Abstract
Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.
Highlights
Observations show that for the last 50 years there has been a decline in both Arctic sea-ice extent (Stroeve et al, 2007; Perovich et al, 2017) and sea-ice thickness (Kwok and Rothrock, 2009)
Many of the results shown will be based on the root mean squared error (RMSE)
We have found that assimilation of more observation types than sea-ice concentration (SIC) into coupled sea-ice–ocean models can lead to significant model improvements
Summary
Observations show that for the last 50 years there has been a decline in both Arctic sea-ice extent (Stroeve et al, 2007; Perovich et al, 2017) and sea-ice thickness (Kwok and Rothrock, 2009). Wang and Overland (2012) estimate the Arctic Ocean to be nearly ice-free within the 2030s. Models show that the sea-ice decline is likely to continue (Zhang and Walsh, 2006). This large change in the global climate system leads to a need for improved models and forecasting systems due to more variable and mobile Arctic sea ice (Eicken, 2013). Accurate initial states can be achieved by assimilating observations into the model system
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