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Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.

Every year, numerous field teams travel to remote field locations on the Greenland ice sheet to carry out polar research, geologic exploration, and other commercial, military, strategic, and recreational activities. In this region, extreme weather can lead to decreased productivity, equipment failure, increased stress, unexpected logistical challenges, and, in the worst cases, a risk of physical injury and loss of life. Here we describe methods for calculating the probability of a “scienceable” day defined as a day when wind, temperature, snowfall, and sunlight conditions are conducive to sustained outdoor activity. Scienceable days have been calculated for six sites on the ice sheet of southern Greenland using meteorological station data between 1996-2016, and compared with indices of large scale atmospheric circulation patterns: the Greenland Blocking Index (GBI) and the North Atlantic Oscillation (NAO). Our findings show that the probability of a scienceable day between 2010 and 2016 in the Greenland Ice Sheet.'s accumulation zone was 46 ± 17% in March-May and 86 ± 11% in July-August on average. Decreases in scienceability due to lower temperatures at higher elevations are made up for by weaker katabatic winds, especially in the shoulder seasons. We also find a strong correlation between the probability of a scienceable day and GBI (R = 0.88, p < 0.001) resulting in a significant decrease in April scienceability since 1996. The methodology presented can help inform expedition planning, the setting of realistic field goals and managing expectations, and aid with accurate risk assessment in Greenland and other harsh, remote environments.

This work is funded by the Netherland Polar Programme, Netherlands Earth System Science Centre (NESSC), NSF OPP research grant 0732946, NERC/GEF grants NE/L006707/1, NE/L005409/1, NE/E012914/1, GEF loans 863, 890, 1028. The surface mass balance (SMB) of the Larsen C ice shelf (LCIS), Antarctica, is poorly constrained due to a dearth of in situ observations. Combining several geophysical techniques, we reconstruct spatial and temporal patterns of SMB over the LCIS. Continuous time series of snow height (2.5–6 years) at five locations allow for multi-year estimates of seasonal and annual SMB over the LCIS. There is high interannual variability in SMB as well as spatial variability: in the north, SMB is 0.40 ± 0.06 to 0.41 ± 0.04 m w.e. year−1, while farther south, SMB is up to 0.50 ± 0.05 m w.e. year−1. This difference between north and south is corroborated by winter snow accumulation derived from an airborne radar survey from 2009, which showed an average snow thickness of 0.34 m w.e. north of 66° S, and 0.40 m w.e. south of 68° S. Analysis of ground-penetrating radar from several field campaigns allows for a longer-term perspective of spatial variations in SMB: a particularly strong and coherent reflection horizon below 25–44 m of water-equivalent ice and firn is observed in radargrams collected across the shelf. We propose that this horizon was formed synchronously across the ice shelf. Combining snow height observations, ground and airborne radar, and SMB output from a regional climate model yields a gridded estimate of SMB over the LCIS. It confirms that SMB increases from north to south, overprinted by a gradient of increasing SMB to the west, modulated in the west by föhn-induced sublimation. Previous observations show a strong decrease in firn air content toward the west, which we attribute to spatial patterns of melt, refreezing, and densification rather than SMB. Publisher PDF

Snow avalanches are a natural hazard in mountainous areas which endanger roads, villages and human lives. To inform the public on the current avalanche situation, avalanche warning services regularly publish avalanche bulletins in winter. However, forecasting snow avalanches is very challenging. Currently, it is not possible to predict the exact timing, location or size of snow avalanches. Avalanche forecasters therefore estimate the degree of avalanche danger at the scale of a region, by linking point observations of the snowpack, consisting of observations of snow stratigraphy and snow instability, with past and future weather. While snowpack observations are very time consuming and thus rather scarce, numerical snow cover models can considerably increase the spatial and temporal resolution of such data, especially if they provide information on snow instability. In our current understanding of avalanche formation, avalanche release is a fracture mechanical problem and snow instability is best understood in terms of failure initiation and crack propagation. Detailed snow cover models exists which can simulate snow stratigraphy, but snow instability information is partly missing or inaccurate. In view of applying snow cover models for avalanche forecasting, the aim of this thesis was to model spatially distributed snow instability. The snow cover model SNOWPACK simulates criteria for failure initiation and crack propagation for each snow layer, namely the stability index and the critical crack length. While the stability index had been validated with field observations and was related to the probability of skier triggering, this was not the case for the critical crack length, which was parameterized based on layer properties including density, shear strength and the elastic modulus. In a first step, we therefore validated the evolution of snow layer properties and the critical crack length in SNOWPACK with novel field measurements. Daily measurements with the snow micro-penetrometer allowed for direct comparison with SNOWPACK. Our results showed that the evolution of layer density was fairly well captured by the model, especially the first two months after deposition. For the validation of the critical crack length, we used results from the propagation saw test performed on a weekly basis over three winter seasons. A comparison to SNOWPACK highlighted some discrepancies, and we thus refined the parameterization with a fit factor depending on weak layer density and grain size. With the refined parameterization, spatially distributed modeling of snow instability in terms of failure initiation and crack propagation became tangible. Spatially distributed snow cover simulations require interpolation and downscaling of meteorological data, which may introduce uncertainties. How these uncertainties impact modeled snow stability remained mostly unknown. For the first time, we therefore investigated the sensitivity of modeled snow instability to meteorological input uncertainty with a global sensitivity analysis. Early in the season, during the period of weak layer formation, modeled instability metrics were mostly sensitive to air temperature and precipitation. After weak layer burial, during the period of slab formation, modeled instability metrics were mostly sensitive to precipitation. These results highlighted that accurate spatial snow depth distributions are required to obtain realistic snow instability patterns. In a last step, we used the distributed snow cover model Alpine3D to simulate snow instability for the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to a grid with 100 m resolution. Precipitation was scaled with highly resolved snow depth measurements from airborne laser scanning to account for realistic snow depth patterns. Modeled snow instability patterns were plausible, e.g south-facing slopes stabilized faster in spring. However, instability metrics were lower for south-facing slopes during the winter months, which was not in line with the forecasted avalanche danger level. While spatial patterns of modeled snow instability still have to be validated, spatially distributed snow cover modeling can greatly improve numerical avalanche forecasting. However, given a lack of accurate input data, simple virtual slopes instead of highly resolved modeling approaches is probably enough at this point in time.