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Keywords:

  • active layer;
  • permafrost;
  • seasonally frozen ground

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[1] Seasonal freezing and thawing processes in cold regions play a major role in ecosystem diversity, productivity, and the Arctic hydrological system. Long-term changes in seasonal freeze and thaw depths are also important indicators of climate change. Only sparse historical measurements of seasonal freeze and thaw depths are available for permafrost and seasonally frozen ground regions. Using mean monthly soil temperature data for 1930–1990 for 242 stations located throughout Russia, we employed a linear interpolation method to determine the depth of the 0°C isotherm based on soil temperature data measured between 0.2 m and 3.2 m depth. The relationship between available observed annual maximum freeze and thaw depths and our interpolated values indicates a perfect correlation. A comprehensive evaluation of long-term trends in these new interpolated data for Russia indicates that in permafrost regions, active layer depths have been steadily increasing. In the period 1956–1990 the active layer exhibited a statistically significant deepening by approximately 20 cm. The changes in the seasonally frozen ground areas are even greater: The depth of the freezing layer decreased 34 cm between 1956 and 1990. Potential forcings of the observed changes include air temperature, freezing and thawing index, and snow depth. Correlation and multiple regression reveal that active layer depth is most strongly related to snow depth. Air temperature, both mean annual and thawing index, is also significantly related to changes in the active layer. Freeze depth is influenced most strongly by the freezing index and mean annual air temperature, although snow depth is also a significant contributor. Air temperature and snow depth have been changing less in the seasonally frozen ground regions of Russia compared to permafrost regions, although observed changes in freeze depth are greater than changes in active layer depth for 1930–1990. This indicates that the seasonally frozen ground regions of the Russian high latitudes are more susceptible to climate change than the Russian permafrost. However, as temperatures have been rising, especially in the high-latitude continental regions, both permafrost and seasonally frozen ground regions are being greatly impacted. These changes can potentially result in increased river runoff and changes in discharge throughout the Russian Arctic drainage basin, as well as changes in high-latitude ecosystems.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[2] Frozen ground, consisting of both permafrost and seasonally frozen ground, plays an essential role in high-latitude environments. Many environmental factors contribute to the formation and degradation of permafrost, such as air temperature, snow depth, vegetation canopy, soil moisture and texture, organic matter accumulation, and hydrologic movement, as well as disturbance by humans [Williams and Smith, 1989; Zhang et al., 1997, 2001a; Jorgenson et al., 2001; Nelson, 2003]. Degradation of permafrost areas can lead to changes in ecosystems, land use, and infrastructures that require permafrost as a foundation [Osterkamp et al., 1998; Nelson et al., 2001]. Furthermore, permafrost also influences soil temperature and moisture, subsurface and surface hydrology, rooting zones, and micro-topography [Hinzman et al., 1991; Kane et al., 1991; Woo, 1992; Zhang et al., 2001b]. Entire ecosystems located on permafrost can be converted to aquatic or wetland systems as the permafrost thaws [Osterkamp et al., 2000; Nelson, 2003]. Therefore, in light of observed climate change and predicted global climate change scenarios, as well as evidence that warming is most prominent in Northern Hemisphere land areas between 40°N and 70°N [Serreze et al., 2000], it is important to evaluate analogous changes in frozen ground.

[3] The spatial extent of permafrost is highly correlated with mean annual air temperature [e.g., Halsey et al., 1995]; however, this relationship is scale dependent, and the extent of permafrost is also sensitive to other environmental factors that control the energy exchange between the atmosphere and the land surface [Williams and Smith, 1989; Vitt et al., 1994; Zhang et al., 1997; Brown et al., 2000]. Therefore the relationship between frozen ground and both air and ground temperature represents a major component of land-atmosphere interactions in high latitudes. Soil temperature is linked to the climate through the ground surface, vegetation, snow cover, and, perhaps most importantly, the top layer of the ground that thaws and freezes each season, the freeze/thaw layer [Lachenbruch and Marshall, 1986; Zhang et al., 1997; Zhang and Stamnes, 1998]. In perennially frozen regions this freeze/thaw layer represents a filter between the air and the permafrost through which heat is transported via radiation, sensible heat advection, and conduction [Lachenbruch and Marshall, 1986]. In areas of permafrost, the portion of the ground that thaws each warm season and refreezes during the cold season is called the active layer [Permafrost Subcommittee, 1988]. In areas of nonpermafrost, i.e., seasonally frozen ground, it is the seasonal freeze/thaw layer.

