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Benazzouz, A., Pelegri, J. L., Demarcq, H., Machin, F., Mason, E., Orbi, A., et al. (2014). On the temporal memory of coastal upwelling off NW Africa. J. Geophys. Res.-Oceans, 119(9), 6356–6380.
Résumé: We use a combination of satellite, in situ, and numerical data to provide a comprehensive view of the seasonal coastal upwelling cycle off NW Africa in terms of both wind forcing and sea surface temperature (SST) response. Wind forcing is expressed in terms of both instantaneous (local) and time-integrated (nonlocal) indices, and the ocean response is expressed as the SST difference between coastal and offshore waters. The classical local index, the cross-shore Ekman transport, reproduces reasonably well the time-latitude distribution of SST differences but with significant time lags at latitudes higher than Cape Blanc. Two nonlocal indices are examined. One of them, a cumulative index calculated as the backward averaged Ekman transport that provides the highest correlation with SST differences, reproduces well the timing of the SST differences at all latitudes (except near Cape Blanc). The corresponding time lags are close to zero south of Cape Blanc and range between 2 and 4 months at latitudes between Cape Blanc and the southern Gulf of Cadiz. The results are interpreted based on calculations of spatial and temporal auto and cross correlations for wind forcing and SST differences. At temporal scales of 2-3 weeks, the alongshore advection of alongshore momentum compensates for interfacial friction, allowing the upwelling jet and associated frontal system to remain active. We conclude that the coastal jet plays a key role in maintaining the structure of coastal upwelling, even at times of relaxed winds, by introducing a seasonal memory to the system in accordance with the atmospheric-forcing annual cycle.
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Martinez, E., Gorgues, T., Lengaigne, M., Fontana, C., Sauzede, R., Menkes, C., et al. (2020). Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach. Front. Mar. Sci., 7, 464.
Résumé: 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 Nino 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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