Articles | Volume 17, issue 24
https://doi.org/10.5194/gmd-17-8927-2024
© 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-17-8927-2024
© 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
| 
19 Dec 2024
Model description paper |
| 19 Dec 2024
| 19 Dec 2024
Simulation of snow albedo and solar irradiance profile with the Two-streAm Radiative TransfEr in Snow (TARTES) v2.0 model
Ghislain Picard
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, IGE, 38000 Grenoble, France
Quentin Libois
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Related authors
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
The Cryosphere, 20, 1297–1314, https://doi.org/10.5194/tc-20-1297-2026, https://doi.org/10.5194/tc-20-1297-2026, 2026
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Microwave radiometry senses the thermal emission from a target, whereas its active counterpart, radar, sends a signal to the target and measures the reflected signal. We simultaneously model radar and radiometry over the East Antarctic ice sheet, which we propose as an analog for icy moons: we can reproduce most data with a unique model. The radar response of Saturn's moons cannot be reproduced and is likely caused by processes unaccounted for in the model and less efficient in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
The Cryosphere, 20, 1199–1216, https://doi.org/10.5194/tc-20-1199-2026, https://doi.org/10.5194/tc-20-1199-2026, 2026
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This study presents a quantitative and synthetic classification of the snowpack in 10 dry-wet status by aggregating separate binary indicators derived from satellite observations. The classification follows the expected evolution of the melt season: night refreezing is frequent at the onset, sustained melting is observed during the summer peak, and remnant liquid water at depth occurs at the end. This dataset improves the knowledge of melt processes using passive microwave remote sensing.
Ghislain Picard, Justin Murfitt, Elena Zakharova, Pierre Zeiger, Laurent Arnaud, Jeremie Aublanc, Jack C. Landy, Michele Scagliola, and Claude Duguay
EGUsphere, https://doi.org/10.5194/egusphere-2025-6056, https://doi.org/10.5194/egusphere-2025-6056, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
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Radar altimeters measure ice-sheet elevation and sea-ice thickness. To improve their accuracy, we developed a model that simulates altimeter waveforms based on the physical properties of snow and sea ice. It computes the power, time, and Doppler shift of radar echoes as waves travel to the surface, interact with snow and ice, and return to the satellite. The model was verified internally, validated against other models and applied in Antarctica using in-situ snow measurements.
Marta Stentella, Ghislain Picard, Petra Heil, Jacqueline Boutin, Emmanuel Dinnat, and Stuart Corney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6437, https://doi.org/10.5194/egusphere-2025-6437, 2026
Short summary
Short summary
Passive microwave radiometers are the primary tool for monitoring Antarctic sea ice, but their reliability decreases in the marginal ice zone between the pack ice and the open ocean. We simulate satellite observations to assess the impact of varying physical parameters on sea ice concentration retrievals. The largest errors are due to wet snow, roughened ocean surfaces, and thin ice. These findings can improve how we interpret satellite observations and forecast sea ice changes.
Adrien Ooms, Mathieu Casado, Ghislain Picard, Laurent Arnaud, Maria Hörhold, Andrea Spolaor, Rita Traversi, Joel Savarino, Patrick Ginot, Pete Akers, Birthe Twarloh, and Valérie Masson-Delmotte
EGUsphere, https://doi.org/10.5194/egusphere-2025-3259, https://doi.org/10.5194/egusphere-2025-3259, 2025
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This work presents a new approach to the estimation of accumulation rates at Concordia Station, East-Antarctica, for the last 20 years, from a new data set of chemical tracers and snow micro-scale properties measured in a snow trench. Multi-annual and meter to decameter scale variability of accumulation rates are compared again in-situ measurements of surface laser scanner and stake farm, with very good agreement. This further constrains SMB estimation for Antarctica at high temporal resolution.
Titouan Tcheng, Elise Fourré, Christophe Leroy-Dos-Santos, Frédéric Parrenin, Emmanuel Le Meur, Frédéric Prié, Olivier Jossoud, Roxanne Jacob, Bénédicte Minster, Olivier Magand, Cécile Agosta, Niels Dutrievoz, Vincent Favier, Léa Baubant, Coralie Lassalle-Bernard, Mathieu Casado, Martin Werner, Alexandre Cauquoin, Laurent Arnaud, Bruno Jourdain, Ghislain Picard, Marie Bouchet, and Amaëlle Landais
EGUsphere, https://doi.org/10.5194/egusphere-2025-2863, https://doi.org/10.5194/egusphere-2025-2863, 2025
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Studying Antarctic ice cores is crucial to assess past climate changes, as they hold historical climate data. This study examines multiple ice cores from three sites in coastal Adélie Land to see if combining cores improves data interpretability. It does at two sites, but at a third, wind-driven snow layer mixing limited benefits. We show that using multiple ice cores from one location can better uncover climate history, especially in areas with less wind disturbance.
Florent Domine, Mireille Quémener, Ludovick Bégin, Benjamin Bouchard, Valérie Dionne, Sébastien Jerczynski, Raphaël Larouche, Félix Lévesque-Desrosiers, Simon-Olivier Philibert, Marc-André Vigneault, Ghislain Picard, and Daniel C. Côté
The Cryosphere, 19, 1757–1774, https://doi.org/10.5194/tc-19-1757-2025, https://doi.org/10.5194/tc-19-1757-2025, 2025
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Shrubs buried in snow absorb solar radiation and reduce irradiance in the snowpack. This decreases photochemical reaction rates and emissions to the atmosphere. By monitoring irradiance in snowpacks with and without shrubs, we conclude that shrubs absorb solar radiation as much as 140 ppb of soot and reduce irradiance by a factor of 2. Shrub expansion in the Arctic may therefore affect tropospheric composition during the snow season with climatic effects.
