Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-3667-2023
© Author(s) 2023. 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-17-3667-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Aug 2023
Research article |
| 31 Aug 2023
| 31 Aug 2023
Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022
Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
Nicholas R. Golledge
Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
Alexandra Gossart
Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
Ghislain Picard
Univ. Grenoble Alpes, CNRS, Institut des Géosciences de l’Environnement (IGE), UMR 5001, Grenoble, France
Marion Leduc-Leballeur
Institute of Applied Physics “Nello Carrara”, National Research Council, 50019 Sesto Fiorentino, Italy
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Clément Cherblanc, Charles Amory, Fredrik Boberg, Alexandra Gossart, Nicolaj Hansen, Marte Gé Hofsteenge, Christine Schøtt Hvidberg, Sylvain Marchi, Masashi Niwano, Florian Sauerland, Niels Souverijns, José Abraham Torres-Alavez, Baptiste Vandecrux, Christiaan van Dalum, Willem Jan van de Berg, Nicole van Lipzig, Maurice van Tiggelen, Kristiina Verro, and Ruth Mottram
EGUsphere, https://doi.org/10.5194/egusphere-2026-4690, https://doi.org/10.5194/egusphere-2026-4690, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Regional Climate Models are known to improve the modelling of meteorological variables, like snow accumulation, in mountainous areas, relative to coarser global models. It was unclear whether they also improve the modelling over the Antarctic Ice Sheet, which is mostly a nearly flat, high-elevation plateau. We find that Regional Climate Models do perform better than global models on most metrics tested, building trust in the regional models for future Antarctic projections.
Ghislain Picard, Justin Murfitt, Elena Zakharova, Pierre Zeiger, Laurent Arnaud, Jeremie Aublanc, Jack C. Landy, Michele Scagliola, and Claude Duguay
Geosci. Model Dev., 19, 9103–9129, https://doi.org/10.5194/gmd-19-9103-2026, https://doi.org/10.5194/gmd-19-9103-2026, 2026
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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.
Adrien Ooms, Mathieu Casado, Sonja Wahl, Thomas Laepple, Alexandra Zuhr, Pete Akers, Ghislain Picard, Laurent Arnaud, Joel Savarino, Kira Rehfeld, Barbara Stenni, Giuliano Dreossi, Mauro Masiol, Amaelle Landais, and Valérie Masson-Delmotte
EGUsphere, https://doi.org/10.5194/egusphere-2026-4801, https://doi.org/10.5194/egusphere-2026-4801, 2026
This preprint is open for discussion and under review for Climate of the Past (CP).
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This work presents new insights in the archival process of multi-annual climatic signal in the near surface firn at Concordia Station, East-Antarctica, from the new data set of high resolution stable water isotope profiles measured in a snow trench. The dated isotopic profiles covering 20 years are compared against overlapping precipitation and surface snow observations. This highlights the strong contribution of stratigraphic noise and post depositional effects to individual trench profiles.
Marta Stentella, Ghislain Picard, Petra Heil, Jacqueline Boutin, Emmanuel Dinnat, and Stuart Corney
The Cryosphere, 20, 4465–4489, https://doi.org/10.5194/tc-20-4465-2026, https://doi.org/10.5194/tc-20-4465-2026, 2026
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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 flooded/wet snow, thin ice, and roughened ocean surfaces. These findings can improve our interpretation of satellite observations and forecast sea ice changes.
