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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 3
The Cryosphere, 6, 539–552, 2012
https://doi.org/10.5194/tc-6-539-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 6, 539–552, 2012
https://doi.org/10.5194/tc-6-539-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 May 2012

Research article | 21 May 2012

Sensitivity of a distributed temperature-radiation index melt model based on AWS observations and surface energy balance fluxes, Hurd Peninsula glaciers, Livingston Island, Antarctica

U. Y. Jonsell1,*, F. J. Navarro1, M. Bañón2, J. J. Lapazaran1, and J. Otero1 U. Y. Jonsell et al.
  • 1Departamento de Matemática Aplicada, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense, 30, 28040 Madrid, Spain
  • 2Observatorio Meteorológico de Alicante, Agencia Estatal de Meteorología (AEMET), C/ Regidor Ocaña, 26, 03011 Alicante, Spain
  • *now at: Swedish Polar Research Secretariat. Box 50003, 104 05 Stockholm, Sweden

Abstract. We use an automatic weather station and surface mass balance dataset spanning four melt seasons collected on Hurd Peninsula Glaciers, South Shetland Islands, to investigate the point surface energy balance, to determine the absolute and relative contribution of the various energy fluxes acting on the glacier surface and to estimate the sensitivity of melt to ambient temperature changes. Long-wave incoming radiation is the main energy source for melt, while short-wave radiation is the most important flux controlling the variation of both seasonal and daily mean surface energy balance. Short-wave and long-wave radiation fluxes do, in general, balance each other, resulting in a high correspondence between daily mean net radiation flux and available melt energy flux. We calibrate a distributed melt model driven by air temperature and an expression for the incoming short-wave radiation. The model is calibrated with the data from one of the melt seasons and validated with the data of the three remaining seasons. The model results deviate at most 140 mm w.e. from the corresponding observations using the glaciological method. The model is very sensitive to changes in ambient temperature: a 0.5 °C increase results in 56 % higher melt rates.

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