Articles | Volume 16, issue 9
https://doi.org/10.5194/tc-16-3431-2022
© 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-16-3431-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Sep 2022
Research article |
| 01 Sep 2022
| 01 Sep 2022
Metamorphism of snow on Arctic sea ice during the melt season: impact on spectral albedo and radiative fluxes through snow
Gauthier Vérin
Takuvik Joint International Laboratory, Université Laval (Canada), CNRS-INSU (France), Université Laval, Québec, Canada
Institut des Géosciences de
l'Environnement (IGE), UMR 5001, Université Grenoble Alpes, CNRS, 38041 Grenoble, France
Takuvik Joint International Laboratory, Université Laval (Canada), CNRS-INSU (France), Université Laval, Québec, Canada
Department of Chemistry, Centre for Northern Studies, Université Laval, Québec, Canada
Marcel Babin
Takuvik Joint International Laboratory, Université Laval (Canada), CNRS-INSU (France), Université Laval, Québec, Canada
Ghislain Picard
Institut des Géosciences de
l'Environnement (IGE), UMR 5001, Université Grenoble Alpes, CNRS, 38041 Grenoble, France
Laurent Arnaud
Institut des Géosciences de
l'Environnement (IGE), UMR 5001, Université Grenoble Alpes, CNRS, 38041 Grenoble, France
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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.
Marin Kneib, Patrick Wagnon, Laurent Arnaud, Louise Balmas, Olivier Laarman, Bruno Jourdain, Amaury Dehecq, Emmanuel Lemeur, Fanny Brun, Andrea Kneib-Walter, Ilaria Santin, Laurane Charrier, Thierry Faug, Giulia Mazzotti, Antoine Rabatel, Delphine Six, and Daniel Farinotti
EGUsphere, https://doi.org/10.5194/egusphere-2026-786, https://doi.org/10.5194/egusphere-2026-786, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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Avalanches are vital for glacier survival, yet their impact is difficult to quantify. We used low-cost cameras and drones to monitor an avalanche cone in the French Alps for two years. By accounting for ice flow, we found that avalanches can deposit 30 meters of snow annually – 50 times more than normal snowfall. This high-frequency data reveals that these cones fill until reaching a specific steepness, after which new snow slides further down to the base.
Léa Elise Bonnefoy, Catherine Prigent, Ghislain Picard, Clément Soriot, Alice Le Gall, Lise Kilic, and Carlos Jimenez
The Cryosphere, 20, 1297–1314, https://doi.org/10.5194/tc-20-1297-2026, https://doi.org/10.5194/tc-20-1297-2026, 2026
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Microwave radiometry senses the thermal emission from a target, whereas its active counterpart, radar, sends a signal to the target and measures the reflected signal. We simultaneously model radar and radiometry over the East Antarctic ice sheet, which we propose as an analog for icy moons: we can reproduce most data with a unique model. The radar response of Saturn's moons cannot be reproduced and is likely caused by processes unaccounted for in the model and less efficient in the Antarctic.
Marion Leduc-Leballeur, Ghislain Picard, Pierre Zeiger, and Giovanni Macelloni
The Cryosphere, 20, 1199–1216, https://doi.org/10.5194/tc-20-1199-2026, https://doi.org/10.5194/tc-20-1199-2026, 2026
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This study presents a quantitative and synthetic classification of the snowpack in 10 dry-wet status by aggregating separate binary indicators derived from satellite observations. The classification follows the expected evolution of the melt season: night refreezing is frequent at the onset, sustained melting is observed during the summer peak, and remnant liquid water at depth occurs at the end. This dataset improves the knowledge of melt processes using passive microwave remote sensing.
Ghislain Picard, Justin Murfitt, Elena Zakharova, Pierre Zeiger, Laurent Arnaud, Jeremie Aublanc, Jack C. Landy, Michele Scagliola, and Claude Duguay
EGUsphere, https://doi.org/10.5194/egusphere-2025-6056, https://doi.org/10.5194/egusphere-2025-6056, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Radar altimeters measure ice-sheet elevation and sea-ice thickness. To improve their accuracy, we developed a model that simulates altimeter waveforms based on the physical properties of snow and sea ice. It computes the power, time, and Doppler shift of radar echoes as waves travel to the surface, interact with snow and ice, and return to the satellite. The model was verified internally, validated against other models and applied in Antarctica using in-situ snow measurements.
Marta Stentella, Ghislain Picard, Petra Heil, Jacqueline Boutin, Emmanuel Dinnat, and Stuart Corney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6437, https://doi.org/10.5194/egusphere-2025-6437, 2026
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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 wet snow, roughened ocean surfaces, and thin ice. These findings can improve how we interpret satellite observations and forecast sea ice changes.
