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Estimates of the past thermal state of the land surface are crucial to assess the magnitude of current anthropogenic climate change as well as to assess the ability of Earth System Models (ESMs) to forecast the evolution of the climate near the ground, which is not included in standard meteorological records. Subsurface temperature reacts to long-term changes in surface energy balance – from decadal to millennial time scales – thus constituting an important record of the dynamics of the climate system that contributes, with low-frequency information, to proxy-based paleoclimatic reconstructions. Broadly used techniques to retrieve past temperature and heat flux histories from subsurface temperature profiles based on a singular value decomposition (SVD) algorithm were able to provide robust global estimates for the last millennium, but the approaches used to derive the corresponding 95 % confidence interval were wrong from a statistical point of view in addition to being difficult to interpret. To alleviate the lack of a meaningful framework for estimating uncertainties in past temperature and heat flux histories at regional and global scales, we combine a new bootstrapping sampling strategy with the broadly used SVD algorithm and assess its performance against the original SVD technique and another technique based on generating perturbed parameter ensembles of inversions. The new bootstrap approach is able to reproduce the prescribed surface temperature series used to derive an artificial profile. Bootstrap results are also in agreement with the global mean surface temperature history and the global mean heat flux history retrieved in previous studies. Furthermore, the new bootstrap technique provides a meaningful uncertainty range for the inversion of large sets of subsurface temperature profiles. We suggest the use of this new approach particularly for aggregating results from a number of individual profiles, and to this end, we release the programs used to derive all inversions in this study as a suite of codes labeled CIBOR v1: Codes for Inverting BORholes, version 1. [ABSTRACT FROM AUTHOR]

Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at 10.1594/PANGAEA.948334 ,​​​​​​​ is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management. [ABSTRACT FROM AUTHOR]

Estimates of the past thermal state of the land surface are crucial to assess the magnitude of current anthropogenic climate change, as well as to assess the ability of Earth System Models to forecast the evolution of the climate near the ground, not included in standard meteorological records. Subsurface temperature data are able to retrieve long-term changes in surface energy balance -from decadal to millennial time scales, thus constituting an important record of the dynamics of the climate system that contributes low-frequency information to proxy-based paleoclimatic reconstructions. A broadly used technique to retrieve past temperature and heat flux histories from subsurface temperature profiles based on a Singular Value Decomposition (SVD) algorithm was able to take into account a limited number of sources of uncertainty, with recent works attempting to increase the number of factors considered in uncertainty estimates. Nevertheless, the SVD methodology did not define a statistical framework for aggregating inversions of individual profiles to derive global results, which lead to estimates of global and regional uncertainties that are difficult to interpret. To alleviate the lack of a conceptual framework for estimating uncertainties in past temperature and heat flux histories at regional and global scales, we combine a new bootstrapping sampling strategy with the broadly used SVD algorithm, and assess its performance against the original SVD technique and another technique based on generating perturbed parameter ensembles of inversions. The new bootstrap approach is able to reproduce the prescribed surface temperature series used to derive an artificial profile. Bootstrap results are also in agreement with the global mean surface temperature history and the global mean heat flux history retrieved in previous studies. Furthermore, the new bootstrap technique provides with a meaningful uncertainty range for the inversion of large sets of subsurface temperature profiles. We suggest the use of this new approach particularly for aggregating results from a number of individual profiles, and to this end, we release the programs used to derive all inversions in this study as a suite of codes labelled CIBOR 1.0: Codes for Inverting BORholes, version 1.0. [ABSTRACT FROM AUTHOR]

Soil freeze–thaw processes play a fundamental role in the hydrology, geomorphology, ecology, thermodynamics, and soil chemistry of cold regions' landscapes. In understanding these processes, the temperature of the soil is used as a proxy to represent the partitioning of soil ice and water content via a soil freezing characteristic curve (SFCC). This mathematical construct relates the soil ice content to a specific temperature for a particular soil. SFCCs depend on many factors, including soil properties (e.g., porosity and composition), soil pore water pressure, dissolved salts, (hysteresis in) freezing/thawing point depression, and the degree of saturation, all of which can be site-specific and time-varying characteristics. SFCCs have been measured using various methods for diverse soils since 1921, but, to date, these data have not been broadly compared. This is in part because they had not previously been compiled in a single dataset. The dataset presented in this publication includes SFCC data digitized or received from authors, and it includes both historic and modern studies. The data are stored in an open-source repository, and an R package is available to facilitate their use. Aggregating the data has pointed out some data gaps, namely that there are few studies on coarse soils and comparably few in situ measurements of SFCCs in mountainous environments. It is hoped that this dataset (10.5281/zenodo.5592825;) will aid in the development of SFCC theory and improve SFCC approximations in soil freeze–thaw modelling activities. [ABSTRACT FROM AUTHOR]

