Permafrost Model Research Articles

Spatiotemporal variations of permafrost extent in Mongolia during 1950–2022

Permafrost in Mongolia is located in the transition zone between high-latitude and high-altitude permafrost regions of the Northern Hemisphere, with large temperature differences and complex subsurface characteristics. In this study, the reliability of the skin temperature data from the ERA5-Land product covering Mongolia is assessed via site observations. The ERA5-Land skin temperature dataset shows a cold bias, which is more pronounced in the cold season. Following calibration based on elevation differences, significant improvements are observed at both the annual scale (92 % improvement in RMSE (root mean square error) and 98 % improvement in MBE (mean bias error)) and the seasonal scale (78 % improvement in RMSE and 82 % improvement in MBE). Additionally, the spatial variations in the surface freezing index (SFI) and surface thawing index (STI) are most pronounced in the central and northeastern Mongolia. The SFI exhibits a significant decreasing trend of 7.16 °C·d/year, while the STI shows a significant increasing trend of 4.49 °C·d/year. Furthermore, the permafrost extent in Mongolia is simulated from 1950 to 2022 using the frost number (Fn) model and the temperature on top of permafrost (TTOP) model. The validated results indicate that the accuracy of the Fn model is relatively high, with an overall accuracy of 0.9 and a Kappa coefficient of 0.47. The permafrost extent in Mongolia has declined from 734.7 × 103 km2 in the 1950 s to 480.1 × 103 km2 in the 2010 s, with a prominent decrease of 3.2 × 103 km2/decade after 1994. According to the variations in permafrost extent during past 72 years, the Hovsgol and Khentii Mountain ranges have experienced significant permafrost degradation.

Internal response of vegetation growth to degrees of permafrost degradation in Northeast China from 2001 to 2020

ABSTRACT Ecosystems at the southern edge of the permafrost distribution are highly sensitive to global warming. Changes in soil freeze-thaw cycles can influence vegetation growth in permafrost regions. Extant studies mainly focused on analyzing the differences of vegetation dynamics in different permafrost regions. However, the intrinsic drivers of permafrost degradation on vegetation growth remain elusive yet. Based on the top temperature of permafrost (TTOP) model, we simulated the spatial distribution of permafrost in Northeast China (NEC) from 2001 to 2020. Using the data of the vegetation Net Primary Productivity (NPP), vegetation phenology, climate and permafrost phenology, and analytical methods including partial correlation, multiple linear regression, and path analysis, we explored the response of vegetation growth and phenology to soil freeze-thaw changes and climate change under different degrees of permafrost degradation. Overall, the start date of the growing season (SOS) was very sensitive to the start date of soil thaw (SOT) changes, and multiple regression analyses showed that SOT was the main factor influencing SOS in 41.8% of the NEC region. Climatic factors remain the main factors affecting vegetation NPP in NEC, and the results of partial correlation analysis showed that only 9.7% of the regional duration of soil thaw (DOT) had a strong correlation with vegetation NPP. Therefore, we determined the mechanism responsible for the soil freeze-thaw changes and vegetation growth relationship using the path analysis. The results indicated that there is a potential inhibitory effect of persistent permafrost degradation on vegetation growth. Our findings would contribute to the improvement of process-based models of forest dynamics in the boreal region, which would help to plan sustainable development and conservation strategies in permafrost areas.

Deciphering the Biophysical Impact of Permafrost Greening on Summer Surface Offset

AbstractSatellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections.

Glacial isostatic adjustment reduces past and future Arctic subsea permafrost

Sea-level rise submerges terrestrial permafrost in the Arctic, turning it into subsea permafrost. Subsea permafrost underlies ~ 1.8 million km2 of Arctic continental shelf, with thicknesses in places exceeding 700 m. Sea-level variations over glacial-interglacial cycles control subsea permafrost distribution and thickness, yet no permafrost model has accounted for glacial isostatic adjustment (GIA), which deviates local sea level from the global mean due to changes in ice and ocean loading. Here we incorporate GIA into a pan-Arctic model of subsea permafrost over the last 400,000 years. Including GIA significantly reduces present-day subsea permafrost thickness, chiefly because of hydro-isostatic effects as well as deformation related to Northern Hemisphere ice sheets. Additionally, we extend the simulation 1000 years into the future for emissions scenarios outlined in the Intergovernmental Panel on Climate Change’s sixth assessment report. We find that subsea permafrost is preserved under a low emissions scenario but mostly disappears under a high emissions scenario.

