Bivariate sea-ice assimilation for global-ocean analysis–reanalysis
In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nev...
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ftcopernicus:oai:publications.copernicus.org:os109632 2023-10-09T21:55:50+02:00 Bivariate sea-ice assimilation for global-ocean analysis–reanalysis Cipollone, Andrea Banerjee, Deep Sankar Iovino, Doroteaciro Aydogdu, Ali Masina, Simona 2023-09-15 application/pdf https://doi.org/10.5194/os-19-1375-2023 https://os.copernicus.org/articles/19/1375/2023/ eng eng doi:10.5194/os-19-1375-2023 https://os.copernicus.org/articles/19/1375/2023/ eISSN: 1812-0792 Text 2023 ftcopernicus https://doi.org/10.5194/os-19-1375-2023 2023-09-18T16:24:16Z In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nevertheless, while univariate algorithms are commonly used to constrain sea-ice area or volume, multivariate approaches have not yet been employed due to the highly non-Gaussian distribution of sea-ice variables together with the low accuracy of thickness observations. This study extends a 3DVar system, called OceanVar, which is routinely employed in the production of global/regional operational/reanalysis products, to process sea-ice variables. The tangent/adjoint versions of an anamorphosis operator are used to locally transform the sea-ice anomalies into Gaussian control variables and back, minimizing in the latter space. The benefit achieved by such a transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The sole assimilation of the CryoSat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of Soil Moisture and Ocean Salinity (SMOS) mission data to converge towards the observation estimates. The intermittent availability of thickness data can lead to potential jumps in the evolution of the volume and requires a dedicated tuning. The use of the merged L4 product CS2SMOS shows the best skill score when validated against independent measurements during the melting season when satellite data are not available. This new sea-ice module is meant to simplify the future coupling with ocean variables. Text Sea ice Copernicus Publications: E-Journals Ocean Science 19 5 1375 1392 |
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Open Polar |
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Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nevertheless, while univariate algorithms are commonly used to constrain sea-ice area or volume, multivariate approaches have not yet been employed due to the highly non-Gaussian distribution of sea-ice variables together with the low accuracy of thickness observations. This study extends a 3DVar system, called OceanVar, which is routinely employed in the production of global/regional operational/reanalysis products, to process sea-ice variables. The tangent/adjoint versions of an anamorphosis operator are used to locally transform the sea-ice anomalies into Gaussian control variables and back, minimizing in the latter space. The benefit achieved by such a transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The sole assimilation of the CryoSat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of Soil Moisture and Ocean Salinity (SMOS) mission data to converge towards the observation estimates. The intermittent availability of thickness data can lead to potential jumps in the evolution of the volume and requires a dedicated tuning. The use of the merged L4 product CS2SMOS shows the best skill score when validated against independent measurements during the melting season when satellite data are not available. This new sea-ice module is meant to simplify the future coupling with ocean variables. |
format |
Text |
author |
Cipollone, Andrea Banerjee, Deep Sankar Iovino, Doroteaciro Aydogdu, Ali Masina, Simona |
spellingShingle |
Cipollone, Andrea Banerjee, Deep Sankar Iovino, Doroteaciro Aydogdu, Ali Masina, Simona Bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
author_facet |
Cipollone, Andrea Banerjee, Deep Sankar Iovino, Doroteaciro Aydogdu, Ali Masina, Simona |
author_sort |
Cipollone, Andrea |
title |
Bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
title_short |
Bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
title_full |
Bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
title_fullStr |
Bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
title_full_unstemmed |
Bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
title_sort |
bivariate sea-ice assimilation for global-ocean analysis–reanalysis |
publishDate |
2023 |
url |
https://doi.org/10.5194/os-19-1375-2023 https://os.copernicus.org/articles/19/1375/2023/ |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
eISSN: 1812-0792 |
op_relation |
doi:10.5194/os-19-1375-2023 https://os.copernicus.org/articles/19/1375/2023/ |
op_doi |
https://doi.org/10.5194/os-19-1375-2023 |
container_title |
Ocean Science |
container_volume |
19 |
container_issue |
5 |
container_start_page |
1375 |
op_container_end_page |
1392 |
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1779320020960018432 |