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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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.
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This preprint is open for discussion and under review for The Cryosphere (TC).
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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.
Ghislain Picard, Justin Murfitt, Elena Zakharova, Pierre Zeiger, Laurent Arnaud, Jeremie Aublanc, Jack C. Landy, Michele Scagliola, and Claude Duguay
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
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Radar altimeters measure ice-sheet elevation and sea-ice thickness. To improve their accuracy, we developed a model that simulates altimeter waveforms based on the physical properties of snow and sea ice. It computes the power, time, and Doppler shift of radar echoes as waves travel to the surface, interact with snow and ice, and return to the satellite. The model was verified internally, validated against other models and applied in Antarctica using in-situ snow measurements.
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This preprint is open for discussion and under review for The Cryosphere (TC).
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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
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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
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
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.
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Alistair Duffey, Walker Lee, Lauren Wheeler, Peter Irvine, Benjamin Wagman, Matthew Henry, Daniele Visioni, Michel Tsamados, and Douglas MacMartin
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Short summary
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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.
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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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1163, https://doi.org/10.5194/egusphere-2025-1163, 2025
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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.
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.
Ida Birgitte 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 Discuss., https://doi.org/10.5194/essd-2024-234, https://doi.org/10.5194/essd-2024-234, 2024
Revised manuscript accepted for ESSD
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Discover the latest advancements in sea ice research with our comprehensive Climate Change Initiative (CCI) sea ice thickness (SIT) Round Robin Data Package (RRDP). This pioneering collection contains reference measurements from 1960 to 2022 from airborne sensors, buoys, visual observations and sonar and covers the polar regions from 1993 to 2021, providing crucial reference measurements for validating satellite-derived sea ice thickness.
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.
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021, https://doi.org/10.5194/tc-15-2857-2021, 2021
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Satellite measurements of radar freeboard allow us to compute the thickness of sea ice from space; however attaining measurements across the entire Arctic basin typically takes up to 30 d. Here we present a statistical method which allows us to combine observations from three separate satellites to generate daily estimates of radar freeboard across the Arctic Basin. This helps us understand how sea ice thickness is changing on shorter timescales and what may be causing these changes.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
