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The Greenland ice sheet mass loss is one of the main sources of contemporary sea-level rise. The mass loss is primarily caused by surface melt and the resulting runoff. During the melt season, the ice sheet’s surface receives energy from sunlight absorption and sensible heating, which subsequently heats the subsurface snow and ice. The energy from the previous melt season can also enhance melting in the following summer as less heating is required to bring the snow and ice to the melting point. Subsurface temperatures are therefore both a result and a driver of the timing and magnitude of surface melt on the ice sheet. We present a dataset of more than 3900 measurements of ice, snow and firn temperature at 10 m depth across the Greenland ice sheet spanning the years from 1912 to 2022. We construct an artificial neural network (ANN) model that takes as input the ERA5 reanalysis monthly near-surface air temperature and snowfall for the 1954-2022 period and train it on our compilation of observed 10-meter temperature. We use our dataset and the ANN to evaluate three broadly used regional climate models (RACMO, MAR and HIRHAM). Our ANN model provides an unprecedented and observation-based description of the recent warming of the ice sheet’s near-surface and our evaluation of the three climate models highlights future development for the models. Overall, these findings improve our understanding of the ice sheet’s response to recent atmospheric warming and will help reduce uncertainties of ice sheet surface mass balance estimates.

Current sea-level rise partly stems from increased surface melting and meltwater runoff from the Greenland ice sheet. Multi-year snow, also known as firn, covers about 80% of the ice sheet and retains part of the surface meltwater. Since the firn cold content integrates its physical and thermal characteristics, it is a valuable tool for determining the meltwater-retention potential of firn. We use gap-filled climatological data from nine automatic weather stations in the ice-sheet accumulation area to drive a surface-energy-budget and firn model, validated against firn density and temperature observations, over the 1998-2017 period. Our results show a stable top 20 m firn cold content (CC20) at most sites. Only at the lower-elevation Dye-2 site did CC20 decrease, by 24% in 2012, before recovering to its original value by 2017. Heat conduction towards the surface is the main process feeding CC20 at all nine sites, while CC20 reduction occurs through low-cold-content fresh-snow addition at the surface during snowfall and latent-heat release when meltwater refreezes. Our simulations suggest that firn densification, while reducing pore space for meltwater retention, increases the firn cold content, enhances near-surface meltwater refreezing and potentially sets favourable conditions for ice-slab formation., SCOPUS: ar.j, info:eu-repo/semantics/published

The quantification of snow accumulation and the temporal evolution of the snow pack is essential when investigating the mass balance conditions of mountain glaciers. In particular, accumulation regions become smaller due to the gradual increase of the equilibrium line, thus reducing mass input into the glacier system. This will have severe consequences on ice flux and thus the mass balance conditions across many mountain regions worldwide. The mass redistribution within the accumulation regions is considerably influenced by migration of melt water in the snow and firn pack and the induced mass and density changes. Here, we study snow and firn processes at a high mountain accumulation plateau on 3470 m asl at Vernagtferner, Austria. Vernagtferner is a major glacier in the drainage basin of Rofenache, with an area of about 6.9 km², covering altitudes between 2900 m and 3550 m. A snow monitoring station, including an upward-looking ground penetrating radar (upGPR) was installed at the highest accumulation basin in 2018. This station allows the continuous determination of the snow pack stratigraphy and of the snow water equivalent (SWE) (Heilig et al. (2009, 2010), Schmid et al. 2014, Heilig et al., 2015). We compare numerical simulations of the 1-dimensional snow cover model SNOWPACK (Bartelt and Lehning, 2002), driven by automatic weather station data, with continuous observations of the installed upGPR system and bi-annual in-situ data. The analysed upGPR data enable continuous evaluation of the SNOWPACK simulations over several melt and accumulation seasons. The upGPR data show that even at high elevations frequent melt-freeze crusts develop during the accumulation period. Even though the crusts are several centimetres, melt water rapidly percolates trough these layers, once the snow pack reaches isothermal conditions in late spring. The simulation results demonstrate, that SNOWPACK is able to reproduce this fast advance of the melt front accurately, while the up-GPR measurements provide an independent proof of the model performance. These measurements also show that firn layers (previous summer surfaces) block water infiltration into depth only for a very short period, indicating that SWE measurements of glacier accumulation only provide realistic values, if carried out before or just at the onset of spring melt. This feasibility study provides important indication on how to extend such studies to larger glacier systems, also in less monitored regions, where in-situ data might be sparse.

