Assimilation von Meereisbeobachtungen in der Arktis in ein gekoppeltes Meereis-Ozeanmodell mit einem lokalen SEIK Filter und verschiedenen a-priori Fehlerschätzungen

Decrease of summer sea ice extent in the Arctic Ocean opens interesting shipping routes and creates potential for many marine operations. For these activities, accurate predictions of sea ice conditions are required to maintain marine safety. In an effort towards Arctic sea ice prediction, the Arctic sea ice data assimilation (DA) system is developed, based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) and a local Singular Evolutive Interpolated Kalman (LSEIK) filter to assimilate Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration operational products from the National Snow and Ice Data Center (NSIDC). The summer of 2010 is selected to implement a DA study. Based on comparisons with both the assimilated NSIDC SSMIS concentration and concentration data from the Ocean and Sea Ice Satellite Application Facility (OSISAF), the forecasted sea-ice edge and concentration are improved over simulations without data assimilation. By nature of the assimilation algorithm with multivariate covariance between ice concentration and thickness, sea ice thickness also shows some improvement over the forecast without data assimilation. The LSEIK system is further extended to investigate the impact of assimilating sea ice thickness data derived from ESA s Soil Moisture and Ocean Salinity (SMOS) satellite together with SSMIS sea ice concentration data. A period of three months from November 1st, 2011 to January 31st, 2012 is selected to assess the forecast skill of the assimilation system. For comparison, the assimilation is repeated only with the SSMIS sea ice concentrations. By running two different assimilation experiments, and making comparison among the unassimilated model, independent satellite derived data and in-situ observation, it is shown that the SMOS ice thickness assimilation leads to improved thickness forecasts. With SMOS thickness data, the sea ice concentration forecasts also have a better agreement with observations, although this improvement is smaller. Then the role of atmospheric uncertainty for the assimilation and prediction of Arctic sea ice is explored by running the MITgcm in data assimilation and prediction mode for the summer of 2010. The atmospheric ensemble forcing is taken from the UK Met Office (UKMO) system available through the TIGGE (THORPEX Interactive Grand Global Ensemble) database. The DA system is also based on LSEIK filter and SSMIS sea ice concentration from the NSIDC are assimilated. Two kinds of experiments are carried out using different LSEIK configuration and forcing: The first one uses a single deterministic control forcing and a forgetting factor necessary to inflate the ensemble spread in the DA phase; the second one uses 23 members from the UKMO atmospheric ensemble prediction system, thereby avoiding any additional ensemble inflation and making further tuning unnecessary. With both systems the model data misfit improves as expected, but the ensemble approach outperforms the deterministic filter. This is because a larger and more realistic ensemble spread, representing model uncertainty, leads to a better adjustment. Finally, the recently released Sea Ice Climate Change Initiative (SICCI) sea ice concentration data during summer are assimilated. Atmospheric forcing uncertainties are modelled by using UKMO atmospheric ensemble forecasting data. Using the original observation uncertainties improves the ensemble mean state of ice concentration compared to using constant data errors, but does not improve the ice thickness. Thickness forecasts can be improved, however, by raising the minimum observation uncertainty to inflate the underestimated data error and ensemble spread