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dc.contributor.authorMartinez, Elodie
dc.contributor.authorGorgues, Thomas
dc.contributor.authorLengaigne, Matthieu
dc.contributor.authorFontana, Clement
dc.contributor.authorSauzède, Raphaëlle
dc.contributor.authorMenkes, Christophe
dc.contributor.authorUitz, Julia
dc.contributor.authorDi Lorenzo, Emanuele
dc.contributor.authorFablet, Ronan
dc.identifier.citationMartinez, E.; Gorgues, T.; Lengaigne, M.; Fontana, C.; Sauzède, R.; Menkes, C.; Uitz, J.; Di Lorenzo, E. and Fablet, R. (2020) Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach. Frontiers in Marine Science, 7:464, 20pp. DOI: 10.3389/fmars.2020.00464en_US
dc.description.abstractMonitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32- years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979–2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability.en_US
dc.rightsAttribution 4.0 International*
dc.subject.otherMachine learningen_US
dc.subject.otherPhytoplankton variabilityen_US
dc.subject.otherSatellite ocean colouren_US
dc.subject.otherDecadal variabilityen_US
dc.subject.otherGlobal scaleen_US
dc.subject.otherChlorophyll aen_US
dc.subject.otherSurface concentrationen_US
dc.titleReconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.en_US
dc.typeJournal Contributionen_US
dc.subject.parameterDisciplineParameter Discipline::Biological oceanography::Phytoplanktonen_US
dc.subject.dmProcessesData Management Practices::Data acquisitionen_US
dc.bibliographicCitation.titleFrontiers in Marine Scienceen_US
dc.bibliographicCitation.issueArticle 464en_US
dc.description.eovPhytoplankton biomass and diversityen_US
dc.description.bptypeManual (incl. handbook, guide, cookbook etc)en_US Martinez

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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International