Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model
Abstract The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retros...
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Online Access: | http://dx.doi.org/10.1007/s00382-020-05196-4 http://link.springer.com/content/pdf/10.1007/s00382-020-05196-4.pdf http://link.springer.com/article/10.1007/s00382-020-05196-4/fulltext.html |
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crspringernat:10.1007/s00382-020-05196-4 2023-05-15T14:49:24+02:00 Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model Dai, Panxi Gao, Yongqi Counillon, François Wang, Yiguo Kimmritz, Madlen Langehaug, Helene R. EU H2020 Blue-Action Nordic Center of Excellence ARCPATH SIU CONNECTED project Norwegian Research Council project SFE Norwegian Program for supercomputing Norwegian Program for storage 2020 http://dx.doi.org/10.1007/s00382-020-05196-4 http://link.springer.com/content/pdf/10.1007/s00382-020-05196-4.pdf http://link.springer.com/article/10.1007/s00382-020-05196-4/fulltext.html en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Climate Dynamics volume 54, issue 9-10, page 3863-3878 ISSN 0930-7575 1432-0894 Atmospheric Science journal-article 2020 crspringernat https://doi.org/10.1007/s00382-020-05196-4 2022-01-04T14:52:16Z Abstract The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill. Article in Journal/Newspaper Arctic Barents Sea Sea ice Springer Nature (via Crossref) Arctic Barents Sea Climate Dynamics 54 9-10 3863 3878 |
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Open Polar |
collection |
Springer Nature (via Crossref) |
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crspringernat |
language |
English |
topic |
Atmospheric Science |
spellingShingle |
Atmospheric Science Dai, Panxi Gao, Yongqi Counillon, François Wang, Yiguo Kimmritz, Madlen Langehaug, Helene R. Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model |
topic_facet |
Atmospheric Science |
description |
Abstract The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill. |
author2 |
EU H2020 Blue-Action Nordic Center of Excellence ARCPATH SIU CONNECTED project Norwegian Research Council project SFE Norwegian Program for supercomputing Norwegian Program for storage |
format |
Article in Journal/Newspaper |
author |
Dai, Panxi Gao, Yongqi Counillon, François Wang, Yiguo Kimmritz, Madlen Langehaug, Helene R. |
author_facet |
Dai, Panxi Gao, Yongqi Counillon, François Wang, Yiguo Kimmritz, Madlen Langehaug, Helene R. |
author_sort |
Dai, Panxi |
title |
Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model |
title_short |
Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model |
title_full |
Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model |
title_fullStr |
Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model |
title_full_unstemmed |
Seasonal to decadal predictions of regional Arctic sea ice by assimilating sea surface temperature in the Norwegian Climate Prediction Model |
title_sort |
seasonal to decadal predictions of regional arctic sea ice by assimilating sea surface temperature in the norwegian climate prediction model |
publisher |
Springer Science and Business Media LLC |
publishDate |
2020 |
url |
http://dx.doi.org/10.1007/s00382-020-05196-4 http://link.springer.com/content/pdf/10.1007/s00382-020-05196-4.pdf http://link.springer.com/article/10.1007/s00382-020-05196-4/fulltext.html |
geographic |
Arctic Barents Sea |
geographic_facet |
Arctic Barents Sea |
genre |
Arctic Barents Sea Sea ice |
genre_facet |
Arctic Barents Sea Sea ice |
op_source |
Climate Dynamics volume 54, issue 9-10, page 3863-3878 ISSN 0930-7575 1432-0894 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1007/s00382-020-05196-4 |
container_title |
Climate Dynamics |
container_volume |
54 |
container_issue |
9-10 |
container_start_page |
3863 |
op_container_end_page |
3878 |
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1766320458228039680 |