Four-dimensional variational data assimilation with a sea-ice thickness emulator

Durand, Charlotte; Finn, Tobias Sebastian; Farchi, Alban; Bocquet, Marc; Brajard, Julien; Bertino, Laurent

doi:10.5194/tc-19-5613-2025

Articles | Volume 19, issue 11

https://doi.org/10.5194/tc-19-5613-2025

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

https://doi.org/10.5194/tc-19-5613-2025

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

Articles | Volume 19, issue 11

Research article

|

12 Nov 2025

Research article |

| 12 Nov 2025

Four-dimensional variational data assimilation with a sea-ice thickness emulator

Charlotte Durand, Tobias Sebastian Finn, Alban Farchi, Marc Bocquet, Julien Brajard, and Laurent Bertino

Data sets

Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) G. Boutin et al. https://doi.org/10.5281/zenodo.7277523

ERA5 hourly data on single levels from 1940 to present Copernicus Climate Change Service, Climate Data Store https://doi.org/10.24381/cds.adbb2d47

Arctic Ocean Sea Ice Analysis and Forecast European Union-Copernicus Marine Service https://doi.org/10.48670/MOI-00004

ECMWF Forecast User Guide R. Owens and T. Hewson https://doi.org/10.21957/M1CS7H

Interactive computing environment

Code and data for 'Four-dimensional variational data assimilation with a sea-ice thickness emulator' Charlotte Durand https://doi.org/10.5281/zenodo.14418068

Short summary

This paper presents a four-dimensional variational data assimilation system based on a neural network emulator for sea-ice thickness, learned from neXtSIM (neXt generation Sea Ice Model) simulation outputs. Testing with simulated and real observation retrievals, the system improves forecasts and bias error, performing comparably to operational methods, demonstrating the promise of sea-ice data-driven data assimilation systems.