, while in other areas it ranges from 10 to 40 days (11%–45%) with a less localized pattern. The frequency in summer is similar to that of spring, ranging from 30 to 40 days in the majority of the study region with an area average of 37.5 days (41%; Fig. 1g). The fall frequency pattern resembles that of winter with localized high frequency centers over the northern part of European Russia and central Siberia (Fig. 1h) and with an average of 40.1 (44%) days.
The mean winter precipitation intensity is 1–2.5 mm day−1 for most stations except for the southern edges of European Russia and southeastern coastal stations (Fig. 2a). The area-adjusted average precipitation intensity is 2.0 mm day−1 in winter. During spring, the precipitation intensity increases to an average of 3.0 mm day−1, most significantly over the southern portion of the study region (Fig. 2b). Summer has the highest precipitation intensity at an average of 5.4 mm day−1 and with a well-defined zonal pattern decreasing toward the north (Fig. 2c). Fall precipitation intensity has a similar distribution pattern to that of spring, with a slightly higher average value of 3.7 mm day−1 (Fig. 2d). For all seasons, the highest intensities are found in the wet regions along the east shore of the Black Sea and along the southeastern coast near the Pacific Ocean. From Figs. 1 and 2, one can clearly see that there are very different precipitation characteristics between winter and summer. Winter precipitation is made up of very frequent low-intensity events, while summer features more-sporadic but intense precipitation. Spring and fall precipitation characteristics lie between those of winter and summer but with an evident zonal pattern more characteristic of summer precipitation intensity rather than the longitudinal pattern that characterizes winter precipitation intensity.
Geographical distribution of mean seasonal precipitation intensity (mm day−1).
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
b. Trends in precipitation characteristics and air temperature
The results of simple linear trend analyses for seasonal precipitation total, frequency, intensity, and seasonal air temperature are listed in Table 1. It is evident that air temperature has been increasing in all seasons; 95%–99% of stations during the study period of 1966–2010 exhibit a positive trend. The largest number of stations (65.5%) with statistically significant increasing trends occurred in the summer, and the smallest number of stations with statistically significant increasing trends (35.9%) occurred in the fall.
Percentage of stations showing statistically significant positive and negative trends (at the 95% confidence level) and all positive- and negative-trend stations (in parentheses). The positive trend is marked by a plus sign and the negative trend is marked by a minus sign at the front of values. Boldface numbers indicate relative dominance in either positive or negative trends (more than 60%).
Precipitation intensity has a predominantly positive trend in all seasons—strongest in winter and spring and weakest in summer (Table 1). As intensity increases, precipitation frequency shows a predominantly decreasing trend in the majority of stations for all seasons. For seasonal precipitation totals, winter and spring show slight increasing trends but not as strong as that of intensity or frequency. These precipitation total trends could be an artifact related to regional warming due to systematic (mostly negative) biases in observation for frozen precipitation (Førland and Hanssen-Bauer 2000; Groisman et al. 2014). The results suggest that warming has been occurring and precipitation intensity has been increasing during the 45 years of study from 1966 to 2010. The increasing intensity seems to be mostly related to decreasing precipitation frequency in all seasons.
Research on the U.K. daily precipitation records during the period 1961–2006 showed increasing precipitation intensity for winter, spring, and (to a lesser extent) fall, while summer intensity has interdecadal variability increasing toward the last decade (Maraun et al. 2008). Increases in U.K. precipitation intensity for all seasons are predicted by climate model projections (HadCM2) based on future increases in greenhouse gases (Hulme et al. 1998). These findings are generally consistent with trend results revealed here for similar time periods at these northern Eurasian stations.
c. Relationships with major atmospheric circulation patterns
Among the 10 dominant atmospheric teleconnection patterns, the AO, SCA, and POL have significant correlation with precipitation intensity in winter (Fig. 3). For the AO, the majority of positive correlations occur over European Russia, central Siberia, and southeastern Siberia; negative correlations are concentrated over northeastern Siberia (Fig. 3a). In winter, significant positive correlations between the AO and precipitation total are mostly over the northern part of the study region, except for far-eastern Siberia, where negative correlations are found. The AO is also positively correlated with frequency over European Russia and western and central Siberia, with negative correlation over southern central Siberia and eastern Siberia (not shown).
