Abstract. Permafrost and related thermo-hydro-mechanical processes are thought to influence high alpine rock wall stability, but a lack of field measurements means that the characteristics and processes of rock wall permafrost are poorly understood. To help remedy this situation, in 2005 work began to install a monitoring system at the Aiguille du Midi (3842 m a.s.l). This paper presents temperature records from nine surface sensors (eight years of records) and three 10 m deep boreholes (4 years of records), installed at locations with different surface and bedrock characteristics. In line with previous studies, our temperature data analyses showed that: micro-meteorology controls the surface temperature, active layer thicknesses are directly related to aspect and ranged from < 2 m to nearly 6 m, and that thin accumulations of snow and open fractures are cooling factors. Thermal profiles empirically demonstrated the coexistence within a single rock peak of warm and cold permafrost (about −1.5 to −4.5 • C at 10 m depth) and the resulting lateral heat fluxes. Our results also extended current knowledge of the effect of snow, in that we found similar thermo-insulation effects as reported for gentle mountain areas. Thick snow warms shaded areas, and may reduce active layer refreezing in winter and delay its thawing in summer. However, thick snow thermo-insulation has little effect compared to the high albedo of snow which leads to cooler conditions at the rock surface in areas exposed to the sun. A consistent inflection in the thermal profiles reflected the cooling effect of an open fracture in the bedrock, which appeared to act as a thermal cutoff in the sub-surface thermal regime. Our field data are the first to be obtained from an Alpine permafrost site where borehole temperatures are below −4 • C, and represent a first step towards the development of strategies to investigate poorly known aspects in steep bedrock permafrost such as the effects of snow cover and fractures.

Abstract. The investigation and modelling of permafrost distribution, particularly in areas of discontinuous permafrost, is challenging due to spatial heterogeneity, remoteness of measurement sites and data scarcity. We have designed a strategy for standardizing different local data sets containing evidence of the presence or absence of permafrost into an inventory for the entire European Alps. With this brief communication, we present the structure and contents of this inventory. This collection of permafrost evidence not only highlights existing data and allows new analyses based on larger data sets, but also provides complementary information for an improved interpretation of monitoring results.

The objective of this paper is to provide a first synthesis on the state and recent evolution of permafrost at the monitoring site of Cime Bianche (3100 m a.s.l.) on the Italian side of the Western Alps. The analysis is based on 7 years of ground temperature observations in two boreholes and seven surface points. The analysis aims to quantify the spatial and temporal variability of ground surface temperature in relation to snow cover, the small-scale spatial variability of the active layer thickness and current temperature trends in deep permafrost. Results show that the heterogeneity of snow cover thickness, both in space and time, is the main factor controlling ground surface temperatures and leads to a mean range of spatial variability (2.5 ± 0.1 °C) which far exceeds the mean range of observed inter-annual variability (1.6 ± 0.1 °C). The active layer thickness measured in two boreholes at a distance of 30 m shows a mean difference of 2.0 ± 0.1 m with the active layer of one borehole consistently deeper. As revealed by temperature analysis and geophysical soundings, such a difference is mainly driven by the ice/water content in the sub-surface and not by the snow cover regimes. The analysis of deep temperature time series reveals that permafrost is warming. The detected trends are statistically significant starting from a depth below 8 m with warming rates between 0.1 and 0.01 °C yr^-1

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Abstract. 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.

International audiencePermafrost and related thermo-hydro-mechanical processes are regarded as crucial factors in rock wall stability in high alpine areas, but a lack of field measurements means that the characteristics of such locations and the processes to which they are subjected are poorly understood. To help remedy this situation, in 2005 work began to install a monitoring system at the Aiguille du Midi (3842 m a.s.l.). This paper presents temperature records from nine surface sensors (eight years of records) and three 10 m-deep boreholes (four years of records), installed at locations with different surface and bedrock characteristics. Annual and seasonal offsets between mean surface temperatures and air temperatures suggest that snow cover and slope aspect are also important factors governing bedrock surface temperatures in steep terrain. Snow-free sensors revealed additional effects of microtopography and micrometeorology. Active layer thicknesses ranged from < 2 m to nearly 6 m, depending on sun-exposure and interannual variations in atmospheric conditions. Warm and cold permafrost (about −1.5 °C to −4.5 °C at 10 m-depth) coexists within the Aiguille du Midi, resulting in high lateral heat fluxes. A temperature inflection associated with a fracture provided evidence of non-conductive processes, most notably cooling due to air ventilation and some intermittent and local warming. Our field data, the first to be obtained from an Alpine permafrost site where temperatures are below −4 °C, confirm the results of previous studies of permafrost in steep bedrock slopes and highlight the importance of factors such as snow cover and fracturing

Abstract. Precipitation orographic enhancement is the result of both synoptic circulation and topography. Since high-elevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement, as well as how they affect precipitation lapse rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual measurements of snow depth (courses) could provide new insights into this process. We leveraged over 11 000 snow course data upstream of two reservoirs in the western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and, consequently, improve predictions of a snow hydrologic modeling chain (Flood-PROOFS). We found that snow water equivalent (SWE) above 3000 m a.s.l. (above sea level) was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m a.s.l., with gradients up to 1000 mm w.e. km−1. Enhancement factors, estimated by blending precipitation gauge and snow course data, were consistent between the two hydropower headwaters (median values above 3000 m a.s.l. between 4.1 and 4.8). Including blended gauge course lapse rates in an iterative precipitation spatialization procedure allowed Flood-PROOFS to remedy underestimations both of SWE above 3000 m a.s.l. (up to 50 %) and – importantly – of precipitation vs. observed streamflow. Annual runoff coefficients based on blended lapse rates were also more consistent from year to year than those based on precipitation gauges alone (standard deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to the essential role of high-elevation headwaters in supporting water security and ecosystem services worldwide.

Abstract. Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.