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Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology

Chen, Y. ORCID: https://orcid.org/0000-0002-2319-6937, Smith, P. ORCID: https://orcid.org/0000-0003-4570-4127, Carrassi, A. ORCID: https://orcid.org/0000-0003-0722-5600, Pasmans, I. ORCID: https://orcid.org/0000-0001-5076-5421, Bertino, L., Bocquet, M., Sebastian Finn, T., Rampal, P. and Dansereau, V. (2024) Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology. The Cryosphere, 18 (5). pp. 2381-2406. ISSN 1994-0424

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To link to this item DOI: 10.5194/tc-18-2381-2024

Abstract/Summary

In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-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 the unobserved model state forecast and parameter accuracy.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:116449
Publisher:European Geosciences Union

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