DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations
To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellit...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2021-12-01
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| Series: | Journal of Glaciology |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0022143021000575/type/journal_article |
| _version_ | 1811155724631277568 |
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| author | Hao Luo Qinghua Yang Longjiang Mu Xiangshan Tian-Kunze Lars Nerger Matthew Mazloff Lars Kaleschke Dake Chen |
| author_facet | Hao Luo Qinghua Yang Longjiang Mu Xiangshan Tian-Kunze Lars Nerger Matthew Mazloff Lars Kaleschke Dake Chen |
| author_sort | Hao Luo |
| collection | DOAJ |
| description | To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation. |
| first_indexed | 2024-04-10T04:39:22Z |
| format | Article |
| id | doaj.art-2f94b1cb962e4770b3a7323e1aebc831 |
| institution | Directory Open Access Journal |
| issn | 0022-1430 1727-5652 |
| language | English |
| last_indexed | 2024-04-10T04:39:22Z |
| publishDate | 2021-12-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Journal of Glaciology |
| spelling | doaj.art-2f94b1cb962e4770b3a7323e1aebc8312023-03-09T12:41:10ZengCambridge University PressJournal of Glaciology0022-14301727-56522021-12-01671235124010.1017/jog.2021.57DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observationsHao Luo0Qinghua Yang1https://orcid.org/0000-0002-7114-2036Longjiang Mu2Xiangshan Tian-Kunze3Lars Nerger4Matthew Mazloff5Lars Kaleschke6Dake Chen7School of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, ChinaSchool of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, ChinaAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven 27570, GermanyAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven 27570, GermanyAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven 27570, GermanyScripps Institution of Oceanography, University of California, San Diego, CA, USAAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven 27570, GermanySchool of Atmospheric Sciences, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, ChinaTo improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.https://www.cambridge.org/core/product/identifier/S0022143021000575/type/journal_articleSouthern Oceandata assimilationsea-ice thicknessSMOS |
| spellingShingle | Hao Luo Qinghua Yang Longjiang Mu Xiangshan Tian-Kunze Lars Nerger Matthew Mazloff Lars Kaleschke Dake Chen DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations Journal of Glaciology Southern Ocean data assimilation sea-ice thickness SMOS |
| title | DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations |
| title_full | DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations |
| title_fullStr | DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations |
| title_full_unstemmed | DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations |
| title_short | DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations |
| title_sort | dasso a data assimilation system for the southern ocean that utilizes both sea ice concentration and thickness observations |
| topic | Southern Ocean data assimilation sea-ice thickness SMOS |
| url | https://www.cambridge.org/core/product/identifier/S0022143021000575/type/journal_article |
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