Sea-ice data assimilation in NEMO-LIM2 and -LIM3 usingthe Ensemble Kalman Filter

We use the Ensemble Kalman Filter (EnKF) to assimilate either sea-ice concentration or sea-ice thickness data into the coupled ocean sea-ice models NEMO-LIM2 and - LIM3. Output from our data assimilation system is intended to be used to initialize decadal forecasts within the EU-project COMBINE. For...

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Bibliographic Details
Main Authors: Konig Beatty, Christof, Mathiot, Pierre, Massonnet, François, Fichefet, Thierry, Goosse, Hugues, Belgian IPY Symposium. The Contribution of Belgian Research to the Achievements of the International Polar Year 2007-2009, Koninklijke Vlaamse Academie van Belgie voor Wetenschappen en Kunsten, pp. 27-29
Other Authors: UCL - SST/ELI/ELIC - Earth & Climate
Format: Conference Object
Language:English
Published: 2010
Subjects:
1443
Sea ice
Online Access:http://hdl.handle.net/2078.1/71116
Description
Summary:We use the Ensemble Kalman Filter (EnKF) to assimilate either sea-ice concentration or sea-ice thickness data into the coupled ocean sea-ice models NEMO-LIM2 and - LIM3. Output from our data assimilation system is intended to be used to initialize decadal forecasts within the EU-project COMBINE. For now assimilated data is model generated (twin experiments). We find that assimilation of data of one variable in NEMO-LIM2 does not only improve the assimilated variable but also non-assimilated variables. Such crossvariable improvement is very promising considering the scarcity of polar data, particularly of sea-ice thickness. We also show preliminary results of data assimilation experiments into the new version of our sea-ice model, NEMO-LIM3, where data assimilation does not yet lead to the expected results

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