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.

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Abstract. Perennial snow, or firn, covers 80 % of the Greenland ice sheet and has the capacity to retain surface meltwater, influencing the ice sheet mass balance and contribution to sea-level rise. Multilayer firn models are traditionally used to simulate firn processes and estimate meltwater retention. We present, intercompare and evaluate outputs from nine firn models at four sites that represent the ice sheet's dry snow, percolation, ice slab and firn aquifer areas. The models are forced by mass and energy fluxes derived from automatic weather stations and compared to firn density, temperature and meltwater percolation depth observations. Models agree relatively well at the dry-snow site while elsewhere their meltwater infiltration schemes lead to marked differences in simulated firn characteristics. Models accounting for deep meltwater percolation overestimate percolation depth and firn temperature at the percolation and ice slab sites but accurately simulate recharge of the firn aquifer. Models using Darcy's law and bucket schemes compare favorably to observed firn temperature and meltwater percolation depth at the percolation site, but only the Darcy models accurately simulate firn temperature and percolation at the ice slab site. Despite good performance at certain locations, no single model currently simulates meltwater infiltration adequately at all sites. The model spread in estimated meltwater retention and runoff increases with increasing meltwater input. The highest runoff was calculated at the KAN_U site in 2012, when average total runoff across models (±2σ) was 353±610 mm w.e. (water equivalent), about 27±48 % of the surface meltwater input. We identify potential causes for the model spread and the mismatch with observations and provide recommendations for future model development and firn investigation.

We present an inverse modeling approach to reconstruct annual accumulation patterns from ground-penetrating radar (GPR) data. A coupled surface energy balance-snow model simulates surface melt and the evolution of subsurface density, temperature, and water content. The inverse problem consists of iteratively calibrating accumulation, serving as input for the model, by finding a match between modeled and observed radar travel times. The inverse method is applied to a 16 km GPR transect on Nordenskiöldbreen, Svalbard, yielding annual accumulation patterns for [2007][2008][2009][2010][2011][2012]. Accumulation patterns with a mean of 0.75 meter water equivalent (mwe) a −1 contain substantial spatial variability, with a mean annual standard deviation of 0.17 mwe a −1 , and show only partial consistency from year to year. In contrast to traditional methods, accounting for melt water percolation, refreezing, and runoff facilitates accurate accumulation reconstruction in areas with substantial melt. Additionally, accounting for horizontal density variability along the transect is shown to reduce spatial variability in reconstructed accumulation, whereas incorporating irreducible water storage lowers accumulation estimates. Correlating accumulation to terrain characteristics in the dominant wind direction indicates a strong preference of snow deposition on leeward slopes, whereas weaker correlations are found with terrain curvature. Sensitivity experiments reveal a nonlinear response of the mass balance to accumulation changes. The related negative impact of small-scale accumulation variability on the mean net mass balance is quantified, yielding a negligible impact in the accumulation zone and a negative impact of −0.09 mwe a −1 in the ablation area.