Articles | Volume 16, issue 12
https://doi.org/10.5194/tc-16-5061-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-5061-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The sensitivity of satellite microwave observations to liquid water in the Antarctic snowpack
Ghislain Picard
CORRESPONDING AUTHOR
Univ. Grenoble Alpes, CNRS, Institut des Géosciences de l’Environnement (IGE), UMR 5001, Grenoble, France
Geological Survey of Denmark and Greenland (GEUS), 1350 Copenhagen, Denmark
Marion Leduc-Leballeur
Institute of Applied Physics “Nello Carrara”, Sesto Fiorentino, Italy
Alison F. Banwell
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
Ludovic Brucker
Center for Satellite Applications and Research, NOAA/NESDIS, the US National Ice Center, College Park, MD, USA
Giovanni Macelloni
Institute of Applied Physics “Nello Carrara”, Sesto Fiorentino, Italy
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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
This preprint is open for discussion and under review for The Cryosphere (TC).
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
This preprint is open for discussion and under review for The Cryosphere (TC).
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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.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
EGUsphere, https://doi.org/10.5194/egusphere-2024-3972, https://doi.org/10.5194/egusphere-2024-3972, 2025
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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 signal reflected back. 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. Saturn's moons' radar brightness cannot be reproduced and must be caused by processes unaccounted for in the model and less active in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
EGUsphere, https://doi.org/10.5194/egusphere-2025-732, https://doi.org/10.5194/egusphere-2025-732, 2025
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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.
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.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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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.
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
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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
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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.
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.
Alison F. Banwell, Rajashree Tri Datta, Rebecca L. Dell, Mahsa Moussavi, Ludovic Brucker, Ghislain Picard, Christopher A. Shuman, and Laura A. Stevens
The Cryosphere, 15, 909–925, https://doi.org/10.5194/tc-15-909-2021, https://doi.org/10.5194/tc-15-909-2021, 2021
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Ice shelves are thick floating layers of glacier ice extending from the glaciers on land that buttress much of the Antarctic Ice Sheet and help to protect it from losing ice to the ocean. However, the stability of ice shelves is vulnerable to meltwater lakes that form on their surfaces during the summer. This study focuses on the northern George VI Ice Shelf on the western side of the AP, which had an exceptionally long and extensive melt season in 2019/2020 compared to the previous 31 seasons.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
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This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
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Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
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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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
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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
Short summary
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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.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
EGUsphere, https://doi.org/10.5194/egusphere-2024-3972, https://doi.org/10.5194/egusphere-2024-3972, 2025
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 signal reflected back. 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. Saturn's moons' radar brightness cannot be reproduced and must be caused by processes unaccounted for in the model and less active in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
EGUsphere, https://doi.org/10.5194/egusphere-2025-732, https://doi.org/10.5194/egusphere-2025-732, 2025
Short summary
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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.
Emily Glen, Amber Leeson, Alison F. Banwell, Jennifer Maddalena, Diarmuid Corr, Olivia Atkins, Brice Noël, and Malcolm McMillan
The Cryosphere, 19, 1047–1066, https://doi.org/10.5194/tc-19-1047-2025, https://doi.org/10.5194/tc-19-1047-2025, 2025
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We compare surface meltwater features from optical satellite imagery in the Russell–Leverett glacier catchment during high (2019) and low (2018) melt years. In the high melt year, features appear at higher elevations, meltwater systems are more connected, small lakes are more frequent, and slush is more widespread. These findings provide insights into how a warming climate, where high melt years become common, could alter meltwater distribution and dynamics on the Greenland Ice Sheet.
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.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
Short summary
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.
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
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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.
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.
Prateek Gantayat, Alison F. Banwell, Amber A. Leeson, James M. Lea, Dorthe Petersen, Noel Gourmelen, and Xavier Fettweis
Geosci. Model Dev., 16, 5803–5823, https://doi.org/10.5194/gmd-16-5803-2023, https://doi.org/10.5194/gmd-16-5803-2023, 2023
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We developed a new supraglacial hydrology model for the Greenland Ice Sheet. This model simulates surface meltwater routing, meltwater drainage, supraglacial lake (SGL) overflow, and formation of lake ice. The model was able to reproduce 80 % of observed lake locations and provides a good match between the observed and modelled temporal evolution of SGLs.
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.
