Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-183-2026
© Author(s) 2026. 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-20-183-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jan 2026
Research article |
| 13 Jan 2026
| 13 Jan 2026
Anticipating CRISTAL: an exploration of multi-frequency satellite altimeter snow depth estimates over Arctic sea ice, 2018–2023
UiT The Arctic University of Norway, Department of Physics and Technology, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
Claude de Rijke-Thomas
University of Bristol, School of Geographical Sciences, Bristol BS8 1SS, UK
Carmen Nab
Centre for Polar Observation and Modelling, Department of Earth Sciences, University College London, UK
Ocean Forecasting Research and Development, Met Office, UK
Isobel Lawrence
ESA ESRIN, Frascati, Italy
Isolde A. Glissenaar
University of Bristol, School of Geographical Sciences, Bristol BS8 1SS, UK
Robbie D. C. Mallett
UiT The Arctic University of Norway, Department of Physics and Technology, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
Renée M. Fredensborg Hansen
DTU Space, National Space Institute, Technical University of Denmark (DTU), Department of Geodesy and Earth Observation, Kgs. Lyngby, Denmark
Norwegian University of Science and Technology (NTNU), Department of Civil and Environmental Engineering, Trondheim, Norway
Department of Arctic Geophysics, The University Centre in Svalbard (UNIS), Longyearbyen, Norway
Alek Petty
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Michel Tsamados
Centre for Polar Observation and Modelling, Department of Earth Sciences, University College London, UK
Amy R. Macfarlane
UiT The Arctic University of Norway, Department of Physics and Technology, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
Northumbria University, Ellison Pl, Newcastle upon Tyne NE1 8ST, UK
Anne Braakmann-Folgmann
UiT The Arctic University of Norway, Department of Physics and Technology, P.O. Box 6050 Langnes, 9037 Tromsø, Norway
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This article summarizes the data sets collected during the Arctic Radiation Cloud aerosol Sea ice Interaction eXperiment (ARCSIX), NASA's most comprehensive Arctic field campaign to date. The overarching goal was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. This paper describes the ARCSIX implementation including the flight strategy, instruments, complementary data sets, access, and usage details.
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This paper introduces a new statistical approach to retrieve ice and snow depth over the Arctic Ocean, using satellite altimeters measurements. We demonstrate the ability of this method to compute efficiently the sea ice thickness and the snow depth over the Arctic, without major assumptions on the snow. In addition to the ice and snow depth, this approach is efficient to study the penetration of radar and laser pulses, paving the way for further research in satellite altimetry.
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Arctic sea ice thickness is commonly estimated using radar measurements from satellites. This technique assumes that the snow on sea ice is 'invisible' to the radar signals. We studied if this assumption is valid and found that the radar measurements work well in cold conditions, when air temperatures are below –10 °C. However, the radar measurements can overestimate ice thickness during warmer periods, especially if there is new snowfall and if the snow contains salt.
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Wenxuan Liu, Ruibo Lei, Taoyong Jin, Heyang Sun, Michel Tsamados, Isolde A. Glissenaar, Jack Christopher Landy, and Yi Zhou
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The thickness and surface shape of Arctic sea ice are difficult to measure accurately, especially in summer when melting occurs. We developed a new method that uses ICESat-2 photon data to map sea ice height throughout the year. By carefully filtering noise and using satellite images to help identify open water, we obtained more detailed and reliable ice heights and freeboards than existing products. The results better capture rough ice features and improve accuracy.
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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.