In recent decades, meltwater runoff has accelerated to become the dominant mechanism for mass loss in the Greenland ice sheet1,2,3. In Greenland’s high- elevation interior, porous snow and firn accumulate; these can absorb surface meltwater and inhibit runoff4, but this buffering effect is limited if enough water refreezes near the surface to restrict percolation5,6. However, the influence of refreezing on runoff from Greenland remains largely unquantified. Here we use firn cores, radar observations and regional climate models to show that recent increases in meltwater have resulted in the formation of metres-thick, low-permeability ‘ice slabs’ that have expanded the Greenland ice sheet’s total runoff area by 26 ± 3 per cent since 2001. Although runoff from the top of ice slabs has added less than one millimetre to global sea-level rise so far, this contribution will grow substantially as ice slabs expand inland in a warming climate. Runoff over ice slabs is set to contribute 7 to 33 millimetres and 17 to 74 millimetres to global sea-level rise by 2100 under moderate- and high-emissions scenarios, respectively—approximately double the estimated runoff from Greenland’s high-elevation interior, as predicted by surface mass balance models without ice slabs. Ice slabs will have an important role in enhancing surface meltwater feedback processes, fundamentally altering the ice sheet’s present and future hydrology.

Recent studies indicate that a declining surface mass balance will dominate the Greenland Ice Sheet’s (GrIS) contribution to 21st century sea level rise. It is therefore crucial to understand the liquid water balance of the ice sheet and its response to increasing temperatures and surface melt if we want to accurately predict future sea level rise. The ice sheet firn layer covers ~90% of the GrIS and provides pore space for storage and refreezing of meltwater. Because of this, the firn layer can retain up to ~45% of the surface meltwater and thus act as an efficient buffer to ice sheet mass loss. However, in a warming climate this buffer capacity of the firn layer is expected to decrease, amplifying meltwater runoff and sea-level rise. Dedicated firn models are used to understand how firn layers evolve and affect runoff. Additionally, firn models are used to estimate the changing thickness of the firn layer, which is necessary in altimetry to convert surface height change into ice sheet mass loss.Here, we present the latest version of our firn model IMAU-FDM. With respect to the previous version, changes have been made to the handling of the freshly fallen snow, the densification rate of the firn and the conduction of heat. These changes lead to an improved representation of firn density and temperature. The results have been thoroughly validated using an extensive dataset of density and temperature measurements that we have compiled covering 126 different locations on the GrIS. Meltwater behaviour in the model is validated with upward-looking GPR measurements at Dye-2. Lastly, we present an in-depth look at the evolution firn characteristics at some typical locations in Greenland.Dedicated, stand-alone firn models offer various benefits to using a regional climate model with an embedded firn model. Firstly, the vertical resolution for buried snow and ice layers can be larger, improving accuracy. Secondly, a stand-alone firn model allows for spinning up the model to a more accurate equilibrium state. And thirdly, a stand-alone model is more cost- and time-effective to use. Firn models are increasingly capable of simulating the firn layer, but areas with large amounts of melt still pose the greatest challenge.

