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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Published in:Climate Dynamics
Main Authors: Wang, Yiguo, Counillon, Francois, Keenlyside, Noel, Svendsen, Lea, Gleixner, Stephanie, Kimmritz, Madlen, Dai, Panxi, Gao, Yongqi
Format: Article in Journal/Newspaper
Language:English
Published: Springer 2020
Subjects:
SST
Online Access:https://hdl.handle.net/1956/22785
https://doi.org/10.1007/s00382-019-04897-9
id ftunivbergen:oai:bora.uib.no:1956/22785
record_format openpolar
spelling 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
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