Vincent Vionnet, Nicolas R. Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
Geosci. Model Dev., 18, 9119–9147, https://doi.org/10.5194/gmd-18-9119-2025, https://doi.org/10.5194/gmd-18-9119-2025, 2025
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Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Raphaël Larouche, Bastian Raulier, Christian Katlein, Simon Lambert-Girard, Simon Thibault, and Marcel Babin
The Cryosphere, 19, 4785–4804, https://doi.org/10.5194/tc-19-4785-2025, https://doi.org/10.5194/tc-19-4785-2025, 2025
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We developed a new method to study how light interacts with sea ice using a compact 360° camera. By lowering this camera into drilled holes in ice, we captured detailed light patterns inside different ice layers. Our research revealed how light is absorbed and scattered in both Arctic multi-year ice and thinner, seasonal ice in Quebec. These findings improve our understanding of sea ice structure and its role in the climate system, helping in the representation of sea ice in models.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Richard O'Keefe, Tiffany Windholz, Kyle A. Arndt, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Mathias Göckede, David Olefeldt, Gerard Rocher-Ros, Edward A. G. Schuur, David Bastviken, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Katrin Attermeyer, Mika Aurela, Sivakiruthika Balathandayuthabani, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andres Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Macall Hock, David Holl, Beth Holmes, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Chersity P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natacha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Misha Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martkainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Daniel F. Nadeau, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykanen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleski Räsänen, Helena Rautakoski, Riika Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Elena Veretennikova, Timo Vesala, Tarmo Virtanen, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-585, https://doi.org/10.5194/essd-2025-585, 2025
Preprint under review for ESSD
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Ariadna Celina Nocera, Lars Stemmann, Marcel Babin, Tristan Biard, Julie Coustenoble, François Carlotti, Laurent Coppola, Lucas Courchet, Laetitia Drago, Amanda Elineau, Lionel Guidi, Helena Hauss, Laëtitia Jalabert, Lee Karp-Boss, Rainer Kiko, Manon Laget, Fabien Lombard, Andrew McDonnell, Camille Merland, Solène Motreuil, Thelma Panaïotis, Marc Picheral, Andreas Rogge, Anya Waite, and Jean-Olivier Irisson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-522, https://doi.org/10.5194/essd-2025-522, 2025
Preprint under review for ESSD
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Plankton and detritus play a key role in ocean health and climate regulation. We present a large global dataset of images and information collected from 2008 to 2018 using specialized underwater camera (UVP). This publicly available dataset will support more accurate ecological models and help train artificial intelligence tools, improving how scientists track ocean biodiversity and monitor environmental changes.
Adrien Ooms, Mathieu Casado, Ghislain Picard, Laurent Arnaud, Maria Hörhold, Andrea Spolaor, Rita Traversi, Joel Savarino, Patrick Ginot, Pete Akers, Birthe Twarloh, and Valérie Masson-Delmotte
EGUsphere, https://doi.org/10.5194/egusphere-2025-3259, https://doi.org/10.5194/egusphere-2025-3259, 2025
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This work presents a new approach to the estimation of accumulation rates at Concordia Station, East-Antarctica, for the last 20 years, from a new data set of chemical tracers and snow micro-scale properties measured in a snow trench. Multi-annual and meter to decameter scale variability of accumulation rates are compared again in-situ measurements of surface laser scanner and stake farm, with very good agreement. This further constrains SMB estimation for Antarctica at high temporal resolution.
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.
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.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
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Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
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.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
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Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Mathilde Dugenne, Marco Corrales-Ugalde, Jessica Y. Luo, Rainer Kiko, Todd D. O'Brien, Jean-Olivier Irisson, Fabien Lombard, Lars Stemmann, Charles Stock, Clarissa R. Anderson, Marcel Babin, Nagib Bhairy, Sophie Bonnet, Francois Carlotti, Astrid Cornils, E. Taylor Crockford, Patrick Daniel, Corinne Desnos, Laetitia Drago, Amanda Elineau, Alexis Fischer, Nina Grandrémy, Pierre-Luc Grondin, Lionel Guidi, Cecile Guieu, Helena Hauss, Kendra Hayashi, Jenny A. Huggett, Laetitia Jalabert, Lee Karp-Boss, Kasia M. Kenitz, Raphael M. Kudela, Magali Lescot, Claudie Marec, Andrew McDonnell, Zoe Mériguet, Barbara Niehoff, Margaux Noyon, Thelma Panaïotis, Emily Peacock, Marc Picheral, Emilie Riquier, Collin Roesler, Jean-Baptiste Romagnan, Heidi M. Sosik, Gretchen Spencer, Jan Taucher, Chloé Tilliette, and Marion Vilain
Earth Syst. Sci. Data, 16, 2971–2999, https://doi.org/10.5194/essd-16-2971-2024, https://doi.org/10.5194/essd-16-2971-2024, 2024
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Plankton and particles influence carbon cycling and energy flow in marine ecosystems. We used three types of novel plankton imaging systems to obtain size measurements from a range of plankton and particle sizes and across all major oceans. Data were compiled and cross-calibrated from many thousands of images, showing seasonal and spatial changes in particle size structure in different ocean basins. These datasets form the first release of the Pelagic Size Structure database (PSSdb).
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, Nander Wever, Adrien Michel, Michael Lehning, and Pierre-Erik Isabelle
The Cryosphere, 18, 2783–2807, https://doi.org/10.5194/tc-18-2783-2024, https://doi.org/10.5194/tc-18-2783-2024, 2024
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Observations over several winters at two boreal sites in eastern Canada show that rain-on-snow (ROS) events lead to the formation of melt–freeze layers and that preferential flow is an important water transport mechanism in the sub-canopy snowpack. Simulations with SNOWPACK generally show good agreement with observations, except for the reproduction of melt–freeze layers. This was improved by simulating intercepted snow microstructure evolution, which also modulates ROS-induced runoff.
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.
Florent Domine, Denis Sarrazin, Daniel F. Nadeau, Georg Lackner, and Maria Belke-Brea
Earth Syst. Sci. Data, 16, 1523–1541, https://doi.org/10.5194/essd-16-1523-2024, https://doi.org/10.5194/essd-16-1523-2024, 2024
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The forest–tundra ecotone is the transition region between the boreal forest and Arctic tundra. It spans over 13 000 km across the Arctic and is evolving rapidly because of climate change. We provide extensive data sets of two sites 850 m apart, one in tundra and one in forest in this ecotone for use in various models. Data include meteorological and flux data and unique snow and soil physics data.
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.