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We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
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Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Robbie D. C. Mallett
The Cryosphere, 15, 1453–1454, https://doi.org/10.5194/tc-15-1453-2021, https://doi.org/10.5194/tc-15-1453-2021, 2021
Cited articles
Aaboe, S., Down, E. J., and Eastwood, S.: Product User Manual for the Global Sea-Ice Edge and Type Product, Norwegian Meteorological Institute, Oslo, Norway, 480, https://doi.org/10.15770/EUM_SAF_OSI_NRT_2006, 2021. a
Adodo, F. I., Remy, F., and Picard, G.: Seasonal variations of the backscattering coefficient measured by radar altimeters over the Antarctic Ice Sheet, The Cryosphere, 12, 1767–1778, https://doi.org/10.5194/tc-12-1767-2018, 2018. a
Babb, D., Landy, J., Lukovich, J., Haas, C., Hendricks, S., Barber, D., and Galley, R.: The 2017 reversal of the Beaufort Gyre: can dynamic thickening of a seasonal ice cover during a reversal limit summer ice melt in the Beaufort Sea?, J. Geophys. Res.-Oceans, 125, e2020JC016796, https://doi.org/10.1029/2020JC016796, 2020. a
Andersen, O. B., Rose, S. K., Abulaitijiang, A., Zhang, S., and Fleury, S.: The DTU21 global mean sea surface and first evaluation, Earth Syst. Sci. Data, 15, 4065–4075, https://doi.org/10.5194/essd-15-4065-2023, 2023. a
Beaven, S., Lockhart, G., Gogineni, S., Hossetnmostafa, A., Jezek, K., Gow, A., Perovich, D., Fung, A., and Tjuatja, S.: Laboratory measurements of radar backscatter from bare and snow-covered saline ice sheets, Remote Sens.-Basel, 16, 851–876, 1995. a
Belter, H. J., Krumpen, T., Hendricks, S., Hoelemann, J., Janout, M. A., Ricker, R., and Haas, C.: Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations, The Cryosphere, 14, 2189–2203, https://doi.org/10.5194/tc-14-2189-2020, 2020. a
Bocquet, M., Fleury, S., Piras, F., Rinne, E., Sallila, H., Garnier, F., and Rémy, F.: Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021, The Cryosphere, 17, 3013–3039, https://doi.org/10.5194/tc-17-3013-2023, 2023. a
Bonan, D. B., Lehner, F., and Holland, M. M.: Partitioning uncertainty in projections of Arctic sea ice, Environ. Res. Lett., 16, 044002, https://doi.org/10.1088/1748-9326/abe0ec, 2021. a
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: incremental but significant improvements for Earth-gridded data sets, ISPRS Int. Geo-Inf., 1, 32–45, 2012. a
Bushuk, M., Msadek, R., Winton, M., Vecchi, G., Gudgel, R., Rosati, A., and Yang, X.: Skillful regional prediction of Arctic sea ice on seasonal timescales, Geophys. Res. Lett., 44, 4953–4964, https://doi.org/10.1002/2017GL073155, 2017. a
de Rijke-Thomas, C.: Deep Learning for Radar Altimetry Echo Classification and Sea Ice Surface Recognition, Master's thesis, University College London, https://doi.org/10.13140/RG.2.2.25854.46408, 2019. a
de Rijke-Thomas, C., Landy, J., Mallett, R., Willatt, R., Tsamados, M., and King, J.: Airborne investigation of quasi-specular Ku-band radar scattering for satellite altimetry over snow-covered Arctic sea ice, IEEE T. Geosci. Remote, 61, 1–19, https://doi.org/10.1109/TGRS.2023.3318263, 2023. a, b, c, d, e, f, g
Dettmering, D., Schwatke, C., and Bosch, W.: Global calibration of SARAL/AltiKa using multi-mission sea surface height crossovers, Marine Geodesy, 38, 206–218, 2015. a
Duncan, K. and Farrell, S. L.: Determining variability in Arctic sea ice pressure ridge topography with ICESat-2, Geophys. Res. Lett., 49, e2022GL100272, https://doi.org/10.1029/2022GL100272, 2022. a, b