Soil freeze-thaw processes play a fundamental role in the hydrology, geomorphology, ecology, thermodynamics and soil chemistry of a cold-regions landscape. In understanding these processes, the temperature of the soil is used as a proxy to represent the soil ice content through a soil freezing characteristic curve (SFCC). This mathematical construct relates the soil ice content to a specific temperature for a particular soil. SFCCs depend on many factors including soil properties (e.g., porosity, composition, etc.), soil pore water pressure, dissolved salts, (hysteresis in) freezing/thawing point depression, and degree of saturation, all of which can be site-specific and time varying. SFCCs have been measured using various methods for diverse soils since 1921, and to date this data has not been broadly compared, in part because it has not previously been compiled in a single data set. The dataset presented in this publication includes SFCC data digitized or received from authors, and includes both historic and modern studies. The data is stored in an open-source repository, and an R package is available to facilitate its use. Aggregating the data has pointed out some data gaps, namely that there are few studies of coarse soils, and comparably few in-situ measurements of SFCCs in mountainous environments. It is hoped that this dataset will aid in the development of SFCC theory, and improve SFCC approximations in soil freeze-thaw modelling activities. [ABSTRACT FROM AUTHOR]

Permafrost warming is coinciding with accelerated mass movements, talking place especially in steep, mountainous topography. While this observation is backed up by evidence and analysis of both remote sensing as well as repeat terrestrial surveys undertaken since decades much knowledge is to be gained about the specific details, the variability and the processes governing these mass movements in the mountain cryosphere. This dataset collates data of continuously acquired kinematic observations obtained through in-situ Global Navigation Satellite Systems (GNSS) instruments that have been designed and implemented in a large-scale multi field-site monitoring campaign across the whole Swiss Alps. The landforms covered include rock glaciers, high-alpine steep bedrock bedrock as well as landslide sites, most of which are situated in permafrost areas. The dataset was acquired at 54 different stations situated at locations from 2304 to 4003 m a.s.l and comprises 209’948 daily positions derived through double-differential GNSS post-processing. Apart from these, the dataset contains down-sampled and cleaned time series of weather station and inclinometer data as well as the full set of GNSS observables in RINEX format. Furthermore the dataset is accompanied by tools for processing and data management in order to facilitate reuse, open alternate usage opportunities and support the life-long living data process with updates. To date this dataset has seen numerous use cases in research as well as natural-hazard mitigation and adaptation due to climate change. [ABSTRACT FROM AUTHOR]

The accurate simulation of heat transfer with phase change is a central problem in cryosphere studies. This is because the non-linear behaviour of enthalpy as function of temperature can prevent thermal models of snow, ice, and frozen soil from converging to the correct solution. Existing numerical techniques rely on increased temporal resolution in trying to keep corresponding errors within acceptable bounds. Here, we propose an algorithm, originally applied to solve water flow in soils, as a method to solve these integration issues with guaranteed convergence and conservation of energy for any time step size. We review common modelling approaches, focusing on the fixed-grid method and on frozen soil. Based on this, we develop a conservative formulation of the governing equation and outline problems of alternative formulations in discretized form. Then, we apply the nested Newton–Casulli–Zanolli (NCZ) algorithm to a one-dimensional finite-volume discretization of the energy–enthalpy formulation. Model performance is demonstrated against the Neumann and Lunardini analytical solutions and by comparing results from numerical experiments with integration time steps of 1 h, 1 d, and 10 d. Using our formulation and the NCZ algorithm, the convergence of the solver is guaranteed for any time step size. With this approach, the integration time step can be chosen to match the timescale of the processes investigated. [ABSTRACT FROM AUTHOR]