[4] The individual and combined interactions of soil temperature, freeze/thaw depth, and permafrost with changes in climatic variables at local, regional, and hemispheric scales are still relatively poorly understood. A major obstacle to understanding the response of frozen ground to climate change, as well as the interactions between the soil and the atmosphere, is the lack of long-term observations of soil temperature. With the exception of research stations in the former Soviet Union, very few-long term records of active layer variability are available, and most studies of changes in the active layer involve only relatively short time periods that do not, and cannot, assess long-term climatic change [Brown et al., 2000]. Furthermore, many investigations of active layer changes are performed as field experiments at individual observing stations, and such site-specific results cannot reliably be extrapolated to large regions. Very few studies exist that examine long-term changes over large regions. Most research of the freeze/thaw cycle seems to focus on Alaska [e.g., Zhang et al., 1997; Romanovsky and Osterkamp, 1997; Nelson et al., 1998, 1999; Brown et al., 2000; Hinkel and Nelson, 2003] and other North American sites [Mackay, 1995; Hinkel et al., 1997; Harris, 2001; Smith et al., 2001], with relatively less focus on active layer depth and freeze depth changes in the Russian high latitudes [Pavlov, 1998].

[5] Observations of soil temperature are available for Russia, with some records dating back as far as the late 19th century and others to the 1930s or 1950s [Gilichinsky et al., 1998; Zhang et al., 2001a]. It is these data that are evaluated here. As air and soil temperatures have been increasing in the Russian high latitudes in recent decades [Serreze et al., 2000], these changes should be reflected in the active layer and in the seasonal freeze/thaw depth in the form of increased thickness of the active layer and decreased seasonal freeze depth. It is the goal of this investigation to evaluate this hypothesis by interpolating the active layer and seasonal freeze depths from the soil temperature data available at various depths. Because these station data have varying lengths of record and long-term analysis is not possible for all stations, spatial averages are created for the permafrost region, as well as the seasonally frozen ground region of Russia. The long-term trends are assessed in both regions, and related to potential external forcing variables such as air temperature, freezing/thawing index, and snow depth. This provides insight into which variables are key in translating observed climate changes to the ground thermal regime, i.e., the active layer and freeze depth.

2. Data and Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

2.1. Active Layer and Freeze Depth Interpolation

[6] The data used in this study include soil temperature, observed freeze/thaw depth, air temperature, and snow depth. Soil temperature data are available for 242 stations located throughout Russia (Figure 1) [Barry et al., 2001; International Permafrost Association Standing Committee on Data Information and Communication, 2003] and can be obtained from the Frozen Ground Data Center (http://nsidc.org/fgdc/). Detailed descriptions of soil temperature measurements in the former Soviet Union were provided by Gilichinsky et al. [1998], Zhang et al. [2001a], and in the instruction manuals of the State Committee of the U.S.S.R. for Hydrometeorology and Environmental Control [1985] and earlier issues from 1946 to 1969 [State Committee of the U.S.S.R. for Hydrometeorology and Environmental Control, 1960–1964, 1964–1972, 1966–1990, 1970–1978]. A brief review follows.

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Figure 1. Map showing the geographic locations of the 242 Russian stations with available data. Grey diamonds represent stations with both soil temperature and freeze/thaw depth data, and black circles represent those stations with soil temperature data only. Expanding the database of active layer and freeze depth stations from 208 to 242 adds many sites throughout Siberia, especially in central and northeastern Siberia (black circles). Denoted also are those 31 stations that are characterized as permafrost (black open circles and grey open diamonds) and those 211 that are on seasonally frozen ground (black closed circles and grey closed diamonds).

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[7] Mean monthly soil temperature was measured by extraction thermometers enclosed in ebonite pipes and installed at a depth of 0.20 m and deeper under natural surface cover such as grass, periodically cut during the warm season, and undisturbed snow cover during the cold season [Gilichinsky et al., 1998]. During the 1960s and 1970s, the soil temperature measurements were performed using electrical resistance thermometers at some stations. It must be noted, however, that taking these soil temperature observations unavoidably causes some site disturbance, which, over time, may cause increased thaw propagation. Therefore any long-term trends in active layer thickness potentially include this nonclimatic component. However, in the former Soviet Union hydrometeorological observation system, these measurements were conducted by well-trained technicians and professionals with established and uniform guidelines. Therefore site disturbance was reduced to minimum [Gilichinsky et al., 1998]. Observations were generally made at standard depths; however, as some of these stations span a century of observations, the standard depths changed several times. The measurements at 0.2 m, 0.4 m, 0.6 m, 0.8 m, 1.2 m, 1.6 m, 2.0 m, 2.4 m, and 3.2 m used here represent those depths that did not change throughout the observational period across all stations. The time period covered by these data varies by location: At some stations, the soil temperature record spans from the late 1800s to 1990, while at others only a few years of measurements are available. However, for many of the stations, data for the 1930–1990 period are available, and therefore this study focuses on this portion of the record. Similarly, observed freeze/thaw depth data, i.e., the monthly maximum depths of penetration of the 0°C isotherm during freezing/thawing, are generally consistently available beginning in 1930, but only until 1985. These freeze/thaw depths are based on daily observations of soil temperature, from which a daily freeze/thaw depth is interpolated. The maximum daily depth was selected from all daily depths for each month to represent that month's thaw or freeze depth. Of the 242 Russian stations, 208 also have these freeze/thaw depth observations (Figure 1).

