A Coupled Transformer–CNN Network: Advancing Sea Surface Temperature Forecast Accuracy

Sea surface temperature (SST) is critically important for understanding ocean dynamics and supporting various marine activities, making accurate short-term SST forecasting highly significant. However, accurately modeling the multiscale variability of SST remains challenging for existing deep learning (DL) models. This study introduces the coupled Transformer-CNN network (CoTCN), a hybrid architecture designed to leverage the multiscale variability of SST. The CoTCN combines the strengths of Transformers and convolutional neural networks (CNNs), significantly enhancing SST forecasts’ spatial continuity and predictive accuracy. Compared to five state-of-the-art DL models based on Transformers or CNNs that include convolutional long short-term memory (ConvLSTM), ConvGRU, adaptive Fourier neural operator (AFNO), PredRNN, and SwinLSTM, the CoTCN demonstrates superior performance in global and local areas of SST forecasting. At 1-day lead time, the CoTCN reduces the global average root-mean-square error (RMSE) by over 15%, with forecast errors ranging from $0.20~^{\circ }$ C to $0.53~^{\circ }$ C across 1–10-day lead times. Moreover, the CoTCN effectively mitigates the checkerboard artifacts inherent to the Vision Transformer (ViT) architecture. These findings highlight the effectiveness of the CoTCN in capturing SST’s multiscale features and underscore the promising potential of hybrid architectures for future DL models.