Philippe Massicotte, Marcel Babin, Frank Fell, Vincent Fournier-Sicre, and David Doxaran
Earth Syst. Sci. Data, 15, 3529–3545, https://doi.org/10.5194/essd-15-3529-2023, https://doi.org/10.5194/essd-15-3529-2023, 2023
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The COASTlOOC oceanographic expeditions in 1997 and 1998 studied the relationship between seawater properties and biology and chemistry across the European coasts. The team collected data from 379 stations using ships and helicopters to support the development of ocean color remote-sensing algorithms. This unique and consistent dataset is still used today by researchers.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Sara Arioli, Ghislain Picard, Laurent Arnaud, and Vincent Favier
The Cryosphere, 17, 2323–2342, https://doi.org/10.5194/tc-17-2323-2023, https://doi.org/10.5194/tc-17-2323-2023, 2023
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To assess the drivers of the snow grain size evolution during snow drift, we exploit a 5-year time series of the snow grain size retrieved from spectral-albedo observations made with a new, autonomous, multi-band radiometer and compare it to observations of snow drift, snowfall and snowmelt at a windy location of coastal Antarctica. Our results highlight the complexity of the grain size evolution in the presence of snow drift and show an overall tendency of snow drift to limit its variations.
Martine Lizotte, Bennet Juhls, Atsushi Matsuoka, Philippe Massicotte, Gaëlle Mével, David Obie James Anikina, Sofia Antonova, Guislain Bécu, Marine Béguin, Simon Bélanger, Thomas Bossé-Demers, Lisa Bröder, Flavienne Bruyant, Gwénaëlle Chaillou, Jérôme Comte, Raoul-Marie Couture, Emmanuel Devred, Gabrièle Deslongchamps, Thibaud Dezutter, Miles Dillon, David Doxaran, Aude Flamand, Frank Fell, Joannie Ferland, Marie-Hélène Forget, Michael Fritz, Thomas J. Gordon, Caroline Guilmette, Andrea Hilborn, Rachel Hussherr, Charlotte Irish, Fabien Joux, Lauren Kipp, Audrey Laberge-Carignan, Hugues Lantuit, Edouard Leymarie, Antonio Mannino, Juliette Maury, Paul Overduin, Laurent Oziel, Colin Stedmon, Crystal Thomas, Lucas Tisserand, Jean-Éric Tremblay, Jorien Vonk, Dustin Whalen, and Marcel Babin
Earth Syst. Sci. Data, 15, 1617–1653, https://doi.org/10.5194/essd-15-1617-2023, https://doi.org/10.5194/essd-15-1617-2023, 2023
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Permafrost thaw in the Mackenzie Delta region results in the release of organic matter into the coastal marine environment. What happens to this carbon-rich organic matter as it transits along the fresh to salty aquatic environments is still underdocumented. Four expeditions were conducted from April to September 2019 in the coastal area of the Beaufort Sea to study the fate of organic matter. This paper describes a rich set of data characterizing the composition and sources of organic matter.
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.
Flavienne Bruyant, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Lise Artigue, Lucas Barbedo de Freitas, Guislain Bécu, Simon Bélanger, Pascaline Bourgain, Annick Bricaud, Etienne Brouard, Camille Brunet, Tonya Burgers, Danielle Caleb, Katrine Chalut, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Marine Cusa, Fanny Cusset, Laeticia Dadaglio, Marty Davelaar, Gabrièle Deslongchamps, Céline Dimier, Julie Dinasquet, Dany Dumont, Brent Else, Igor Eulaers, Joannie Ferland, Gabrielle Filteau, Marie-Hélène Forget, Jérome Fort, Louis Fortier, Martí Galí, Morgane Gallinari, Svend-Erik Garbus, Nicole Garcia, Catherine Gérikas Ribeiro, Colline Gombault, Priscilla Gourvil, Clémence Goyens, Cindy Grant, Pierre-Luc Grondin, Pascal Guillot, Sandrine Hillion, Rachel Hussherr, Fabien Joux, Hannah Joy-Warren, Gabriel Joyal, David Kieber, Augustin Lafond, José Lagunas, Patrick Lajeunesse, Catherine Lalande, Jade Larivière, Florence Le Gall, Karine Leblanc, Mathieu Leblanc, Justine Legras, Keith Lévesque, Kate-M. Lewis, Edouard Leymarie, Aude Leynaert, Thomas Linkowski, Martine Lizotte, Adriana Lopes dos Santos, Claudie Marec, Dominique Marie, Guillaume Massé, Philippe Massicotte, Atsushi Matsuoka, Lisa A. Miller, Sharif Mirshak, Nathalie Morata, Brivaela Moriceau, Philippe-Israël Morin, Simon Morisset, Anders Mosbech, Alfonso Mucci, Gabrielle Nadaï, Christian Nozais, Ingrid Obernosterer, Thimoté Paire, Christos Panagiotopoulos, Marie Parenteau, Noémie Pelletier, Marc Picheral, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Llúcia Ribot Lacosta, Jean-François Rontani, Blanche Saint-Béat, Julie Sansoulet, Noé Sardet, Catherine Schmechtig, Antoine Sciandra, Richard Sempéré, Caroline Sévigny, Jordan Toullec, Margot Tragin, Jean-Éric Tremblay, Annie-Pier Trottier, Daniel Vaulot, Anda Vladoiu, Lei Xue, Gustavo Yunda-Guarin, and Marcel Babin
Earth Syst. Sci. Data, 14, 4607–4642, https://doi.org/10.5194/essd-14-4607-2022, https://doi.org/10.5194/essd-14-4607-2022, 2022
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This paper presents a dataset acquired during a research cruise held in Baffin Bay in 2016. We observed that the disappearance of sea ice in the Arctic Ocean increases both the length and spatial extent of the phytoplankton growth season. In the future, this will impact the food webs on which the local populations depend for their food supply and fisheries. This dataset will provide insight into quantifying these impacts and help the decision-making process for policymakers.
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.