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Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Patrick C. Taylor, Armin Sorooshian, Rei Ueyama, Sebastian Schmidt, Ihab Abboud, Quincy Allison, Kevin Barry, Sebastian Becker, Holly A. Bender, Joseph R. Bennett, James B. Blair, Niklas Bohn, Linette Boisvert, Matthew D. Brown, Roelof Bruintjes, Anthony Bucholtz, Megan Buzanowicz, Brian Cairns, Filippo Calì Quaglia, Eduard Chemyakin, Bo Chen, Gao Chen, Hong Chen, Yu-Wen Chen, Zezhen Cheng, Swarup China, Dan Chirica, Yonghoon Choi, Peter Colarco, Brian Collister, Ewan Crosbie, Maurice J. Cross, Janet Daniels, Paul DeMott, Joshua P. DiGangi, Alcide Giorgio di Sarra, Glenn S. Diskin, Erica K. Dolinar, Eva-Lou Edwards, Samuel Ephraim, Nikolaos Evangeliou, Romanos Foskinis, Francesca Gallo, Lan Gao, José Luis Gómez-Amo, Daisy Gonzalez, Christine Groot Zwaaftink, Pawan Gupta, Ivan Heckman, Michael Hendrickson, Miguel Ricardo A. Hilario, Ken Hirata, Michelle Hofton, Andrew L. Holen, Ulas Im, Alia L. Khan, Ralph Kahn, Alexei V. Korolev, Sonia Kreidenweis, Thomas Krumpen, Nathan Kurtz, Leslie Lait, Bradley Lamkin, Jack Landy, Nurun Nahar Lata, Paul Lawson, Samuel LeBlanc, Sean Leavor, Jing Li, Thorsten Markus, Hal Maring, Andreas H. Massling, Camille Mavis, Flynn McGinnity, Kerry Meyer, Gabriel Mojica, Richard H. Moore, Parker Morris, Giovanni Muscari, Vikas Nataraja, Amin R. Nehrir, Athanasios Nenes, Edward P. Nowottnick, Matteo Ottaviani, Chelsea Parker, Ryan Patnaude, Michael Perez, Russell J. Perkins, Colten Peterson, Alek Petty, Stevie Phothisane, Chris Polashenski, Kerri A. Pratt, Kayla M. Preisler, John Prytherch, David Rabine, Jens Redemann, Ju-Mee Ryoo, Joseph S. Schlosser, Vanessa Selimovic, Michael A. Shook, Morgan Silverman, Henrik Skov, Alexander Smirnov, Cassidy Soloff, Amy Solomon, Snorre Stamnes, Azusa Takeishi, David R. Thompson, K. Lee Thornhill, Rachel Tilling, Michael Tjernström, Monica Tosco, Pedro C. Valdelomar, David Van Gilst, Jian Wang, Zhien Wang, Andrzej Wasilewski, Manfred Wendisch, Brent Wilder, Edward L. Winstead, Albert Wu, Peng Xian, Lauren M. Zamora, Jiaoshi Zhang, Lei Zhang, Lu Zhang, Luke Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-241, https://doi.org/10.5194/essd-2026-241, 2026
Preprint under review for ESSD
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This article summarizes the data sets collected during the Arctic Radiation Cloud aerosol Sea ice Interaction eXperiment (ARCSIX), NASA's most comprehensive Arctic field campaign to date. The overarching goal was to quantify the contributions of surface, clouds, aerosol particles, and precipitation to summer sea ice melt. This paper describes the ARCSIX implementation including the flight strategy, instruments, complementary data sets, access, and usage details.
Elie René-Bazin, Michel Tsamados, Sabrina Sofea Binti Aliff Raziuddin, Joel Perez Ferrer, Tudor Suciu, Carmen Nab, Chamkaur Ghag, Harry Heorton, Rosemary Willatt, Jack Landy, Matthew Fox, and Thomas Bodin
The Cryosphere, 20, 3913–3932, https://doi.org/10.5194/tc-20-3913-2026, https://doi.org/10.5194/tc-20-3913-2026, 2026
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This paper introduces a new statistical approach to retrieve ice and snow depth over the Arctic Ocean, using satellite altimeters measurements. We demonstrate the ability of this method to compute efficiently the sea ice thickness and the snow depth over the Arctic, without major assumptions on the snow. In addition to the ice and snow depth, this approach is efficient to study the penetration of radar and laser pulses, paving the way for further research in satellite altimetry.
Lanqing Huang, Julienne Stroeve, Thomas Newman, Robbie Mallett, Rosemary Willatt, Lu Zhou, Malin Johansson, Carmen Nab, and Alicia Fallows
The Cryosphere, 20, 2825–2849, https://doi.org/10.5194/tc-20-2825-2026, https://doi.org/10.5194/tc-20-2825-2026, 2026
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Understanding snow depth on sea ice is key for measuring ice thickness, studying ecosystems, and modelling climate. Using snow and ice thickness measurements from Arctic and Antarctic campaigns, this study examines sub-kilometre-scale (<1 km²) snow depth variations and identifies the most suitable statistical models for different ice ages, thicknesses, and weather conditions. These results can improve sub-grid snow parameterisations in snow models and remote sensing algorithms.
