Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter.
International audience A sequence of one-year combined state–parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical–biogeochemical model HYCOM-NORWECOM over the period 2007–2010. The aim is to evaluate the ability of an ensemble...
Published in: | Journal of Marine Systems |
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Main Authors: | , , , |
Other Authors: | , , , , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2015
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Subjects: | |
Online Access: | https://hal.science/hal-02960624 https://hal.science/hal-02960624/document https://hal.science/hal-02960624/file/simon_15387.pdf https://doi.org/10.1016/j.jmarsys.2015.07.004 |
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ftunivtoulouse2:oai:HAL:hal-02960624v1 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
Université Toulouse 2 - Jean Jaurès: HAL |
op_collection_id |
ftunivtoulouse2 |
language |
English |
topic |
Data assimilation Ensemble Kalman filter Combined state–parameter estimation Clustering analysis Ecosystem modeling [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] |
spellingShingle |
Data assimilation Ensemble Kalman filter Combined state–parameter estimation Clustering analysis Ecosystem modeling [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Simon, Ehouarn Samuelsen, Annette Bertino, Laurent Mouysset, Sandrine Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. |
topic_facet |
Data assimilation Ensemble Kalman filter Combined state–parameter estimation Clustering analysis Ecosystem modeling [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] |
description |
International audience A sequence of one-year combined state–parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical–biogeochemical model HYCOM-NORWECOM over the period 2007–2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom. |
author2 |
Algorithmes Parallèles et Optimisation (IRIT-APO) Institut de recherche en informatique de Toulouse (IRIT) Université Toulouse Capitole (UT Capitole) Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J) Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP) Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI) Université Toulouse - Jean Jaurès (UT2J) Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole) Université de Toulouse (UT) Institut National Polytechnique (Toulouse) (Toulouse INP) Nansen Environmental and Remote Sensing Center Bergen (NERSC) Hjort Center for Marine Ecosystem Dynamics |
format |
Article in Journal/Newspaper |
author |
Simon, Ehouarn Samuelsen, Annette Bertino, Laurent Mouysset, Sandrine |
author_facet |
Simon, Ehouarn Samuelsen, Annette Bertino, Laurent Mouysset, Sandrine |
author_sort |
Simon, Ehouarn |
title |
Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. |
title_short |
Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. |
title_full |
Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. |
title_fullStr |
Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. |
title_full_unstemmed |
Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. |
title_sort |
experiences in multiyear combined state-parameter estimation with an ecosystem model of the north atlantic and arctic oceans using the ensemble kalman filter. |
publisher |
HAL CCSD |
publishDate |
2015 |
url |
https://hal.science/hal-02960624 https://hal.science/hal-02960624/document https://hal.science/hal-02960624/file/simon_15387.pdf https://doi.org/10.1016/j.jmarsys.2015.07.004 |
long_lat |
ENVELOPE(157.300,157.300,-79.433,-79.433) |
geographic |
Arctic Arctic Ocean Longhurst |
geographic_facet |
Arctic Arctic Ocean Longhurst |
genre |
Arctic Arctic Ocean North Atlantic Zooplankton |
genre_facet |
Arctic Arctic Ocean North Atlantic Zooplankton |
op_source |
ISSN: 0924-7963 Journal of Marine Systems https://hal.science/hal-02960624 Journal of Marine Systems, 2015, 152, pp.1-17. ⟨10.1016/j.jmarsys.2015.07.004⟩ https://www.sciencedirect.com/science/article/pii/S0924796315001190?via%3Dihub |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmarsys.2015.07.004 hal-02960624 https://hal.science/hal-02960624 https://hal.science/hal-02960624/document https://hal.science/hal-02960624/file/simon_15387.pdf doi:10.1016/j.jmarsys.2015.07.004 OATAO: 15387 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1016/j.jmarsys.2015.07.004 |
