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Reliable projections of permafrost change are crucial for estimating permafrost carbon loss. However, potential model biases in surface air temperature may yield unrealistic projections of future permafrost area. Here, by leveraging the emergent relationship between equilibrium climate sensitivity and projected changes in mean annual air temperature over High Mountain Asia (HMA), we mitigate the overestimated local warming rates and excessive thawing of permafrost associated with the “hot model” problem in models participating in the Coupled Model Intercomparison Project phase 6. After constraint, permafrost area over HMA will reduce by 37%, 64% and 99% in 2081–2100 relative to present‐day under the SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. In contrast, the unconstrained projections tend to overestimate the loss of permafrost area by nearly 10% under the low and mid‐emission scenarios due to the overestimation of local warming rates. These findings are crucial for policymaking and provide valuable insights into global permafrost projection. Plain Language Summary This study shows a comprehensive picture of permafrost changes over High Mountain Asia (HMA) in the coming future. A subset of the newest generation of climate models participating in Coupled Model Intercomparison Project phase 6 (CMIP6) have a “hot model” problem with high equilibrium climate sensitivity (ECS) exceeding the likely range of 2.5°C–4°C assessed by Intergovernmental Panel on Climate Change Sixth Assessment Report. This indicates that the surface temperature projections in response to changes in atmospheric carbon dioxide concentrations are higher than those expected based on other evidence. Here, taking HMA as a case study, we establish a relationship between ECSs and the future changes in mean annual air temperature over HMA to constrain the future projections of warming rates and permafrost degradation. The constrained projection is 0.2°C, 0.4°C and 0.5°C lower than the unconstrained warming simulated by the CMIP6 ensemble during 2081–2100 under low, intermediate, and very high emission scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5). Based on the more reliable projections of future warming, reductions of permafrost area by 37%, 64% and 99% in 2081–2100 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively, are expected. The constraining solves the exaggerated projection of permafrost degradation caused by CMIP6 models. Key Points Models that overestimate warming rates tend to also overestimate permafrost degradation over High Mountain Asia An emergent relationship between equilibrium climate sensitivity and surface warming is used to constrain future permafrost projections The permafrost area will reduce by 37%, 64% and 99% in 2081–2100 relative to 2000–2016 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios

Permafrost over the Qinghai–Tibet Plateau (QTP) has received increasing attention due to its high sensitivity to climate change. Numerous spatial modeling studies have been conducted on the QTP to assess the status of permafrost, project future changes in permafrost, and diagnose contributors to permafrost degradation. Due to the scarcity of ground stations on the QTP, these modeling studies are often hampered by the lack of validation references, calibration targets, and model constraints; however, a high-quality permafrost distribution map would be a good option as a benchmark for spatial simulations. Existing permafrost distribution maps for the QTP can poorly serve this purpose. An ideal benchmark map for spatial modeling should be methodologically sound, of sufficient accuracy, and based on observations from mapping years rather than all historical data spanning several decades. Therefore, in this study, we created a new permafrost distribution map for the QTP in 2010 using a novel permafrost mapping approach with satellite-derived ground surface thawing and freezing indices as inputs and survey-based subregion permafrost maps as constraints. This approach accounted for the effects of local factors by incorporating (into the model) an empirical soil parameter whose values were optimally estimated through spatial clustering and parameter optimization constrained by survey-based subregion permafrost maps, and the approach was also improved to reduce parametric equifinality. This new map showed a total permafrost area of about 1.086×106 km2 (41.2 % of the QTP area) and seasonally frozen ground of about 1.447×106 km2 (54.9 %) in 2010, excluding glaciers and lakes. Validations using survey-based subregion permafrost maps (κ=0.74) and borehole records (overall accuracy =0.85 and κ=0.43) showed a higher accuracy of this map compared with two other recent maps. Inspection of regions with obvious distinctions between the maps affirms that the permafrost distribution on this map is more realistic than that on the Zou et al. (2017) map. Given the demonstrated excellent accuracy, this map can serve as a benchmark map for constraining/validating land surface simulations on the QTP and as a historical reference for projecting future permafrost changes on the QTP in the context of global warming. The dataset is available from the repository hosted on Figshare (Cao et al., 2022): https://doi.org/10.6084/m9.figshare.19642362.

