TC The Cryosphere TC The Cryosphere 1994-0424 Copernicus Publications Göttingen, Germany 10.5194/tc-2-191-2008 Exploring uncertainty in glacier mass balance modelling with Monte Carlo simulation Machguth H. 1 Purves R. S. 1 Oerlemans J. 2 Hoelzle M. 1 Paul F. 1 Department of Geography, University of Zurich, Zurich, Switzerland Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, The Netherlands 08 12 2008 2 2 191 204 Copyright: © 2008 H. Machguth et al. 2008 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://tc.copernicus.org/articles/2/191/2008/tc-2-191-2008.html The full text article is available as a PDF file from https://tc.copernicus.org/articles/2/191/2008/tc-2-191-2008.pdf

By means of Monte Carlo simulations we calculated uncertainty in modelled cumulative mass balance over 400 days at one particular point on the tongue of Morteratsch Glacier, Switzerland, using a glacier energy balance model of intermediate complexity. Before uncertainty assessment, the model was tuned to observed mass balance for the investigated time period and its robustness was tested by comparing observed and modelled mass balance over 11 years, yielding very small deviations. Both systematic and random uncertainties are assigned to twelve input parameters and their respective values estimated from the literature or from available meteorological data sets. The calculated overall uncertainty in the model output is dominated by systematic errors and amounts to 0.7 m w.e. or approximately 10% of total melt over the investigated time span. In order to provide a first order estimate on variability in uncertainty depending on the quality of input data, we conducted a further experiment, calculating overall uncertainty for different levels of uncertainty in measured global radiation and air temperature. Our results show that the output of a well calibrated model is subject to considerable uncertainties, in particular when applied for extrapolation in time and space where systematic errors are likely to be an important issue.

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