Articles | Volume 20, issue 7
https://doi.org/10.5194/tc-20-3913-2026
https://doi.org/10.5194/tc-20-3913-2026
Research article
 | 
17 Jul 2026
Research article |  | 17 Jul 2026

Bayesian inversion of satellite altimetry for Arctic sea ice and snow thickness

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

Data sets

Sea-ice type climate data record October 1978-August 2023 A. Sørensen et al. https://doi.org/10.24381/CDS.29C46D83

IceBridge Sea Ice Freeboard, Snow Depth, and Thickness Quick Look, Version 1 N. Kurtz et al. https://doi.org/10.5067/GRIXZ91DE0L9

ATLAS/ICESat-2 L3A Sea Ice Freeboard, Version 6 R. Kwok et al. https://doi.org/10.5067/ATLAS/ATL10.006

Magnaprobe snow and melt pond depth measurements from the 2019-2020 MOSAiC expedition P. Itkin et al. https://doi.org/10.1594/PANGAEA.937781

Airborne sea ice parameters during the IceBird Winter 2019 campaign in the Arctic Ocean A. Jutila et al. https://doi.org/10.1594/PANGAEA.933912

Model code and software

Elierb/Bayesian-trans-dimensional-inversion-from-satellite-altimeters-for-Arctic-ice-and-snow-retrievals: v2 (Version v2) Elierb https://doi.org/10.5281/zenodo.17475067

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Short summary
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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