FN Archimer Export Format
PT J
TI Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach
BT 
AF Martinez, Elodie
   Gorgues, Thomas
   Lengaigne, Matthieu
   Fontana, Clement
   Sauzède, Raphaëlle
   Menkes, Christophe
   Uitz, Julia
   Di Lorenzo, Emanuele
   Fablet, Ronan
AS 1:1,2;2:1;3:3;4:2;5:2;6:4;7:5;8:6;9:7;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:;
C1 LOPS, IUEM, IRD, Ifremer, CNRS, Univ. Brest, Brest, France
   EIO, IRD, Ifremer, UPF and ILM, Tahiti, French Polynesia
   LOCEAN-IPSL, Sorbonne Universités/UPMC-CNRS-IRD-MNHN, Paris, France
   ENTROPIE (UMR 9220), IRD, Univ. de la Réunion, CNRS, Noumea, New Caledonia
   Laboratoire d’Océanographie de Villefranche, CNRS and Sorbonne Université, Villefranche-sur-Mer, France
   Georgia Institute of Technology, Atlanta, GA, United States
   IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France
C2 IRD, FRANCE
   IRD, FRANCE
   IPSL, FRANCE
   IRD, FRANCE
   CNRS, FRANCE
   GEORGIA INST TECHNOL, USA
   IMT ATLANTIQUE, FRANCE
UM LOPS
   EIO
   ENTROPIE
IN WOS Cotutelle UMR
   DOAJ
   copubli-france
   copubli-int-hors-europe
IF 5.247
TC 23
UR https://archimer.ifremer.fr/doc/00641/75314/75810.pdf
   https://archimer.ifremer.fr/doc/00641/75314/75812.pdf
   https://archimer.ifremer.fr/doc/00641/75314/78654.pdf
LA English
DT Article
DE ;machine learning;phytoplankton variability;satellite ocean color;decadel variability;global scale
AB 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. 
PY 2020
PD JUL
SO Frontiers In Marine Science
SN 2296-7745
PU Frontiers Media SA
VL 7
IS 464
UT 000548192800001
DI 10.3389/fmars.2020.00464
ID 75314
ER

EF