Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system
Source at https://doi.org/10.5194/tc-13-491-2019 . 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 int...
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Online Access: | https://hdl.handle.net/10037/16412 https://doi.org/10.5194/tc-13-491-2019 |
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ftunivtroemsoe:oai:munin.uit.no:10037/16412 2023-05-15T14:28:02+02:00 Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon Rostosky, Philip Wang, Keguang 2019-02-08 https://hdl.handle.net/10037/16412 https://doi.org/10.5194/tc-13-491-2019 eng eng European Geosciences Union The model output used for the analysis in this study is published in the NIRD Research Data Archive, https://doi.org/10.11582/2019.00005 . Fritzner, S.M. (2020). On sea-ice forecasting. (Doctoral thesis). https://hdl.handle.net/10037/18141 . The Cryosphere info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Fritzner, 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. The Cryosphere., 13 (2), 491-509. https://doi.org/10.5194/tc-13-491-2019 FRIDAID 1691896 doi:10.5194/tc-13-491-2019 1994-0416 1994-0424 https://hdl.handle.net/10037/16412 openAccess VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 Journal article Tidsskriftartikkel Peer reviewed 2019 ftunivtroemsoe https://doi.org/10.5194/tc-13-491-2019 https://doi.org/10.11582/2019.00005 2021-06-25T17:56:45Z Source at https://doi.org/10.5194/tc-13-491-2019 . 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 months, but possibly even longer. Article in Journal/Newspaper Arctic Arctic Sea ice The Cryosphere University of Tromsø: Munin Open Research Archive Arctic The Cryosphere 13 2 491 509 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 |
spellingShingle |
VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon Rostosky, Philip Wang, Keguang Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modelling system |
topic_facet |
VDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452 |
description |
Source at https://doi.org/10.5194/tc-13-491-2019 . 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 months, but possibly even longer. |
format |
Article in Journal/Newspaper |
author |
Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon Rostosky, Philip Wang, Keguang |
author_facet |
Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon Rostosky, Philip Wang, Keguang |
author_sort |
Fritzner, Sindre Markus |
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 |
European Geosciences Union |
publishDate |
2019 |
url |
https://hdl.handle.net/10037/16412 https://doi.org/10.5194/tc-13-491-2019 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice The Cryosphere |
genre_facet |
Arctic Arctic Sea ice The Cryosphere |
op_relation |
The model output used for the analysis in this study is published in the NIRD Research Data Archive, https://doi.org/10.11582/2019.00005 . Fritzner, S.M. (2020). On sea-ice forecasting. (Doctoral thesis). https://hdl.handle.net/10037/18141 . The Cryosphere info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Fritzner, 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. The Cryosphere., 13 (2), 491-509. https://doi.org/10.5194/tc-13-491-2019 FRIDAID 1691896 doi:10.5194/tc-13-491-2019 1994-0416 1994-0424 https://hdl.handle.net/10037/16412 |
op_rights |
openAccess |
op_doi |
https://doi.org/10.5194/tc-13-491-2019 https://doi.org/10.11582/2019.00005 |
container_title |
The Cryosphere |
container_volume |
13 |
container_issue |
2 |
container_start_page |
491 |
op_container_end_page |
509 |
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