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Preprints
https://doi.org/10.5194/egusphere-2023-1809
https://doi.org/10.5194/egusphere-2023-1809
16 Oct 2023
 | 16 Oct 2023

Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology

Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau

Abstract. In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamic-only model that uses the novel Maxwell-Elasto-Brittle (MEB) sea ice rheology and in which we estimate not only the sea ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea ice model especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics-thermodynamics) sea ice model, we show that, with the current observation network, it is possible to improve both the observed and unobserved model state forecast and parameters accuracy.

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Journal article(s) based on this preprint

14 May 2024
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024,https://doi.org/10.5194/tc-18-2381-2024, 2024
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We explore the multivariate state and parameter estimation using data assimilation approach...
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