Inès Ollivier, Hans Christian Steen-Larsen, Barbara Stenni, Laurent Arnaud, Mathieu Casado, Alexandre Cauquoin, Giuliano Dreossi, Christophe Genthon, Bénédicte Minster, Ghislain Picard, Martin Werner, and Amaëlle Landais
The Cryosphere, 19, 173–200, https://doi.org/10.5194/tc-19-173-2025, https://doi.org/10.5194/tc-19-173-2025, 2025
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The role of post-depositional processes taking place at the ice sheet's surface on the water stable isotope signal measured in polar ice cores is not fully understood. Using field observations and modelling results, we show that the original precipitation isotopic signal at Dome C, East Antarctica, is modified by post-depositional processes and provide the first quantitative estimation of their mean impact on the isotopic signal observed in the snow.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Romilly Harris Stuart, Amaëlle Landais, Laurent Arnaud, Christo Buizert, Emilie Capron, Marie Dumont, Quentin Libois, Robert Mulvaney, Anaïs Orsi, Ghislain Picard, Frédéric Prié, Jeffrey Severinghaus, Barbara Stenni, and Patricia Martinerie
The Cryosphere, 18, 3741–3763, https://doi.org/10.5194/tc-18-3741-2024, https://doi.org/10.5194/tc-18-3741-2024, 2024
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Ice core δO2/N2 records are useful dating tools due to their local insolation pacing. A precise understanding of the physical mechanism driving this relationship, however, remain ambiguous. By compiling data from 15 polar sites, we find a strong dependence of mean δO2/N2 on accumulation rate and temperature in addition to the well-documented insolation dependence. Snowpack modelling is used to investigate which physical properties drive the mechanistic dependence on these local parameters.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
Preprint archived
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Naomi E. Ochwat, Ted A. Scambos, Alison F. Banwell, Robert S. Anderson, Michelle L. Maclennan, Ghislain Picard, Julia A. Shates, Sebastian Marinsek, Liliana Margonari, Martin Truffer, and Erin C. Pettit
The Cryosphere, 18, 1709–1731, https://doi.org/10.5194/tc-18-1709-2024, https://doi.org/10.5194/tc-18-1709-2024, 2024
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On the Antarctic Peninsula, there is a small bay that had sea ice fastened to the shoreline (
fast ice) for over a decade. The fast ice stabilized the glaciers that fed into the ocean. In January 2022, the fast ice broke away. Using satellite data we found that this was because of low sea ice concentrations and a high long-period ocean wave swell. We find that the glaciers have responded to this event by thinning, speeding up, and retreating by breaking off lots of icebergs at remarkable rates.
Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha Lemmetyinen
The Cryosphere, 18, 869–888, https://doi.org/10.5194/tc-18-869-2024, https://doi.org/10.5194/tc-18-869-2024, 2024
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This research focuses on the interaction between microwave signals and lake ice under wet conditions. Field data collected for Lake Oulujärvi in Finland were used to model backscatter under different conditions. The results of the modelling likely indicate that a combination of increased water content and roughness of different interfaces caused backscatter to increase. These results could help to identify areas where lake ice is unsafe for winter transportation.
Claudio Stefanini, Giovanni Macelloni, Marion Leduc-Leballeur, Vincent Favier, Benjamin Pohl, and Ghislain Picard
The Cryosphere, 18, 593–608, https://doi.org/10.5194/tc-18-593-2024, https://doi.org/10.5194/tc-18-593-2024, 2024
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Local and large-scale meteorological conditions have been considered in order to explain some peculiar changes of snow grains on the East Antarctic Plateau from 2000 to 2022, by using remote sensing observations and reanalysis. We identified some extreme grain size events on the highest ice divide, resulting from a combination of conditions of low wind speed and low temperature. Moreover, the beginning of seasonal grain growth has been linked to the occurrence of atmospheric rivers.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Thomas Dethinne, Quentin Glaude, Ghislain Picard, Christoph Kittel, Patrick Alexander, Anne Orban, and Xavier Fettweis
The Cryosphere, 17, 4267–4288, https://doi.org/10.5194/tc-17-4267-2023, https://doi.org/10.5194/tc-17-4267-2023, 2023
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We investigate the sensitivity of the regional climate model
Modèle Atmosphérique Régional(MAR) to the assimilation of wet-snow occurrence estimated by remote sensing datasets. The assimilation is performed by nudging the MAR snowpack temperature. The data assimilation is performed over the Antarctic Peninsula for the 2019–2021 period. The results show an increase in the melt production (+66.7 %) and a decrease in surface mass balance (−4.5 %) of the model for the 2019–2020 melt season.
Yaowen Zheng, Nicholas R. Golledge, Alexandra Gossart, Ghislain Picard, and Marion Leduc-Leballeur
The Cryosphere, 17, 3667–3694, https://doi.org/10.5194/tc-17-3667-2023, https://doi.org/10.5194/tc-17-3667-2023, 2023
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Positive degree-day (PDD) schemes are widely used in many Antarctic numerical ice sheet models. However, the PDD approach has not been systematically explored for its application in Antarctica. We have constructed a novel grid-cell-level spatially distributed PDD (dist-PDD) model and assessed its accuracy. We suggest that an appropriately parameterized dist-PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
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Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
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To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Ghislain Picard, Marion Leduc-Leballeur, Alison F. Banwell, Ludovic Brucker, and Giovanni Macelloni
The Cryosphere, 16, 5061–5083, https://doi.org/10.5194/tc-16-5061-2022, https://doi.org/10.5194/tc-16-5061-2022, 2022
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Using a snowpack radiative transfer model, we investigate in which conditions meltwater can be detected from passive microwave satellite observations from 1.4 to 37 GHz. In particular, we determine the minimum detectable liquid water content, the maximum depth of detection of a buried wet snow layer and the risk of false alarm due to supraglacial lakes. These results provide information for the developers of new, more advanced satellite melt products and for the users of the existing products.
Dominic Saunderson, Andrew Mackintosh, Felicity McCormack, Richard Selwyn Jones, and Ghislain Picard
The Cryosphere, 16, 4553–4569, https://doi.org/10.5194/tc-16-4553-2022, https://doi.org/10.5194/tc-16-4553-2022, 2022
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We investigate the variability in surface melt on the Shackleton Ice Shelf in East Antarctica over the last 2 decades (2003–2021). Using daily satellite observations and the machine learning approach of a self-organising map, we identify nine distinct spatial patterns of melt. These patterns allow comparisons of melt within and across melt seasons and highlight the importance of both air temperatures and local controls such as topography, katabatic winds, and albedo in driving surface melt.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
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Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
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Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Alvaro Robledano, Ghislain Picard, Laurent Arnaud, Fanny Larue, and Inès Ollivier
The Cryosphere, 16, 559–579, https://doi.org/10.5194/tc-16-559-2022, https://doi.org/10.5194/tc-16-559-2022, 2022
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Topography controls the surface temperature of snow-covered, mountainous areas. We developed a modelling chain that uses ray-tracing methods to quantify the impact of a few topographic effects on snow surface temperature at high spatial resolution. Its large spatial and temporal variations are correctly simulated over a 50 km2 area in the French Alps, and our results show that excluding a single topographic effect results in cooling (or warming) effects on the order of 1 °C.
