Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach

Edel, Léo; Xie, Jiping; Korosov, Anton; Brajard, Julien; Bertino, Laurent

doi:https://doi.org/10.5194/tc-19-731-2025

Articles | Volume 19, issue 2

https://doi.org/10.5194/tc-19-731-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-19-731-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 2

Research article

|

18 Feb 2025

Research article |

| 18 Feb 2025

Reconstruction of Arctic sea ice thickness (1992–2010) based on a hybrid machine learning and data assimilation approach

Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino

Supplement

https://doi.org/10.5194/tc-19-731-2025-supplement

Data sets

TOPAZ4-ML Sea Ice Thickness (1992–2022) Léo Edel et al. https://doi.org/10.5281/zenodo.11191853

ESA Sea Ice Climate Change Initiative: Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C) S. Hendricks et al. https://doi.org/10.5285/f4c34f4f0f1d4d0da06d771f6972f180

Arctic Sea Ice Freeboard and Thickness D. Yi and H. J. Zwally https://doi.org/10.5067/SXJVJ3A2XIZT

TOPAZ4-ML Sea Ice Thickness (1992-2022) L. Edel et al. https://doi.org/10.5281/zenodo.11191854

Global Low Resolution Sea Ice Drift - Multimission OSI SAF https://doi.org/10.15770/EUM_SAF_OSI_NRT_2007

North Pole Environmental Observatory (NPEO) Oceanographic Mooring Data J. H. Morison et al. https://doi.org/10.5065/D6P84921

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Arctic Ocean Physics Reanalysis, E.U. Copernicus Marine Service Information (CMEMS) E.U. Copernicus Marine Service Information (CMEMS) https://doi.org/10.48670/moi-00007

Arctic Sea Ice Freeboard and Thickness, Version 1 D. Yi and H. J. Zwally https://doi.org/10.5067/SXJVJ3A2XIZT

ESA Sea Ice Climate Change Initiative: Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v2.0 S. Hendricks et al. https://doi.org/10.5285/f4c34f4f0f1d4d0da06d771f6972f180

Mooring data Beaufort Gyre Exploration Program https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/

North Pole Environmental Observatory (NPEO) Oceanographic Mooring Data J. H. Morison et al. https://doi.org/10.5065/D6P84921

Arctic and Antarctic sea ice thickness climate data record (ERS-1, ERS-2, Envisat, CryoSat-2) M. Bocquet and S. Fleury https://doi.org/10.6096/ctoh_sit_2023_01

PMW Sea Ice Thickness - from SSM/I and SSMIS CDR (v1.0) C. Soriot et al. https://doi.org/10.5281/zenodo.13880123

SIN'XS - Sea Ice-thickness product iNter-comparison eXerciSe SIN'XS https://sinxs.noveltis.fr

Video supplement

TOPAZ4-ML Sea Ice Thickness and Volume (1992–2022) Léo Edel https://doi.org/10.5446/68161

Short summary

This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011 to 2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes in sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.