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Accurate snow cover modeling is a high stake for mountain regions. Alpine snow evolution and spatial variability result from a multitude of complex processes including interactions between wind and snow. The SnowPappus blowing snow model was designed to add blowing snow modeling capabilities to the SURFEX/Crocus simulation system for applications across large spatial and temporal extents. This paper presents the very first spatialized evaluation of this simulation system over a 902 km2 domain in the French Alps. Here we compare snow cover simulations to the spatial distribution of snow height obtained from Pleiades satellites stereo-imagery and to Snow Melt-Out Dates from Sentinel-2 time series over three snow seasons. We analyzed the sensitivity of the simulations to three different precipitation datasets and two horizontal resolutions. The evaluations are presented as a function of elevation and landform types. The results show that the SnowPappus model forced with high-resolution wind fields enhances the snow cover spatial variability at high elevations allowing a better agreement with observations above 2500 m and near peaks and ridges. Model improvements are not obvious at low to medium altitudes where precipitation errors are the prevailing uncertainty. Our study illustrates the necessity to consider error contributions from blowing snow, precipitation forcings, and unresolved subgrid variability for robust evaluations of spatialized snow simulations. Despite the significant effect of the unresolved spatial scales of snow transport, 250 m horizontal resolution snow simulations using SnowPappus are found to be a promising avenue for large-scale modeling of alpine snowpacks. [ABSTRACT FROM AUTHOR]

The European Space Agency SnowSAR instrument is a side-looking, dual-polarised (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from various sizes of aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland, for a single flight mission in March 2011 and 12 missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones representing a montane valley, Alpine tundra and a glacier environment. In Canada, a total of two missions were flown in March and April 2013 over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data and coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record, the datasets described here are deposited in a permanent data repository (10.1594/PANGAEA.933255, Lemmetyinen et al., 2021). [ABSTRACT FROM AUTHOR]

Mountain areas are biodiversity hotspots and provide a multitude of ecosystem services of irreplaceable socio‐economic value. In the European Alps, air temperature has increased at a rate of about 0.36°C decade−1 since 1970, leading to glacier retreat and significant snowpack reduction. Due to these rapid environmental changes, this mountainous region is undergoing marked changes in spring phenology and elevational distribution of animals, plants and fungi. Long‐term monitoring in the European Alps offers an excellent natural laboratory to synthetize climate‐related changes in spring phenology and elevational distribution for a large array of taxonomic groups. This review assesses the climatic changes that have occurred across the European Alps during recent decades, spring phenological changes and upslope shifts of plants, animals and fungi from evidence in published papers and previously unpublished data. Our review provides evidence that spring phenology has been shifting earlier during the past four decades and distribution ranges show an upwards trend for most of the taxonomic groups for which there are sufficient data. The first observed activity of reptiles and terrestrial insects (e.g. butterflies) in spring has shifted significantly earlier, at an average rate of −5.7 and −6.0 days decade−1, respectively. By contrast, the first observed spring activity of semi‐aquatic insects (e.g. dragonflies and damselflies) and amphibians, as well as the singing activity or laying dates of resident birds, show smaller non‐significant trends ranging from −1.0 to +1.3 days decade−1. Leaf‐out and flowering of woody and herbaceous plants showed intermediate trends with mean values of −2.4 and −2.8 days decade−1, respectively. Regarding species distribution, plants, animals and fungi (N = 2133 species) shifted the elevation of maximum abundance (optimum elevation) upslope at a similar pace (on average between +18 and +25 m decade−1) but with substantial differences among taxa. For example, the optimum elevation shifted upward by +36.2 m decade−1 for terrestrial insects and +32.7 m decade−1 for woody plants, whereas it was estimated to range between −1.0 and +11 m decade−1 for semi‐aquatic insects, ferns, birds and wood‐decaying fungi. The upper range limit (leading edge) of most species also shifted upslope with a rate clearly higher for animals (from +47 to +91 m decade−1) than for plants (from +17 to +40 m decade−1), except for semi‐aquatic insects (−4.7 m decade−1). Although regional land‐use changes could partly explain some trends, the consistent upward shift found in almost all taxa all over the Alps is likely reflecting the strong warming and the receding of snow cover that has taken place across the European Alps over recent decades. However, with the possible exception of terrestrial insects, the upward shift of organisms seems currently too slow to track the pace of isotherm shifts induced by climate warming, estimated at about +62 to +71 m decade−1 since 1970. In the light of these results, species interactions are likely to change over multiple trophic levels through phenological and spatial mismatches. This nascent research field deserves greater attention to allow us to anticipate structural and functional changes better at the ecosystem level. [ABSTRACT FROM AUTHOR]

Measurements of the spectrum of the atmospheric emission in the far-infrared (FIR) range, between 100 and 667 cm-1 (100–15 µm) are scarce because of the detection complexity and of the strong absorption of air at ground level preventing the sounding of the FIR from low altitude. Consequently, FIR measurements need to be made from high-altitude sites or on board airborne platforms or satellites. This paper describes the dataset of FIR spectral radiances of the atmosphere and snow surface emission measured in the 100–1000 cm-1 range by the Far-Infrared Radiation Mobile Observation System (FIRMOS) instrument during a 2-month campaign carried out from the ground at about 3000 m of altitude on the top of Mt. Zugspitze in the German Alps in 2018–2019. This campaign is part of the preparatory activity of a new space FIR mission, named Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM), which is under development by the European Space Agency (ESA). The dataset acquired during the campaign also includes all the additional measurements needed to provide a full characterisation of the observed atmospheric state and the local atmospheric and surface conditions. It includes co-located spectral measurements in the infrared range from 400 to 1800 cm-1 ; lidar backscatter profiles; radio soundings of temperature, humidity and aerosol backscatter profiles; local weather parameters; and snow/ice microphysical properties. These measurements provide a unique dataset that can be used to perform radiative closure experiments to improve modelling parameters in the FIR that are not well-characterised, such as water vapour spectroscopy, scattering properties of cirrus clouds, and the FIR emissivity of the surface covered by snow. The consolidated dataset is freely available via the ESA campaign dataset website at 10.5270/ESA-38034ee. [ABSTRACT FROM AUTHOR]

Soil erosion is addressed as one of the main hydrogeological risks in the European Union. Since the average annual soil loss rate exceeds the annual average formation rate, soil is considered as a non-renewable resource. Besides, human activities, human-induced forces and climate change have further accelerated the erosion processes. Therefore, understanding soil erosion spatial and temporal trends could provide important information for supporting government land-use policies and strategies for its reduction. This paper describes the Dynamic Revised Universal Soil Loss Equation (D-RUSLE) model, a modified version of the well-known RUSLE model. The RUSLE model formulation was modified to include variations in rainfall erosivity and land-cover to provide more accurate estimates of the potential soil erosion in the Italian Alps. Specifically, the modelling of snow occurrence and the inclusion of Earth Observation data allow dynamic estimation of both spatial and temporal land-cover changes. Results obtained in Val Camonica (Italy) show that RUSLE model tends to overestimate erosion rates in Autumn/Winter because not considering snow cover and vegetation dynamics. The assimilation of satellite-derived information in D-RUSLE allows a better representation of soil erosion forcing, thus proving a more accurate erosion estimate for supporting government land-use policies and strategies for reducing this phenomenon. [ABSTRACT FROM AUTHOR]

• Scientific literature since 1920 is analysed quantitatively and qualitatively. • All cryospheric elements (glacier, snow, (perma) frost) are considered. • Almost half (46.2 %) of the papers focus on soil formation after glacier retreat. • Half of the papers study primarily direct interactions between cryosphere and soil. • Literature that covers geomorphodynamical changes is represented the least. A review of the status of research on high mountain soils and their alterations caused by changes in the cryosphere in the European Alps is given. Soils of high mountain environments are not only exposed to atmospheric warming, rising CO 2 levels, and changing precipitation patterns but also to climate-driven changes in the cryosphere. The massive reduction of glacier coverage as well as snow cover and (perma) frost extent can affect soils in various ways. We performed a comprehensive literature analysis and considered both the direct impacts (changes in surface coverage or ground thermal conditions) and indirect impacts (changing hydrosphere, lithosphere/geomorphodynamics, or biosphere) of cryosphere changes on soil. All considered studies had a multidisciplinary character: around 34 % of the articles covered two spheres (cryosphere, pedosphere), 40 % covered three spheres (cryosphere, pedosphere, and an additional sphere), and 26 % covered more than three spheres. Most studies focused on initial soil formation in glacier forefields. The impact of changing geomorphodynamics on soils is underrepresented in literature, even though it is one of the major consequences of changes in the cryosphere. We therefore finally discuss possible consequences of changing geomorphodynamics due to changes in the cryosphere for high mountain soils. [ABSTRACT FROM AUTHOR]

