Preprints
https://doi.org/10.5194/egusphere-2023-2604
https://doi.org/10.5194/egusphere-2023-2604
23 Nov 2023
 | 23 Nov 2023

Exploring the sensitivity to precipitation, blowing snow, and horizontal resolution of the spatial distribution of simulated snow cover

Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamp-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont

Abstract. Accurate snow cover modeling is a high stake for mountain regions. Alpine snow evolution and spatial variability result from a multitude of complex processes including interactions between wind and snow. The SnowPappus blowing snow model was designed to add blowing snow modeling capabilities to the SURFEX/Crocus simulation system for applications across large spatial and temporal extents. This paper presents the very first spatialized evaluation of this simulation system over a 902 km2 domain in the French Alps. Here we compare snow cover simulations to the spatial distribution of snow height obtained from Pleiades satellites stereo-imagery and to Snow Melt-Out Dates from Sentinel-2 time series over three snow seasons. We analyzed the sensitivity of the simulations to three different precipitation datasets and two horizontal resolutions. The evaluations are presented as a function of elevation and landform types. The results show that the SnowPappus model forced with high-resolution wind fields enhances the snow cover spatial variability at high elevations allowing a better agreement with observations above 2500 m and near peaks and ridges. Model improvements are not obvious at low to medium altitudes where precipitation errors are the prevailing uncertainty. Our study illustrates the necessity to consider error contributions from blowing snow, precipitation forcings, and unresolved subgrid variability for robust evaluations of spatialized snow simulations. Despite the significant effect of the unresolved spatial scales of snow transport, 250 m horizontal resolution snow simulations using SnowPappus are found to be a promising avenue for large-scale modeling of alpine snowpacks.

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Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamp-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2604', Anonymous Referee #1, 12 Jan 2024
    • AC2: 'Reply on RC1', Ange Haddjeri, 27 Mar 2024
  • RC2: 'Comment on egusphere-2023-2604', Anonymous Referee #2, 12 Jan 2024
    • AC1: 'Reply on RC2', Ange Haddjeri, 27 Mar 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2604', Anonymous Referee #1, 12 Jan 2024
    • AC2: 'Reply on RC1', Ange Haddjeri, 27 Mar 2024
  • RC2: 'Comment on egusphere-2023-2604', Anonymous Referee #2, 12 Jan 2024
    • AC1: 'Reply on RC2', Ange Haddjeri, 27 Mar 2024
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamp-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamp-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont

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Latest update: 22 Jun 2024
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Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations by disentangling error contributions between blowing snow, precipitation forcings and unresolved subgrid variability. We evaluated simulated snow cover against snow depths from Pléiades stereo-imagery and Snow Melt-Out Dates from Sentinel 2. The simulation of snow transport enhances the snow spatial variance at high elevations while precipitation biases are the prevailing error source in other areas.