Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1297-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1297-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Feb 2024
Model description paper |
| 14 Feb 2024
| 14 Feb 2024
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
Matthieu Baron
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Grenoble, France
Ange Haddjeri
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Matthieu Lafaysse
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Louis Le Toumelin
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
Vincent Vionnet
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Mathieu Fructus
Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, Grenoble, France
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Audrey Goutard, Marion Réveillet, Fanny Brun, Delphine Six, Kevin Fourteau, Charles Amory, Xavier Fettweis, Mathieu Fructus, Arbindra Khadka, and Matthieu Lafaysse
EGUsphere, https://doi.org/10.5194/egusphere-2025-2947, https://doi.org/10.5194/egusphere-2025-2947, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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A new scheme has been developed in the SURFEX/ISBA-Crocus model, to consider the impact of liquid water dynamics on bare ice, including albedo feedback and refreezing. When applied to the Mera Glacier in Nepal, the model reveals strong seasonal effects on the energy and mass balance, with increased melting in dry seasons and significant refreezing during the monsoon. This development improves mass balance modeling under increasing rainfall and bare ice exposure due to climate warming.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
The Cryosphere, 19, 2949–2962, https://doi.org/10.5194/tc-19-2949-2025, https://doi.org/10.5194/tc-19-2949-2025, 2025
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Measuring snow mass from radar measurements is possible with information on snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layers yielding richer information but at increased computational cost. Here, we show the capabilities of a new method for simplifying a complex snowpack while preserving the scattering behavior of the snowpack and conserving its mass.
Colleen Mortimer and Vincent Vionnet
Earth Syst. Sci. Data, 17, 3619–3640, https://doi.org/10.5194/essd-17-3619-2025, https://doi.org/10.5194/essd-17-3619-2025, 2025
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In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management. NorSWE is a dataset of in situ SWE observations covering North America, Norway, Finland, Switzerland, Russia, and Nepal over the period 1979–2021. It includes more than 11.5 million observations from more than 10 000 different locations compiled from nine different sources. Snow depth and derived bulk snow density are included when available.
Vincent Vionnet, Nicolas Romain Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Zacharie Barrou Dumont, Simon Gascoin, Jordi Inglada, Andreas Dietz, Jonas Köhler, Matthieu Lafaysse, Diego Monteiro, Carlo Carmagnola, Arthur Bayle, Jean-Pierre Dedieu, Olivier Hagolle, and Philippe Choler
The Cryosphere, 19, 2407–2429, https://doi.org/10.5194/tc-19-2407-2025, https://doi.org/10.5194/tc-19-2407-2025, 2025
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We generated annual maps of snow melt-out days at 20 m resolution over a period of 38 years from 10 different satellites. This study fills a knowledge gap regarding the evolution of mountain snow in Europe by covering a much longer period and characterizing trends at much higher resolutions than previous studies. We found a trend for earlier melt-out with average reductions of 5.51 d per decade over the French Alps and 4.04 d per decade over the Pyrenees for the period 1986–2023.
Alireza Amani, Marie-Amélie Boucher, Alexandre R. Cabral, Vincent Vionnet, and Étienne Gaborit
Hydrol. Earth Syst. Sci., 29, 2445–2465, https://doi.org/10.5194/hess-29-2445-2025, https://doi.org/10.5194/hess-29-2445-2025, 2025
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Accurately estimating groundwater recharge using numerical models is particularly difficult in cold regions with snow and soil freezing. This study evaluated a physics-based model against high-resolution field measurements. Our findings highlight a need for a better representation of soil-freezing processes, offering a roadmap for future model development. This leads to more accurate models to aid in water resource management decisions in cold climates.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Wooley, Nicolas R. Leroux, Paul Siqueira, J. Max Adams, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2317, https://doi.org/10.5194/egusphere-2025-2317, 2025
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This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, TSMM. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–19. We detail important considerations to have in the context of an Earth Observation mission over a vast region such as Canada. The results show that it is possible to achieve the desired accuracy for TSMM, over an Arctic environment.
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).
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The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
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
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Saharan dust deposits frequently color alpine glaciers orange. Mineral dust reduces snow albedo and increases snow and glaciers melt rate. Using physical modeling, we quantified the impact of dust on the Argentière Glacier over the period 2019–2022. We found that that the contribution of mineral dust to the melt represents between 6 and 12 % of Argentière Glacier summer melt. At specific locations, the impact of dust over one year can rise to an equivalent of 1 meter of melted ice.