Maria Belke-Brea, Florent Domine, Ghislain Picard, Mathieu Barrere, and Laurent Arnaud
Biogeosciences, 18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, https://doi.org/10.5194/bg-18-5851-2021, 2021
Short summary
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Expanding shrubs in the Arctic change snowpacks into a mix of snow, impurities and buried branches. Snow is a translucent medium into which light penetrates and gets partly absorbed by branches or impurities. Measurements of light attenuation in snow in Northern Quebec, Canada, showed (1) black-carbon-dominated light attenuation in snowpacks without shrubs and (2) buried branches influence radiation attenuation in snow locally, leading to melting and pockets of large crystals close to branches.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
The Cryosphere, 20, 1297–1314, https://doi.org/10.5194/tc-20-1297-2026, https://doi.org/10.5194/tc-20-1297-2026, 2026
Short summary
Short summary
Microwave radiometry senses the thermal emission from a target, whereas its active counterpart, radar, sends a signal to the target and measures the reflected signal. We simultaneously model radar and radiometry over the East Antarctic ice sheet, which we propose as an analog for icy moons: we can reproduce most data with a unique model. The radar response of Saturn's moons cannot be reproduced and is likely caused by processes unaccounted for in the model and less efficient in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
The Cryosphere, 20, 1199–1216, https://doi.org/10.5194/tc-20-1199-2026, https://doi.org/10.5194/tc-20-1199-2026, 2026
Short summary
Short summary
This study presents a quantitative and synthetic classification of the snowpack in 10 dry-wet status by aggregating separate binary indicators derived from satellite observations. The classification follows the expected evolution of the melt season: night refreezing is frequent at the onset, sustained melting is observed during the summer peak, and remnant liquid water at depth occurs at the end. This dataset improves the knowledge of melt processes using passive microwave remote sensing.
Ghislain Picard, Justin Murfitt, Elena Zakharova, Pierre Zeiger, Laurent Arnaud, Jeremie Aublanc, Jack C. Landy, Michele Scagliola, and Claude Duguay
EGUsphere, https://doi.org/10.5194/egusphere-2025-6056, https://doi.org/10.5194/egusphere-2025-6056, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Radar altimeters measure ice-sheet elevation and sea-ice thickness. To improve their accuracy, we developed a model that simulates altimeter waveforms based on the physical properties of snow and sea ice. It computes the power, time, and Doppler shift of radar echoes as waves travel to the surface, interact with snow and ice, and return to the satellite. The model was verified internally, validated against other models and applied in Antarctica using in-situ snow measurements.
Marta Stentella, Ghislain Picard, Petra Heil, Jacqueline Boutin, Emmanuel Dinnat, and Stuart Corney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6437, https://doi.org/10.5194/egusphere-2025-6437, 2026
Short summary
Short summary
Passive microwave radiometers are the primary tool for monitoring Antarctic sea ice, but their reliability decreases in the marginal ice zone between the pack ice and the open ocean. We simulate satellite observations to assess the impact of varying physical parameters on sea ice concentration retrievals. The largest errors are due to wet snow, roughened ocean surfaces, and thin ice. These findings can improve how we interpret satellite observations and forecast sea ice changes.
Adrien Ooms, Mathieu Casado, Ghislain Picard, Laurent Arnaud, Maria Hörhold, Andrea Spolaor, Rita Traversi, Joel Savarino, Patrick Ginot, Pete Akers, Birthe Twarloh, and Valérie Masson-Delmotte
EGUsphere, https://doi.org/10.5194/egusphere-2025-3259, https://doi.org/10.5194/egusphere-2025-3259, 2025
Short summary
Short summary
This work presents a new approach to the estimation of accumulation rates at Concordia Station, East-Antarctica, for the last 20 years, from a new data set of chemical tracers and snow micro-scale properties measured in a snow trench. Multi-annual and meter to decameter scale variability of accumulation rates are compared again in-situ measurements of surface laser scanner and stake farm, with very good agreement. This further constrains SMB estimation for Antarctica at high temporal resolution.
Titouan Tcheng, Elise Fourré, Christophe Leroy-Dos-Santos, Frédéric Parrenin, Emmanuel Le Meur, Frédéric Prié, Olivier Jossoud, Roxanne Jacob, Bénédicte Minster, Olivier Magand, Cécile Agosta, Niels Dutrievoz, Vincent Favier, Léa Baubant, Coralie Lassalle-Bernard, Mathieu Casado, Martin Werner, Alexandre Cauquoin, Laurent Arnaud, Bruno Jourdain, Ghislain Picard, Marie Bouchet, and Amaëlle Landais
EGUsphere, https://doi.org/10.5194/egusphere-2025-2863, https://doi.org/10.5194/egusphere-2025-2863, 2025
Short summary
Short summary
Studying Antarctic ice cores is crucial to assess past climate changes, as they hold historical climate data. This study examines multiple ice cores from three sites in coastal Adélie Land to see if combining cores improves data interpretability. It does at two sites, but at a third, wind-driven snow layer mixing limited benefits. We show that using multiple ice cores from one location can better uncover climate history, especially in areas with less wind disturbance.
Florent Domine, Mireille Quémener, Ludovick Bégin, Benjamin Bouchard, Valérie Dionne, Sébastien Jerczynski, Raphaël Larouche, Félix Lévesque-Desrosiers, Simon-Olivier Philibert, Marc-André Vigneault, Ghislain Picard, and Daniel C. Côté
The Cryosphere, 19, 1757–1774, https://doi.org/10.5194/tc-19-1757-2025, https://doi.org/10.5194/tc-19-1757-2025, 2025
Short summary
Short summary
Shrubs buried in snow absorb solar radiation and reduce irradiance in the snowpack. This decreases photochemical reaction rates and emissions to the atmosphere. By monitoring irradiance in snowpacks with and without shrubs, we conclude that shrubs absorb solar radiation as much as 140 ppb of soot and reduce irradiance by a factor of 2. Shrub expansion in the Arctic may therefore affect tropospheric composition during the snow season with climatic effects.
Inès Ollivier, Hans Christian Steen-Larsen, Barbara Stenni, Laurent Arnaud, Mathieu Casado, Alexandre Cauquoin, Giuliano Dreossi, Christophe Genthon, Bénédicte Minster, Ghislain Picard, Martin Werner, and Amaëlle Landais
The Cryosphere, 19, 173–200, https://doi.org/10.5194/tc-19-173-2025, https://doi.org/10.5194/tc-19-173-2025, 2025
Short summary
Short summary
The role of post-depositional processes taking place at the ice sheet's surface on the water stable isotope signal measured in polar ice cores is not fully understood. Using field observations and modelling results, we show that the original precipitation isotopic signal at Dome C, East Antarctica, is modified by post-depositional processes and provide the first quantitative estimation of their mean impact on the isotopic signal observed in the snow.
