Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF
This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble...
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Online Access: | https://hdl.handle.net/1956/22785 https://doi.org/10.1007/s00382-019-04897-9 |
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ftunivbergen:oai:bora.uib.no:1956/22785 2023-05-15T15:09:19+02:00 Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF Wang, Yiguo Counillon, Francois Keenlyside, Noel Svendsen, Lea Gleixner, Stephanie Kimmritz, Madlen Dai, Panxi Gao, Yongqi 2020-01-14T14:55:57Z application/pdf https://hdl.handle.net/1956/22785 https://doi.org/10.1007/s00382-019-04897-9 eng eng Springer Norges forskningsråd: 270733 Norges forskningsråd: 229774/E10 Nordforsk: 76654 Trond Mohn stiftelse: BFS2018TMT01 Notur/NorStore: NS9039K EU: 648982 Notur/NorStore: NS9207K Notur/NorStore: NN9039K Notur/NorStore: NN9385K urn:issn:0930-7575 urn:issn:1432-0894 https://hdl.handle.net/1956/22785 https://doi.org/10.1007/s00382-019-04897-9 cristin:1715484 Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Author(s) Climate Dynamics Seasonal prediction Advanced data assimilation EnKF SST NorCPM ENSO Sea ice extent Peer reviewed Journal article 2020 ftunivbergen https://doi.org/10.1007/s00382-019-04897-9 2023-03-14T17:43:08Z This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content. publishedVersion Article in Journal/Newspaper Arctic North Atlantic Sea ice University of Bergen: Bergen Open Research Archive (BORA-UiB) Arctic Climate Dynamics 53 9-10 5777 5797 |
institution |
Open Polar |
collection |
University of Bergen: Bergen Open Research Archive (BORA-UiB) |
op_collection_id |
ftunivbergen |
language |
English |
topic |
Seasonal prediction Advanced data assimilation EnKF SST NorCPM ENSO Sea ice extent |
spellingShingle |
Seasonal prediction Advanced data assimilation EnKF SST NorCPM ENSO Sea ice extent Wang, Yiguo Counillon, Francois Keenlyside, Noel Svendsen, Lea Gleixner, Stephanie Kimmritz, Madlen Dai, Panxi Gao, Yongqi Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF |
topic_facet |
Seasonal prediction Advanced data assimilation EnKF SST NorCPM ENSO Sea ice extent |
description |
This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Wang, Yiguo Counillon, Francois Keenlyside, Noel Svendsen, Lea Gleixner, Stephanie Kimmritz, Madlen Dai, Panxi Gao, Yongqi |
author_facet |
Wang, Yiguo Counillon, Francois Keenlyside, Noel Svendsen, Lea Gleixner, Stephanie Kimmritz, Madlen Dai, Panxi Gao, Yongqi |
author_sort |
Wang, Yiguo |
title |
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF |
title_short |
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF |
title_full |
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF |
title_fullStr |
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF |
title_full_unstemmed |
Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF |
title_sort |
seasonal predictions initialised by assimilating sea surface temperature observations with the enkf |
publisher |
Springer |
publishDate |
2020 |
url |
https://hdl.handle.net/1956/22785 https://doi.org/10.1007/s00382-019-04897-9 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic North Atlantic Sea ice |
genre_facet |
Arctic North Atlantic Sea ice |
op_source |
Climate Dynamics |
op_relation |
Norges forskningsråd: 270733 Norges forskningsråd: 229774/E10 Nordforsk: 76654 Trond Mohn stiftelse: BFS2018TMT01 Notur/NorStore: NS9039K EU: 648982 Notur/NorStore: NS9207K Notur/NorStore: NN9039K Notur/NorStore: NN9385K urn:issn:0930-7575 urn:issn:1432-0894 https://hdl.handle.net/1956/22785 https://doi.org/10.1007/s00382-019-04897-9 cristin:1715484 |
op_rights |
Attribution CC BY http://creativecommons.org/licenses/by/4.0/ Copyright 2019 The Author(s) |
op_doi |
https://doi.org/10.1007/s00382-019-04897-9 |
container_title |
Climate Dynamics |
container_volume |
53 |
container_issue |
9-10 |
container_start_page |
5777 |
op_container_end_page |
5797 |
_version_ |
1766340535800299520 |