Alistair Duffey, Walker Lee, Lauren Wheeler, Peter Irvine, Benjamin Wagman, Matthew Henry, Daniele Visioni, Michel Tsamados, and Douglas MacMartin
Earth Syst. Dynam., 17, 353–385, https://doi.org/10.5194/esd-17-353-2026, https://doi.org/10.5194/esd-17-353-2026, 2026
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Ida Lundtorp Olsen, Henriette Skourup, Heidi Sallila, Stefan Hendricks, Renée Mie Fredensborg Hansen, Stefan Kern, Stephan Paul, Marion Bocquet, Sara Fleury, Dmitry Divine, and Eero Rinne
Earth Syst. Sci. Data, 18, 2469–2505, https://doi.org/10.5194/essd-18-2469-2026, https://doi.org/10.5194/essd-18-2469-2026, 2026
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Catherine Taelman, Jack Landy, Robbie Mallett, and Polona Itkin
EGUsphere, https://doi.org/10.5194/egusphere-2026-1662, https://doi.org/10.5194/egusphere-2026-1662, 2026
Short summary
Short summary
Arctic sea ice thickness is commonly estimated using radar measurements from satellites. This technique assumes that the snow on sea ice is 'invisible' to the radar signals. We studied if this assumption is valid and found that the radar measurements work well in cold conditions, when air temperatures are below –10 °C. However, the radar measurements can overestimate ice thickness during warmer periods, especially if there is new snowfall and if the snow contains salt.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Isobel Lawrence, Lars Nerger, Jack Landy, and Geoffrey Dawson
EGUsphere, https://doi.org/10.5194/egusphere-2026-742, https://doi.org/10.5194/egusphere-2026-742, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
In this study we present three satellite era Arctic sea ice reanalyses, each assimilating different combinations of sea ice concentration and thickness observations. Results show that using year-round thickness observations substantially improves reanalysis compared to winter-only data. The best-performing reanalysis reveals a seasonally compensating bias cycle, suggesting errors in ice growth, leads, refreezing, marginal ice zone dynamics, redistribution, and melt timing mask model errors.
Wenxuan Liu, Ruibo Lei, Taoyong Jin, Heyang Sun, Michel Tsamados, Isolde A. Glissenaar, Jack Christopher Landy, and Yi Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2026-766, https://doi.org/10.5194/egusphere-2026-766, 2026
Short summary
Short summary
The thickness and surface shape of Arctic sea ice are difficult to measure accurately, especially in summer when melting occurs. We developed a new method that uses ICESat-2 photon data to map sea ice height throughout the year. By carefully filtering noise and using satellite images to help identify open water, we obtained more detailed and reliable ice heights and freeboards than existing products. The results better capture rough ice features and improve accuracy.
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
Short summary
Short summary
Radar altimeters measure ice-sheet elevation and sea-ice thickness. To improve their accuracy, we developed a model that simulates altimeter waveforms based on the physical properties of snow and sea ice. It computes the power, time, and Doppler shift of radar echoes as waves travel to the surface, interact with snow and ice, and return to the satellite. The model was verified internally, validated against other models and applied in Antarctica using in-situ snow measurements.
Julienne Stroeve, Rosemary Willatt, Madeleine Downie, Monojit Saha, Carmen Nab, Alicia Fallows, Clement Soriot, Robbie Mallett, Anton Komarov, Vishnu Nandan, Thomas Newman, and John Yackel
EGUsphere, https://doi.org/10.5194/egusphere-2026-212, https://doi.org/10.5194/egusphere-2026-212, 2026
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We evaluate polarimetry for snow depth retrieval on Arctic winter transport routes. Over landfast ice and tundra, traditional dual-frequency (Ku/Ka) methods often fail. We demonstrate that cross-polarization (VH) provides a more reliable marker for snow/ice and snow/ground interfaces. For lakes, we successfully resolve air, snow, and ice interfaces to retrieve snow and ice thickness simultaneously. These results establish a baseline for future polarimetric satellite missions.
