[1] The East Siberian transect, which has been designated by the International Geosphere‐Biosphere Program (IGBP) as its Far East transect, has unique permafrost conditions. Not only does permafrost underlie the entire transect, but also about one third of the region is underlain by an “ice complex,” consisting of extremely ice‐rich Late Pleistocene sediments. Given the possibility of a predicted future increase in global temperatures, an evaluation of the magnitude of changes in the ground thermal regime becomes desirable for assessments of possible ecosystem responses and impacts on infrastructure. A soil model developed at the Geophysical Institute Permafrost Laboratory was used to simulate the dynamics of the active layer thickness and ground temperature in this transect, both retrospectively and prognostically, using climate forcing from six global climate models (GCMs). Analysis of future permafrost dynamics showed that within the southwestern part of the transect, widespread permafrost thawing from the surface can begin as early as 2050. The spatial extent and temporal dynamics of the zone with thawing permafrost vary significantly among the different GCMs. According to all the GCMs the mean annual ground temperatures could rise by 2°–6°C, and the active layer thickness could increase by 0.5–2 m everywhere within the transect by 2099. However, the increases in mean annual ground temperature and active layer thickness are not uniform in time. Relatively cold and warm periods associated with natural fluctuations in air temperature and precipitation are superimposed on the background warming trend. The most significant increases in mean annual ground temperatures and in the active layer thickness are projected to occur in the southwestern part of the transect and in areas with coarse‐grained sediments, characterized by low water content and high thermal conductivity.

1. Introduction

[2] Permafrost is a product of severe climate conditions and is one of the most sensitive parts of the Arctic system [University Corporation for Atmospheric Research (UCAR), 1988]. The active layer of permafrost is an upper layer that thaws every summer and refreezes every winter. Because of the fact that almost all belowground biological processes and activities take place within this layer, any changes in the active layer can affect the carbon cycle. When the annual mean temperature at the base of the active layer exceeds 0°C, the depth of thaw increases in successive summers and becomes greater than the depth of refreezing in the following winters. In this situation, known as “permafrost degradation,” the thawing of the permafrost can significantly impact infrastructure, regional hydrology and ecosystems. Permafrost dynamics must be considered in order to understand changes in the active layer thickness and in mean annual ground temperatures.

[3] Interactions between the atmosphere and permafrost are complex. They involve snow cover and vegetation, conduction through various soil types with varying ice content, and phase changes of water. Permafrost, in turn, influences the atmosphere by its effects on the surface heat fluxes, evaporation and runoff, and trace gas exchanges. As global climate models (GCMs) are increasingly used to project climate changes and to understand the impact of global climate change on the ecosystems, it will eventually be possible to address changes in permafrost and the role of permafrost in climate change over the entire circumpolar region.

[4] At present, the data necessary for an evaluation of permafrost dynamics over the entire circumpolar region are not available. However, limited areas in both northern Asia and North America are amenable to the transect approach. The main idea of the transect approach is to study permafrost's spatial distribution and its temporal dynamics along latitudinal gradients in regions for which detailed information on climate, vegetation and soils is available [International Geosphere‐Biosphere Program (IGBP), 1996; McGuire et al., 2002]. This approach represents an intermediate step between single‐point studies and extrapolation/interpolation between transects to obtain a circumpolar picture of permafrost conditions and their dynamics.

[5] For our studies, which are part of the Arctic System Science Program (ARCSS) of the National Science Foundation, we address permafrost conditions along two north‐south transects: the Alaskan North Slope transect and the Tiksi‐Yakutsk East Siberian transect (Figure 1). The two transects differ in the amount and form of available information. The Alaskan transect has been an area of intensive study during the last 15–25 years and especially during the last 10 years [e.g., Romanovsky and Osterkamp, 1997]. Most of the data gathered in these studies is stored electronically in the Geographic Information Systems (GIS) in the ARCSS database. Although the studies along the East Siberian transect have more than 40 years of history, this area has been studied less intensely than the Alaskan transect. In this paper we will focus on the East Siberian transect by presenting the results of our research on past, present and future permafrost conditions as well as on the permafrost dynamics of this region.

**Figure 1**
Open in figure viewer PowerPoint

Geographic location of the East Siberian transect.

2. Environmental and Permafrost Conditions Along the East Siberian Transect

2.1. Geographic Location

[6] The East Siberian transect occupies almost the entire central part (between 120° and 140°E) of the Yakutia (Sakha) Republic in the eastern Siberia (Figure 1). The Laptev Sea of the Arctic Ocean borders the transect on the north. The territory of the transect is a land of mountains and plateaus, which occupy over 70% of the area. The main rivers of the East Siberian transect are the Lena River and its major tributaries, the Vilyuy River and the Aldan River. The Lena River watershed begins in the steep mountains that border the western shores of Lake Baikal. Then the river meanders northeast and is joined by the Vitim River, followed by the Olyokma, Aldan, Amga, and Vilui Rivers before flowing through a wide delta and into the Arctic Ocean. The Lena River is over 4000 km long, and its entire basin is 2.5 million sq. km.

[7] Yakutia's greatest mountain range, the Verkhoyansk Range, runs parallel to and east of the Lena River (Figure 1), forming a great arch that begins in the Sea of Okhotsk and ends near the Laptev Sea (Arctic Ocean). This range has hundreds of small tributaries that feed the Lena River as it moves northward.

2.2. Climate

[8] The climate in the most parts of the East Siberian transect is extremely continental. The mean annual air temperature ranges from −10° to −15°C within the transect [Gavrilova, 1981]. The Arctic Laptev Sea Lowlands (Figure 1) are characterized by an oceanic type of climate in their northern part and by a continental type farther south in their subarctic belt. The Arctic Ocean's influence makes the climate much less continental near the Arctic shore. The seasonal range of air temperature near the coast is 32°–36°C, in comparison with 50°–54°C within the inland parts of the Lowlands. Mean annual air temperatures in the Lowlands range from −13.5° to −15°C in the northeastern part to −11.6° to −13.5°C in the southern part. Total annual precipitation in the Arctic Lowlands ranges from 140–170 mm/yr on the shoreline and on islands to 250–270 mm/yr in the interior. In spite of the low values of total annual precipitation, the ground (vegetation) surface is abundantly moist because evaporation rates are small. Winter snow thicknesses (or snow depths) generally decrease from 0.6–0.7 m in the foothills to 0.15–0.25 m near the Arctic shoreline and on the islands [Yershov, 1998a]. Snow density increases in the same direction because of the increase of wind speeds.

[9] The Verhoyansk Range is in the zone of severe continental climate with long cold winters and short hot summers. Seasonal temperature variations exceed 100°C (from +40°C during the summer to −60°C during the winter). This region is famous for being the coldest region of the Northern hemisphere during winter. The most extreme winter temperatures occur at Verkhoyansk, where the temperature can drop here as low as −71.2°C [Gavrilova, 1978].

[10] Smooth relief characterizes the Central Yakutian Lowlands, which represent a typical alluvial‐lacustrine and erosion‐denudation plain. High points reach 50–300 m above sea level. The climate within this territory is severely continental, with little precipitation and large seasonal variations in air temperatures [Gavrilova, 1981]. During winter, the center of the Siberian anticyclone is located within the Central Yakutian Lowlands, bringing clear skies and very cold air temperatures (−50° to −60°C). Mean January air temperatures decrease from −32°C in the west to −44°C in the east. Summer monthly mean temperatures are usually around 16°–18°C. Mean annual air temperatures are −9° to −11°C and the range of seasonal variations is from 50° to 62°C. Winter inversions of air temperatures are very common in valleys. As a result, the air temperatures in valleys in wintertime can be as low as −70°C. Total annual precipitation does not exceed 250–300 mm, and the thickness of the winter snow cover typically ranges from 20 to 40 cm [Yershov, 1998b].

[11] Southern Yakutia is also characterized by a severe continental climate. Mean annual air temperatures vary from −6° to −15°C and depend on elevation [Kudryavtsev, 1975]. Snow cover is comparatively thick (winter‐average snow cover thickness is 0.6–0.8 m) and at the same time quite porous. Annual precipitation is 400–500 mm.

2.3. Permafrost and Ground Ice

[12] The entire transect is located within the permafrost area. The Arctic Laptev Sea Lowlands are one of the most severe regions on Earth in terms of climatic conditions (not only at present, but also during the Pleistocene) [Danilova, 1989]. Permafrost is continuous with mean annual ground temperatures in the range of −5° to −13°C (Figure 2). Mean annual ground temperatures gradually decrease from south (−5°C) to north (−11° to −15°C). Through its insulating effect, the snow cover can increase mean annual ground temperatures by up to 8°C and is therefore a major factor determining mean annual ground temperatures in this region [Gavrilova, 1978].

**Figure 2**
Open in figure viewer PowerPoint

Active layer thickness and mean annual ground temperatures within the East Siberian transect. Derived by using data from *Melnikov* [1988]. Digitized by the Permafrost Laboratory, Geophysical Institute, University of Alaska, Fairbanks.

[13] Active layer thickness ranges from 0.15–0.3 m in peat and in silty sediments with organic inclusions to 0.9–1.5 m in sand and deluvial sediments [Dunaeva and Kondratieva, 1989]. The active layer becomes gradually shallower from south to north because of the northward decrease in summer air temperatures. Permafrost thickness ranges from about 200 m under rivers beds and lakes to 500 m near the Arctic shoreline. The volumetric ice content of the soils ranges from about 30% (in sand) up to 70% (in silt) [Karpov, 1978].

[14] Permafrost within the Verhoyansk Range is continuous with mean annual ground temperatures between −3°C and −9°C (Figure 2). Mean annual ground temperatures are warmer in valleys than on the ridges and mountaintops. Active layer thickness is generally less than 0.5 m in silt and 1.2 m in coarse‐grain sediments [Gavrilova, 1978].