Correlation between precipitation intensity and major atmospheric circulation patterns. Red denotes positive correlation, blue denotes negative correlation, and solid dots denote statistical significance at the 95% confidence level or higher.
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
The distribution of correlation results with the AO is very similar to that with the POL (Figs. 3a,e), as well as with the SCA but with opposite signs (Fig. 3c). This is not surprising because the winter time series of the AO is significantly correlated with the SCA (coefficient of −0.5976) and the POL (coefficient of 0.3238) over the study period of 1966–2010. Given the similar geographical patterns of correlation among AO, SCA, and POL as well as the intercorrelated nature of these three time series, AO, the first primary mode of the atmospheric teleconnection, is used to represent atmospheric influence in winter for further analyses.
During spring, the SCA is found to be most significantly correlated with precipitation intensity (Fig. 3b), although correlation is not as high as during winter. Significant negative correlations are found over the northern coast and southern edge of European Russia, northwestern Siberia, and the southern central and southern coastal regions of eastern Siberia (Fig. 3b). The pattern resembles that of winter correlation (Fig. 3c), with fewer statistically significant stations over northern European Russia. The SCA has a very strong decreasing trend during the study period of 1966–2010 (Fig. 5f; described in greater detail below), while spring precipitation intensity has a dominant increasing trend. However, a similar relationship is also detected after the trend is removed from both time series, suggesting that SCA has an impact on spring precipitation intensity.
During summer, EA and EAWR are most significantly correlated with precipitation intensity. EA has dominant positive correlations (Fig. 3d) while EAWR has dominant negative correlation with summer precipitation intensity, except for a few stations over northern European Russia where positive correlations are found (Fig. 3f). Also, summer EA and EAWR time series have a statistically significant negative correlation (coefficient of −0.3856) during the study period. EA has a significant increasing trend while EAWR has a decreasing trend during the study period of 1966–2010 summers. The correlations between EA and precipitation intensity are no longer significant when the trend is removed from the time series, but the significant relationship between EAWR and precipitation intensity remains. We can conclude that the relationship between EA and precipitation intensity in summer is due to both positive trends. Thus, only the EAWR is considered to be related to summer precipitation intensity.
In summary, the AO has significant correlations with precipitation intensity in winter, the SCA is significantly correlated with precipitation intensity in spring, and the EAWR is related to summer precipitation. Thus, when evaluating the relationship of precipitation characteristics with air temperature, partial correlation is used to separate the effect of each of these atmospheric circulation patterns.
d. Relationship with air temperature
Correlation between precipitation intensity and air temperature is predominantly positive, except for summer when the relationship is weakest. Figure 4 shows the correlations of air temperature with precipitation total, frequency, and intensity for winter and summer. During winter, precipitation total and frequency are statistically significantly correlated with air temperature over the study region, except for southern central Siberia and an area between the Aral and Black Seas (Figs. 4a,c). During summer, precipitation total and frequency have predominantly negative correlations with air temperature (Figs. 4b,d). But correlation with precipitation intensity shows a split between positive and negative correlations—generally positive over the northern study regions and around Lake Baikal and negative over the southern regions.
Correlation between air temperature and precipitation characteristics for (left) winter and (right) summer. Red denotes positive correlation, blue denotes negative correlation, and solid dots denote statistical significance at the 95% confidence level or higher.
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
For spring, temperature is most positively correlated with precipitation intensity and negatively correlated with frequency (Table 2). For fall, air temperature is most significantly negatively correlated with frequency and positively with intensity. For both seasons, there is no clear geographical pattern of distribution, and the positive correlations for precipitation intensity are scattered across the study region.