[8] To expand the number of stations with freeze/thaw depth data from 208 to 242 and to expand the time series from 1985 to 1990 for all stations, thereby obtaining a larger and longer database of observations characterizing freeze/thaw depth in the permafrost and seasonally frozen ground regions throughout Russia, we linearly interpolated the depth of the 0°C isotherm throughout the 0.2–3.2 m temperature profile. It should be noted, however, that although the depth of the 0°C isotherm can be used as an estimate of the freeze/thaw depth it is not necessarily the same as the “true” freeze/thaw depth. For wet/ice-rich soils latent heat can play an important role which could lead to errors in estimating the freeze/thaw depth using the propagation of the 0°C isotherm. The stations were first classified as either permafrost or seasonally frozen ground, depending on soil temperature at the 3.2 m depth. If, for the entire record, a station's soil temperature at 3.2 m was negative, that station was classified as permafrost. The remaining stations were considered to be characterized as seasonally frozen ground. Soil temperature time series at 3.2 m depth were also plotted and analyzed visually, to ensure proper classification. The two subsets of stations were then analyzed separately. Active layer depths were interpolated in the permafrost region, where the temperature from one depth to the next switched from being positive to negative. Similarly, in the seasonally frozen ground regions, the freeze depth was interpolated between those layers where the temperature switched from negative to positive.

[9] In the best-case scenario, the 0°C isotherm falls between a positive and an adjacent negative value. However, because of missing values, this interpolation is often not possible. Therefore interpolations were performed between multiple layers, depending on data availability across the 0.2–3.2 m profile. There is a trade-off between sample-size and interpolation accuracy, though, such that the most restrictive interpolation (where the depth of the 0°C isotherm is interpolated between available adjacent values only) results in the smallest sample-size. Conversely, if the freeze/thaw depth is interpolated between any and all available depths, the interpolation skill decreases, since the temperature profile between, for example, the 0.2 m depth and the 3.2 m depth is not linear. Therefore our interpolation was limited to between any four standard soil depths only (i.e., allowing soil temperature at two depths to be missing), providing an optimal sample size and interpolation skill.

[10] The resulting data set of interpolated monthly freeze/thaw depths, as well as the observed daily freeze/thaw depths, were then analyzed further: Annual maximum depth of penetration of the 0°C isotherm during both freezing and thawing was calculated, as it is the changes in this variable over time that provide insight into climate change in the Russian high latitudes. The maximum depth of freezing values were selected from the months of March, April, and May only, and the maximum depth of thawing was selected from the months of August, September, and October only. It is during these months that the maximum depths are expected to occur, and this restriction was necessary because of missing data. For instance, if the maximum freeze depth was always chosen from all months of the calendar year, but the winter and spring half of a year is missing, the maximum depth of freezing could be severely underestimated because it would be chosen from the warm season, when thawing has already begun.

[11] The accuracy of the interpolation is dependent on interpolation method, vertical spacing of measuring devices, as well as other factors, and we therefore carefully assess the accuracy of our interpolation: The monthly interpolated freeze/thaw depths were compared to the observed daily values for those stations where both variables are available. This was done via linear regression between the observed daily freeze/thaw depths and the monthly interpolated ones. It is this analysis that assesses the accuracy of our interpolation, where a perfect 1:1 relationship would be described by a regression R-value of 1 and a regression slope of 1.

[12] Given this new and expanded data set of monthly freeze/thaw depths, the long-term trends were then assessed for the Russian high latitudes. An average time series was generated for the annual active layer depth departures in the permafrost region, and an average time series was generated for the maximum annual freezing layer depth departures in the seasonally frozen ground regions. The permafrost region average was formed by averaging, for each year, all available stations' annual active layer depth departures from each station's mean for the available record, for those stations that are classified as permafrost. Similarly, the seasonally frozen ground average was formed by averaging all available stations' maximum annual freezing depth departures from each station's mean, for all seasonally frozen ground stations. Linear least squares regression was then applied to these two time series to quantify their long-term changes.