Benoit S. Lecavalier, Lev Tarasov, Greg Balco, Perry Spector, Claus-Dieter Hillenbrand, Christo Buizert, Catherine Ritz, Marion Leduc-Leballeur, Robert Mulvaney, Pippa L. Whitehouse, Michael J. Bentley, and Jonathan Bamber
Earth Syst. Sci. Data, 15, 3573–3596, https://doi.org/10.5194/essd-15-3573-2023, https://doi.org/10.5194/essd-15-3573-2023, 2023
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The Antarctic Ice Sheet Evolution constraint database version 2 (AntICE2) consists of a large variety of observations that constrain the evolution of the Antarctic Ice Sheet over the last glacial cycle. This includes observations of past ice sheet extent, past ice thickness, past relative sea level, borehole temperature profiles, and present-day bedrock displacement rates. The database is intended to improve our understanding of past Antarctic changes and for ice sheet model calibrations.
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.
Marco Brogioni, Mark J. Andrews, Stefano Urbini, Kenneth C. Jezek, Joel T. Johnson, Marion Leduc-Leballeur, Giovanni Macelloni, Stephen F. Ackley, Alexandra Bringer, Ludovic Brucker, Oguz Demir, Giacomo Fontanelli, Caglar Yardim, Lars Kaleschke, Francesco Montomoli, Leung Tsang, Silvia Becagli, and Massimo Frezzotti
The Cryosphere, 17, 255–278, https://doi.org/10.5194/tc-17-255-2023, https://doi.org/10.5194/tc-17-255-2023, 2023
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In 2018 the first Antarctic campaign of UWBRAD was carried out. UWBRAD is a new radiometer able to collect microwave spectral signatures over 0.5–2 GHz, thus outperforming existing similar sensors. It allows us to probe thicker sea ice and ice sheet down to the bedrock. In this work we tried to assess the UWBRAD potentials for sea ice, glaciers, ice shelves and buried lakes. We also highlighted the wider range of information the spectral signature can provide to glaciological studies.
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.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
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.
Devon Dunmire, Alison F. Banwell, Nander Wever, Jan T. M. Lenaerts, and Rajashree Tri Datta
The Cryosphere, 15, 2983–3005, https://doi.org/10.5194/tc-15-2983-2021, https://doi.org/10.5194/tc-15-2983-2021, 2021
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Here, we automatically detect buried lakes (meltwater lakes buried below layers of snow) across the Greenland Ice Sheet, providing insight into a poorly studied meltwater feature. For 2018 and 2019, we compare areal extent of buried lakes. We find greater buried lake extent in 2019, especially in northern Greenland, which we attribute to late-summer surface melt and high autumn temperatures. We also provide evidence that buried lakes form via different processes across Greenland.
Alison F. Banwell, Rajashree Tri Datta, Rebecca L. Dell, Mahsa Moussavi, Ludovic Brucker, Ghislain Picard, Christopher A. Shuman, and Laura A. Stevens
The Cryosphere, 15, 909–925, https://doi.org/10.5194/tc-15-909-2021, https://doi.org/10.5194/tc-15-909-2021, 2021
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Ice shelves are thick floating layers of glacier ice extending from the glaciers on land that buttress much of the Antarctic Ice Sheet and help to protect it from losing ice to the ocean. However, the stability of ice shelves is vulnerable to meltwater lakes that form on their surfaces during the summer. This study focuses on the northern George VI Ice Shelf on the western side of the AP, which had an exceptionally long and extensive melt season in 2019/2020 compared to the previous 31 seasons.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
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This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
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Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Cited articles
Abdalati, W. and Steffen, K.: Snowmelt on the Greenland Ice Sheet as Derived
from Passive Microwave Satellite Data, J. Climate, 10, 165–175, 1997. a
Agosta, C., Amory, C., Kittel, C., Orsi, A., Favier, V., Gallée, H., van den Broeke, M. R., Lenaerts, J. T. M., van Wessem, J. M., van de Berg, W. J., and Fettweis, X.: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979–2015) and identification of dominant processes, The Cryosphere, 13, 281–296, https://doi.org/10.5194/tc-13-281-2019, 2019. a
