Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations
Abstract Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first year‐round SIT observations, retrieved from CryoSat‐2 from 2011 to 2020, are assimilated into the GF...
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Online Access: | https://doi.org/10.1029/2023GL105672 https://doaj.org/article/d339bbe64182499fbd312ebd78929ab7 |
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ftdoajarticles:oai:doaj.org/article:d339bbe64182499fbd312ebd78929ab7 2024-09-15T18:01:59+00:00 Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations Yong‐Fei Zhang Mitchell Bushuk Michael Winton Bill Hurlin William Gregory Jack Landy Liwei Jia 2023-12-01T00:00:00Z https://doi.org/10.1029/2023GL105672 https://doaj.org/article/d339bbe64182499fbd312ebd78929ab7 EN eng Wiley https://doi.org/10.1029/2023GL105672 https://doaj.org/toc/0094-8276 https://doaj.org/toc/1944-8007 1944-8007 0094-8276 doi:10.1029/2023GL105672 https://doaj.org/article/d339bbe64182499fbd312ebd78929ab7 Geophysical Research Letters, Vol 50, Iss 24, Pp n/a-n/a (2023) Arctic sea ice thickness data assimilation seasonal prediction Geophysics. Cosmic physics QC801-809 article 2023 ftdoajarticles https://doi.org/10.1029/2023GL105672 2024-08-05T17:49:23Z Abstract Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first year‐round SIT observations, retrieved from CryoSat‐2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to SIC‐only assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions. Article in Journal/Newspaper Chukchi Chukchi Sea Sea ice Directory of Open Access Journals: DOAJ Articles Geophysical Research Letters 50 24 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic sea ice thickness data assimilation seasonal prediction Geophysics. Cosmic physics QC801-809 |
spellingShingle |
Arctic sea ice thickness data assimilation seasonal prediction Geophysics. Cosmic physics QC801-809 Yong‐Fei Zhang Mitchell Bushuk Michael Winton Bill Hurlin William Gregory Jack Landy Liwei Jia Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations |
topic_facet |
Arctic sea ice thickness data assimilation seasonal prediction Geophysics. Cosmic physics QC801-809 |
description |
Abstract Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt‐season sea ice thickness (SIT) observations. The first year‐round SIT observations, retrieved from CryoSat‐2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the observations, particularly in the Central Arctic. Although the short observational period makes forecast assessment challenging, we find that the addition of May–August SIT assimilation improves September local sea ice concentration (SIC) and extent forecasts similarly to SIC‐only assimilation. Although most regional forecasts are improved by SIT assimilation, the Chukchi Sea forecasts are degraded. This degradation is likely due to the introduction of negative correlations between September SIC and earlier SIT introduced by SIT assimilation, contrary to the increased correlations found in other regions. |
format |
Article in Journal/Newspaper |
author |
Yong‐Fei Zhang Mitchell Bushuk Michael Winton Bill Hurlin William Gregory Jack Landy Liwei Jia |
author_facet |
Yong‐Fei Zhang Mitchell Bushuk Michael Winton Bill Hurlin William Gregory Jack Landy Liwei Jia |
author_sort |
Yong‐Fei Zhang |
title |
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations |
title_short |
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations |
title_full |
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations |
title_fullStr |
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations |
title_full_unstemmed |
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat‐2 Sea Ice Thickness Observations |
title_sort |
improvements in september arctic sea ice predictions via assimilation of summer cryosat‐2 sea ice thickness observations |
publisher |
Wiley |
publishDate |
2023 |
url |
https://doi.org/10.1029/2023GL105672 https://doaj.org/article/d339bbe64182499fbd312ebd78929ab7 |
genre |
Chukchi Chukchi Sea Sea ice |
genre_facet |
Chukchi Chukchi Sea Sea ice |
op_source |
Geophysical Research Letters, Vol 50, Iss 24, Pp n/a-n/a (2023) |
op_relation |
https://doi.org/10.1029/2023GL105672 https://doaj.org/toc/0094-8276 https://doaj.org/toc/1944-8007 1944-8007 0094-8276 doi:10.1029/2023GL105672 https://doaj.org/article/d339bbe64182499fbd312ebd78929ab7 |
op_doi |
https://doi.org/10.1029/2023GL105672 |
container_title |
Geophysical Research Letters |
container_volume |
50 |
container_issue |
24 |
_version_ |
1810439054042333184 |