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Calculating distributed glacier mass balance for the Swiss Alps from regional climate model output: A methodical description and interpretation of the results

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Kotlarski,  S.
The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

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Citation

Machguth, H., Paul, F., Kotlarski, S., & Hoelzle, M. (2009). Calculating distributed glacier mass balance for the Swiss Alps from regional climate model output: A methodical description and interpretation of the results. Journal of Geophysical Research - Atmospheres, 114: D19106. doi:10.1029/2009JD011775.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0011-F795-F
Abstract
This study aims at giving a methodical description of the use of gridded output from a regional climate model (RCM) for the calculation of glacier mass balance distribution for the perimeter of the Swiss Alps. The mass balance model runs at daily steps and 100 m spatial resolution, while the regional model (REMO) RCM provides daily grids (∼18 km resolution) of dynamically downscaled reanalysis data. A combination of interpolation techniques and simple subgrid parameterizations is applied to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation, and precipitation. Interpolation schemes are a key element and thus we test different interpolators. For validation, computed mass balances are compared to stake measurements and time series (1979–2003) of observed mass balance. The meteorological input fields are compared to measurements at weather stations. The applied inverse distance weighting introduces systematic biases due to spatial autocorrelation, whereas thin plate splines preserve the characteristics of the RCM output. While summer melt at point locations on several glaciers is well reproduced by the model, accumulation is mostly underestimated. These systematic shifts are correlated to biases of the meteorological input fields. Time series of mass balance obtained from the model run agree well with observed time series. We conclude that the gap in spatial resolution is not a major drawback, given that interpolators and parameterizations are selected upon detailed considerations. Biases in RCM precipitation are a major source for the observed underestimations in mass balance and have to be corrected prior to operational use of the presented approach