[15] The thickness of permafrost is greatest (700–900 m and more) on ridges and mountaintops. In foothills, the permafrost thickness does not exceed 500–700 m, and in river valleys it is typically 100–300 m [Yakupov et al., 1984; Varlamov et al., 1993]. The volumetric ice content in the frozen soils is typically 5% to 10%. Within the glacial sediments on mountain slopes, ice content can reach 20–30% [Yershov, 1998a].

[16] Permafrost is continuous everywhere in the Central Yakutian Lowlands. Mean annual ground temperatures decrease gradually from south (−2° to −4°C) to the north (−7° to −9°C) (Figure 2). Within the Lena‐Vilui Plain, the mean annual ground temperatures vary from +0.2° to −6°C. The mean annual ground temperatures are similar in Yakutsk. The warmest temperatures are observed in sandy‐silt and sandy sediments with low ice content. Active layer thickness is about 1–2 m in sand and 0.5–1 m in peat [Dunaeva and Kondratieva, 1989]. The depth of permafrost ranges from about 100 m in the southern part (Olekminsk, Russkaya River) to about 400 m (Namtsy) in the northern part of the Plain.

[17] The transition from continuous to discontinuous permafrost exists in Southern Yakutia in spite of the fact that mean annual air temperatures are very close to those typical for Central Yakutia, which is in a zone of continuous permafrost. Southern Yakutia's uniqueness arises from the wide variety of climatic, geographical, hydrological and geological conditions. The combination enables large taliks (unfrozen ground occupying small areas and existing for more than a year [Yershov, 1998a]) with mean annual ground temperatures around 0°C to coexist with 200–300 m thick permafrost [Kudryavtsev, 1975]. Bedrock in the region consists of gneiss, schist, and carbonates and is rarely covered by Quaternary sediments.

[18] The most peculiar feature of the East Siberian transect is the “yedoma” or “ice complex.” The ice complex occupies approximately 30% of the transect (Figure 3). It can be described as syngenetic ice wedges in a matrix of silt. The volumetric ice content can be as high as 60–80% [Soloviev, 1959; Tomirdiaro, 1980]. The genesis of the ice complex is still an active topic of discussion and research. The most common theories about its origin can be categorized as alluvial, alluvial‐lacustrine, glacial, deluvial and eolian [Tomirdiaro, 1980]. At least some sections of the ice complex contain evidences of water sedimentation [Kaplina, 1981; Rozenbaum, 1976].

**Figure 3**
Open in figure viewer PowerPoint

Schematic map of ice complex distribution within the East Siberian transect (courtesy of M. N. Grigoriev).

[19] Ice complex is covered with a so called “protection layer,” usually silt or sandy silt that often contains significant amounts of organic material [Karpov, 1984]. This layer was developed during the Holocene after cessation of syngenetic ice wedges growth. The development of this layer probably was a result of both increased active layer thickness during the Holocene optimum and limited deposition of mineral and organic material at the ground surface. The thickness of the protection layer does not exceed 2.8 m and is usually 1.5–2.0 m thick [Balobaev, 1996]. If seasonal thawing reaches the ice horizon or if mean annual ground temperature exceeds 0°C, long‐term thawing will start from the surface and thermokarst development will accompany the process of permafrost degradation.

2.4. Soils and Their Thermophysical Properties

[20] Thermophysical properties of soils and rocks are among the crucial initial parameters for calculating the active layer thickness and ground temperatures. These properties include thermal conductivity, volumetric heat capacity and the latent heat of changes of water phase. The thermophysical properties of soils depend on factors such as texture, structure, mineral composition, organic content, water content (ice content), density, salt content and temperature [Feldman et al., 1988]. All of these factors are the result of the geological history of the sediment formation and the present‐day landscape type and climate.

[21] Within the East Siberian transect, most of the soils formed during the Quaternary and overlay Mezozoic and Paleozoic bedrock. Bedrock can be highly weathered and is visible on the surface in some places [Hrutskyi and Derevyagin, 1981]. Quaternary deposits have alluvial, eluvial, glacial, lacustrine and fluvioglacial genesis. Alluvial sediments consist of sand with different grain size, sandy‐silt and silt, and gravel and boulders in sandy or silt matrices in some places. In alluvial silty‐organic sediments, which are sometimes up to 100 m thick, one can often find massive underground ice [Ivanov, 1984].

[22] It is practically impossible to distinguish the thermophysical properties of all genetically different types of soils, even for a small area. For regional studies and for engineering purposes it is sufficient to distinguish between the following types of soils: peat, sand, sandy silt, silt, soils with organic, gravel and bedrock [Feldman et al., 1988].

[23] The thermal properties of peat have major effects on mean annual ground temperatures and active layer thickness [Yershov, 1998a]. Peat is very common in tundra and in marshes within the taiga zone. The thickness of a peat layer can be as large as 40–50 cm, and in some places can exceed 1 m. Within the East Siberian transect peat can be found in the valleys of all major and small rivers (Figure 4). In the north, it is found within the deltas of the Lena and Yana Rivers and on the Arctic Lowlands of Northern Yakutia. The thermal conductivity of the unfrozen peat varies from 0.29 to 0.52 W/(m K) [Gavriliev, 1998]; the volumetric heat capacity varies from 1760 to 3780 kJ/(m³ K); the thermal conductivity of frozen peat can reach 1.3 W/(m K).

**Figure 4**
Open in figure viewer PowerPoint

Soil types within the East Siberian transect. Derived using data from *Elovskaya et al.* [1978] and *Krasnii* [1978].

[24] Most alluvial sediments within the East Siberian transect can be put into three categories: sand, sandy silt and silt (Table 1 and Figure 4). Thermal conductivity, heat capacity and latent heat vary significantly within each category, depending on grain size, water content, presence of organic material and genesis. The presence of organic material in soils can alter the physical and thermophysical properties of the soils [Yershov, 1998a]. The majority of alluvial soils within the transect contain organic material [Yershov, 1989; Elovskaya et al., 1978]. For most of the territory, the quantity and character of the organic is not known.

Table 1. Thermal Properties of Soils Within the East Siberian Transect^aa Data from Feldman et al. [1988].

Groups of Soils	Volumetric Water Content	Thermal Conductivity of Frozen Soils, W/m K	Thermal Conductivity of Thawed State, W/m K	Heat Capacity of Frozen Soils, kJ/m³	Heat Capacity of Thawed Soils, kJ/m³
Pebbles, gravel, boulders, silt	0.10	2.80	2.45	1776	1985
Fine sand	0.17	2.04	1.68	1401	1757
Sandy‐silt with peat	0.15	2.48	1.52	1705	2019
Peat with silt	0.40	0.46	0.37	1706	2020
Gravel, sand	0.20	2.48	1.52	1810	2229
Gravel, pebbles, sand, silt	0.20	2.36	1.35	2156	2783

a Data from Feldman et al. [1988].

[25] Gravel and coarse‐grained sediments of glacial, fluvial glacial or deluvial origin can be found in mountainous and uplands regions (Figure 4). Those sediments are characterized by low water and ice content and by high thermal conductivity (up to 3 W/(m K) [Feldman et al., 1988]. The thermophysical properties chosen for our calculations were adopted from Feldman et al. [1988] and are averaged characteristics for each group.

3. Description of the Model and Input Parameters Used for the Hindcast and Projection of Permafrost Dynamics

[26] The Geophysical Institute Permafrost Laboratory (GIPL) model was used to simulate the spatial and temporal dynamics of permafrost in the East Siberian transect for the past and the future. This model is a quasi‐two‐dimensional, quasi‐transitional, spatially distributed, physically based analytical model for the calculation of active layer thickness and mean annual ground temperature. Details of the formulation and the analysis of performance quality of this model are provided by Sazonova and Romanovsky [2003]. In brief, the model contains a horizontal grid of points without explicit resolution into vertical layers. It allows for temporal variations driven by prescribed forcing from above. The core of GIPL is a modification of the formulations of Kudryavtsev et al. [1974], Romanovsky [1989], and Romanovsky and Osterkamp [1995, 1997] for calculating mean annual ground temperatures and active layer thickness. Input parameters for the calculations are stored in GIS format for a series of layers. Digital maps of mean annual ground temperatures and active layer thickness (ALT) are the output of the model. The combination of GIS and a modified Kudryavtsev's approach can be referred to as an interactive GIS. The calculations of the ALT and mean annual ground temperatures were performed on a grid consisting of 1000 grid cells with spatial dimensions of 0.5° latitude × 0.5° longitude.

[27] The introduction of GCMs made it possible to evaluate permafrost dynamics on a global scale. Anisimov and Nelson [1997] used a frost index approach [Nelson and Outcalt, 1987], coupled with three GCMs, to develop the first GCM‐based assessment of permafrost dynamics over the Northern Hemisphere. The results indicated that a large, nearly circumpolar zone of relict permafrost would develop by the end of the 21st century. The most recent application of GCMs to determine permafrost dynamics has been done by Stendel and Christensen [2002]. They calculated active layer thickness (ALT) using the modified Stefan's equation. The results of this work indicated that the ALT would increase by 30–40% by the end of the 21st century, although it should be noted that the 1961–1990 discontinuous and sporadic permafrost zones in Stendel and Christensen's results (see their Figure 1) are much smaller than observed, so much so that they are virtually absent.

[28] The models developed by Anisimov and Nelson [1997] and Stendel and Christensen [2002] have a spatial resolution of about 2.5°–5° of latitude/longitude. This resolution does not allow for complex relief and vegetation patterns, and is a rough approximation for applications in ecosystem modeling and infrastructure risk assessment. For the purposes of this project, it was determined that a spatial resolution of 0.5 × 0.5° was required to capture the regional variations of features such as topography and would be appropriate for modeling on a regional scale. It should be mentioned that the present study is not aimed at creating a highly accurate model, which is not feasible with the resolution scale of 0.5 × 0.5° latitude/longitude. Rather, the aim is a physically based model that will describe the temporal dynamic well.

