Towards an Early Warning System for Ocean Heat Extremes Through AI-Ocean Dynamics Synergy

Abstract

Ocean heat extremes, including marine heatwaves and the El Ni\~no–Southern Oscillation (ENSO), exert profound impacts on marine ecosystems and socio-economic stability. Establishing robust early warning systems is critical for proactive risk management; however, conventional predictive models often fail to generalize to the intensifying, non-stationary extremes driven by rapid global warming. This project introduces a novel AI-Ocean Dynamics synergy designed to provide an integrated early warning system. By synthesizing multi-source observations with physics-informed neural networks, it ensures predictions remain constrained by fundamental physical laws. The system forecasts event onset, intensity, duration, and spatial extent while simultaneously attributing the underlying mechanisms, such as ocean advection and air–sea heat exchange. To validate performance, we establish a specialized ocean heat extremes benchmark to assess predictive skill and attribution reliability. Furthermore, the system incorporates an incremental learning mechanism, enabling continuous adaptation to long-term climatic and environmental evolutions. This project advances the development of reliable, interpretable, and adaptive early warning systems, providing a vital tool for informed policy and maritime decision-making.