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. The accuracy of the initial state is very important for thequality of a forecast, and data assimilation is crucial for obtaining thebest-possible initial state. For many years, sea-ice concentration was theonly parameter used for assimilation into numerical sea-ice models. Sea-iceconcentration can easily be observed by satellites, and satelliteobservations provide a full Arctic coverage. During the last decade, anincreasing number of sea-ice related variables have become available, whichinclude sea-ice thickness and snow depth, which are both important parametersin the numerical sea-ice models. In the present study, a coupledocean–sea-ice model is used to assess the assimilation impact of sea-icethickness and snow depth on the model. The model system with the assimilationof these parameters is verified by comparison with a system assimilating onlyice concentration and a system having no assimilation. The observationsassimilated are sea ice concentration from the Ocean and Sea Ice SatelliteApplication Facility, thin sea ice from the European Space Agency's(ESA) Soil Moisture and Ocean Salinity mission, thick sea ice fromESA's CryoSat-2 satellite, and a new snow-depth product derived from theNational Space Agency's Advanced Microwave Scanning Radiometer(AMSR-E/AMSR-2) satellites. The model results are verified by comparingassimilated observations and independent observations of ice concentrationfrom AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridgecampaign. It is found that the assimilation of ice thickness stronglyimproves ice concentration, ice thickness and snow depth, while the snowobservations have a smaller but still positive short-term effect on snowdepth and sea-ice concentration. In our study, the seasonal forecast showedthat assimilating snow depth led to a less accurate long-term estimation ofsea-ice extent compared to the other assimilation systems. The other threegave similar results. The improvements due to assimilation were found to lastfor at least 3–4 months, but possibly even longer.

Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system