Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1297-2024
https://doi.org/10.5194/gmd-17-1297-2024
Model description paper
 | 
14 Feb 2024
Model description paper |  | 14 Feb 2024

SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme

Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus

Related authors

Simulating snow properties and Ku-band backscatter across the forest-tundra ecotone
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498,https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Saharan dust impacts on the surface mass balance of Argentière Glacier (French Alps)
Léon Roussel, Marie Dumont, Marion Réveillet, Delphine Six, Marin Kneib, Pierre Nabat, Kevin Fourteau, Diego Monteiro, Simon Gascoin, Emmanuel Thibert, Antoine Rabatel, Jean-Emmanuel Sicart, Mylène Bonnefoy, Luc Piard, Olivier Laarman, Bruno Jourdain, Mathieu Fructus, Matthieu Vernay, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1741,https://doi.org/10.5194/egusphere-2025-1741, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Radar-based high-resolution ensemble precipitation analyses over the French Alps
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
Atmos. Meas. Tech., 18, 1731–1755, https://doi.org/10.5194/amt-18-1731-2025,https://doi.org/10.5194/amt-18-1731-2025, 2025
Short summary
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
The Cryosphere, 19, 769–792, https://doi.org/10.5194/tc-19-769-2025,https://doi.org/10.5194/tc-19-769-2025, 2025
Short summary
Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021)
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-602,https://doi.org/10.5194/essd-2024-602, 2025
Revised manuscript accepted for ESSD
Short summary

Related subject area

Cryosphere
Coupling framework (1.0) for the Úa (2023b) ice sheet model and the FESOM-1.4 z-coordinate ocean model in an Antarctic domain
Ole Richter, Ralph Timmermann, G. Hilmar Gudmundsson, and Jan De Rydt
Geosci. Model Dev., 18, 2945–2960, https://doi.org/10.5194/gmd-18-2945-2025,https://doi.org/10.5194/gmd-18-2945-2025, 2025
Short summary
A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)
Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Troels Petersen, and Sebastiano Vascon
Geosci. Model Dev., 18, 2545–2568, https://doi.org/10.5194/gmd-18-2545-2025,https://doi.org/10.5194/gmd-18-2545-2025, 2025
Short summary
Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)
Jan Svoboda, Marc Ruesch, David Liechti, Corinne Jones, Michele Volpi, Michael Zehnder, and Jürg Schweizer
Geosci. Model Dev., 18, 1829–1849, https://doi.org/10.5194/gmd-18-1829-2025,https://doi.org/10.5194/gmd-18-1829-2025, 2025
Short summary
Quantitative sub-ice and marine tracing of Antarctic sediment provenance (TASP v1.0)
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev., 18, 1673–1708, https://doi.org/10.5194/gmd-18-1673-2025,https://doi.org/10.5194/gmd-18-1673-2025, 2025
Short summary
Tuning parameters of a sea ice model using machine learning
Anton Korosov, Yue Ying, and Einar Ólason
Geosci. Model Dev., 18, 885–904, https://doi.org/10.5194/gmd-18-885-2025,https://doi.org/10.5194/gmd-18-885-2025, 2025
Short summary

Cited articles

Aksamit, N. O. and Pomeroy, J. W.: The Effect of Coherent Structures in the Atmospheric Surface Layer on Blowing-Snow Transport, Bound.-Lay. Meteorol., 167, 211–233, https://doi.org/10.1007/s10546-017-0318-2, 2017. a
Amory, C., Kittel, C., Le Toumelin, L., Agosta, C., Delhasse, A., Favier, V., and Fettweis, X.: Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica, Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, 2021. a, b, c, d
Baba, M. W., Gascoin, S., Kinnard, C., Marchane, A., and Hanich, L.: Effect of Digital Elevation Model Resolution on the Simulation of the Snow Cover Evolution in the High Atlas, Water Resour. Res., 55, 5360–5378, https://doi.org/10.1029/2018wr023789, 2019. a
Baron, M.: Supplementary material to “SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme”: Pleiades snow depth maps analysis, Zenodo [data set], https://doi.org/10.5281/zenodo.10204743, 2023. a
Baron, M., Haddjeri, A., Lafaysse, M., le Toumelin, L., Vionnet, V., and Fructus, M.: Supplementary material to “SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme”, user manual, Zenodo [code], https://doi.org/10.5281/zenodo.7681340, 2023a. a
Download
Short summary
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
Share