Permafrost Distribution Research Articles

Thermal regime of peatlands in Western Siberia near the southern border of the permafrost

Relevance. The permafrost degradation under the effect of global warming. It determines the necessity for a predictive assessment of permafrost stability to minimize disturbances to engineering installations in the permafrost zone. Aim. To assess the stability of frozen peatlands in the southern part of the zone of insular distribution of permafrost. Objects. Soils of flat-mound and high-mound palsa mires, which preserve permafrost on the southern border of the insular permafrost zone. Methods. Measurements of the temperature of peat and mineral soil in geocryological boreholes from 0 to 10 m deep using the SAM-N automatic monitoring surveillance network; determination of surface temperature from thermal channels of MODIS satellite images from 2000 to 2022; analysis of meteorological indices and determination of air temperature trends for predictive assessment of permafrost stability; calculation of indicator values of permafrost state, such as freezing degree-days and thawing degree-days, frost index, freezing and thawing N-factors. Results. Numerous features of unstable permafrost have been observed. In particular, the mean annual temperature of the surface layer was positive in all studied boreholes (+0.8...+1.3°C), and the temperature at the depth of zero amplitudes (10 m) is close to the melting point (with the predominance of temperatures in the range of –0.2...–0.3°C). Besides, a layer of soil that does not freeze throughout the year (non-merging permafrost) was identified. The thawing degree-days and frost number values correspond to areas with unfrozen soils. Positive trends in air temperature, Earth's surface temperature, and snow depth were observed. For 2000–2022, surface air temperature increased by 0.76°C/10 years on average. The land surface temperature increased in summer by an average of 0.42°C/10 years. If the positive trend in surface air temperatures continues, frozen peatlands in the southern part of the permafrost zone will completely melt in 50–70 years.

Current understanding, knowledge gaps, and challenges of mountain permafrost research in the Chilean and Argentinean Andes

Mountain permafrost extends over a vast area throughout the Chilean and Argentinean Andes, making it a key component of these mountain ecosystems. To develop an overview of the current state of knowledge on southern Andean permafrost, it is essential to outline appropriate research strategies in a warmer climate context. Based on a comprehensive review of existing literature, this work identifies eight main research themes on mountain permafrost in the Chilean and Argentinean Andes: paleoenvironmental reconstructions, permafrost-derived landforms inventories, permafrost distribution models, internal structure analysis, hydrogeochemistry, permafrost dynamics, geological hazards, and transitional landscape studies. This extensive review work also highlights key debates concerning the potential of permafrost as a water resource and the factors influencing its distribution. Furthermore, we identified several challenges the scientific community must address to gain a deeper understanding of mountain permafrost dynamics. Among these challenges, we suggest tackling the need to broaden spatial focus, along with the use of emerging technologies and methodologies. Additionally, we emphasize the importance of developing interdisciplinary approaches to effectively identify the impacts of climate change on mountain permafrost. Such efforts are essential for adequately preparing scientists, institutional entities, and society to address future scenarios.

Permafrost Hydrogeology of Taylor Valley, Antarctica: Insights From Deep Electrical Resistivity Tomography

AbstractGlobal warming has prompted globally widespread permafrost thawing, resulting in enhanced greenhouse gas release into the atmosphere. Studies conducted in the Northern Hemisphere reveal an alarming increase in permafrost thawing. However, similar data from Antarctica are scarce. We conducted a 2‐D Deep Electrical Resistivity Tomography (DERT) survey in Taylor Valley, Antarctica, to image the distribution of permafrost, its thicknesses, lower boundaries, and hydrogeology. Results show resistive, discontinuous domains that we suggest represent permafrost units. We also find highly conductive layers (5–10 Ω·m), between 300–350 m and 600–650 m below ground level and a shallower (∼50–100 m depth) conductive layer. The combined data set reveals a broad brine system in Taylor Valley, implying multi‐tiered groundwater circulation: a shallow, localized system linked with surface water bodies and a separate deeper, regional circulation system. The arrangement of these brines across different levels, coupled with the uneven permafrost distribution, underscores potential interplay between the two systems.