Fox-Kemper, B., Hewitt, H.T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S.S., Edwards, T.L., Golledge, N.R., Hemer, M., Kopp, R.E., Krinner, G. and Mix, A.: Ocean, cryosphere and sea level change, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Zhai, P. and Pirani, A., https://doi.org/10.1017/9781009157896.011, 2021. a
Fredensborg Hansen, R. M., Rinne, E., and Skourup, H.: Classification of sea ice types in the Arctic by radar echoes from SARAL/AltiKa, Remote Sens.-Basel, 13, 3183, https://doi.org/10.3390/rs13163183, 2021. a, b
Fredensborg Hansen, R. M., Skourup, H., Rinne, E., Høyland, K. V., Landy, J. C., Merkouriadi, I., and Forsberg, R.: Arctic freeboard and snow depth from near-coincident CryoSat-2 and ICESat-2 (CRYO2ICE) observations: a first examination of winter sea ice during 2020–2022, Earth and Space Science, 11, e2023EA003313, https://doi.org/10.1029/2023EA003313, 2024. a, b
Fung, A. and Chen, K.: An update on the IEM surface backscattering model, IEEE Geosci. Remote S., 1, 75–77, https://doi.org/10.1109/LGRS.2004.826564, 2004. a
Fung, A. and Eom, H.: Coherent scattering of a spherical wave from an irregular surface, IEEE Transactions on Antennas and Propagation, 31, 68–72, 1983. a
Garnier, F., Fleury, S., Garric, G., Bouffard, J., Tsamados, M., Laforge, A., Bocquet, M., Fredensborg Hansen, R. M., and Remy, F.: Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements, The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, 2021. a, b, c, d
Glissenaar, I. A., Landy, J. C., Petty, A. A., Kurtz, N. T., and Stroeve, J. C.: Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness, The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, 2021. a, b
Graham, R.M., Itkin, P., Meyer, A., Sundfjord, A., Spreen, G., Smedsrud, L.H., Liston, G.E., Cheng, B., Cohen, L., Divine, D. and Fer, I.,: Winter storms accelerate the demise of sea ice in the Atlantic sector of the Arctic Ocean, Sci. Rep.-UK, 9, 9222, https://doi.org/10.1038/s41598-019-45574-5, 2019. a
Guerreiro, K., Fleury, S., Zakharova, E., Kouraev, A., Rémy, F., and Maisongrande, P.: Comparison of CryoSat-2 and ENVISAT radar freeboard over Arctic sea ice: toward an improved Envisat freeboard retrieval, The Cryosphere, 11, 2059–2073, https://doi.org/10.5194/tc-11-2059-2017, 2017. a, b
Hendricks, S., Ricker, R., and Paul, S.: Product User Guide and Algorithm Specification: AWI CryoSat-2 Sea Ice Thickness (version 2.4), https://hdl.handle.net/10013/epic.eee8e1c4-56fd-4127-89c0-9bfddb9ec38b (last access: 27 September 2025), 2021. a
Itkin, P., Webster, M., Hendricks, S., Oggier, M., Jaggi, M., Ricker, R., Arndt, S., Divine, D. V., von Albedyll, L., Raphael, I., Rohde, J., and Liston, G. E.: Magnaprobe snow and melt pond depth measurements from the 2019–2020 MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.937781, 2021. a
Itkin, P., Hendricks, S., Webster, M., von Albedyll, L., Arndt, S., Divine, D., Jaggi, M., Oggier, M., Raphael, I., Ricker, R. and Rohde, J.: Sea ice and snow characteristics from year-long transects at the MOSAiC Central Observatory, Elem. Sci. Anth., 11, 00048, https://doi.org/10.1525/elementa.2022.00048, 2023. a
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., Krumpen, T., and Haas, C.: Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements, The Cryosphere, 16, 259–275, https://doi.org/10.5194/tc-16-259-2022, 2022a. a
Jutila, A., King, J., Paden, J., Ricker, R., Hendricks, S., Polashenski, C., Helm, V., Binder, T., and Haas, C.: High-resolution snow depth on Arctic sea ice from low-altitude airborne microwave radar data, IEEE T. Geosci. Remote, 60, 1–16, 2022b. a