The DC resistivity method is a common tool in periglacial research because it can delineate zones of large resistivities, which are often associated with frozen water. The interpretation can be ambiguous, however, because large resistivities may also have other causes, like solid dry rock. One possibility to reduce the ambiguity is to measure the frequency-dependent resistivity. At low frequencies (< 100 Hz) the corresponding method is called induced polarization, which has also been used in periglacial environments. For the detection and possibly quantification of water ice, a higher frequency range, between 100 Hz and 100 kHz, may be particularly interesting because in that range, the electrical properties of water ice exhibit a characteristic behaviour. In addition, the large frequencies allow a capacitive coupling of the electrodes, which may have logistical advantages. The capacitively coupled resistivity (CCR) method tries to combine these logistical advantages with the potential scientific benefit of reduced ambiguity. In this paper, we discuss CCR data obtained at two field sites with cryospheric influence: the Schilthorn massif in the Swiss Alps and the frozen Lake Prestvannet in the northern part of Norway. One objective is to add examples to the literature where the method is assessed in different conditions. Our results agree reasonably well with known subsurface structure: at the Prestvannet site, the transition from a frozen lake to the land is clearly visible in the inversion results, whereas at the Schilthorn site, the boundary between a snow cover and the bedrock below can be nicely delineated. In both cases, the electrical parameters are consistent with those expected from literature. The second objective is to discuss useful methodological advancements: first, we investigate the effect of capacitive sensor height above the surface and corroborate the assumption that it is negligible for highly resistive conditions. For the inversion of the data, we modified an existing 2-D inversion code originally developed for low-frequency induced polarization data by including a parametrization of electrical permittivity. The new inversion code allows the extraction of electrical parameters that may be directly compared with literature values, which was previously not possible. [ABSTRACT FROM AUTHOR]

The input of mineral dust from arid regions impacts snow optical properties. The induced albedo reduction generally alters the melting dynamics of the snowpack, resulting in earlier snowmelt. In this paper, we evaluate the impact of dust depositions on the melting dynamics of snowpack at a high-elevation site (2160 m) in the European Alps (Torgnon, Aosta Valley, Italy) during three hydrological years (2013–2016). These years were characterized by several Saharan dust events that deposited significant amounts of mineral dust in the European Alps. We quantify the shortening of the snow season due to dust deposition by comparing observed snow depths and those simulated with the Crocus model accounting, or not, for the impact of impurities. The model was run and tested using meteorological data from an automated weather station. We propose the use of repeated digital images for tracking dust deposition and resurfacing in the snowpack. The good agreement between model prediction and digital images allowed us to propose the use of an RGB index (i.e. snow darkening index – SDI) for monitoring dust on snow using images from a digital camera. We also present a geochemical characterization of dust reaching the Alpine chain during spring in 2014. Elements found in dust were classified as a function of their origin and compared with Saharan sources. A strong enrichment in Fe was observed in snow containing Saharan dust. In our case study, the comparison between modelling results and observations showed that impurities deposited in snow anticipated the disappearance of snow up to 38 d a out of a total 7 months of typical snow duration. This happened for the season 2015–2016 that was characterized by a strong dust deposition event. During the other seasons considered here (2013–2014 and 2014–2015), the snow melt-out date was 18 and 11 d earlier, respectively. We conclude that the effect of the Saharan dust is expected to reduce snow cover duration through the snow-albedo feedback. This process is known to have a series of further hydrological and phenological feedback effects that should be characterized in future research. [ABSTRACT FROM AUTHOR]

A meteorological and blowing snow data set from the high-elevation experimental site of Col du Lac Blanc (2720 m a.s.l., Grandes Rousses mountain range, French Alps) is presented and detailed in this paper. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In situ data consist of wind (speed and direction), snow depth and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical profile of snow particle counters (2010–2016). Observations span the period from 1 December to 31 March for each winter season from 2000–2001 to 2015–2016. The time resolution has varied from 15 min until 2014 to 10 min for the last years. Atmospheric data from the meteorological reanalysis are also provided from 1 August 2000 to 1 August 2016. A digital elevation model (DEM) of the study area (1.5 km 2) at 1 m resolution is also provided in RGF 93 Lambert 93 coordinates. This data set has been used in the past to develop and evaluate physical parameterizations and numerical models of blowing and drifting snow in alpine terrain. Col du Lac Blanc is also a target site to evaluate meteorological and climate models in alpine terrain. It belongs to the CRYOBS-CLIM observatory (the CRYosphere, an OBServatory of the CLIMate), which is a part of the national research infrastructure OZCAR (Critical Zone Observatories – Application and Research) and have been a Global Cryospheric Watch Cryonet site since 2017. The data are available from the repository of the OSUG data centre 10.17178/CRYOBSCLIM.CLB.all. [ABSTRACT FROM AUTHOR]

Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data. [ABSTRACT FROM AUTHOR]

The European Space Agency SnowSAR instrument is a side looking, dual polarized (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from a small aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps, and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow -covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland for a single flight mission in March 2011 and twelve missions at two sites (tundra and boreal forest) in the winter of 2011-2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones, representing a montane valley, Alpine tundra, and a glacier environment. In Canada, a total of two missions were flown in March and April 2013, over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data, as well as coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record the datasets described here are deposited in a permanent data repository (https://doi.pangaea.de/ 10.1594/PANGAEA.933255; Lemmetyinen et al., 2021). A temporary link to access the data without login information is provided for reviewers of this manuscript: https://www.pangaea.de/tok/e8c562c3c8a15ac34daa83d00 c76fcb347330884. [ABSTRACT FROM AUTHOR]

• Guidelines for the use of snow data combined with runoff to calibrate models. • Improvement of the snow simulations without impairing runoff performance. • The improvement in the model consistency is not accompanied by lower equifinality. • Using snow data does not improve model robustness to climate variability. This paper compares different calibration strategies for using snow data combined with streamflow records to constrain model optimisation in mountain catchments. In particular, it assesses to what extent the use of snow observations makes it possible to improve the consistency, identifiability and robustness of the calibrated parameters. To answer this question, a semi-distributed snow and ice model was used on top of a rainfall-runoff model using the SAFRAN meteorological reanalysis as input dataset on several catchments in the Alps and Pyrenees. Model calibration and control were based on streamflow observations, remotely-sensed snow-covered areas and in-situ snow water equivalent (SWE) measurements. The snow and rainfall-runoff parameters were calibrated sequentially and simultaneously against objective functions integrating different combinations of runoff, snow cover and SWE criteria. Statistical assessment of model performances in independent evaluation periods showed that sequential calibration of the snow parameters gives too much weight to snow compared to runoff. Instead, incorporating snow data in the simultaneous calibration of the parameters improves snow simulations without impairing runoff performance. This can be achieved by limiting the weight of the snow criteria to 25% in the objective function. Although local SWE measurements were found to be more useful than satellite observations for identifying more consistent parameters, it is advisable to include both in the calibration process through a compromise objective function. However, the improved model consistency was not accompanied by a significant reduction in equifinality and optimisation times. On the other hand, improving internal consistency made it possible to reduce the interdependence between the parameters of the snow model and those of the rainfall-runoff model. This also made it possible to identify the least sensitive snow parameters in order to fix them at general values without impairing model performance while reducing equifinality with a more parsimonious model. Finally, there was no evidence that using snow observations in the calibration process improves model robustness with respect to climate variability. [ABSTRACT FROM AUTHOR]