An indicator of relative distribution probability of field-scale permafrost in Northeast China: Using a particle swarm optimization (PSO)-based indicator composition algorithm

Under the influence of climate changing, permafrost in Northeast China (NEC) has been consistently degrading in recent years. Numerous scholars have investigated the spatial and temporal distribution patterns of permafrost in the NEC region. However, due to constraints in data availability and methodological approaches, only a limited number of studies have extended their analyses to the field scale. In this study, we established a particle swarm optimization (PSO)-based indicator composition algorithm (PSO-ICA) to obtain an indicator factor, η, that indicates the relative distribution probability of permafrost at the field scale. PSO-ICA screened and combined 12 high-resolution environmental variables to compose η. The spatial distribution data of permafrost with a length of 765.378 km provided by the engineering geological investigation report (EGIR) of six highways were used to train and validate the effectiveness of η in indicating permafrost. At the field scale, η was found to be similar to the surface freezing number (SFN) in its ability to indicate permafrost, with AUC values of 0.7046 and 0.7063 for the two by the ROC test. In addition, η has a good performance in predicting highway distresses in the permafrost region in the absence of survey data. This study also confirmed that the resolution and accuracy of permafrost mapping results can be improved by utilizing η. After downscaling the 1 km resolution SFN to 30 m resolution using η, the R2 of the linear relationship between SFN and permafrost temperatures from 43 monitoring boreholes was improved from 0.7010 to 0.8043. If η can help understand the distribution of permafrost at field scale, many engineering and environmental practices could potentially benefit.

Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal

Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited.

A Comprehensive Inventory, Characterization, and Analysis of Rock Glaciers in the Jhelum Basin, Kashmir Himalaya, Using High-Resolution Google Earth Data

Rock glaciers are crucial freshwater resources, yet detailed knowledge about their distribution, characteristics, and dynamics in the Himalayan region is scarce. This study presents a comprehensive rock glacier inventory of the Jhelum basin, Kashmir Himalaya, India, using high-resolution Google Earth data. We identified 240 rock glaciers covering an area of 41.24 ± 2.2 km2, with ~76% classified as active, ~20% inactive, and 3.7% relict. The average areas and lengths of these rock glacier types were 0.19 km2, 0.06 km2, and 0.29 km2, and 699 m, 426 m, and 952 m, respectively. Most rock glaciers (~90%) were oriented northwards (N, NE, NW), while only 5% faced southwards (S, SE, SW). The lower limit of permafrost in the Jhelum basin is about 3316 m asl. Furthermore, we estimated the ice storage of rock glaciers in the Jhelum basin at 0.80 ± 0.13 km3, equivalent to 0.72 ± 0.12 km3 of water volume. This study enhances our understanding of permafrost distribution and the characteristics and dynamics in the basin. Given their greater resilience to climate change compared to clean glaciers, the hydrological significance of rock glaciers is expected to increase under projected climate change scenarios. This study highlights their importance as a vital water resource amidst the accelerated recession of clean glaciers.

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.

Discontinuous permafrost detection from neural network-ensemble learning based electrical resistivity tomography

Electrical resistivity tomography (ERT) is an effective method for detecting the distribution of permafrost. However, the general inversion method of ERT cannot satisfy the engineering designation demand, resulting in the foundation of thaw settlement in discontinuous permafrost regions. In this study, we proposed a neural network-ensemble learning inversion method to improve the detection accuracy of discontinuous permafrost. First, a series of different resistivity distributions was evaluated to establish forward models for the training of a backpropagation neural network (BPNN). The resistivity distributions of the forward models varied with the temperature gradient, similar to the resistivity distribution of real discontinuous permafrost. The bagging algorithm of ensemble learning was then used to optimize the BPNN inversion models. Finally, three discontinuous permafrost resistivity models and two field data examples are considered to demonstrate the feasibility of the proposed inversion model. The inversion results of synthetic and field examples show that the neural network-ensemble learning model achieved a greater inversion effect with better accuracy and less noisy points than a single BPNN model or the Res2Dinv method. The trained ensemble learning inversion method has good application in field permafrost exploration.

