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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 12
Geosci. Model Dev., 8, 3911-3928, 2015
https://doi.org/10.5194/gmd-8-3911-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 3911-3928, 2015
https://doi.org/10.5194/gmd-8-3911-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 10 Dec 2015

Methods for assessment of models | 10 Dec 2015

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

T. Sauter1,2 and F. Obleitner1 T. Sauter and F. Obleitner
  • 1Institute of Meteorology and Geophysics, University of Innsbruck, Innsbruck, Austria
  • 2Institute of Geography, University of Erlangen-Nürnberg, Erlangen, Germany

Abstract. State-of-the-art numerical snowpack models essentially rely on observational data for initialization, forcing, parametrization, and validation. Such data are available in increasing amounts, but the propagation of related uncertainties in simulation results has received rather limited attention so far. Depending on their complexity, even small errors can have a profound effect on simulations, which dilutes our confidence in the results. This paper aims at quantification of the overall and fractional contributions of some archetypical measurement uncertainties on snowpack simulations in arctic environments. The sensitivity pattern is studied at two sites representing the accumulation and ablation area of the Kongsvegen glacier (Svalbard), using the snowpack scheme Crocus. The contribution of measurement errors on model output variance, either alone or by interaction, is decomposed using global sensitivity analysis. This allows one to investigate the temporal evolution of the fractional contribution of different factors on key model output metrics, which provides a more detailed understanding of the model's sensitivity pattern. The analysis demonstrates that the specified uncertainties in precipitation and long-wave radiation forcings had a strong influence on the calculated surface-height changes and surface-energy balance components. The model output sensitivity patterns also revealed some characteristic seasonal imprints. For example, uncertainties in long-wave radiation affect the calculated surface-energy balance throughout the year at both study sites, while precipitation exerted the most influence during the winter and at the upper site. Such findings are valuable for identifying critical parameters and improving their measurement; correspondingly, updated simulations may shed new 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 of avalanche and hydrological forecasting.

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Even small errors in observational data can have a profound effect on snowpack simulations, which dilutes our confidence in the results. This paper aims at quantification of the overall contributions of these errors on the uncertainty of snowpack simulations in Arctic environments. The analysis demonstrates that the uncertainties in the data can have a profound impact on glacier mass balance estimates.
Even small errors in observational data can have a profound effect on snowpack simulations,...
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