Perennial snow, or firn, covers 80 % of the Greenland ice sheet and has the capacity to retain part of the surface meltwater, buffering the ice sheet’s contribution to sea level. Multi-layer firn models are traditionally used to simulate the firn processes and estimate meltwater retention. We present the output from nine firn models, forced by weather-station-derived mass and energy fluxes at four sites representative of the dry snow, percolation, ice slab and firn aquifer areas. We compare the model outputs and evaluate them against in situ observations. Models that explicitly account for deep meltwater percolation overestimate percolation depth and consequently firn temperature at the percolation and ice slab sites although they accurately simulate the recharge of the firn aquifer. Models using Darcy's law and a bucket scheme compare favourably to observations at the percolation site but only the Darcy models accurately simulate firn temperature and thus meltwater percolation at the ice slab site. We find that Eulerian models, that transfer firn through fixed layers, smooth sharp gradients in firn temperature and density over time. From the model spread, we find that simulated densities (respectively temperature) have an uncertainty envelope of ±60 kg m−3 (resp. ±14 °C) in the dry snow area and up to ±280 kg m−3 (resp. ±15–18 °C) at warmer sites.

Increasing melt over the Greenland Ice Sheet (GrIS) recorded over the past several years has resulted in significant changes of the percolation regime of the ice sheet. It remains unclear whether Greenland's percolation zone will act as a meltwater buffer in the near future through gradually filling all pore space or if near-surface refreezing causes the formation of impermeable layers, which provoke lateral runoff. Homogeneous ice layers within perennial firn, as well as near-surface ice layers of several meter thickness have been observed in firn cores. Because firn coring is a destructive method, deriving stratigraphic changes in firn and allocation of summer melt events is challenging. To overcome this deficit and provide continuous data for model evaluations on snow and firn density, temporal changes in liquid water content and depths of water infiltration, we installed an upward-looking radar system (upGPR) 3.4 m below the snow surface in May 2016 close to Camp Raven (66.4779∘ N, 46.2856∘ W) at 2120 m a.s.l. The radar is capable of quasi-continuously monitoring changes in snow and firn stratigraphy, which occur above the antennas. For summer 2016, we observed four major melt events, which routed liquid water into various depths beneath the surface. The last event in mid-August resulted in the deepest percolation down to about 2.3 m beneath the surface. Comparisons with simulations from the regional climate model MAR are in very good agreement in terms of seasonal changes in accumulation and timing of onset of melt. However, neither bulk density of near-surface layers nor the amounts of liquid water and percolation depths predicted by MAR correspond with upGPR data. Radar data and records of a nearby thermistor string, in contrast, matched very well for both timing and depth of temperature changes and observed water percolations. All four melt events transferred a cumulative mass of 56 kg m−2 into firn beneath the summer surface of 2015. We find that continuous observations of liquid water content, percolation depths and rates for the seasonal mass fluxes are sufficiently accurate to provide valuable information for validation of model approaches and help to develop a better understanding of liquid water retention and percolation in perennial firn.

In order to assess the climate-glacier relationship in the Southern European Alps, two small glaciers in Lombardy, Suretta Sud and Lupo, have been chosen for investigation. GPS profiling in 2016 allowed the determination of elevation changes since 2007 and thus the determination of the glacier mass balance using the geodetic method. Older maps have been digitized to reconstruct mass changes back to 1962. The two glaciers react synchronously and show overall mass losses, but the narrower observation pattern at Suretta Sud reveals positive mass changes from 1970 to 1982. Long-term meteorological series of precipitation and air temperature of representative stations have been analysed, they show highly significant warming trends of 0.32-0.40°C per decade during summer, but no significant trend in winter precipitation. Since 2009/2010, annual mass balance measurements applying the glaciological method were undertaken by the Servizio Glaciologico Lombardo. These annual observations reveal that, despite the decadal mass loss observed by geodetic observations, years with positive mass changes still occur. These years showed below average winter precipitation and below average summer temperatures, indicating that glacier behaviour is influenced by both parameters. Ice thicknesses observed by echo radio sounding allow to calculate glacier volume and the bedrock topography. Maximum ice thicknesses are around 40 m and the cirques are not overdeepened by glacial erosion. Using very simple extrapolations of observed rates of downwasting results in theoretical life expectancies of these glacierets of 50-70 yr.