2.2. Forcing Variables

[13] To explore possible causes for potential long-term changes in the freeze/thaw depths, the averaged time series were related to a number of external forcing variables, such as air temperature, freezing index, thawing index, and snow depth. Air temperature was based on the CRU TS 1.0 (1901–1995) and CRU TS 1.1 (1996–1998) data [New et al., 2000], which are available as globally gridded monthly air temperature data on a 0.5° × 0.5° grid for 1901–1998. The air temperatures corresponding to the 242 Russian stations were extracted from that grid on the basis of which grid cell each station occupies, i.e., the closest grid cell center to each station, for 1930–1990. Mean annual air temperatures were calculated for January–December of each year from the monthly data. The freezing and thawing indices were also calculated from the New et al. [2000] air temperature data. The freezing index is a measure of the combined duration and magnitude of below 0°C temperatures during any given freezing season, and is generally calculated as the sum of the average daily temperatures for all days with below-zero temperatures [Permafrost Subcommittee, 1988]. Similarly, the thawing index is a measure of the duration and magnitude of above-zero temperatures during the thawing season, and is generally calculated as the sum of the daily temperatures for all days with positive temperatures [Permafrost Subcommittee, 1988]. In this study, however, the freezing index was calculated on the basis of mean monthly air temperature. Zhang et al. [1996] found that the use of mean monthly air temperature to estimate freezing and thawing indices is accurate to approximately 95% for northern Alaska. The freezing and thawing indices were calculated for July (year t-1)–June (year t) and January (year t)–December (year t), respectively, to ensure that the entire freeze/thaw season was captured.

[14] Snow depth data were obtained from the Historical Soviet Daily Snow Depth (HSDSD) data set [Armstrong, 2001]. The HSDSD data are based on observations from 284 World Meteorological Organization (WMO) stations located throughout the former Soviet Union for the period 1881–1995. Those WMO stations closest to the 242 Russian soil temperature stations were selected (Figure 2), and the daily snow depths were averaged into monthly snow depths for 1930–1990, as well as annual averages. Similarly, monthly maximum snow depth was acquired from the daily snow depths, as well as annual maximum snow depth. The annual averages and maxima were not calculated for the calendar year but rather for July (year t-1)–June (year t). Therefore, in establishing potential relationships between snow depth and freeze/thaw depth, we are linking the previous winter's snow depth with concurrent or subsequent freeze/thaw depth. It was possible to match 92 of the Russian soil temperature stations to the WMO snow depth stations, of which 9 were in the permafrost region and the remaining 83 in the seasonally frozen ground region. In some instances, it was possible to match a WMO snow station to two soil temperature stations; in this case, the same snow station data were used for both soil temperature stations.

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Figure 2. Geographic location of the snow depth data stations and Russian soil temperature stations. The 92 matched-up snow and soil temperature stations are circled. Of these 92 sites with both snow and soil temperature data, 9 are located on permafrost and 83 are located on seasonally frozen ground.

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[15] Air temperature, freezing/thawing index, and snow depth corresponding to the 242 Russian soil temperature stations were then averaged for the permafrost and the seasonally frozen ground regions separately, and related to the changes in maximum annual freeze/thaw depth using both correlation and multiple regression analysis. For the permafrost region, active layer depth was correlated with air temperature, the thawing index, and both average and maximum snow depth, and a multiple regression model was also built to evaluate the combined effect of these variables on the active layer. The temporal trend of each variable is also determined for the permafrost region using linear regression. For the seasonally frozen ground region, freeze depth was correlated with air temperature, freezing index, and average as well as maximum snow depth, and multiple regression was again employed. The long-term trend of each variable was also assessed to determine the changes of those variables in the seasonally frozen ground region.

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

3.1. Active Layer and Freeze Depth Interpolation

[16] The interpolation of the 0°C isotherm was first performed on the set of permafrost stations. Analysis of the soil temperature at the 3.2 m depth revealed that, of the 242 Russian stations, only 31 could be characterized as being located on permafrost (Figure 1). It should be noted that although permafrost is defined as ground (soil or rock) that remains at or below 0°C for at least two years [Muller, 1943; Washburn, 1979; Permafrost Subcommittee, 1988], our definition is much more conservative, requiring the ground at 3.2 m to be below 0°C for the entire available record. Of the 31 permafrost stations, 18 had both monthly soil temperature data and observed daily active layer depths. After the active layer depth was calculated for each year, the interpolated maximum 0°C isotherm depths were compared to those based on the daily observations for those 18 stations in order to assess the accuracy of the interpolation (Figure 3). The equation of the regression line, in m, is

  • equation image

The statistically significant linear regression slope of 1.0 reveals a perfect 1:1 relationship, while the intercept of 0.059 m indicates that the monthly interpolated values tend to underestimate observed active layer depth based on daily data by approximately 6 cm. The correlation between the monthly interpolated and daily interpolated active layer depths is also a statistically significant and perfect 1.0.

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Figure 3. Scatterplot and linear least squares regression line showing the relationship between the annual active layer depth based on monthly interpolations and daily observations for 18 permafrost stations. Grid lines are drawn at the standard observing depths for soil temperature.

