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dc.contributor.authorFritzner, Sindre Markus
dc.contributor.authorGraversen, Rune
dc.contributor.authorChristensen, Kai Håkon
dc.contributor.authorRostosky, Philip
dc.contributor.authorWang, Keguang
dc.date.accessioned2019-10-16T11:59:18Z
dc.date.available2019-10-16T11:59:18Z
dc.date.issued2019-02-08
dc.description.abstractThe accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the 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, which 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 assess 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 from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (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 smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term 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, but possibly even longer.en_US
dc.descriptionSource at <a href=https://doi.org/10.5194/tc-13-491-2019>https://doi.org/10.5194/tc-13-491-2019</a>.en_US
dc.identifier.citationFritzner, S., Graversen, R., Christensen, K.H., Rostosky, P. & Wang, K. (2019). Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system. <i>The Cryosphere., 13</i>(2), 491-509. https://doi.org/10.5194/tc-13-491-2019en_US
dc.identifier.cristinIDFRIDAID 1691896
dc.identifier.doi10.5194/tc-13-491-2019
dc.identifier.issn1994-0416
dc.identifier.issn1994-0424
dc.identifier.urihttps://hdl.handle.net/10037/16412
dc.language.isoengen_US
dc.publisherEuropean Geosciences Unionen_US
dc.relation.isbasedonThe model output used for the analysis in this study is published in the NIRD Research Data Archive, <a href=https://doi.org/10.11582/2019.00005>https://doi.org/10.11582/2019.00005</a>.en_US
dc.relation.ispartofFritzner, S.M. (2020). On sea-ice forecasting. (Doctoral thesis). <a href=https://hdl.handle.net/10037/18141>https://hdl.handle.net/10037/18141</a>.
dc.relation.journalThe Cryosphere
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452en_US
dc.titleImpact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling systemen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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