Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system
The 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 c...
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ftdoajarticles:oai:doaj.org/article:53c9a1d0d4ff4d1c9d2bc038e6793d17 2023-05-15T15:17:41+02:00 Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system S. Fritzner R. Graversen K. H. Christensen P. Rostosky K. Wang 2019-02-01T00:00:00Z https://doi.org/10.5194/tc-13-491-2019 https://doaj.org/article/53c9a1d0d4ff4d1c9d2bc038e6793d17 EN eng Copernicus Publications https://www.the-cryosphere.net/13/491/2019/tc-13-491-2019.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-13-491-2019 1994-0416 1994-0424 https://doaj.org/article/53c9a1d0d4ff4d1c9d2bc038e6793d17 The Cryosphere, Vol 13, Pp 491-509 (2019) Environmental sciences GE1-350 Geology QE1-996.5 article 2019 ftdoajarticles https://doi.org/10.5194/tc-13-491-2019 2022-12-31T12:16:06Z The 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 ... Article in Journal/Newspaper Arctic Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles Arctic The Cryosphere 13 2 491 509 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 S. Fritzner R. Graversen K. H. Christensen P. Rostosky K. Wang Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
The 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 ... |
format |
Article in Journal/Newspaper |
author |
S. Fritzner R. Graversen K. H. Christensen P. Rostosky K. Wang |
author_facet |
S. Fritzner R. Graversen K. H. Christensen P. Rostosky K. Wang |
author_sort |
S. Fritzner |
title |
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
title_short |
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
title_full |
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
title_fullStr |
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
title_full_unstemmed |
Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
title_sort |
impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-13-491-2019 https://doaj.org/article/53c9a1d0d4ff4d1c9d2bc038e6793d17 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice The Cryosphere |
genre_facet |
Arctic Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 13, Pp 491-509 (2019) |
op_relation |
https://www.the-cryosphere.net/13/491/2019/tc-13-491-2019.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-13-491-2019 1994-0416 1994-0424 https://doaj.org/article/53c9a1d0d4ff4d1c9d2bc038e6793d17 |
op_doi |
https://doi.org/10.5194/tc-13-491-2019 |
container_title |
The Cryosphere |
container_volume |
13 |
container_issue |
2 |
container_start_page |
491 |
op_container_end_page |
509 |
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1766347929766854656 |