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., and Haas, C.: Airborne sea ice parameters during the IceBird Winter 2019 campaign in the Arctic Ocean, Version 2, PANGAEA [dataset publication series], https://doi.org/10.1594/PANGAEA.966057, 2024. a
Kacimi, S. and Kwok, R.: Arctic snow depth, ice thickness, and volume from ICESat 2 and CryoSat 2: 2018–2021, Geophys. Res. Lett., 49, 2018–2021, https://doi.org/10.1029/2021gl097448, 2022. a
Kern, M., Cullen, R., Berruti, B., Bouffard, J., Casal, T., Drinkwater, M. R., Gabriele, A., Lecuyot, A., Ludwig, M., Midthassel, R., Navas Traver, I., Parrinello, T., Ressler, G., Andersson, E., Martin-Puig, C., Andersen, O., Bartsch, A., Farrell, S., Fleury, S., Gascoin, S., Guillot, A., Humbert, A., Rinne, E., Shepherd, A., van den Broeke, M. R., and Yackel, J.: The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission, The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, 2020. a, b, c, d
King, J., Howell, S., Derksen, C., Rutter, N., Toose, P., Beckers, J., Haas, C., Kurtz, N., and Richter-Menge, J.: Evaluation of Operation IceBridge quick-look snow depth estimates on sea ice, Geophys, Res. Lett, 42, 9302–9310, https://doi.org/10.1002/2015GL066389, 2015. a, b
King, J., Skourup, H., Hvidegaard, S. M., Rösel, A., Gerland, S., Spreen, G., Polashenski, C., Helm, V., and Liston, G. E.: Comparison of freeboard retrieval and ice thickness calculation from ALS, ASIRAS, and CryoSat-2 in the Norwegian Arctic to field measurements made during the N-ICE2015 expedition, J. Geophys. Res.-Oceans, 123, 1123–1141, 2018. a
King, J., Brady, M., and Newman, T.: kingjml/pySnowRadar: Updated IEEE TGRS Submission (v1.1.1), Zenodo, https://doi.org/10.5281/zenodo.4071947, 2020. a
Kurtz, N. T., Galin, N., and Studinger, M.: An improved CryoSat-2 sea ice freeboard retrieval algorithm through the use of waveform fitting, The Cryosphere, 8, 1217–1237, https://doi.org/10.5194/tc-8-1217-2014, 2014. a, b, c
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018, Environ. Res. Lett., 13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018. a
Kwok, R. and Cunningham, G.: ICESat over Arctic sea ice: estimation of snow depth and ice thickness, J. Geophys. Res., 113, 08010, https://doi.org/10.1029/2008JC004753, 2008. a
Kwok, R. and Cunningham, G.: Variability of arctic sea ice thickness and volume from CryoSat-2, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 373, https://doi.org/10.1098/rsta.2014.0157, 2015. a
Kwok, R., Kurtz, N. T., Brucker, L., Ivanoff, A., Newman, T., Farrell, S. L., King, J., Howell, S., Webster, M. A., Paden, J., Leuschen, C., MacGregor, J. A., Richter-Menge, J., Harbeck, J., and Tschudi, M.: Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge, The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, 2017. a
Kwok, R., Markus, T., Kurtz, N., Petty, A., Neumann, T., Farrell, S., Cunningham, G., Hancock, D., Ivanoff, A., and Wimert, J.: Surface height and sea ice freeboard of the Arctic Ocean from ICESat-2: characteristics and early results, J. Geophys. Res.-Oceans, 124, 6942–6959, 2019b. a
Kwok, R., Kacimi, S., Webster, M., Kurtz, N., and Petty, A.: Arctic snow depth and sea ice thickness from ICESat 2 and CryoSat 2 freeboards: a first examination, J. Geophys. Res.-Oceans, 125, 2019 016008, https://doi.org/10.1029/2019JC016008, 2020. a, b, c, d
Kwok, R., Petty, A. A., Bagnardi, M., Kurtz, N. T., Cunningham, G. F., Ivanoff, A., and Kacimi, S.: Refining the sea surface identification approach for determining freeboards in the ICESat-2 sea ice products, The Cryosphere, 15, 821–833, https://doi.org/10.5194/tc-15-821-2021, 2021. a
Kwok, R., Petty, A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A., Wimert, J., Bagnardi, M., and Kurtz, N.: ATLAS/ICESat-2 L3A sea ice height, version 6, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL07.006, 2023. a