Oxygen and hydrogen isotopes in snow were measured in weekly profiles during the growth and decline of a sloped subalpine snowpack, southern Idaho, 2011-2012. Isotopic steps (10 parts per thousand, delta O-18;80 parts per thousand, delta D) were preserved relative to physical markers throughout the season, albeit with some diffusive smoothing. Melting stripped off upper layers without shifting isotopes within the snowpack. Meltwater is in isotopic equilibrium with snow at the top but not with snow at each respective collection height. Transport of meltwater occurred primarily along pipes and lateral flow paths allowing the snowpack to melt initially in reverse stratigraphic order. Isotope diffusivities are similar to 2 orders of magnitude faster than estimated from experiments but can be explained by higher temperature and porosity. A better understanding of how snowmelt isotopes change during meltout improves hydrograph separation methods, whereas constraints on isotope diffusivities under warm conditions improve models of ice core records in low-latitude settings.

Monitoring seasonal snow cover properties is critical for properly managing natural hazards such as snow avalanches or snowmelt floods. However, measurements often cannot be conducted in difficult terrain or lack the high temporal resolution needed to account for rapid changes in the snowpack, e.g., liquid water content (LWC). To monitor essential snowpack properties, we installed an upward looking ground-penetrating radar (upGPR) and a low-cost GPS system below the snow cover and observed in parallel its evolution during two winter seasons. Applying external snow height (HS) information, both systems provided consistent LWC estimates in snow, based on independent approaches, namely measurements of travel time and attenuation of electromagnetic waves. By combining upGPR and GPS, we now obtain a self-contained approach instead of having to rely on external information such as HS. This allows for the first time determining LWC, HS, and snow water equivalent (SWE) nondestructively and continuously potentially also in avalanche-prone slopes.

An increase of the spatial and temporal resolution of snowpack measurements in Alpine or Arctic regions will improve the predictability of flood and avalanche hazards and increase the spatial validity of snowpack simulation models. In the winter season 2009, we installed a ground-penetrating radar (GPR) system beneath the snowpack to measure snowpack conditions above the antennas. In comparison with modulated frequency systems, GPR systems consist of a much simpler technology, are commercially available and therefore are cheaper. The radar observed the temporal alternation of the snow height over more than 2·5 months. The presented data showed that with moved antennas, it is possible to record the snow height with an uncertainty of less than 8% in comparison with the probed snow depth. Three persistent melt crusts, which formed at the snow surface and were buried by further new snow events, were used as reflecting tracers to follow the snow cover evolution and to determine the strain rates of underlaying layers between adjacent measurements. The height in two-way travel time of each layer changed over time, which is a cumulative effect of settlement and variation of wave speed in response to densification and liquid water content. The infiltration of liquid water with depth during melt processes was clearly observed during one event. All recorded reflections appeared in concordance with the physical principles (e.g. in phase structure), and one can assume that distinct density steps above a certain threshold result in reflections in the radargram. The accuracy of the used impulse radar system in determining the snow water equivalent is in good agreement with previous studies, which used continuous wave radar systems. The results of this pilot study encourage further investigations with radar measurements using the described test arrangement on a daily basis for continuous destruction-free monitoring of the snow cover.

Operational remote monitoring of snowpack stratigraphy, melt water intrusions and their evolution with time for forecasting snowpack stability is not possible to date. Determination of the spatial variability of snowpack conditions on various scales requires a number of point measurements with various methods. These methods are either destructive or do not provide information about the internal structure of the snowpack. The application of a remotely controlled non-destructive sensor system would help to gain a higher spatio-temporal resolution about information of the snowpack. In this study we present results from upward-looking ground-penetrating radar (GPR) surveys from horizontal caves dug in the front wall of snow pits at the bottom of the snowpack. GPR data are compared with vertical profiles of snow hardness and density, obtained in the snow pit. Data were acquired in different areas with varying snow conditions with various GPR impulse systems, frequencies and polarizations. Radar experiments with high frequencies (> 1 GHz) detect internal layers in the snowpack in dry snow, but fail to provide clear reflections at the upper snow-air transition because of attenuation. In wet snow, the radar signals < 1 GHz are capable to penetrate a meter-thick snowpack and detect the snow surface, although the signal is strongly attenuated. Analysis of reflection phases and magnitudes allows interpretation of their physical origin in terms of changes in electrical permittivity. Varying antenna polarization causes a strongly different signal response, likely caused by the snow-pit wall present in our set-up. Forward calculation of density-based reflection coefficients between neighboring layers of varying hardness yields ambiguous results in terms of correspondence with observed radar reflections apart except for interferences of neighboring reflections. Moreover, we identify several pitfalls for future applications. The system set-up used here represents a basis for further developments towards a system, which is capable of improving information on the spatial and temporal snowpack characteristics.

