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State-of-the-art numerical snowpack models essentially rely on observationaldata for initialization, forcing, parametrization, and validation. Such data areavailable in increasing amounts, but the propagation of related uncertaintiesin simulation results has received rather limited attention so far. Dependingon their complexity, even small errors can have a profound effect onsimulations, which dilutes our confidence in the results. This paper aims atquantification of the overall and fractional contributions of some archetypicalmeasurement uncertainties on snowpack simulations in arctic environments.  Thesensitivity pattern is studied at two sites representing the accumulation andablation area of the Kongsvegen glacier (Svalbard), using the snowpack schemeCrocus. The contribution of measurement errors on model output variance, eitheralone or by interaction, is decomposed using global sensitivity analysis. Thisallows one to investigate the temporal evolution of the fractionalcontribution of different factors on key model output metrics, which provides amore detailed understanding of the model's sensitivity pattern. The analysisdemonstrates that the specified uncertainties in precipitation and long-waveradiation forcings had a strong influence on the calculated surface-heightchanges and surface-energy balance components.  The model output sensitivitypatterns also revealed some characteristic seasonal imprints. For example,uncertainties in long-wave radiation affect the calculated surface-energybalance throughout the year at both study sites,while precipitation exerted the most influence during the winter and at theupper site. Such findings are valuable for identifying critical parameters andimproving their measurement; correspondingly, updated simulations may shednew light on the confidence of results from snow or glacier mass- and energy-balance models. This is relevant for many applications, for example in the fields ofavalanche and hydrological forecasting

Assessing the uncertainty of glacier mass-balance simulations in the European Arctic based on variance decomposition