Deep learning identifies the climate warming signal in global ocean chlorophyll from satellite records

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Lin, Lei and Dong, Chen and Henson, Stephanie and Chen, Bingzhang (2026) Deep learning identifies the climate warming signal in global ocean chlorophyll from satellite records. Geophysical Research Letters, 53 (4). e2025GL120669. ISSN 0094-8276 (https://doi.org/10.1029/2025GL120669)

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Abstract

Satellite remote sensing of chlorophyll-a (Chl-a) provides the only continuous global-scale monitoring of phytoplankton abundance for over two decades. While certain trends have been observed in the satellite Chl-a data, it remains uncertain whether the changes are attributable to climate warming, because the data is not long enough to separate the role of climate warming from natural variability. Here, using a deep-learning model trained with an ensemble of 10 Earth System Model (ESM) simulations, we identified the climate-warming signal in satellite-derived global Chl-a fields. By comparison, a null model trained on ESM simulations forced only by natural variability was unable to identify a warming signal, confirming the role of climate warming. The warming signal is primarily derived from the spatial pattern of global Chl-a trends, and eastern and western boundary regions are most sensitive to warming. Our results explicitly reveal the ongoing climate-warming effect on global marine phytoplankton this century.

ORCID iDs

Lin, Lei, Dong, Chen, Henson, Stephanie and Chen, Bingzhang ORCID logoORCID: https://orcid.org/0000-0002-1573-7473;
CORE (COnnecting REpositories)