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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-221
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-221
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  27 Aug 2020

27 Aug 2020

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This preprint is currently under review for the journal TC.

Fractional snow-covered area: Scale-independent peak of winter parameterization

Nora Helbig1, Yves Bühler1, Lucie Eberhard1, César Deschamps-Berger2,3, Simon Gascoin2, Marie Dumont3, Jesus Revuelto3,4, Jeff S. Deems5, and Tobias Jonas1 Nora Helbig et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Centre d’Etudes Spatiales de la Biosphère, UPS/CNRS/IRD/INRAE/CNES, Toulouse, France
  • 3Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, 38000 Grenoble, France
  • 4Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Zaragoza, Spain
  • 5National Snow and Ice Data Center, University of Colorado, Boulder, CO, USA

Abstract. The spatial distribution of snow in the mountains is significantly influenced through interactions of topography with wind, precipitation, shortwave and longwave radiation, and avalanches that may relocate the accumulated snow. One of the most crucial model parameters for various applications such as weather forecasts, climate predictions and in hydrological modeling is the fraction of the ground surface that is covered by snow, also called fractional snow-covered area (fSCA). While previous subgrid parameterizations for the spatial snow depth distribution and fSCA work well, performances were scale-dependent. Here, we were able to confirm a previously established empirical relationship of the peak of winter parameterization for the standard deviation of snow depth σ>sub>HS by evaluating it on 11 spatial snow depth data sets from 7 different geographic regions and snow climates with resolutions ranging from 0.1 m to 3 m. Enhanced performance (mean percentage errors (MPE) decreased by 25 %) across all spatial scales ≥ 200 m was achieved by recalibrating and introducing a scale-dependency in the dominant scaling variables. Scale-dependent MPEs vary between −7 % and 3 % for σ>sub>HS and between 0 % and 1 % for fSCA. A scale- as well as region-dependent evaluation revealed that for the majority of the regions the MPEs mostly lie between ±10 % for σ>sub>HS and between −1 % and 1.5 % for fSCA. This suggests that the new parameterizations perform similarly well in most geographical regions.

Nora Helbig et al.

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Short summary
The spatial variability of snow depth in mountains is driven by topography-wind, -precipitation and -radiation-interactions. In applications such as weather, climate and hydrological predictions this is accounted for by the fractional snow-covered area describing the fraction of the ground surface that is covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
The spatial variability of snow depth in mountains is driven by topography-wind, -precipitation...
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