Zili He, Quentin Libois, Najda Villefranque, Hartwig Deneke, Jonas Witthuhn, and Fleur Couvreux
Atmos. Chem. Phys., 24, 11391–11408, https://doi.org/10.5194/acp-24-11391-2024, https://doi.org/10.5194/acp-24-11391-2024, 2024
Short summary
Short summary
This study uses observations and simulations to analyze how cumulus clouds affect spacial solar radiation variability on the ground. Results show that the simulations reproduce the observations well and improve understanding of cloud impacts on radiation. The research also indicates that a few strategically placed sensors, capitalizing on measurement timing, can effectively measure these variations, aiding in the development of detailed weather prediction models.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Romilly Harris Stuart, Amaëlle Landais, Laurent Arnaud, Christo Buizert, Emilie Capron, Marie Dumont, Quentin Libois, Robert Mulvaney, Anaïs Orsi, Ghislain Picard, Frédéric Prié, Jeffrey Severinghaus, Barbara Stenni, and Patricia Martinerie
The Cryosphere, 18, 3741–3763, https://doi.org/10.5194/tc-18-3741-2024, https://doi.org/10.5194/tc-18-3741-2024, 2024
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Ice core δO2/N2 records are useful dating tools due to their local insolation pacing. A precise understanding of the physical mechanism driving this relationship, however, remain ambiguous. By compiling data from 15 polar sites, we find a strong dependence of mean δO2/N2 on accumulation rate and temperature in addition to the well-documented insolation dependence. Snowpack modelling is used to investigate which physical properties drive the mechanistic dependence on these local parameters.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Naomi E. Ochwat, Ted A. Scambos, Alison F. Banwell, Robert S. Anderson, Michelle L. Maclennan, Ghislain Picard, Julia A. Shates, Sebastian Marinsek, Liliana Margonari, Martin Truffer, and Erin C. Pettit
The Cryosphere, 18, 1709–1731, https://doi.org/10.5194/tc-18-1709-2024, https://doi.org/10.5194/tc-18-1709-2024, 2024
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On the Antarctic Peninsula, there is a small bay that had sea ice fastened to the shoreline (
fast ice) for over a decade. The fast ice stabilized the glaciers that fed into the ocean. In January 2022, the fast ice broke away. Using satellite data we found that this was because of low sea ice concentrations and a high long-period ocean wave swell. We find that the glaciers have responded to this event by thinning, speeding up, and retreating by breaking off lots of icebergs at remarkable rates.
Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha Lemmetyinen
The Cryosphere, 18, 869–888, https://doi.org/10.5194/tc-18-869-2024, https://doi.org/10.5194/tc-18-869-2024, 2024
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This research focuses on the interaction between microwave signals and lake ice under wet conditions. Field data collected for Lake Oulujärvi in Finland were used to model backscatter under different conditions. The results of the modelling likely indicate that a combination of increased water content and roughness of different interfaces caused backscatter to increase. These results could help to identify areas where lake ice is unsafe for winter transportation.
Claudio Stefanini, Giovanni Macelloni, Marion Leduc-Leballeur, Vincent Favier, Benjamin Pohl, and Ghislain Picard
The Cryosphere, 18, 593–608, https://doi.org/10.5194/tc-18-593-2024, https://doi.org/10.5194/tc-18-593-2024, 2024
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Local and large-scale meteorological conditions have been considered in order to explain some peculiar changes of snow grains on the East Antarctic Plateau from 2000 to 2022, by using remote sensing observations and reanalysis. We identified some extreme grain size events on the highest ice divide, resulting from a combination of conditions of low wind speed and low temperature. Moreover, the beginning of seasonal grain growth has been linked to the occurrence of atmospheric rivers.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
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With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Thomas Dethinne, Quentin Glaude, Ghislain Picard, Christoph Kittel, Patrick Alexander, Anne Orban, and Xavier Fettweis
The Cryosphere, 17, 4267–4288, https://doi.org/10.5194/tc-17-4267-2023, https://doi.org/10.5194/tc-17-4267-2023, 2023
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We investigate the sensitivity of the regional climate model
Modèle Atmosphérique Régional(MAR) to the assimilation of wet-snow occurrence estimated by remote sensing datasets. The assimilation is performed by nudging the MAR snowpack temperature. The data assimilation is performed over the Antarctic Peninsula for the 2019–2021 period. The results show an increase in the melt production (+66.7 %) and a decrease in surface mass balance (−4.5 %) of the model for the 2019–2020 melt season.
Yaowen Zheng, Nicholas R. Golledge, Alexandra Gossart, Ghislain Picard, and Marion Leduc-Leballeur
The Cryosphere, 17, 3667–3694, https://doi.org/10.5194/tc-17-3667-2023, https://doi.org/10.5194/tc-17-3667-2023, 2023
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Positive degree-day (PDD) schemes are widely used in many Antarctic numerical ice sheet models. However, the PDD approach has not been systematically explored for its application in Antarctica. We have constructed a novel grid-cell-level spatially distributed PDD (dist-PDD) model and assessed its accuracy. We suggest that an appropriately parameterized dist-PDD model can be a valuable tool for exploring Antarctic surface melt beyond the satellite era.
Esteban Alonso-González, Simon Gascoin, Sara Arioli, and Ghislain Picard
The Cryosphere, 17, 3329–3342, https://doi.org/10.5194/tc-17-3329-2023, https://doi.org/10.5194/tc-17-3329-2023, 2023
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Data assimilation techniques are a promising approach to improve snowpack simulations in remote areas that are difficult to monitor. This paper studies the ability of satellite-observed land surface temperature to improve snowpack simulations through data assimilation. We show that it is possible to improve snowpack simulations, but the temporal resolution of the observations and the algorithm used are critical to obtain satisfactory results.
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.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
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To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Ghislain Picard, Marion Leduc-Leballeur, Alison F. Banwell, Ludovic Brucker, and Giovanni Macelloni
The Cryosphere, 16, 5061–5083, https://doi.org/10.5194/tc-16-5061-2022, https://doi.org/10.5194/tc-16-5061-2022, 2022
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Using a snowpack radiative transfer model, we investigate in which conditions meltwater can be detected from passive microwave satellite observations from 1.4 to 37 GHz. In particular, we determine the minimum detectable liquid water content, the maximum depth of detection of a buried wet snow layer and the risk of false alarm due to supraglacial lakes. These results provide information for the developers of new, more advanced satellite melt products and for the users of the existing products.
