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: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: article; Verlagsveröffentlichung; Sea ice
• Online Access: https://doi.org/10.5194/egusphere-2023-1809; https://noa.gwlb.de/receive/cop_mods_00069323; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00067710/egusphere-2023-1809.pdf; https://egusphere.copernicus.org/preprints/2023/egusphere-2023-1809/egusphere-2023-1809.pdf

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.