Snow avalanche victims have only a good chance to survive when they are located within a short time. This requires an active beacon for them to wear or a very rapid deployment of a search-and-rescue team with dogs. Customary ground-penetrating radar (GPR) instruments used on the snow surface are not able to reduce fatality numbers because they are slow to search a field. A potential alternative could be an airborne search using radar. An airborne radar search is technologically challenging because a very large data stream has to be processed and visualized in real time, and the interaction of the electromagnetic waves with snow, subsurface, and objects must be understood. We studied a two-step algorithm to locate avalanche victims in real time. The algorithm was validated using realistic test arrangements and conditions using an aerial tramway. The distance dependence of the reflection energy with increased flight heights, the coherence between the use of more antennas and the detectable range, and the reflection images of different avalanche victims were measured. The algorithm detected an object for each investigated case, where the reflection energy of the scans was higher than for the scans of pure snow. Airborne GPR has a large potential to become a rapid search method in dry snow avalanches. However, a fully operational version still requires substantial improvements in hardware and software.

Slightly more than one third of all buried avalanche victims of the last 4 years in Austria and Switzerland did not wear any locating device. This is a demanding problem for rescue operations. A fast and reliable method to locate these victims is desired. A ground-penetrating radar (GPR) mounted on a helicopter with an automatic location of buried objects via processing the radar data and GPS referencing is suggested to be a tremendous support to organised rescue teams. To set up the basis of such a system, we performed field experiments in winter 2006 to investigate three fundamental topics: (i) the influence of the snow properties on the radar signal, (ii) the maximum horizontal distance of a victim from the flight direction and (iii) the influence of the orientation of the victim with respect to the flight direction. A numerical simulation of radar signals on an inhomogeneous moist snow pack was computed and compared to field measurements. Post processing of the radar data was developed based on statistical evaluation of the reflected radar energy. To simulate a helicopter flight the radar antenna, a 400 MHz GPR system, was mounted on an approximately 6 m high aerial railway system. The victim was substituted by water bags in various depths. In experiments with dry snow the water bags could be detected easily, independent of the large spatial variation of snow densities in the test arrangement. In moist snow however, it was impossible to detect the target. The numerical simulation of this situation gives reason to presume that the variation of electrical conductivity, due to the spatial variability of the moisture content, causes this difficulty. The post processing was able to provide a numerical cheap automatic location procedure using a simple threshold value. The maximum transversal distance of a victim from the direction of a flight in 6 m height turned out to be approximately 1.5 m. Victims buried longitudinal to the flight direction are slightly better to detect than orthogonal buried victims.

Variations of snow surface and snowpack properties affect avalanche formation. In up to four north-facing slopes above the tree line near Davos, Switzerland, snow surface properties were characterized. Penetration resistance was measured with a snow micro-penetrometer. The sampling scheme was designed to allow a multi-scale approach covering the snowpack depth scale (0.5–5 m), the slope scale (5–100 m) and the basin scale (100–1000 m). Observations and measurements were compared to the data of a nearby automatic weather station (AWS). The AWS data were also used to model snow-cover stratigraphy and its evolution with the numerical snow-cover model SNOWPACK. Comparing the four slopes showed that surface properties observed manually were similar among the three slopes that were sheltered, and often different from the slope that was wind-exposed. However, the penetration resistance of the surface layer was in most cases significantly different among slopes, although most values were