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
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This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
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
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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.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
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Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Diego Monteiro, Cécile Caillaud, Matthieu Lafaysse, Adrien Napoly, Mathieu Fructus, Antoinette Alias, and Samuel Morin
Geosci. Model Dev., 17, 7645–7677, https://doi.org/10.5194/gmd-17-7645-2024, https://doi.org/10.5194/gmd-17-7645-2024, 2024
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Modeling snow cover in climate and weather forecasting models is a challenge even for high-resolution models. Recent simulations with CNRM-AROME have shown difficulties when representing snow in the European Alps. Using remote sensing data and in situ observations, we evaluate modifications of the land surface configuration in order to improve it. We propose a new surface configuration, enabling a more realistic simulation of snow cover, relevant for climate and weather forecasting applications.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
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As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
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Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
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Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig
Nonlin. Processes Geophys., 31, 75–97, https://doi.org/10.5194/npg-31-75-2024, https://doi.org/10.5194/npg-31-75-2024, 2024
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Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
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The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Oscar Dick, Léo Viallon-Galinier, François Tuzet, Pascal Hagenmuller, Mathieu Fructus, Benjamin Reuter, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 17, 1755–1773, https://doi.org/10.5194/tc-17-1755-2023, https://doi.org/10.5194/tc-17-1755-2023, 2023
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Saharan dust deposition can drastically change the snow color, turning mountain landscapes into sepia scenes. Dust increases the absorption of solar energy by the snow cover and thus modifies the snow evolution and potentially the avalanche risk. Here we show that dust can lead to increased or decreased snowpack stability depending on the snow and meteorological conditions after the deposition event. We also show that wet-snow avalanches happen earlier in the season due to the presence of dust.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
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We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
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Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Christoph Kittel, Charles Amory, Stefan Hofer, Cécile Agosta, Nicolas C. Jourdain, Ella Gilbert, Louis Le Toumelin, Étienne Vignon, Hubert Gallée, and Xavier Fettweis
The Cryosphere, 16, 2655–2669, https://doi.org/10.5194/tc-16-2655-2022, https://doi.org/10.5194/tc-16-2655-2022, 2022
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Model projections suggest large differences in future Antarctic surface melting even for similar greenhouse gas scenarios and warming rates. We show that clouds containing a larger amount of liquid water lead to stronger melt. As surface melt can trigger the collapse of the ice shelves (the safety band of the Antarctic Ice Sheet), clouds could be a major source of uncertainties in projections of sea level rise.
Matthieu Vernay, Matthieu Lafaysse, Diego Monteiro, Pascal Hagenmuller, Rafife Nheili, Raphaëlle Samacoïts, Deborah Verfaillie, and Samuel Morin
Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, https://doi.org/10.5194/essd-14-1707-2022, 2022
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This paper introduces the latest version of the freely available S2M dataset which provides estimates of both meteorological and snow cover variables, as well as various avalanche hazard diagnostics at different elevations, slopes and aspects for the three main French high-elevation mountainous regions. A complete description of the system and the dataset is provided, as well as an overview of the possible uses of this dataset and an objective assessment of its limitations.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
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The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
Florent Veillon, Marie Dumont, Charles Amory, and Mathieu Fructus
Geosci. Model Dev., 14, 7329–7343, https://doi.org/10.5194/gmd-14-7329-2021, https://doi.org/10.5194/gmd-14-7329-2021, 2021
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In climate models, the snow albedo scheme generally calculates only a narrowband or broadband albedo. Therefore, we have developed the VALHALLA method to optimize snow spectral albedo calculations through the determination of spectrally fixed radiative variables. The development of VALHALLA v1.0 with the use of the snow albedo model TARTES and the spectral irradiance model SBDART indicates a considerable reduction in calculation time while maintaining an adequate accuracy of albedo values.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
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Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021, https://doi.org/10.5194/npg-28-467-2021, 2021
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Forecasting the height of new snow is essential for avalanche hazard surveys, road and ski resort management, tourism attractiveness, etc. Météo-France operates a probabilistic forecasting system using a numerical weather prediction system and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.
Louis Le Toumelin, Charles Amory, Vincent Favier, Christoph Kittel, Stefan Hofer, Xavier Fettweis, Hubert Gallée, and Vinay Kayetha
The Cryosphere, 15, 3595–3614, https://doi.org/10.5194/tc-15-3595-2021, https://doi.org/10.5194/tc-15-3595-2021, 2021
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Snow is frequently eroded from the surface by the wind in Adelie Land (Antarctica) and suspended in the lower atmosphere. By performing model simulations, we show firstly that suspended snow layers interact with incoming radiation similarly to a near-surface cloud. Secondly, suspended snow modifies the atmosphere's thermodynamic structure and energy exchanges with the surface. Our results suggest snow transport by the wind should be taken into account in future model studies over the region.
