Currently in revision
Menard, C. B., Rasmus, S., Merkouriadi, I., Balsamo, G., Bartsch, A., Derksen, C., Domine, F., Dumont, M., Ehrich, D., Essery, R., Forbes, B. C., Krinner, G., Lawrence, D., Liston, G., Matthes, H., Rutter, N., Sandells, M., Schneebeli, M., and Stark, S.: Exploring the decision-making process in model development: focus on the Arctic snowpack, EGUsphere [preprint],
https://doi.org/10.5194/egusphere-2023-2926, 2024.
the big question of arctic snowpack modelling ?
Harris Stuart, R., Landais, A., Arnaud, L., Buizert, C., Capron, E., Dumont, M., Libois, Q., Mulvaney, R., Orsi, A., Picard, G., Prié, F., Severinghaus, J., Stenni, B., and Martinerie, P.: Towards an understanding of the controls on δO2/N2 variability in ice core records, EGUsphere [preprint],
https://doi.org/10.5194/egusphere-2023-2585, 2023.
How does surface meteorological conditions influence firn physical properties ?
2024
Fourteau, K., Brondex, J., Brun, F., and Dumont, M.: A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers, Geosci. Model Dev., 17, 1903–1929,
https://doi.org/10.5194/gmd-17-1903-2024, 2024.
How to couple accurately the snow surface and the inner part of the snowpack ?
2023
Brondex, J., Fourteau, K., Dumont, M., Hagenmuller, P., Calonne, N., Tuzet, F., and Löwe, H.: A finite-element framework to explore the numerical solution of the coupled problem of heat conduction, water vapor diffusion and settlement in dry snow (IvoriFEM v0.1.0), Geosci. Model Dev., 16, 7075–7106,
https://doi.org/10.5194/gmd-16-7075-2023, 2023
How to couple accurately heat and water vapor diffusion in dry snow ?
Sicart, J. E., Ramseyer, V., Picard, G., Arnaud, L., Coulaud, C., Freche, G., Soubeyrand, D., Lejeune, Y.,
Dumont, M., Gouttevin, I., Le Gac, E., Berger, F., Monnet, J.-M., Borgniet, L., Mermin, É., Rutter, N., Webster, C., and Essery, R. : Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude) : the Snow Under Forest (SnoUF) field campaign data set, Earth Syst. Sci. Data, 15, 5121–5133,
https://doi.org/10.5194/essd-15-5121-2023, 2023.
Ezzedine, J.A., Uwizeye, C., Si Larbi, G. et al. Adaptive traits of cysts of the snow alga Sanguina nivaloides unveiled by 3D subcellular imaging. Nat Commun 14, 7500 (2023).
https://doi.org/10.1038/s41467-023-43030-7Brun, F., King, O., Réveillet, M., Amory, C., Planchot, A., Berthier, E., Dehecq, A., Bolch, T.,
Fourteau, K.,
Brondex, J.,
Dumont, M., Mayer, C., Leinss, S., Hugonnet, R., and Wagnon, P.: Everest South Col Glacier did not thin during the period 1984–2017, The Cryosphere, 17, 3251–3268,
https://doi.org/10.5194/tc-17-3251-2023, 2023.
On the impact of surface energy balance modelling
How does snow look like for sunlight ?
Dumont, M., Gascoin, S.,
Réveillet, M., Voisin, D.,
Tuzet, F., Arnaud, L., Bonnefoy, M., Bacardit Peñarroya, M., Carmagnola, C., Deguine, A., Diacre, A., Dürr, L., Evrard, O., Fontaine, F., Frankl, A., Fructus, M., Gandois, L., Gouttevin, I., Gherab, A.,
Hagenmuller, P., Hansson, S., Herbin, H., Josse, B., Jourdain, B., Lefevre, I., Le Roux, G., Libois, Q., Liger, L., Morin, S., Petitprez, D., Robledano, A., Schneebeli, M., Salze, P., Six, D., Thibert, E., Trachsel, J., Vernay, M., Viallon-Galinier, L., and Voiron, C.: Spatial variability of Saharan dust deposition revealed through a citizen science campaign, Earth Syst. Sci. Data, 15, 3075–3094,
https://doi.org/10.5194/essd-15-3075-2023, 2023.