Wind-induced snow transport has a strong influence on snow spatial variability, especially at spatial scales between 1 and 500 m in alpine environments. Thus, the evolution of operational snow modelling systems towards 100–500 m resolutions requires representing this process at these resolutions over large domains and entire snow seasons. We developed SnowPappus, a parsimonious blowing-snow model coupled to the state-of-the-art Crocus snow model able to cope with these requirements. SnowPappus simulates blowing-snow occurrence, horizontal transport flux and sublimation rate at each grid cell as a function of 2D atmospheric forcing and snow surface properties. Then, it computes a mass balance using an upwind scheme to provide eroded or accumulated snow amounts to Crocus. Parameterizations used to represent the different processes are described in detail and discussed against existing literature. A point-scale evaluation of blowing-snow fluxes was conducted, mainly at the Col du Lac Blanc observatory in the French Alps. Evaluations showed that SnowPappus performs as well as the currently operational scheme SYTRON in terms of blowing-snow occurrence detection, while the latter does not give access to spatialized information. Evaluation of the simulated suspension fluxes highlighted a strong sensitivity to the suspended particle's terminal fall speed. Proper calibrations allow the model to reproduce the correct order of magnitude of the mass flux in the suspension layer. Numerical performances of gridded simulations of Crocus coupled with SnowPappus were assessed, showing the feasibility of using it for operational snow forecast at the scale of the entire French Alps. [ABSTRACT FROM AUTHOR]

Despite the increasing use of physical snow-cover simulations in regional avalanche forecasting, avalanche forecasting is still an expert-based decision-making process. However, recently, it has become possible to obtain fully automated avalanche danger level predictions with satisfying accuracy by combining physically-based snow-cover models with machine learning approaches. These predictions are made at the location of automated weather stations close to avalanche starting zones. To bridge the gap between these local predictions and fully data- and model-driven regional avalanche danger maps, we developed and evaluated a three-stage model pipeline (RAvaFcast v1.0.0), involving the steps classification, interpolation, and aggregation. More specifically, we evaluated the impact of various terrain features on the performance of a Gaussian process-based model for interpolation of local predictions to unobserved locations on a dense grid. Aggregating these predictions using an elevation-based strategy, we estimated the regional danger level and the corresponding elevation range for predefined warning regions, resulting in a forecast similar to the human-made avalanche forecast in Switzerland. The best-performing model matched the human-made forecasts with a mean day accuracy of approximately 66 % for the entire forecast domain, and 70 % specifically for the Alps. However, the performance depended strongly on the classifier's accuracy (i.e., a mean day accuracy of 68 %) and the density of local predictions available for the interpolation task. Despite these limitations, we believe that the proposed three-stage model pipeline has the potential to improve the interpretability of machine-made danger level predictions and has, thus, the potential to assist avalanche forecasters during forecast preparation, for instance, by being integrated in the forecast process in the form of an independent virtual forecaster. [ABSTRACT FROM AUTHOR]

• Three distinct groups are identified amongst diverse cross-sector stakeholders based on their specific social perspectives. • Q methodology revealed broad value consensus even among divergent stakeholders. • Consensus finding is hindered by polarized stakeholder views on tourism development. • Relying solely on snowmaking as a climate change adaptation strategy is unsustainable. • Nuances in stakeholder attitudes and value systems may undermine climate change adaptation if not fully recognized. Climate change has reduced the snow cover in the Swiss Alps, negatively impacting the winter tourist sector. The adaptation of artificial or technical snow, as a solution to combat a decline in tourism, is pervasive, yet controversial. This paper uses Q methodology to analyse the perspectives of stakeholders in relation to artificial snow production with regard to the three pillars of sustainable development. While all stakeholders agreed that there are ecological constraints to socioeconomic development, three distinct perspectives were identified. Perspective 1 prioritizes the environment, not accepting ecological compromises for socioeconomic development. Perspective 2 is more willing to accept trade-offs, focusing on economic diversification and long-term strategies. Perspective 3 focuses on the economy, with a preference for the status quo. The ecological awareness of all stakeholders provides a promising basis for sustainable development. However, the diverse views on priority setting present nontrivial obstacles towards devising future strategies for sustainable development. [ABSTRACT FROM AUTHOR]

The alpine snow cover exhibits a high spatial variability in the horizontal and vertical directions even on a very small scale, mainly caused by the high variability of alpine terrain. To quantify the annual and inter-annual snow dynamics continuously reliable measurements of the temporal and spatial variability are required. While remote sensing from satellite and aerial platforms have been successfully used to estimate snow cover at larger scales, especially in mountain areas spatial and temporal resolution are too low to capture local changes. In the alpine region, webcam images are freely available for touristic purposes capturing images at high frequency intervals. Within the WebSnow project the feasibility of using such images for the detection of snow was investigated. With the developed workflow, processing times could be reduced and satisfactory results obtained. Our results show, that webcam networks have the potential for monitoring snow at high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

In 2022, a large part of Europe experienced an extremely dry and hot summer. In the Alps, this episode occurred after an unusually low-snowfall winter, which aggravated the dryness of soils. This study examines the impact of this particular year on the canopy greenness of above-treeline ecosystems by comparison with previous heat waves that hit the Alps during the last 2 decades. Normalized difference vegetation index (NDVI) time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were processed to extract the temporal variability in yearly maximum NDVI (NDVImax). The responsiveness of NDVImax to snow cover duration and growing-season weather conditions was evaluated in contrasting hydroclimate regions of the Alps using linear mixed-effect models. The year 2022 was unique in that the summer heat wave led to a widespread negative anomaly of NDVImax. The magnitude of this anomaly was unprecedented in the southwestern, driest part of the Alps, where vegetation activity was found to be particularly responsive to snow cover duration and early summer precipitation. In the colder and wetter regions, all warm to very warm summers before 2022 had led to increased canopy greenness, but the combination of a reduced snow cover and low early summer precipitation counteracted this expected beneficial effect in 2022. This study provides evidence that the control of canopy greenness by temperature and water balance differs markedly across regions of the Alps and that the year 2022 bears witness to a shift toward an increasing importance of moisture availability for regulating plant growth at high elevation. This is viewed as a warning sign of what could become the new norm in the years ahead in the context of increasing frequency and intensity of extreme droughts throughout temperate mountain ecosystems. [ABSTRACT FROM AUTHOR]

Avalanches result from an interaction of weather and terrain, where past weather and internal snow cover processes play important roles. So far, climatology was mainly based on weather data, as regional snow instability information, such as avalanche activity, is scarce on climatological time scales. We present a new approach to create a snow avalanche climatology from simulations of avalanche problem types based on snow cover simulations of reanalysis data and a cluster analysis. Analyzing the winters between 1958 and 2020 in the French Alps, wet-snow situations dominated natural release. Dry-snow situations with non-persistent and persistent weak layers occurred each on at least one third of the days. Four typical patterns of avalanche problem types were identified. They follow the main orography with more new snow situations in the northern regions and more cases of persistent weak layers in inner-Alpine regions. In the front-ranges and in southern regions wet-snow situations occurred early in winter – typical for coastal snow climates. Agreement with the standard snow climate classification and the geography of the French Alps suggests that mountain regions with similar conditions can now be outlined. This method for snow avalanche climatology will inform avalanche forecasting and facilitate climate change impact studies. [ABSTRACT FROM AUTHOR]