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[17] The remaining 211 stations that were not classified as being in permafrost regions were evaluated as seasonally frozen ground stations (Figure 1). The 0°C isotherm was linearly interpolated throughout the 0.2–3.2 m profile to find the monthly depth of the freezing layer. The maximum depth was obtained from the monthly values for each year, and compared to the available 190 stations' maximum depths based on the daily data. The relationship is again strikingly good, with a statistically significant and perfect 1.0 correlation coefficient between the two (Figure 4). The equation of the regression line, in m, is

  • equation image

indicating a statistically significant perfect 1:1 relationship with an intercept suggesting, again, a 6 cm underestimation of the freezing layer. The simple linear interpolation between up to four standard soil depths was thereby verified as accurately reproducing the observed active layer and seasonal freeze depths as based on the daily soil temperature data, and the resulting data sets of 31 permafrost stations and 211 seasonally frozen ground stations can now be evaluated for long-term trends and their potential causes.

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Figure 4. Scatterplot and linear least squares regression line showing the relationship between the maximum annual seasonal freeze depths based on monthly interpolations and daily observations for 190 seasonally frozen ground stations. Grid lines are drawn at the standard observing depths for soil temperature.

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3.2. Trends in Freeze/Thaw Depth

[18] While some of the Russian stations have long continuous records, analyzing individual stations would prove difficult as most individual station records contain too many missing observations and some only cover short periods in time. However, compositing these stations results in a long-term time series that provides an integrated view of the Russian high latitudes (40°–70°N). The 31 permafrost stations were therefore averaged to represent the areas characterized by perennially frozen ground, and the 211 seasonally frozen ground stations were averaged into a time series representing the nonpermafrost regions.

[19] In creating the averaged permafrost time series, it became evident that although the time series can be created for 1930–1990, of the 31 total stations, very few actually contribute to the mean values during the 1930s to mid 1950s (Figure 5a). Having only one to two stations contribute to the early years' means is clearly not representative of the permafrost region as a whole, and at the same time makes the overall trend very susceptible to potential outliers. Therefore 1956 was chosen as the point in time when the long-term trend can reliably be assessed, because ten or more stations contribute to each year's mean from then on. However, the entire 1930–1990 time series is presented (along with the trend for 1930–1990), in addition to the 1956–1990 values, to ensure and demonstrate that we have not made a biased decision that favors a certain outcome.

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Figure 5a. Number of permafrost stations used in creating each year's mean active layer depth.

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[20] The trend in active layer thickness shows a statistically significant increase for both 1930–1990 and 1956–1990 (Figure 5b), indicating a deepening of the active layer. This illustrates that throughout the entire available record, permafrost is thawing to greater depths each year. The 1956–1990 slope is greater, suggesting an increased rate of thawing since the mid-1950s. The net changes in active layer thickness, as approximated by the least squares regression lines, show a deepening by 23 cm for 1930–1990, and a deepening by 20 cm for 1956–1990 (note that this does not imply a deepening of 3 cm for 1930–1956, as the slope/trend for 1956–1990 is greater).

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Figure 5b. Time series of the mean annual active layer depth departures in the Russian permafrost regions. Linear least squares regression lines are included for 1930–1990 and 1956–1990.

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[21] The averaged time series for the seasonally frozen ground stations illustrates a similar increase in the number of stations that contribute to each year's mean in the mid 1950s. While even during the early years, from 1930 to the mid 1950s, between 20 and 40 stations contribute to each month's mean, and the long-term trend can potentially be assessed reasonably well from 1930 on, the number of stations increases sharply, to over 100, in the mid 1950s (Figure 6a). Therefore 1956 was again chosen for a more reliable starting point for trend assessment, which also corresponds to the starting point of the active layer depth assessment and thereby allows for comparisons. The seasonally frozen ground regions of the Russian high latitudes exhibit a statistically significant decrease in freeze depth during both 1930–1990 and 1956–1990 (Figure 6b). Similar to the active layer depth trend, the freezing depth trend is greater for the 1956–1990 period. These decreasing trends indicate that, in areas of seasonally frozen ground, the ground is freezing to shallower and shallower depths each year. The net change is a 27 cm decrease in freeze depth in 1990 compared to 1930, and an even greater 34 cm decrease from 1956–1990. The time series also indicates large negative departures beginning in the early 1970s, a feature that was not evident in the active layer depths throughout the permafrost region.

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Figure 6a. Number of seasonally frozen ground (SFG) stations used in creating annual mean freezing depth.

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Figure 6b. Time series of the mean maximum annual freeze depth departures in the Russian permafrost regions. Linear least squares regression lines are included for 1930–1990 and 1956–1990.