Philippe Gamet, Bimal K. Bhattacharya, Jean-Louis Roujean, Gilles Boulet, Corinne Salcedo, Albert Olioso, Kanishka Mallick, Marie Weiss, Emmanuelle Sarrazin, Emmanuelle Autret, Laure Roupioz, José Antonio Sobrino, Olivier Merlin, Ghislain Picard, Thomas H. G. Vidal, Mark Irvine, Rahul Nigam, Mehul Pandya, Simon Gascoin, Sébastien Marcq, Manoj K. Mishra, Emilie Delogu, Vincent Rivalland, Jordi Etchanchu, Jean-Louis Raynaud, Meenakshi Sarkar, Rishi K. Gangwar, Sushil K. Singh, Renaud Binet, Sara Arioli, Hervé Yésou, Isabelle Dadou, Alexei Kouraev, Julien Michel, Benoit Coudert, Samuel Mwangi, Nesrine Farhani, Ayan Das, Stéphane Saux-Picart, Thierry Tormos, Jean-François Piollé, Charles Verpoorter, Auline Rodler, Jonathan Leon Tavares, Jennifer Adams, Anand S. Sahadevan, Besavaraju Santhi Sree, Manikumar Vedantam, Dheeraj Adlakha, Laurence Buffet, Kathrin Naegeli, Itziar Barat, Jerome Demarty, Olivier Hagolle, and Philippe Maisongrande
EGUsphere, https://doi.org/10.5194/egusphere-2026-4492, https://doi.org/10.5194/egusphere-2026-4492, 2026
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This paper highlights how the upcoming TRISHNA satellite mission (launching 2027) will transform Earth observation by providing high-resolution (60 m) thermal infrared imagery with sub-weekly revisits. It details the mission's design and its synergies with other future missions to significantly improve the monitoring of global surface temperature and its dynamics, water stress, and ecosystem energy processes.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Ghislain Picard, Richard Essery, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Michele Scagliola, Jerome Bouffard, Alessandro Di Bella, and Christian Haas
EGUsphere, https://doi.org/10.5194/egusphere-2026-3615, https://doi.org/10.5194/egusphere-2026-3615, 2026
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurements, but uncertainty arises from the 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 algorithms to retrieve snow depth and sea ice thickness for radar altimeter missions.
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
The Cryosphere, 20, 3235–3255, https://doi.org/10.5194/tc-20-3235-2026, https://doi.org/10.5194/tc-20-3235-2026, 2026
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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
The Cryosphere, 20, 1599–1618, https://doi.org/10.5194/tc-20-1599-2026, https://doi.org/10.5194/tc-20-1599-2026, 2026
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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.
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.
Alexandra Gossart, Alena Malyarenko, Liv Cornelissen, Craig Stevens, Una Miller, Christopher J. Zappa, Nancy Luca, Pasquale Castagno, and Giorgio Budillon
EGUsphere, https://doi.org/10.5194/egusphere-2025-4332, https://doi.org/10.5194/egusphere-2025-4332, 2025
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Polynyas are areas of open water that form within the sea ice during the Antarctic winter. They occur along the coastline and act as hotspots for air–sea interactions and sea-ice formation. We use the P-SKRIPSv2 model to simulate air–sea–ice–ocean interactions in the Ross Sea. Model results are compared with observations, and we test how changes in air–sea–ice coupling affect the system. Our findings show that P-SKRIPSv2 provides a valuable tool to study polynyasa and understand their dynamics.
Johanna Beckmann, Ronja Reese, Felicity S. McCormack, Sue Cook, Lawrence Bird, Dawid Gwyther, Daniel Richards, Matthias Scheiter, Yu Wang, Hélène Seroussi, Ayako Abe‐Ouchi, Torsten Albrecht, Jorge Alvarez‐Solas, Xylar S. Asay‐Davis, Jean‐Baptiste Barre, Constantijn J. Berends, Jorge Bernales, Javier Blasco, Justine Caillet, David M. Chandler, Violaine Coulon, Richard Cullather, Christophe Dumas, Benjamin K. Galton‐Fenzi, Julius Garbe, Fabien Gillet‐Chaulet, Rupert Gladstone, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, G. Hilmar Gudmundsson, Holly Kyeore Han, Trevor R. Hillebrand, Matthew J. Hoffman, Philippe Huybrechts, Nicolas C. Jourdain, Ann Kristin Klose, Petra M. Langebroek, Gunter R. Leguy, William H. Lipscomb, Daniel P. Lowry, Pierre Mathiot, Marisa Montoya, Mathieu Morlighem, Sophie Nowicki, Frank Pattyn, Antony J. Payne, Tyler Pelle, Aurélien Quiquet, Alexander Robinson, Leopekka Saraste, Erika G. Simon, Sainan Sun, Jake P. Twarog, Luke D. Trusel, Benoit Urruty, Jonas Van Breedam, Roderik S. W. van de Wal, Chen Zhao, and Thomas Zwinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4069, https://doi.org/10.5194/egusphere-2025-4069, 2025
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Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
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.