Dominic Saunderson, Andrew Mackintosh, Felicity McCormack, Richard Selwyn Jones, and Ghislain Picard
The Cryosphere, 16, 4553–4569, https://doi.org/10.5194/tc-16-4553-2022, https://doi.org/10.5194/tc-16-4553-2022, 2022
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We investigate the variability in surface melt on the Shackleton Ice Shelf in East Antarctica over the last 2 decades (2003–2021). Using daily satellite observations and the machine learning approach of a self-organising map, we identify nine distinct spatial patterns of melt. These patterns allow comparisons of melt within and across melt seasons and highlight the importance of both air temperatures and local controls such as topography, katabatic winds, and albedo in driving surface melt.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
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Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
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Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Alvaro Robledano, Ghislain Picard, Laurent Arnaud, Fanny Larue, and Inès Ollivier
The Cryosphere, 16, 559–579, https://doi.org/10.5194/tc-16-559-2022, https://doi.org/10.5194/tc-16-559-2022, 2022
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Topography controls the surface temperature of snow-covered, mountainous areas. We developed a modelling chain that uses ray-tracing methods to quantify the impact of a few topographic effects on snow surface temperature at high spatial resolution. Its large spatial and temporal variations are correctly simulated over a 50 km2 area in the French Alps, and our results show that excluding a single topographic effect results in cooling (or warming) effects on the order of 1 °C.
Maria Belke-Brea, Florent Domine, Ghislain Picard, Mathieu Barrere, and Laurent Arnaud
Biogeosciences, 18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, https://doi.org/10.5194/bg-18-5851-2021, 2021
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Expanding shrubs in the Arctic change snowpacks into a mix of snow, impurities and buried branches. Snow is a translucent medium into which light penetrates and gets partly absorbed by branches or impurities. Measurements of light attenuation in snow in Northern Quebec, Canada, showed (1) black-carbon-dominated light attenuation in snowpacks without shrubs and (2) buried branches influence radiation attenuation in snow locally, leading to melting and pockets of large crystals close to branches.
Cited articles
Aoki, T., Kuchiki, K., Niwano, M., Kodama, Y., Hosaka, M., and Tanaka, T.: Physically based snow albedo model for calculating broadband albedos and the solar heating profile in snowpack for general circulation models, J. Geophys. Res., 116, D11114, https://doi.org/10.1029/2010jd015507, 2011. a
Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and van den Bosch, J.: MODTRAN® 6: A major upgrade of the MODTRAN® radiative transfer code, in: 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland, 24–27 June 2014, https://doi.org/10.1109/whispers.2014.8077573, 2014. a
Bisiaux, M. M., Edwards, R., McConnell, J. R., Curran, M. A. J., Van Ommen, T. D., Smith, A. M., Neumann, T. A., Pasteris, D. R., Penner, J. E., and Taylor, K.: Changes in black carbon deposition to Antarctica from two high-resolution ice core records, 1850–2000 AD, Atmos. Chem. Phys., 12, 4107–4115, https://doi.org/10.5194/acp-12-4107-2012, 2012. a
Bohren, C. F.: Multiple scattering of light and some of its observable consequences, Am. J. Phys., 55, 524–533, https://doi.org/10.1119/1.15109, 1987. a
Bohren, C. F. and Barkstrom, B. R.: Theory of the optical properties of snow, J. Geophys. Res., 79, 4527–4535, https://doi.org/10.1029/jc079i030p04527, 1974. a
Bond, T. C. and Bergstrom, R. W.: Light Absorption by Carbonaceous Particles: An Investigative Review, Aerosol Sci. Tech., 40, 27–67, https://doi.org/10.1080/02786820500421521, 2006. a
Box, J. E., Wehrlé, A., van As, D., Fausto, R. S., Kjeldsen, K. K., Dachauer, A., Ahlstrøm, A. P., and Picard, G.: Greenland Ice Sheet Rainfall, Heat and Albedo Feedback Impacts From the Mid-August 2021 Atmospheric River, Geophys. Res. Lett., 49, e2021GL097356, https://doi.org/10.1029/2021gl097356, 2022. a
Caponi, L., Formenti, P., Massabó, D., Di Biagio, C., Cazaunau, M., Pangui, E., Chevaillier, S., Landrot, G., Andreae, M. O., Kandler, K., Piketh, S., Saeed, T., Seibert, D., Williams, E., Balkanski, Y., Prati, P., and Doussin, J.-F.: Spectral- and size-resolved mass absorption efficiency of mineral dust aerosols in the shortwave spectrum: a simulation chamber study, Atmos. Chem. Phys., 17, 7175–7191, https://doi.org/10.5194/acp-17-7175-2017, 2017. a, b, c
Carmagnola, C. M., Domine, F., Dumont, M., Wright, P., Strellis, B., Bergin, M., Dibb, J., Picard, G., Libois, Q., Arnaud, L., and Morin, S.: Snow spectral albedo at Summit, Greenland: measurements and numerical simulations based on physical and chemical properties of the snowpack, The Cryosphere, 7, 1139–1160, https://doi.org/10.5194/tc-7-1139-2013, 2013. a
Chevrollier, L.-A., Cook, J. M., Halbach, L., Jakobsen, H., Benning, L. G., Anesio, A. M., and Tranter, M.: Light absorption and albedo reduction by pigmented microalgae on snow and ice, J. Glaciol., 69, 333–341, https://doi.org/10.1017/jog.2022.64, 2022. a
Colbeck, S. C.: An Overview of Seasonal Snow Metamorphism, Rev. Geophys., 20, 45–61, https://doi.org/10.1029/RG020i001p00045, 1982. a
Cook, J. M., Hodson, A. J., Gardner, A. S., Flanner, M., Tedstone, A. J., Williamson, C., Irvine-Fynn, T. D. L., Nilsson, J., Bryant, R., and Tranter, M.: Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo, The Cryosphere, 11, 2611–2632, https://doi.org/10.5194/tc-11-2611-2017, 2017. a
