EGU24-8942, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Chlorophyll-a satellite climate time series: How machine learning can help distinguish between bias and consistency

Etienne Pauthenet1, Elodie Martinez1, Thomas Gorgues1, Joana Roussillon1,4, Lucas Drumetz2, Ronan Fablet2, and Maïlys Roux3
Etienne Pauthenet et al.
  • 1IRD, CNRS, Univ. Brest, Ifremer, Laboratoire d'Océanographie Physique et Spatiale (LOPS), IUEM, Plouzané, France.
  • 2IMT Atlantique, UMR CNRS LabSTICC, Technopole Brest Iroise, France
  • 3École Normale Supérieure, Université Paris Sciences et Lettres, Ulm, Paris, France
  • 4IRD, Géosciences Environnement Toulouse (GET), UMR5563, CNRS, Université Toulouse 3, Toulouse, France

Phytoplankton sustains marine ecosystems and influences global carbon dioxide levels through photosynthesis. To grow, phytoplankton rely on nutrient availability in the upper sunlit layer, closely related to ocean dynamics and specifically ocean stratification. Human-caused climate change is responsible, among others, for an increase in global temperature and regional modifications of winds, thus affecting the stratification of the ocean's surface. Consequently, phytoplankton biomass is expected to be impacted by these environmental changes. While most existing studies focus on one or two satellite products to investigate phytoplankton trends in the global ocean, in this study, we analyze surface chlorophyll-a concentration (chl-a), a proxy for phytoplankton biomass, using six merged satellite products from January 1998 to December 2020. Significant regional discrepancies are observed among the different products, displaying opposing trends. To distinguish trends arising from changes in the physical ocean from those potentially resulting from sensor biases, a convolutional neural network is employed to examine the relationship between chl-a and physical ocean variables (sea surface temperature, sea surface height, sea surface currents, wind, and solar radiation). The training is conducted over 2002-2009 when the number of merged sensors is constant, and chl-a is reconstructed over 2010-2020. Our results suggest that the merging algorithm of the Globcolour Garver, Siegel, Maritorena (GSM) bio-optical model is not reliable for trend detection. Specifically, changes in chl-a after 2016 are not supported by changes in the physical ocean but rather by the introduction of the VIIRS sensor. These results emphasize the need for a careful interpretation of chl-a trends and highlight the potential of machine learning to study the evolution of marine ecosystems.

How to cite: Pauthenet, E., Martinez, E., Gorgues, T., Roussillon, J., Drumetz, L., Fablet, R., and Roux, M.: Chlorophyll-a satellite climate time series: How machine learning can help distinguish between bias and consistency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8942,, 2024.