Permafrost Distribution in the Southern Carpathians, Romania, Derived From Machine Learning Modeling

ABSTRACTComputer modeling of sporadic and isolated patches of mountain permafrost distribution is difficult to implement without overestimating it. The main challenge is to determine the very areas where the criteria for permafrost maintenance are met. This paper aims to modeling the permafrost distribution in the Southern Carpathians (SC), a typical marginal periglacial mountain range. For this purpose, a collection of 883 bottom temperature of late winter snow cover (BTS) points was used as a proxy for permafrost presence or absence in order to train several machine learning models. The performances of each model were evaluated with AUC with varying between 0.99 for Maxent and 0.74 for K‐nearest neighbors and most models (five) exhibiting values between 0.82 and 0.86. Other tests such as confusion matrices, sensitivity analyses, data shuffling, and data size reduction tests indicated that Maxent, AdaBoost, and support vector machine offered the best results while logistic regression, neural network, and gradient boosting exhibited rather poor permafrost distributions. The final ensemble median model indicated a total permafrost area of 19.2 km2 occupying 1%–9% of the alpine area of the studied massifs. NDVI proved crucial for permafrost prediction because it allows delimiting the debris surfaces where permafrost is probable.

Exploring the impact of surface lapse rate change scenarios on mountain permafrost distribution in four dissimilar valleys in Yukon, Canada

A scenario-based approach was used to test air and ground response to warming with and without changes to inverted surface lapse rates in four Yukon valleys. Generally, climate warming coupled with weakening of temperature inversions resulted in the greatest increase in air temperature at low elevations. However, ground temperatures at high elevations showed the greatest response to warming and variability between scenarios due to increased connectivity between air and ground. Low elevations showed less of a response to warming and permafrost was largely preserved in these locations. Local models also predicted higher permafrost occurrence compared to a regional permafrost probability model, due to the inclusion of differential surface and thermal offsets. Results show that the spatial warming patterns in these mountains may not follow those predicted in other mountain environments following elevation dependent warming (EDW). As a result, the concept of EDW should be expanded to become more inclusive of a wider range of possible spatial warming distributions. The purpose of this paper is not to provide exact estimations of warming, but rather to provide hypothetical spatial warming patterns, based on logical predictions of changes to temperature inversion strength, which may not directly follow the distribution projected through EDW.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

Deep-learning viscoelastic seismic inversion for mapping subsea permafrost

Marine seismic surveys can be used to map ice-bearing subsea permafrost on a large scale. However, current seismic processing technologies have limited capacity to image permafrost distribution at depth, mainly due to the low sensitivity of primary reflections and refractions to the velocity inversion found at the base of permafrost. Guided waves and multiples are more sensitive to the velocity variations below the top of permafrost, but they remain challenging to use in physics-based inversion approaches. A deep-learning-based seismic inversion has the potential to improve seismic imaging below the top of permafrost by automatically extracting information from all wave modes. We develop a multi-input neural network (NN) to estimate seismic velocities from marine seismic data. The network is trained on synthetic data generated from the representative distributions of the seismic properties of subsea permafrost. We find that our network can image large velocity contrasts and reversals in depth, typical of subsea permafrost. We use our network to estimate the P- and S-wave velocity and Q-factor models from a seismic line in the Beaufort Sea. The NN indicates highly discontinuous subsea permafrost with variable thickness in the area. Our work shows that deep-learning-based seismic inversion could become a cost-effective technology to map the distribution of subsea permafrost on a large scale and, more generally, high-velocity geologic layers located in shallow waters.

