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Modeling an extreme dust deposition event to the French alpine seasonal snowpack in April 2018: Meteorological context and predictions of dust deposition

Abstract : Mineral dust is an important aerosol in the atmosphere and is known to reduce snow albedo upon deposition. Model predictions of dust deposition events in snow covered mountain regions are challenging due to the complexity of aerosol-cloud interactions and the specifics of mountain meteorological systems. We use a case study of dust deposition between 30 March and 5 April 2018 to the French alpine snowpack to study the processes that control dust deposition to the seasonal snowpack. To understand processes controlling dust transport and deposition to snow we use a combination of in situ observations at Col du Lautaret in the French Alps, satellite remote sensing, the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis global atmospheric composition, and the regional model WRF-Chem. Specifically, we investigate the role of increased model spatial resolution within WRF-chem in capturing mountain meteorology, precipitation, and predicted dust deposition. Regional model results are also compared to the reanalysis global CAMS products including aerosols in the atmosphere and predicted dust deposition fluxes. We conclude that predicted mountain meteorology (e.g. precipitation) is better with increased model resolution (3 x 3 km resolution WRF-Chem domain). This improved meteorology has significant impacts on predicted dry and wet dust deposition to the alpine snowpack. Dry deposition is important in the western part of the French Alps at low altitudes, while wet deposition dominates over the complex higher altitude mountain terrain.
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Submitted on : Friday, June 3, 2022 - 9:36:41 AM
Last modification on : Friday, August 5, 2022 - 12:00:29 PM

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Foteini Baladima, Jennie L. Thomas, Didier Voisin, Marie Dumont, Clementine Junquas, et al.. Modeling an extreme dust deposition event to the French alpine seasonal snowpack in April 2018: Meteorological context and predictions of dust deposition. Journal of Geophysical Research: Atmospheres, American Geophysical Union, 2022, 127 (8), pp.e2021JD035745. ⟨10.1029/2021JD035745⟩. ⟨insu-03617771⟩

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