Debris cover either enhances or reduces glacier melting, thereby modulating glacier response to increasing temperatures. Debris cover variation and glacier recession were investigated on five glaciers; Pensilungpa (PG), Drung Drung (DD), Haskira (HK), Kange (KG) and Hagshu (HG), situated in the topographically and climatically similar zone in the Zanskar Himalaya using satellite data between 2000 and 2020. Analyses reveals that the HK, KG, and HG had a debris-covered area of similar to 24% in 2020, while PG and DD had a debris cover of <10%. Comparing PG to the other four glaciers, it had the highest shrinkage (5.7 +/- 0.3%) and maximum thinning (1.6 +/- 0.6 m a(-1)). Accordingly, detailed measurements of PG's debris cover thickness, temperature and ablation were conducted for eleven days in August 2020. The results indicated a significant variation of temperature and the highest melting was observed near dirty and thin debris-covered ice surface. Thermal conductivity of 0.9 +/- 0.1 Wm(-1) K-1 and 1.1 +/- 0.1 Wm(-1) K-1 was observed at 15 cm and 20 cm debris-depth, respectively. The ablation measurements indicated an average cumulative melting of 21.5 cm during eleven days only. Degree-day factor showed a decreasing trend towards debris cover depth with the highest value (4.8 mm w.e.degrees C-1 d(-1)) found for the dirty ice near the glacier surface and the lowest value (0.4 mm w.e.degrees C-1 d(-1)) found at 30 cm depth. The study highlights the importance of in-situ debris cover, temperature and ablation measurements for better understanding the impact of debris cover on glacier melting.

Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model's sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

The Qinghai-Tibet Plateau (QTP) has the largest amount of permafrost in the low and middle latitudes, making it highly susceptible to the effects of global warming. In particular, the degradation of permafrost can be intensified by anomalous amplified warming. To accurately model the hydrothermal dynamics of permafrost and its future trends, the accumulation of high -precision, long-term data for the soil thermal conductivity (STC) in the active layer is crucial. However, no previous research has systematically investigated the spatio-temporal variation in the STC on the QTP over an extended period. Therefore, this study aims to fill this gap using the XGBoost model to analyze the STC in the permafrost on the QTP from 1980 to 2020. The findings of this study provide some preliminary insights. First, areas with high variation in the STC between the freeze-thaw periods over the 40 years gradually migrated from the western region to the central region. Second, since 2015, STC in more than 90 % of the permafrost region in the thawing period has shown positive growth. While, during the freezing period, the STC also exhibited an increase over most regions of the QTP, though the western region and parts of the northeastern region exhibited a decrease. Third, the spatial center of gravity for the STC during the freezing and thawing periods from 1980 to 2020 shifted. The mean STC was larger in the eastern and northeastern regions during the freezing period and larger in the western region during the thawing period. Fourth, both alpine swamp meadow and alpine meadow exhibited a gradual increase in the STC during the freeze-thaw period from 1980 to 2020. The conclusions and data products from this study are expected to support spatiotemporal modeling of the permafrost on the QTP and assist in the prognosis for its future.

High-latitude permafrost, including hydrate-bearing frozen ground, changes its properties in response to natural climate change and to impacts from petroleum production. Of special interest is the behavior of thermal conductivity, one of the key parameters that control the thermal processes in permafrost containing gas hydrate accumulations. Thermal conductivity variations under pressure and temperature changes were studied in the laboratory through physical modeling using sand sampled from gas-bearing permafrost of the Yamal Peninsula (northern West Siberia, Russia). When gas pressure drops to below equilibrium at a constant negative temperature (about -6(degrees)C), the thermal conductivity of the samples first becomes a few percent to 10% lower as a result of cracking and then increases as pore gas hydrate dissociates and converts to water and then to ice. The range of thermal conductivity variations has several controls: pore gas pressure, hydrate saturation, rate of hydrate dissociation, and amount of additionally formed pore ice. In general, hydrate dissociation can cause up to 20% thermal conductivity decrease in frozen hydrate-bearing sand. As the samples are heated to positive temperatures, their thermal conductivity decreases by a magnitude depending on residual contents of pore gas hydrate and ice: the decrease reaches similar to 30% at 20-40% hydrate saturation. The thermal conductivity decrease in hydrate-free saline frozen sand is proportional to the salinity and can become similar to 40% lower at a salinity of 0.14%. The behavior of thermal conductivity in frozen hydrate-bearing sediments under a pressure drop below the equilibrium and a temperature increase to above 0 C-degrees is explained in a model of pore space changes based on the experimental results.