[Display omitted] • An enhanced degree-day model is developed based on a flexible modeling approach. • Calibration and control were based on local and remotely-sensed snow data, and runoff. • The development resulted in a one-free parameter snow- and ice-accounting routine. • The model is statistically equivalent or better than the alternative models tested. This paper evaluates the degrees of freedom and complexity warranted in temperature-index models to jointly simulate local snow measurements, remotely-sensed snow cover and runoff in mountainous areas. To address this issue, the snow- and ice-accounting routine (SIAR) on top of the GR4J model was developed on a dataset covering 17 mountainous catchments (45 to 3580 km2) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow cover (FSC) computed from MODIS snow products and at least one chronicle of local measurements of snow water equivalent (SWE) acquired in each catchment for the period 2004–2016. SIAR was applied according to an adaptable number of elevation bands and different parametrizations ranging from 11 free parameters (precipitation orographic correction, temperature lapse rate, variation in the temperature lapse rate, snowfall adjustment, rainfall lapse rate, thermal inertia of the snow pack, constant and variable part of the degree-day snow melt factor, degree-day ice melt factor, 2-parameter hysteresis between SWE and FSC), to only fixed parameters. Results showed that the one-free-parameter SIAR is as efficient as more parametrized versions in simulating both local and distributed snow dynamics as well as runoff. Interestingly, using SIAR without any free parameters by fixing the snowfall adjustment to a median value of 60% only led to slight impairment of local SWE dynamics. Certain processes represented in SIAR (glacier-component, sublimation, simple energy balance, snowpack cold-content, variable melt factor) were then alternatively turned off to justify those retained in its final version. The modeling performances were also compared by applying SIAR with different distribution options ranging from full distribution according to 0.25 km2 cells to lumped mode. A number of equal-area elevation bands according to the catchment hypsometry proved to be a good compromise as it allowed snow and runoff simulations of similar accuracy to the full distribution mode, while limiting computational time. Finally, SIAR was compared with the Cemaneige snow routine, which showed its modeling performance was better. These findings suggest that it is possible, and even advisable, to limit the number of free parameters in temperature-index models in order to reduce problems of over-parameterization and equifinality. [ABSTRACT FROM AUTHOR]

Assessing past distributions, variability and trends in the mountain snow cover and its first-order drivers, temperature and precipitation, is key for a wide range of studies and applications. In this study, we compare the results of various modeling systems (global and regional reanalyses ERA5, ERA5-Land, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX and MTMSI and regional climate model simulations CNRM-ALADIN and CNRM-AROME driven by the global reanalysis ERA-Interim) against observational references (in situ, gridded observational datasets and satellite observations) across the European Alps from 1950 to 2020. The comparisons are performed in terms of monthly and seasonal snow cover variables (snow depth and snow cover duration) and their main atmospherical drivers (near-surface temperature and precipitation). We assess multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales, mainly for the winter period (from November through April). ERA5, ERA5-Crocus, MESCAN-SURFEX, CERRA-Land and MTMSI offer a satisfying description of the monthly snow evolution. However, a spatial comparison against satellite observation indicates that all datasets overestimate the snow cover duration, especially the melt-out date. CNRM-AROME and CNRM-ALADIN simulations and ERA5-Land exhibit an overestimation of the snow accumulation during winter, increasing with elevations. The analysis of the interannual variability and trends indicates that modeling snow cover dynamics remains complex across multiple scales and that none of the models evaluated here fully succeed to reproduce this compared to observational reference datasets. Indeed, while most of the evaluated model outputs perform well at representing the interannual to multi-decadal winter temperature and precipitation variability, they often fail to address the variability in the snow depth and snow cover duration. We discuss several artifacts potentially responsible for incorrect long-term climate trends in several reanalysis products (ERA5 and MESCAN-SURFEX), which we attribute primarily to the heterogeneities of the observation datasets assimilated. Nevertheless, many of the considered datasets in this study exhibit past trends in line with the current state of knowledge. Based on these datasets, over the last 50 years (1968–2017) at a regional scale, the European Alps have experienced a winter warming of 0.3 to 0.4 ∘ C per decade, stronger at lower elevations, and a small reduction in winter precipitation, homogeneous with elevation. The decline in the winter snow depth and snow cover duration ranges from -7 % to -15 % per decade and from -5 to -7 d per decade, respectively, both showing a larger decrease at low and intermediate elevations. Overall, we show that no modeling strategy outperforms all others within our sample and that upstream choices (horizontal resolution, heterogeneity of the observations used for data assimilation in reanalyses, coupling between surface and atmosphere, level of complexity, configuration of the snow scheme, etc.) have great consequences on the quality of the datasets and their potential use. Despite their limitations, in many cases they can be used to characterize the main features of the mountain snow cover for a range of applications. [ABSTRACT FROM AUTHOR]

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm , coefficient of determination (R2) of 0.97, and bias of - 0.3 cm ; it is able to achieve RMSE of 20.5 cm , R2 of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm , R2 of 0.97, and bias of - 0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used. [ABSTRACT FROM AUTHOR]

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier's energy budget and melt rates. [ABSTRACT FROM AUTHOR]

Monitoring variations in the extent of wet snow over space and time is essential for many applications, such as hydrology, mountain ecosystems, meteorology and avalanche forecasting. The Synthetic Aperture Radar (SAR) measurements from the Sentinel-1 satellite help detect wet snow in almost all weather conditions. Most detection methods use a fixed threshold to a winter image ratio with one or two reference images (with no snow or dry snow). This study aimed to explore the potential of image segmentation methods from different families applied to Sentinel-1 SAR images to improve the detection of wet snow over the French Alps. Several segmentation methods were selected and tested on a large alpine area of 100 × 100 km2. The segmentation methods were evaluated over one season using total snow masks from Sentinel-2 optical measurements and outputs from forecasters' bulletins combining model and in-situ observations. Different metrics were used (such as snow probability, correlations, Hamming distance, and structure similarity scores). The standard scores illustrated that filtering globally improved the segmentation results. Using a probabilistic score as a function of altitude highlights the interest in some segmentation methods, and we show that these scores could be relevant to calibrate the parameters of these methods better. [ABSTRACT FROM AUTHOR]

Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how combining snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a random forest approach to predict avalanche situations at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent evaluation set, confusion matrices and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the machine learning model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically based stability indices and their time derivatives in addition to simple snow and meteorological variables increases the probability of avalanche situation detection from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche situations in the training dataset, the predicted avalanche situations that are really observed remains low, around 3.3 %. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting. [ABSTRACT FROM AUTHOR]

In mountain regions, forests that overlap with seasonal snow mostly reside in complex terrain. Due to persisting major observational challenges in these environments, the combined impact of forest structure and topography on seasonal snow cover dynamics is still poorly understood. Recent advances in forest snow process representation and increasing availability of detailed canopy structure datasets, however, now allow for hyper-resolution (<5 m) snow model simulations capable of resolving tree-scale processes. These can shed light on the complex process interactions that govern forest snow dynamics. We present multi-year simulations at 2 m resolution obtained with FSM2, a mass- and energy-balance-based forest snow model specifically developed and validated for metre-scale applications. We simulate an ∼3 km 2 model domain encompassing forested slopes of a sub-alpine valley in the eastern Swiss Alps and six snow seasons. Simulations thus span a wide range of canopy structures, terrain characteristics, and meteorological conditions. We analyse spatial and temporal variations in forest snow energy balance partitioning, aiming to quantify and understand the contribution of individual energy exchange processes at different locations and times. Our results suggest that snow cover evolution is equally affected by canopy structure, terrain characteristics, and meteorological conditions. We show that the interaction of these three factors can lead to snow accumulation and ablation patterns that vary between years. We further identify higher snow distribution variability and complexity in slopes that receive solar radiation early in winter. Our process-level insights corroborate and complement existing empirical findings that are largely based on snow distribution datasets only. Hyper-resolution simulations as presented here thus help to better understand how snowpacks and ecohydrological regimes in sub-alpine regions may evolve due to forest disturbances and a warming climate. They could further support the development of process-based sub-grid forest snow cover parameterizations or tiling approaches for coarse-resolution modelling applications. [ABSTRACT FROM AUTHOR]

Saharan dust deposits can turn snow-covered mountains into a spectacular orange landscape. When avalanches release, a formerly buried dust layer can become apparent, possibly marking the failure plane. This appearance may suggest a relation between avalanche release and the previously deposited dust, which found mention among recreationists and avalanche professionals alike. While dust deposition affects the absorption of solar energy altering snowpack temperatures and melt rates, to date, there is no clear scientific evidence that dust deposition can significantly modify snow stability. Here we investigate, using an ensemble snow cover model, the impact of dust deposition on snow properties and mechanical stability by comparing simulations with and without dust deposition for synthetic and observed dust deposition events. The study focuses on two typical avalanche situations: artificial triggering on persistent weak layers and natural release of wet-snow avalanches. We study several situations with and without dust deposition and demonstrate how sensitive the impact of dust deposition is to the deposited dust mass, the slope aspect, the elevation and the meteorological conditions following the dust deposition. The additional energy absorbed by the dust layer speeds up warming and may advance surface wetting to ease the formation of a melt-freeze crust. If the crust is buried, the phenomenon of a strong temperature gradient close to the crust may promote the formation of persistent weak layers inside the snowpack. On the other hand, the melt-freeze crust may also lead to an increase in snowpack stability by redistributing the stress applied to weak layers buried below. Regarding wet-snow instability, we show that dust deposition can advance the onset of wet-snow avalanche activity by up to 1 month in spring, as hypothesized in previous studies. Thus, the impact of Saharan dust deposition on snowpack stability can be either neutral, positive or negative, depending on the topographical, snow and meteorological conditions. Even though not all physical processes are implemented, state-of the art snow cover models are able to mimic the speed-up of crust formation, and snow instability models can point out relevant situations for avalanche forecasting. [ABSTRACT FROM AUTHOR]