Charles Amory, Christoph Kittel, Louis Le Toumelin, Cécile Agosta, Alison Delhasse, Vincent Favier, and Xavier Fettweis
Geosci. Model Dev., 14, 3487–3510, https://doi.org/10.5194/gmd-14-3487-2021, https://doi.org/10.5194/gmd-14-3487-2021, 2021
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This paper presents recent developments in the drifting-snow scheme of the regional climate model MAR and its application to simulate drifting snow and the surface mass balance of Adélie Land in East Antarctica. The model is extensively described and evaluated against a multi-year drifting-snow dataset and surface mass balance estimates available in the area. The model sensitivity to input parameters and improvements over a previously published version are also assessed.
Bertrand Cluzet, Matthieu Lafaysse, Emmanuel Cosme, Clément Albergel, Louis-François Meunier, and Marie Dumont
Geosci. Model Dev., 14, 1595–1614, https://doi.org/10.5194/gmd-14-1595-2021, https://doi.org/10.5194/gmd-14-1595-2021, 2021
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In the mountains, the combination of large model error and observation sparseness is a challenge for data assimilation. Here, we develop two variants of the particle filter (PF) in order to propagate the information content of observations into unobserved areas. By adjusting observation errors or exploiting background correlation patterns, we demonstrate the potential for partial observations of snow depth and surface reflectance to improve model accuracy with the PF in an idealised setting.
Vincent Vionnet, Christopher B. Marsh, Brian Menounos, Simon Gascoin, Nicholas E. Wayand, Joseph Shea, Kriti Mukherjee, and John W. Pomeroy
The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, https://doi.org/10.5194/tc-15-743-2021, 2021
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Mountain snow cover provides critical supplies of fresh water to downstream users. Its accurate prediction requires inclusion of often-ignored processes. A multi-scale modelling strategy is presented that efficiently accounts for snow redistribution. Model accuracy is assessed via airborne lidar and optical satellite imagery. With redistribution the model captures the elevation–snow depth relation. Redistribution processes are required to reproduce spatial variability, such as around ridges.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
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Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Guoqiang Tang, Martyn P. Clark, Andrew J. Newman, Andrew W. Wood, Simon Michael Papalexiou, Vincent Vionnet, and Paul H. Whitfield
Earth Syst. Sci. Data, 12, 2381–2409, https://doi.org/10.5194/essd-12-2381-2020, https://doi.org/10.5194/essd-12-2381-2020, 2020
Short summary
Short summary
Station observations are critical for hydrological and meteorological studies, but they often contain missing values and have short measurement periods. This study developed a serially complete dataset for North America (SCDNA) from 1979 to 2018 for 27 276 precipitation and temperature stations. SCDNA is built on multiple data sources and infilling/reconstruction strategies to achieve high-quality estimates which can be used for a variety of applications.
Patrick Le Moigne, François Besson, Eric Martin, Julien Boé, Aaron Boone, Bertrand Decharme, Pierre Etchevers, Stéphanie Faroux, Florence Habets, Matthieu Lafaysse, Delphine Leroux, and Fabienne Rousset-Regimbeau
Geosci. Model Dev., 13, 3925–3946, https://doi.org/10.5194/gmd-13-3925-2020, https://doi.org/10.5194/gmd-13-3925-2020, 2020
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The study describes how a hydrometeorological model, operational at Météo-France, has been improved. Particular emphasis is placed on the impact of climatic data, surface, and soil parametrizations on the model results. Model simulations and evaluations carried out on a variety of measurements of river flows and snow depths are presented. All improvements in climate, surface data, and model physics have a positive impact on system performance.