Transient Modeling of Permafrost Distribution From 1986 to 2016 in Mongolia

ABSTRACTThe distribution of permafrost in the southernmost zones is complicated in both time and space. We assessed this complexity by applying transient heat transfer schemes to model permafrost in Mongolia between 1986 and 2016. The modeling took into account the spatial distribution of soil thermal properties and time series of ground temperature profiles observed at 69 borehole sites. Spatial modeling was forced by downscaled ERA5 air temperatures that were further debiased with local meteorological records. The results indicated that permafrost warmed by 0°C to 0.4°C/decade, most significantly in eastern and northern Hangai and at high elevations in the Altai Mountains. The extent of permafrost remained stable, but the active layer was thickened, especially after the late 1990s. Forested wet soils experienced less warming, while temperatures in soils with low organic matter and low moisture content increased by 0.5°C over the modeling period. The lower limits of mountain permafrost were estimated to be between 1320 and 2030 m a.s.l., depending on the latitude. Our permafrost map allows the delineation of a complex distribution of permafrost in Mongolia, which is strongly controlled by the spatially complex topography, landcovers, and soil wetness.

A climate-driven, altitudinal transition in rock glacier dynamics detected through integration of geomorphological mapping and synthetic aperture radar interferometry (InSAR)-based kinematics

Abstract. In dry southwestern South Tyrol, Italy, rock glaciers are dominant landforms of the high-mountain cryosphere. Their spatial distribution and degree of activity hold critical information on the current state of discontinuous permafrost and consequently on the response potential to climate warming. Traditional geomorphologic mapping, however, owing to the qualitative expert-based nature, typically displays a high degree of uncertainty and variability among operators with respect to the dynamic classification of intact (permafrost-bearing) and relict (permafrost-devoid) rock glaciers. This limits the reliability of geomorphologic rock glacier inventories for basic and applied purposes. To address this limitation, (i) we conduct a systematic evaluation of the improvements that synthetic aperture radar interferometry (InSAR) can afford to the detection and dynamic classification of rock glaciers and (ii) build an integrated inventory that combines the strengths of geomorphologic- and InSAR-based approaches. To exploit fully InSAR-based information towards a better understanding of the topo-climatic conditions that sustain creeping permafrost, we further explore how velocity and the spatial distribution of moving areas (MAs) within rock glaciers may vary as a function of simple topographic variables known to exert first-order controls on incoming solar radiation, such as elevation and aspect. Starting from a geomorphologic inventory (n=789), we characterize the kinematics of InSAR-based MAs and the relevant hosting rock glaciers on 36 Sentinel-1 interferograms in the 2018–2019 period. With respect to the original inventory, InSAR analysis allowed us to identify 14 previously undetected rock glaciers. Further, it confirmed that 246 (76 %) landforms, originally interpreted as intact, do exhibit detectable movement (i.e., ≥1 cm yr−1) and that 270 (60 %) of the relict labeled counterparts do not, whereas 144 (18 %) were kinematically undefined due to decorrelation. Most importantly, InSAR proved critical for reclassifying 121 (15 %) rock glaciers, clarifying that 41 (13 %) of those interpreted as being intact do not exhibit detectable movement and that 80 (17 %) of the original relict ones do move. Reclassification (i) allowed us to identify a cluster of intact rock glaciers below 2000 m a.s.l. associated with positive mean annual air temperature (MAAT), and (ii) by increasing the altitudinal overlap between intact and relict rock glaciers, it depicts a broad transition belt in the aspect–elevation space, which varies from 50 m on west-facing slopes to 500 m on easterly ones. This finding deteriorates the significance of elevation and aspect as topographic proxies for modeling permafrost occurrence and highlights the importance of using InSAR to inform such models. From a process-oriented standpoint, InSAR information proves fundamental for imaging how this altitudinal transition manifests through changing rates and styles of rock glacier surface deformation. Specifically, we find that, as rock glaciers move faster, an increasingly larger proportion of their surface becomes kinematically involved (i.e., percent MA cover) and that this proportion increases with elevation up to 2600–2800 m, beyond which an inflection occurs and consistent average values are attained. Considering that the inflection falls between the −1 and −2 °C MAAT – the lower boundary for discontinuous permafrost – and is independent of slope gradient, we conclude that this altitudinal pattern represents a geomorphic signature: the dynamic expression of increasing permafrost distribution, from sporadic to discontinuous.