Anne Braakmann-Folgmann, Jack C. Landy, Geoffrey Dawson, and Robert Ricker
The Cryosphere, 20, 905–929, https://doi.org/10.5194/tc-20-905-2026, https://doi.org/10.5194/tc-20-905-2026, 2026
Short summary
Short summary
To calculate sea ice thickness from altimetry, returns from ice and leads need to be differentiated. During summer, melt ponds complicate this task, as they resemble leads. In this study, we improve a previously suggested neural network classifier by expanding the training dataset fivefold, tuning the network architecture and introducing an additional class for thinned floes. We show that this increases the accuracy from 77 ± 5 % to 84 ± 2 % and that more leads are found.
Valentin Ludwig, Caroline Ribere, Sara Fleury, Christian Haas, Michel Tsamados, Mahmoud El Hajj, Jerome Bouffard, Michele Scagliola, Marion Bocquet, Eric de Boisseson, Vincent Boulenger, Guillaume Boutin, Laurence Connor, Léo Edel, Stefan Hendricks, Ferran Hernández Macià, Marcus Huntemann, Lars Kaleschke, Frank Kauker, Jack Landy, Tom Megain, Alek Petty, Till Soya Rasmussen, Mads Hvid Ribergaard, Robert Ricker, Axel Schweiger, Hoyeon Shi, Xiangshan Tian-Kunze, Donghui Yi, and Alessandro Di Bella
EGUsphere, https://doi.org/10.5194/egusphere-2025-6201, https://doi.org/10.5194/egusphere-2025-6201, 2026
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Our paper compares Arctic sea-ice thickness datasets from models, reanalyses, satellite-only, and multi-product sources. We validate them against Beaufort Sea reference data, compare large-scale products, and analyse time series. Cross-product biases range from 0.2–0.4 m, RMSDs from 0.4–0.9 m, and correlations from 0.5–0.8. We find no 2010–2023 trend, but 1995–2023 thinning of ~ 0.5 m in November and ~ 0.3 m in March.
Stephen E. L. Howell, Alex Cabaj, David G. Babb, Jack C. Landy, Jackie Dawson, Mallik Mahmud, and Mike Brady
The Cryosphere, 19, 6711–6725, https://doi.org/10.5194/tc-19-6711-2025, https://doi.org/10.5194/tc-19-6711-2025, 2025
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The Northwest Passage provides a shorter transit route connecting the Atlantic Ocean to the Pacific Ocean but ever-present sea ice has prevented its practical navigation. Sea ice area in the northern route of the Northwest Passage on September 30, 2024 fell to a minimum of 4×103 km2 or ~3% of its total area, the lowest ice area observed since 1960. This paper describes the unique processes that contributed to the record low sea ice area in the northern route of the Northwest Passage in 2024.
Yubing Cheng, Bin Cheng, Roberta Pirazzini, Amy R. Macfarlane, Timo Vihma, Wolfgang Dorn, Ruzica Dadic, Martin Schneebeli, Stefanie Arndt, and Annette Rinke
The Cryosphere, 19, 6001–6021, https://doi.org/10.5194/tc-19-6001-2025, https://doi.org/10.5194/tc-19-6001-2025, 2025
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We study snow density from the MOSAiC expedition. Several snow density schemes were tested and compared with observation. A thermodynamic ice model was employed to assess the impact of snow density and precipitation on the thermal regime of sea ice. The parameterized mean snow densities are consistent with observations. Increased snow density reduces snow and ice temperatures, promoting ice growth, while increased precipitation leads to warmer snow and ice temperatures and reduced ice thickness.
Youngmin Choi, Alek Petty, Denis Felikson, and Jonathan Poterjoy
The Cryosphere, 19, 5423–5444, https://doi.org/10.5194/tc-19-5423-2025, https://doi.org/10.5194/tc-19-5423-2025, 2025
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We combined numerical models with satellite observations using the ensemble Kalman filter to improve predictions of glacier states and their basal conditions. Our simulations show that incorporating more data generally improves prediction accuracy. We also tested different types of data and found that the high-resolution observations provide the greatest improvements. This method can help guide the design of future observing systems and improve long-term projections of ice sheet change.