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
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”, Zenodo, https://doi.org/10.5281/zenodo.7681551, 2023b. a
Baron, M., Haddjeri, A., Lafaysse, M., le Toumelin, L., Vionnet, V., and Fructus, M.: supplementary to “SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme”, 2D wind forcing, Zenodo [data set], https://doi.org/10.5281/zenodo.7681661, 2023c. a
Baron, M., Haddjeri, A., Lafaysse, M., le Toumelin, L., Vionnet, V., and Fructus, M.: Supplementary to “SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme”: SURFEX codes and dependancies, Zenodo [code], https://doi.org/10.5281/zenodo.7687821, 2023d. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002. a
Bernhardt, M., Zängl, G., Liston, G., Strasser, U., and Mauser, W.: Using wind fields from a high-resolution atmospheric model for simulating snow dynamics in mountainous terrain, Hydrol. Process., 23, 1064–1075, https://doi.org/10.1002/hyp.7208, 2009. a
Bowling, L., Pomeroy, J., and Lettenmaier, D.: Parameterization of blowing-snow sublimation in a macroscale hydrology model, J. Hydrometeorol., 5, 745–762, https://doi.org/10.1175/1525-7541(2004)005%3C0745:POBSIA%3E2.0.CO;2, 2004. a, b, c, d
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteor. Soc., 142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016. a
Carmagnola, C. M., Morin, S., Lafaysse, M., Domine, F., Lesaffre, B., Lejeune, Y., Picard, G., and Arnaud, L.: Implementation and evaluation of prognostic representations of the optical diameter of snow in the SURFEX/ISBA-Crocus detailed snowpack model, The Cryosphere, 8, 417–437, https://doi.org/10.5194/tc-8-417-2014, 2014. a, b, c, d
Castelle, T., Sivardiere, F., Guyomarc'H, G., Buisson, L., and Mérindol, L.: Drifting zone phenomena and avalanches, in: International symposium on snow and related manifestations, Manali, IND, September 1994, p. 7, https://hal.inrae.fr/hal-02573988 (last access: 6 February 2024), 1994. a
Choler, P.: Growth response of temperate mountain grasslands to inter-annual variations in snow cover duration, Biogeosciences, 12, 3885–3897, https://doi.org/10.5194/bg-12-3885-2015, 2015. a
Chritin, V., Bolognesi, R., and Gubler, H.: FlowCapt: a new acoustic sensor to measure snowdrift and wind velocity for avalanche forecasting, Cold Reg. Sci. Technol., 30, 125–133, https://doi.org/10.1016/S0165-232X(99)00012-9, 1999. a
Cierco, F.-X., Naaim-Bouvet, F., and Bellot, H.: Acoustic sensors for snowdrift measurements: How should they be used for research purposes?, Cold Reg. Sci. Technol., 49, 74–87, https://doi.org/10.1016/j.coldregions.2007.01.002, 2007. a
Clarke, L., Glendinning, I., and Hempel, R.: The MPI message passing interface standard, in: Programming environments for massively parallel distributed systems, Springer, 213–218, https://doi.org/10.1007/978-3-0348-8534-8_21, 1994. a
Clifton, A., Rüedi, J.-D., and Lehning, M.: Snow saltation threshold measurements in a drifting-snow wind tunnel, J. Glaciol., 52, 585–596, https://doi.org/10.3189/172756506781828430, 2006. a
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. a
Comola, F. and Lehning, M.: Energy-and momentum-conserving model of splash entrainment in sand and snow saltation, Geophys. Res. Lett., 44, 1601–1609, https://doi.org/10.1002/2016GL071822, 2017. a
Comola, F., Kok, J. F., Gaume, J., Paterna, E., and Lehning, M.: Fragmentation of wind-blown snow crystals, Geophys. Res. Lett., 44, 4195–4203, https://doi.org/10.1002/2017GL073039, 2017. a
Cryobs-Clim-CLB: Cryobs-Clim-CLB/Col du Lac Blanc: a meteorological and blowing snow observatory, CNRS – OSUG – Meteo France – Irstea [data set], https://doi.org/10.17178/CRYOBSCLIM.CLB.all, 2000. a
Déry, S. J. and Yau, M.: A bulk blowing snow model, Bound.-Lay. Meteorol., 93, 237–251, https://doi.org/10.1023/A:1002065615856, 1999. a
Déry, S. J. and Yau, M.: Simulation of blowing snow in the Canadian Arctic using a double-moment model, Bound.-Lay. Meteorol., 99, 297–316, https://doi.org/10.1023/A:1018965008049, 2001. a
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020. a, b