container_title |
Journal of Marine Systems |
container_volume |
152 |
container_start_page |
1 |
op_container_end_page |
17 |
_version_ |
1785573111241900032 |
spelling |
ftunivtoulouse2:oai:HAL:hal-02960624v1 2023-12-17T10:25:05+01:00 Experiences in multiyear combined state-parameter estimation with an ecosystem model of the North Atlantic and Arctic Oceans using the Ensemble Kalman Filter. Simon, Ehouarn Samuelsen, Annette Bertino, Laurent Mouysset, Sandrine Algorithmes Parallèles et Optimisation (IRIT-APO) Institut de recherche en informatique de Toulouse (IRIT) Université Toulouse Capitole (UT Capitole) Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J) Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP) Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI) Université Toulouse - Jean Jaurès (UT2J) Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole) Université de Toulouse (UT) Institut National Polytechnique (Toulouse) (Toulouse INP) Nansen Environmental and Remote Sensing Center Bergen (NERSC) Hjort Center for Marine Ecosystem Dynamics 2015-07 https://hal.science/hal-02960624 https://hal.science/hal-02960624/document https://hal.science/hal-02960624/file/simon_15387.pdf https://doi.org/10.1016/j.jmarsys.2015.07.004 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmarsys.2015.07.004 hal-02960624 https://hal.science/hal-02960624 https://hal.science/hal-02960624/document https://hal.science/hal-02960624/file/simon_15387.pdf doi:10.1016/j.jmarsys.2015.07.004 OATAO: 15387 info:eu-repo/semantics/OpenAccess ISSN: 0924-7963 Journal of Marine Systems https://hal.science/hal-02960624 Journal of Marine Systems, 2015, 152, pp.1-17. ⟨10.1016/j.jmarsys.2015.07.004⟩ https://www.sciencedirect.com/science/article/pii/S0924796315001190?via%3Dihub Data assimilation Ensemble Kalman filter Combined state–parameter estimation Clustering analysis Ecosystem modeling [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [SDE.BE]Environmental Sciences/Biodiversity and Ecology [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] info:eu-repo/semantics/article Journal articles 2015 ftunivtoulouse2 https://doi.org/10.1016/j.jmarsys.2015.07.004 2023-11-21T23:34:50Z International audience A sequence of one-year combined state–parameter estimation experiments has been conducted in a North Atlantic and Arctic Ocean configuration of the coupled physical–biogeochemical model HYCOM-NORWECOM over the period 2007–2010. The aim is to evaluate the ability of an ensemble-based data assimilation method to calibrate ecosystem model parameters in a pre-operational setting, namely the production of the MyOcean pilot reanalysis of the Arctic biology. For that purpose, four biological parameters (two phyto- and two zooplankton mortality rates) are estimated by assimilating weekly data such as, satellite-derived Sea Surface Temperature, along-track Sea Level Anomalies, ice concentrations and chlorophyll-a concentrations with an Ensemble Kalman Filter. The set of optimized parameters locally exhibits seasonal variations suggesting that time-dependent parameters should be used in ocean ecosystem models. A clustering analysis of the optimized parameters is performed in order to identify consistent ecosystem regions. In the north part of the domain, where the ecosystem model is the most reliable, most of them can be associated with Longhurst provinces and new provinces emerge in the Arctic Ocean. However, the clusters do not coincide anymore with the Longhurst provinces in the Tropics due to large model errors. Regarding the ecosystem state variables, the assimilation of satellite-derived chlorophyll concentration leads to significant reduction of the RMS errors in the observed variables during the first year, i.e. 2008, compared to a free run simulation. However, local filter divergences of the parameter component occur in 2009 and result in an increase in the RMS error at the time of the spring bloom. Article in Journal/Newspaper Arctic Arctic Ocean North Atlantic Zooplankton Université Toulouse 2 - Jean Jaurès: HAL Arctic Arctic Ocean Longhurst ENVELOPE(157.300,157.300,-79.433,-79.433) Journal of Marine Systems 152 1 17 |