Correlation and partial correlation (controlled for AO for winter, SCA for spring, or EAWP for summer) results of air temperature with precipitation total, precipitation frequency, and precipitation intensity, presented by the percentages of stations showing statistically significant positive (negative) correlations at the ≥95% confidence level in the first (second) line and all stations showing positive (negative) correlations (shown in parentheses) in the first (second) line. Boldface highlights significant positive and/or negative relationships.
The partial correlation between air temperature and precipitation intensity after controlling for the AO gives an increased number of statistically significant stations in winter, especially for positive correlation (Table 2). But for spring, there is a slight reduction in number of stations with positive correlation after control for the SCA. For summer, the control of the EAWR influence has increased the number of statistically significant negatively correlated stations over the southern study region, while the positively correlated stations are not affected. This suggests that the atmospheric circulation pattern does significantly influence summer precipitation intensity.
The 11-yr moving-averaged time series and residual time series, averaged over all stations for winter and spring, are shown in Fig. 5. Winter temperature time series show steady increases until around the mid-1990s and then stay relatively flat, while precipitation intensity shows larger increases starting in the 1990s and then stays relatively flat in the 2000s (Fig. 5a). The residual time series, after removal of the AO contribution through a linear regression of both temperature and precipitation intensity, show synchronized interdecadal variation in addition to an increasing trend during the study period. They increase until 1985, decrease slightly until 1990, and then rapidly increase after 1990 (Fig. 5c). In spring, both air temperature and precipitation intensity of the original time series show slight decreases until the early 1980s followed by rapid increases (Fig. 5b), while the residual time series after removal of the SCA stay synchronized between air temperature and precipitation intensity, with a slight departure during 1985–95 (Fig. 5d). This suggests that the SCA is related to precipitation intensity at both interannual and decadal time scales.
The 11-yr moving-averaged precipitation intensity and air temperature averaged from all 517 stations for (a) winter, (b) spring, (c) winter with AO variability removed, and (d) spring with SCA variability removed. Also shown are the 11-yr moving-averaged (e) winter AO and (f) spring SCA time series. Black triangles denote precipitation intensity (mm day−1), and red crosses denote air temperature or atmospheric circulation index [only applies to (a)–(d)].
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
The time series of summer and fall air temperature and precipitation intensity show consistent fluctuations and a general increasing trend throughout the study time period (Fig. 6). These temperature and precipitation intensity time series are not as tightly matched as those of winter and spring time series since a smaller number of stations demonstrated statistically significant correlation. For example, the rapid temperature increases in the last decade during the fall season is not reflected in increasing precipitation intensity, which stays relatively flat (Fig. 6b). Also, the summertime series of the EAWR shows an opposite phase from that of precipitation intensity and opposite trends during the study period (Fig. 6c).
The 11-yr moving-averaged precipitation intensity and air temperature averaged from all 517 stations for (a) summer and (b) fall. (c) The 11-yr moving-averaged precipitation intensity and EAWR for summer.
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
e. Quantitative assessment
The rate of change is averaged from all stations to estimate changes associated with increasing air temperature over the entire study region. Although there are overall increases in precipitation totals of 2.8 mm (3.35%), 4.76 mm (4.85%), and 1.02 mm (0.71%) for winter, spring, and fall, respectively, the large decrease in summer precipitation of 7.46 mm (3.65%) results in only a marginal annual precipitation increase for each degree of air temperature increase (1.1 mm). However, decreases in precipitation frequency in spring (0.35 days), summer (2.43 days), and fall (0.73 days), respectively, more than compensate for the small increase in winter frequency (0.18 days); thus, annual precipitation frequency decreases 3.3 days yr−1 for each degree of air temperature increase.