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 Resour. Res., 58, e2021WR030271, https://doi.org/10.1029/2021WR030271, 2022. a, b, c
Doorschot, J. J. and Lehning, M.: Equilibrium saltation: mass fluxes, aerodynamic entrainment, and dependence on grain properties, Bound.-Lay. Meteorol., 104, 111–130, https://doi.org/10.1023/A:1015516420286, 2002. a, b
Doorschot, J. J., Lehning, M., and Vrouwe, A.: Field measurements of snow-drift threshold and mass fluxes, and related model simulations, Bound.-Lay. Meteorol., 113, 347–368, 2004. a
Dujardin, J. and Lehning, M.: Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning, Q. J. Roy. Meteor. Soc., 148, 1368–1388, https://doi.org/10.1002/qj.4265, 2022. a
Gallée, H., Guyomarc'h, G., and Brun, E.: Impact of snow drift on the Antarctic ice sheet surface mass balance: possible sensitivity to snow-surface properties, Bound.-Lay. Meteorol., 99, 1–19, https://doi.org/10.1023/A:1018776422809, 2001. a, b, c
Gordon, M., Simon, K., and Taylor, P. A.: On snow depth predictions with the Canadian land surface scheme including a parametrization of blowing snow sublimation, Atmos.-Ocean, 44, 239–255, https://doi.org/10.3137/ao.440303, 2006. a
Gordon, M., Savelyev, S., and Taylor, P. A.: Measurements of blowing snow, part II: Mass and number density profiles and saltation height at Franklin Bay, NWT, Canada, Cold Reg. Sci. Technol., 55, 75–85, https://doi.org/10.1016/j.coldregions.2008.07.001, 2009. a, b, c
Greenshields, C. and Weller, H.: Notes on Computational Fluid Dynamics: General Principles, CFD Direct Ltd, Reading, UK, ISBN 978-1-3999-2078-0, 291 pp., https://doc.cfd.direct/notes/cfd-general-principles/ (last access: 6 February 2024), 2022. a
Groot Zwaaftink, C., Diebold, M., Horender, S., Overney, J., Lieberherr, G., Parlange, M., and Lehning, M.: Modelling small-scale drifting snow with a Lagrangian stochastic model based on large-eddy simulations, Bound.-Lay. Meteorol., 153, 117–139, https://doi.org/10.1007/s10546-014-9934-2, 2014. a
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010. a
Guyomarc'h, G., Bellot, H., Vionnet, V., Naaim-Bouvet, F., Déliot, Y., Fontaine, F., Puglièse, P., Nishimura, K., Durand, Y., and Naaim, M.: A meteorological and blowing snow data set (2000–2016) from a high-elevation alpine site (Col du Lac Blanc, France, 2720 m a.s.l.), Earth Syst. Sci. Data, 11, 57–69, https://doi.org/10.5194/essd-11-57-2019, 2019. a, b, c, d, e
Günther, D., Marke, T., Essery, R., and Strasser, U.: Uncertainties in Snowpack Simulations – Assessing the Impact of Model Structure, Parameter Choice, and Forcing Data Error on Point‐Scale Energy Balance Snow Model Performance, Water Resour. Res. 55, 2779–2800, https://doi.org/10.1029/2018wr023403, 2019. a
Haddjeri, A., Baron, M., Lafaysse, M., Le Toumelin, L., Deschamp-Berger, C., Vionnet, V., Gascoin, S., Vernay, M., and Dumont, M.: Exploring the sensitivity to precipitation, blowing snow, and horizontal resolution of the spatial distribution of simulated snow cover, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2604, 2023. a, b, c
He, S. and Ohara, N.: A New Formula for Estimating the Threshold Wind Speed for Snow Movement, J. Adv. Model. Earth Sy., 9, 2514–2525, https://doi.org/10.1002/2017ms000982, 2017. a
Helbig, N., Mott, R., Van Herwijnen, A., Winstral, A., and Jonas, T.: Parameterizing surface wind speed over complex topography, J. Geophys. Res.-Atmos., 122, 651–667, https://doi.org/10.1002/2016JD025593, 2017. a
Helfricht, K., Hartl, L., Koch, R., Marty, C., and Olefs, M.: Obtaining sub-daily new snow density from automated measurements in high mountain regions, Hydrol. Earth Syst. Sci., 22, 2655–2668, https://doi.org/10.5194/hess-22-2655-2018, 2018. a
IAV Technologies: ISAW network, IAV Technologies [data set], http://iav-portal.com/index.php?nav=iodmisawlist, last access: 6 February 2024. a
IGN©: BD Foret® V2, https://geoservices.ign.fr/bdforet (last access: 6 February 2024), 2021a. a
IGN©: BD Topo®, https://geoservices.ign.fr/bdtopo (last access: 6 February 2024), 2021b. a
