ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1528489
This article is part of the Research TopicAdvanced Monitoring, Modelling, and Analysis of Coastal Environments and EcosystemsView all 35 articles
Physical drivers and reconstruction of the interannual variability of satellite-derived chlorophyll-a in key regions of the tropical and south Atlantic
Provisionally accepted
- 1University of Bergen, Bergen, Norway
- 2Departamento de Oceanografía Biológica, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico
- 3Bjerknes Centre for Climate Research, University of Bergen, Bergen, Hordaland, Norway
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Understanding drivers of variability in oceanic primary productivity is essential to increase our understanding of the functioning of marine ecosystems and biogeochemical cycles. Here, interannual variability of satellite-derived chlorophyll-a (CHL) and its underlying oceanographic processes are analyzed in six coastal regions of the tropical and south Atlantic. Along the South American coast, sea-surface height (SSH) and alongshore velocity, proxies for surface flows, were identified as the main drivers.Along the African coast, variations in sea-surface temperature (SST) and SSH related to coastal upwelling, were the dominant drivers. Important links to the Tropical Southern Atlantic, Dipole Mode Index, Western Hemisphere Warm Pool, and Southern Oscillation Index indices were identified, indicating potential role of teleconnections in the CHL-variability. The identified driver-linked variables were used to reconstruct the regional CHL series using multi-linear regressions and a neural-network model. The multi-linear models were able to reproduce significant fractions of the observed CHL variance. In particular, a model based on eigenvalues from an empirical orthogonal function decomposition of SST, outperformed the others. The neural-network model shows the highest performance reproducing most of the CHL variance (> 70%), but it presents difficulty to deduce the relative importance of individual drivers. Beyond this fitting/training period, the multi-linear model show better results respect to the neural-network model, especially that based on oceanographic variables. These CHLreconstruction models present the possibility to reproduce CHL in periods when its observation is unavailable and even to predict it in multi-year climate projections.
Keywords: Chlorophyll-a interannual variability, satellite and reanalysis products, Teleconnection indices, Correlation analysis, Regional biogeochemistry
Received: 14 Nov 2024; Accepted: 02 Sep 2025.
Copyright: © 2025 Rivas, Fransner, Koseki and Keenlyside. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: David Rivas, University of Bergen, Bergen, Norway
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.