A Physics-Guided Hierarchical Transformer Framework for Sea Surface Temperature Forecasting and Marine Heatwave Detection

Accurate forecasting of sea surface temperature (SST) and early detection of marine heatwave (MHW) events are critical yet challenging tasks due to the complex, nonlinear, and multiscale dynamics of the ocean-atmosphere system. To address these challenges, we propose a novel physics-guided hierarchical Transformer framework that combines deep spatiotemporal learning with physical process constraints. The architecture integrates a U-Net-style encoder-decoder with a Temporal-Spatial Predictor (TSP) module. It introduces a physics-constrained branch based on the mixed-layer heat budget equation, enhancing physical consistency and interpretability. A data-driven anomaly compensation mechanism is further employed to adaptively fuse physically-derived predictions with complex dynamic corrections through a learnable weighting scheme. This dual-stream architecture enables robust multi-step rolling forecasting and accurate detection of both gradual SST trends and abrupt MHW events. Extensive experiments on high-resolution SST datasets show that our model significantly outperforms state-of-the-art deep learning baselines such as ConvLSTM, DeepONet, FNO, and hybrid CNN-Transformer models across various performance metrics. These results highlight the framework’s ability to bridge physical oceanography and modern AI, providing a powerful tool for operational ocean forecasting and climate risk assessment.