Preprints
https://doi.org/10.5194/tc-2021-9
https://doi.org/10.5194/tc-2021-9

  26 Jan 2021

26 Jan 2021

Review status: a revised version of this preprint is currently under review for the journal TC.

Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile

Annelies Voordendag1,a, Marion Réveillet2,b, Shelley MacDonell2, and Stef Lhermitte1 Annelies Voordendag et al.
  • 1Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
  • 2Centro de Estudios Avanzados en Zonas Áridas (CEAZA), ULS-Campus Andrés Bello, Raúl Britan 1305, La Serena, Chile
  • anow at: Department of Atmospheric and Cryospheric Sciences (ACINN), University of Innsbruck, Innsbruck, Austria
  • bnow at: Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Etudes de la Neige, 38100 Grenoble, France

Abstract. Physically-based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, issues still arise, especially in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point, over one snow season. Their sensitivity relative i) to physical calibration for albedo and fresh snow density and ii) to forcing perturbation is evaluated based on ensemble approaches. Results indicate larger uncertainty depending on the model calibration than between the two models (even though the significant differences in their physical complexity). We also confirm the importance of albedo parameterization, even though ablation is driven by sublimation. SnowModel is particularly sensitive to this choice as it strongly affects both the sublimation and the melt rates. However, the day of snow-free snow surface is not sensitive to the parameterization as it only varies every eight days. The albedo parameterization of SNOWPACK has stronger consequences on melt at the end of the season leading to a date difference of the end of the season of 41 days. However, despite these differences, the sublimation ratio ranges are in agreement for the two models: 42.7–63.5 % for SnowModel and 51.3 and 64.6 % for SNOWPACK, and are related to the albedo calibration choice for the two models. Finally, the sensitivity of both models to the forcing data was in the same order of magnitude and highly influenced by the precipitation uncertainties.

Annelies Voordendag et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-9', Anonymous Referee #1, 06 Mar 2021
    • AC1: 'Reply on RC1', Annelies Voordendag, 05 May 2021
  • RC2: 'Comment on tc-2021-9', Michael Lehning, 11 Mar 2021
    • AC2: 'Reply on RC2', Annelies Voordendag, 05 May 2021
  • RC3: 'Review', Anonymous Referee #3, 17 Mar 2021
    • AC3: 'Reply on RC3', Annelies Voordendag, 05 May 2021

Annelies Voordendag et al.

Annelies Voordendag et al.

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Short summary
The sensitivity of two snow models (SNOWPACK and SnowModel) to various parameterizations and atmospheric forcing perturbations is assessed in the semi-arid Andes of Chile in winter 2017. Models show that sublimation is the main driver of ablation during the snow season and that its relative contribution to total ablation is highly sensitive to the selected albedo parameterization. Atmospheric forcing perturbations also strongly impact model results mainly through precipitation uncertainties.