Abstract
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.
Highlights
Phytoplankton—the microalgae that populate the upper lit layers of the ocean—fuels the oceanic food web and regulates oceanic and atmospheric carbon dioxide levels through photosynthetic carbon fixation
Our results indicate that this non-linear method accurately hindcasts interannual-to-decadal variations of the phytoplankton biomass simulated at global scale by the
This results in a NRMSE generally weaker when reconstructing satellite data compared to the model one, the predictors used are identical
Summary
Phytoplankton—the microalgae that populate the upper lit layers of the ocean—fuels the oceanic food web and regulates oceanic and atmospheric carbon dioxide levels through photosynthetic carbon fixation. The launch of the modern radiometric Sea-viewing Wide Field-of-View Sensor (SeaWiFS; McClain et al, 2004) in late 1997 followed later by other satellites allowed monitoring and understanding the spatio-temporal Chl variations at global scale over the past two decades It revealed that El Niño events induce a Chl decrease in the central and eastern equatorial Pacific in response to reduced upwelled nutrients to the surface layers (e.g., Chavez et al, 1999; Wilson and Adamec, 2001; McClain et al, 2002; Radenac et al, 2012) and a Chl signature outside the tropical Pacific through atmospheric teleconnections (Behrenfeld et al, 2001; Yoder and Kennelly, 2003; Dandonneau et al, 2004; Messié and Chavez, 2012). These discrepancies suggest that detection of robust global trend may require several decades of continuous observations (Beaulieu et al, 2013)
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