[29] The strategy that was followed in this study included the use of a reference simulation of permafrost dynamics in the East Siberian transect. This reference simulation consists of a hindcast for the 20th century blended with a projection (forecast) for the 21st century. The hindcast simulation, for the period 1900–1966, was forced by observationally based monthly grids of surface air temperature (Jones [1994] and update; Leemans and Cramer [1991]) and precipitation (Hulme [1994] and update), gridded to 0.5° resolution in latitude and longitude, as described by McGuire et al. [2001]. For the period 1966–1994, the observational grids were blended, by the procedure of McGuire et al. [2001], with the monthly simulated fields of surface air temperature and precipitation from a HadCM2 GCM for the same period to provide the upper boundary conditions for GIPL (Figure 5). The boundary conditions for 1994–2100 were obtained directly from the output of the HadCM2 global climate model, forced by prescribed values of greenhouse gas concentrations and sulfate aerosols [e.g., Johns et al., 1997]. The observational data were also used to establish the credibility of HadCM2, as described in the following section. The reasoning behind this strategy is that a hindcast simulation that captures present‐day climate characteristics reasonably accurately can provide a 21st‐century reference scenario against which other simulations, forced by climate output from different climate models, can then be compared.

**Figure 5**
Open in figure viewer PowerPoint

Comparison between measured and modeled using HadCM2 GCM mean annual air temperatures, seasonal range of air temperatures and snow cover thickness (averaged for the period of 1966–1989).

3.1. Boundary Conditions and Input Parameters for the Period 1901–2000

3.1.1. Climatic Parameters

[30] The climatic parameters used to drive GIPL include mean annual air temperatures, seasonal range of air temperatures, snow thickness and snow density. We note here that GIPL utilizes only a single (winter average) snow thickness for each year. The seasonal variation of this thickness is included implicitly through the use of air temperature's annual range, which is one of the three GCM output parameters used in driving GIPL. The annual range of air temperature effectively introduces into the permafrost simulation the effects of the seasonality of the snow cover. Because of the air temperature and snow cover simulated by HadCM2 are essential to the GIPL simulation beyond the late 20th century, we first summarize HadCM2's configuration and then examine the air temperature and snow distributions simulated by HadCM2 over the East Siberian transect.

[31] The HadCM2 GCM was developed in the Hadley Centre (a branch of the UK Meteorological Office) in 1994 as a modification of the Unified Model [Gullen, 1993]. A coupled, ocean‐atmosphere GCM, HadCM2 was among the first to be used in historical climate change experiments forced by past concentrations of greenhouse gases. HadCM2 has been run with a spatial resolution of 2.5 × 3.75 degrees of latitude/longitude, corresponding to approximately 295 km × 278 km at 45° N and S. This GCM has 19 levels representing the atmospheric components. The equilibrium climate sensitivity of HadCM2, which is the global‐mean temperature response to a doubling of effective CO₂ concentration, is in the midrange of estimates for the real climate system behavior [Johns et al., 1997; Mitchell et al., 1995] and is approximately 3.0°C (http://www.cru.uea.ac.uk/).

[32] The mean annual air temperatures simulated by HadCM2 generally decrease northward and eastward in the East Siberian transect. The warmest (−8° to −5°C) air temperatures are in the southern part of the transect (between 55° and 60°N), while the coldest mean annual air temperatures (down to −16°C) are in the northernmost parts of the transect (the Lena River delta and Arctic Lowlands) and along the eastern slope of the Verkhoyansk Range (Figure 5). Mean annual air temperatures are generally colder in the eastern part of the transect (east of 130°E) than in the western part. Interannual variations of mean annual air temperatures are 3°–4°C (Figure 6).

**Figure 6**
Open in figure viewer PowerPoint

Output climatic parameters from HadCM2 GCM for the year 2000.

[33] The seasonal range of air temperatures has been derived from the mean monthly values. The average magnitude of the seasonal range is about 60°–65°C. The lowest seasonal range of air temperatures is in the northernmost and southernmost parts of the transect (up to 35°–40°C).

[34] HadCM2 as well as other GCMs used for the hindcast and forecast provide the snow‐water equivalent instead of snow thickness and snow density. The latter two important parameters for the ground thermal regime were derived from snow‐water equivalents by the relation w_eq = ρ_snH_sn (where w_eq is snow‐water equivalent in mm of water, ρ_sn is the snow density in kg/m³, and H_sn is the snow cover thickness in meters), together with data from meteorological stations and Feldman et al.'s [1988] method based on an analysis of snow cover properties measured at 166 meteorological stations within Yakutia. The processing and analysis of the measured data permitted a grouping of the meteorological stations (five groups altogether) on the basis of the relation ρ_sn(H_sn), which in general form can be represented as

where a_i, b_i, c_i are coefficients that are specific to each group of meteorological stations, i = 1,…,5.

[35] For each group, the following relationships between snow density and snow thickness have been established [Feldman et al., 1988]:

[36] A spatial interpolation between meteostations was then performed to obtain the spatial extent for each group, and each grid cell was assigned to a particular group (Figure 7). The largest portion of the transect area belongs to the fourth group with relatively low snow density. Low snow density together with thick (0.3–0.5 m) snow cover can warm the mean annual ground temperatures by up to 6°–8°C [Feldman et al., 1988]. By using HadCM2's and the observationally based snow‐water equivalents (W_eq), the relation W_eq = ρ_snH_sn, and the linear or quadratic polynomials listed above, one can solve two equations for the two unknowns (ρ_sn and H_sn), thereby obtaining the snow thickness for each grid cell for each year of the calculation.

**Figure 7**
Open in figure viewer PowerPoint

Snow cover type distribution map derived by using the data from a snow density classification [after *Feldman et al.*, 1988].

[37] The northern part of the transect, including the Lena River delta and the Arctic Lowlands, is in the first group, for which snow thickness is generally smaller and snow density is relatively high. As a result, the warming effect of the snow in these areas is as small as 2°–4°C. The high snow density can be explained by high moisture content in the air and in the snow during autumn and early winter, and by strong winds that cause the snow to compact.

3.1.2. Validation of the HadCM2 GCM

[38] The measured air temperatures and snow thickness from 32 sites within the East Siberian transect were used for the comparison with the outputs of HadCM2 model for the period 1966–1989. Snow cover thickness is available from 8 sites of the 32 sites. HadCM2's output climatic parameters (courtesy of D. McGuire) were interpolated over the entire transect. The same interpolation has been done for observed climatic data. The results are shown in Figure 5.

[39] For most parts of the transect, the simulated and observed mean annual temperatures are within 2°C of each other. For the northernmost and southernmost parts, this difference can increase to 4°–5°C. The difference between measured and calculated temperatures also decreases when approaching a data site, implying that the error between measured and calculated temperatures can be partially explained by errors in the spatial interpolation.

[40] The model's seasonal range of air temperature is smaller than observed over the most of the transect. For the central part, the difference between observed and modeled seasonal ranges is as large as 20°C (Figure 5). This difference can lead to an error of up to 0.2 m in the estimate of the active layer thickness. The observed and modeled snow cover thicknesses are similar, except for the northernmost parts of the transect.

[41] Overall, the validation of HadCM2 showed that this model can serve as a useful benchmark for comparisons with other GCMs and can be used to hindcast mean annual ground temperatures and active layer thickness for the 1900–2000 period, subject to small errors resulting from differences in the observed and simulated values of certain climatic parameters.

3.1.3. Vegetation and Soil Thermal Properties

[42] The thermal effects of surface vegetation depend on vegetation type, the presence of water and the season [Tyrtikov, 1979]. Moss is one of the most important and widespread surface vegetation types [Beringer et al., 2001]. Because moss has a large capacity for moisture, the wintertime thermal conductivity of moss is high, sometimes as large as 1.8 W/(m K) [Feldman et al., 1988]. Hence moss allows the effects of cold waves to penetrate deep into the ground. During summer, when evaporation rates are high, moss loses moisture and its thermal conductivity can decrease to 0.2 W/(m K) and possibly even lower [Feldman et al., 1988; Beringer et al., 2001]. For this reason, moss is an effective insulator during summer. Consequently, permafrost is more stable and the active layer is shallower in areas with moss.

[43] Shrubs, grass and lichens generally, but not always, act like insulators in winter and in summer. Shrubs play an important role in snow distribution and redistribution [Sturm et al., 2001]. In the presence of shrubs, the ground temperatures are warmer and the active layer is usually thicker. The only exception occurs in the presence of very thick shrubs, which create shadows and thus lead to a cooling of the surface during summer [Yershov, 1998a].

[44] In our study we considered only a low level of vegetation that is less than 1 m high, because the information about higher vegetation such as trees and tall shrubs is already incorporated into the monthly surface air temperature output from GCMs. Our vegetation typing was based on a data set for the East Siberian transect created in GIS format by D. O. Sergueev using a topsoil cover vegetation map from the Global Characteristics Database (http://edcdaac.usgs.gov/glcc/globdoc2_0.html). Surface vegetation was generalized into six classes: sparse moss or lichen, moss or lichen, moss with grass tussocks, moss or lichen with grass, sparse grass, and dense grass. Surface vegetation affects the ground temperature and is very important for the active layer thickness.

[45] Soils properties such as volumetric moisture content, thermal conductivity in frozen and thawed state, and heat capacity for frozen and thawed soils were assigned to each grid cell. The thermophysical properties assigned to each group of soils, discussed previously, are shown in Table 1.

3.2. Permafrost Dynamics Within the East Siberian Transect During 1900–2000

[46] The active layer thickness (ALT) and the mean annual ground temperatures are highly variable parameters, and the interannual range of the variations in the simulated time series can be considerable (0.5 m for the ALT and 3°–4°C for the mean annual ground temperatures). Over decadal timescales (10–20 years), the mean annual ground temperatures can vary by as much as 5°–6°C, while variations in the ALT can be as large as 1.5 m [Dunaeva and Kondratieva, 1989].