Influences of Snow Cover on the Thermal Regimes of Xing'an Permafrost in Northeast China in 1960s–2010s

ABSTRACTThe distributive characteristics of snow cover and their impacting mechanisms on ground thermal regimes in Northeast China remain evasive because of limited systematic studies. In this study, based on long‐term ground‐based observational data and auxiliary topographic data, geographically weighted regression kriging (GWRK) method and the temperature at the top of permafrost (TTOP) model were used to analyze the influences of snow cover duration (SCD) and average snow depth over the SCD (ASDSCD) on the thermal regimes of Xing'an permafrost in Northeast China in the 1960s–2010s. The results show a remarkable reduction of permafrost extent in Northeast China from the 1960s to 2010s, with an average reduction rate of 4.115 × 104 km2/decade. In permafrost regions, from the 1960s to 2010s, average SCD varied between 150 and 160 days, and the regional average of ASDSCD between 8 and 14 cm. Increases in ASDSCD led to a rise of TTOP. From the 1960s to 2010s, the regional average of ASDSCD increased by 3.53 cm, and that of TTOP by 2.02°C. The research results can provide scientific basis and data support for evaluating the responses of permafrost and cold region ecosystems to climate change in Northeast China.

Modelling permafrost distribution using the temperature at top of permafrost model in the boreal forest environment of Whatì, NT

Current permafrost models in Canadian boreal forests are generally of low spatial resolution as they cover regional or continental scales. This study aims to understand the viability of creating a temperature at the top of permafrost (TTOP) model on a local scale in the boreal wetland environment of Whatì, Northwest Territories from short-term field-collected temperature data. The model utilizes independent variables of vegetation, topographic position index, and elevation, with the dependent variables being ground surface temperature collected from 60 ground temperature nodes and 1.5 m air temperature collected from 10 temperature stations. In doing this, the study investigates the relationship vegetation and disturbance have on ground temperature and permafrost distribution. The model predicts that 31% of the ground is underlain by permafrost, based on a mean annual temperature at TTOP of <0 °C. This model shows an accuracy of 62.5% when compared to cryotic assessment sites (CAS). Most inaccuracies, showing the limitations of the TTOP model, came from peat plateaus that had been burned in the most recent forest fire in 2014. These resulted in out-of-equilibrium permafrost and climatic conditions that TTOP cannot handle well. Commonly, permafrost mapping places Whatì in the extensive discontinuous zone, estimating that between 50% and 90% of the ground is underlain by permafrost. The study shows that a climatically driven TTOP model calibrated with CAS can be used to illustrate ground temperature heterogeneity from short-term data in boreal forest wetland environments. However, this approach likely underestimates permafrost extent and is perhaps not the best-suited modelling choice for near-surface permafrost, which is currently out of equilibrium with the current climate.

Spatiotemporal characteristics and variability in the thermal state of permafrost on the Qinghai–Tibet Plateau