Soil thermal conductivity (lambda), which describes the ability of the soil to transfer heat, is critical to understand the thermal regime of ground surfaces. In this study, in situ measurements of lambda were conducted at two field sites in the permafrost region of the central Qinghai-Tibet Plateau (QTP) and the results were used to evaluate 11 schemes of lambda at depths of 10-50 cm during the freeze-thaw cycle period. Our analyses revealed that lambda had a remarkable seasonal variation, due to the significant effects of soil moisture content and ice-water phase changes as temperature changed during the freeze-thaw cycle period. Among the selected schemes, the Johansen scheme, its three derivatives (i.e., the He scheme, Yang scheme and Zhao scheme), and the Campbell scheme were significantly superior to others. Moreover, the Johansen scheme ranked among the top schemes for frozen soil, while the Campbell scheme gave the most accurate values for unfrozen soil. The effects of different estimation methods of quartz content (q), dry lambda and the Kersten number (K-e) on the predicted schemes results were also evaluated. The results showed that, the methods used for the estimation of q and K-e had the greatest influence on the calculation results for the permafrost region. Overall, this research provides insights for the development of a lambda scheme for the permafrost region of the central QTP.

The Qinghai-Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai-Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai-Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai-Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai-Tibet Plateau.

The Arctic amplification (AA) has exacerbated permafrost degradation, posing a serious threat to infrastructure security and other areas. Therefore, it is crucial to accurately assess the current status and future changes of permafrost, and reliable soil thermal conductivity (STC) is an important prerequisite for permafrost prediction. However, few methods and products are available for regional-scale STC simulations in permafrost of the Arctic, which lead to greater uncertainty in the simulation of land surface temperatures. This study conducted a preliminary STC simulation based on the XGBoost method. The results show that the average STC during the freezing period is between 0.71 similar to 0.73 W center dot m-1K-1, and around 0.67 W center dot m-1K-1 during the thawing period; The variation of STC between the thawing and freezing period ranged from -0.34-0.23 W center dot m-1K-1, with an average value of -0.02 W center dot m-1K-1; The areas where STC of the thawing period is smaller than that of the freezing period are mainly concentrated in the marginal areas near the sea on the continental side of North America and in the typical areas of plains, lowlands, and plateaus on the continental side of Eurasia. The areas with large STC during the thawing period are concentrated in mountainous areas.

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation-evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai-Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.

Soil thermal conductivity (lambda), describing the ability of transferring heat in the soil, plays an important role in soil thermal behavior. The estimation of lambda at dryness (lambda(dry)) is essential for obtaining accurate lambda. This study aims to develop a new model for lambda(dry) across a wide range of the soil dry density (rho(d)) for soils with different textures. The lambda(dry) measurements of 75 soil samples from literature and 19 new soils from Qinghai-Tibet Plateau are used to establish the segmented relationships between lambda(dry) and rho(d) based on clustering algorithms. Our analyses reveal that when rho(d) = 1.4 g cm(-3), other soil properties must be taken into account. So, the performances of 12 widely used models are evaluated in these two different rho(d)& nbsp;ranges. Results show that when rho(d)& nbsp;=& nbsp;1.4 g cm(-3). This further confirms the necessity of segmentation. Finally, with a demarcation point of 1.4 g cm(-3), a new model with different calculation methods is proposed herein for predicting dry. The new model exhibits the highest accuracy in predicting & nbsp;lambda(dry) with the highest correlation coefficient (R), lowest root mean square error (RMSE), and smaller mean bias error values; compared to the previous models, the new model RMSE values are reduced by 16.6% on average for soils with rho(d)& nbsp;=& nbsp;1.4 g cm(-3), respectively. Namely, the new model is highly suitable for studying lambda(dry)& nbsp;for different rho(d)& nbsp;due to its simplicity and applicability.

Thermal conduction control is important for retarding permafrost degradation and mitigating of frost geohazards. Similar to a thermodiode, high thermal conductivity contrast (HTCC) materials can serve as good thermal insulators. A preferred HTCC material for ground cooling is larger in thermal resistance in summer and smaller in winter. Because of contrasting thermal conductivity under frozen and thawed states, organic soil is blessed with such a property. This study quantified and reported the HTCC effects on a range of soil organic matter concentrations (SOMC) and soil moisture saturation degree (SMSD). Using the COMSOL, influences of different SOMC and SMSD on ground temperatures were simulated and compared with laboratory-measured properties. Simulation results demonstrated that with constant SMSD at 20% throughout the year, the thermal insulation effect was strengthened with increasing SOMC. A better insulating effect was judged by lower annual amplitudes and smaller depths of zero annual amplitude of ground temperatures. In case of low SMSD in summer (20%) and high SMSD in winter (60-80%), the HTCC effect of soil is enhanced with increasing SOMC. This enhancement was evidenced by increased thermal offsets and decreased maximum summer and average nearsurface soil temperatures. With constant SOMC and increasing SMSD, the rising HTCC effect gradually cools the ground. An integral analysis indicates that the higher the SOMC and SMSD in winter, the larger the thermal offset and the lower the ground temperature, i.e., the greater the HTCC effect of organic soil. This study may provide geocryological bases for engineering and environmental applications in cold regions.