[47] The distribution of the ALT for the sample year of 1905 (Figure 8) shows that the soil thermophysical properties are the major factor affecting ground thermal regime. The largest ALT can be found in mountains and uplands with glacial‐fluvioglacial sediments and eluvial sediments consisting mostly of sand, pebbles, and cobbles, with inclusions of silt (Figure 4). Sand, pebbles and cobbles have high thermal conductivity and relatively low volumetric ice content (15–25%). The shallowest ALT usually occurs in areas with significant peat or organic material content in soils and with a moss cover on the ground surface. In northern parts of the transect, the active layer is also shallow because of very low mean annual air temperatures and cold summers.

**Figure 8**
Open in figure viewer PowerPoint

Active layer thickness dynamics within the East Siberian transect in the 20th century calculated using climatic parameters from HadCM2 GCM.

[48] The retrospective analysis of the dynamics of the ALT and the mean annual ground temperatures shows periods of relative warming of ground temperatures and accompanying increases in the ALT, as well as periods with relative cooling in mean annual ground temperatures and decreases in the ALT. The first relatively warm period in the model's 1900–2000 occurred during approximately 1930–1940 (Figure 8). In comparison with the model's initial state, the ALT was deeper everywhere in the East Siberian transect during that time. The most significant increase took place in uplands and mountainous areas (up to 1 m for the ALT and up to 3°C for mean annual ground temperatures). In the lowlands, near the coastline and adjacent regions, the increase in the ALT was much smaller, typically less than 0.3 m. The zone of the shallow ALT, where thickness does not exceed 0.3 m, shifted to the north and contracted.

[49] After the warm period of 1930–1940, a relatively cold period occurred in the simulation during 1960–1970, when the ALT decreased and mean annual ground temperatures decreased (Figure 8). The area with shallow ALT moved southward, and the ALT did not exceed 2 m anywhere in the transect. The most significant changes in the ALT and in the mean annual ground temperatures again took place in uplands and mountainous regions.

[50] The next warm period in the simulation occurred in 1990–2000, which was warmer than the 1930–1940 period. The increase in mean annual air temperatures caused a deepening of the ALT (Figure 8) and an increase in the mean annual ground temperatures.

[51] The analysis of the ALT and mean annual ground temperatures for the past 100 years (1900–2000 years) showed that under the influence of natural variability of mean annual air temperatures [e.g., Polyakov et al., 2002], significant changes occurred in the ALT (up to 1 m) and in the mean annual ground temperatures (±3°C) of the East Siberian transect. In the period since 1900, there were two warm subperiods during which the ALT was deeper and mean annual ground temperatures were higher. Those two warm periods were separated by a cold period, during which the mean annual ground temperatures were cooler and ALT was shallower.

3.3. Boundary Conditions and Parameters for the Forecast for the Period 2000–2099

[52] Climatic output parameters from five GCMs, in addition to HadCM2, were used as upper boundary conditions to obtain 21st‐century projections of ALT and ground temperatures. The five additional GCMs were all designated for use by the Arctic Climate Impact Assessment program (ACIA) (http://www.acia.uaf.edu).

[53] The ACIA is using simulations forced by the B2 scenario [Intergovernmental Panel on Climage Change (IPCC), 2000], which is a “moderate” scenario of CO₂ increase in the atmosphere. The outputs of 21st‐century simulations (through 2100) forced by the B2 scenario were used here as input to the GIPL permafrost model. The five ACIA‐designated GCMs are those of Canadian Center for Climate Modeling and Analysis (CCC), the National Center for Atmospheric Research/Climate System Model (CSM), Geophysical Fluid Dynamics Laboratory (GFDL), Hadley Climate Center's Version 3 (HadCM3) and the Max Planck Institute for Meteorology (ECHAM). The spatial resolution of each model, together with a reference for each, is shown in Table 2. In order to incorporate climatic data from each GCM into GIPL, the climatic outputs were formatted and interpolated to fit the 0.5° × 0.5° latitude/longitude grid of the GIPL.

Table 2. ACIA‐Designated GCMs

Full Model Name (Model “ID” for ACIA)	Center and Country	Reference	Atmospheric Resolution	Ocean Resolution
CGCM2 (CGC)	CCCma, Canada	Flato et al. [2000]	T32 (3.8 × 3.8) L10	1.8 × 1.8 L29
CSM 1.4 (CSM)	NCAR, USA	Boville et al. [2001]	T42 (2.8 × 2.8) L18	2.0 × 2.4 L45
ECHAM4/OPYC3 (ECH)	MPI, Germany	Roeckner et al. [1999]	T42 (2.8 × 2.8) L19	2.8 × 2.8 L11
GFDL_R30_c (GFD)	GFDL, USA	Knutson et al. [1999]	R30 (2.25 × 3.75) L14	2.25 × 1.875 L18
HadCM3 (HAD)	UKMO, UK	Gordon et al. [2000]	2.5 × 3.75 L19	1.25 × 1.25 L20

[54] The various GCMs differ significantly in terms of their projected mean annual temperatures, amplitudes of seasonal variations of temperature, and snow thickness for the period 2000–2099. The increase in mean annual air temperatures by the end of the year 2100 ranges from 2° to 9°C among the models. The largest increase is projected to occur at high latitudes (to the north of 65°N).

[55] The CCC simulation provides a good approximation to mean annual air temperatures of the reference (HadCM2) simulation in 2000. Latitudinal and longitudinal zonality is present. The CCC model's coldest mean annual air temperatures (−14° to −16°C) are in the western part of the Lena River delta; the warmest region (−8° to −6°C) is the southern (south of 60°N) part of the transect. The CCC model gives generally warmer (about 1°–2°C) mean annual air temperatures for the central and southern parts of the transect in comparison with HadCM2. Interannual variations of mean annual air temperatures are typically 2 to 4°C, and the magnitudes become smaller by the year 2100 (Figure 9). CCC projects increases in mean annual air temperatures by up to 3°–4°C, which puts this simulation in the category of a moderate warming. The seasonal range of CCC air temperatures (35°–55°C) is 10°–20°C smaller than in the reference simulation over much of the transect. This difference in amplitudes can lead to an error of up to 0.2–0.3 m in the estimated ALT.

**Figure 9**
Open in figure viewer PowerPoint

Output climatic parameters from CCC GCM for the year 2000.

[56] The CSM (Figure 10) can be categorized as a “cold” model. The warmest mean annual air temperatures (−12° to −10°C) are in the far southwestern part of the transect. The coldest temperatures (−20° to −18°C) occupy a wide zone between 70° and 75°N. In the central part of the transect, mean annual air temperatures do not exceed −12°C. In comparison with reference data, this GCM gives much colder (by up to 4°C) mean annual air temperatures. The variation of the CSM temperatures in the East Siberian transect is almost entirely zonal; that is, the temperatures vary with latitude but only negligibly with longitude. The interannual variability of mean annual air temperatures is high (5° to 7°C), with a slight decrease toward 2100. CSM projects increases in mean annual air temperatures of up to 4°–6°C by 2100. The seasonal range of air temperatures in this model is low (35°–50°C) in comparison with the reference simulation. Because of its low mean annual air temperatures and small seasonal range, CSM predicts very cold summers. In the northern parts of the transect, the model simulates no summer at all, with snow cover persisting through the summer. The snow thickness is low in comparison with the reference, with a range from 0.1 to 0.3 m over much of the transect.

**Figure 10**
Open in figure viewer PowerPoint

Output climatic parameters from CSM GCM for the year 2000.

[57] The annual mean air temperatures simulated by the Geophysics Fluid Dynamics Laboratory (GFDL) model are generally colder than those of HadCM2 by 1°–3°C in 2000. For most parts of the transect, the mean annual air temperatures range from −10°C to −14°C (Figure 11). There is a slight longitudinal variation of the simulated temperatures. The warmest temperatures are to the south of 60°N and do not exceed −10°C. Interannual variability of mean annual air temperatures reaches 6°–8°C. In comparison with other models, there is no decrease in interannual variability by the end of the 21st century. Mean annual air temperatures increase by 5°–7°C during the 2000–2100 simulation period. The seasonal range of air temperatures is realistic, 60°–65°C over most of the transect. This model's snow cover is very thick, ranging from 0.4 m in the far north to 0.8 m in the central and southern parts of the transect.

**Figure 11**
Open in figure viewer PowerPoint

Output climatic parameters from GFDL GCM for the year 2000.

[58] HadCM3 is the second GCM from Hadley Climate Center used for our East Siberian climatic forcing. In comparison with reference HadCM2, it gives slightly colder air temperature and a shallower snow cover. Mean annual air temperatures generally show a realistic variation in latitude and longitude (Figure 12). The coldest temperatures are in the northernmost part of the transect and range from −14° to −18°C. The warmest temperatures (−5° to −10°C) are in the southern and central parts. The interannual variability of temperatures is low (2°–4°C).

**Figure 12**
Open in figure viewer PowerPoint

Output climatic parameters from HadCM3 GCM for the year 2000.

[59] The ECHAM GCM developed at the Max Planck Institute for Meteorology produces what can be called the “worst case scenario” for permafrost. The increase in mean annual air temperatures for the period 2000–2100 is large, typically 8°–11°C. Mean annual air temperatures are in good agreement with the reference values everywhere except in the northernmost part of the transect and in the Arctic lowlands, where the model's temperatures are warmer than the reference values (Figure 13). Mean annual air temperatures vary realistically with latitude and longitude. The warmest mean annual air temperatures (−5° to −8°C) occupy the southwestern part of the transect, while the coldest air temperatures (−14° to −16°C) are found in the center of the easternmost part of the transect. Averages for the central portion range from −10° to −14°C. The interannual variability of the air temperatures is 3°–4°C. The seasonal range of air temperature, generally from 40° to 60°C, is slightly lower than the corresponding reference values. The highest amplitudes are in the central and western parts of the transect. Snow cover thickness is 0.2 m to 0.3 m for the entire transect. There is no zonality in the snow cover distribution.