AbstractPermafrost degradation on the Qinghai–Tibet Plateau (QTP) has significant impacts on climate, hydrology, and engineering and environmental systems. To understand the temporal and spatial characteristics of permafrost on the QTP, we quantified the variation in active layer thickness (ALT), permafrost thermal state, and future permafrost change under different scenarios using observational data, reanalysis data, and the numerical permafrost model. Generally, ALT ranged from 0.5 to 6.0 m with an average of 2.39 m, and mean annual ground temperature (at a depth of zero annual amplitude for ground temperature) mainly ranged between 0 and −3°C with an average of −0.85°C. The soil temperatures in different layers based on the ERA5‐Land data revealed even stronger increasing trends, for example, 0.245, 0.245, 0.244, and 0.238°C/decade at depths of 0–7, 7–28, 28–100, and 100–289 cm from 1980 to 2021, compared to those during the period from 1960 to 2021, which were 0.153, 0.156, 0.155, and 0.149°C/decade, respectively. The average warming trends in annual mean soil temperature were 0.153 and 0.243°C/decade from 1960 to 2021 and 1980 to 2021, respectively. The average rate of thickening of the ALT among the 10 active layer observation sites was 2.84 cm/year. There was a significant warming trend in ground temperature above ~15 m with warming of 0.063 to 0.120, 0.026 to 0.182, 0.101 to 0.314, and 0.189 to 0.303°C/decade at the QTB01, QTB06, QTB08, and XDTGT sites, respectively, and yearly minimum ground temperatures exhibited stronger warming trends than maximum ground temperatures. In addition, the simulation revealed significant increases in ground temperature at the Xidatan (XDT) and Tanggula (TGL) sites under both historical and future Representative Concentration Pathway (RCP) scenarios, but the increases in ground temperature were significantly greater at TGL than XDT. These findings provide important information for understanding the variability in permafrost degradation processes and improving simulations of permafrost change under climate change on the QTP.

The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model

Abstract. Understanding the future evolution of permafrost requires a better understanding of its climatological past. This requires permafrost models to efficiently simulate the thermal dynamics of permafrost over the past centuries to millennia, taking into account highly uncertain soil and snow properties. In this study, we present a computationally efficient numerical permafrost model which satisfactorily reproduces the current ground temperatures and active layer thicknesses of permafrost in the Arctic and their trends over recent centuries. The performed simulations provide insights into the evolution of permafrost since the 18th century and show that permafrost on the North American continent is subject to early degradation, while permafrost on the Eurasian continent is relatively stable over the investigated 300-year period. Permafrost warming since industrialization has occurred primarily in three “hotspot” regions in northeastern Canada, northern Alaska, and, to a lesser extent, western Siberia. We find that the extent of areas with a high probability (p3 m>0.9) of near-surface permafrost (i.e., 3 m of permafrost within the upper 10 m of the subsurface) has declined substantially since the early 19th century, with loss accelerating during the last 50 years. Our simulations further indicate that short-term climate cooling due to large volcanic eruptions in the Northern Hemisphere in some cases favors permafrost aggradation within the uppermost 10 m of the ground, but the effect only lasts for a relatively short period of a few decades. Despite some limitations, e.g., with respect to the representation of vegetation, the presented model shows great potential for further investigation of the climatological past of permafrost, especially in conjunction with paleoclimate modeling.

Effects of Ground Subsidence on Permafrost Simulation Related to Climate Warming

We develop a moving-mesh permafrost model that contains a ground subsidence computation module to estimate the effects of ground subsidence on permafrost simulation under different warming scenarios. Including the ground subsidence process in the permafrost simulation produces only a relatively small improvement in the simulation performance of the ground temperature field, as validated by observations from two sites on the Qinghai–Tibetan Plateau (QTP). It is shown that ignoring ground subsidence tends to achieve a larger active layer thickness (ALT) but a smaller original thickness of permafrost that has thawed when simulating permafrost changes in a warming climate. The heat consumed by permafrost changes will be underestimated in simulations that do not consider ground subsidence. The effects that ground subsidence exerts within the permafrost simulation are clearly demonstrated under a strong warming scenario, which will influence the global energy budget. Projections indicate that the permafrost in the continuous permafrost area of the QTP may be close to the phase transition temperature to become zero thermal gradients in 2030–2040 under the SSP5-8.5 scenario, and there will be a great risk of ground subsidence by that stage. For permafrost regions with rich ground ice, the downward propagating temperature signals caused by ground subsidence are more attenuated. However, the heat calculation error will be larger in a simulation that does not consider ground subsidence there. This study quantifies the effects of ground subsidence, which can provide a better understanding of the permafrost thaw and energy budget of the QTP.