Rainer Kiko, Marc Picheral, David Antoine, Marcel Babin, Léo Berline, Tristan Biard, Emmanuel Boss, Peter Brandt, Francois Carlotti, Svenja Christiansen, Laurent Coppola, Leandro de la Cruz, Emilie Diamond-Riquier, Xavier Durrieu de Madron, Amanda Elineau, Gabriel Gorsky, Lionel Guidi, Helena Hauss, Jean-Olivier Irisson, Lee Karp-Boss, Johannes Karstensen, Dong-gyun Kim, Rachel M. Lekanoff, Fabien Lombard, Rubens M. Lopes, Claudie Marec, Andrew M. P. McDonnell, Daniela Niemeyer, Margaux Noyon, Stephanie H. O'Daly, Mark D. Ohman, Jessica L. Pretty, Andreas Rogge, Sarah Searson, Masashi Shibata, Yuji Tanaka, Toste Tanhua, Jan Taucher, Emilia Trudnowska, Jessica S. Turner, Anya Waite, and Lars Stemmann
Earth Syst. Sci. Data, 14, 4315–4337, https://doi.org/10.5194/essd-14-4315-2022, https://doi.org/10.5194/essd-14-4315-2022, 2022
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The term
marine particlescomprises detrital aggregates; fecal pellets; bacterioplankton, phytoplankton and zooplankton; and even fish. Here, we present a global dataset that contains 8805 vertical particle size distribution profiles obtained with Underwater Vision Profiler 5 (UVP5) camera systems. These data are valuable to the scientific community, as they can be used to constrain important biogeochemical processes in the ocean, such as the flux of carbon to the deep sea.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 3357–3373, https://doi.org/10.5194/tc-16-3357-2022, https://doi.org/10.5194/tc-16-3357-2022, 2022
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We compared the snowpack at two sites separated by less than 1 km, one in shrub tundra and the other one within the boreal forest. Even though the snowpack was twice as thick at the forested site, we found evidence that the vertical transport of water vapor from the bottom of the snowpack to its surface was important at both sites. The snow model Crocus simulates no water vapor fluxes and consequently failed to correctly simulate the observed density profiles.
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.
Georg Lackner, Florent Domine, Daniel F. Nadeau, Annie-Claude Parent, François Anctil, Matthieu Lafaysse, and Marie Dumont
The Cryosphere, 16, 127–142, https://doi.org/10.5194/tc-16-127-2022, https://doi.org/10.5194/tc-16-127-2022, 2022
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The surface energy budget is the sum of all incoming and outgoing energy fluxes at the Earth's surface and has a key role in the climate. We measured all these fluxes for an Arctic snowpack and found that most incoming energy from radiation is counterbalanced by thermal radiation and heat convection while sublimation was negligible. Overall, the snow model Crocus was able to simulate the observed energy fluxes well.
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.
Christophe Perron, Christian Katlein, Simon Lambert-Girard, Edouard Leymarie, Louis-Philippe Guinard, Pierre Marquet, and Marcel Babin
The Cryosphere, 15, 4483–4500, https://doi.org/10.5194/tc-15-4483-2021, https://doi.org/10.5194/tc-15-4483-2021, 2021
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Characterizing the evolution of inherent optical properties (IOPs) of sea ice in situ is necessary to improve climate and arctic ecosystem models. Here we present the development of an optical probe, based on the spatially resolved diffuse reflectance method, to measure IOPs of a small volume of sea ice (dm3) in situ and non-destructively. For the first time, in situ vertically resolved profiles of the dominant IOP, the reduced scattering coefficient, were obtained for interior sea ice.
Florent Domine, Georg Lackner, Denis Sarrazin, Mathilde Poirier, and Maria Belke-Brea
Earth Syst. Sci. Data, 13, 4331–4348, https://doi.org/10.5194/essd-13-4331-2021, https://doi.org/10.5194/essd-13-4331-2021, 2021
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Current sophisticated snow physics models were mostly designed for alpine conditions and cannot adequately simulate the physical properties of Arctic snowpacks. New snow models will require Arctic data sets for forcing and validation. We provide an extensive driving and testing data set from a high Arctic herb tundra site in Canada. Unique validating data include continuous time series of snow and soil thermal conductivity and temperature profiles. Field observations in spring are provided.
Kévin Fourteau, Florent Domine, and Pascal Hagenmuller
The Cryosphere, 15, 2739–2755, https://doi.org/10.5194/tc-15-2739-2021, https://doi.org/10.5194/tc-15-2739-2021, 2021
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The thermal conductivity of snow is an important physical property governing the thermal regime of a snowpack and its substrate. We show that it strongly depends on the kinetics of water vapor sublimation and that previous experimental data suggest a rather fast kinetics. In such a case, neglecting water vapor leads to an underestimation of thermal conductivity by up to 50 % for light snow. Moreover, the diffusivity of water vapor in snow is then directly related to the thermal conductivity.
Philippe Massicotte, Rainer M. W. Amon, David Antoine, Philippe Archambault, Sergio Balzano, Simon Bélanger, Ronald Benner, Dominique Boeuf, Annick Bricaud, Flavienne Bruyant, Gwenaëlle Chaillou, Malik Chami, Bruno Charrière, Jing Chen, Hervé Claustre, Pierre Coupel, Nicole Delsaut, David Doxaran, Jens Ehn, Cédric Fichot, Marie-Hélène Forget, Pingqing Fu, Jonathan Gagnon, Nicole Garcia, Beat Gasser, Jean-François Ghiglione, Gaby Gorsky, Michel Gosselin, Priscillia Gourvil, Yves Gratton, Pascal Guillot, Hermann J. Heipieper, Serge Heussner, Stanford B. Hooker, Yannick Huot, Christian Jeanthon, Wade Jeffrey, Fabien Joux, Kimitaka Kawamura, Bruno Lansard, Edouard Leymarie, Heike Link, Connie Lovejoy, Claudie Marec, Dominique Marie, Johannie Martin, Jacobo Martín, Guillaume Massé, Atsushi Matsuoka, Vanessa McKague, Alexandre Mignot, William L. Miller, Juan-Carlos Miquel, Alfonso Mucci, Kaori Ono, Eva Ortega-Retuerta, Christos Panagiotopoulos, Tim Papakyriakou, Marc Picheral, Louis Prieur, Patrick Raimbault, Joséphine Ras, Rick A. Reynolds, André Rochon, Jean-François Rontani, Catherine Schmechtig, Sabine Schmidt, Richard Sempéré, Yuan Shen, Guisheng Song, Dariusz Stramski, Eri Tachibana, Alexandre Thirouard, Imma Tolosa, Jean-Éric Tremblay, Mickael Vaïtilingom, Daniel Vaulot, Frédéric Vaultier, John K. Volkman, Huixiang Xie, Guangming Zheng, and Marcel Babin
Earth Syst. Sci. Data, 13, 1561–1592, https://doi.org/10.5194/essd-13-1561-2021, https://doi.org/10.5194/essd-13-1561-2021, 2021
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The MALINA oceanographic expedition was conducted in the Mackenzie River and the Beaufort Sea systems. The sampling was performed across seven shelf–basin transects to capture the meridional gradient between the estuary and the open ocean. The main goal of this research program was to better understand how processes such as primary production are influencing the fate of organic matter originating from the surrounding terrestrial landscape during its transition toward the Arctic Ocean.