Recent studies have shown the cold and arid trans-Himalayan region comprises significant areas underlain by permafrost. While the information on the permafrost characteristics of this region started emerging, the governing energy regime is of particular interest. This paper presents the results of a surface energy balance (SEB) study carried out in the upper Ganglass catchment in the Ladakh region of India which feeds directly into the Indus River. The point-scale SEB is estimated using the 1D mode of the GEOtop model for the period of 1 September 2015 to 31 August 2017 at 4727 m a.s.l. elevation. The model is evaluated using field-monitored snow depth variations (accumulation and melting), outgoing long-wave radiation and near-surface ground temperatures and showed good agreement with the respective simulated values. For the study period, the SEB characteristics of the study site show that the net radiation (29.7 Wm-2) was the major component, followed by sensible heat flux (-15.6 Wm-2), latent heat flux (-11.2 Wm-2) and ground heat flux (-0.5 Wm-2). During both years, the latent heat flux was highest in summer and lowest in winter, whereas the sensible heat flux was highest in post-winter and gradually decreased towards the pre-winter season. During the study period, snow cover builds up starting around the last week of December, facilitating ground cooling during almost 3 months (October to December), with sub-zero temperatures down to -20 ∘ C providing a favourable environment for permafrost. It is observed that the Ladakh region has a very low relative humidity in the range of 43 % compared to e.g. ∼70 % in the European Alps, resulting in lower incoming long-wave radiation and strongly negative net long-wave radiation averaging ∼-90 Wm-2 compared to -40 Wm-2 in the European Alps. Hence, land surfaces at high elevation in cold and arid regions could be overall colder than the locations with higher relative humidity, such as the European Alps. Further, it is found that high incoming short-wave radiation during summer months in the region may be facilitating enhanced cooling of wet valley bottom surfaces as a result of stronger evaporation. [ABSTRACT FROM AUTHOR]

The central Slave Geological Province is situated 450–650 km from the presumed spreading centre of the Keewatin Dome of the Laurentide Ice Sheet, and it differs from the western Canadian Arctic, where recent thaw-induced landscape changes in Laurentide ice-marginal environments are already abundant. Although much of the terrain in the central Slave Geological Province is mapped as predominantly bedrock and ice-poor, glacial deposits of varying thickness occupy significant portions of the landscape in some areas, creating a mosaic of permafrost conditions. Limited evidence of ice-rich ground, a key determinant of thaw-induced landscape change, exists. Carbon and soluble cation contents in permafrost are largely unknown in the area. Twenty-four boreholes with depths up to 10 m were drilled in tundra north of Lac de Gras to address these regional gaps in knowledge and to better inform projections and generalizations at a coarser scale. Excess-ice contents of 20 %–60 %, likely remnant Laurentide basal ice, are found in upland till, suggesting that thaw subsidence of metres to more than 10 m is possible if permafrost were to thaw completely. Beneath organic terrain and in fluvially reworked sediment, aggradational ice is found. The variability in abundance of ground ice poses long-term challenges for engineering, and it makes the area susceptible to thaw-induced landscape change and mobilization of sediment, solutes and carbon several metres deep. The nature and spatial patterns of landscape changes, however, are expected to differ from ice-marginal landscapes of western Arctic Canada, for example, based on greater spatial and stratigraphic heterogeneity. Mean organic-carbon densities in the top 3 m of soil profiles near Lac de Gras are about half of those reported in circumpolar statistics; deeper deposits have densities ranging from 1.3–10.1 kg C m -3 , representing a significant additional carbon pool. The concentration of total soluble cations in mineral soils is lower than at previously studied locations in the western Canadian Arctic. This study can inform permafrost investigations in other parts of the Slave Geological Province, and its data can support scenario simulations of future trajectories of permafrost thaw. Preserved Laurentide basal ice can support new ways of studying processes and phenomena at the base of an ice sheet. [ABSTRACT FROM AUTHOR]

ERA5-Land (ERA5L) is a reanalysis product derived by running the land component of ERA5 at increased resolution. This study evaluates ERA5L soil temperature in permafrost regions based on observations and published permafrost products. We find that ERA5L overestimates soil temperature in northern Canada and Alaska but underestimates it in mid–low latitudes, leading to an average bias of -0.08 ∘ C. The warm bias of ERA5L soil is stronger in winter than in other seasons. As calculated from its soil temperature, ERA5L overestimates active-layer thickness and underestimates near-surface (<1.89 m) permafrost area. This is thought to be due in part to the shallow soil column and coarse vertical discretization of the land surface model and to warmer simulated soil. The soil temperature bias in permafrost regions correlates well with the bias in air temperature and with maximum snow height. A review of the ERA5L snow parameterization and a simulation example both point to a low bias in ERA5L snow density as a possible cause for the warm bias in soil temperature. The apparent disagreement of station-based and areal evaluation techniques highlights challenges in our ability to test permafrost simulation models. While global reanalyses are important drivers for permafrost simulation, we conclude that ERA5L soil data are not well suited for informing permafrost research and decision making directly. To address this, future soil temperature products in reanalyses will require permafrost-specific alterations to their land surface models. [ABSTRACT FROM AUTHOR]

