Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model
The impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SI...
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Cambridge University Press
2020
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Online Access: | https://doi.org/10.1017/aog.2020.13 https://doaj.org/article/8486ba2f7d7a4db3accd6ddb725c22bd |
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ftdoajarticles:oai:doaj.org/article:8486ba2f7d7a4db3accd6ddb725c22bd 2023-05-15T13:29:30+02:00 Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model Jun Ono Yoshiki Komuro Hiroaki Tatebe 2020-09-01T00:00:00Z https://doi.org/10.1017/aog.2020.13 https://doaj.org/article/8486ba2f7d7a4db3accd6ddb725c22bd EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0260305520000130/type/journal_article https://doaj.org/toc/0260-3055 https://doaj.org/toc/1727-5644 doi:10.1017/aog.2020.13 0260-3055 1727-5644 https://doaj.org/article/8486ba2f7d7a4db3accd6ddb725c22bd Annals of Glaciology, Vol 61, Pp 97-105 (2020) Arctic sea ice ice thickness initialization pacific sector predictability Meteorology. Climatology QC851-999 article 2020 ftdoajarticles https://doi.org/10.1017/aog.2020.13 2023-03-12T01:31:55Z The impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September. Article in Journal/Newspaper Annals of Glaciology Arctic Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Pacific Annals of Glaciology 61 82 97 105 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic sea ice ice thickness initialization pacific sector predictability Meteorology. Climatology QC851-999 |
spellingShingle |
Arctic sea ice ice thickness initialization pacific sector predictability Meteorology. Climatology QC851-999 Jun Ono Yoshiki Komuro Hiroaki Tatebe Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model |
topic_facet |
Arctic sea ice ice thickness initialization pacific sector predictability Meteorology. Climatology QC851-999 |
description |
The impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September. |
format |
Article in Journal/Newspaper |
author |
Jun Ono Yoshiki Komuro Hiroaki Tatebe |
author_facet |
Jun Ono Yoshiki Komuro Hiroaki Tatebe |
author_sort |
Jun Ono |
title |
Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model |
title_short |
Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model |
title_full |
Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model |
title_fullStr |
Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model |
title_full_unstemmed |
Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model |
title_sort |
impact of sea-ice thickness initialized in april on arctic sea-ice extent predictability with the miroc climate model |
publisher |
Cambridge University Press |
publishDate |
2020 |
url |
https://doi.org/10.1017/aog.2020.13 https://doaj.org/article/8486ba2f7d7a4db3accd6ddb725c22bd |
geographic |
Arctic Pacific |
geographic_facet |
Arctic Pacific |
genre |
Annals of Glaciology Arctic Sea ice |
genre_facet |
Annals of Glaciology Arctic Sea ice |
op_source |
Annals of Glaciology, Vol 61, Pp 97-105 (2020) |
op_relation |
https://www.cambridge.org/core/product/identifier/S0260305520000130/type/journal_article https://doaj.org/toc/0260-3055 https://doaj.org/toc/1727-5644 doi:10.1017/aog.2020.13 0260-3055 1727-5644 https://doaj.org/article/8486ba2f7d7a4db3accd6ddb725c22bd |
op_doi |
https://doi.org/10.1017/aog.2020.13 |
container_title |
Annals of Glaciology |
container_volume |
61 |
container_issue |
82 |
container_start_page |
97 |
op_container_end_page |
105 |
_version_ |
1766000974151811072 |