Francesca Baldacchino, Nicholas R. Golledge, Mathieu Morlighem, Huw Horgan, Alanna V. Alevropoulos-Borrill, Alena Malyarenko, Alexandra Gossart, Daniel P. Lowry, and Laurine van Haastrecht
The Cryosphere, 19, 107–127, https://doi.org/10.5194/tc-19-107-2025, https://doi.org/10.5194/tc-19-107-2025, 2025
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Understanding how the Ross Ice Shelf flow is changing in a warming world is important for predicting ice sheet change. Field measurements show clear intra-annual variations in ice flow; however, it is unclear what mechanisms drive this variability. We show that local perturbations in basal melt can have a significant impact on ice flow speed, but a combination of forcings is likely driving the observed variability in ice flow.
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
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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.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, https://doi.org/10.5194/tc-17-5197-2023, 2023
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Mass loss from Antarctica is a key contributor to sea level rise over the 21st century, and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
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.
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.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
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.
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.
Francesca Baldacchino, Mathieu Morlighem, Nicholas R. Golledge, Huw Horgan, and Alena Malyarenko
The Cryosphere, 16, 3723–3738, https://doi.org/10.5194/tc-16-3723-2022, https://doi.org/10.5194/tc-16-3723-2022, 2022
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Understanding how the Ross Ice Shelf will evolve in a warming world is important to the future stability of Antarctica. It remains unclear what changes could drive the largest mass loss in the future and where places are most likely to trigger larger mass losses. Sensitivity maps are modelled showing that the RIS is sensitive to changes in environmental and glaciological controls at regions which are currently experiencing changes. These regions need to be monitored in a warming world.
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.
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.
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
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
Banwell, A. F., Wever, N., Dunmire, D., and Picard, G.: Quantifying
Antarctic-Wide Ice-Shelf Surface Melt Volume Using Microwave and Firn Model
Data: 1980 to 2021, Geophys. Res. Lett., 50, e2023GL102744, https://doi.org/10.1029/2023GL102744, 2023. a, b
Barrand, N. E., Vaughan, D. G., Steiner, N., Tedesco, M., Kuipers Munneke, P.,
Van Den Broeke, M. R., and Hosking, J. S.: Trends in Antarctic Peninsula
surface melting conditions from observations and regional climate modeling,
J. Geophys. Res.-Earth, 118, 315–330, 2013. a
Braithwaite, R. J.: Positive degree-day factors for ablation on the Greenland
ice sheet studied by energy-balance modelling, J. Glaciol., 41,
153–160, 1995. a
Chang, T. and Gloersen, P.: Microwave emission from dry and wet snow, in:
Operational Applications of Satellite Snowcover Observations: The Proceedings
of a Workshop Held August 18–20, 1975 at the Waystation, South Lake Tahoe,
California, edited by: Rango, A., Aeronautics, U. S. N., Administration, S.,
and University of Nevada, R., NASA SP, Scientific and Technical Information
Office, National Aeronautics and Space Administration,
https://ntrs.nasa.gov/citations/19760009500 (last access: 30 August 2023), 1975. a, b
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
Costi, J., Arigony-Neto, J., Braun, M., Mavlyudov, B., Barrand, N. E.,
Da Silva, A. B., Marques, W. C., and Simoes, J. C.: Estimating surface melt
and runoff on the Antarctic Peninsula using ERA-Interim reanalysis data,
Antarct. Sci., 30, 379–393, 2018. a
Fausto, R. S., Ahlstrøm, A. P., Van As, D., and Steffen, K.: Present-day
temperature standard deviation parameterization for Greenland, J.
Glaciol., 57, 1181–1183, 2011. a
Fricker, H. A., Arndt, P., Brunt, K. M., Datta, R. T., Fair, Z., Jasinski, M. F., Kingslake, J., Magruder, L. A., Moussavi, M., Pope, A., Spergel, J. J., Stoll, J. D., and Wouters, B.:
ICESat-2 meltwater depth estimates: application to surface melt on Amery Ice
Shelf, East Antarctica, Geophys. Res. Lett., 48, e2020GL090550, https://doi.org/10.1029/2020GL090550,
2021. a
Glasser, N. and Scambos, T. A.: A structural glaciological analysis of the 2002
Larsen B ice-shelf collapse, J. Glaciol., 54, 3–16, 2008. a
Golledge, N. R., Everest, J. D., Bradwell, T., and Johnson, J. S.: Lichenometry
on adelaide island, antarctic peninsula: size-frequency studies, growth rates
and snowpatches, Geogr. Ann. A, 92,
111–124, 2010. 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. D., 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. D., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47 (last access: 30 August 2023), 2023a. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6 (last access: 30 August 2023), 2023b. a, b, c, d, e, f
Ismail, M. F., Bogacki, W., Disse, M., Schäfer, M., and Kirschbauer, L.: Estimating degree-day factors of snow based on energy flux components, The Cryosphere, 17, 211–231, https://doi.org/10.5194/tc-17-211-2023, 2023. a
Jakobs, C. L., Reijmer, C. H., Smeets, C. P., Trusel, L. D., Van De Berg,
W. J., Van Den Broeke, M. R., and Van Wessem, J. M.: A benchmark dataset of
in situ Antarctic surface melt rates and energy balance, J.
