Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach

Type Article
Date 2020-06
Language English
Author(s) Martinez Elodie1, 2, Gorgues Thomas1, Lengaigne Matthieu3, Fontana Clement2, Sauzède Raphaëlle2, Menkes Christophe4, Uitz Julia5, Di Lorenzo Emanuele6, Fablet Ronan7
Affiliation(s) 1 : LOPS, IUEM, IRD, Ifremer, CNRS, Univ. Brest, Brest, France
2 : EIO, IRD, Ifremer, UPF and ILM, Tahiti, French Polynesia
3 : LOCEAN-IPSL, Sorbonne Universités/UPMC-CNRS-IRD-MNHN, Paris, France
4 : ENTROPIE (UMR 9220), IRD, Univ. de la Réunion, CNRS, Noumea, New Caledonia
5 : Laboratoire d’Océanographie de Villefranche, CNRS and Sorbonne Université, Villefranche-sur-Mer, France
6 : Georgia Institute of Technology, Atlanta, GA, United States
7 : IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France
Source Frontiers In Marine Science (2296-7745) (Frontiers Media SA), 2020-06 , Vol. 7 , N. 464 , P. (20p.)
DOI 10.3389/fmars.2020.00464
WOS© Times Cited 21
Keyword(s) machine learning, phytoplankton variability, satellite ocean color, decadel variability, global scale

Monitoring 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.

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Martinez Elodie, Gorgues Thomas, Lengaigne Matthieu, Fontana Clement, Sauzède Raphaëlle, Menkes Christophe, Uitz Julia, Di Lorenzo Emanuele, Fablet Ronan (2020). Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach. Frontiers In Marine Science, 7(464), (20p.). Publisher's official version : , Open Access version :