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017 (data available at: https://data.catds.fr/cpdc/Common_products/GRIDDED/L3/RE07/MIR_CDF3TS/, last access: 14 March 2022). a, b
Arthur, J. F., Stokes, C. R., Jamieson, S. S. R., Carr, J. R., and Leeson, A. A.: Distribution and seasonal evolution of supraglacial lakes on Shackleton Ice Shelf, East Antarctica, The Cryosphere, 14, 4103–4120, https://doi.org/10.5194/tc-14-4103-2020, 2020. a
Arthur, J. F., Stokes, C. R., Jamieson, S. S. R., Carr, J. R., Leeson, A. A.,
and Verjans, V.: Large interannual variability in supraglacial lakes around
East Antarctica, Nat. Commun., 13, 1711, https://doi.org/10.1038/s41467-022-29385-3,
2022. a
Banwell, A. F., MacAyeal, D. R., and Sergienko, O. V.: Breakup of the Larsen B
Ice Shelf triggered by chain reaction drainage of supraglacial lakes,
Geophys. Res. Lett., 40, 5872–5876, https://doi.org/10.1002/2013gl057694,
2013. a, b
Banwell, A. F., Caballero, M., Arnold, N. S., Glasser, N. F., Cathles, L. M.,
and MacAyeal, D. R.: Supraglacial lakes on the Larsen B ice shelf,
Antarctica, and at Paakitsoq, West Greenland: a comparative study, Annals of
Glaciology, 55, 1–8, https://doi.org/10.3189/2014aog66a049, 2014. a, b
Banwell, A. F., Datta, R. T., Dell, R. L., Moussavi, M., Brucker, L., Picard, G., Shuman, C. A., and Stevens, L. A.: The 32-year record-high surface melt in 2019/2020 on the northern George VI Ice Shelf, Antarctic Peninsula, The Cryosphere, 15, 909–925, https://doi.org/10.5194/tc-15-909-2021, 2021. a, b, c
Bell, R. E., Chu, W., Kingslake, J., Das, I., Tedesco, M., Tinto, K. J., Zappa,
C. J., Frezzotti, M., Boghosian, A., and Lee, W. S.: Antarctic ice shelf
potentially stabilized by export of meltwater in surface river, Nature, 544,
344–348, https://doi.org/10.1038/nature22048, 2017. a
Bell, R. E., Banwell, A. F., Trusel, L. D., and Kingslake, J.: Antarctic
surface hydrology and impacts on ice-sheet mass balance, Nat. Clim.
Change, 8, 1044–1052, https://doi.org/10.1038/s41558-018-0326-3, 2018. a, b, c
Beven, K. and Binley, A.: The future of distributed models: Model calibration
and uncertainty prediction, Hydrol. Process., 6, 279–298,
https://doi.org/10.1002/hyp.3360060305, 1992. 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
Brogioni, M., Pettinato, S., Macelloni, G., Paloscia, S., Pampaloni, P.,
Pierdicca, N., and Ticconi, F.: Sensitivity of bistatic scattering to soil
moisture and surface roughness of bare soils, Int. J. Remote
Sens., 31, 4227–4255, https://doi.org/10.1080/01431160903232808, 2010. a
Brucker, L., Picard, G., and Fily, M.: Snow grain size profiles deduced from
microwave snow emissivities in Antarctica, J. Glaciol., 56, 514–526,
https://doi.org/10.3189/002214310792447806, 2010. a, b, c, d
Burr, A., Lhuissier, P., Martin, C. L., and Philip, A.: In situ X-ray
tomography densification of firn: The role of mechanics and diffusion
processes, Acta Mater., 167, 210–220,
https://doi.org/10.1016/j.actamat.2019.01.053, 2019. a
Chopra, K. L. and Reddy, G. B.: Optically selective coatings, Pramana, 27,
193–217, https://doi.org/10.1007/bf02846338, 1986. a
Colbeck, S. C.: An Overview of Seasonal Snow Metamorphism, Rev. Geophys., 20,
45–61, https://doi.org/10.1029/RG020i001p00045, 1982. a
Colliander, A., Mousavi, M., Marshall, S., Samimi, S., Kimball, J. S., Miller,
J. Z., Johnson, J., and Burgin, M.: Ice Sheet Surface and Subsurface Melt
Water Discrimination Using Multi-Frequency Microwave Radiometry, Geophys. Res. Lett., 49, e2021GL096599, https://doi.org/10.1029/2021gl096599, 2022. a
Colosio, P., Tedesco, M., Ranzi, R., and Fettweis, X.: Surface melting over the Greenland ice sheet derived from enhanced resolution passive microwave brightness temperatures (1979–2019), The Cryosphere, 15, 2623–2646, https://doi.org/10.5194/tc-15-2623-2021, 2021. a
Comiso, J., Cavalieri, D., and Markus, T.: Sea ice concentration, ice
temperature, and snow depth using AMSR-E data, IEEE T.
Geosci. Remote, 41, 243–252, https://doi.org/10.1109/tgrs.2002.808317,
2003. a
Cook, A. J., Fox, A. J., Vaughan, D. G., and Ferrigno, J. G.: Retreating
glacier fronts on the Antarctic Peninsula over the past half-century,
Science, 308, 541–544, 2005. a
Dell, R., Arnold, N., Willis, I., Banwell, A., Williamson, A., Pritchard, H., and Orr, A.: Lateral meltwater transfer across an Antarctic ice shelf, The Cryosphere, 14, 2313–2330, https://doi.org/10.5194/tc-14-2313-2020, 2020. a
Dell, R. L., Banwell, A. F., Willis, I. C., Arnold, N. S., Halberstadt, A.