Heave and subsidence of the ground surface can offer insight into processes of heat and mass transfer in freezing and thawing soils. Additionally, subsidence is an important metric for monitoring and understanding the transformation of permafrost landscapes under climate change. Corresponding ground observations, however, are sparse and episodic. A simple tilt-arm apparatus with logging inclinometer has been developed to measure heave and subsidence of the ground surface with hourly resolution and millimeter accuracy. This contribution reports data from the first two winters and the first full summer, measured at three sites with contrasting organic and frost-susceptible soils in warm permafrost. The patterns of surface movement differ significantly between sites and from a prediction based on the Stefan equation and observed ground temperature. The data are rich in features of heave and subsidence that are several days to several weeks long and that may help elucidate processes in the ground. For example, late-winter heave followed by thawing and subsidence, as reported in earlier literature and hypothesized to be caused by infiltration and refreezing of water into permeable frozen ground, has been detected. An early-winter peak in heave, followed by brief subsidence, is discernible in a previous publication but so far has not been interpreted. An effect of precipitation on changes in surface elevation can be inferred with confidence. These results highlight the potential of ground-based observation of subsidence and heave as an enabler of progress in process understanding, modeling and interpretation of remotely sensed data. [ABSTRACT FROM AUTHOR]

Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses – currently ERA5, ERA-Interim, JRA-55 and MERRA-2 – to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for 10 terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulations and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data. [ABSTRACT FROM AUTHOR]

The Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component of the Canadian Earth System Model (CanESM). Here, we investigate the impact of changes to CLASS-CTEM that are designed to improve the simulation of permafrost physics. Overall, 18 tests were performed, including changing the model configuration (number and depth of ground layers, different soil permeable depth datasets, adding a surface moss layer), and investigating alternative parameterizations of soil hydrology, soil thermal conductivity, and snow properties. To evaluate these changes, CLASS-CTEM outputs were compared to 1570 active layer thickness (ALT) measurements from 97 observation sites that are part of the Global Terrestrial Network for Permafrost (GTN-P), 105 106 monthly ground temperature observations from 132 GTN-P borehole sites, a blend of five observation-based snow water equivalent (SWE) datasets (Blended-5), remotely sensed albedo, and seasonal discharge for major rivers draining permafrost regions. From the tests performed, the final revised model configuration has more ground layers (increased from 3 to 20) extending to greater depth (from 4.1 to 61.4 m) and uses a new soil permeable depths dataset with a surface layer of moss added. The most beneficial change to the model parameterizations was incorporation of unfrozen water in frozen soils. These changes to CLASS-CTEM cause a small improvement in simulated SWE with little change in surface albedo but greatly improve the model performance at the GTN-P ALT and borehole sites. Compared to the GTN-P observations, the revised CLASS-CTEM ALTs have a weighted mean absolute error (wMAE) of 0.41–0.47 m (depending on configuration), improved from >2.5 m for the original model, while the borehole sites see a consistent improvement in wMAE for most seasons and depths considered, with seasonal wMAE values for the shallow surface layers of the revised model simulation of at most 3.7 ∘ C, which is 1.2 ∘ C more than the wMAE of the screen-level air temperature used to drive the model as compared to site-level observations (2.5 ∘ C). Subgrid heterogeneity estimates were derived from the standard deviation of ALT on the 1 km 2 measurement grids at the GTN-P ALT sites, the spread in wMAE in grid cells with multiple GTN-P ALT sites, as well as from 35 boreholes measured within a 1200 km 2 region as part of the Slave Province Surficial Materials and Permafrost Study. Given the size of the model grid cells (approximately 2.8 ∘), subgrid heterogeneity makes it likely difficult to appreciably reduce the wMAE of ALT or borehole temperatures much further. [ABSTRACT FROM AUTHOR]

Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses - currently ERA-5, ERA-Interim, JRA-55 and MERRA-2 - to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for ten terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulation and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time-series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data. [ABSTRACT FROM AUTHOR]