Modelling present and future rock wall permafrost distribution in the Sisimiut mountain area, West Greenland

Abstract. Degrading rock wall permafrost was found responsible for the increase in rockfall and landslide activity in several cold mountain regions across the globe. In Greenland, rock wall permafrost has so far received little attention from the scientific community, despite mountains being a predominant feature on the ice-free coastline and landslide activity being significant. In this study, we aim to make a first step towards a better understanding of rock wall permafrost in Greenland by modelling rock wall temperatures in the mountain area around the town of Sisimiut, which is 68° N on the west coast of Greenland. We first acquire rock surface temperature (RST) data for the period September 2020–September 2022 to model rock surface temperatures from weather forcing. The model is then applied to weather data from 1870 to 2022, generating rock surface temperatures to force transient heat transfer simulations over the same period. By extrapolating this method at the landscape scale, we obtain permafrost distribution maps and ad hoc simulations for complex topographies. Our model results are compared to temperature data from two lowland boreholes (100 m depth) and geophysical data describing frozen and unfrozen conditions across a mid-elevation mountain ridge. Finally, we use regional carbon pathway scenarios 2.6 and 8.5 to evaluate future evolution of rock wall temperatures until the end of the 21st century. Our data and simulation describe discontinuous permafrost distribution in rock walls up to roughly 400 m a.s.l. Future scenarios suggest a decline of deep frozen bodies up to 800 m a.s.l., i.e. the highest summits in the area. In summary, this study depicts a picture of warm permafrost in this area, highlighting its sensitivity to ongoing climate change.

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.

Investigation, Monitoring, and Simulation of Permafrost on the Qinghai‐Tibet Plateau: A Review

ABSTRACTThe Qinghai‐Tibet Plateau (QTP) is the largest permafrost region in the world at low and middle latitudes and high elevation. Permafrost is being degraded on the QTP due to global warming, which has a significant effect on regional climate, hydrological, and ecological processes. This paper provides a summary of recent progress in methods used in permafrost research, the permafrost distribution, and basic data relevant to permafrost research on the QTP. The area of permafrost was 1.32 × 106 km2 over the QTP, which accounts for approximately 46% of the QTP. Moreover, simulation studies of the hydrothermal process and permafrost change were reviewed and evaluated the effect of permafrost degradation on hydrological and ecological processes. The results revealed that the effects of permafrost on runoff were closely related to soil temperature, and the effect of permafrost degradation on the carbon cycle requires further study. Finally, current challenges in simulation of permafrost change processes on the QTP were discussed, emphasizing that permafrost degradation under climate change is a slow and non‐linear process. This review will aid future studies examining the mechanism underlying the interaction between permafrost and climate change, and environmental protection in permafrost regions on the QTP.

