Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects
We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean t...
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ftcopernicus:oai:publications.copernicus.org:os16162 2023-05-15T15:13:07+02:00 Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects Tietsche, S. Notz, D. Jungclaus, J. H. Marotzke, J. 2018-01-15 application/pdf https://doi.org/10.5194/os-9-19-2013 https://os.copernicus.org/articles/9/19/2013/ eng eng doi:10.5194/os-9-19-2013 https://os.copernicus.org/articles/9/19/2013/ eISSN: 1812-0792 Text 2018 ftcopernicus https://doi.org/10.5194/os-9-19-2013 2020-07-20T16:25:35Z We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions. Text Arctic Sea ice Copernicus Publications: E-Journals Arctic Ocean Science 9 1 19 36 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions. |
format |
Text |
author |
Tietsche, S. Notz, D. Jungclaus, J. H. Marotzke, J. |
spellingShingle |
Tietsche, S. Notz, D. Jungclaus, J. H. Marotzke, J. Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
author_facet |
Tietsche, S. Notz, D. Jungclaus, J. H. Marotzke, J. |
author_sort |
Tietsche, S. |
title |
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
title_short |
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
title_full |
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
title_fullStr |
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
title_full_unstemmed |
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
title_sort |
assimilation of sea-ice concentration in a global climate model – physical and statistical aspects |
publishDate |
2018 |
url |
https://doi.org/10.5194/os-9-19-2013 https://os.copernicus.org/articles/9/19/2013/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
eISSN: 1812-0792 |
op_relation |
doi:10.5194/os-9-19-2013 https://os.copernicus.org/articles/9/19/2013/ |
op_doi |
https://doi.org/10.5194/os-9-19-2013 |
container_title |
Ocean Science |
container_volume |
9 |
container_issue |
1 |
container_start_page |
19 |
op_container_end_page |
36 |
_version_ |
1766343709917446144 |