Simulating ice segregation and thaw consolidation in permafrost environments with the CryoGrid community model

Abstract. The ground ice content in cold environments influences the permafrost thermal regime and the thaw trajectories in a warming climate, especially for soils containing excess ice. Despite their importance, the amount and distribution of ground ice are often unknown due to lacking field observations. Hence, modeling the thawing of ice-rich permafrost soils and associated thermokarst is challenging as ground ice content has to be prescribed in the model setup. In this study, we present a model scheme, capable of simulating segregated ice formation during a model spinup together with associated ground heave. It provides the option to add a constant sedimentation rate throughout the simulation. Besides ice segregation, it can represent thaw consolidation processes and ground subsidence under a warming climate. The computation is based on soil mechanical processes, soil hydrology by the Richards equation and soil freezing characteristics. The code is implemented in the CryoGrid community model (version 1.0), a modular land surface model for simulations of the ground thermal regime. The simulation of ice segregation and thaw consolidation with the new model scheme allows us to analyze the evolution of ground ice content in both space and time. To do so, we use climate data from two contrasting permafrost sites to run the simulations. Several influencing factors are identified, which control the formation and thaw of segregated ice. (i) Model results show that high temperature gradients in the soil as well as moist conditions support the formation of segregated ice. (ii) We find that ice segregation increases in fine-grained soils and that especially organic-rich sediments enhance the process. (iii) Applying external loads suppresses ice segregation and speeds up thaw consolidation. (iv) Sedimentation leads to a rise of the ground surface and the formation of an ice-enriched layer whose thickness increases with sedimentation time. We conclude that the new model scheme is a step forward to improve the description of ground ice distributions in permafrost models and can contribute towards the understanding of ice segregation and thaw consolidation in permafrost environments under changing climatic conditions.

A New Land Cover Map of Two Watersheds under Long-Term Environmental Monitoring in the Swedish Arctic Using Sentinel-2 Data

A land cover map of two arctic catchments near the Abisko Scientific Research Station was obtained based on a classification from a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. Two classification algorithms, support vector machine and random forest, were tested and gave very similar results. The percentage of correctly classified pixels was over 88% in both cases. The developed workflow relies solely on open-source software and acquired ground observations. Space organization was directed by the altitude as demonstrated by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 shows some trends in vegetation cover evolution in response to climate change in the considered area. This land cover map is key input data for permafrost modeling and, hence, for the quantification of climate change impacts in the studied area.

Understanding seismic velocity variations of subsea permafrost: A sensitivity study

Monitoring the change in permafrost conditions and distribution is crucial for forecasting global warming. As seismic velocities increase with ice content, marine seismic surveys can map the top of ice-bearing subsea permafrost on a large scale. However, conventional seismic methods cannot fully map internal velocity variations linked to changes in ice content because the removal of guided waves and multiples is challenging for reflection processing. Nevertheless, these arrivals carry information about velocity variations with depth. We investigate if a joint analysis of various wave arrivals could provide information about velocity variations within permafrost and accurately estimate the permafrost thickness. Through a sensitivity analysis, we estimate the feasibility of using different wave arrivals, such as reflections, refractions, multiples, and guided waves, to better characterize permafrost conditions. We find that guided waves are sensitive to velocity variations within permafrost and can detect the depth of the permafrost’s base under certain geologic conditions. Then, we combine the analysis of all seismic arrivals to derive a subsurface permafrost model for a seismic line collected in the Beaufort Sea. The joint analysis reveals the transitions in depth between ice-bonded and partially ice-bonded permafrost and offers a crude estimate of permafrost thickness.