IGN: RGE Alti, IGN [data set], https://geoservices.ign.fr/documentation/donnees/alti/rgealti, last access: 6 February 2024. a
IPCC: The Ocean and Cryosphere in a Changing Climate, Cambridge University Press, https://doi.org/10.1017/9781009157964, 2022. a
Jordan, R. E.: A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM. 89, http://hdl.handle.net/11681/11677 (last access: 6 February 2024) 1991. a
Kind, R.: One-dimensional aeolian suspension above beds of loose particles—A new concentration-profile equation, Atmos. Environ., 26, 927–931, 1992. a
Lafaysse, M.: Modélisation numérique de la neige: la fin du déterminisme?, Habilitation à diriger des recherches, Université Toulouse III – Paul Sabatier, https://hal.science/tel-04130109 (last access: 6 February 2024), 2023. a
Largeron, C., Dumont, M., Morin, S., Boone, A., Lafaysse, M., Metref, S., Cosme, E., Jonas, T., Winstral, A., and Margulis, S. A.: Toward snow cover estimation in mountainous areas using modern data assimilation methods: a review, Front. Earth Sci., 8, 325, https://doi.org/10.3389/feart.2020.00325, 2020. a
Le Toumelin, L., Gouttevin, I., Helbig, N., Galiez, C., Roux, M., and Karbou, F.: Emulating the adaptation of wind fields to complex terrain with deep-learning, Artificial Intelligence for the Earth Systems, 2, 1–39, https://doi.org/10.1175/AIES-D-22-0034.1, 2022. a, b
Lehning, M., Doorschot, J., and Bartelt, P.: A snowdrift index based on SNOWPACK model calculations, Ann. Glaciol., 31, 382–386, https://doi.org/10.3189/172756400781819770, 2000. a
Lehning, M., Löwe, H., Ryser, M., and Raderschall, N.: Inhomogeneous precipitation distribution and snow transport in steep terrain, Water Resour. Res., 44, W07404, https://doi.org/10.1029/2007WR006545, 2008. a, b
Li, L. and Pomeroy, J. W.: Estimates of threshold wind speeds for snow transport using meteorological data, J. Appl. Meteorol., 36, 205–213, https://doi.org/10.1175/1520-0450(1997)036<0205:EOTWSF>2.0.CO;2, 1997. a, b
Liston, G. E., Haehnel, R. B., Sturm, M., Hiemstra, C. A., Berezovskaya, S., and Tabler, R. D.: Simulating complex snow distributions in windy environments using SnowTran-3D, J. Glaciol., 53, 241–256, https://doi.org/10.3189/172756507782202865, 2007. a
MacDonald, M. K., Pomeroy, J. W., and Pietroniro, A.: Parameterizing redistribution and sublimation of blowing snow for hydrological models: tests in a mountainous subarctic catchment, Hydrol. Process., 23, 2570–2583, https://doi.org/10.1002/hyp.7356, 2009. a
Mann, G., Anderson, P., and Mobbs, S.: Profile measurements of blowing snow at Halley, Antarctica, J. Geophys. Res.-Atmos., 105, 24491–24508, https://doi.org/10.1029/2000JD900247, 2000. a, b, c, d
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016. a
Michaux, J.-L.: Etude, compréhension, et modélisation des phénomènes liés au transport de la neige par le vent, Ph.D. thesis, Doctorat Environnement risques naturels, Université Joseph Fourier, Grenoble, https://hal.inrae.fr/tel-02581549/ (last access: 6 February 2024), 2003. a
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., Ližar, M., Mitterer, C., Monti, F., Müller, K., Olefs, M., Snook, J. S., van Herwijnen, A., and Vionnet, V.: Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future, Cold Reg. Sci. Technol., 170, 102910, https://doi.org/10.1016/j.coldregions.2019.102910, 2020a. a
Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., Ližar, M., et al.: Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future, Cold Reg. Sci. Technol., 170, 102910, https://doi.org/10.1016/j.coldregions.2019.102910, 2020b. a, b
Mott, R., Vionnet, V., and Grünewald, T.: The seasonal snow cover dynamics: review on wind-driven coupling processes, Front. Earth Sci., 6, 197, https://doi.org/10.3389/feart.2018.00197, 2018. a, b, c, d
Musselman, K. N., Pomeroy, J. W., Essery, R. L., and Leroux, N.: Impact of windflow calculations on simulations of alpine snow accumulation, redistribution and ablation, Hydrol. Process., 29, 3983–3999, https://doi.org/10.1002/hyp.10595, 2015. a, b
