Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach

TL;DR

This paper introduces a physics-guided deep learning model for 10-day forecasts of extreme marine heatwaves, significantly improving accuracy and interpretability over traditional methods.

Contribution

A novel neural network framework incorporating physical modules for improved extreme marine heatwave prediction and understanding of driving mechanisms.

Findings

Enhanced forecast accuracy for extreme MHWs.

Reduced computational resources compared to numerical models.

Identified wind forcing as the primary driver of MHW evolution.

Abstract

The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel deep learning neural network that is capable of accurate 10-day MHW forecasting. Our framework significantly improves the forecast ability of extreme MHWs through two specially designed modules inspired by numerical models: a coupler and a probabilistic data argumentation. The coupler simulates the driving effect of atmosphere on MHWs while the probabilistic data argumentation approaches significantly boost the forecast ability of extreme MHWs based on the idea of ensemble forecast. Compared with traditional numerical prediction, our framework has significantly higher accuracy and requires fewer computational resources. What's more, explainable AI methods show that wind forcing is the primary driver of MHW evolution and reveal its relation with air-sea heat exchange. Overall, our model provides a framework for understanding MHWs' driving processes and operational forecasts in the future.