The percentage of precipitation intensity change for each degree of air temperature increase at each station and season is plotted against its corresponding seasonal climatological mean values of air temperature in Fig. 7. An averaged value corresponding to each degree of air temperature increment is calculated for all stations and seasons that fall into that temperature range. It is clear that the rate of precipitation intensity increase hovers around 1%–3% for all seasons, except for summer, when it switches to negative values when the stations’ air temperatures reach above 15.5°C. The fairly consistent rate of change for stations with air temperature below 15.5°C suggests a nonlinear relationship between air temperature and precipitation intensity, similar to the relationship of water vapor to air temperature but at a much lower rate. This calculated rate of precipitation intensity change is consistent with that of 2% K−1 averaged from multimodel projections for the twenty-first century (Sun et al. 2007).
Percentage rates of change in precipitation intensity plotted against their corresponding seasonal air temperatures for all stations and all seasons. The mean values (solid black line) for each degree of air temperature aggregation is calculated by averaging all station values where their corresponding seasonal air temperatures fall within the corresponding 1°C temperature range. Similarly, the mean percentage rates of change for precipitation total (light blue line) and frequency (orange line) are plotted. The thresholds of air temperature when rate switches from positive to negative values are plotted with dashed lines as marked.
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
It is also interesting to see that precipitation total switches from positive to negative correlation around a temperature of −0.5°C and that frequency switches around −5.5°C (Fig. 7). The temperature threshold at which the correlation with precipitation frequency changes sign is very close to −6°C, as shown by Ye (2008) in a study of 80 weather stations over the time period from 1936 to 1990 from a different dataset.
When carefully examining the averaged rates of change of precipitation total and frequency (solid lines in Fig. 7), the rates of precipitation total are predominantly slightly higher than those of frequency for all low temperatures, until the station air temperature reaches 15.5°C, at which point the rate of frequency decrease is higher than that of precipitation total. This is the threshold when precipitation intensity starts to decrease with air temperature.
To further examine this relationship at the regional scale, mean seasonal precipitation intensity averaged from all available stations is plotted against the corresponding seasonal air temperature in Fig. 8. The higher precipitation intensity associated with higher air temperature is apparent for all seasons with correlation coefficients of 0.248, 0.515, 0.520, and 0.250 for winter, spring, summer, and fall, respectively (statistically significant at 99% or higher for spring and summer and 90% or higher for winter and fall). It is also clear that precipitation intensity switches to decreasing with air temperature when the averaged mean air temperature over the study region becomes higher than 15°–16°C in the summer season (Fig. 8c).
Mean seasonal precipitation intensity, averaged from all available stations, plotted against corresponding seasonal air temperatures. Solid line is the averaged value for each 0.5° or 1°C increment in air temperature.
Citation: Journal of Climate 29, 2; 10.1175/JCLI-D-14-00771.1
The reversed relationship between precipitation intensity and air temperature is likely to be related to that of the relationship between water vapor and air temperature. Ye and Fetzer (2009) found a threshold Eurasian summer air temperature of about 19°C above which the vapor pressure changes from increasing to decreasing, based on 80 station records. They also revealed that the threshold may be highly location dependent. Since the atmospheric circulation has a significant role in precipitation intensity changes over the southern region studied here during the summer, the threshold of air temperature at which precipitation intensity changes sign needs to be further investigated.
The unique response of summer precipitation is likely due to the fact that the precipitation-producing weather patterns are responding to warmer temperature in different ways in different seasons. During winter of a warmer climate, increases in water vapor and changes in large-scale circulation can cause extratropical winter storms to shift and intensify locally, giving rise to positive precipitation intensity in northern Eurasia (Bengtsson et al. 2006; Ulbrich et al. 2008). On the other hand, during summer, reduced relative humidity associated with changes in large-scale circulation, local convection, and water recycling through atmosphere–land feedback may lead to increased dryness and suppressed precipitation (Lau and Kim 2015; Ye et al. 2014).