Dang, C., Zender, C. S., and Flanner, M. G.: Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces, The Cryosphere, 13, 2325–2343, https://doi.org/10.5194/tc-13-2325-2019, 2019. a, b
Di Mauro, B., Garzonio, R., Ravasio, C., Orlandi, V., Baccolo, G., Gilardoni, S., Remias, D., Leoni, B., Rossini, M., and Colombo, R.: Combined effect of algae and dust on snow spectral and broadband albedo, J. Quant. Spectrosc. Ra., 316, 108906, https://doi.org/10.1016/j.jqsrt.2024.108906, 2024. a, b
Dombrovsky, L. A., Kokhanovsky, A. A., and Randrianalisoa, J. H.: On snowpack heating by solar radiation: A computational model, J. Quant. Spectrosc. Ra., 227, 72–85, https://doi.org/10.1016/j.jqsrt.2019.02.004, 2019. a
Domine, F., Salvatori, R., Legagneux, L., Salzano, R., Fily, M., and Casacchia, R.: Correlation between the specific surface area and the short wave infrared (SWIR) reflectance of snow, Cold Reg. Sci. Technol., 46, 60–68, https://doi.org/10.1016/j.coldregions.2006.06.002, 2006. a
Domine, F., Albert, M., Huthwelker, T., Jacobi, H.-W., Kokhanovsky, A. A., Lehning, M., Picard, G., and Simpson, W. R.: Snow physics as relevant to snow photochemistry, Atmos. Chem. Phys., 8, 171–208, https://doi.org/10.5194/acp-8-171-2008, 2008. a
Donahue, C., Skiles, S. M., and Hammonds, K.: Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements, The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, 2022. a
Dumont, M., Arnaud, L., Picard, G., Libois, Q., Lejeune, Y., Nabat, P., Voisin, D., and Morin, S.: In situ continuous visible and near-infrared spectroscopy of an alpine snowpack, The Cryosphere, 11, 1091–1110, https://doi.org/10.5194/tc-11-1091-2017, 2017. a
Dumont, M., Tuzet, F., Gascoin, S., Picard, G., Kutuzov, S., Lafaysse, M., Cluzet, B., Nheili, R., and Painter, T. H.: Accelerated Snow Melt in the Russian Caucasus Mountains After the Saharan Dust Outbreak in March 2018, J. Geophys. Res.-Earth, 125, e2020JF005641, https://doi.org/10.1029/2020jf005641, 2020. a
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. a
Dunkle, R. V. and Bevans, J. T.: An Approximate Analysis of the Solar Reflectance and Transmittance of a Snow Cover, J. Meteorol., 13, 212–216, https://doi.org/10.1175/1520-0469(1956)013<0212:aaaots>2.0.co;2, 1956. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a
Fair, Z., Flanner, M., Schneider, A., and Skiles, S. M.: Sensitivity of modeled snow grain size retrievals to solar geometry, snow particle asphericity, and snowpack impurities, The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, 2022. a
Fierz, C., Armstrong, R. L., Durand, Y., Etchevers, P., Greene, E., McClung, D. M., Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The international classification for seasonal snow on the ground, UNESCO/IHP, https://unesdoc.unesco.org/ark:/48223/pf0000186462 (last access: 17 December 2024), 2009. a
Flanner, M. G. and Zender, C. S.: Snowpack radiative heating: Influence on Tibetan Plateau climate, Geophys. Res. Lett., 32, L06501, https://doi.org/10.1029/2004GL022076, 2005. a, b
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.: Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155, https://doi.org/10.1038/ngeo1062, 2011. a
Flanner, M. G., Liu, X., Zhou, C., Penner, J. E., and Jiao, C.: Enhanced solar energy absorption by internally-mixed black carbon in snow grains, Atmos. Chem. Phys., 12, 4699–4721, https://doi.org/10.5194/acp-12-4699-2012, 2012. a
France, J. L., King, M. D., Frey, M. M., Erbland, J., Picard, G., Preunkert, S., MacArthur, A., and Savarino, J.: Snow optical properties at Dome C (Concordia), Antarctica; implications for snow emissions and snow chemistry of reactive nitrogen, Atmos. Chem. Phys., 11, 9787–9801, https://doi.org/10.5194/acp-11-9787-2011, 2011. a
Gallet, J.-C., Domine, F., Zender, C. S., and Picard, G.: Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm, The Cryosphere, 3, 167–182, https://doi.org/10.5194/tc-3-167-2009, 2009. a, b
Gallet, J.-C., Domine, F., Arnaud, L., Picard, G., and Savarino, J.: Vertical profile of the specific surface area and density of the snow at Dome C and on a transect to Dumont D'Urville, Antarctica – albedo calculations and comparison to remote sensing products, The Cryosphere, 5, 631–649, https://doi.org/10.5194/tc-5-631-2011, 2011. a
Glendinning, J. H. G. and Morris, E. M.: Incorporation of spectral and directional radiative transfer in a snow model, Hydrol. Process., 13, 1761–1772, https://doi.org/10.1002/(sici)1099-1085(199909)13:12/13<1761::aid-hyp856>3.0.co;2-y, 1999. a
Green, R. O., Dozier, J., Roberts, D., and Painter, T.: Spectral snow-reflectance models for grain-size and liquid-water fraction in melting snow for the solar-reflected spectrum, Ann. Glaciol., 34, 71–73, https://doi.org/10.3189/172756402781817987, 2002. a
Grenfell, T. C. and Warren, S. G.: Representation of a nonspherical ice particle by a collection of independent spheres for scattering and absorption of radiation, J. Geophys. Res., 104, 31697–31710, https://doi.org/10.1029/1999JD900496, 1999. a
Gueymard, C. A.: Parameterized transmittance model for direct beam and circumsolar spectral irradiance, Sol. Energy, 71, 325–346, https://doi.org/10.1016/s0038-092x(01)00054-8, 2001. a
Hall, A.: The Role of Surface Albedo Feedback in Climate, J. Climate, 17, 1550–1568, https://doi.org/10.1175/1520-0442(2004)017<1550:trosaf>2.0.co;2, 2004. a
He, C. and Flanner, M.: Snow Albedo and Radiative Transfer: Theory, Modeling, and Parameterization, pp. 67–133, Springer International Publishing, ISBN 9783030386962, https://doi.org/10.1007/978-3-030-38696-2_3, 2020. a
Henyey, L. C. and Greenstein, J. L.: Diffuse radiation in the Galaxy, Astrophys. J., 93, 70–83, https://doi.org/10.1086/144246, 1941. a