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3.3. External Forcing Variables

[22] To explore potential causes for the observed trends in active layer and freeze layer depth since 1930 and/or 1956, the averaged time series are related to mean annual air temperature, freezing/thawing index, and the previous cold season's maximum and average snow depth (Figure 7). We found that for the 1930–1990 period, the active layer depth is significantly correlated with snow depth, but not with thawing index or air temperature. However, this is most likely due to the unrepresentative nature of the averaged time series prior to the mid 1950s. For the 1956–1990 period, the active layer is significantly correlated with all four variables; the strongest correlations are observed for the thawing index and snow depth, with mean annual air temperature correlations of similar magnitude (Table 1). The positive sign of the correlation coefficients indicates that, as temperature/thawing index increases, so does the depth of the active layer. Similarly, an increase in the preceding winter's snow depth results in a deeper active layer. The strength of the correlation between active layer and snow depth is perhaps somewhat surprising given that during times of greatest active layer depth there is likely no snow cover at all; however, the snow depth used here is that of the preceding winter, and the relationship could therefore be explained in terms of soil moisture content and soil temperature. Anomalous winter snow depth likely results in anomalous spring and summer soil moisture which, given the higher thermal conductivity of water versus air, could result in a faster thaw of the frozen ground. Also factoring into this argument is the fact that a deeper snow cover during the preceding winter acts as a greater insulator, resulting in higher soil temperatures during the cold season and at the onset of thaw, which then requires less energy inputs and allows for thawing to greater depths. However, these explanations are highly speculative and the link between snow depth and active layer thickness is certainly very complex.

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Figure 7. Time series of mean annual air temperature, thawing index, and maximum snow depth in the permafrost region (black lines). Included in each panel is also the time series of active layer depth (grey line) to illustrate its relationship to the forcing variables. All variables were standardized with respect to their mean and standard deviation.

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Table 1. Correlation Coefficients Between Active Layer Depth and Various Potential Forcing Variables, as Well as the Trends of the Individual Variablesa
Active Layer DepthAir TemperatureThawing IndexSnow DepthmaxSnow Depthavg
  • a

    Statistically significant values (α = 0.05) are in bold italics.

Correlations
1930–19900.160.140.300.33
1956–19900.600.650.650.53
 
Trends
1930–19900.01−0.100.320.28
1956–19900.470.340.430.59

[23] Building a multiple regression model for 1956–1990 to predict active layer depth based on the above variables results in a statistically significant model in which air temperature, thawing index, and maximum snow depth account for 69% of the variance in active layer depth. It should be noted that average snow depth was not used in the model, as it is collinear with maximum snow depth and would hence result in overfitting. Combining all three variables in a multiple regression results in maximum snow depth being the most influential predictor, again illustrating the importance of antecedent soil conditions. The two measures of air temperature, thawing index and mean annual air temperature, are of approximately equal magnitude as demonstrated by their standardized coefficients (Table 2). The previous season's maximum snow depth therefore plays an important role in the following warm season's maximum active layer depth, with mean annual air temperature and thawing index also factoring into the equation significantly.

Table 2. ANOVA Table, Regression Summary, and Regression Coefficients for the Multiple Regression Between Active Layer Depth and Air Temperature, Thawing Index, and Maximum Snow Depth for 1956–1990
SourceSum of SquaresDFMean SquareFSignificanceSummary
Regression0.16530.055126.70.000R = 0.85
Residual0.0639310.00206  R2 = 0.72
Total0.22934   adj. R2 = 0.69
 
Independent VariableUnstandardized CoefficientsStandard ErrorStandardized Coefficientst-StatisticSignificance 
Constant−0.2120.264 −0.800.428 
Air Temperature0.02800.0110.3182.520.017 
Thawing Index0.0002800.0000.2982.300.029 
Snow Depthmax0.007110.0010.5195.270.000 

[24] Exploring potential causes for the observed trend in freeze depth in seasonally frozen ground regions, we found that the depth of the freezing layer is significantly correlated with mean annual air temperature, freezing index, and both maximum and average snow depth for both 1930–1990 and 1956–1990 (Figure 8 and Table 3). The strongest relationship is a positive correlation with freezing index, indicating that the combined magnitude and duration of below-zero temperatures results in an increase in freeze depth; not surprisingly, the longer the temperatures remain below 0°C, the deeper the ground freezes. The second strongest correlation is with air temperature, and this inverse relationship indicates that as mean annual air temperatures decrease, the freeze depth increases. It should be noted that while maximum freeze depth is generally observed during spring, the mean annual air temperature used here is averaged over the calendar year and therefore represents, in part, average conditions subsequent to the maximum freeze depth. Snow depth is also inversely correlated with freeze depth, such that decreased snow depth results in increased freeze depth, obviously because of the insulating effects of snow cover during the cold season.