The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere

Abstract. The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice–water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

Burning trees in frozen soil: Simulating fire, vegetation, soil, and hydrology in the boreal forests of Alaska

Boreal ecosystems account for 29% of the world's total forested area and contain more carbon than any other terrestrial biome. Over the past 60 years, Alaska has warmed twice as rapidly as the contiguous U.S. and wildfire activity has increased, including the number of fires, area burned, and frequency of large wildfire seasons. These recent and rapid changes in climate and wildfire have implications for future vegetation composition, structure, and biomass in interior Alaska, given that the vegetation is highly dependent on active layer thickness, soil moisture, organic layer depth, and plant-available nutrients. Here we developed a new succession extension (DGS) of the LANDIS-II forest landscape model which integrates a vegetation dynamics model (NECN) with a soil carbon model (DAMM-McNiP), a hydrologic model (SHAW), and a deep soil profile permafrost model (GIPL) in a spatially-explicit framework. DGS Succession uses the algorithms in the NECN Succession extension of LANDIS-II to simulate growth, mortality and reproduction of vegetation but has three major improvements. First, the simple bucket model in NECN was replaced with a physically-based model (SHAW) that simulates energy and water fluxes (e.g. snow depth, evapotranspiration, soil moisture) at multiple levels in the canopy and soil. Second, the active, slow, and passive soil pools in NECN were replaced by seven soil pools that are measurable in the field, with carbon and nitrogen dynamics dictated by DAMM-McNiP. Finally, soil temperature and soil moisture are simulated only at one depth in NECN, but in DGS, soil temperature (and hence permafrost dynamics) are simulated at as many as 50 user-defined depths down to 4 m with SHAW and 75 m with GIPL. During the initial calibration phase, DGS was applied at three inventory sites at the Bonanza Creek Long Term Ecological Research area in Interior Alaska where climate forcings, species biomass, soil temperature, and/or soil moisture were available. For the landscape-scale simulations, DGS was run with the SCRPPLE fire extension of LANDIS-II under two scenarios of climate using a ∼400,000 ha landscape that included the inventory sites. Across all three sites, DGS generally captured the variation in soil moisture and temperature across depths, seasons, and years reasonably well, though there were some discrepancies at each site. DGS had better agreement with field measurements of soil moisture and temperature than its predecessor NECN which produced unrealistically low soil moisture and unrealistically high seasonal fluctuations in soil temperature. At the landscape scale, ignitions, area burned, and soil temperature increased under climate change, as expected, while soil moisture was relatively unchanged across climate scenarios. Biomass tended to decline under climate change, which differs from other modeling studies in this region but is consistent with the browning trends observed from remote sensing data. Simulating climate, vegetation succession, hydrology, permafrost, carbon and nutrient cycling, and wildfire in an integrated, spatially-explicit framework like LANDIS-II will allow us to disentangle the drivers and ecosystem responses in this rapidly changing ecosystem, as well as other forested systems with complex hydrologic, biochemical, cryospheric, and vegetation feedbacks.

Effects of soil parameterization on permafrost modeling in the Qinghai-Tibet Plateau: A calibration-constrained analysis

Borehole-measured soil temperatures have been routinely used to calibrate soil parameters in permafrost modeling, although they are sparse in the Qinghai-Tibet Plateau (QTP). A feasible alternative is to calibrate models using land surface temperatures. However, the quantitative impacts of various soil parameterizations on permafrost modeling remain unexplored. To quantify these impacts, two sets of soil parameters (denoted as Psoil and Psurf) were obtained via calibration using borehole temperature measurement and ERA5-Land (the land component of the fifth generation of European Re-Analysis) skin temperature, respectively, and applied to the Geophysical Institute Permafrost Laboratory Version 2 (GIPL 2.0) model. Comparing against the borehole-measured soil temperatures of 4 soil layers, the ERA5-Psurf simulation (with Root Mean Squared Error, i.e., RMSE from 1.4 °C to 3.9 °C) outperform ERA5-Psoil simulation (RMSE from 1.4 °C to 3.9 °C) during 2006–2014. The obtained Psoil and Psurf were then utilized as soil parameters in GIPL 2.0 to model permafrost dynamics for a long period from 1983 to 2019, respectively, using ERA5-Land as forcing data. Simulations revealed significant disparities. In comparison to the simulation using Psurf results using Psoil show that the mean annual soil temperature at 1 m depth was 2.72 °C lower with a 0.01 °C/a (50.0%) lower trend; the active layer thickness was 0.81 m (35.7%) less with a 2.16 cm/a (82.1%) lower trend; the duration of the thawing season at 1 m depth was underestimated by about one month, and the zero-curtain period was about 23 days (37.7%) shorter. The change rates of the zero-curtain period, however, were comparable. This study implies that choosing soil parameterizations is critical for model evaluation against observations and long-term model prediction.

