|Title||Comparing Arctic winter sea-ice thickness from SMOS and ORAS5|
|Publication Type||Technical memorandum|
|Secondary Title||ECMWF Technical Memorandum|
|Authors||Tietsche, S, Alonso-Balmaseda, M, Zuo, H, de Rosnay, P|
In this report, Level 3 sea ice thickness observations from SMOS provided by the University of Hamburg (SMOSIce data) are compared to the ocean-sea ice analysis ORAS5. It is concluded that SMOS provides valuable and unique information on thin sea ice during winter. This is immediately relevant to numerical weather prediction, because thin ice in winter allows appreciable heating of the surface atmosphere by the relatively warm ocean underneath, with heat fluxes of the order of several tens of Wm-2. There is a promising match between SMOS and ORAS5 early in the freezing season (October-December), while later in winter, sea ice is consistently modelled thicker than observed. This seems to be mostly due to deficiencies of the model to simulate polynyas and fracture zones. However, there are regions where biases in the observational data seem to play a role. In the current data version 2, the uncertainties provided with the data set do not seem to characterise well the complex uncertainties of the retrieval model; future data versions will bring further refinements. The large and poorly characterised uncertainties make a direct use of SMOSIce data for model evaluation and data assimilation difficult. However, in order to exploit the unique observational information that SMOS provides about thin sea ice, an operational diagnostic monitoring facility has been implemented into ORAS5, so that statistics of departures between the observed and modelled sea ice state can be routinely monitored, and can help to quantify the impact of thin sea ice on operational ECMWF forecasts. In conclusion, it seems premature to consider assimilation of SMOS sea ice thickness, but it is recommended to use SMOSIce observations for the qualitative detection of thin ice during winter, until further improvements in sea ice model, data assimilation, and retrieval algorithms allow a more direct use of the SMOS sea ice thickness data.