A Deep Learning Framework for Chlorophyll Prediction in Large Marine Ecosystems: Benchmarking with a Dynamic Model and Implications for Fish Catch Forecasts

Abstract. Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluctuations and for predicting future climate by considering interactions between Earth’s physical and biogeochemical fields. Earth System Models (ESMs) simulate Earth’s multi-facet features, but their predictive capabilities remain limited due to sparse biogeochemical observations and structural uncertainties in marine biogeochemical models. Here, we develop a deep learning–based prediction system to forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs). 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 arises from 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.