Multivariate State And Parameter Estimation With Data Assimilation Applied To Sea-Ice Models Using A Maxwell Elasto-Brittle Rheology

• Published in: The Cryosphere
• Main Authors: Chen, Yumeng, Smith, Polly, Carrassi, Alberto, Pasmans, Ivo, Bertino, Laurent, Bocquet, Marc, Finn, Tobias Sebastian, Rampal, Pierre, Dansereau, Véronique
• Other Authors: EDF (EDF), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut des Sciences de la Terre (ISTerre), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2024
• Subjects: [SDU]Sciences of the Universe [physics]; Sea ice; The Cryosphere
• Online Access: https://insu.hal.science/insu-04604398; https://insu.hal.science/insu-04604398/document; https://insu.hal.science/insu-04604398/file/tc-18-2381-2024.pdf; https://doi.org/10.5194/tc-18-2381-2024

International audience 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.