Hoffer, A., Gelencsér, A., Guyon, P., Kiss, G., Schmid, O., Frank, G. P., Artaxo, P., and Andreae, M. O.: Optical properties of humic-like substances (HULIS) in biomass-burning aerosols, Atmos. Chem. Phys., 6, 3563–3570, https://doi.org/10.5194/acp-6-3563-2006, 2006. a
Joseph, J. H., Wiscombe, W. J., and Weinman, J. A.: The Delta-Eddington Approximation for Radiative Flux Transfer, J. Atmos. Sci., 33, 2452–2459, https://doi.org/10.1175/1520-0469(1976)033<2452:tdeafr>2.0.co;2, 1976. a
Kaempfer, T. U., Schneebeli, M., and Sokratov, S. A.: A microstructural approach to model heat transfer in snow, Geophys. Res. Lett., 32, L21503, https://doi.org/10.1029/2005GL023873, 2005. a
Kang, S., Zhang, Y., Qian, Y., and Wang, H.: A review of black carbon in snow and ice and its impact on the cryosphere, Earth-Sci. Rev., 210, 103346, https://doi.org/10.1016/j.earscirev.2020.103346, 2020. a
King, M. D. and Simpson, W. R.: Extinction of UV radiation in Arctic snow at Alert, Canada (82° N), J. Geophys. Res., 106, 12499–12507, https://doi.org/10.1029/2001jd900006, 2001. a
Kokhanovsky, A. A.: Light Scattering Reviews, Vol. 6: Light Scattering and Remote Sensing of Atmosphere and Surface, Springer Berlin Heidelberg, ISBN 9783642155314, https://doi.org/10.1007/978-3-642-15531-4, 2012. a
Kokhanovsky, A. A.: Light penetration in snow layers, J. Quant. Spectrosc. Ra., 278, 108040, https://doi.org/10.1016/j.jqsrt.2021.108040, 2022. a
Kokhanovsky, A. A. and Macke, A.: Integral light-scattering and absorption characteristics of large, nonspherical particles, Appl. Optics, 36, 8785, https://doi.org/10.1364/ao.36.008785, 1997. a, b, c, d
Krol, Q. and Löwe, H.: Relating optical and microwave grain metrics of snow: the relevance of grain shape, The Cryosphere, 10, 2847–2863, https://doi.org/10.5194/tc-10-2847-2016, 2016. a
Larue, F., Picard, G., Arnaud, L., Ollivier, I., Delcourt, C., Lamare, M., Tuzet, F., Revuelto, J., and Dumont, M.: Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model, The Cryosphere, 14, 1651–1672, https://doi.org/10.5194/tc-14-1651-2020, 2020. a, b
Lee-Taylor, J. and Madronich, S.: Calculation of actinic fluxes with a coupled atmosphere–snow radiative transfer model, J. Geophys. Res.-Atmos., 107, 4796, https://doi.org/10.1029/2002jd002084, 2002. a
Leroux, C. and Fily, M.: Modeling the effect of sastrugi on snow reflectance, J. Geophys. Res., 103, 25779, https://doi.org/10.1029/98je00558, 1998. a
Letcher, T., Parno, J., Courville, Z., Farnsworth, L., and Olivier, J.: A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics, The Cryosphere, 16, 4343–4361, https://doi.org/10.5194/tc-16-4343-2022, 2022. a
Libois, Q., Picard, G., Arnaud, L., Dumont, M., Lafaysse, M., Morin, S., and Lefebvre, E.: Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau, The Cryosphere, 9, 2383–2398, https://doi.org/10.5194/tc-9-2383-2015, 2015. a
Malinka, A.: Stereological approach to radiative transfer in porous materials. Application to the optics of snow, J. Quant. Spectrosc. Ra., 295, 108410, https://doi.org/10.1016/j.jqsrt.2022.108410, 2023. a
Malinka, A. V.: Light scattering in porous materials: Geometrical optics and stereological approach, J. Quant. Spectrosc. Ra., 141, 14–23, https://doi.org/10.1016/j.jqsrt.2014.02.022, 2014. a, b, c, d
Manninen, T., Anttila, K., Jääskeläinen, E., Riihelä, A., Peltoniemi, J., Räisänen, P., Lahtinen, P., Siljamo, N., Thölix, L., Meinander, O., Kontu, A., Suokanerva, H., Pirazzini, R., Suomalainen, J., Hakala, T., Kaasalainen, S., Kaartinen, H., Kukko, A., Hautecoeur, O., and Roujean, J.-L.: Effect of small-scale snow surface roughness on snow albedo and reflectance, The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, 2021. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Meador, W. E. and Weaver, W. R.: Two-Stream Approximations to Radiative Transfer in Planetary Atmospheres: A Unified Description of Existing Methods and a New Improvement, J. Atmos. Sci., 37, 630–643, https://doi.org/10.1175/1520-0469(1980)037<0630:tsatrt>2.0.co;2, 1980. a
Mie, G.: Beitraege zur Optik trueber Medien, speziell kolloidaler Metalloesungen, Annals of Physics, 330, 377–445, 1908. a
Mishchenko, M. I., Travis, L. D., and Mackowski, D. W.: T-matrix computations of light scattering by nonspherical particles: A review, J. Quant. Spectrosc. Ra., 55, 535–575, https://doi.org/10.1016/0022-4073(96)00002-7, 1996. a
Mishchenko, M. I., Dlugach, J. M., Yanovitskij, E. G., and Zakharova, N. T.: Bidirectional reflectance of flat, optically thick particulate layers: an efficient radiative transfer solution and applications to snow and soil surfaces, J. Quant. Spectrosc. Ra., 63, 409–432, https://doi.org/10.1016/s0022-4073(99)00028-x, 1999. a
Onuma, Y., Takeuchi, N., Tanaka, S., Nagatsuka, N., Niwano, M., and Aoki, T.: Physically based model of the contribution of red snow algal cells to temporal changes in albedo in northwest Greenland, The Cryosphere, 14, 2087–2101, https://doi.org/10.5194/tc-14-2087-2020, 2020. a
Perovich, D. K.: Light reflection and transmission by a temperate snow cover, J. Glaciol., 53, 201–210, https://doi.org/10.3189/172756507782202919, 2007. a
Picard, G.: snowtartes: a web application to perform snow albedo and irradiance profile simulation with the TARTES model, https://snow.univ-grenoble-alpes.fr/snowtartes/ (last access: 17 December 2024), 2021. a
Picard, G. and Libois Q.: ghislainp/tartes: v2.0, Zenodo [code], https://doi.org/10.5281/zenodo.13950598, 2024. a
Picard, G., Arnaud, L., Domine, F., and Fily, M.: Determining snow specific surface area from near-infrared reflectance measurements: Numerical study of the influence of grain shape, Cold Reg. Sci. Technol., 56, 10–17, https://doi.org/10.1016/j.coldregions.2008.10.001, 2009. a
