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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/tc-2018-171
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2018-171
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Oct 2018

Research article | 10 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal The Cryosphere (TC).

Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modeling system

Sindre Fritzner1, Rune Graversen1, Kai H. Christensen2, Philip Rostosky3, and Keguang Wang4 Sindre Fritzner et al.
  • 1UiT The Arctic University of Norway, Tromsø, Norway
  • 2The Norwegian Meteorological Institute, Oslo, Norway
  • 3Institute of Environmental Physics, University of Bremen, Germany
  • 4The Norwegian Meteorological Institute, Tromsø, Norway

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining a 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, these 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 asses 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 thickness from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice thickness from ESA’s CryoSat satellite, and a new snow depth product derived from the National Space Agency’s Advanced Microwave Scanning Radiometers (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 positive effect on snow thickness and ice concentration. In our study, the seasonal forecast showed that assimilating snow depth lead to a worse 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, possibly even longer.

Sindre Fritzner et al.
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Sindre Fritzner et al.
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Short summary
In this work, a coupled ocean and sea-ice ensemble based assimilation system is used to asses the observation impact of different observations on the assimilation system. The focus of this study is on sea-ice observations, where observations of sea-ice concentration, sea-ice thickness and snow depth are used for assimilation. The study showed that assimilation of sea-ice thickness in addition to sea-ice concentration has a large positive impact on the model.
In this work, a coupled ocean and sea-ice ensemble based assimilation system is used to asses...
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