Publication Date:
2015-04-21
Description:
We present sensitivity experiments in which the Ocean and Sea Ice Satellite Application Facility (OSISAF) near-real time sea ice concentration data and the recently released Sea Ice Climate Change Initiative (SICCI) data are assimilated during summer. The data assimilation system uses the MIT general circulation model (MITgcm) and a local Singular Evolutive Interpolated Kalman (LSEIK) filter. Atmospheric forcing uncertainties are modelled by using atmospheric ensemble forcing which is taken from the UK Met Office (UKMO) system available through the TIGGE (THORPEX Interactive Grand Global Ensemble) database. When a constant data uncertainty is assumed, the assimilation of SICCI concentrations outperforms the assimilation of OSISAF data in both concentration and thickness forecasts. This is probably because the SICCI data retrieval uses an improved processing algorithms and methodologies. For the assimilation of SICCI data, using the observation uncertainties that are provided with the data improves the ensemble mean state of ice concentration compared to using constant data errors, but does not improve the ice thickness. This is caused by a mismatch between the SICCI concentration and the modelled physical ice concentration. To account for this mismatch the SICCI product should feature larger uncertainties in summer. Consistently, thickness forecasts can be improved by raising the minimum observation uncertainty to inflate the underestimated data error and ensemble spread.
Print ISSN:
1994-0432
Electronic ISSN:
1994-0440
Topics:
Geography
,
Geosciences
