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

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

doi:https://doi.org/10.5194/tc-19-769-2025

Articles | Volume 19, issue 2

https://doi.org/10.5194/tc-19-769-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-19-769-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

Research article

|

20 Feb 2025

Research article |

| 20 Feb 2025

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

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

Supplement

https://doi.org/10.5194/tc-19-769-2025-supplement

Data sets

Improved snow darkening coefficient for large-scale albedo modelling with Crocus Manon Gaillard et al. https://doi.org/10.5281/zenodo.14194990

Global climatology of light-absorbing particle deposition on snow Manon Gaillard et al. https://doi.org/10.5281/zenodo.14195003

Model code and software

Code of the land surface scheme Soil Vegetation and Snow version 2 integrated in the MESH platform V. Vionnet et al. https://doi.org/10.5281/zenodo.14859640

Short summary

This study presents an efficient method to improve large-scale snow albedo simulations by considering the spatial variability in 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 10 global sites improved albedo predictions by 10 % on average and over 25 % in the Arctic. This method can enhance other snow models' predictions without complex simulations.