Landy, J.: Facet-Based SAR Altimeter Echo Model, GitHub [code], https://github.com/jclandy/FBEM (last access: 3 March 2023), 2022. a
Landy, J.: University of Tromso Arctic Ocean freeboard and snow depth product from CryoSat-2, AltiKa and ICESat-2 (Version v3), Zenodo [data set], https://doi.org/10.5281/zenodo.13774843, 2024. a
Larue, F., Picard, G., Aublanc, J., Arnaud, L., Robledano-Perez, A., Meur, E. L., Favier, V., Jourdain, B., Savarino, J., and Thibaut, P.: Radar altimeter waveform simulations in antarctica with the snow microwave radiative transfer model (SMRT), Remote Sens. Environ., 263, 112534, https://doi.org/10.1016/j.rse.2021.112534, 2021. a
Lawrence, I. R., Tsamados, M. C., Stroeve, J. C., Armitage, T. W. K., and Ridout, A. L.: Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards, The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Lawrence, I., Tsamados, M., Stroeve, J., Dinardo, S., Ridout, A., Muir, A., Tilling, R., and Shepherd, A.: Extending the Arctic sea ice freeboard and sea level record with the Sentinel-3 radar altimeters, Adv. Space Res., https://doi.org/10.1016/j.asr.2019.10.011, 2019. a
Long, D. and Ulaby, F.: Microwave radar and radiometric remote sensing, Artech, ISBN 9781630810504, 2015. a
Macfarlane, A. R., Schneebeli, M., Dadic, R., Tavri, A., Immerz, A., Polashenski, C., Krampe, D., Clemens-Sewall, D., Wagner, D. N., Perovich, D. K. and Henna-Reetta, H.,: A database of snow on sea ice in the central Arctic collected during the MOSaiC expedition, Scientific Data, 10, 398, https://doi.org/10.1038/s41597-023-02273-1, 2023. a, b
Mallett, R. D. C., Lawrence, I. R., Stroeve, J. C., Landy, J. C., and Tsamados, M.: Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates, The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, 2020. a
Mallett, R. D.: A methodologically robust densification function for snow on multiyear Arctic sea ice, J. Glaciol., 71, e24, https://doi.org/10.1017/jog.2025.5, 2025. a
Mallett, R. D. C., Stroeve, J. C., Tsamados, M., Landy, J. C., Willatt, R., Nandan, V., and Liston, G. E.: Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover, The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, 2021. a, b
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D. and Jasinski, M.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, 2017. a
Massonnet, F., Vancoppenolle, M., Goosse, H., Docquier, D., Fichefet, T., and Blanchard-Wrigglesworth, E.: Arctic sea-ice change tied to its mean state through thermodynamic processes, Nat. Clim. Change, 8, 599–603, 2018. a
Mätzler, C.: Improved Born approximation for scattering of radiation in a granular medium, J. Appl. Phys., 83, 6111–6117, 1998. a
Meloche, J., Sandells, M., Löwe, H., Rutter, N., Essery, R., Picard, G., Scharien, R. K., Langlois, A., Jaggi, M., King, J., Toose, P., Bouffard, J., Di Bella, A., and Scagliola, M.: Altimetric Ku-band Radar Observations of Snow on Sea Ice Simulated with SMRT, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1583, 2024. a, b, c
Merkouriadi, I., Gallet, J.-C., Graham, R. M., Liston, G. E., Polashenski, C., Rösel, A., and Gerland, S.: Winter snow conditions on Arctic sea ice north of Svalbard during the Norwegian young sea ICE (N-ICE2015) expedition, J. Geophys. Res.-Atmos., 122, 10–837, 2017. a
Müller, F. L., Paul, S., Hendricks, S., and Dettmering, D.: Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery, The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, 2023. a, b