In the Alpine environment, snow plays a key role in many processes involving ecosystems, biogeochemical cycles, and human wellbeing. Due to the inaccessibility of mountain areas and the high spatial and temporal heterogeneity of the snowpack, satellite spatio-temporal data without gaps offer a unique opportunity to monitor snow on a fine scale. In this study, we present a random forest approach within three different workflows to combine MODIS and Sentinel-2 snow products to retrieve daily gap-free snow cover maps at 20 m resolution. The three workflows differ in terms of the type of ingested snow products and, consequently, in the type of random forest used. The required inputs are the MODIS/Terra Snow Cover Daily L3 Global dataset at 500 m and the Sentinel-2 snow dataset at 20 m, automatically retrieved through the recently developed revised-Let It Snow workflow, from which the selected inputs are, alternatively, the Snow Cover Extent (SCE) map or the Normalized Difference Snow Index (NDSI) map, and a Digital Elevation Model (DEM) of consistent resolution with Sentinel-2 imagery. The algorithm is based on two steps, the first to fill the gaps of the MODIS snow dataset and the second to downscale the data and obtain the high resolution daily snow time series. The workflow is applied to a case study in Gran Paradiso National Park. The proposed study represents a first attempt to use the revised-Let It Snow with the purpose of extracting temporal parameters of snow. The validation was achieved by comparison with both an independent dataset of Sentinel-2 to assess the spatial accuracy, including the snowline elevation prediction, and the algorithm's performance through the different topographic conditions, and with in-situ data collected by meteorological stations, to assess temporal accuracy, with a focus on seasonal snow phenology parameters. Results show that all of the approaches provide robust time series (overall accuracies of A1 = 93.4%, and A2 and A3 = 92.6% against Sentinel-2, and A1 = 93.1%, A2 = 93.7%, and A3 = 93.6% against weather stations), but the first approach requires about one fifth of the computational resources needed for the other two. The proposed workflow is fully automatic and requires input data that are readily and globally available, and promises to be easily reproducible in other study areas to obtain high-resolution daily time series, which is crucial for understanding snow-driven processes at a fine scale, such as vegetation dynamics after snowmelt. [ABSTRACT FROM AUTHOR]

A comprehensive assessment of twenty-first century climate change in the European Alps is presented. The analysis is based on the EURO-CORDEX regional climate model ensemble available at two grid spacings (12.5 and 50 km) and for three different greenhouse gas emission scenarios (RCPs 2.6, 4.5 and 8.5). The core simulation ensemble has been subject to a dedicated evaluation exercise carried out in the frame of the CH2018 Climate Scenarios for Switzerland. Results reveal that the entire Alpine region will face a warmer climate in the course of the twenty-first century for all emission scenarios considered. Strongest warming is projected for the summer season, for regions south of the main Alpine ridge and for the high-end RCP 8.5 scenario. Depending on the season, medium to high elevations might experience an amplified warming. Model uncertainty can be considerable, but the major warming patterns are consistent across the ensemble. For precipitation, a seasonal shift of precipitation amounts from summer to winter over most parts of the domain is projected. However, model uncertainty is high and individual simulations can show change signals of opposite sign. Daily precipitation intensity is projected to increase in all seasons and all sub-domains, while the wet-day frequency will decrease in the summer season. The projected temperature change in summer is negatively correlated with the precipitation change, i.e. simulations and/or regions with a strong seasonal mean warming typically show a stronger precipitation decrease. By contrast, a positive correlation between temperature change and precipitation change is found for winter. Among other indicators, snow cover will be strongly affected by the projected climatic changes and will be subject to a widespread decrease except for very high elevation settings. In general and for all indicators, the magnitude of the change signals increases with the assumed greenhouse gas forcing, i.e., is smallest for RCP 2.6 and largest for RCP 8.5 with RCP 4.5 being located in between. These results largely agree with previous works based on older generations of RCM ensembles but, due to the comparatively large ensemble size and the high spatial resolution, allow for a more decent assessment of inherent projection uncertainties and of spatial details of future Alpine climate change. [ABSTRACT FROM AUTHOR]

• Shrubs growth rings are relevant indicators of the response of alpine ecosystem to global warming. • At the high-elevation site, our results demonstrate a negative effect of snow cover duration. • At lower elevations, late frost exposure is shown to lead to growth reduction. • Global warming induced a divergence in the growth response of a dominant shrub species to climate. Enhanced shrub growth in a warming alpine climate has potential far-reaching implications, including soil nutrient cycling, carbon storage, or water and surface energy exchanges. Growth ring analysis can yield mid- to long-term, annually resolved records of shrub growth, and thereby offer valuable insights into how growth is driven by interannual climate variability. In the European Alps, dendroecological approaches have shown that dwarf shrub productivity is influenced by interannual variations of growing season temperature but results also point to a negative effect of winter precipitation on radial growth. However, as past work lacked snow cover data, links between snow cover duration, growing season length, energy availability and inter-annual shrub growth remain poorly understood. In this paper, we combined multi-decadal shrub-ring series from 49 individuals sampled at three sites along a 600-m elevational gradient in the Taillefer massif, located in the French Alps to assess growth sensitivity of long-lived and widespread Rhododendron ferrugineum shrubs to both snow cover dynamics and temperature changes. To this end, we computed structural equation models to track the response of shrub radial growth to extending growing season at 1800, 2000 and 2400 m above sea level and for two time periods (i.e. 1959–1988 and 1989–2016). The second period is marked by a significant advance in snow melt-out resulting in a regime shift highlighted at the end of the 1980s by a breakpoint analysis. At the high-elevation site, our results demonstrate a positive effect of increasing growing season length on shrub growth, which is strongly dependent on snowpack depth and snow cover duration. Conversely, at lower elevations, earlier melt-out dates and associated late frost exposure are shown to lead to radial growth reduction. Moreover, the climate signal in ring-width chronologies of R. ferrugineum portrays a weakening since 1988 – similar to a phenomenon observed in series from circumpolar and alpine tree-ring sites and referred to as "divergence". By analyzing long-term records of radial growth along an elevation gradient, our work provides novel insights into the complex responses of shrub growth to climate change in alpine environments. This paper demonstrates that R. ferrugineum , as a dominant alpine shrub species, behave as an ecological indicator of the response of alpine ecosystem to global warming. [ABSTRACT FROM AUTHOR]

In full-scale snow avalanche test sites, structures such as pylons, plates, or dams have been used to measure impact forces and pressures from avalanches. Impact pressures are of extreme importance when dealing with issues such as hazard mapping and the design of buildings exposed to avalanches. In this paper, we present the force measurements recorded for five selected avalanches that occurred at the Seehore test site in Aosta Valley (NW Italian Alps). The five avalanches were small to medium-sized and cover a wide range in terms of snow characteristics and flow dynamics. Our aim was to analyze the force and pressure measurements with respect to the avalanche characteristics. We measured pressures in the range of 2 to 30 kPa. Though without exhaustive measurements of the avalanche flows, we found indications of different flow regimes. For example, we could appreciate some differences in the vertical profile of the pressures recorded for wet dense avalanches and powder ones. Being aware of the fact that more complete measurements are necessary to fully describe the avalanche flows, we think that the data of the five avalanches triggered at the Seehore test site might add some useful information to the ongoing scientific discussion on avalanche flow regimes and impact pressure. [ABSTRACT FROM AUTHOR]