4. Summary and conclusions
This study carefully examines changes in seasonal precipitation characteristics associated with air temperature and the major atmospheric circulation patterns on interannual and longer time scales at 517 stations over northern Eurasia. Results suggest that 1) precipitation intensity has been increasing in all four seasons at a rate of about 1%–3% per each degree of air temperature increase across the study region; 2) increasing precipitation intensity is most likely related to decreasing precipitation frequency, as the precipitation total shows little change; 3) there may be a threshold of air temperature of 15°–16°C above which precipitation intensity switches to decreasing with air temperature over the study region; 4) the correlation of precipitation intensity with the Arctic Oscillation as well as the Scandinavian and polar–Eurasian patterns have similar regional patterns of opposite signs between far-eastern Siberia and the rest of the study region during winter; 5) the Scandinavian pattern has an overall positive relationship with spring precipitation intensity; and 6) the east Atlantic–western Russian pattern is significantly related to summer precipitation intensity. The increase in precipitation intensity is most likely due to increasing atmospheric water vapor holding capacity associated with higher air temperatures. The heaviest precipitation events occur when effectively all water vapor in the column of air is precipitated out, resulting in higher precipitation intensity during extreme events. It is expected that the uppermost quartile of precipitation will increase by 6.7% K−1 in high-latitude regions (Allen and Ingram 2002). In this study, the mean annual daily precipitation intensity increase is about 1%–3% for the study region. It is likely that precipitation intensity stops increasing when water vapor can no longer increase at extremely high temperatures as a result of limited local evaporation sources (Ye and Fetzer 2009) associated with increased hydrologic drought conditions over northern Eurasia (Dai 2013). This study concludes that warming air temperature is responsible for increasing precipitation intensity across northern Eurasia during the last 45 years.
Acknowledgments
The first author is supported by NSF Grant BCS-1060788, and the seventh author is supported by NSF Grant BCS-106033. Some of the research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The first author and the JPL authors are supported by NASA Grant NNX15AQ06A, the NASA JPL AIRS project, the NASA MeaSUREs project, and the NASA Earth System Data Record Uncertainty Analysis project.
REFERENCES
Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 321, 1481–1484, doi:10.1126/science.1160787.
Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224–232, doi:10.1038/nature01092.
Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083–1126, doi:10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.
Bengtsson, L., K. I. Hodges, and E. Roeckner, 2006: Storm tracks and climate change. J. Climate, 19, 3518–3543, doi:10.1175/JCLI3815.1.
Berg, P., J. O. Haerter, P. Thejll, C. Piani, S. Hagemann, and J. H. Christensen, 2009: Seasonal characteristics of the relationship between daily precipitation intensity and surface temperature. J. Geophys. Res., 114, D18102, doi:10.1029/2009JD012008.
Bogdanova, E. G., B. M. Ilyin, and I. V. Dragomilova, 2002: Application of a comprehensive bias-correction model to precipitation measured at Russian North Pole drifting stations. J. Hydrometeor., 3, 700–713, doi:10.1175/1525-7541(2002)003<0700:AOACBC>2.0.CO;2.
Bulygina, O. N., and V. N. Razuvaev, 2012: Daily temperature and precipitation data for 518 Russian meteorological stations. Carbon Dioxide Information Analysis Center, accessed 12 December 2012, doi:10.3334/CDIAC/cli.100.
Bulygina, O. N., V. N. Razuvaev, N. N. Korshunova, and P. Ya Groisman, 2007: Climate variations and changes in extreme climate events in Russia. Environ. Res. Lett., 2, 045020, doi:10.1088/1748-9326/2/4/045020.
Chou, C., C. Chen, P. Tan, and K. T. Chen, 2012: Mechanisms for global warming impacts on precipitation frequency and intensity. J. Climate, 25, 3291–3306, doi:10.1175/JCLI-D-11-00239.1.
Collins, M., and Coauthors, 2013: Long-term climate change: Projections, commitments and irreversibility. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1029–1136. [Available online at http://www.climatechange2013.org/images/report/WG1AR5_Chapter12_FINAL.pdf.]
Dai, A., 2013: Increasing drought under global warming in observations and models. Nat. Climate Change, 3, 52–58, doi:10.1038/nclimate1633.