Under a pronounced climate warming, permafrost has been degrading in most areas globally, but it is still unclear in the northwestern part of the Da Xing'anling Mountains, Northeast China. According to a 10-year observation of permafrost and active-layer temperatures, the multi-year average of mean annual ground temperatures at 20 m was −2.83, −0.94, −0.80, −0.70, −0.60, and −0.49 ∘C, respectively, at boreholes Gen'he4 (GH4), Mangui3 (MG3), Mangui1 (MG1), Mangui2 (MG2), Gen'he5 (GH5), and Yituli'he2 (YTLH2), with the depths of the permafrost table varying from 1.1 to 7.0 m. Ground cooling at shallow depths has been detected, resulting in declining thaw depths in Yituli'he during 2009–2020, possibly due to relatively stable mean positive air temperature and declining snow cover and a dwindling local population. In most study areas (e.g., Mangui and Gen'he), permafrost warming is particularly pronounced at larger depths (even at 80 m). These results can provide important information for regional development and engineering design and maintenance and also provide a long-term ground temperature dataset for the validation of models relevant to the thermal dynamics of permafrost in the Da Xing'anling Mountains. All of the datasets are published through the National Tibetan Plateau Data Center (TPDC), and the link is https://doi.org/10.11888/Geocry.tpdc.271752 (Chang, 2021).

Rapid landscape changes in the Arctic are occurring at an accelerated rate. The most common disturbances that cover the largest areas of the Arctic are wildfires. It is well known that lakes serve as a good proxy for identifying thermokarst processes. However, the impact of fires is often overlooked in analyses of lake dynamics. This paper aims to validate if thermokarst lakes can be treated as geoindicators of permafrost degradation after wildfires. We analyzed changes in the spatial extent of the thermokarst lakes after wildfires at the part of the Mackenzie River watershed. For that purpose, we examined changes in thermokarst lakes in five test areas before the fire, short and long term after fires, and compared the results with similar unburned areas. Our findings indicate that lake responses to wildfires vary significantly, with no consistent area increase or decrease pattern. However, burned areas exhibit greater variability in lake size compared to unburned sites, suggesting that wildfire disturbances create a state of geoecological disharmony. Long-term reductions in the lakes area point to permanent permafrost degradation, while some lakes expanded, likely due to thermokarst processes. Our results emphasize the potential of thermokarst lakes as indicators of post-fire permafrost changes, reinforcing the need for further research to develop quantitative methods for assessing permafrost degradation in wildfire-affected regions.

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.

Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.

The history of permafrost landscape map compilation is related to the study of ecological problems with permafrost. Permafrost-landscape studies are now widely used in geocryological mapping. Permafrost-landscape classifications and mapping are necessary for studying the trends in development of the natural environment in northern and high-altitude permafrost regions. The cryogenic factor in the permafrost zone plays a leading role in the differentiation of landscapes, so it must be considered during classification construction. In this study, a map’s special content was developed using publications about Yakutian nature, archive sources from academic institutes, the interpretation of satellite images, and special field studies. Overlays of 20 types of terrain, identified by geological and geomorphological features, and 36 types of plant groupings, allowed the systematization of permafrost temperature and active layer thickness in 145 landscape units with relatively homogeneous permafrost-landscape conditions in the Sakha (Yakutia) Republic. This map serves as a basis for applied thematic maps related to the assessment and forecast of permafrost changes during climate warming and anthropogenic impacts.