Nab, C., Mallett, R., Gregory, W., Landy, J., Lawrence, I., Willatt, R., Stroeve, J., and Tsamados, M.: Synoptic variability in satellite altimeter-derived radar freeboard of Arctic sea ice, Geophys. Res. Lett., 50, e2022GL100696, https://doi.org/10.1029/2022GL100696, 2023. a, b, c, d
Nandan, V., Geldsetzer, T., Yackel, J., Mahmud, M., Scharien, R., Howell, S., and Else, B.: Effect of snow salinity on CryoSat 2 Arctic first year sea ice freeboard measurements, Geophys. Res. Lett., 44, 10419–10426, https://doi.org/10.1002/2017GL074506, 2017. a, b, c, d
Nandan, V., Scharien, R., Geldsetzer, T., Kwok, R., Yackel, J., Mahmud, M., Rösel, A., Tonboe, R., Granskog, M., Willatt, R., Stroeve, J., Nomura, D., and Frey, M.: Snow property controls on modeled Ku-band altimeter estimates of first-year sea ice thickness: case studies from the Canadian and Norwegian Arctic, IEEE J. Sel. Top. Appl., 13, 1082–1096, https://doi.org/10.1109/JSTARS.2020.2966432, 2020. a
Nandan, V., Willatt, R., Mallett, R., Stroeve, J., Geldsetzer, T., Scharien, R., Tonboe, R., Yackel, J., Landy, J., Clemens-Sewall, D., Jutila, A., Wagner, D. N., Krampe, D., Huntemann, M., Mahmud, M., Jensen, D., Newman, T., Hendricks, S., Spreen, G., Macfarlane, A., Schneebeli, M., Mead, J., Ricker, R., Gallagher, M., Duguay, C., Raphael, I., Polashenski, C., Tsamados, M., Matero, I., and Hoppmann, M.: Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice, The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, 2023. a, b, c, d, e
Notz, D. and Community, S.: Arctic sea ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Petty, A., Bagnardi, M., Kurtz, N., Tilling, R., Fons, S., Armitage, T., Horvat, C., and Kwok, R.: Assessment of ICESat-2 sea ice surface classification with Sentinel-2 imagery: implications for freeboard and new estimates of lead and floe geometry, Earth and Space Science, 8, e2020EA001491, https://doi.org/10.1029/2020EA001491, 2021. a
Petty, A. A., Keeney, N., Cabaj, A., Kushner, P., and Bagnardi, M.: Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, 2023a. a
Petty, A., Kwok, R., Bagnardi, M., Ivanoff, A., Kurtz, N., Lee, J., Wimert, J., and Hancock, D.: ATLAS/ICESat-2 L3B Daily and Monthly Gridded Sea Ice Freeboard, Version 4, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL20.004, 2023b. a
Quartly, G.D., Rinne, E., Passaro, M., Andersen, O.B., Dinardo, S., Fleury, S., Guillot, A., Hendricks, S., Kurekin, A.A., Müller, F.L. and Ricker, R.: Retrieving sea level and freeboard in the Arctic: a review of current radar altimetry methodologies and future perspectives, Remote Sens.-Basel, 11, 881, https://doi.org/10.3390/rs11070881, 2019. a
Ricker, R., Hendricks, S., Helm, V., Skourup, H., and Davidson, M.: Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation, The Cryosphere, 8, 1607–1622, https://doi.org/10.5194/tc-8-1607-2014, 2014. a, b, c
Ricker, R., Hendricks, S., and Beckers, J. F.: The impact of geophysical corrections on sea-ice freeboard retrieved from satellite altimetry, Remote Sens.-Basel, 8, 317, https://doi.org/10.3390/rs8040317, 2016. a
Rösel, A., Farrell, S. L., Nandan, V., Richter-Menge, J., Spreen, G., Divine, D. V., Steer, A., Gallet, J.-C., and Gerland, S.: Implications of surface flooding on airborne estimates of snow depth on sea ice, The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, 2021. a
Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all seasons, Environ. Res. Lett., 13, https://doi.org/10.1088/1748-9326/aade56, 2018. a
Stroeve, J., Nandan, V., Willatt, R., Tonboe, R., Hendricks, S., Ricker, R., Mead, J., Mallett, R., Huntemann, M., Itkin, P., Schneebeli, M., Krampe, D., Spreen, G., Wilkinson, J., Matero, I., Hoppmann, M., and Tsamados, M.: Surface-based Ku- and Ka-band polarimetric radar for sea ice studies, The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, 2020. a, b, c, d