Easterling, D. R., J. L. Evans, P. Ya. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417–425, doi:10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2.
Førland, E. J., and I. Hanssen-Bauer, 2000: Increased precipitation in the Norwegian Arctic: True or false? Climatic Change, 46, 485–509, doi:10.1023/A:1005613304674.
Frey, K. E., and L. Smith, 2003: Recent temperature and precipitation increases in west Siberia and their associations with the Arctic Oscillation. Polar Res., 22, 287–300.
Groisman, P. Ya., V. V. Koknaeva, T. A. Belokrylova, and T. R. Karl, 1991: Overcoming biases of precipitation measurement: A history of the USSR experience. Bull. Amer. Meteor. Soc., 72, 1725–1733, doi:10.1175/1520-0477(1991)072<1725:OBOPMA>2.0.CO;2.
Groisman, P. Ya., R. W. Knight, D. R. Easterling, and T. R. Karl, 2005: Trends in intense precipitation in climate record. J. Climate, 18, 1326–1350, doi:10.1175/JCLI3339.1.
Groisman, P. Ya., E. G. Bogdanova, V. A. Alexeev, J. E. Cherry, and O. N. Bulygina, 2014: Impact of snowfall measurement deficiencies on quantification of precipitation and its trends over northern Eurasia [in Russian]. Ice Snow, 54 (2), 29–43, doi:10.15356/2076-6734-2014-2-29-43.
Hardwick Jones, R., S. Westra, and A. Sharma, 2010: Observed relationships between extreme sub-daily precipitation, surface temperature, and relative humidity. Geophys. Res. Lett., 37, L22805, doi:10.1029/2010GL045081.
Hennessy, K. J., J. M. Gregory, and J. F. B. Mitchell, 1997: Changes in daily precipitation under enhanced greenhouse conditions. Climate Dyn., 13, 667–680, doi:10.1007/s003820050189.
Hulme, M., and Coauthors, 1998: Climate change scenarios for the United Kingdom. U.K. Climate Impacts Programme Tech. Rep. UKCIP02, 112 pp.
Johnson, R. J., 1978: Multivariate Statistical Analysis in Geography: A Primer on the General Linear Model. Longman, 280 pp.
Khon, V. Ch., I. I. Mokhov, E. Roeckner, and V. A. Semenov, 2007: Regional changes of precipitation characteristics in northern Eurasia from simulations with global climate model. Global Planet. Change, 57, 118–123, doi:10.1016/j.gloplacha.2006.11.006.
Korzun, V. I., 1978: World Water Balance and Water Resources of the Earth. UNESCO Press, 663 pp.
Lau, W. K. M., and K. M. Kim, 2015: Robust Hadley circulation changes and increasing global dryness due to CO2 warming from CMIP5 model projections. Proc. Natl. Acad. Sci. USA, 112, 3630–3635, doi:10.1073/pnas.1418682112.
Lau, W. K. M., H. T. Wu, and K. M. Kim, 2013: A canonical response of precipitation characteristics to global warming from CMIP5 models. Geophys. Res. Lett., 40, 3163–3169, doi:10.1002/grl.50420.
Lydolph, P. E., 1977: Climates of the Soviet Union. Elsevier, 456 pp.
Maraun, D., T. J. Osborn, and N. P. Gillett, 2008: United Kingdom daily precipitation intensity: Improved early data, error estimates and an update from 2000 to 2006. Int. J. Climatol., 28, 833–842, doi:10.1002/joc.1672.
Meehl, G. A., J. M. Arblaster, and C. Tebaldi, 2005: Understanding future patterns of increased precipitation intensity in climate model simulations. Geophys. Res. Lett., 32, L18719, doi:10.1029/2005GL023680.
Meehl, G. A., and Coauthors, 2007: Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–845.
Pall, P., M. R. Allen, and D. A. Stone, 2007: Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming. Climate Dyn., 28, 351–363, doi:10.1007/s00382-006-0180-2.
