Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic
There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model (NorCPM) that combines the fully-coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly-coupled data assimilation (wCDA)...
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ftunivbergen:oai:bora.uib.no:1956/23589 2023-05-15T14:29:16+02:00 Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic Kimmritz, Madlen Counillon, Francois Smedsrud, Lars H. Bethke, Ingo Keenlyside, Noel Ogawa, Fumiaki Wang, Yiguo 2020-02-03T18:16:41Z application/pdf https://hdl.handle.net/1956/23589 https://doi.org/10.1029/2019ms001825 eng eng American Geophysical Union Notur/NorStore: ns9039k Notur/NorStore: nn9602k Nordforsk: 81512 Nordforsk: 76654 Notur/NorStore: ns9602k Norges forskningsråd: 275268 Notur/NorStore: nn9039k Norges forskningsråd: 270733 Trond Mohn stiftelse: BFS2018TMT01 EC/H2020: 727890 urn:issn:1942-2466 https://hdl.handle.net/1956/23589 https://doi.org/10.1029/2019ms001825 cristin:1748624 Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Authors Journal of Advances in Modeling Earth Systems Peer reviewed Journal article 2020 ftunivbergen https://doi.org/10.1029/2019ms001825 2023-03-14T17:43:10Z There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model (NorCPM) that combines the fully-coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly-coupled data assimilation (wCDA) when assimilating ocean hydrogaphy (by updating the ocean alone) and strongly-coupled data assimilation (sCDA) when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of NorCPM, the first climate prediction system using sCDA, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data, exhibits a skillful hydrography in the upper Arctic ocean, and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10-12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial sea ice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialised through wCDA of only ocean data. Particularly high skill is found for July-initialised autumn SIE predictions. publishedVersion Article in Journal/Newspaper Arctic Basin Arctic Arctic Ocean Barents Sea Sea ice University of Bergen: Bergen Open Research Archive (BORA-UiB) Arctic Arctic Ocean Barents Sea Journal of Advances in Modeling Earth Systems 11 12 4147 4166 |
institution |
Open Polar |
collection |
University of Bergen: Bergen Open Research Archive (BORA-UiB) |
op_collection_id |
ftunivbergen |
language |
English |
description |
There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model (NorCPM) that combines the fully-coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly-coupled data assimilation (wCDA) when assimilating ocean hydrogaphy (by updating the ocean alone) and strongly-coupled data assimilation (sCDA) when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of NorCPM, the first climate prediction system using sCDA, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data, exhibits a skillful hydrography in the upper Arctic ocean, and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10-12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial sea ice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialised through wCDA of only ocean data. Particularly high skill is found for July-initialised autumn SIE predictions. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Kimmritz, Madlen Counillon, Francois Smedsrud, Lars H. Bethke, Ingo Keenlyside, Noel Ogawa, Fumiaki Wang, Yiguo |
spellingShingle |
Kimmritz, Madlen Counillon, Francois Smedsrud, Lars H. Bethke, Ingo Keenlyside, Noel Ogawa, Fumiaki Wang, Yiguo Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic |
author_facet |
Kimmritz, Madlen Counillon, Francois Smedsrud, Lars H. Bethke, Ingo Keenlyside, Noel Ogawa, Fumiaki Wang, Yiguo |
author_sort |
Kimmritz, Madlen |
title |
Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic |
title_short |
Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic |
title_full |
Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic |
title_fullStr |
Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic |
title_full_unstemmed |
Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic |
title_sort |
impact of ocean and sea ice initialisation on seasonal prediction skill in the arctic |
publisher |
American Geophysical Union |
publishDate |
2020 |
url |
https://hdl.handle.net/1956/23589 https://doi.org/10.1029/2019ms001825 |
geographic |
Arctic Arctic Ocean Barents Sea |
geographic_facet |
Arctic Arctic Ocean Barents Sea |
genre |
Arctic Basin Arctic Arctic Ocean Barents Sea Sea ice |
genre_facet |
Arctic Basin Arctic Arctic Ocean Barents Sea Sea ice |
op_source |
Journal of Advances in Modeling Earth Systems |
op_relation |
Notur/NorStore: ns9039k Notur/NorStore: nn9602k Nordforsk: 81512 Nordforsk: 76654 Notur/NorStore: ns9602k Norges forskningsråd: 275268 Notur/NorStore: nn9039k Norges forskningsråd: 270733 Trond Mohn stiftelse: BFS2018TMT01 EC/H2020: 727890 urn:issn:1942-2466 https://hdl.handle.net/1956/23589 https://doi.org/10.1029/2019ms001825 cristin:1748624 |
op_rights |
Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Authors |
op_doi |
https://doi.org/10.1029/2019ms001825 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
11 |
container_issue |
12 |
container_start_page |
4147 |
op_container_end_page |
4166 |
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1766303320305041408 |