Introduction:

Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluc-

tuations and for predicting future climate by considering interactions between Earth’s physical and biogeochemi-

cal fields. Earth System Models (ESMs) capture large-scale physical–biogeochemical coupling, but their biogeo-

chemical prediction skill varies substantially across regions and lead times due to sparse observational records,

structural uncertainties in biogeochemical models. Here, we develop a deep learning-based prediction system to

forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs) at monthly to annual

timescales with lead times up to two years. Trained on multi-decadal simulations from various climate models

and a coupled physical–biogeochemical reanalysis from a data assimilative ESM run, the system demonstrates

skillful chlorophyll predictions comparable to ESM-based dynamic forecasts. The prediction skill is associated

with physical-biogeochemical coupling processes triggered by large-scale climate variability, consistent with

the mechanisms previously identified in dynamical forecasts. Furthermore, predicted chlorophyll anomalies are

significantly linked to interannual variability in fish catch in several LMEs, demonstrating the promise of data-

driven biogeochemical forecasting to support adaptive, climate-informed marine resource management.

pdf : Park et al.(2026).pdf