Predicting Marine Heatwaves in the South China Sea Using a 3D U-Net Model Based on Intraseasonal Oscillation Signals from Atmosphere-Ocean Data

With the intensification of global warming, marine heatwaves (MHWs) have emerged as a significant extreme hazard, garnering widespread attention and creating a pressing need for accurate prediction. The development of artificial intelligence, particularly the application of deep learning to sea surface temperature (SST), has significantly improved the feasibility of predictions. This study utilizes SST and Outgoing Longwave Radiation (OLR) data to train a 3D U-Net model for predicting MHWs in the South China Sea (SCS) with lead times ranging from 1 to 7 days, based on the characteristics of intraseasonal weather processes. Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns, with summer MHWs primarily concentrated in the northern and central SCS, and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines. The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data. Incorporating OLR data enhances forecast accuracy compared to SST-only inputs, and training the model exclusively with summer data further improves prediction accuracy. These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.