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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 1
The Cryosphere, 4, 35–51, 2010
https://doi.org/10.5194/tc-4-35-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
The Cryosphere, 4, 35–51, 2010
https://doi.org/10.5194/tc-4-35-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jan 2010

21 Jan 2010

Simulation of the specific surface area of snow using a one-dimensional physical snowpack model: implementation and evaluation for subarctic snow in Alaska

H.-W. Jacobi1, F. Domine1, W. R. Simpson2, T. A. Douglas3, and M. Sturm3 H.-W. Jacobi et al.
  • 1CNRS/Université Joseph Fourier – Grenoble 1, Laboratoire de Glaciologie et Géophysique de l'Environnement, St. Martin d'Hères, France
  • 2Geophysical Institute and Department of Chemistry and Biochemistry, University of Alaska, Fairbanks, Alaska
  • 3US Army Cold Regions Research and Engineering Laboratory, Fort Wainwright, Alaska

Abstract. The specific surface area (SSA) of the snow constitutes a powerful parameter to quantify the exchange of matter and energy between the snow and the atmosphere. However, currently no snow physics model can simulate the SSA. Therefore, two different types of empirical parameterizations of the specific surface area (SSA) of snow are implemented into the existing one-dimensional snow physics model CROCUS. The parameterizations are either based on diagnostic equations relating the SSA to parameters like snow type and density or on prognostic equations that describe the change of SSA depending on snow age, snowpack temperature, and the temperature gradient within the snowpack. Simulations with the upgraded CROCUS model were performed for a subarctic snowpack, for which an extensive data set including SSA measurements is available at Fairbanks, Alaska for the winter season 2003/2004. While a reasonable agreement between simulated and observed SSA values is obtained using both parameterizations, the model tends to overestimate the SSA. This overestimation is more pronounced using the diagnostic equations compared to the results of the prognostic equations. Parts of the SSA deviations using both parameterizations can be attributed to differences between simulated and observed snow heights, densities, and temperatures. Therefore, further sensitivity studies regarding the thermal budget of the snowpack were performed. They revealed that reducing the thermal conductivity of the snow or increasing the turbulent fluxes at the snow surfaces leads to a slight improvement of the simulated thermal budget of the snowpack compared to the observations. However, their impact on further simulated parameters like snow height and SSA remains small. Including additional physical processes in the snow model may have the potential to advance the simulations of the thermal budget of the snowpack and, thus, the SSA simulations.

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