R. W., Chudley, T. R., and Pritchard, H. D.: Supervised classification of
slush and ponded water on Antarctic ice shelves using Landsat 8 imagery,
J. Glaciol., 68, 401–414, https://doi.org/10.1017/jog.2021.114, 2021. a, b
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H.,
Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J.,
Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N.,
McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer,
M. W., Thurman, S. W., Tsang, L., and Zyl, J. V.: The Soil Moisture Active
Passive (SMAP) Mission, Proc. IEEE, 98, 704–716,
https://doi.org/10.1109/jproc.2010.2043918, 2010. a
Fettweis, X., Tedesco, M., van den Broeke, M., and Ettema, J.: Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models, The Cryosphere, 5, 359–375, https://doi.org/10.5194/tc-5-359-2011, 2011. a, b
Fretwell, P., Pritchard, H. D., Vaughan, D. G., Bamber, J. L., Barrand, N. E., Bell, R., Bianchi, C., Bingham, R. G., Blankenship, D. D., Casassa, G., Catania, G., Callens, D., Conway, H., Cook, A. J., Corr, H. F. J., Damaske, D., Damm, V., Ferraccioli, F., Forsberg, R., Fujita, S., Gim, Y., Gogineni, P., Griggs, J. A., Hindmarsh, R. C. A., Holmlund, P., Holt, J. W., Jacobel, R. W., Jenkins, A., Jokat, W., Jordan, T., King, E. C., Kohler, J., Krabill, W., Riger-Kusk, M., Langley, K. A., Leitchenkov, G., Leuschen, C., Luyendyk, B. P., Matsuoka, K., Mouginot, J., Nitsche, F. O., Nogi, Y., Nost, O. A., Popov, S. V., Rignot, E., Rippin, D. M., Rivera, A., Roberts, J., Ross, N., Siegert, M. J., Smith, A. M., Steinhage, D., Studinger, M., Sun, B., Tinto, B. K., Welch, B. C., Wilson, D., Young, D. A., Xiangbin, C., and Zirizzotti, A.: Bedmap2: improved ice bed, surface and thickness datasets for Antarctica, The Cryosphere, 7, 375–393, https://doi.org/10.5194/tc-7-375-2013, 2013 (data available at: https://secure.antarctica.ac.uk/data/bedmap2, last access: 25 November 2022). a, b
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple
Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc.,
146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Houtz, D., Mätzler, C., Naderpour, R., Schwank, M., and Steffen, K.:
Quantifying Surface Melt and Liquid Water on the Greenland Ice Sheet using
L-band Radiometry, Remote Sens. Environ., 256, 112341,
https://doi.org/10.1016/j.rse.2021.112341, 2021. a
Jezek, K. C., Johnson, J. T., Tan, S., Tsang, L., Andrews, M. J., Brogioni, M.,
Macelloni, G., Durand, M., Chen, C.-C., Belgiovane, D. J., Duan, Y., Yardim,
C., Li, H., Bringer, A., Leuski, V., and Aksoy, M.:
500–2000-MHz Brightness Temperature Spectra of the Northwestern
Greenland Ice Sheet, IEEE T. Geosci. Remote,
56, 1485–1496, https://doi.org/10.1109/tgrs.2017.2764381, 2018. a
Johnson, A., Fahnestock, M., and Hock, R.: Evaluation of passive microwave melt
detection methods on Antarctic Peninsula ice shelves using time series of
Sentinel-1 SAR, Remote Sens. Environ., 250, 112044,
https://doi.org/10.1016/j.rse.2020.112044, 2020. a, b
Johnson, A., Hock, R., and Fahnestock, M.: Spatial variability and regional
trends of Antarctic ice shelf surface melt duration over
1979–2020 derived from passive microwave data, J. Glaciol., 68, 533–546, https://doi.org/10.1017/jog.2021.112, 2021. a, b, c, d
Kasahara, M., Kachi, M., Inaoka, K., Fujii, H., Kubota, T., Shimada, R., and
Kojima, Y.: Overview and current status of GOSAT-GW mission and AMSR3
instrument, in: Sensors, Systems, and Next-Generation Satellites XXIV,
edited by: Neeck, S. P., Kimura, T., and Hélière, A., SPIE,
https://doi.org/10.1117/12.2573914, 2020. a
Kern, M., Cullen, R., Berruti, B., Bouffard, J., Casal, T., Drinkwater, M. R., Gabriele, A., Lecuyot, A., Ludwig, M., Midthassel, R., Navas Traver, I., Parrinello, T., Ressler, G., Andersson, E., Martin-Puig, C., Andersen, O., Bartsch, A., Farrell, S., Fleury, S., Gascoin, S., Guillot, A., Humbert, A., Rinne, E., Shepherd, A., van den Broeke, M. R., and Yackel, J.: The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission, The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, 2020. a
