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

• Main Authors: Chen, Yumeng, Smith, Polly, Carrassi, Alberto, Pasmans, Ivo, Bertino, Laurent, Bocquet, Marc, Finn, Tobias Sebastian, Rampal, Pierre, Dansereau, Véronique
• Format: Text
• Language: English
• Published: 2024
• Subjects: Sea ice
• Online Access: https://doi.org/10.5194/egusphere-2023-1809; https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1809/

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.