A long-term dataset of climatic mass balance, snow conditions, and runoff in Svalbard (1957–2018)

Abstract

Abstract. The climate in Svalbard is undergoing amplified change compared to the global mean. This has major implications for runoff from glaciers and seasonal snow on land. We use a coupled energy balance–subsurface model, forced with downscaled regional climate model fields, and apply it to both glacier-covered and land areas in Svalbard. This generates a long-term (1957–2018) distributed dataset of climatic mass balance (CMB) for the glaciers, snow conditions, and runoff with a 1 km×1 km spatial and 3-hourly temporal resolution. Observational data including stake measurements, automatic weather station data, and subsurface data across Svalbard are used for model calibration and validation. We find a weakly positive mean net CMB (+0.09 m w.e. a−1) over the simulation period, which only fractionally compensates for mass loss through calving. Pronounced warming and a small precipitation increase lead to a spatial-mean negative net CMB trend (−0.06 m w.e. a−1 decade−1), and an increase in the equilibrium line altitude (ELA) by 17 m decade−1, with the largest changes in southern and central Svalbard. The retreating ELA in turn causes firn air volume to decrease by 4 % decade−1, which in combination with winter warming induces a substantial reduction of refreezing in both glacier-covered and land areas (average −4 % decade−1). A combination of increased melt and reduced refreezing causes glacier runoff (average 34.3 Gt a−1) to double over the simulation period, while discharge from land (average 10.6 Gt a−1) remains nearly unchanged. As a result, the relative contribution of land runoff to total runoff drops from 30 % to 20 % during 1957–2018. Seasonal snow on land and in glacier ablation zones is found to arrive later in autumn (+1.4 d decade−1), while no significant changes occurred on the date of snow disappearance in spring–summer. Altogether, the output of the simulation provides an extensive dataset that may be of use in a wide range of applications ranging from runoff modelling to ecosystem studies.

A 20-Year MODIS-Based Snow Cover Dataset for Svalbard and Its Link to Phenological Timing and Sea Ice Variability

“…Our results indicate that while the majority of the central and southern areas of Svalbard are completely snow-free on this date, there are nevertheless areas in the northernmost part of Spitsbergen and Nordaustlandet that remain partially snow covered, with >50% snow cover fraction for many years of the 20-year dataset, explaining why the average minimum snow cover fraction for the whole of Svalbard remains above zero. However, the time series of the land-averaged FSFD did not reveal any significant trend, which is in agreement with earlier modeling studies of seasonal snow disappearance on Svalbard [17,48]. On the other hand, studies of snow disappearance date over glaciated regions of Svalbard using QuikSCAT datasets have earlier reported on a general trend toward earlier snow disappearance [21], despite their time period of study being less than half that which was studied in this work.…”

Reconciling Svalbard Glacier Mass Balance

“…Therefore, the operational records are biased and are not representative for large parts of the mountainous archipelago. The CMB modeling studies make partial use of glaciological data from higher elevations and from eastern regions (Aas et al, 2016;Østby et al, 2017;Möller and Kohler, 2018;Van Pelt et al, 2019), not only to evaluate the performance of the CMB model but also to evaluate the downscaling of meteorological variables. In less rough topography, such as the smooth surface of the Austfonna ice cap, snow distribution on the surface is largely dominated by the spatial pattern of precipitation (Taurisano et al, 2007).…”

“…The availability of global atmospheric reanalysis data at improved spatial and temporal resolution, and improved consistency with available observations (e.g., Schuler and Østby, 2020), has sparked the application of gap-free meteorologically forced climatic mass balance models that cover the entire archipelago (e.g., Østby et al, 2017;Van Pelt et al, 2019). These models either directly incorporate local measurements or have been optimized to ensure agreement between simulated and observed values, and therefore play an important role in synthesizing the wealth of information that has become available.…”

Coastal Erosion Affecting Cultural Heritage in Svalbard. A Case Study in Hiorthhamn (Adventfjorden)—An Abandoned Mining Settlement

“…The present ongoing research focuses on modelling the velocity of glaciers and ice caps [10], land cover and ice-wedge polygon mapping [11,12], the surface morphology of fans [13], retrogressive thaw slumps triggering [14,15], coastal erosion [16], human impact [17], etc. One of the most exposed Arctic areas is Svalbard, which is experiencing amplified climate change when compared to the global average [18,19]. Svalbard's coastal area is under high pressure from natural [20] and in some area's anthropogenic changes [21,22].Studying the physical dynamics of coastal areas is a challenging task with a huge potential to be applied in future coastal management plans and sustainable development of coastal areas.…”