Biogeosciences
Biogeosciences
BG
Articles | Volume 23, issue 7
Articles | Volume 23, issue 7
https://doi.org/10.5194/bg-23-2601-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-2601-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 7
Research article
 | 
17 Apr 2026
Research article |  | 17 Apr 2026

Forecasting seasonal global sea surface chlorophyll a with a lightweight data-driven approach

Gabriela Martinez Balbontin, Julien Jouanno, Rachid Benshila, Julien Lamouroux, Coralie Perruche, and Stefano Ciavatta

Viewed

Total article views: 1,283 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
906 335 42 1,283 39 47
  • HTML: 906
  • PDF: 335
  • XML: 42
  • Total: 1,283
  • BibTeX: 39
  • EndNote: 47
Views and downloads (calculated since 09 Apr 2025)
Cumulative views and downloads (calculated since 09 Apr 2025)

Viewed (geographical distribution)

Total article views: 1,283 (including HTML, PDF, and XML) Thereof 1,283 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Apr 2026
Download
Short summary
This study uses machine learning to predict global sea surface chlorophyll a, which is important for monitoring marine ecosystems and the carbon cycle. Using forecasts of sea surface temperature, salinity, height, and mixed layer depth, we generate global predictions up to six months ahead in just minutes. Our approach matches state-of-the-art numerical methods while being faster and more resource-efficient.
Read more
Share
Altmetrics
Final-revised paper
Preprint