Mountain seasonal snow cover is undergoing major changes due to global climate change. Assessments of future snow cover usually rely on physically based models, and often include post-processed meteorology. Alternatively, we here propose a direct statistical adjustment of snow cover fraction from regional climate models by using long-term remote-sensing observations. We compared different bias-adjustment routines (delta change, quantile mapping, and quantile delta mapping) and explored a downscaling based on historical observations for the Greater Alpine Region in Europe. All bias-adjustment methods account for systematic biases, for example due to topographic smoothing, and reduce model spread in future projections. The trend-preserving methods delta change and quantile delta mapping were found to be more suitable for snow cover fraction than quantile mapping. Averaged over the study region and whole year, snow cover fraction decreases from 12.5 % in 2001–2020 to 10.4 % (8.9 %, 11.5 %; model spread) in 2071–2100 under RCP2.6 (representative concentration pathway), and to 6.4 % (4.1 %, 7.8 %) under RCP8.5 (bias-adjusted estimates from quantile delta mapping). In addition, changes strongly depended on season and elevation. The comparison of the statistical downscaling to a high-resolution physically based model yields similar results for the elevation range covered by the climate models, but different elevation gradients of change above and below. Downscaling showed overall potential but requires further research. Since climate model and remote-sensing observations are available globally, the proposed methods are potentially widely applicable but are limited to snow cover fraction. [ABSTRACT FROM AUTHOR]

Modeled snow stratigraphy and instability data are a promising source of information for avalanche forecasting. While instability indices describing the mechanical processes of dry-snow avalanche release have been implemented into snow cover models, there exists no readily applicable method that combines these metrics to predict snow instability. We therefore trained a random forest (RF) classification model to assess snow instability from snow stratigraphy simulated with SNOWPACK. To do so, we manually compared 742 snow profiles observed in the Swiss Alps with their simulated counterparts and selected the simulated weak layer corresponding to the observed rutschblock failure layer. We then used the observed stability test result and an estimate of the local avalanche danger to construct a binary target variable (stable vs. unstable) and considered 34 features describing the simulated weak layer and the overlying slab as potential explanatory variables. The final RF classifier aggregates six of these features into the output probability Punstable , corresponding to the mean vote of an ensemble of 400 classification trees. Although the subset of training data only consisted of 146 profiles labeled as either unstable or stable, the model classified profiles from an independent validation data set (N=121) with high reliability (accuracy 88 %, precision 96 %, recall 85 %) using manually predefined weak layers. Model performance was even higher (accuracy 93 %, precision 96 %, recall 92 %), when the weakest layers of the profiles were identified with the maximum of Punstable. Finally, we compared model predictions to observed avalanche activity in the region of Davos for five winter seasons. Of the 252 avalanche days (345 non-avalanche days), 69 % (75 %) were classified correctly. Overall, the results of our RF classification are very encouraging, suggesting it could be of great value for operational avalanche forecasting. [ABSTRACT FROM AUTHOR]

In mountain rivers, long‐term observations of water and sediment fluxes are crucial for understanding the dynamics of bedload fluctuations. We analyze 7 years of continuous data gathered from eight geophone plates at a monitoring station in the glacier‐fed Sulden/Solda River (South Tyrol, Italy) to estimate the bedload flux at 1‐min scale. Sixty‐five bedload samples were used to derive the calibration equations adopted to quantify the transported bedload mass. The signal power is proposed as a more effective metric than threshold‐based impulses for calculating bedload masses. Results show (a) a remarkable variability of bedload rates for the same value of flow discharge, (b) the joint effect of storm‐driven flood events and seasonal changes in sediment supply on bedload rates, and (c) the strong impact of climatic factors (i.e., temperature and snow cover) on bedload fluxes. Moderate bedload rates occurring in late spring/early summer are likely related to the mobilization of riverbed sediments, while sustained bedload transport during melt flows in July–August—corresponding to the effective bedload discharge range—is associated with the activation of glacial and proglacial sediment sources. The data set shows a complex climatic control on bedload transport at the basin scale, where precipitation, air temperature, and snow cover determine flow and glacier melting dynamics. These findings suggest how the effects of climate change in the Alps likely will lead to an increase in peak bedload rates in a context of declining annual bedload yields as melt flows will progressively reduce in the next decades. Plain Language Summary: Water flowing in mountain rivers can transport a large amount of bedload, which is the coarse sediments that jump and roll on the river bed. The knowledge of the bedload flux matters for both improved understanding of the ecosystem and of the effects of human activities. Measuring how much bedload is transported is difficult as particles move below the water level. In this study, we employ seismic sensors installed on the river bed to quantify bedload transported by an Alpine river originating from a glacierized area. Moving particles produce ground vibrations that are more intense when the river transports more material. We found that the intensity of bedload measured by this "seismic noise" is extremely variable during the year and controlled by weather conditions. In particular, the river can rapidly increase the transport of sediments during high runoff peaks induced by glacier melt and summer thunderstorms. These findings are particularly relevant in the context of ongoing global warming that is dramatically impacting mountain areas where newly exposed stream reaches are emerging from the rapidly melting glaciers. Key Points: The power of the geophone‐plate seismic signal better reflects the variability of bedload rate than the number of threshold‐based impulsesHighest bedload rates (>103 kg m−1 min−1) produced by summer rainstorm events occur during snow‐ and glacier‐melt periodsTemperature‐controlled glacier melt flows are most effective in transporting bedload, followed by rainfall‐driven 1.5–2 years floods [ABSTRACT FROM AUTHOR]

By darkening the snow surface, mineral dust and black carbon (BC) deposition enhances snowmelt and triggers numerous feedbacks. Assessments of their long-term impact at the regional scale are still largely missing despite the environmental and socio-economic implications of snow cover changes. Here we show, using numerical simulations, that dust and BC deposition advanced snowmelt by 17 ± 6 days on average in the French Alps and the Pyrenees over the 1979–2018 period. BC and dust also advanced by 10-15 days the peak melt water runoff, a substantial effect on the timing of water resources availability. We also demonstrate that the decrease in BC deposition since the 1980s moderates the impact of current warming on snow cover decline. Hence, accounting for changes in light-absorbing particles deposition is required to improve the accuracy of snow cover reanalyses and climate projections, that are crucial for better understanding the past and future evolution of mountain social-ecological systems. Black carbon and dust deposition advanced the end of the snow season by 17 days on average over the last 40 years in the French Alps and the Pyrenees. The warming-induced snow cover decline was partly offset by decreases in black carbon deposition observed since the 1980s. [ABSTRACT FROM AUTHOR]

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach. [ABSTRACT FROM AUTHOR]

The longest time series of surface velocities recorded on a rock glacier in the French Alps, covering more than three decades, has been recorded since 1983 on the Laurichard rock glacier (Ecrins range). The time signal of velocity changes is extracted from variance analyses separating time and space variabilities on the rock glacier surface to provide an average‐wide time signal. We show that changes in velocity from year to year are virtually uniform at all locations with homogeneous accelerations or decelerations on the scale of the rock glacier as a whole. The spatial structure of velocity was found to be nearly at steady state over 35 years. Nonlinear effects are located in low‐velocity areas such as the rock glacier margins where accelerations/decelerations tend to be proportional to the local velocity. Over the period of record, a long‐term trend in rock glacier acceleration was detected with a rate of +0.2 m/yr per decade. Two main phases of acceleration were identified from the mid‐1980s to 1999 and from 2010 to 2015. In between, those two periods were interrupted by a 10‐year period of almost steady‐state velocities with an abrupt deceleration from 2006 to 2009 of −0.35 m/yr. The process of internal increases in ice temperatures alone (and associated changes in creep rates) would seem insufficient to explain the long‐term rise of surface velocities and their annual variations. Changes in the liquid water are a possible contributing factor, due to the injection of seasonal water caused by melting snow cover or internal melt due to heat generated by enhanced ice creep and friction in the ice/debris mixture. [ABSTRACT FROM AUTHOR]