Naaim-Bouvet, F., Naaim, M., and Martinez, H.: Profils de concentration de la neige soufflée. Théorie, résolution numérique et validation expérimentale in situ, La Houille Blanche, 82, 53–56, https://doi.org/10.1051/lhb/1996052, 1996. a, b
Patankar, S. V.: Numerical heat transfer and fluid flow, CRC press, https://doi.org/10.1201/9781482234213, 2018. a
Pomeroy, J., Gray, D., Brown, T., Hedstrom, N., Quinton, W., Granger, R., and Carey, S.: The cold regions hydrological model: a platform for basing process representation and model structure on physical evidence, Hydrol. Proc., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007. a
Raderschall, N., Lehning, M., and Schär, C.: Fine-scale modeling of the boundary layer wind field over steep topography, Water Resour. Res., 44, W09425, https://doi.org/10.1029/2007WR006544, 2008. a, b
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015. a
Revuelto, J., Lecourt, G., Lafaysse, M., Zin, I., Charrois, L., Vionnet, V., Dumont, M., Rabatel, A., Six, D., Condom, T., Morin, S., Viani, A., and Sirguey, P.: Multi-criteria evaluation of snowpack simulations in complex alpine terrain using satellite and in situ observations, Remote Sens., 10, 1171, https://doi.org/10.3390/rs10081171, 2018. a
Saarinen, S., Hamrud, M., Salmond, D., and Hague, J.: Dr. hook instrumentation tool, Technical documentation, ECMWF, https://www.umr-cnrm.fr/surfex/IMG/pdf/DrHook.pdf (last access: 6 February 2024), 2005. a
Sato, T., Kimura, T., Ishimaru, T., and Maruyama, T.: Field test of a new snow-particle counter (SPC) system, Ann. Glaciol., 18, 149–154, https://doi.org/10.3189/S0260305500011411, 1993. a
Sato, T., Mochizuki, S., Kosugi, K., and Nemoto, M.: Effects of particle shape on mass flux measurement of drifting snow by snow particle counter, J. Jpn. Soc. Snow Ice, 67, 493–503, https://doi.org/10.5331/seppyo.67.493, 2005. a
Sato, T., Kosugi, K., Mochizuki, S., and Nemoto, M.: Wind speed dependences of fracture and accumulation of snowflakes on snow surface, Cold Reg. Sci. Technol., 51, 229–239, https://doi.org/10.1016/j.coldregions.2007.05.004, 2008. a
SBSM: Code documentation, https://documentation.help/CRHM_Borland/modules_sbsm.htm, last access: 4 June 2022. a
Schlögl, S., Marty, C., Bavay, M., and Lehning, M.: Sensitivity of Alpine3D modeled snow cover to modifications in DEM resolution, station coverage and meteorological input quantities, Environ. Model. Softw., 83, 387–396, https://doi.org/10.1016/j.envsoft.2016.02.017, 2016. a
Schmidt, D. S., Schmidt, R., and Dent, J.: Electrostatic force in blowing snow, Bounda.-Lay. Meteorol., 93, 29–45, https://doi.org/10.1023/A:1002045818907, 1999. a
Schmidt, R.: Threshold wind-speeds and elastic impact in snow transport, J. Glaciol., 26, 453–467, https://doi.org/10.1017/S0022143000010972, 1980. a
Schneiderbauer, S. and Prokop, A.: The atmospheric snow-transport model: SnowDrift3D, J. Glaciol., 57, 526–542, https://doi.org/10.3189/002214311796905677, 2011a. a
Schneiderbauer, S. and Prokop, A.: The atmospheric snow-transport model: SnowDrift3D, J. Glaciol. 57, 526–542, https://doi.org/10.3189/002214311796905677, 2011b. a
Schweizer, J., Jamieson, J. B., and Schneebeli, M.: Snow avalanche formation, Rev. Geophys., 41, 1016, https://doi.org/10.1029/2002rg000123, 2003. a, b
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., and Masson, V.: The AROME-France convective-scale operational model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Sexstone, G. A., Clow, D. W., Fassnacht, S. R., Liston, G. E., Hiemstra, C. A., Knowles, J. F., and Penn, C. A.: Snow Sublimation in Mountain Environments and Its Sensitivity to Forest Disturbance and Climate Warming, Water Resour. Res., 54, 1191–1211, https://doi.org/10.1002/2017wr021172, 2018. a, b
Shao, Y.: A similarity theory for saltation and application to aeolian mass flux, Bound.-Lay. Meteorol., 115, 319–338, https://doi.org/10.1007/s10546-004-4632-0, 2005. a
Sharma, V., Gerber, F., and Lehning, M.: Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling, Geosci. Model Dev., 16, 719–749, https://doi.org/10.5194/gmd-16-719-2023, 2023. a, b, c, d