Studinger, M., Smith, B. E., Kurtz, N., Petty, A., Sutterley, T., and Tilling, R.: Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models, The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, 2024. a
Tilling, R., Ridout, A., and Shepherd, A.: Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data, Adv. Space Res., 62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018. a
Tonboe, R. T., Nandan, V., Yackel, J., Kern, S., Pedersen, L. T., and Stroeve, J.: Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice, The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, 2021. a
Tran, N., Vandemark, D., Labroue, S., Feng, H., Chapron, B., Tolman, H., Lambin, J., and Picot, N.: Sea state bias in altimeter sea level estimates determined by combining wave model and satellite data, J. Geophys. Res.-Oceans, 115, https://doi.org/10.1029/2009JC005534, 2010. a
Tsang, L., Pan, J., Liang, D., Li, Z., Cline, D. W., and Tan, Y.: Modeling active microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple-scattering effects, IEEE T. Geosci. Remote, 45, 990–1004, 2007. a
Ulaby, F., Moore, R., and Fung, A.: K.: Microwave Remote Sensing: Active and Passive, Vol. 3, ISBN 19860041708, 1986. a
Verron, J., Sengenes, P., Lambin, J., Noubel, J., Steunou, N., Guillot, A., Picot, N., Coutin-Faye, S., Sharma, R., Gairola, R.M. and Murthy, D.R.: The SARAL/AltiKa altimetry satellite mission, Marine Geodesy, 38, 2–21, https://doi.org/10.1080/01490419.2014.1000471, 2015. a
Warren, S., Rigor, I., Untersteiner, N., Radionov, V., Bryazgin, N., Aleksandrov, Y., and Colony, R.: Snow depth on Arctic sea ice, J. Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012%3C1814:SDOASI%3E2.0.CO;2, 1999. a
Watts, M., Maslowski, W., Lee, Y. J., Kinney, J. C., and Osinski, R.: A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations, J. Climate, 34, 6399–6420, 2021. a
Webster, M., Gerland, S., Holland, M., Hunke, E., Kwok, R., Lecomte, O., Massom, R., Perovich, D., and Sturm, M.: Snow in the changing sea-ice systems, Nat. Clim. Change, 8, 946–953, 2018. a
Webster, M. A., Parker, C., Boisvert, L., and Kwok, R.: The role of cyclone activity in snow accumulation on Arctic sea ice, Nat. Commun., 10, 5285, https://doi.org/10.1038/s41467-019-13299-8, 2019. a
Willatt, R., Giles, K., Laxon, S., Stone-Drake, L., and Worby, A.: Field investigations of Ku-band radar penetration into snow cover on Antarctic sea ice, IEEE T. Geosci. Remote, 48, 365–372, https://doi.org/10.1109/TGRS.2009.2028237, 2009. a
Willatt, R., Laxon, S., Giles, K., Cullen, R., Haas, C., and Helm, V.: Ku-band radar penetration into snow cover on Arctic sea ice using airborne data, Ann. Glaciol., 52, 197–205, 2011. a
Willatt, R., Stroeve, J.C., Nandan, V., Newman, T., Mallett, R., Hendricks, S., Ricker, R., Mead, J., Itkin, P., Tonboe, R. and Wagner, D.N.: Retrieval of snow depth on Arctic sea ice from surface-based, polarimetric, dual-frequency radar altimetry, Geophys. Res. Lett., 50, e2023GL104461, https://doi.org/10.1029/2023GL104461, 2023. a, b, c, d, e
Wingham, D., Francis, C., Baker, S., Bouzinac, C., Brockley, D., Cullen, R., and Phalippou, L.: CryoSat: a mission to determine the fluctuations in Earth's land and marine ice fields, Adv. Space Res., 37, 841–871, https://doi.org/10.1016/j.asr.2005.07.027, 2006. a, b, c
Zakharova, E., Fleury, S., Guerreiro, K., Willmes, S., Rémy, F., Kouraev, A., and Heinemann, G.: Sea ice leads detection using SARAL/AltiKa altimeter, Mar. Geod., 38, 522–533, https://doi.org/10.1080/01490419.2015.1019655, 2015. a
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...