The widely used detailed SNOWPACK model hasundergone constant development over the years. A notablerecent extension is the introduction of a Richards equation(RE) solver as an alternative for the bucket-type approachfor describing water transport in the snow and soil layers.In addition, continuous updates of snow settling and newsnow density parameterizations have changed model behav-ior. This study presents a detailed evaluation of model per-formance against a comprehensive multiyear data set fromWeissfluhjoch near Davos, Switzerland. The data set is col-lected by automatic meteorological and snowpack measure-ments and manual snow profiles. During the main win-ter season, snow height (RMSE

In this paper we present results from the tomographic analysis of L-Band SAR data acquired in February/March 2014 over the Mittelbergferner glacier, Austrian Alps, during the ESA campaign AlpTomoSAR. The campaign includes coincident in-situ measurements of snow and ice properties, as well as high-frequency Ground Penetrating Radar (GPR) data acquired over a total length of 18 km. The analyses of three-dimensional TomoSAR data cubes shows the complexity of the glacier sub-surface scattering. Most areas are characterized by surface scattering in proximity of the Lidar surface, plus a complex pattern of in-depth volumetric scattering beneath. Various subsurface features observed in GPR transects at 600 MHz and 200 MHz clearly showed up in TomoSAR sections as well. In particular: firn bodies, crevasses, and even the bedrock down to 50 m below the ice surface.

Evaluating and improving snow models and outflow predictions for hydrological applications is hindered by the lack of continuous data on bulk volumetric liquid water content (θw) and storage capacity of the melting snowpack. The combination of upward looking ground-penetrating radar and conventional snow height sensors enable continuous, nondestructive determinations of θw in natural snow covers from first surficial wetting until shortly before melt out. We analyze diurnal and seasonal cycles of θw for 4 years in a flat study site and for three melt seasons on slopes and evaluate model simulations for two different water transport schemes in the snow cover model SNOWPACK. Observed maximum increases in θw during a day are below 1.7 vol % (90th percentile) at the flat site. Concerning seasonal characteristics of θw, less than 10% of recorded data exceed 5 vol % at the flat site and 3.5 vol % at slopes. Both water transport schemes in SNOWPACK underestimate maximum θw at the flat site systematically for all observed melt seasons, while simulated θw maxima on slopes are accurate. Implementing observed changes in θw per day in outflow predictions increases model performance toward higher agreement with lysimeter measurements. Hence, continuously monitoring θw improves our understanding of liquid water percolation and retention in snow, which is highly relevant for several aspects of the cryosphere such as avalanche formation, catchment hydrology, and ice sheet mass balances.

Snow stratigraphy information is among other sources the key data for assessing avalanche danger—not only for dry snow but also for wet-snow conditions. Until now this information is obtained by traditional snow pit observations or more recently by applying more quantitative methods such as the snow micro-penetrometer or dielectric devices. All these methods are destructive and only provide a snap shot in time of snowpack evolution. We used an upward-looking ground-penetrating radar system (upGPR) to monitor snowpack evolution on a daily or, whenever necessary, hourly basis to obtain information on wet-snow properties. We focused on determining the volumetric liquid water content ( θ w ) by calculating the effective permittivity ( e eff ) of the wet snow above the radar antennas, the advance of a wetting front and the wet-snow stratigraphy. e eff was obtained using the signal velocity and snow depth recorded with nearby ultrasonic gauges; θ w was calculated with different mixing model approaches. Results were compared to in-situ measured permittivity, modelled wetting front advance and modelled and measured outflow at the bottom of the snowpack. The upGPR system clearly showed the advance of a wetting front and the arrival time was similar to the one recorded with a nearby lysimeter. Possibly weak wet layers with high liquid water content ( θ w > 6%) were detected within the radar signal by multiple reflections. However, determining the exact amount of liquid water for each layer separately is still a task for future research.