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Seasonal predictions at the Met Office are made using the GloSea5 coupled forecasting system which has contributed September sea ice predictions to SIPN since its implementation in 2013. The ocean and sea ice components of GloSea5 are initialised using analysis fields from the FOAM ocean-sea ice analysis and forecast system. Both GloSea and FOAM are run daily at the Met Office and use the Nucleus for European Modelling of the Ocean (NEMO) model coupled to the Los Alamos sea ice model (CICE).
Data assimilation in the FOAM analysis system is performed using the NEMOVAR 3D-Var First Guess at Appropriate Time (FGAT) scheme. Satellite and in-situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day. Sea ice assimilation is performed using SSMI/S sea ice concentration data obtained from the EUMETSAT OSI-SAF. At present assimilation of sea ice is unbalanced from the rest of the ocean-sea ice system and is performed in a separate NEMOVAR minimisation. In this talk a brief overview of the challenges associated with assimilating sea ice concentration in a multi-category sea ice model will be given and the various assumptions made will be discussed. Details will be provided of planned sensitivity studies intended to increase our awareness of how these assumptions affect the predictability of sea ice on short-seasonal time scales.
Although the FOAM analyses used to initialise GloSea benefit from assimilation of sea ice concentration data, other aspects of the sea ice - particularly thickness - is unconstrained by observations. Work is under way at the Met Office to implement assimilation of sea ice thickness data derived from satellite altimetry (i.e., CryoSat-2) within the FOAM-GloSea system. A key goal of this involves assessing the potential impact of sea ice thickness initialisation for the GloSea forecasts. Details of planned work designed to achieve these goals will be presented which includes exploring the link between winter thickness biases/errors and the evolution of concentration forecast errors in GloSea. This assessment can include participation/involvement from other modelling groups within SIPN and we would be interested in exploring potential collaboration in this regard.