**Figure 13**
Open in figure viewer PowerPoint

Output climatic parameters from ECHAM GCM for the year 2000.

[60] The surface air temperature and snow cover from the models described above provided the climate forcing data for the GIPL model. Other parameters for the GIPL simulations, including soil properties and vegetation types, were the same as for the HadCM2 hindcast in all the permafrost simulations.

4. Projections of Permafrost Dynamics for the Period 2000–2099

[61] The ALT distributions for 2000 (Figure 14) permit evaluations of the different GCMs relative to the reference HadCM2 simulation. In comparison with this reference simulation, CSM gives a very shallow active layer (ALT less than 1.5 m) in the central part, where HadCM2 simulates an ALT of 2–3 m. In contrast, CCC and GFDL give generally larger values of the ALT. ECHAM and HadCM3 are the closest to the reference GCM.

**Figure 14**
Open in figure viewer PowerPoint

Active layer thickness distribution for the year 2000 according to six GCMs used in the forecast.

[62] According to all models, the smallest ALT occurs in the valleys of large rivers, where soils are composed of silt with peat, and in the colder northern parts of the transect (to the north of 70°N). CSM gives the largest zone with a shallow active layer (less than 0.6 m) to the north of 70°N; in the simulations by the other models, this zone includes only the Lena River delta and the Arctic Lowlands. The deepest (up to 2–3 m) ALT is simulated within territory occupied by glacial, fluvioglacial coarse deposits. Within the Central Yakutian lowlands, the ALT ranges from 1.2 m to 2.4 m according to all GCMs.

[63] By 25 years after the beginning of the forecast, almost all GCMs, except for CSM, show that the mean annual ground temperatures will be warmer by up to 1°–2°C and the ALT will be deeper by up to 1 m (Figure 15). CSM predicts a cooling in 2020–2030. According to this model, the zone with the shallowest ALT (less than 0.6 m) occupies most of the eastern part of the transect; in the rest of the area, the ALT simulated by CSM does not exceed 1.8 m. On average, the decrease in CSM's ALT ranges from 0.2 to 0.4 m. Mean annual ground temperatures are projected to be colder as well. In comparison with the reference model, ECHAM and CCC produce a larger increase of ALT by up to 1 m in the southwestern part of the transect. GFDL and HadCM3 produce ALT values that remain the closest to the reference GCM.

**Figure 15**
Open in figure viewer PowerPoint

Active layer thickness distribution for the year 2025 according to six GCMs used in our forecast.

[64] By 50 years after the beginning of the forecast (Figure 16), all the models predict an increase of 0.5–1 m in the ALT, but in comparison with the reference simulation, only ECHAM predicts a zone with thawing permafrost. The reference model and ECHAM predict the formation of a zone with widespread permafrost degradation (annual mean temperature at the base of the active layer exceeds 0°C) in the southwestern part of the transect, where uplands are widespread. According to ECHAM, this zone will be twice as large as in the reference model. CCC and ECHAM give the largest increases in the ALT (up to 1–2 m) in the central and southern parts of the transect. CSM predicts a northward shift of the zone with shallowest ALT, with a net increase of the ALT by up to 0.8–1 m. All the GCMs predict that the shallowest ALT will be in the Lena River delta and in the valleys of large rivers. The deepest ALT (from 1.2 m to 3.6 m) will be in the southwestern part of the transect. ECHAM gives the deepest ALT (3.6 m). The most significant increase in the ALT will take place in the areas with coarse‐grained soils and in the southern part of the transect.

**Figure 16**
Open in figure viewer PowerPoint

Active layer thickness distribution for the year 2050 according to six GCMs used in our forecast.

[65] By 75 years after the beginning of the forecast, the ECHAM and CSM models indicate that there will be a relatively cold period, during which the zone with permafrost degradation that existed in 2050 (ECHAM) will totally disappear (Figure 17). The ALT will decrease by up to 1 m according to ECHAM and CSM. According to the CSM scenario, the zone with shallowest ALT will shift farther south. Other GCMs predict an increase in mean annual ground temperatures up to 2°–3°C, together with a deepening of the ALT. A zone with permafrost degradation appears by 2075 in the CCC‐driven simulation. This zone occupies the central part of southeastern uplands. According to the reference model, the zone where permafrost degradation has begun will progress farther and almost triple in size.

**Figure 17**
Open in figure viewer PowerPoint

Active layer thickness distribution for the year 2075 according to six GCMs used in our forecast.

[66] By the year 2099 (Figure 18), which is the end of the forecast, the zone with thawing permafrost will be widespread in the southwestern upland part of the transect, according to all simulations except CSM. ECHAM predicts the largest zone, which expands almost to the Vilui River and occupies approximately 19% from the total area of the transect. The reference model gives a smaller zone with permafrost degradation (about 10% of the transect area). CCC and HadCM3 give even smaller areas of thawing permafrost. ECHAM, CCC, GFDL and HadCM3 predict deep ALT for uplands in the southwestern and central parts of the East Siberian transect. In these simulations, the ALT ranges from 3 to 4 m in central part of the transect where coarse‐grained sediments are found. CSM produces comparably smaller values of the ALT. According to this model, the deepest ALT is found in the southwestern part of the transect, and the depth will not exceed 2.4 m.

**Figure 18**
Open in figure viewer PowerPoint

Active layer thickness distribution for the year 2099 according to six GCMs used in our forecast.

[67] The results presented above show that the permafrost evolution varies considerably with the choice of the climate model used to drive the permafrost simulation. The set of climate models used here is essentially the same as the set chosen for use by the Climate Impact Assessment (http://www.acia.uaf.edu/) on the assumption that a group of state‐of‐the‐art climate models provides a more solid basis for such applications than any single model. It is known (e.g., V. Kattsov, personal communication, 2003) that the “consensus” or “composite” of a suite of climate simulations by different state‐of‐the‐art models is generally superior to any single model's present‐day climate simulation when subjected to a comprehensive validation.

[68] Although no single model is superior to the others in all respects, each model has its own strengths and weaknesses. The biases vary from model to model for various reasons, some of which are understood while others are not. The CSM model, for example, is colder in the Arctic during summer than the other models because the optically thick clouds in this version of the CSM deplete the solar beam excessively. The CSM cloud parameterization has been investigated and modified in subsequent model versions. The greenhouse simulations by the CCC model are known to show greater warming in the Arctic relative to the other models, in part because the present‐day summer sea ice was undersimulated by this version of the model. The origin of other differences, such as the stronger low‐frequency variability in ECHAM, is not well known.

5. Conclusions

[69] The primary conclusions are the following:

[70] 1. CCC, HadCM3, HadCM2 and ECHAM predict that a zone with permafrost degradation will develop within the southwestern part of the transect. The starting point in time and the character of dynamics of this zone vary among the models. ECHAM and HadCM2 predict that this zone will develop by 2050. According to HadCM2, this zone grows continuously through time and will reach its maximum (11% from the total area of the transect) in 2099. ECHAM indicates that the dynamics of the zone with permafrost degradation varies nonuniformly in time. Thus, in 2075, this zone totally disappears because of a relatively cold period, but it forms again and reaches almost 25% of the total area by 2099. According to CCC, this zone will develop by 2075 and will double in size by 2099. HadCM3 predict that permafrost degradation will not start until the 2080s.

[71] 2. All GCMs except CSM predict that the ALT will deepen by 0.5–2 m everywhere within the transect, and that mean annual ground temperatures will rise by 2°–6°C. However, the increase in mean annual ground temperatures and the ALT will not be uniform in time. There will be relatively cold and warm periods caused by natural variations of air temperatures. These variations will be superimposed on the background warming trend. The timing of cold and warm periods varies among the GCMs. For example, the 2020s and 2030s are relatively cold according to CSM and GFDL, and the 2070s and 2080s according to ECHAM and CSM.

[72] 3. The most significant increase in mean annual ground temperatures and in the ALT take place in southwestern part of the transect and in the areas with coarse‐grained sediments, which are characterized by low water content and high thermal conductivity. Such thermophysical properties allow those soils to respond quickly to changes in air temperature.

Acknowledgments

[73] This research was funded by the ARCSS Program, by the Polar Earth Science Program, Office of Polar Programs, National Science Foundation (OPP‐9721347, OPP‐9732126, and OPP‐9870635), and by the State of Alaska. The temperature data used in this study are available to other researchers through the JOSS project (http://www.joss.ucar.edu) and from NSIDC (http://nsidc.org). We would like to thank J. Brigham‐Grette and an anonymous reviewer for very helpful suggestions that were used to improve the manuscript.