Academician Guodong Cheng's Contributions to Permafrost Science

ABSTRACTPermafrost is strongly associated with human well‐being and has become a frontier of cryospheric science. Professor Guodong Cheng is one of the most outstanding geocryologists in China. He was elected as an academician of the Chinese Academy of Sciences in 1993 and served as the president of the International Permafrost Association from 1993–1998. In the early 1980s, Professor Cheng proposed the hypothesis of the repeated‐segregation mechanism for the formation of thick‐layered ground ice near the permafrost table. Subsequently, in the early 2000s, he proposed the “proactive roadbed cooling” concept and led the successful development of a series of specific engineering measures that were fully applied in the Qinghai–Tibet Railway Project. Furthermore, he developed a conceptual model to describe the influences of changing permafrost on the groundwater system and discovered the “sink‐holing effect” (channeling with improved hydraulic conductivity of warming permafrost). Professor Cheng has also developed theories on the three‐dimensional zonation and proposed a classification system and an altitude model for high‐altitude permafrost distribution. On this special occasion of Professor Cheng's 80th birthday, this paper summarizes his outstanding achievements on permafrost science, hoping the permafrost research community will carry forward the momentum to further advance permafrost science worldwide.

An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau

Abstract. Ground surface temperature (GST), measured at approximately 5 cm in depth, is a key controlling parameter for subsurface biophysical processes at the land–atmosphere boundary. This work presents a valuable dataset of GST observations at various spatial scales in the Headwater Area of the Yellow River (HAYR), a representative area of high-plateau permafrost on the northeastern Qinghai–Tibet Plateau (QTP). GST was measured every 3 h using 72 iButton temperature loggers (DS1922L) at 39 sites from 2019 to 2020. At each site, GST was recorded in two plots at distances from 2 to 16 m under similar and different land-cover conditions (steppe, meadow, swamp meadow, and bare ground). These sensors proved their reliability in harsh environments because there were only 165 biased measurements from a total of 210 816. A high significant correlation (>0.96, p<0.001) was observed between plots, with a mean absolute error (MAE) of 0.2 to 1.2 °C. The daily intra-plot differences in GST were mainly <2 °C for sites with similar land cover in both plots and >2 °C when GST of bare ground was compared to that of sites with vegetation. From autumn to spring, the differences in GST could increase to 4–5 °C for up to 15 d. The values of the frost number (FN) were quite similar between the plots with differences in FN <0.05 for most of the sites. This dataset complements the sparse observations of GST on the QTP and helps to identify the permafrost distribution and degradation at high resolution as well as to validate and calibrate the permafrost distribution models. The datasets are openly available in the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.272945, Şerban and Jin, 2022).

Response of alpine ground temperatures to a rising atmospheric 0 °C isotherm in the period 1955–2021

Ground temperatures in alpine terrain vary considerably over short distances, particularly due to differences in elevation and incoming short-wave radiation but also snow and surface conditions. To allow for direct cross-site, interregional, and cross-disciplinary comparisons, we introduce a parameter called the potential 0 °C isotherm (PZDI) in the ground. This parameter represents an aspect- and elevation independent geo-projection of ground temperatures. It was calculated for several ground temperature datasets from the (sub-)permafrost zone in the European Alps. We analyse the reaction of the PZDI at different depths to long-term changes in atmospheric temperature, represented by the atmospheric 0 °C isotherm (AZDI). The close correspondence of PZDI and AZDI mainly provided two new insights: Based on 15 boreholes, the PZDI/AZDI interaction allowed a depth-dependent analysis of the magnitude and temporal evolution of the disequilibrium between atmosphere and ground and the potential heat transfer processes involved. Moreover, it allowed a reconstruction of the development of ground temperatures in the European Alps during the second half of the 20th century, adding 50 years to the longest existing time series measured in ice-poor permafrost. This extension into the past reveals a major warming at the end of the 1980s, which occurred before the start of the available permafrost temperature observations. In the 1955–2021 period, the 5-year running mean of the AZDI rose by 400 m, while the PZDI at 15 m depth rose by about 300 m. Projecting this warming into a permafrost distribution map shows a decline in shallow, ice-poor permafrost area by roughly 60 % since the 1980s. We expect the PZDI to follow the AZDI and to increase by another 100 m in the coming decade and up to at least 14 m depth. This would increase the area with loss of shallow permafrost to approximately 80 % since the 1980s.