SAS Institute, 2010: Base SAS 9.2 procedures guide: Statistical procedures. SAS Institute Rep., 486 pp. [Available online at https://support.sas.com/documentation/cdl/en/procstat/63104/PDF/default/procstat.pdf.]
Shiu, C., S. Liu, C. Fu, A. Dai, and Y. Sun, 2012: How much do precipitation extremes change in a warming climate? Geophys. Res. Lett., 39, L17707, doi:10.1029/2012GL052762.
Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. Atmos., 118, 2473–2493, doi:10.1002/jgrd.50188.
Sun, Y., S. Solomon, A. Dai, and R. W. Portmann, 2007: How often will it rain? J. Climate, 20, 4801–4818, doi:10.1175/JCLI4263.1.
Tebaldi, C., K. Hayhoe, J. M. Arblaster, and G. A. Meehl, 2006: Going to the extremes. Climatic Change, 79, 185–211, doi:10.1007/s10584-006-9051-4.
Thompson, D. W., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 1297–1300, doi:10.1029/98GL00950.
Trenberth, K. E., and D. J. Shea, 2005: Relationships between precipitation and surface temperature. Geophys. Res. Lett., 32, L14703, doi:10.1029/2005GL022760.
Ulbrich, U., J. G. Pinto, H. Kupfer, G. C. Leckebusch, T. Spangehl, and M. Reyers, 2008: Changing Northern Hemisphere storm tracks in an ensemble of IPCC climate change simulations. J. Climate, 21, 1669–1679, doi:10.1175/2007JCLI1992.1.
Utsumi, N., S. Seto, S. Kanae, E. E. Maeda, and T. Oki, 2011: Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett., 38, L16708, doi:10.1029/2011GL048426.
Westra, S., L. V. Alexander, and F. W., Zwiers, 2013: Global increasing trends in annual maximum daily precipitation. J. Climate, 26, 3904–3918, doi:10.1175/JCLI-D-12-00502.1.
Willmott, C. J., C. M. Rowe, and W. D. Philpot, 1985: Small-scale climate maps: A sensitivity analysis of some common assumptions associated with the grid-point interpolation and contouring. Amer. Cartogr., 12, 5–16, doi:10.1559/152304085783914686.
Ye, H., 2001: Characteristics of winter precipitation variation over northern central Eurasia and their connections to sea surface temperatures over the Atlantic and Pacific Oceans. J. Climate, 14, 3140–3155, doi:10.1175/1520-0442(2001)014<3140:COWPVO>2.0.CO;2.
Ye, H., 2008: Changes in frequency of precipitation types associated with surface air temperature over northern Eurasia during 1936–90. J. Climate, 21, 5807–5819, doi:10.1175/2008JCLI2181.1.
Ye, H., 2009: The influence of air temperature and atmospheric circulation on winter fog frequency over Northern Eurasia. Int. J. Climatol., 29, 729–734, doi:10.1002/joc.1741.
Ye, H., and E. J. Fetzer, 2009: Atmospheric moisture content associated with surface air temperatures over northern Eurasia. Int. J. Climatol., 30, 1463–1471, doi:10.1002/joc.1991.
Ye, H., and J. Cohen, 2013: A shorter snowfall season associated with higher air temperatures over northern Eurasia. Environ. Res. Lett., 8, 014052, doi:10.1088/1748-9326/8/1/014052.
Ye, H., E. J. Fetzer, S. Wong, A. Behrangi, E. T. Olsen, J. Cohen, B. H. Lambrigtsen, and L. Chen, 2014: Impact of increased water vapor on precipitation efficiency over northern Eurasia. Geophys. Res. Lett., 41, 2941–2947, doi:10.1002/2014GL059830.
Zolina, O., C. Simmer, S. K. Gulev, and S. Kollet, 2010: Changing structure of European precipitation: Longer wet periods leading to more abundant rainfalls. Geophys. Res. Lett., 37, L06704, doi:10.1029/2010GL042468.