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines and started in 2006 with the goals of realizing observations that previously have not been possible. Specifically, the aims are to obtain measurements in unprecedented quantity and quality based on technological advances. This paper describes a unique >10-year data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 ma.s.l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make these data available in near real time and tightly monitor and control the running experiments. This data set (10.1594/PANGAEA.897640,) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of 10 or more years. By documenting and sharing these data in this form we contribute to making our past research reproducible and facilitate future research based on these data, e.g., in the areas of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. Finally, the cross-validation of four different data types clearly indicates the dominance of thawing-related kinematics. [ABSTRACT FROM AUTHOR]

The Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component of the Canadian Earth System model (CanESM). Here we investigate the impact of changes to CLASS-CTEM that are designed to improve the simulation of permafrost physics. Eighteen tests were performed including changing the model configuration (number and depth of ground layers, different soil permeable depth datasets, adding a surface moss layer), and investigating alternative parameterizations of soil hydrology, soil thermal conductivity and snow properties. To evaluate these changes, outputs from CLASS-CTEM were compared to 1570 active layer thickness (ALT) measurements from 97 observation sites that are part of the Global Terrestrial Network for Permafrost (GTN-P), 105 106 monthly ground temperature observations from 132 GTN-P borehole sites, a blend of 5 observation-based snow water equivalent (SWE) datasets (Blended-5), remotely-sensed albedo, and seasonal discharge for major rivers draining permafrost regions. From the tests performed, the final revised model configuration has more ground layers (increased from 3 to 20) extending to greater depth (from 4.1m to 61.4m) and uses a new soil permeable depths dataset with a surface layer of moss added. The most beneficial change to the model parameterizations was incorporation of unfrozen water in frozen soils. These changes to CLASS-CTEM cause a small improvement in simulated SWE with little change in surface albedo but greatly improve the model performance at the GTN-P ALT and borehole sites. Compared to the GTN-P observations, the revised CLASS-CTEM ALTs have a weighted mean absolute error (wMAE) of 0.41-0.47m (depending on configuration), improved from >2.5m for the original model, while the borehole sites see a consistent improvement in wMAE for most seasons and depths considered, with seasonal wMAE values for the shallow surface layers of the revised model simulation at most 1.2°C greater than those calculated for the model driving screen-level air temperature compared to observations at the sites. Sub-grid heterogeneity estimates were derived from the standard deviation of ALT on the 1km² measurement grids at the GTN-P ALT sites, the spread in wMAE in grid cells with multiple GTN-P ALT sites, as well as from 35 boreholes measured within a 1200km² region as part of the Slave Province Surficial Materials and Permafrost Study. Given the size of the model grid cells (ca. 2.8°), sub-grid heterogeneity makes it likely difficult to appreciably reduce the wMAE of ALT or borehole temperatures much further. [ABSTRACT FROM AUTHOR]

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines started in 2006 with the goals to make observations possible that previously have not been possible. Specifically the aims are to obtain measurements data in unprecedented quantity and quality based on technological advances. This paper describes a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt Switzerland at 3500 m a.s.l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make this data available in near real-time and tightly monitor and control the running experiments. This data set (DOI: https://doi.org/10.1594/PANGAEA.897640, Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of ten+ years. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. [ABSTRACT FROM AUTHOR]

In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation. [ABSTRACT FROM AUTHOR]

The cryosphere reacts sensitively to climate change, as evidenced by the widespread retreat of mountain glaciers. Subsurface ice contained in permafrost is similarly affected by climate change, causing persistent impacts on natural and human systems. In contrast to glaciers, permafrost is not observable spatially and therefore its presence and possible changes are frequently overlooked. Correspondingly, little is known about permafrost in the mountains of the Hindu Kush Himalaya (HKH) region, despite permafrost area exceeding that of glaciers in nearly all countries. Based on evidence and insight gained mostly in other permafrost areas globally, this review provides a synopsis on what is known or can be inferred about permafrost in the mountains of the HKH region. Given the extreme nature of the environment concerned, it is to be expected that the diversity of conditions and phenomena encountered in permafrost exceed what has previously been described and investigated. We further argue that climate change in concert with increasing development will bring about diverse permafrost-related impacts on vegetation, water quality, geohazards, and livelihoods. To better anticipate and mitigate these effects, a deepened understanding of high-elevation permafrost in subtropical latitudes as well as the pathways interconnecting environmental changes and human livelihoods are needed. [ABSTRACT FROM AUTHOR]