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Figure 8. Time series of mean annual air temperature, freezing index, and maximum snow depth in the seasonally frozen ground region (black lines). Included in each panel is also the time series of freeze depth (grey line) to illustrate its relationship to the forcing variables. All variables were standardized with respect to their mean and standard deviation.

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Table 3. Correlation Coefficients Between Freezing Layer Depth and Various Potential Forcing Variables, as Well as the Trends of the Individual Variablesa
Freeze DepthAir TemperatureFreezing IndexSnow DepthmaxSnow Depthavg
  • a

    Statistically significant values (α = 0.05) are in bold italics.

Correlations
1930–1990−0.620.73−0.50−0.39
1956–1990−0.730.79−0.52−0.48
 
Trends
1930–19900.21−0.100.280.19
1956–19900.42−0.260.590.53

[25] Employing multiple regression for 1956–1990 to determine the combined effect of these variables on freeze depth, as well as to determine which variable is most important, we found that all three variables are significant in the model and account for approximately 80% of the variance in freeze depth (Table 4). Again, only maximum snow depth was used in the model and average depth was excluded, to avoid multicollinearity issues. As in the correlation analysis, freezing index is found to be the most important predictor, followed by snow depth and mean annual air temperature (Table 4).

Table 4. ANOVA Table, Regression Summary, and Regression Coefficients for the Multiple Regression Between Freezing Layer Depth and Air Temperature, Thawing Index, and Maximum Snow Depth for 1956–1990
SourceSum of SquaresDFMean SquareFSignificanceSummary
Regression0.78930.26341.460.000R = 0.90
Residual0.197310.00634  R2 = 0.80
Total0.98534   adj. R2 = 0.78
 
Independent VariableUnstandardized CoefficientsStandard ErrorStandardized Coefficientst-StatisticSignificance 
Constant−0.4270.227 −1.890.069 
Air Temperature−0.05700.0220−0.275−2.550.016 
Freezing Index0.0003760.0000.5455.170.000 
Snow Depthmax−0.01500.004−0.323−3.840.001 

[26] We also determined the temporal trends of the individual potential forcing variables to assess whether the permafrost or the seasonally frozen ground region is more/less susceptible to external forcing. Comparing the trends in mean annual air temperature indicates that temperatures increased more in the permafrost region than the seasonally frozen ground region (Table 1 versus Table 3). Similarly, the trends in freezing/thawing index and snow depth were greater in the permafrost region. Since the overall change in freeze depth was greater than the change in active layer thickness, this indicates that the seasonally frozen ground region could be more susceptible to external forcing than the permafrost region.

4. Summary and Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[27] This analysis represents a comprehensive large-scale investigation of active layer thickness and seasonal freeze depth in the high latitudes of Russia. The freeze/thaw depth, affected by varying conditions in the soil as well as the overlying surface, is an important indicator of climate variability because it integrates changes from many variables, such as temperature and precipitation [Lachenbruch and Marshall, 1986; Brown et al., 2000]. Interpolating the depth of the active layer and the seasonal freeze depth works remarkably well in reproducing the observed/daily freeze/thaw depths, as indicated by perfect 1:1 relationships between the daily observed and monthly interpolated values. Therefore working with monthly interpolated values of the 0°C isotherm allows for the long-term characterization of the permafrost region of Russia with 31 stations, and the seasonally frozen ground region with 211 stations.