Kerr, Y., Waldteufel, P., Wigneron, J.-P., Martinuzzi, J., Font, J., and
Berger, M.: Soil moisture retrieval from space: the Soil Moisture and Ocean
Salinity (SMOS) mission, IEEE T. Geosci. Remote, 39, 1729–1735, https://doi.org/10.1109/36.942551, 2001. a, b
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin,
J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S. E.,
Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.:
The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water
Cycle, Proc. IEEE, 98, 666–687,
https://doi.org/10.1109/jproc.2010.2043032, 2010. a
King, J. C., Kirchgaessner, A., Bevan, S., Elvidge, A. D., Munneke, P. K.,
Luckman, A., Orr, A., Renfrew, I. A., and van den Broeke, M. R.: The Impact
of Föhn Winds on Surface Energy Balance During the 2010–2011 Melt Season
Over Larsen C Ice Shelf, Antarctica, J. Geophys. Res.-Atmos., 122, 12,062–12,076, https://doi.org/10.1002/2017jd026809, 2017. a
Kuipers Munneke, P., Picard, G., van den Broeke, M. R., Lenaerts, J. T. M., and
van Meijgaard, E.: Insignificant change in Antarctic snowmelt volume since
1979, Geophys. Res. Lett., 39, 5, https://doi.org/10.1029/2011GL050207,
2012. a, b, c
Kumar, R., Carroll, C., Hartikainen, A., and Martin, O.: ArviZ a unified
library for exploratory analysis of Bayesian models in Python, J.
Open Source Softw., 4, 1143, https://doi.org/10.21105/joss.01143, 2019. a
Kunz, L. and Long, D.: Melt Detection in Antarctic Ice Shelves Using
Scatterometers and Microwave Radiometers, IEEE T. Geosci. Remote, 44, 2461–2469, https://doi.org/10.1109/tgrs.2006.874138, 2006. a
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of
hydrologic models using multiple-try DREAM(ZS) and high-performance
computing, Water Resour. Res., 48, W01526, https://doi.org/10.1029/2011wr010608, 2012. a
Leeson, A. A., Forster, E., Rice, A., Gourmelen, N., and Wessem, J. M.:
Evolution of Supraglacial Lakes on the Larsen B Ice Shelf in the Decades
Before it Collapsed, Geophys. Res. Lett., 47, e2019GL085591,
https://doi.org/10.1029/2019gl085591, 2020. a
Lenaerts, J. T. M., Lhermitte, S., Drews, R., Ligtenberg, S. R. M., Berger, S.,
Helm, V., Smeets, C. J. P. P., van den Broeke, M. R., van de Berg, W. J., van
Meijgaard, E., Eijkelboom, M., Eisen, O., and Pattyn, F.: Meltwater produced
by wind–albedo interaction stored in an East Antarctic ice shelf,
Nat. Clim. Change, 7, 58–62, https://doi.org/10.1038/nclimate3180, 2016. a
Liu, H., Wang, L., and Jezek, K. C.: Spatiotemporal variations of snowmelt in
Antarctica derived from satellite scanning multichannel microwave radiometer
and Special Sensor Microwave Imager data (1978–2004), J. Geophys.
Res., 111, F01003, https://doi.org/10.1029/2005JF000318, 2006. a
Luckman, A., Elvidge, A., Jansen, D., Kulessa, B., Munneke, P. K., King, J.,
and Barrand, N. E.: Surface melt and ponding on Larsen C Ice Shelf and the
impact of föhn winds, Antarct. Sci., 26, 625–635,
https://doi.org/10.1017/s0954102014000339, 2014. a
Macelloni, G., Leduc-Leballeur, M., Brogioni, M., Ritz, C., and Picard, G.:
Analyzing and modeling the SMOS spatial variations in the East Antarctic
Plateau, Remote Sens. Environ., 180, 193–204,
https://doi.org/10.1016/j.rse.2016.02.037, 2016. a, b
Macelloni, G., Leduc-Leballeur, M., Montomoli, F., Brogioni, M., Ritz, C., and
Picard, G.: On the retrieval of internal temperature of Antarctica Ice Sheet
by using SMOS observations, Remote Sens. Environ., 233, 111405,
https://doi.org/10.1016/j.rse.2019.111405, 2019. a
Mätzler, C.: Microwave permittivity of dry snow, Geoscience and Remote
Sensing, IEEE Transactions on, 34, 573–581, 1996. a
Mätzler, C. and Wiesmann, A.: Documentation for MEMLS, Version 3, Tech.