Cited articles
Alvarez-Aviles, L., Simpson, W. R., Douglas, T. A., Sturm, M., Perovich, D., and Domine, F.: Frost flower chemical composition during growth and its implications for aerosol production and bromine activation, J. Geophys. Res., 113, D21304, https://doi.org/10.1029/2008jd010277, 2008.
Aoki, T., Hachikubo, A., and Hori, M.: Effects of snow physical parameters on shortwave broadband albedos, J. Geophys. Res., 108, D4616, https://doi.org/10.1029/2003jd003506, 2003.
Ardyna, M. and Arrigo, K. R.: Phytoplankton dynamics in a changing Arctic
Ocean, Nat. Clim. Change, 10, 892–903, https://doi.org/10.1038/s41558-020-0905-y, 2020.
Ardyna, M., Mundy, C. J., Mayot, N., Matthes, L. C., Oziel, L., Horvat, C.,
Leu, E., Assmy, P., Hill, V., Matrai, P. A., Gale, M., Melnikov, I. A., and
Arrigo, K. R.: Under-Ice Phytoplankton Blooms: Shedding Light on the
“Invisible” Part of Arctic Primary Production, Front. Mar. Sci., 7, 608032, https://doi.org/10.3389/fmars.2020.608032, 2020.
Arrigo, K. R. and van Dijken, G. L.: Secular trends in Arctic Ocean net primary production, J. Geophys. Res.-Oceans, 116, C09011, https://doi.org/10.1029/2011JC007151, 2011.
Belke-Brea, M., Domine, F., Barrere, M., Picard, G., and Arnaud, L.: Impact
of Shrubs on Winter Surface Albedo and Snow Specific Surface Area at a Low
Arctic Site: In Situ Measurements and Simulations, J. Climate, 33, 597–609,
https://doi.org/10.1175/jcli-d-19-0318.1, 2020.
Belke-Brea, M., Domine, F., Picard, G., Barrere, M., and Arnaud, L.: On the
influence of erect shrubs on the irradiance profile in snow, Biogeosciences,
18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, 2021.
Bond, T. C. and Bergstrom, R. W.: Light absorption by carbonaceous particles: An investigative review, Aerosol Sci. Tech., 40, 27–67, https://doi.org/10.1080/02786820500421521, 2006.
Caponi, L., Formenti, P., Massabó, D., Di Biagio, C., Cazaunau, M., Pangui, E., Chevaillier, S., Landrot, G., Andreae, M. O., Kandler, K., Piketh, S., Saeed, T., Seibert, D., Williams, E., Balkanski, Y., Prati, P.,
and Doussin, J. F.: Spectral- and size-resolved mass absorption efficiency
of mineral dust aerosols in the shortwave spectrum: a simulation chamber
study, Atmos. Chem. Phys., 17, 7175–7191, https://doi.org/10.5194/acp-17-7175-2017, 2017.
Colbeck, S. C.: Theory of metamorphism of wet snow, USACRREL Report 73, United States Army Corps of Engineers, Hanover, NH, 1–11, https://erdc-library.erdc.dren.mil/jspui/bitstream/11681/5894/1/CRREL-Research-Report-313.pdf (last access: 29 August 2022), 1973.
Colbeck, S. C.: Theory of metamorphism of dry snow, J. Geophys. Res., 88,
5475–5482, https://doi.org/10.1029/JC088iC09p05475, 1983.
Comiso, J. C.: Large Decadal Decline of the Arctic Multiyear Ice Cover, J.
Climate, 25, 1176–1193, https://doi.org/10.1175/jcli-d-11-00113.1, 2012.
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the
Arctic Sea ice cover: Results from different techniques, J. Geophys. Res., 122, 6883–6900, https://doi.org/10.1002/2017jc012768, 2017.
Conger, S. M. and McClung, D. M.: Comparison of density cutters for snow profile observations, J. Glaciol., 55, 163–169, https://doi.org/10.3189/002214309788609038, 2009.
Derksen, C., Silis, A., Sturm, M., Holmgren, J., Liston, G. E., Huntington, H., and Solie, D.: Northwest Territories and Nunavut Snow Characteristics from a Subarctic Traverse: Implications for Passive Microwave Remote Sensing, J. Hydrometeorol., 10, 448–463, https://doi.org/10.1175/2008jhm1074.1, 2009.
Domine, F., Cabanes, A., and Legagneux, L.: Structure, microphysics, and
surface area of the Arctic snowpack near Alert during the ALERT 2000 campaign, Atmos. Environ., 36, 2753–2765, https://doi.org/10.1016/S1352-2310(02)00108-5, 2002.
Domine, F., Salvatori, R., Legagneux, L., Salzano, R., Fily, M., and Casacchia, R.: Correlation between the specific surface area and the short
wave infrared (SWIR) reflectance of snow, Cold Reg. Sci. Technol., 46, 60–68, https://doi.org/10.1016/j.coldregions.2006.06.002, 2006.