Climate warming has resulted in significant changes in permafrost in Northeast China, leading to notable alterations in soil organic carbon (SOC) stocks. These changes are crucial for both the global carbon cycle and climate change, as well as directly impacting the sustainable development of ecosystems. In order to examine the SOC dynamics and the impact of permafrost changes on SOC, we investigate the changes of permafrost extent based on a regression model and TTOP (top temperature of permafrost) model and the relationship between land use and land cover (LULC), SOC stocks, and permafrost changes in Northeast China. The results showing a shrinking permafrost area from 37.43 × 104 km2 to 16.48 × 104 km2 during the period from the 1980s to the 2010s in Northeast China, and the SOC stock decreased by 24.18 Tg C from the 1980s to the 1990s and then rapidly increased by 102.84 Tg C in the 2000s. Permafrost degradation speeds up the succession of LULC, impacting about 90% of the SOC in permafrost regions. The relationship between permafrost changes and SOC in Northeast China shows that permafrost degradation significantly reduces SOC stocks in the short term but increases SOC stocks in the long term, and that LULC play a crucial role in regulating this relationship. The goals of this study are to acquire an understanding of permafrost status and deepening insights into the dynamics of SOC. Simultaneously, the study aims to furnish valuable scientific references for shaping policies on sustainable land use and management in the future, all the while advancing the cause of ecological equilibrium and sustainable development in Northeast China and other areas.

The current spatial atmospheric forcing data cannot accurately depict the actual conditions of the Qinghai–Tibet Plateau (QTP), where monitoring stations are scarce and unevenly distributed. This deficiency in atmospheric data hinders accurate simulation of plateau permafrost changes on the plateau. In this study, we develop a new approach to evaluate regional permafrost changes, which does not rely on spatially distributed meteorological data but instead uses the regional climate change processes or temperature change rates. Centred on a transient heat conduction permafrost model, this approach was applied to the Qinghai Hoh Xil National Nature Reserve (referred to as Hoh Xil) within the QTP from 1960 to 2015, using the rate of air temperature change provided by the Wudaoliang Meteorological Station, the only national station in Hoh Xil. Simulation results showed that the difference between the simulated and observed change rates of mean annual ground temperature (MAGT) was less than 0.04 °C per decade from 2001 to 2015 at five long-term monitoring sites. The simulated ground temperature profiles in four boreholes from various permafrost zones revealed an error of less than 0.7 °C below 5 m in depth. Model validation demonstrates the reliability of this approach for predicting long-term permafrost changes. Future regional permafrost changes were further simulated based on the latest warming scenarios (BCC-CSM2-MR) from the Coupled Model Intercomparison Project Phase 6. Predictions revealed significant differences in the regional permafrost degradation rate under different climate warming scenarios. Under the most severe warming scenario (SSP5-8.5), permafrost in the study area is projected to still cover 72.2% of the total area by 2100, with most of the Hoh Xil's permafrost becoming warm (MAGT > −1 °C) permafrost. This approach not only facilitates the simulation of frozen ground changes in areas with few meteorological monitoring stations but also provides a new perspective for using coarse-resolution palaeoclimate data to investigate permafrost formation and evolution over long time scales.

There is an exceptional opportunity of achieving simultaneous and complementary data from a multitude of geoscience and environmental near-earth orbiting artificial satellites to study phenomena related to the climate change. These satellite missions provide the information about the various phenomena, such as sea level change, ice melting, soil moisture variation, temperature changes and earth surface deformations. In this study, we focus on permafrost thawing and its associated gravity change (in terms of the groundwater storage), and organic material changes using the gravity recovery and climate experiment (GRACE) data and other satellite- and ground-based observations. The estimation of permafrost changes requires combining information from various sources, particularly using the gravity field change, surface temperature change, and glacial isostatic adjustment. The most significant factor for a careful monitoring of the permafrost thawing is the fact that this process could be responsible for releasing an additional enormous amount of greenhouse gases emitted to the atmosphere, most importantly to mention carbon dioxide (CO2) and methane that are currently stored in the frozen ground. The results of a preliminary numerical analysis reveal a possible existence of a high correlation between the secular trends of greenhouse gases (CO2), temperature and equivalent water thickness (in permafrost active layer) in the selected regions. Furthermore, according to our estimates based on processing the GRACE data, the groundwater storage attributed due to permafrost thawing increased at the annual rates of 3.4, 3.8, 4.4 and 4.0 cm, respectively, in Siberia, North Alaska and Canada (Yukon and Hudson Bay). Despite a rather preliminary character of our results, these findings indicate that the methodology developed and applied in this study should be further improved by incorporating the in situ permafrost measurements.