rep., University of Bern, Research report, 2007. a
Mätzler, C., Rosenkranz, P. W., Battaglia, A., and Wigneron, J. P.: Thermal
microwave radiation – applications for remote sensing, no. 52 in IET,
Electromagnetic Waves, London, UK, 2006. a
Meier, W. N., Markus, T., and Comiso, J. C.: AMSR-E/AMSR2 Unified L3 Daily 12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth Polar Grids, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/RA1MIJOYPK3P, 2018. a
Miller, J. Z., Long, D. G., Jezek, K. C., Johnson, J. T., Brodzik, M. J., Shuman, C. A., Koenig, L. S., and Scambos, T. A.: Brief communication: Mapping Greenland's perennial firn aquifers using enhanced-resolution L-band brightness temperature image time series, The Cryosphere, 14, 2809–2817, https://doi.org/10.5194/tc-14-2809-2020, 2020. a
Montgomery, L., Miège, C., Miller, J., Scambos, T. A., Wallin, B.,
Miller, O., Solomon, D. K., Forster, R., and Koenig, L.: Hydrologic
Properties of a Highly Permeable Firn Aquifer in the Wilkins Ice Shelf,
Antarctica, Geophys. Res. Lett., 47, e2020GL089552, https://doi.org/10.1029/2020gl089552,
2020. a, b
Montpetit, B., Royer, A., Roy, A., Langlois, A., and Derksen, C.: Snow
Microwave Emission Modeling of Ice Lenses Within a Snowpack Using the
Microwave Emission Model for Layered Snowpacks, IEEE T.
Geosci. Remote, 51, 4705–4717,
https://doi.org/10.1109/tgrs.2013.2250509, 2013. a
Mousavi, S., Colliander, A., Miller, J. Z., and Kimball, J. S.: Antarctica Ice
Sheet Melt Detection Using a Machine Learning Algorithm Based on SMAP
Microwave Radiometery, in: 2021 IEEE International Geoscience and Remote
Sensing Symposium IGARSS, IEEE, https://doi.org/10.1109/igarss47720.2021.9553812,
2021. a
Munneke, P. K., Ligtenberg, S. R., Broeke, M. R. V. D., and Vaughan, D. G.:
Firn air depletion as a precursor of Antarctic ice-shelf collapse, J.
Glaciol., 60, 205–214, https://doi.org/10.3189/2014jog13j183, 2014. a
Munneke, P. K., Luckman, A. J., Bevan, S. L., Smeets, C. J. P. P., Gilbert, E.,
van den Broeke, M. R., Wang, W., Zender, C., Hubbard, B., Ashmore, D., Orr,
A., King, J. C., and Kulessa, B.: Intense Winter Surface Melt on an Antarctic
Ice Shelf, Geophys. Res. Lett., 45, 7615–7623,
https://doi.org/10.1029/2018gl077899, 2018. a
Naderpour, R. and Schwank, M.: Snow Wetness Retrieved from L-Band Radiometry,
Remote Sensing, 10, 359, https://doi.org/10.3390/rs10030359, 2018. a, b, c, d
Naderpour, R., Schwank, M., and Mätzler, C.: Davos-Laret Remote Sensing Field
Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and
Analysis, Remote Sens., 9, 1185, https://doi.org/10.3390/rs9111185, 2017. a
Picard, G.: smrt-model/smrt_liquid_water_paper: Initial release, Zenodo [code],
https://doi.org/10.5281/zenodo.7302799, 2022. a
Picard, G. and Fily, M.: Surface melting observations in Antarctica by microwave radiometers: Correcting 26-year time series from changes in acquisition hours, Remote Sens. Environ., 104, 325–336, https://doi.org/10.1016/j.rse.2006.05.010, 2006 (data available at: https://snow.univ-grenoble-alpes.fr/melting/, last access: 25 November 2022). a, b
Picard, G., Fily, M., and Gallee, H.: Surface melting derived from microwave
radiometers: a climatic indicator in Antarctica, Ann. Glaciol., 46, 29–34,
https://doi.org/10.3189/172756407782871684, 2007. a, b
Picard, G., Brucker, L., Fily, M., Gallee, H., and Krinner, G.: Modeling
timeseries of microwave brightness temperature in Antarctica, J. Glaciol.,
55, 537–551, https://doi.org/10.3189/002214309788816678, 2009. a, b
Picard, G., Sandells, M., and Löwe, H.: SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0), Geosci. Model Dev., 11, 2763–2788, https://doi.org/10.5194/gmd-11-2763-2018, 2018. a, b, c, d
Picard, G., Löwe, H., Domine, F., Arnaud, L., Larue, F., Favier, V., Meur,
E. L., Lefebvre, E., Savarino, J., and Royer, A.: The Microwave Snow Grain