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
The Cryosphere, 19, 5175–5199, https://doi.org/10.5194/tc-19-5175-2025, https://doi.org/10.5194/tc-19-5175-2025, 2025
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In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
Renée Mie Fredensborg Hansen, Henriette Skourup, Eero Rinne, Arttu Jutila, Isobel R. Lawrence, Andrew Shepherd, Knut Vilhelm Høyland, Jilu Li, Fernando Rodriguez-Morales, Sebastian Bjerregaaard Simonsen, Jeremy Wilkinson, Gaelle Veyssiere, Donghui Yi, René Forsberg, and Taniâ Gil Duarte Casal
The Cryosphere, 19, 4193–4209, https://doi.org/10.5194/tc-19-4193-2025, https://doi.org/10.5194/tc-19-4193-2025, 2025
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An airborne campaign collected unprecedented coincident multi-frequency radar and lidar data over sea ice along a CryoSat-2 and ICESat-2 (CRYO2ICE) orbit in the Weddell Sea, useful for evaluating microwave snow penetration. Ka-band and Ku-band had limited penetration with significant contributions from the air–snow interface, contradicting traditional assumptions with discrepancies between commonly used C/S-band "snow-radar" methodologies, all challenging comparisons of airborne and spaceborne estimates.
Renée Mie Fredensborg Hansen, Henriette Skourup, Eero Rinne, Arttu Jutila, Isobel R. Lawrence, Andrew Shepherd, Knut Vilhelm Høyland, Jilu Li, Fernando Rodriguez-Morales, Sebastian Bjerregaaard Simonsen, Jeremy Wilkinson, Gaelle Veyssiere, Donghui Yi, René Forsberg, and Taniâ Gil Duarte Casal
The Cryosphere, 19, 4167–4192, https://doi.org/10.5194/tc-19-4167-2025, https://doi.org/10.5194/tc-19-4167-2025, 2025
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An airborne campaign collected unprecedented coincident multi-frequency radar and lidar data over sea ice along a CryoSat-2 and ICESat-2 (CRYO2ICE) orbit in the Weddell Sea, useful for evaluating microwave snow penetration. Ka-band and Ku-band had limited penetration with significant contributions from the air–snow interface, contradicting traditional assumptions with discrepancies between commonly used C/S-band "snow-radar" methodologies, all challenging comparisons of airborne and spaceborne estimates.
Alek Petty, Christopher Cardinale, and Madison Smith
Geosci. Model Dev., 18, 6313–6340, https://doi.org/10.5194/gmd-18-6313-2025, https://doi.org/10.5194/gmd-18-6313-2025, 2025
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We use total freeboard data from NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) across both hemispheres and estimates of winter Arctic sea ice thickness to evaluate climate model simulations of sea ice, providing constraints beyond the traditional sea ice area metric. ICESat-2 provides accurate freeboard data, but its short observational record requires careful consideration of natural variability.
Isolde Glissenaar, Klaas Folkert Boersma, Isidora Anglou, Pieter Rijsdijk, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Jean-Christopher Lambert, Michel Van Roozendael, and Henk Eskes
Earth Syst. Sci. Data, 17, 4627–4650, https://doi.org/10.5194/essd-17-4627-2025, https://doi.org/10.5194/essd-17-4627-2025, 2025
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We developed a new global dataset of nitrogen dioxide (NO2) levels in the lower atmosphere, using data from TROPOMI for 2018–2021. This dataset offers improved accuracy and detail compared to earlier versions, meeting high international standards for climate data. By refining how measurement errors are calculated and reduced over time and space, we provide clearer insights into pollution patterns. This work supports better air quality monitoring and informs actions to address pollution globally.
Alex Cabaj, Paul J. Kushner, and Alek A. Petty
The Cryosphere, 19, 3033–3064, https://doi.org/10.5194/tc-19-3033-2025, https://doi.org/10.5194/tc-19-3033-2025, 2025
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The output of snow-on-sea-ice models is influenced by the choice of snowfall input used. We ran such a model with different snowfall inputs and calibrated it to observations, produced a new calibrated snow product, and regionally compared the model outputs to outputs from another snow-on-sea-ice model. The two models agree best on the seasonal cycle of snow in the central Arctic Ocean. Observational comparisons highlight ongoing challenges in estimating the depth and density of snow on Arctic sea ice.
