Articles | Volume 16, issue 5
https://doi.org/10.5194/os-16-1297-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-1297-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Oct 2020
Research article |
| 29 Oct 2020
| 29 Oct 2020
Assimilation of chlorophyll data into a stochastic ensemble simulation for the North Atlantic Ocean
Yeray Santana-Falcón
CORRESPONDING AUTHOR
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Pierre Brasseur
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Jean Michel Brankart
Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Florent Garnier
LEGOS, University of Toulouse, CNRS, IRD, CNES, UPS, Toulouse, France
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Related subject area
Approach: Data Assimilation | Depth range: All Depths | Geographical range: Deep Seas: North Atlantic | Phenomena: Biological Processes
Toward a multivariate reanalysis of the North Atlantic Ocean biogeochemistry during 1998–2006 based on the assimilation of SeaWiFS chlorophyll data
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Short summary
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the ocean. Here, surface Chl a data are daily assimilated into a 24-member NEMO–PISCES ensemble configuration to implement a complete 4D assimilation system. Results show the assimilation increases the skills of the ensemble, though a regional diagnosis suggests that the description of model and observation uncertainties needs to be refined according to the biogeochemical characteristics of each region.
Data assimilation is the most comprehensive strategy to estimate the biogeochemical state of the...