Size: A New Concept to Predict Satellite Observations Over Snow-Covered
Regions, AGU Advances, 3, e2021AV000630, https://doi.org/10.1029/2021av000630, 2022a. a
Picard, G., Löwe, H., and Mätzler, C.: Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles, The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, 2022b. a
Picard, G., Sandells, M., and Löwe, H.: smrt-model/smrt: v1.1.0, Zenodo [code],
https://doi.org/10.5281/zenodo.7086080, 2022c. a
Polder, D. and van Santen, J. H.: The effective permeability of mixtures of
solids, Physica, 12, 257–271, 1946. a
Ridley, J.: Surface melting on Antartic Peninsula ice shelves detected by
passive microwave sensors, Geophys. Res. Lett., 20, 2639–2642, 1993. a
Robel, A. A. and Banwell, A. F.: A Speed Limit on Ice Shelf Collapse Through
Hydrofracture, Geophys. Res. Lett., 46, 12092–12100,
https://doi.org/10.1029/2019gl084397, 2019. a, b
Sandells, M., Lowe, H., Picard, G., Dumont, M., Essery, R., Floury, N., Kontu,
A., Lemmetyinen, J., Maslanka, W., Morin, S., Wiesmann, A., and Matzler, C.:
X-Ray Tomography-Based Microstructure Representation in the Snow Microwave
Radiative Transfer Model, IEEE T. Geosci. Remote, 60, 1–15, https://doi.org/10.1109/tgrs.2021.3086412, 2021. a
Saunderson, D., Mackintosh, A., McCormack, F., Jones, R. S., and Picard, G.: Surface melt on the Shackleton Ice Shelf, East Antarctica (2003–2021), The Cryosphere, 16, 4553–4569, https://doi.org/10.5194/tc-16-4553-2022, 2022. a
Scambos, T. A.: Glacier acceleration and thinning after ice shelf collapse in
the Larsen B embayment, Antarctica, Geophys. Res. Lett., 31, L18402,
https://doi.org/10.1029/2004GL020670, 2004. a
Shi, J. and Dozier, J.: Inferring snow wetness using C-band data from
SIR-C′s polarimetric synthetic aperture radar, IEEE
T. Geosci. Remote, 33, 905–914,
https://doi.org/10.1109/36.406676, 1995. a
Shockley, E. M., Vrugt, J. A., and Lopez, C. F.: PyDREAM: high-dimensional
parameter inference for biological models in python, Bioinformatics, 34,
695–697, https://doi.org/10.1093/bioinformatics/btx626, 2017. a
Sihvola, A.: Electromagnetic Mixing Formulas and Applications, INSTITUTION OF
ENGINEERING & T,
http://www.ebook.de/de/product/21470462/a_sihvola_electromagnetic_mixing_formulas_and_applications.html (last access: 14 March 2022),
1999. a
Sihvola, A., Nyfors, E., and Tiuri, M.: Mixing formulae and experimental
results for the dielectric constant of snow, J. Glaciol., 31, 163–170, 1985. a
Spergel, J. J., Kingslake, J., Creyts, T., van Wessem, M., and Fricker, H. A.:
Surface meltwater drainage and ponding on Amery Ice Shelf, East Antarctica,
1973–2019, J. Glaciol., 67, 985–998,
https://doi.org/10.1017/jog.2021.46, 2021. a
Surdyk, S.: Using microwave brightness temperature to detect short-term surface
air temperature changes in Antarctica: An analytical approach, Remote Sens. Environ., 80, 256–271, 2002. a
Tedesco, M.: Assessment and development of snowmelt retrieval algorithms over
Antarctica from K-band spaceborne brightness temperature (1979–2008), Remote
Sens. Environ., 113, 979–997, https://doi.org/10.1016/j.rse.2009.01.009, 2009. a, b
Tedesco, M. and Kim, E. J.: Intercomparison of Electromagnetic Models for
Passive Microwave Remote Sensing of Snow, Geoscience and Remote Sensing,
IEEE Transactions on, 44, 2654–2666, https://doi.org/10.1109/TGRS.2006.873182, 2006. a, b
Tedesco, M., Kim, E. J., England, A. W., de Roo, R. D., and Hardy, J. P.:
Brightness Temperatures of Snow Melting/Refreezing Cycles: Observations and
Modeling Using a Multilayer Dense Medium Theory-Based Model, Geoscience and
Remote Sensing, IEEE Transactions on, 44, 3563–3573,
https://doi.org/10.1109/TGRS.2006.881759, 2006. a
Tedesco, M., Abdalati, W., and Zwally, H. J.: Persistent surface snowmelt over
Antarctica (1987-2006) from 19.35 GHz brightness temperatures, Geophys.