Spatial distribution of permafrost degradation and its impact on vegetation phenology from 2000 to 2020

As global temperatures rise, permafrost is degraded. Permafrost degradation alters vegetation phenology and community composition, thereby affecting local and regional ecosystems. The Xing'an Mountains, located on the southern edge of the Eurasian permafrost region, are very sensitive to the impact of permafrost degradation on ecosystems. Climate change has direct effects on permafrost and vegetation growth, and analysis of the indirect effects of permafrost degradation on vegetation phenology based on the normalized difference vegetation index (NDVI) can explain the internal impact mechanisms of ecosystem components. Based on the temperature at the top of permafrost (TTOP) model, which was used to simulate the spatial distribution of permafrost areas in the Xing'an Mountains from 2000 to 2020, the areas of the three permafrost types showed a decreasing trend. The mean annual surface temperature (MAST) increased significantly at a rate of 0.008 °C year−1 from 2000 to 2020, and the southern boundary of the permafrost region moved north by 0.1–1 degrees. The average NDVI value of the permafrost region increased significantly in 8.34 % of the region. The significant correlations between NDVI and permafrost degradation, temperature and precipitation in the permafrost degradation region were 92.06 % (80.19 % positive, 11.87 % negative), 50.37 % (42.72 % positive, 7.65 % negative), and 81.59 % (36.25 % positive, 45.34 % negative), and were mainly distributed along the southern boundary of the permafrost region. A significance test of phenology in the Xing'an Mountains showed that the end of the growing season (EOS) and the length of the growing season (GLS) were significantly delayed and prolonged in the southern sparse island permafrost region. Sensitivity analysis showed that permafrost degradation was the main factor that affected the start of the growing season (SOS) and GLS. When the effects of temperature, precipitation, and sunshine duration were excluded, the regions with a significant positive correlation between permafrost degradation and SOS (20.96 %) and GLS (28.55 %) were located in both continuous and discontinuous permafrost regions. The regions with a significant negative correlation between permafrost degradation and SOS (21.11 %) and GLS (8.98 %) were mainly distributed on the southern edge of the island permafrost region. In summary, the NDVI changed significantly in the southern boundary of the permafrost region, and this change was mainly attributed to permafrost degradation.

Two-dimensional simulation of island permafrost degradation in Northeastern Tibetan Plateau

Despite the response of ground temperatures to increases in air temperature, discontinuous and isolated bodies of permafrost are strongly affected by lateral heat fluxes from adjacent unfrozen zones. However, many current simulations consider heat flow in only one-dimension (1D) and thus cannot represent two or three-dimensional effects. To address this issue, we use fine-scale in situ measurements and two-dimension (2D) heat conduction simulations and compare the simulated ground thermal evolution and degradation of island permafrost to a 1D simulation. The 2D simulation reasonably represented seasonal and interannual features of the ground thermal regime, such as temperature profile, thawing/frozen depth, and permafrost body width. Lateral thawing of the island permafrost was dominant, and was about one order of magnitude greater than the modelled vertical thaw. In the vertical direction, downward permafrost thaw caused by increase in air temperature rise was restricted by phase change of ground ice near the permafrost table during the simulation period, contributing little to the overall permafrost degradation. Rates of permafrost degradation in the 1D simulation were 1.6–1.8 times lower than the simulation considering lateral heat fluxes. The field monitoring and numerical simulation highlight the importance of coupling lateral heat transfer in permafrost models.