Even today, the assessment of avalanche danger is by and large a subjective yet data-based decision-making process. Human experts analyse heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to assess the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF 1) was trained relying on the forecast danger levels published in the official Swiss avalanche bulletin. To reduce the uncertainty resulting from using the forecast danger level as target variable, we trained a second classifier (RF 2) that relies on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the models, i.e. the percentage of correct danger level predictions, ranged between 74 % and 76 % for RF 1 and between 72 % and 78 % for RF 2. We assessed the accuracy of forecasts with nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the agreement rate between forecast and nowcast assessments of the current experience-based Swiss avalanche forecasts (which is estimated to be 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. Our results suggest that the models may well have potential to become a valuable supplementary decision support tool for avalanche forecasters when assessing avalanche hazard. [ABSTRACT FROM AUTHOR]

Summer mountain pastures (also called alpages) are a central element for many agro-pastoral livestock systems in the alpine region, by providing the feedstock for herds during the summer transhumance. However, vegetation phenology and productivity in mountain pastures are increasingly affected by climate hazards exacerbated by climate change, such as early snow removal, late frost events, or droughts. Difficulties can then arise to match animal demand with forage resource on alpages and, in the long term, threaten the sustainable management of these highly multifunctional socio-ecological systems. To help agro-pastoral actors adapt, an essential step is to quantify the risk of impacts on the forage resource, due to an increased occurrence or intensity of climate hazards. Exposure to climate hazards on alpages is defined locally by topographic aspects in combination with the broader influence of the regional climate. Our work therefore aimed at providing a tailored assessment of potential climate risk for the forage resource at the individual scale of each alpage in the French Alps. To this end, we developed agro-climatic indicators based on atmospheric and snow cover data accounting for geographic and topographic conditions, and applied them to a database providing unique spatially explicit information at the alpage level. For the first time, we introduce a description of agro-climatic conditions and provide a classification of agro-climatic profiles of alpages in the French Alps, ranging from low to high potential risk for the forage resource, mainly following a North-South gradient combined with altitude. We also bring insights on the evolutions of the climate risk with climate change and discuss management implications for agro-pastoral livestock systems using alpages. We finally present a web-based visualization tool that aim at communicating agro-climatic profiles and their evolution to practitioners and at assisting decision makers in understanding climate-related risks on the alpages of the French Alps. [ABSTRACT FROM AUTHOR]

Mineral dust is an important aerosol in the atmosphere and is known to reduce snow albedo upon deposition. Model predictions of dust deposition events in snow covered mountain regions are challenging due to the complexity of aerosol‐cloud interactions and the specifics of mountain meteorological systems. We use a case study of dust deposition between 30 March and 5 April 2018 to the French alpine snowpack to study the processes that control dust deposition to the seasonal snowpack. To understand processes controlling dust transport and deposition to snow, we use a combination of in situ observations at Col du Lautaret in the French Alps, satellite remote sensing, the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis global atmospheric composition, and the regional model WRF‐Chem. Specifically, we investigate the role of increased model spatial resolution within WRF‐Chem in capturing mountain meteorology, precipitation, and predicted dust deposition. Regional model results are also compared to the reanalysis global CAMS products including aerosols in the atmosphere and predicted dust deposition fluxes. We conclude that predicted mountain meteorology (e.g., precipitation) is better with increased model resolution (3 × 3 km resolution WRF‐Chem domain). This improved meteorology has significant impacts on predicted dry and wet dust deposition to the alpine snowpack. Dry deposition is important in the western part of the French Alps at low altitudes, while wet deposition dominates over the complex higher altitude mountain terrain. Key Points: An extreme dust deposition event occurred in April 2018, which is in the top 95% of dust events to the Alps during the winter snow seasonWet (∼80%) and dry deposition (∼20%) contribute to total; dry deposition increases up to 1,700 m (40%) in the western part of the French AlpsDifferences in dust deposition when increasing the model resolution are co‐located with regions with improved predictions of precipitation [ABSTRACT FROM AUTHOR]

The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterizations, and unresolved terrain features. In situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large-scale modeling errors by means of data assimilation. In this work, we assimilate HS observations from an in situ network of 295 stations covering the French Alps, Pyrenees, and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialized snow cover modeling framework, we investigate whether such observations can be used to correct neighboring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modeling, based on a particle filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localization strategies to assimilate snow observations. These approaches are evaluated in a leave-one-out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that an intermediate localization radius of 35–50 km yields a slightly lower root mean square error (RMSE), and a better spread–skill than the strategy of assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modeling errors are the largest, e.g., the Haute-Ariège, Andorra, and the extreme southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses and, therefore, snow simulations. In situ HS observations thus show an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas. [ABSTRACT FROM AUTHOR]

This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope in that it does not rely directly on surface observations and is able to generate the full suite of model forcing variables required for hydrological and land surface modelling in hourly time steps. It achieves this by utilizing the previously published TopoSCALE scheme to generate synthetic observations of the current climate at the hillslope scale while accounting for a broad range of surface–atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical–statistical methods, including speed of use, while it avoids the need for ground data, which are often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high-elevation stations across the Swiss Alps, and a test application in which the impacts of climate change on Alpine snow cover are modelled. [ABSTRACT FROM AUTHOR]

The surface energy balance is a key factor influencing the ground thermal regime. With ongoing climate change, it is crucial to understand the interactions of the individual heat fluxes at the surface and within the subsurface layers, as well as their relative impacts on the permafrost thermal regime. A unique set of high-altitude meteorological measurements was analysed to determine the energy balance at three mountain permafrost sites in the Swiss Alps (Murtèl–Corvatsch, Schilthorn and Stockhorn), where data have been collected since the late 1990s in the framework of the Swiss Permafrost Monitoring Network (PERMOS). All stations are equipped with sensors for four-component radiation, air temperature, humidity, and wind speed and direction, as well as ground temperatures and snow height. The three sites differ considerably in their surface and ground material composition, as well as their ground ice contents. The energy fluxes were calculated based on two decades of field measurements. While the determination of the radiation budget and the ground heat flux is comparatively straightforward (by the four-component radiation sensor and thermistor measurements within the boreholes), larger uncertainties exist for the determination of turbulent sensible and latent heat fluxes. Our results show that mean air temperature at Murtèl–Corvatsch (1997–2018, 2600 m a.s.l.) is -1.66 ∘ C and has increased by about 0.8 ∘ C during the measurement period. At the Schilthorn site (1999–2018, 2900 m a.s.l.) a mean air temperature of -2.60 ∘ C with a mean increase of 1.0 ∘ C was measured. The Stockhorn site (2003–2018, 3400 m a.s.l.) recorded lower air temperatures with a mean of -6.18 ∘ C and an increase of 0.5 ∘ C. Measured net radiation, as the most important energy input at the surface, shows substantial differences with mean values of 30.59 W m -2 for Murtèl–Corvatsch, 32.40 W m -2 for Schilthorn and 6.91 W m -2 for Stockhorn. The calculated turbulent fluxes show values of around 7 to 13 W m -2 using the Bowen ratio method and 3 to 15 W m -2 using the bulk method at all sites. Large differences are observed regarding the energy used for the melting of the snow cover: at Schilthorn a value of 8.46 W m -2 , at Murtèl–Corvatsch 4.17 W m -2 and at Stockhorn 2.26 W m -2 are calculated, reflecting the differences in snow height at the three sites. In general, we found considerable differences in the energy fluxes at the different sites. These differences help to explain and interpret the causes of a warming atmosphere. We recognise a strong relation between the net radiation and the ground heat flux. Our results further demonstrate the importance of long-term monitoring to better understand the impacts of changes in the surface energy balance components on the permafrost thermal regime. The dataset presented can be used to improve permafrost modelling studies aiming at, for example, advancing knowledge about permafrost thaw processes. The data presented and described here are available for download at the following site: 10.13093/permos-meteo-2021-01. [ABSTRACT FROM AUTHOR]

This work introduces the S2M (SAFRAN–SURFEX/ISBA–Crocus–MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2021. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2021) and the best possible set of available in situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset, available at https://doi.org/10.25326/37#v2020.2. The S2M data are provided by Météo-France – CNRS, CNRM, Centre d'Études de la Neige, through AERIS. [ABSTRACT FROM AUTHOR]

