Preprints
https://doi.org/10.5194/egusphere-2024-1795
https://doi.org/10.5194/egusphere-2024-1795
15 Jul 2024
 | 15 Jul 2024

Improving large-scale snow albedo modelling using a climatology of light-absorbing particle deposition

Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux

Abstract. Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. The explicit simulation of these effects with advanced snow radiative transfer models is generally associated with a large computational cost. Consequently, many albedo schemes used in snowpack models still rely on empirical parameterizations that do not account for the spatial variability of LAP deposition. In this study, a new strategy of intermediate complexity that includes the effects of spatially variable LAP deposition on snow albedo is tested with the snowpack model Crocus. It relies on an optimization of the parameter that controls the evolution of snow albedo in the visible range. Optimized values for multi-year snow albedo simulations with Crocus were generated at ten reference experimental sites spanning a large variety of climates across the world. A regression was then established between these optimal values and climatological deposition of LAP on snow at the location of the experimental sites extracted from a global climatology developed in this study. This regression was finally combined with the global climatology to obtain an LAP-informed and spatially variable parameter for the Crocus albedo parameterization. The revised parameter improved snow albedo simulations on average by 10 % with the largest improvements found in the Arctic (more than 25 %). This methodology can be applied to other land surface models using the global climatology of LAP deposition on snow developed for this study.

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Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1795', Anonymous Referee #1, 18 Aug 2024
    • AC1: 'Reply on RC1', Vincent Vionnet, 18 Oct 2024
  • RC2: 'Comment on egusphere-2024-1795', Anonymous Referee #2, 25 Aug 2024
    • AC2: 'Reply on RC2', Vincent Vionnet, 18 Oct 2024
Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux

Data sets

Improved snow ageing parameter for large-scale albedo modelling with Crocus Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux https://zenodo.org/doi/10.5281/zenodo.11554925

Global climatology of light-absorbing particle deposition on snow Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux https://zenodo.org/doi/10.5281/zenodo.11554782

Manon Gaillard, Vincent Vionnet, Matthieu Lafaysse, Marie Dumont, and Paul Ginoux

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Short summary
This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability of light-absorbing particles (LAPs) like black carbon and dust. A global climatology of LAP deposition was created and used to optimize a parameter in the Crocus snow model. Testing at ten global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can also enhance other snow models' predictions without complex simulations.