Picard, G., Domine, F., Krinner, G., Arnaud, L., and Lefebvre, E.: Inhibition of the positive snow-albedo feedback by precipitation in interior Antarctica, Nat. Clim. Change, 2, 795–798, https://doi.org/10.1038/nclimate1590, 2012. a
Picard, G., Arnaud, L., Caneill, R., Lefebvre, E., and Lamare, M.: Observation of the process of snow accumulation on the Antarctic Plateau by time lapse laser scanning, The Cryosphere, 13, 1983–1999, https://doi.org/10.5194/tc-13-1983-2019, 2019. a
Qu, X. and Hall, A.: What Controls the Strength of Snow-Albedo Feedback?, J. Climate, 20, 3971–3981, https://doi.org/10.1175/jcli4186.1, 2007. a, b
Räisänen, P., Makkonen, R., Kirkevåg, A., and Debernard, J. B.: Effects of snow grain shape on climate simulations: sensitivity tests with the Norwegian Earth System Model, The Cryosphere, 11, 2919–2942, https://doi.org/10.5194/tc-11-2919-2017, 2017. a, b
Réveillet, M., Dumont, M., Gascoin, S., Lafaysse, M., Nabat, P., Ribes, A., Nheili, R., Tuzet, F., Ménégoz, M., Morin, S., Picard, G., and Ginoux, P.: Black carbon and dust alter the response of mountain snow cover under climate change, Nat. Commun., 13, 5279, https://doi.org/10.1038/s41467-022-32501-y, 2022. a
Ricchiazzi, P., Yang, S., Gautier, C., and Sowle, D.: SBDART: A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's Atmosphere, B. Am. Meteorol. Soc., 79, 2101–2114, https://doi.org/10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2, 1998. a, b
Robledano, A., Picard, G., Arnaud, L., Larue, F., and Ollivier, I.: Modelling surface temperature and radiation budget of snow-covered complex terrain, The Cryosphere, 16, 559–579, https://doi.org/10.5194/tc-16-559-2022, 2022. a
Schaepman-Strub, G., Schaepman, M., Painter, T., Dangel, S., and Martonchik, J.: Reflectance quantities in optical remote sensing – definitions and case studies, Remote Sens. Environ., 103, 27–42, https://doi.org/10.1016/j.rse.2006.03.002, 2006. a
Shao, D., Xu, W., Li, H., Wang, J., and Hao, X.: Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models, IEEE T. Geosci. Remote, 56, 6969–6985, https://doi.org/10.1109/tgrs.2018.2846681, 2018. a
Stamnes, K., Tsay, S. C., Jayaweera, K., and Wiscombe, W.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optic., 27, 2502–2509, https://doi.org/10.1364/AO.27.002502, 1988a. a
Stamnes, K., Tsay, S.-C., and Nakajima, T.: Computation of eigenvalues and eigenvectors for the discrete ordinate and matrix operator methods in radiative transfer, J. Quant. Spectrosc. Ra., 39, 415–419, https://doi.org/10.1016/0022-4073(88)90107-0, 1988b. a
Stephens, G. L.: Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes, J. Atmos. Sci., 35, 2123–2132, https://doi.org/10.1175/1520-0469(1978)035<2123:rpiewc>2.0.co;2, 1978. a
Toon, O. B., McKay, C. P., Ackerman, T. P., and Santhanam, K.: Rapid calculation of radiative heating rates and photodissociation rates in inhomogeneous multiple scattering atmospheres, J. Geophys. Res.-Atmos., 94, 16287–16301, https://doi.org/10.1029/jd094id13p16287, 1989. a
Tuzet, F., Dumont, M., Lafaysse, M., Picard, G., Arnaud, L., Voisin, D., Lejeune, Y., Charrois, L., Nabat, P., and Morin, S.: A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow, The Cryosphere, 11, 2633–2653, https://doi.org/10.5194/tc-11-2633-2017, 2017. a, b
Tuzet, F., Dumont, M., Arnaud, L., Voisin, D., Lamare, M., Larue, F., Revuelto, J., and Picard, G.: Influence of light-absorbing particles on snow spectral irradiance profiles, The Cryosphere, 13, 2169–2187, https://doi.org/10.5194/tc-13-2169-2019, 2019. a
Tuzet, F., Dumont, M., Picard, G., Lamare, M., Voisin, D., Nabat, P., Lafaysse, M., Larue, F., Revuelto, J., and Arnaud, L.: Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps), The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, 2020. a
Usha, K. H., Nair, V. S., and Babu, S. S.: Modeling of aerosol induced snow albedo feedbacks over the Himalayas and its implications on regional climate, Clim. Dynam., 54, 4191–4210, https://doi.org/10.1007/s00382-020-05222-5, 2020. a
van Dalum, C. T., van de Berg, W. J., Libois, Q., Picard, G., and van den Broeke, M. R.: A module to convert spectral to narrowband snow albedo for use in climate models: SNOWBAL v1.2, Geosci. Model Dev., 12, 5157–5175, https://doi.org/10.5194/gmd-12-5157-2019, 2019. a, b
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. a, b
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b
Walden, V. P., Warren, S. G., and Tuttle, E.: Atmospheric Ice Crystals over the Antarctic Plateau in Winter, J. Appl. Meteorol., 42, 1391–1405, https://doi.org/10.1175/1520-0450(2003)042<1391:AICOTA>2.0.CO;2, 2003. a
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res., 113, D14220, https://doi.org/10.1029/2007JD009744, 2008. a, b, c, d
Warren, S. G. and Wiscombe, W. J.: A Model for the Spectral Albedo of Snow. II: Snow Containing Atmospheric Aerosols, J. Atmos. Sci., 37, 2734–2745, https://doi.org/10.1175/1520-0469(1980)037<2734:amftsa>2.0.co;2, 1980. a, b, c
Warren, S. G., Brandt, R. E., and O’Rawe Hinton, P.: Effect of surface roughness on bidirectional reflectance of Antarctic snow, J. Geophys. Res., 103, 25789, https://doi.org/10.1029/98je01898, 1998. a, b
Wiscombe, W. J.: The delta-Eddington approximation for a vertically inhomogeneous atmosphere, National Center for Atmospheric Research Boulder, Colorado, https://doi.org/10.5065/D65H7D6Z, 1977. a, b, c, d
Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow. I: Pure Snow, J. Atmos. Sci., 37, 2712–2733, https://doi.org/10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2, 1980. a, b, c
Xiong, C. and Shi, J.: Simulating polarized light scattering in terrestrial snow based on bicontinuous random medium and Monte Carlo ray tracing, J. Quant. Spectrosc. Ra., 133, 177–189, https://doi.org/10.1016/j.jqsrt.2013.07.026, 2014. a
Short summary
The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow...