Domine, F., Gallet, J.-C., Bock, J., and Morin, S.: Structure, specific surface area and thermal conductivity of the snowpack around Barrow, Alaska,
J. Geophys. Res., 117, D00R14, https://doi.org/10.1029/2011jd016647, 2012.
Domine, F., Barrere, M., and Morin, S.: The growth of shrubs on high Arctic
tundra at Bylot Island: impact on snow physical properties and permafrost
thermal regime, Biogeosciences, 13, 6471–6486, https://doi.org/10.5194/bg-13-6471-2016, 2016a.
Domine, F., Barrere, M., and Sarrazin, D.: Seasonal evolution of the effective thermal conductivity of the snow and the soil in high Arctic herb tundra at Bylot Island, Canada, The Cryosphere, 10, 2573–2588,
https://doi.org/10.5194/tc-10-2573-2016, 2016b.
Domine, F., Lackner, G., Sarrazin, D., Poirier, M., and Belke-Brea, M.:
Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models, Earth Syst. Sci. Data, 13, 4331–4348,
https://doi.org/10.5194/essd-13-4331-2021, 2021.
Filhol, S., and Sturm, M.: Snow bedforms: A review, new data, and a formation model, J. Geophys. Res., 120, 1645–1669, https://doi.org/10.1002/2015jf003529, 2015.
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res., 111, D12208, https://doi.org/10.1029/2005jd006834, 2006.
Gallet, J.-C., Domine, F., Zender, C. S., and Picard, G.: Measurement of the
specific surface area of snow using infrared reflectance in an integrating
sphere at 1310 and 1550 nm, The Cryosphere, 3, 167–182, https://doi.org/10.5194/tc-3-167-2009, 2009.
Gallet, J.-C., Domine, F., Arnaud, L., Picard, G., and Savarino, J.: Vertical profile of the specific surface area and density of the snow at Dome C and on a transect to Dumont D'Urville, Antarctica – albedo calculations and comparison to remote sensing products, The Cryosphere, 5, 631–649, https://doi.org/10.5194/tc-5-631-2011, 2011.
Gallet, J.-C., Domine, F., and Dumont, M.: Measuring the specific surface area of wet snow using 1310 nm reflectance, The Cryosphere, 8, 1139–1148,
https://doi.org/10.5194/tc-8-1139-2014, 2014a.
Gallet, J.-C., Domine, F., Savarino, J., Dumont, M., and Brun, E.: The growth of sublimation crystals and surface hoar on the Antarctic plateau, The Cryosphere, 8, 1205–1215, https://doi.org/10.5194/tc-8-1205-2014, 2014b.
Gallet, J. C., Merkouriadi, I., Liston, G. E., Polashenski, C., Hudson, S.,
Rosel, A., and Gerland, S.: Spring snow conditions on Arctic sea ice north
of Svalbard, during the Norwegian Young Sea ICE (N-ICE2015) expedition, J.
Geophys. Res., 122, 10820–10836, https://doi.org/10.1002/2016jd026035, 2017.
Grenfell, T. C. and Maykut, G. A.: The Optical Properties of Ice and Snow
in the Arctic Basin, J. Glaciol., 18, 445–463, https://doi.org/10.3189/S0022143000021122, 1977.
Grenfell, T. C. and Perovich, D. K.: Seasonal and spatial evolution of albedo in a snow-ice-land-ocean environment, J. Geophys. Res.-Oceans, 109, C01001, https://doi.org/10.1029/2003JC001866, 2004.
Grenfell, T. C. and Warren, S. G.: Representation of a nonspherical ice
particle by a collection of independent spheres for scattering and absorption of radiation, J. Geophys. Res., 104, 31697–31709, https://doi.org/10.1029/1999JD900496, 1999.
Haussener, S., Gergely, M., Schneebeli, M., and Steinfeld, A.: Determination
of the macroscopic optical properties of snow based on exact morphology and
direct pore-level heat transfer modeling, J. Geophys. Res., 117, F03009, https://doi.org/10.1029/2012jf002332, 2012.
Jimenez-Aquino, J. I. and Varela, J. R.: Two stream approximation to radiative transfer equation: An alternative method of solution, Revista Mexicana De Fisica, 51, 82–86, 2005.
Kokhanovsky, A.: Spectral reflectance of solar light from dirty snow: a simple theoretical model and its validation, The Cryosphere, 7, 1325–1331,
https://doi.org/10.5194/tc-7-1325-2013, 2013.
Kokhanovsky, A. A. and Zege, E. P.: Scattering optics of snow, Appl. Optics,
43, 1589–1602, https://doi.org/10.1364/AO.43.001589, 2004.
Krnavek, L., Simpson, W. R., Carlson, D., Domine, F., Douglas, T. A., and
Sturm, M.: The chemical composition of surface snow in the Arctic: Examining
marine, terrestrial, and atmospheric influences, Atmos. Environ., 50, 349–359, https://doi.org/10.1016/j.atmosenv.2011.11.033, 2012.
Kwok, R. and Rothrock, D. A.: Decline in Arctic sea ice thickness from
submarine and ICESat records: 1958–2008, Geophys. Res. Lett., 36, L15501, https://doi.org/10.1029/2009gl039035, 2009.
Legagneux, L. and Domine, F.: A mean field model of the decrease of the
specific surface area of dry snow during isothermal metamorphism, J. Geophys. Res., 110, F04011, https://doi.org/10.1029/2004jf000181, 2005.
Letcher, T., Parno, J., Courville, Z., Farnsworth, L., and Olivier, J.: A generalized photon-tracking approach to simulate spectral snow albedo and transmissivity using X-ray microtomography and geometric optics, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-310, in review, 2021.
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013.