On the benefits of citizen science to document the spatial variability of Saharan dust deposition
Dick, O., Viallon-Galinier, L.,
Tuzet, F.,
Hagenmuller, P., Fructus, M., Reuter, B.,
Lafaysse, M., and
Dumont, M. : Can Saharan dust deposition impact snowpack stability in the French Alps ?, The Cryosphere, The Cryosphere, 17, 1755–1773,
https://doi.org/10.5194/tc-17-1755-2023, 2023.
Is there a link between snow avalanche danger and Saharan dust deposition ?
How challenging is it to represente arctic snowpack with snow models ?
2022
Or how dust and black carbon have modified the evolution of the snow cover in the French Mountain ranges over the past 40 years.
Link to the video abstract : https://youtu.be/f_F8Ia2q7mo
Baladima, F., Thomas, J. L., Voisin, D., Dumont, M., Junquas, C., Kumar, R., et al. (2022). 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, 127, e2021JD035745. https://doi. org/10.1029/2021JD035745
What is the added value of using high resolution models for simulating dust deposition in mountains areas ?
Cluzet, B.,
Lafaysse, M., Deschamps-Berger, C., Vernay, M., and
Dumont, M. : Propagating information from snow observations with CrocO ensemble data assimilation system : a 10-years case study over a snow depth observation network, The Cryosphere, 16, 1281–1298,
https://doi.org/10.5194/tc-16-1281-2022, 2022.
How to spatially propagate the information of a punctal observation in a snow data assimilation system ?
Deschamps‐Berger, C., Cluzet, B.,
Dumont, M.,
Lafaysse, M., Berthier, E., Fanise, P. and Gascoin, S., Improving the spatial distribution of snow cover simulations by assimilation of satellite stereoscopic imagery. Water Resources Research,
https://doi.org/10.1029/2021WR030271
Is the assimilation of high resolution snow depth maps derived from satellite date beneficial for snow simulation ?
2021
Barrou Dumont, Z., Gascoin, S., Hagolle, O., Ablain, M., Jugier, R., Salgues, G., Marti, F., Dupuis, A.,
Dumont, M., and Morin, S.: Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service, The Cryosphere, 15, 4975–4980,
https://doi.org/10.5194/tc-15-4975-2021, 2021.
On the accuracy of the snow cover area detection from satellite data
Royer A, Picard G, Vargel C, Langlois A, Gouttevin I and Dumont M (2021) Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures. Front. Earth Sci. 9:685140. doi: 10.3389/feart.2021.685140
Challenges and workarounds to simulate arctic snow cover
Veillon, F.,
Dumont, M., Amory, C., and Fructus, M.: A versatile method for computing optimized snow albedo from spectrally fixed radiative variables: VALHALLA v1.0, Geosci. Model Dev., 14, 7329–7343,
https://doi.org/10.5194/gmd-14-7329-2021, 2021.
The color of snow, more rigourously snow albedo, is a key climate variable since it drives the amount of solar energy absorbed by the Earth surface. The albedo of snow has subtle spectral variations with the snow microstructure, the impurities content and the atmospheric properties. In climate and land surface model, the accurate representation of these subtle variations is crucial to represent climate feedback. However, this is generally too demanding in term of computing time for climate simulations. Here we present a method to drastically reduce the computation time for snow albedo while keeping an high accuracy.
Dumont, M., Flin, F., Malinka, A., Brissaud, O.,
Hagenmuller, P., Lapalus, P., Lesaffre, B., Dufour, A.,
Calonne, N., Rolland du Roscoat, S., and Ando, E. : Experimental and model-based investigation of the links between snow bidirectional reflectance and snow microstructure, The Cryosphere, 15, 3921–3948,
https://doi.org/10.5194/tc-15-3921-2021, 2021.
What are the main drivers of the changes in snow albedo as observed by satellite ? We know that the length scale of the snow microstructure, also referred as to grain size, is an important driver. Here we investigate the impact of the morphology beyond size, i.e. shape, on the variations of snow reflectance. We show that using the distribution of chord length in the snow microstructure provides an accurate simulation of the optical properties of snow.