References

Anisimov, O. A., and F. E. Nelson (1997), Influence of climate change on continental permafrost in the Northern Hemisphere (in Russian), Meteorol. Gidrol., 1997/5, 71– 80. (Russ. Meteorol. Hydrol., Engl. Transl., 1997/5, 47–53, 1997.).
Google Scholar
Balobaev, V. T. (1996), Climate influence on permafrost in central Yakutia (in Russian), 25 pp., Permafrost Inst., Siberian Branch, Acad. of Sci. of the USSR, Yakutsk.
Google Scholar
Beringer, J., A. H. Lynch, F. S. Chapin III, M. Mack, and G. B. Bonan (2001), The representation of Arctic soils in the Land Surface Model (LSM): The importance of mosses, J. Clim., 14, 3324– 3335.
Crossref ADS Web of Science®Google Scholar
Boville, B. A., J. T. Kiehl, P. J. Rasch, and F. O. Bryan (2001), Improvements to the NCAR CSM‐1 for transient climate simulations, J. Clim., 14, 164– 179.
Crossref ADS Web of Science®Google Scholar
Danilova, N. S. (1989), Major features of natural conditions in middle Siberia during permafrost formation (in Russian), in Geocryology of the USSR: Middle Siberia, pp. 100– 115, Nedra, Moscow.
Google Scholar
Dunaeva, E. N., and K. A. Kondratieva (1989), Seasonal freezing and thawing of soils (in Russian), in Geocryology of the USSR: Middle Siberia, pp. 68– 71, Nedra, Moscow.
Google Scholar
Elovskaya, L. G., E. I. Petrova, and L. V. Teterina (1978), Soil map of Yakutia, scale 1:5,000,000, Permafrost Inst., Siberian Branch, Acad. of Sci. of the USSR, Yakutsk.
Google Scholar
Feldman, G. M., A. S. Tetelbaum, N. I. Shender, and R. I. Gavriliev (1988), The Guidebook for Temperature Regime Forecast in Yakutia (in Russian), 240 pp., Permafrost Inst., Siberian Branch, Acad. of Sci. of the USSR, Yakutsk.
Google Scholar
Flato, G., G. J. Boer, W. G. Lee, N. A. McFarlane, D. Ramsden, M. C. Reader, and A. J. Weaver (2000), The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate, Clim. Dyn., 16, 451– 468.
Crossref ADS Web of Science®Google Scholar
Gavriliev, R. I. (1998), Thermophysical Properties of Soils and Soils' Covers Within Cryolithozone (in Russian), 220 pp., Permafrost Inst., Siberian Branch, Acad. of Sci. of the USSR, Novosibirsk, Russia.
Google Scholar
Gavrilova, M. K. (1978), Climate and Long‐Term Freezing of Soils (in Russian), 214 pp., Nauka, Moscow.
Google Scholar
Gavrilova, M. K. (1981), Contemporary Climate and Permafrost on the Continents (in Russian), 112 pp., Nauka, Moscow.
Google Scholar
Gordon, C., C. Cooper, C. A. Senior, H. T. Banks, J. M. Gregory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood (2000), The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dyn., 16, 147– 168.
Crossref ADS Web of Science®Google Scholar
Gullen, M. J. P. (1993), The Unified forecast/climate model, Meteorol. Mag., 122, 81– 95.
Google Scholar
Hrutskyi, C. F., and A. U. Derevyagin (1981), Cryolitological characteristics of upper part of Anabarskyi‐Olenekskyi Plate cryolithozone (in Russian), Probl. Kriolitol., 9, 139– 141.
Google Scholar
Hulme, M. (1994), Validation of large‐scale precipitation fields in general circulation models, in Global Precipitation and Climate Change, edited by M. Desbois, and F. Desalmand, NATO ASI Ser., Ser. I, 26, 387– 406.
Crossref Google Scholar
Intergovernmental Panel on Climate Change (IPCC) (2000), Special Report on Emission Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, edited by N. J. Nakicenovic et al., 599 pp., Cambridge Univ. Press, New York.
Google Scholar
International Geosphere‐Biosphere Program (IGBP) (1996), Northern Eurasia Study: Prospectus for an integrated Global Change Research Project, Rep. 37, 95 pp., Stockholm.
Google Scholar
Ivanov, M. S. (1984), Cryogenic Structure of Quaternary Deposits in Leno‐Aldanskaya Depression (in Russian), 35 pp., Nauka, Moscow.
Google Scholar
Johns, T. C., R. E. Carnell, J. F. Crossley, J. M. Gregory, J. F. B. Mitchell, C. A. Senior, S. F. B. Tett, and R. A. Wood (1997), The Second Hadley Centre Coupled Ocean‐Atmosphere GCM: Model description, spinup and validation, Clim. Dyn., 13, 103– 134.
Crossref ADS Web of Science®Google Scholar
Jones, P. D. (1994), Hemispheric surface air temperature variations: A reanalysis and an update to 1993, J. Clim., 7, 1794– 1802.
Crossref ADS Web of Science®Google Scholar
Kaplina, T. N. (1981), The Late Cenozoic history of permafrost in northern Yakutia (in Russian), in The History of Permafrost Dynamics in Eurasia, pp. 153– 181, Nauka, Moscow.
Google Scholar
Karpov, E. G. (1978), Morphology and temperature of contemporary permafrost on shoreline and shallow parts of Eniseisko‐Pyasinskyi northern rivers (in Russian), in Geocryological and Hydrogeological Research in Yakutia, pp. 105– 114, Permafrost Inst., Siberian Branch, Acad. of Sci. of the USSR, Yakutsk.
Google Scholar
Karpov, E. G. (1984), Genesis of thick ice complex in Enisei Lowlands (in Russian), Geol. Geofiz., 1, 118– 123.
Google Scholar
Knutson, T. R., T. L. Delworth, K. W. Dixon, and R. J. Stouffer (1999), Model assessment of regional surface temperature trends (1949–1997), J. Geophys. Res., 104, 30,981– 30,996.
Wiley Online Library ADS Web of Science®Google Scholar
Krasnii, L. I. (1978), Geological map of Yakutia, scale 1:1,500,000, GUGK, Moscow.
Google Scholar
A. V. Kudryavtsev, (Ed.) 1975, Southern Yakutia (in Russian), 135 pp., MSU Press, Moscow.
Google Scholar
Kudryavtsev, V. A., L. S. Garagula, K. A. Kondrat'yeva, and V. G. Melamed (1974), Fundamentals of Frost Forecasting in Geological Engineering Investigations (in Russian), MGU Press, Moscow. (English translation, CRREL Draft Transl. 606, 489 pp., Cold Reg. Res. and Eng. Lab., Hanover, N. H., 1977.).
Google Scholar
Leemans, R., and W. Cramer (1991), The IIASA database for mean monthly temperature, precipitation and cloudiness of a global terrestrial grid, RR‐91‐18, 61 pp., Int. Inst. for Appl. Anal., Laxenburg, Austria.
Google Scholar
McGuire, A. D., et al. (2001), Carbon balance of the terrestrial biosphere in the twentieth century: Analysis of CO₂, climate and land use effects with four process‐based ecosystem models, Global Biogeochem. Cycles, 15, 183– 206.
Wiley Online Library CAS ADS Web of Science®Google Scholar
McGuire, A. D., et al. (2002), Environmental variation, vegetation distribution, carbon dynamics, and water/energy exchange in high latitudes, J. Vegetation Sci., 13, 301– 314.
Wiley Online Library Web of Science®Google Scholar
P. I. Melnikov, (Ed.) 1988, Map of landscapes and permafrost conditions in Yakutia (scale 1:2,500,000), Permafrost Inst., Siberian Branch, Acad. of Sci. of the USSR, Yakutsk.
Google Scholar
Mitchell, J. F. B., T. C. Johns, J. M. Gregory, and S. Tett (1995), Climate response to increasing levels of greenhouse gases and sulphate aerosols, Nature, 376, 501– 504.
Crossref CAS ADS Web of Science®Google Scholar
Nelson, F. E., and S. I. Outcalt (1987), A frost index number for spatial prediction of ground frost zones, Arct. Alp. Res., 19, 279– 288.
Crossref Web of Science®Google Scholar
Polyakov, I., S. Akasofu, U. Bhatt, R. Colony, M. Ikeda, A. Makshtas, C. Swingley, D. Walsh, and J. Walsh (2002), Trends and variations in Arctic climate system, Eos Trans. AGU, 83(47), 547– 548.
Wiley Online Library ADS Google Scholar
Roeckner, E., L. Bengtsson, J. Feichter, J. Lebeveld, and H. Rodhe (1999), Transient climate change simulations with a coupled atmosphere‐ocean GCM including the tropospheric sulfur cycle, J. Clim., 12, 3004– 3012.
Crossref ADS Web of Science®Google Scholar
Romanovsky, V. E. (1989), Approximate calculation of the insulation effect of the snow cover (in Russian), in Geokriologicheskie Issledovania, Geologiia, vol. 23, pp. 145– 157, MGU Press, Moscow.
Google Scholar
Romanovsky, V. E., and T. E. Osterkamp (1995), Interannual variations of the thermal regime of the active layer and near‐surface permafrost in northern Alaska, Permafrost Periglacial Processes, 6, 313– 335.
Wiley Online Library Web of Science®Google Scholar
Romanovsky, V. E., and T. E. Osterkamp (1997), Thawing of the active layer on the coastal plain of the Alaskan Arctic, Permafrost Periglacial Processes, 8, 1– 22.
Wiley Online Library Web of Science®Google Scholar
Rozenbaum, G. E. (1976), Cryogenic structure of the alluvium of subarctic rivers (in Russian), Probl. Kriolitol., 5, 61– 65.
Google Scholar
Sazonova, T. S., and V. E. Romanovsky (2003), A model for regional‐scale estimation of temporal and spatial variability of the active layer thickness and mean annual ground temperatures, Permafrost Periglacial Processes, 14, 125– 139.
Wiley Online Library Web of Science®Google Scholar
Soloviev, P. A. (1959), Cryolithozone of the Northern Part of the Lena‐Amga Rivers Watershed (in Russian), pp. 130– 144, Acad. of Sci. USSR, Moscow.
Google Scholar
Stendel, M., and J. H. Christensen (2001), Impact of global warming on permafrost conditions in a coupled GCM, Geophys. Res. Lett., 29(13), 1632, doi:10.1029/2001GL014345, 2002.
Wiley Online Library Web of Science®Google Scholar
Sturm, M., J. P. McFadden, G. E. Liston, F. S. Chapin III, C. H. Racine, and J. Holmgren (2001), Snow‐shrub interactions in Arctic tundra: A hypothesis with climatic implications, J. Clim., 14, 336– 344.
Crossref ADS Web of Science®Google Scholar
Tomirdiaro, S. V. (1980), Loess‐Ice Late Pleistocene‐Holocene Sediments in Eastern Siberia (in Russian), 100 pp., Nauka, Moscow.
Google Scholar
Tyrtikov, A. P. (1979), The Dynamics of Vegetation Cover and the Development of Cryogenic Processes (in Russian), pp. 90– 116, Nauka, Moscow.
Google Scholar
University Corporation for Atmospheric Research (UCAR) (1988), Arctic interactions: Recommendations for an arctic component in the International Geosphere‐Biosphere Programme, Off. for Interdiscip. Earth Stud., Boulder, Colo.
Google Scholar
Varlamov, S. P., Yu. B. Skachkov, and P. N. Skryabin (1993), Variability of the ground hydrothermal regime within the slope landscapes in central Yakutia (in Russian), in The Regularities of the Permafrost Landscapes Evolution, pp. 12– 21, Permafrost Inst. Press, Yakutsk, Russia.
Web of Science®Google Scholar
Yakupov, V. S., V. S. Danilov, and V. M. Kalinin (1984), Permafrost and cryopegs in western Yakutia based on geophysical data, in XXVII International Geological Congress, vol. 8, pp. 184– 186, Nauka, Moscow.
Google Scholar
E. D. Yershov, (Ed.) 1989, Geocryology of the USSR: Eastern Siberia and Far East (in Russian), 515 pp., Nedra, Moscow.
Google Scholar
Yershov, E. D. (1998a), General Geocryology, 580 pp., Cambridge Univ. Press, New York.
Google Scholar
E. D. Yershov, (Ed.) (1998b), Regional and historical world geocryology (in Russian), in The Basics of Geocryology, Part 3, pp. 276– 388, MSU Press, Moscow.
Google Scholar