Glaciol., 66, 291–302, 2020. a
Kingslake, J., Ely, J. C., Das, I., and Bell, R. E.: Widespread movement of
meltwater onto and across Antarctic ice shelves, Nature, 544, 349–352, 2017. a
Kittel, C., Amory, C., Agosta, C., Jourdain, N. C., Hofer, S., Delhasse, A., Doutreloup, S., Huot, P.-V., Lang, C., Fichefet, T., and Fettweis, X.: Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236, https://doi.org/10.5194/tc-15-1215-2021, 2021. a
Larour, E., Seroussi, H., Morlighem, M., and Rignot, E.: Continental scale, high order, high spatial resolution, icesheet modeling using the Ice Sheet System Model (ISSM), J. Geophys. Res., 117, F01022, https://doi.org/10.1029/2011JF002140,
2012. a, b
Lenaerts, J., Lhermitte, S., Drews, R., Ligtenberg, S., Berger, S., Helm, V.,
Smeets, C., Van Den Broeke, M., Van De Berg, W. J., Van Meijgaard, E.,
et al.: Meltwater produced by wind–albedo interaction stored in an East
Antarctic ice shelf, Nat. Clim. Change, 7, 58–62, 2017. a
Liu, H., Wang, L., and Jezek, K. C.: Spatiotemporal variations of snowmelt in Antarctica derived from satellitescanning multichannel microwave radiometer and Special Sensor Microwave Imager data (1978–2004), J. Geophys. Res., 111, F01003, https://doi.org/10.1029/2005JF000318, 2006. a
Mernild, S. H., Mote, T. L., and Liston, G. E.: Greenland ice sheet surface
melt extent and trends: 1960–2010, J. Glaciol., 57, 621–628,
2011. a
Mottram, R., Hansen, N., Kittel, C., van Wessem, J. M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates, The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, 2021. a
Nowicki, S., Bindschadler, R. A., Abe-Ouchi, A., Aschwanden, A., Bueler, E., Choi, H., Fastook, J., Granzow, G., Greve, R., Gutowski, G., Herzfeld, U., Jackson, C., Johnson, J., Khroulev, C., Larour, E., Levermann, A., Lipscomb, W. H., Martin, M. A., Morlighem, M., Parizek, B. R., Pollard, D., Price, S. F., Ren, D., Rignot, E., Saito, F., Sato, T., Seddik, H., Seroussi, H., Takahashi, K., Walker, R., and Wang, W. L.: Insights
into spatial sensitivities of ice mass response to environmental change from
the SeaRISE ice sheet modeling project I: Antarctica, J. Geophys.
Res.-Earth, 118, 1002–1024, 2013. a
NSIDC: A Guide to NSIDC's Polar Stereographic Projection,
https://nsidc.org/data/polar-stereo/ps_grids.html (last access: 30 August 2023), 2022. a
Picard, G.: Snow status (wet/dry) in Antarctica from SMMR, SSM/I, AMSR-E and
AMSR2 passive microwave radiometers, PerSCiDO [data set], https://doi.org/10.18709/perscido.2022.09.ds376 (last access: 30 August 2023), 2022. a
Reeh, N.: Parameterization of melt rate and surface temperature in the
Greenland ice sheet, Polarforschung, 59, 113–128, 1991. a
Ryan, J., Smith, L., Van As, D., Cooley, S., Cooper, M., Pitcher, L., and
Hubbard, A.: Greenland Ice Sheet surface melt amplified by snowline migration
and bare ice exposure, Science Advances, 5, eaav3738, https://doi.org/10.1126/sciadv.aav3738, 2019. a
Schulzweida, U.: CDO User Guide, Zenodo [software], https://doi.org/10.5281/zenodo.5614769, 2021. a
Sellevold, R. and Vizcaino, M.: First application of artificial neural networks
to estimate 21st century Greenland ice sheet surface melt, Geophys.