The Alps are an interesting case for studying the relationship between tourism and climate change. Despite a growing number of studies, the impacts of climate change on the tourism sector remain uncertain, when the regional and local scale or seasonality are considered. This article presents a risk methodology to assess the spatial distribution of the main challenges and opportunities for winter and summer tourism due to climate change at the sub-regional level on a 2021-2050 scenario. This methodology has been tested on an Italian Alpine area, which consists of very different landscapes from plain to high mountains. The results show that high-altitude municipalities will face the stronger risks for winter touristic activities, due to reduced snow cover duration, but also opportunities to attract in summer tourists escaping from the hotter temperatures of the plain. At the same time, climate change could have secondary negative effects in these areas, as it will increase the frequency and the magnitude of extreme events. The results show that impacts of CC cannot be generalised, even in a limited area; same hazards due to changes in temperature and precipitation patterns can generate very different risk scores, because of local conditions related to exposure and vulnerability factors. [ABSTRACT FROM AUTHOR]

Snow reliability is a key climatic impact driver for the ski tourism industry, although there are only a few studies addressing past changes in snow reliability in ski resorts accounting for snow management practices (grooming and snowmaking, in particular). This study provides an assessment of past changes in natural and managed snow cover reliability from 1961 to 2019 in the French Alps. In particular, we used snowmaking investment figures to infer the evolution of snowmaking coverage at the ski resort scale for 16 ski resorts in the French Alps, which we used together with a detailed snow cover modelling system driven by a local atmospheric reanalysis. We find different benefits of snow management to reduce the variability and long-term decrease in snow cover reliability because of the heterogeneity of the snowmaking deployment trajectories across ski resorts. The frequency of challenging conditions for ski resort operation over the 1991–2019 period increased in November and February to April compared to the 30-year reference period 1961–1990. In general, snowmaking had a positive impact on snow reliability, especially in December to January. While for the highest-elevation ski resorts, snowmaking improved snow reliability for the core of the winter season, it did not counterbalance the decreasing trend in snow cover reliability for lower-elevation ski resorts and in the spring. [ABSTRACT FROM AUTHOR]

There are on average 35 fatal mountaineering accidents per summer in France. On average, since 1990, 3.7 of them have occurred every summer in the Grand Couloir du Goûter, on the classic route up Mont Blanc (4809 m a.s.l.). Rockfall is one of the main factors that explain this high accident rate and contribute to making it one of the most accident-prone areas in the Alps for mountaineers. In this particular context, the objective of this study is to document the rockfall activity and its triggering factors in the Grand Couloir du Goûter in order to disseminate the results to mountaineers and favour their adaptation to the local rockfall hazard. Using a multi-method monitoring system (five seismic sensors, an automatic digital camera, three rock subsurface temperature sensors, a traffic sensor, a high-resolution topographical survey, two weather stations and a rain gauge), we acquired a continuous database on rockfalls during a period of 68 d in 2019 and some of their potential triggering factors (precipitation, ground and air temperatures, snow cover, frequentation by climbers). At the seasonal scale, our results confirm previous studies showing that rockfalls are most frequent during the snowmelt period in permafrost-affected rockwalls. Furthermore, the unprecedented time precision and completeness of our rockfall database at high elevation thanks to seismic sensors allowed us to investigate the factors triggering rockfalls. We found a clear correlation between rockfall frequency and air temperature, with a 2 h delay between peak air temperature and peak rockfall activity. A small number of rockfalls seem to be triggered by mountaineers. Our data set shows that climbers are not aware of the variations in rockfall frequency and/or cannot/will not adapt their behaviour to this hazard. These results should help to define an adaptation strategy for climbers. Therefore, we disseminated our results within the mountaineering community thanks to the full integration of our results into the management of the route by local actors. Knowledge built during this experiment has already been used for the definition and implementation of management measures for the attendance in summer 2020. [ABSTRACT FROM AUTHOR]

We study the probability for snow cover at a climate station. Connecting stations with the same probability yields the corresponding snow line (a figure between zero and unity). The climatological snow lines in the Alps are implicit in the state function of snow duration. This function, specified by just five parameters, depends upon the mountain temperature, a linear combination of the mean temperature over Europe and the 3D-coordinates of the stations. The influence of external parameters other than temperature (like snowfall, melting processes, station exposition) is treated as stochastic. The five state function parameters are gained for both winter (DJF) and summer (JJA) through a fit algorithm from routine snow depth observations in 1961-2010 in Austria and Switzerland. Any desired snow line is defined by a linear surface with a characteristic value of the mountain temperature. The snow line appears when there is a cut between the surface and the orography. Temperature sensitivity of snow cover duration, analytically derived from the state function, is extreme at the median snow line (snow probability 0.50). Alpine-wide mean altitude of the median snow line is 793(+ / −36)m in winter and 3.083(+ / −1.121)m in summer. The snowline field slopes gently from west to east across the Alps (downward in winter, upward in summer) and oscillates up and down with the seasons. The sensitivity of the median snowline altitude to European temperature over the five decades of Alpine data is 166 (±5) m/°C in winter and 123 (±18) m/°C in summer. Global warming causes the snow lines to shift upward with time; in parallel, the area of the Alps that is at least 50 % snow covered in winter shrinks by −7.0 (±4.1) %/10 years. [ABSTRACT FROM AUTHOR]

In alpine environments, tectonic processes, past glaciation and weathering processes fracture rock and prepare or trigger rockfalls, which are important processes of rock slope evolution and natural hazards. In this study, I quantify thermally and ice-induced rock and fracture kinematics and place these in the context of their role in producing rockfall and climate change. I conducted laboratory measurements on intact rock samples and installed temperature loggers and crackmeters at four rockwalls reaching from 2585 to 2935 m in elevation in the Hungerli Valley, Swiss Alps. My laboratory data show that thermal expansion followed three phases of rock kinematics, which resulted in a hysteresis effect. In the field, control crackmeters on intact rock reflected these temperature phases, and based on thermal expansion coefficients of these observed phases, I modelled thermal stress. Model results show that thermal stress magnitudes were predominantly below rock strengths. Crackmeters across fractures revealed fracture opening during cooling and reverse closing behaviour during warming on daily timescales. Elevation-dependent snow cover controlled the number of daily temperature changes and thermal stresses affecting both intact and fractured rock, while the magnitude is controlled by topographic factors influencing insolation. On a seasonal scale, slow ice-segregation-induced fracture opening can occur within lithology-dependent temperature regimes called frost cracking windows. Shear plane dipping controlled whether fractures opened or closed irreversibly with time due to thermally induced block crawling on an annual scale. Climate change will shorten snow duration and increase temperature extremes and will, therefore, affect the number and the magnitude of thermal changes and associated stresses. Earlier snowmelt in combination with temperature increase will shift the ice-induced kinematic processes to higher elevations. In conclusion, climate change will affect and change rock and fracture kinematics and, therefore, change rockfall patterns in alpine environments. Future work should quantify rockfall patterns and link these patterns to climatic drivers. [ABSTRACT FROM AUTHOR]

Earth's climate is warming and will continue to warm as the century progresses. High mountains and high latitudes are experiencing the greatest warming of all regions on Earth and also are some of the most sensitive areas to climate change, in part because ecosystems and natural processes in these areas are intimately linked to the cryosphere. Evidence is mounting that warming will further reduce permafrost and snow and ice cover in high mountains, which in turn will destabilize many slopes, alter sediment delivery to streams, and change subalpine and alpine ecosystems. This paper contributes to the continuing discussion of impacts of climate change on mountain environments by comparing and discussing processes and trends in the mountains of western Canada and the European Alps. We highlight the effects of physiography and climate on physical processes occurring in the two regions. Processes of interest include landslides and debris flows induced by glacier debuttressing, alpine permafrost thaw, changes in rainfall regime, formation and sudden drainage of glacier- and moraine-dammed lakes, ice avalanches, glacier surges, and large-scale sediment transfers due to rapid deglacierization. Our analysis points out the value of integrating observations and data from different areas of the world to better understand these processes and their impacts. • Climate change is accelerating mass wasting in high mountains. • Deglaciation and permafrost degradation is the main driver of slope instability. • Regional differences are controlled by climate, topography and geology. • Integration of data from different regions can help fill knowledge gaps. [ABSTRACT FROM AUTHOR]