Citing Literature

Number of times cited according to CrossRef: 58

Mark R Welford, Robert A Yarbrough, Mark R. Welford, Robert A. Yarbrough, Thresholds, Human-Environment Interactions, 10.1007/978-3-030-56032-4, (91-121), (2021).
Crossref
Ren Li, Lin Zhao, Tonghua Wu, Qinxue Wang, Yongjian Ding, Jimin Yao, Xiaodong Wu, Guojie Hu, Yao Xiao, Yizhen Du, Xiaofan Zhu, Yanhui Qin, Shuhua Yang, Rui Bai, Erji Du, Guangyue Liu, Defu Zou, Yongping Qiao, Jianzong Shi, Soil thermal conductivity and its influencing factors at the Tanggula permafrost region on the Qinghai–Tibet Plateau, Agricultural and Forest Meteorology, 10.1016/j.agrformet.2018.10.011, 264, (235-246), (2019).
Crossref
Sergey R. Chalov, Shuguang Liu, Roman S. Chalov, Ekaterina R. Chalova, Alexey V. Chernov, Ekaterina V. Promakhova, Konstantin M. Berkovitch, Aleksandra S. Chalova, Aleksandr S. Zavadsky, Nadezhda Mikhailova, Environmental and human impacts on sediment transport of the largest Asian rivers of Russia and China, Environmental Earth Sciences, 10.1007/s12665-018-7448-9, 77, 7, (2018).
Crossref
Chenghai Wang, Di Wu, Ying Kong, Ruolin Li, Hongxia Shi, Changes of Soil Thermal and Hydraulic Regimes in Northern Hemisphere Permafrost Regions over the 21st Century, Arctic, Antarctic, and Alpine Research, 10.1657/AAAR0016-026, 49, 2, (305-319), (2018).
Crossref
Matthias Fuchs, Guido Grosse, Jens Strauss, Frank Günther, Mikhail Grigoriev, Georgy M. Maximov, Gustaf Hugelius, Carbon and nitrogen pools in thermokarst-affected permafrost landscapes in Arctic Siberia, Biogeosciences, 10.5194/bg-15-953-2018, 15, 3, (953-971), (2018).
Crossref
Subodh Kumar Saha, K. Sujith, Samir Pokhrel, Hemantkumar S. Chaudhari, Anupam Hazra, Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2, Journal of Advances in Modeling Earth Systems, 10.1002/2016MS000845, 9, 1, (271-290), (2017).
Wiley Online Library
Xiaofan Zhu, Tonghua Wu, Ren Li, Changwei Xie, Guojie Hu, Yanhui Qin, Weihua Wang, Junming Hao, Shuhua Yang, Jie Ni, Cheng Yang, Impacts of Summer Extreme Precipitation Events on the Hydrothermal Dynamics of the Active Layer in the Tanggula Permafrost Region on the Qinghai‐Tibetan Plateau, Journal of Geophysical Research: Atmospheres, 10.1002/2017JD026736, 122, 21, (11,549-11,567), (2017).
Wiley Online Library
Munkhtsetseg Zorigt, Jaap Kwadijk, Eelco Van Beek, Scott Kenner, Estimating thawing depths and mean annual ground temperatures in the Khuvsgul region of Mongolia, Environmental Earth Sciences, 10.1007/s12665-016-5687-1, 75, 10, (2016).
Crossref
Jim McLaughlin, Kara Webster, Effects of Climate Change on Peatlands in the Far North of Ontario, Canada: A Synthesis, Arctic, Antarctic, and Alpine Research, 10.1657/1938-4246-46.1.84, 46, 1, (84-102), (2014).
Crossref
Tobias Kluge, Thomas Marx, Werner Aeschbach-Hertig, Christoph Spötl, Detlev K. Richter, Noble gas concentrations in fluid inclusions as tracer for the origin of coarse-crystalline cryogenic cave carbonates, Chemical Geology, 10.1016/j.chemgeo.2014.01.006, 368, (54-62), (2014).
Crossref
DongLiang Luo, Jin HuiJun, Sergei Marchenko, Vladimir Romanovsky, Distribution and changes of active layer thickness (ALT) and soil temperature (TTOP) in the source area of the Yellow River using the GIPL model, Science China Earth Sciences, 10.1007/s11430-014-4852-1, 57, 8, (1834-1845), (2014).
Crossref
Qingbai Wu, Peng Zhang, Guanli Jiang, Yuzhong Yang, Yousheng Deng, Xianbin Wang, Bubble emissions from thermokarst lakes in the Qinghai–Xizang Plateau, Quaternary International, 10.1016/j.quaint.2013.11.028, 321, (65-70), (2014).
Crossref
Kevin Schaefer, Hugues Lantuit, Vladimir E Romanovsky, Edward A G Schuur, Ronald Witt, The impact of the permafrost carbon feedback on global climate, Environmental Research Letters, 10.1088/1748-9326/9/8/085003, 9, 8, (085003), (2014).
Crossref
Yu Zhang, Spatio‐temporal features of permafrost thaw projected from long‐term high‐resolution modeling for a region in the Hudson Bay Lowlands in Canada, Journal of Geophysical Research: Earth Surface, 10.1002/jgrf.20045, 118, 2, (542-552), (2013).
Wiley Online Library
C. Beer, A. N. Fedorov, Y. Torgovkin, Permafrost temperature and active-layer thickness of Yakutia with 0.5-degree spatial resolution for model evaluation, Earth System Science Data, 10.5194/essd-5-305-2013, 5, 2, (305-310), (2013).
Crossref
C. Beer, A. N. Fedorov, Y. Torgovkin, Permafrost temperature and active-layer thickness of Yakutia with 0.5 degree spatial resolution for model evaluation, Earth System Science Data Discussions, 10.5194/essdd-6-153-2013, 6, 1, (153-162), (2013).
Crossref
S. Zubrzycki, L. Kutzbach, G. Grosse, A. Desyatkin, E.-M. Pfeiffer, Organic carbon and total nitrogen stocks in soils of the Lena River Delta, Biogeosciences, 10.5194/bg-10-3507-2013, 10, 6, (3507-3524), (2013).
Crossref
A. Rinke, H. Matthes, J.H. Christensen, P. Kuhry, V.E. Romanovsky, K. Dethloff, Arctic RCM simulations of temperature and precipitation derived indices relevant to future frozen ground conditions, Global and Planetary Change, 10.1016/j.gloplacha.2011.10.011, 80-81, (136-148), (2012).
Crossref
Karianne S. Lilleøren, Bernd Etzelmüller, Thomas V. Schuler, Kjersti Gisnås, Ole Humlum, The relative age of mountain permafrost — estimation of Holocene permafrost limits in Norway, Global and Planetary Change, 10.1016/j.gloplacha.2012.05.016, 92-93, (209-223), (2012).
Crossref
Ruo He, Matthew J. Wooller, John W. Pohlman, John Quensen, James M. Tiedje, Mary Beth Leigh, Shifts in Identity and Activity of Methanotrophs in Arctic Lake Sediments in Response to Temperature Changes, Applied and Environmental Microbiology, 10.1128/AEM.00853-12, 78, 13, (4715-4723), (2012).
Crossref
Yueyang Jiang, Qianlai Zhuang, Jonathan A. O'Donnell, Modeling thermal dynamics of active layer soils and near‐surface permafrost using a fully coupled water and heat transport model, Journal of Geophysical Research: Atmospheres, 10.1029/2012JD017512, 117, D11, (2012).
Wiley Online Library
T. Hipp, B. Etzelmüller, H. Farbrot, T. V. Schuler, S. Westermann, Modelling borehole temperatures in Southern Norway – insights into permafrost dynamics during the 20th and 21st century, The Cryosphere, 10.5194/tc-6-553-2012, 6, 3, (553-571), (2012).
Crossref
T. Hipp, B. Etzelmüller, H. Farbrot, T. V. Schuler, S. Westermann, Modelling borehole temperatures in Southern Norway – insights into permafrost dynamics during the 20th and 21st century, The Cryosphere Discussions, 10.5194/tcd-6-341-2012, 6, 1, (341-385), (2012).
Crossref
S. Zubrzycki, L. Kutzbach, G. Grosse, A. Desyatkin, E.-M. Pfeiffer, Organic carbon and total nitrogen stocks in soils of the Lena River Delta, Biogeosciences Discussions, 10.5194/bgd-9-17263-2012, 9, 12, (17263-17311), (2012).
Crossref
Yan Kong, Hong Jiang, Wei Xu, Xiuying Zhang, undefined, 2011 19th International Conference on Geoinformatics, 10.1109/GeoInformatics.2011.5980690, (1-6), (2011).
Crossref
Tara J. Troy, Justin Sheffield, Eric F. Wood, Estimation of the Terrestrial Water Budget over Northern Eurasia through the Use of Multiple Data Sources, Journal of Climate, 10.1175/2011JCLI3936.1, 24, 13, (3272-3293), (2011).
Crossref
Reginald Muskett, Vladimir Romanovsky, Alaskan Permafrost Groundwater Storage Changes Derived from GRACE and Ground Measurements, Remote Sensing, 10.3390/rs3020378, 3, 2, (378-397), (2011).
Crossref
T. Hipp, B. Etzelmüller, H. Farbrot, T. V. Schuler, Modelling the temperature evolution of permafrost and seasonal frost in southern Norway during the 20th and 21st century, The Cryosphere Discussions, 10.5194/tcd-5-811-2011, 5, 2, (811-854), (2011).
Crossref