Sommer, C. G., Lehning, M., and Fierz, C.: Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow, Front. Earth Sci., 6, 4, https://doi.org/10.3389/feart.2018.00004, 2018. a
Sørensen, M.: On the rate of aeolian sand transport, Geomorphology, 59, 53–62, https://doi.org/10.1016/j.geomorph.2003.09.005, 2004. a, b
Strasser, U., Bernhardt, M., Weber, M., Liston, G. E., and Mauser, W.: Is snow sublimation important in the alpine water balance?, The Cryosphere, 2, 53–66, https://doi.org/10.5194/tc-2-53-2008, 2008. a, b
Takahashi, S.: Characteristics of drifting snow at Mizuho Station, Antarctica, Ann. Glaciol., 6, 71–75, https://doi.org/10.3189/1985AoG6-1-71-75, 1985. a, b, c
Trouvilliez, A., Naaim-Bouvet, F., Bellot, H., Genthon, C., and Gallée, H.: Evaluation of the FlowCapt acoustic sensor for the aeolian transport of snow, J. Atmos. Ocean. Tech., 32, 1630–1641, https://doi.org/10.1175/JTECH-D-14-00104.1, 2015. a, b, c
Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoïts, R., Verfaillie, D., and Morin, S.: The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021), Earth Syst. Sci. Data, 14, 1707–1733, https://doi.org/10.5194/essd-14-1707-2022, 2022. a, b
Vionnet, V.: Études du transport de la neige par le vent en conditions alpines: observations et simulations à l'aide d'un modèle couplé atmosphère/manteau neigeux, Ph.D. thesis, Paris Est, https://pastel.hal.science/tel-00781279v3 (last access: 6 February 2024), 2012. a, b, c, d, e, f, g, h, i, j, k
Vionnet, V., Guyomarc’h, G., Bouvet, F. N., Martin, E., Durand, Y., Bellot, H., Bel, C., and Puglièse, P.: Occurrence of blowing snow events at an alpine site over a 10-year period: Observations and modelling, Adv. Water Resour., 55, 53–63, https://doi.org/10.1016/j.advwatres.2012.05.004, 2013. a, b, c, d, e, f, g, h, i, j, k, l
Vionnet, V., Martin, E., Masson, V., Guyomarc'h, G., Naaim-Bouvet, F., Prokop, A., Durand, Y., and Lac, C.: Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model, The Cryosphere, 8, 395–415, https://doi.org/10.5194/tc-8-395-2014, 2014. a, b, c, d, e, f, g, h, i, j, k
Vionnet, V., Dombrowski-Etchevers, I., Lafaysse, M., Quéno, L., Seity, Y., and Bazile, E.: Numerical weather forecasts at kilometer scale in the French Alps: Evaluation and application for snowpack modeling, J. Hydrometeorol., 17, 2591–2614, https://doi.org/10.1175/JHM-D-15-0241.1, 2016. a
Vionnet, V., Martin, E., Masson, V., Lac, C., Naaim Bouvet, F., and Guyomarc'h, G.: High-resolution large eddy simulation of snow accumulation in Alpine terrain, J. Geophys. Res.-Atmos., 122, 11–005, https://doi.org/10.1002/2017JD026947, 2017. a
Vionnet, V., Guyomarc’h, G., Lafaysse, M., Naaim-Bouvet, F., Giraud, G., and Deliot, Y.: Operational implementation and evaluation of a blowing snow scheme for avalanche hazard forecasting, Cold Reg. Sci. Technol., 147, 1–10, https://doi.org/10.1016/j.coldregions.2017.12.006, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Vionnet, V., Marsh, C. B., Menounos, B., Gascoin, S., Wayand, N. E., Shea, J., Mukherjee, K., and Pomeroy, J. W.: Multi-scale snowdrift-permitting modelling of mountain snowpack, The Cryosphere, 15, 743–769, https://doi.org/10.5194/tc-15-743-2021, 2021. a, b, c
White, B. R. and Tsoar, H.: Slope effect on saltation over a climbing sand dune, Geomorphology, 22, 159–180, https://doi.org/10.1016/S0169-555X(97)00058-5, 1998. a
Wilcox, R.: A note on the Theil-Sen regression estimator when the regressor is random and the error term is heteroscedastic, Biometrical J., 40, 261–268, https://doi.org/10.1002/(SICI)1521-4036(199807)40:3<261::AID-BIMJ261>3.0.CO;2-V, 1998. a
Xue, M., Droegemeier, K. K., and Wong, V.: The Advanced Regional Prediction System (ARPS)–A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification, Meteorol. Atmos. Phys., 75, 161–193, https://doi.org/10.1007/s007030070003, 2000. a
Yang, J. and Yau, M.: A new triple-moment blowing snow model, Bound.-Lay. Meteorol., 126, 137–155, https://doi.org/10.1007/s10546-007-9215-4, 2008. a
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.
Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain...