Res. Lett., 34, 6, https://doi.org/10.1029/2007GL031199, 2007. a, b
ter Braak, C. J. F. and Vrugt, J. A.: Differential Evolution Markov Chain with
snooker updater and fewer chains, Stat. Comput., 18, 435–446,
https://doi.org/10.1007/s11222-008-9104-9, 2008. a
Tinga, W. R., Voss, W. A. G., and Blossey, D. F.:
Generalized approach to multiphase dielectric mixture theory,
J. Appl. Phys., 44, 3897–3902, https://doi.org/10.1063/1.1662868, 1973.
Tiuri, L. and Schultz, H.: Theoretical and experimental studies of microwave
radiation from a natural snow field, in: In Rango A. ed. Microwave remote
sensing of snowpack properties, Proceedings of a workshop, 225–234,
National Aeronautics and Space Center, Fort Collins, Colorado, 1980. a
Torinesi, O., Fily, M., and Genthon, C.: Variability and Trends of the Summer
Melt Period of Antarctic Ice Margins since 1980 from Microwave Sensors, J.
Climate, 16, 1047–1060, https://doi.org/10.1175/1520-0442(2003)016<1047:VATOTS>2.0.CO;2,
2003b. a
Torquato, S. and Kim, J.: Nonlocal Effective Electromagnetic Wave
Characteristics of Composite Media: Beyond the Quasistatic Regime, Phys.
Rev. X, 11, 021002, https://doi.org/10.1103/physrevx.11.021002, 2021. a
Trusel, L. D., Frey, K. E., Das, S. B., Munneke, P. K., and van den Broeke,
M. R.: Satellite-based estimates of Antarctic surface meltwater fluxes,
Geophys. Res. Lett., 40, 6148–6153, https://doi.org/10.1002/2013GL058138,
2013. a
Van Wessem, J. M., Reijmer, C., Morlighem, M., Mouginot, J., Rignot, E.,
Medley, B., Joughin, I., Wouters, B., Depoorter, M., Bamber, J., Lenaerts,
J., Berg, W. V. D., Broeke, M. V. D., and Meijgaard, E. V.: Improved
representation of East Antarctic surface mass balance in a regional
atmospheric climate model, J. Glaciol., 60, 761–770,
https://doi.org/10.3189/2014jog14j051, 2014.
a
van Wessem, J. M., Steger, C. R., Wever, N., and van den Broeke, M. R.: An exploratory modelling study of perennial firn aquifers in the Antarctic Peninsula for the period 1979–2016, The Cryosphere, 15, 695–714, https://doi.org/10.5194/tc-15-695-2021, 2021. a
Wiesmann, A., Hatzler, C., and Hiltbrunner, D.: Modeling microwave emission
spectra of layered snowpacks, in: IGARSS ′98. Sensing and
Managing the Environment. 1998 IEEE International Geoscience and Remote
Sensing. Symposium Proceedings. (Cat. No.98CH36174), IEEE,
https://doi.org/10.1109/igarss.1998.691371, 1998. a
Wille, J. D., Favier, V., Dufour, A., Gorodetskaya, I. V., Turner, J., Agosta,
C., and Codron, F.: West Antarctic surface melt triggered by atmospheric
rivers, Nat. Geosci., 12, 911–916, https://doi.org/10.1038/s41561-019-0460-1,
2019. a, b
Wille, J. D., Favier, V., Jourdain, N. C., Kittel, C., Turton, J. V., Agosta,
C., Gorodetskaya, I. V., Picard, G., Codron, F., Santos, C. L.-D., Amory, C.,
Fettweis, X., Blanchet, J., Jomelli, V., and Berchet, A.: Intense atmospheric
rivers can weaken ice shelf stability at the Antarctic Peninsula,
Commun. Earth Environ., 3, 90, https://doi.org/10.1038/s43247-022-00422-9,
2022. a
Short summary
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.
Using a snowpack radiative transfer model, we investigate in which conditions meltwater can be...