Libois, Q., Picard, G., Dumont, M., Arnaud, L., Sergent, C., Pougatch, E.,
Sudul, M., and Vial, D.: Experimental determination of the absorption
enhancement parameter of snow, J. Glaciol., 60, 714–724,
https://doi.org/10.3189/2014JoG14J015, 2014.
Maksimovich, E. and Vihma, T.: The effect of surface heat fluxes on interannual variability in the spring onset of snow melt in the central Arctic Ocean, J. Geophys. Res.-Oceans, 117, C07012, https://doi.org/10.1029/2011JC007220, 2012.
Marbouty, D.: An experimental study of temperature-gradient metamorphism, J.
Glaciol., 26, 303–312, https://doi.org/10.3189/S0022143000010844, 1980.
Massicotte, P., Amiraux, R., Amyot, M. P., Archambault, P., Ardyna, M., Arnaud, L., Artigue, L., Aubry, C., Ayotte, P., Bécu, G., Bélanger,
S., Benner, R., Bittig, H. C., Bricaud, A., Brossier, É., Bruyant, F.,
Chauvaud, L., Christiansen-Stowe, D., Claustre, H., Cornet-Barthaux, V., Coupel, P., Cox, C., Delaforge, A., Dezutter, T., Dimier, C., Domine, F.,
Dufour, F., Dufresne, C., Dumont, D., Ehn, J., Else, B., Ferland, J., Forget, M. H., Fortier, L., Galí, M., Galindo, V., Gallinari, M., Garcia, N., Gérikas Ribeiro, C., Gourdal, M., Gourvil, P., Goyens, C., Grondin, P. L., Guillot, P., Guilmette, C., Houssais, M. N., Joux, F., Lacour, L., Lacour, T., Lafond, A., Lagunas, J., Lalande, C., Laliberté, J., Lambert-Girard, S., Larivière, J., Lavaud, J., LeBaron, A., Leblanc, K., Le Gall, F., Legras, J., Lemire, M., Levasseur, M., Leymarie, E., Leynaert, A., Lopes dos Santos, A., Lourenço, A., Mah, D., Marec, C., Marie, D., Martin, N., Marty, C., Marty, S., Massé, G., Matsuoka, A., Matthes, L., Moriceau, B., Muller, P. E., Mundy, C. J., Neukermans, G., Oziel, L., Panagiotopoulos, C., Pangrazi, J. J., Picard, G., Picheral, M., Pinczon du Sel, F., Pogorzelec, N., Probert, I., Quéguiner, B., Raimbault, P., Ras, J., Rehm, E., Reimer, E., Rontani, J. F., Rysgaard, S., Saint-Béat, B., Sampei, M., Sansoulet, J., Schmechtig, C., Schmidt, S., Sempéré, R., Sévigny, C., Shen, Y., Tragin, M., Tremblay, J. É., Vaulot, D., Verin, G., Vivier, F., Vladoiu, A., Whitehead, J., and Babin, M.: The Green Edge initiative: understanding the processes controlling the under-ice Arctic phytoplankton spring bloom, SEANOE [data set], https://doi.org/10.17882/59892, 2019.
Massicotte, P., Amiraux, R., Amyot, M. P., Archambault, P., Ardyna, M., Arnaud, L., Artigue, L., Aubry, C., Ayotte, P., Bécu, G., Bélanger,
S., Benner, R., Bittig, H. C., Bricaud, A., Brossier, É., Bruyant, F.,
Chauvaud, L., Christiansen-Stowe, D., Claustre, H., Cornet-Barthaux, V., Coupel, P., Cox, C., Delaforge, A., Dezutter, T., Dimier, C., Domine, F.,
Dufour, F., Dufresne, C., Dumont, D., Ehn, J., Else, B., Ferland, J., Forget, M. H., Fortier, L., Galí, M., Galindo, V., Gallinari, M., Garcia, N., Gérikas Ribeiro, C., Gourdal, M., Gourvil, P., Goyens, C., Grondin, P. L., Guillot, P., Guilmette, C., Houssais, M. N., Joux, F., Lacour, L., Lacour, T., Lafond, A., Lagunas, J., Lalande, C., Laliberté, J., Lambert-Girard, S., Larivière, J., Lavaud, J., LeBaron, A., Leblanc, K., Le Gall, F., Legras, J., Lemire, M., Levasseur, M., Leymarie, E., Leynaert, A., Lopes dos Santos, A., Lourenço, A., Mah, D., Marec, C., Marie, D., Martin, N., Marty, C., Marty, S., Massé, G., Matsuoka, A., Matthes, L., Moriceau, B., Muller, P. E., Mundy, C. J., Neukermans, G., Oziel, L., Panagiotopoulos, C., Pangrazi, J. J., Picard, G., Picheral, M., Pinczon du Sel, F., Pogorzelec, N., Probert, I., Quéguiner, B., Raimbault, P., Ras, J., Rehm, E., Reimer, E., Rontani, J. F., Rysgaard, S., Saint-Béat, B., Sampei, M., Sansoulet, J., Schmechtig, C., Schmidt, S., Sempéré, R., Sévigny, C., Shen, Y., Tragin, M., Tremblay, J. É., Vaulot, D., Verin, G., Vivier, F., Vladoiu, A., Whitehead, J., and Babin, M.: Green Edge ice camp campaigns: understanding the processes controlling the under-ice Arctic phytoplankton spring bloom, Earth Syst. Sci. Data, 12, 151–176, https://doi.org/10.5194/essd-12-151-2020, 2020.
Mortin, J., Svensson, G., Graversen, R. G., Kapsch, M.-L., Stroeve, J. C.,
and Boisvert, L. N.: Melt onset over Arctic sea ice controlled by atmospheric moisture transport, Geophys. Res. Lett., 43, 6636–6642, https://doi.org/10.1002/2016GL069330, 2016.