Spatiotemporal Variations of Soil Temperature at 10 and 50 cm Depths in Permafrost Regions along the Qinghai-Tibet Engineering Corridor

Soil temperature plays an essential role in the permafrost thermal state and degradation process. Especially the soil temperatures at 10 cm and 50 cm depths in the active layer, which are much easier to be observed in situ, have great effects on the surface water cycles and vegetation, and could be used as the upper boundary for permafrost models to simulate the thermal state of the permafrost and active layer thicknesses. However, due to the limitations of the observation data, there are still large uncertainties in the soil temperature data, including at these two depths, in the permafrost region of Qinghai–Tibet Plateau (QTP). In this study, we evaluated and calibrated the applicability of four daily shallow soil temperature datasets (i.e., MERRA-2, GLDAS-Noah, ERA5-Land, and CFSR) by using the in situ soil temperature data from eight observation sites from 2004 to 2018 in the permafrost region along the Qinghai–Tibet Engineering Corridor. The results revealed that there were different uncertainties for all four sets of reanalysis data, which were the largest (Bias = −2.44 °C) in CFSR and smallest (Bias= −0.43 °C) in GLDAS-Noah at depths of 10 cm and 50 cm. Overall, the reanalysis datasets reflect the trends of soil temperature, and the applicability of reanalysis data at 50 cm depth is better than at 10 cm depth. Furthermore, the GLDAS-Noah soil temperatures were recalibrated based on our observations using multiple linear regression and random forest models. The accuracy of the corrected daily soil temperature was significantly improved, and the RMSE was reduced by 1.49 °C and 1.28 °C at the depth of 10 cm and 50 cm, respectively. The random forest model performed better in the calibration of soil temperature data from GLDAS-Noah. Finally, the warming rates of soil temperature were analyzed, which were 0.0994 °C/a and 0.1005 °C/a at 10 cm and 50 cm depth from 2004 to 2018, respectively.

The importance of topographic gradients in alpine permafrost modeling

Alpine permafrost environments are highly vulnerable and sensitive to changes in regional and global climate trends. Thawing and degradation of permafrost has numerous adverse environmental, economic, and societal impacts. Mathematical modeling and numerical simulations provide powerful tools for predicting the degree of degradation and evolution of subsurface permafrost as a result of global warming. A particularly significant characteristic of alpine environments is the high variability in their surface geometry which drives large lateral thermal and fluid fluxes along topographic gradients. The combination of these topography-driven fluxes and unsaturated ground makes alpine systems markedly different from Arctic permafrost environments and general geotechnical ground freezing applications, and therefore, alpine permafrost demands its own specialized modeling approaches. In this work, we present a multi-physics permafrost model tailored to subsurface processes of alpine regions. In particular, we resolve the ice–water phase transitions, unsaturated conditions, and capillary actions, and account for the impact of the evolving pore space through freezing and thawing processes. Moreover, the approach is multi-dimensional, and therefore, inherently resolves the topography-driven horizontal fluxes. Through numerical case studies based on the elevation profiles of the Zugspitze (DE) and the Matterhorn (CH), we show the strong influence of lateral fluxes in 2D on active layer dynamics and the distribution of permafrost.

КЛИМАТООБРАЗУЮЩАЯ РОЛЬ ЭМИССИИ МЕТАНА НА ШЕЛЬФЕ МОРЕЙ ВОСТОЧНОЙ АРКТИКИ

Satellite data on tropospheric methane and dynamical permafrost model simulations were used to analyze the methane emission in the subaquatic permafrost on the East Siberian Arctic shelf. The hypothesis of potential methane release from the dissociation of submarine gas hydrates was studied. It was demonstrated that the methane emission on the Arctic shelf is governed mainly by geological factors and is not related to contemporary climate change. Numerical experiments with the INM-CM48 Earth system model showed that there is no feedback between the methane emission on the shelf and global air temperature. A conceptual model of methane emission on the East Siberian Arctic shelf was developed.