[28] We found that as temperatures have been rising globally, more so in the high-latitude continental regions than elsewhere, the permafrost and seasonally frozen ground have been profoundly impacted. Permafrost degradation has occurred to the extent that in 1990, the active layer had deepened by 23 cm since 1930, and 20 cm since the mid 1950s. These changes are related to changes in snow depth as well as air temperature. Snow depth affecting active layer depth is perhaps unexpected, and numerical modeling studies show that snow depth has very limited impact on active layer depth [Zhang and Stamnes, 1998; Ling and Zhang, 2003]. However, ground temperatures at some Alaskan stations do not show forcing from air temperature, instead suggesting that antecedent snow cover, among other influences, could be more important than summer climate [Sokratov and Barry, 2002; Brown et al., 2000]. Similarly, Pavlov [1994] argues that increases in permafrost temperatures from 1970 to 1990 in northern Russia are due to deeper snow cover rather than higher air temperatures, and Stieglitz et al. [2003] also find that snow cover influences ground temperatures to a similar degree as air temperature. Furthermore, increases in air temperature are found to be accompanied by increases in snow depth, both of which have also contributed to the degradation of permafrost in central Alaska [Jorgenson et al., 2001]. The effect of snow depth on active layer depth can perhaps be explained in terms of soil moisture dynamics, although some modeling results suggest that snow cover does not have a significant correlation with the following summer soil moisture at a Russian site at Valdai [Luo et al., 2003]. However, it could be argued that greater snow depth during the preceding winter results in greater snow melt, which potentially results in higher soil moisture. Moist soil has a higher thermal conductivity than dry soil, allowing surface energy to penetrate the ground more effectively, and resulting in thawing to greater depths. This reasoning is complicated by the fact that increased soil moisture also increases soil heat capacity, therefore requiring more energy to raise the temperature of the soil. Furthermore, greater soil moisture could result in greater evaporation at the surface, which consumes energy and is therefore not available to heat the ground. Nonetheless, Zhang and Stamnes [1998] found that changes in soil water content indeed have a significant impact on active layer thickness, especially in the top soil layer, such that active layer thickness increases significantly with increasing soil water content. Similarly, Hinkel et al. [2001] conclude that the rapid downward movement of melt water during the brief snow melt period in spring has a measurable impact on both the thermal field and the soil moisture content, causing rapid warming of the upper soil. The insulating properties of snow perhaps also factor into the relationship between snow depth and the active layer. The greater the snow depth during the preceding winter, the more the ground is insulated, thereby maintaining a higher soil temperature throughout the cold season and requiring less energy at the onset of thawing. These soil moisture hypotheses represent speculations only, and it is the focus of our ongoing research to further explore the link between snow depth and active layer thickness in the Russian high latitudes. Mean annual air temperature is also found to significantly influence active layer depth, as does the cumulative effect of the magnitude and duration of positive temperatures, the thawing index.

[29] Changes in freeze depth in the seasonally frozen ground regions of Russia are even greater than those in active layer thickness, and have accelerated since the early 1970s. We found that freeze depth has been decreasing, indicating that as surface air temperatures have generally been rising in the high latitudes, less of the seasonally frozen ground freezes during the cold season. These changes are indeed related to air temperature: both mean annual air temperature and the freezing index. Snow depth also factors into the changes in freeze depth, such that as snow depth has been increasing, so has its insulating effect, thereby resulting in higher soil temperatures and less frozen ground during the cold season. Quantifying the long-term changes shows that in 1990, the freeze depth was 27 cm less than in 1930, while the change from 1956 to 1990 is even greater, at 34 cm. The fact that the freeze depth changed more than the active layer depth perhaps indicates that regions of seasonally frozen ground are more susceptible to climate change, especially in light of the fact that mean annual temperature increases, as well as increases in the freezing index (thawing index in the case of permafrost) and snow depth, were less in the seasonally frozen ground region than in the permafrost areas. Similarly, the freeze depth has undergone unprecedented negative departures and accelerated shoaling beginning in the early to mid 1970s, coinciding with observed increases in global surface air temperature records.

[30] These trends in active layer and seasonal freeze depth therefore agree well with observed local changes in mean annual air temperature, freezing/thawing index, and snow depth at the network of stations throughout the former Soviet Union. These changes seem to also agree with reported large-scale trends in surface air temperature, atmospheric temperature, and atmospheric circulation over Eurasia as a whole [e.g., Hurrell, 1996; Thompson and Wallace, 2001; Frauenfeld and Davis, 2003]. It is the focus of our ongoing research to relate changes in the freeze/thaw cycle not only to local variables, but also to these observed large-scale circulation trends such as those observed for the Arctic Oscillation and the circumpolar vortex, as well as to feedbacks of the frozen ground on the overlying atmosphere. An important next step is to expand the number of stations throughout the Russian high latitudes to allow for more accurate representation of long-term changes, as well as enable spatial analyses. While the spatially averaged approach employed here provides an average depiction of large areas using point (station) measurements, active layer and seasonal freeze depth variability is highly geographically variable, and determining in which localized regions the changes are greatest/least will provide further insight into potential causes for climate change. The station records need to also be updated through at least 2000, since significant warming has occurred throughout the last decade, especially so during the late 1990s. Hinkel and Nelson [2003] note maximum thaw depth in northern Alaska in 1998, in response to the warmest summer in their record. In western Siberia, active layer thickness increased by another 12 cm from 1992 to 1995 alone [Brown et al., 2000]. Therefore it is likely that both the active layer and seasonal freeze depth underwent further, and perhaps even greater, changes throughout the 1990s.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

[31] We thank Andrew Etringer for calculating the freezing/thawing indices, Lyne Yohe for editing the original manuscript, and the three anonymous reviewers for their valuable suggestions. This study was supported by the U.S. National Science Foundation through NSF grants OPP-9907541 and OPP-0229766. Financial support does not constitute an endorsement of the views expressed in this report.

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  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Summary and Conclusions
  7. Acknowledgments
  8. References
  9. Supporting Information

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