Suvarna Fadnavis, Yasin Elshorbany, Jerald Ziemke, Brice Barret, Alexandru Rap, P. R. Satheesh Chandran, Richard J. Pope, Vijay Sagar, Domenico Taraborrelli, Eric Le Flochmoen, Juan Cuesta, Catherine Wespes, Folkert Boersma, Isolde Glissenaar, Isabelle De Smedt, Michel Van Roozendael, Hervé Petetin, and Isidora Anglou
Atmos. Chem. Phys., 25, 8229–8254, https://doi.org/10.5194/acp-25-8229-2025, https://doi.org/10.5194/acp-25-8229-2025, 2025
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Satellites and model simulations show enhancement in tropospheric ozone, which is highly impacted by human-produced nitrous oxides compared to volatile organic compounds. The increased amount of ozone enhances ozone radiative forcing. The ozone enhancement and associated radiative forcing are the highest over South and East Asia. The emissions of nitrous oxides show a higher influence on shifting ozone photochemical regimes than volatile organic compounds.
Evgenii Salganik, Odile Crabeck, Niels Fuchs, Nils Hutter, Philipp Anhaus, and Jack Christopher Landy
The Cryosphere, 19, 1259–1278, https://doi.org/10.5194/tc-19-1259-2025, https://doi.org/10.5194/tc-19-1259-2025, 2025
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To measure Arctic ice thickness, we often check how much ice sticks out of the water. This method depends on knowing the ice's density, which drops significantly in summer. Our study, validated with sonar and laser data, shows that these seasonal changes in density can complicate melt measurements. We stress the importance of considering these density changes for more accurate ice thickness readings.
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
The Cryosphere, 19, 325–346, https://doi.org/10.5194/tc-19-325-2025, https://doi.org/10.5194/tc-19-325-2025, 2025
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Snow on sea ice is vital for near-shore sea ice geophysical and biological processes. Past studies have measured snow depths using the satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice), but estimating sea surface height from leadless landfast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in situ data after adjusting for tides. Realistic snow depths are retrieved, but differences in roughness, satellite footprints, and snow geophysical properties are identified.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
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We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
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The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Moein Mellat, Amy R. Macfarlane, Camilla F. Brunello, Martin Werner, Martin Schneebeli, Ruzica Dadic, Stefanie Arndt, Kaisa-Riikka Mustonen, Jeffrey M. Welker, and Hanno Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-719, https://doi.org/10.5194/egusphere-2024-719, 2024
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Our research, utilizing data from the Arctic MOSAiC expedition, reveals how snow on Arctic sea ice changes due to weather conditions. By analyzing snow samples collected over a year, we found differences in snow layers that tell us about their origins and how they've been affected by the environment. We discovered variations in snow and vapour that reflect the influence of weather patterns and surface processes like wind and sublimation.
Amy R. Macfarlane, Henning Löwe, Lucille Gimenes, David N. Wagner, Ruzica Dadic, Rafael Ottersberg, Stefan Hämmerle, and Martin Schneebeli
The Cryosphere, 17, 5417–5434, https://doi.org/10.5194/tc-17-5417-2023, https://doi.org/10.5194/tc-17-5417-2023, 2023
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Snow acts as an insulating blanket on Arctic sea ice, keeping the underlying ice "warm", relative to the atmosphere. Knowing the snow's thermal conductivity is essential for understanding winter ice growth. During the MOSAiC expedition, we measured the thermal conductivity of snow. We found spatial and vertical variability to overpower any temporal variability or dependency on underlying ice type and the thermal resistance to be directly influenced by snow height.
Alistair Duffey, Robbie Mallett, Peter J. Irvine, Michel Tsamados, and Julienne Stroeve
Earth Syst. Dynam., 14, 1165–1169, https://doi.org/10.5194/esd-14-1165-2023, https://doi.org/10.5194/esd-14-1165-2023, 2023
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The Arctic is warming several times faster than the rest of the planet. Here, we use climate model projections to quantify for the first time how this faster warming in the Arctic impacts the timing of crossing the 1.5 °C and 2 °C thresholds defined in the Paris Agreement. We show that under plausible emissions scenarios that fail to meet the Paris 1.5 °C target, a hypothetical world without faster warming in the Arctic would breach that 1.5 °C target around 5 years later.
Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond
The Cryosphere, 17, 4675–4690, https://doi.org/10.5194/tc-17-4675-2023, https://doi.org/10.5194/tc-17-4675-2023, 2023
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In this study, we propose a deep neural network to map the extent of giant Antarctic icebergs in Sentinel-1 images automatically. While each manual delineation requires several minutes, our U-net takes less than 0.01 s. In terms of accuracy, we find that U-net outperforms two standard segmentation techniques (Otsu, k-means) in most metrics and is more robust to challenging scenes with sea ice, coast and other icebergs. The absolute median deviation in iceberg area across 191 images is 4.1 %.
Geoffrey J. Dawson and Jack C. Landy
The Cryosphere, 17, 4165–4178, https://doi.org/10.5194/tc-17-4165-2023, https://doi.org/10.5194/tc-17-4165-2023, 2023
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In this study, we compared measurements from CryoSat-2 and ICESat-2 over Arctic summer sea ice to understand any possible biases between the two satellites. We found that there is a difference when we measure elevation over summer sea ice using CryoSat-2 and ICESat-2, and this is likely due to surface melt ponds. The differences we found were in good agreement with theoretical predictions, and this work will be valuable for summer sea ice thickness measurements from both altimeters.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
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In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Isolde A. Glissenaar, Jack C. Landy, David G. Babb, Geoffrey J. Dawson, and Stephen E. L. Howell
The Cryosphere, 17, 3269–3289, https://doi.org/10.5194/tc-17-3269-2023, https://doi.org/10.5194/tc-17-3269-2023, 2023
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Observations of large-scale ice thickness have unfortunately only been available since 2003, a short record for researching trends and variability. We generated a proxy for sea ice thickness in the Canadian Arctic for 1996–2020. This is the longest available record for large-scale sea ice thickness available to date and the first record reliably covering the channels between the islands in northern Canada. The product shows that sea ice has thinned by 21 cm over the 25-year record in April.
Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli
Geosci. Model Dev., 16, 4521–4550, https://doi.org/10.5194/gmd-16-4521-2023, https://doi.org/10.5194/gmd-16-4521-2023, 2023
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Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
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We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Robert Ricker, Steven Fons, Arttu Jutila, Nils Hutter, Kyle Duncan, Sinead L. Farrell, Nathan T. Kurtz, and Renée Mie Fredensborg Hansen
The Cryosphere, 17, 1411–1429, https://doi.org/10.5194/tc-17-1411-2023, https://doi.org/10.5194/tc-17-1411-2023, 2023
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Information on sea ice surface topography is important for studies of sea ice as well as for ship navigation through ice. The ICESat-2 satellite senses the sea ice surface with six laser beams. To examine the accuracy of these measurements, we carried out a temporally coincident helicopter flight along the same ground track as the satellite and measured the sea ice surface topography with a laser scanner. This showed that ICESat-2 can see even bumps of only few meters in the sea ice cover.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
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We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
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Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
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Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
William Gregory, Julienne Stroeve, and Michel Tsamados
The Cryosphere, 16, 1653–1673, https://doi.org/10.5194/tc-16-1653-2022, https://doi.org/10.5194/tc-16-1653-2022, 2022
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This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
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Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
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Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Anne Braakmann-Folgmann, Andrew Shepherd, and Andy Ridout
The Cryosphere, 15, 3861–3876, https://doi.org/10.5194/tc-15-3861-2021, https://doi.org/10.5194/tc-15-3861-2021, 2021
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We investigate the disintegration of the B30 iceberg using satellite remote sensing and find that the iceberg lost 378 km3 of ice in 6.5 years, corresponding to 80 % of its initial volume. About two thirds are due to fragmentation at the sides, and one third is due to melting at the iceberg’s base. The release of fresh water and nutrients impacts ocean circulation, sea ice formation, and biological production. We show that adding a snow layer is important when deriving iceberg thickness.
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Short summary
In this study, we use three satellites to test the planned remote sensing approach of the upcoming mission Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) over sea ice and that its dual radars will accurately measure the heights of the top and base of snow sitting atop floating sea ice floes. Our results suggest that CRISTAL's dual radars will not necessarily measure the snow top and base under all conditions. We find that accurate height measurements depend more on surface roughness than on snow properties, as is commonly assumed.
In this study, we use three satellites to test the planned remote sensing approach of the...