Hisashi Sato, Hideki Kobayashi, Nicolas Delbart, Simulation study of the vegetation structure and function in eastern Siberian larch forests using the individual-based vegetation model SEIB-DGVM, Forest Ecology and Management, 10.1016/j.foreco.2009.10.019, 259, 3, (301-311), (2010).
Crossref
I M Shkolnik, E D Nadyozhina, T V Pavlova, E K Molkentin, A A Semioshina, Snow cover and permafrost evolution in Siberia as simulated by the MGO regional climate model in the 20th and 21st centuries, Environmental Research Letters, 10.1088/1748-9326/5/1/015005, 5, 1, (015005), (2010).
Crossref
J. E. Vonk, L. Sánchez-García, I. Semiletov, O. Dudarev, T. Eglinton, A. Andersson, Ö. Gustafsson, Molecular and radiocarbon constraints on sources and degradation of terrestrial organic carbon along the Kolyma paleoriver transect, East Siberian Sea, Biogeosciences, 10.5194/bg-7-3153-2010, 7, 10, (3153-3166), (2010).
Crossref
J. E. Vonk, L. Sánchez-García, I. P. Semiletov, O. V. Dudarev, T. I. Eglinton, A. Andersson, Ö. Gustafsson, Molecular and radiocarbon constraints on sources and degradation of terrestrial organic carbon along the Kolyma paleoriver transect, East Siberian Sea, Biogeosciences Discussions, 10.5194/bgd-7-5191-2010, 7, 4, (5191-5226), (2010).
Crossref
Reginald R Muskett, Vladimir E Romanovsky, Groundwater storage changes in arctic permafrost watersheds from GRACE and in situ measurements , Environmental Research Letters, 10.1088/1748-9326/4/4/045009, 4, 4, (045009), (2009).
Crossref
J. F. González-Rouco, H. Beltrami, E. Zorita, M. B. Stevens, Borehole climatology: a discussion based on contributions from climate modeling, Climate of the Past, 10.5194/cp-5-97-2009, 5, 1, (97-127), (2009).
Crossref
Tonghua Wu, Qinxue Wang, Masataka Watanabe, Ji Chen, Dorjgotov Battogtokh, Mapping vertical profile of discontinuous permafrost with ground penetrating radar at Nalaikh depression, Mongolia, Environmental Geology, 10.1007/s00254-008-1255-7, 56, 8, (1577-1583), (2008).
Crossref
Yu Zhang, Wenjun Chen, Daniel W. Riseborough, Transient projections of permafrost distribution in Canada during the 21st century under scenarios of climate change, Global and Planetary Change, 10.1016/j.gloplacha.2007.05.003, 60, 3-4, (443-456), (2008).
Crossref
David M. Lawrence, Andrew G. Slater, Vladimir E. Romanovsky, Dmitry J. Nicolsky, Sensitivity of a model projection of near‐surface permafrost degradation to soil column depth and representation of soil organic matter, Journal of Geophysical Research: Earth Surface, 10.1029/2007JF000883, 113, F2, (2008).
Wiley Online Library
K. M. Walter, J. P. Chanton, F. S. Chapin, E. A. G. Schuur, S. A. Zimov, Methane production and bubble emissions from arctic lakes: Isotopic implications for source pathways and ages, Journal of Geophysical Research: Biogeosciences, 10.1029/2007JG000569, 113, G3, (2008).
Wiley Online Library
Zhaosheng Fan, Jason C. Neff, Jennifer W. Harden, Kimberly P. Wickland, Boreal soil carbon dynamics under a changing climate: A model inversion approach, Journal of Geophysical Research: Biogeosciences, 10.1029/2008JG000723, 113, G4, (2008).
Wiley Online Library
J. F. González-Rouco, H. Beltrami, E. Zorita, M. B. Stevens, Borehole climatology: a discussion based on contributions from climate modeling, Climate of the Past Discussions, 10.5194/cpd-4-1-2008, 4, 1, (1-80), (2008).
Crossref
K. M. Walter, M. E. Edwards, G. Grosse, S. A. Zimov, F. S. Chapin, Thermokarst Lakes as a Source of Atmospheric CH4 During the Last Deglaciation, Science, 10.1126/science.1142924, 318, 5850, (633-636), (2007).
Crossref
Martin Stendel, Vladimir E. Romanovsky, Jens H. Christensen, Tatiana Sazonova, Using dynamical downscaling to close the gap between global change scenarios and local permafrost dynamics, Global and Planetary Change, 10.1016/j.gloplacha.2006.07.014, 56, 1-2, (203-214), (2007).
Crossref
E. Uhlířová, H. Šantrůčková, S.P. Davidov, Quality and potential biodegradability of soil organic matter preserved in permafrost of Siberian tussock tundra, Soil Biology and Biochemistry, 10.1016/j.soilbio.2007.02.018, 39, 8, (1978-1989), (2007).
Crossref
Kenneth M. Hinkel, Benjamin M. Jones, Wendy R. Eisner, Chris J. Cuomo, Richard A. Beck, Robert Frohn, Methods to assess natural and anthropogenic thaw lake drainage on the western Arctic coastal plain of northern Alaska, Journal of Geophysical Research: Earth Surface, 10.1029/2006JF000584, 112, F2, (2007).
Wiley Online Library
Keiji Kushida, Alexander P. Isaev, Trofim C. Maximov, Gen Takao, Masami Fukuda, Remote sensing of upper canopy leaf area index and forest floor vegetation cover as indicators of net primary productivity in a Siberian larch forest, Journal of Geophysical Research: Biogeosciences, 10.1029/2006JG000269, 112, G2, (2007).
Wiley Online Library
D. J. Nicolsky, V. E. Romanovsky, V. A. Alexeev, D. M. Lawrence, Improved modeling of permafrost dynamics in a GCM land‐surface scheme, Geophysical Research Letters, 10.1029/2007GL029525, 34, 8, (2007).
Wiley Online Library
K. Isaksen, R. E. Benestad, C. Harris, J. L. Sollid, Recent extreme near‐surface permafrost temperatures on Svalbard in relation to future climate scenarios, Geophysical Research Letters, 10.1029/2007GL031002, 34, 17, (2007).
Wiley Online Library
O A Anisimov, Potential feedback of thawing permafrost to the global climate system through methane emission, Environmental Research Letters, 10.1088/1748-9326/2/4/045016, 2, 4, (045016), (2007).
Crossref
K. M Walter, L. C Smith, F Stuart Chapin, Methane bubbling from northern lakes: present and future contributions to the global methane budget, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 10.1098/rsta.2007.2036, 365, 1856, (1657-1676), (2007).
Crossref
D. J. Nicolsky, V. E. Romanovsky, G. S. Tipenko, Using in-situ temperature measurements to estimate saturated soil thermal properties by solving a sequence of optimization problems, The Cryosphere, 10.5194/tc-1-41-2007, 1, 1, (41-58), (2007).
Crossref
D. J. Nicolsky, V. E. Romanovsky, G. S. Tipenko, Estimation of thermal properties of saturated soils using in-situ temperature measurements, The Cryosphere Discussions, 10.5194/tcd-1-213-2007, 1, 1, (213-269), (2007).
Crossref
D. Archer, Methane hydrate stability and anthropogenic climate change, Biogeosciences Discussions, 10.5194/bgd-4-993-2007, 4, 2, (993-1057), (2007).
Crossref
D. Archer, Methane hydrate stability and anthropogenic climate change, Biogeosciences, 10.5194/bg-4-521-2007, 4, 4, (521-544), (2007).
Crossref
L. Sushama, R. Laprise, M. Allard, Modeled current and future soil thermal regime for northeast Canada, Journal of Geophysical Research: Atmospheres, 10.1029/2005JD007027, 111, D18, (2006).
Wiley Online Library
S. K. Saha, A. Rinke, K. Dethloff, P. Kuhry, Influence of a complex land surface scheme on Arctic climate simulations, Journal of Geophysical Research: Atmospheres, 10.1029/2006JD007188, 111, D22, (2006).
Wiley Online Library
Yu Zhang, Wenjun Chen, Daniel W. Riseborough, Temporal and spatial changes of permafrost in Canada since the end of the Little Ice Age, Journal of Geophysical Research: Atmospheres, 10.1029/2006JD007284, 111, D22, (2006).
Wiley Online Library
K. M. Walter, S. A. Zimov, J. P. Chanton, D. Verbyla, F. S. Chapin, Methane bubbling from Siberian thaw lakes as a positive feedback to climate warming, Nature, 10.1038/nature05040, 443, 7107, (71-75), (2006).
Crossref
David M. Lawrence, Andrew G. Slater, A projection of severe near‐surface permafrost degradation during the 21st century, Geophysical Research Letters, 10.1029/2005GL025080, 32, 24, (2005).
Wiley Online Library

Volume109, IssueD1

16 January 2004

Permafrost dynamics in the 20th and 21st centuries along the East Siberian transect

Abstract

1. Introduction