Nicolaus, M., Gerland, S., Hudson, S. R., Hanson, S., Haapala, J., and Perovich, D. K.: Seasonality of spectral albedo and transmittance as observed in the Arctic Transpolar Drift in 2007, J. Geophys. Res.-Oceans, 115, C11011, https://doi.org/10.1029/2009JC006074, 2010.
Perovich, D., Polashenski, C., Arntsen, A., and Stwertka, C.: Anatomy of a late spring snowfall on sea ice, Geophys. Res. Lett., 44, 2802–2809,
https://doi.org/10.1002/2016GL071470, 2017.
Perovich, D. K.: Theoretical estimates of light reflection and transmission
by spatially complex and temporally varying sea ice covers, J. Geophys. Res.-Oceans, 95, 9557–9567, https://doi.org/10.1029/JC095iC06p09557, 1990.
Perovich, D. K. and Polashenski, C.: Albedo evolution of seasonal Arctic sea ice, Geophys. Res. Lett., 39, L08501, https://doi.org/10.1029/2012gl051432, 2012.
Perovich, D. K., Grenfell, T. C., Light, B., and Hobbs, P. V.: Seasonal evolution of the albedo of multiyear Arctic sea ice, J. Geophys. Res.-Oceans, 107, SHE 20-21–SHE 20-13, https://doi.org/10.1029/2000JC000438, 2002.
Perovich, D. K., Nghiem, S. V., Markus, T., and Schweiger, A.: Seasonal evolution and interannual variability of the local solar energy absorbed by
the Arctic sea ice-ocean system, J. Geophys. Res., 112, C03005, https://doi.org/10.1029/2006jc003558, 2007.
Picard, G.: Two-streAm Radiative TransfEr in Snow model (TARTES), ghislainp/tartes: v1.1.0, Zenodo [code], https://doi.org/10.5281/zenodo.7031229, 2022.
Picard, G., Libois, Q., and Arnaud, L.: Refinement of the ice absorption
spectrum in the visible using radiance profile measurements in Antarctic snow, The Cryosphere, 10, 2655–2672, https://doi.org/10.5194/tc-10-2655-2016, 2016a.
Picard, G., Libois, Q., Arnaud, L., Verin, G., and Dumont, M.: Development
and calibration of an automatic spectral albedometer to estimate near-surface snow SSA time series, The Cryosphere, 10, 1297–1316, https://doi.org/10.5194/tc-10-1297-2016, 2016b.
Picard, G., Dumont, M., Lamare, M., Tuzet, F., Larue, F., Pirazzini, R., and
Arnaud, L.: Spectral albedo measurements over snow-covered slopes: theory and slope effect corrections, The Cryosphere, 14, 1497–1517, https://doi.org/10.5194/tc-14-1497-2020, 2020.
Ricchiazzi, P., Yang, S. R., Gautier, C., and Sowle, D.: SBDART: A research
and teaching software tool for plane-parallell radiative transfer in the
Earth's atmosphere, B. Am. Meteorol. Soc., 79, 2101–2114, https://doi.org/10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2, 1998.
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Global Planet. Change, 77, 85–96,
https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011.
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T., Holland,
M., and Meier, W. N.: Trends in Arctic sea ice extent from CMIP5, CMIP3 and
observations, Geophys. Res. Lett., 39, L16502, https://doi.org/10.1029/2012gl052676, 2012.
Sturm, M., Holmgren, J., and Perovich, D. K.: Winter snow cover on the sea
ice of the Arctic Ocean at the Surface Heat Budget of the Arctic Ocean (SHEBA): Temporal evolution and spatial variability, J. Geophys. Res., 107, 8047, https://doi.org/10.1029/2000jc000400, 2002.
Taillandier, A. S., Domine, F., Simpson, W. R., Sturm, M., Douglas, T. A.,
and Severin, K.: Evolution of the snow area index of the subarctic snowpack
in central Alaska over a whole season. Consequences for the air to snow transfer of pollutants, Environ. Sci. Technol., 40, 7521–7527,
https://doi.org/10.1021/es060842j, 2006.
Taillandier, A. S., Domine, F., Simpson, W. R., Sturm, M., and Douglas, T. A.: Rate of decrease of the specific surface area of dry snow: Isothermal and temperature gradient conditions, J. Geophys. Res., 112, F03003, https://doi.org/10.1029/2006jf000514, 2007.
Tassan, S. and Ferrari, G. M.: An alternative approach to absorption measurements of aquatic particles retained on filters, Limnol. Oceanogr.,
40, 1358–1368, https://doi.org/10.4319/lo.1995.40.8.1358, 1995.
Tassan, S. and Ferrari, G. M.: A sensitivity analysis of the
`Transmittance-Reflectance' method for measuring light absorption by aquatic
particles, J. Plankton Res., 24, 757–774, https://doi.org/10.1093/plankt/24.8.757, 2002.
Tuzet, F., Dumont, M., Arnaud, L., Voisin, D., Lamare, M., Larue, F., Revuelto, J., and Picard, G.: Influence of light-absorbing particles on snow
spectral irradiance profiles, The Cryosphere, 13, 2169–2187,
https://doi.org/10.5194/tc-13-2169-2019, 2019.
Wagner, R., Ajtai, T., Kandler, K., Lieke, K., Linke, C., Müller, T.,
Schnaiter, M., and Vragel, M.: Complex refractive indices of Saharan dust
samples at visible and near UV wavelengths: a laboratory study, Atmos. Chem.
Phys., 12, 2491–2512, https://doi.org/10.5194/acp-12-2491-2012, 2012.
Warren, S. G.: Optical-properties of snow, Rev. Geophys., 20, 67–89, 1982.
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N.
N., Aleksandrov, Y. I., and Colony, R.: Snow Depth on Arctic Sea Ice, J.
Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:sdoasi>2.0.co;2, 1999.
Wiscombe, W. J. and Warren, S. G.: A model for the spectral albedo of snow.
1. Pure snow, J. Atmos. Sci., 37, 2712–2733, 1980.
Short summary
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.
Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains...