Res. Lett., 48, e2021GL092449, https://doi.org/10.1029/2021GL092449, 2021. 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, 2021. a
Tedesco, M. and Monaghan, A. J.: An updated Antarctic melt record through 2009 and its linkages to high-latitude and tropical climate variability, Geophys. Res. Lett., 36, L18502, https://doi.org/10.1029/2009GL039186, 2009. a, b, c
Tetzner, D., Thomas, E., and Allen, C.: A validation of ERA5 reanalysis data in
the Southern Antarctic Peninsula–Ellsworth land region, and its
implications for ice core studies, Geosciences, 9, 289, https://doi.org/10.3390/geosciences9070289, 2019. a
Trusel, L., Frey, K. E., and Das, S. B.: Antarctic surface melting dynamics:
Enhanced perspectives from radar scatterometer data, J. Geophys.
Res.-Earth, 117, F02023, https://doi.org/10.1029/2011JF002126, 2012. a, b
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, 2013. a
Turton, J. V., Kirchgaessner, A., Ross, A. N., King, J. C., and Kuipers Munneke, P.: The influence of föhn winds on annual and seasonal surface melt on the Larsen C Ice Shelf, Antarctica, The Cryosphere, 14, 4165–4180, https://doi.org/10.5194/tc-14-4165-2020, 2020. a, b
van den Broeke, M., Bus, C., Ettema, J., and Smeets, P.: Temperature thresholds
for degree-day modelling of Greenland ice sheet melt rates, Geophys.
Res. Lett., 37, L18501, https://doi.org/10.1029/2010GL044123, 2010.
a, b
van Wessem, J. M., van de Berg, W. J., Noël, B. P. Y., van Meijgaard, E., Amory, C., Birnbaum, G., Jakobs, C. L., Krüger, K., Lenaerts, J. T. M., Lhermitte, S., Ligtenberg, S. R. M., Medley, B., Reijmer, C. H., van Tricht, K., Trusel, L. D., van Ulft, L. H., Wouters, B., Wuite, J., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016), The Cryosphere, 12, 1479–1498, https://doi.org/10.5194/tc-12-1479-2018, 2018. a, b, c, d, e, f
van Wessem, J. M., van de Berg, W. J., and van den Broeke, M. R.: Data set: Monthly averaged RACMO2.3p2 variables (1979–2022); Antarctica, Zenodo [data set], https://doi.org/10.5281/zenodo.7845736, 2023. a
Wake, L. and Marshall, S.: Assessment of current methods of positive degree-day
calculation using in situ observations from glaciated regions, J.
Glaciol., 61, 329–344, 2015. 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, 2019. a
Wilton, D. J., Jowett, A., Hanna, E., Bigg, G. R., Van Den Broeke, M. R.,
Fettweis, X., and Huybrechts, P.: High resolution (1 km) positive degree-day
modelling of Greenland ice sheet surface mass balance, 1870–2012 using
reanalysis data, J. Glaciol., 63, 176–193, 2017. a
Winkelmann, R., Martin, M. A., Haseloff, M., Albrecht, T., Bueler, E., Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model (PISM-PIK) – Part 1: Model description, The Cryosphere, 5, 715–726, https://doi.org/10.5194/tc-5-715-2011, 2011. a, b
Zheng, Y., Golledge, N. R., and Gossart, A.: Data set: Statistically parameterizing and evaluating a positive degree-day model to estimate surface melt in Antarctica from 1979 to 2022 (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.7131459, 2023. a
Zhu, J., Xie, A., Qin, X., Wang, Y., Xu, B., and Wang, Y.: An assessment of
ERA5 reanalysis for antarctic near-surface air temperature, Atmosphere, 12,
217, https://doi.org/10.3390/atmos12020217, 2021. a, b, c, d
Short summary
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.
Positive degree-day (PDD) schemes are widely used in many Antarctic numerical ice sheet models....
