Dynamic Graph Contrastive Learning Neural Network for Sea Surface Temperature Prediction

Accurate daily mean sea surface temperature (SST) forecasting not only contributes to a deeper understanding of natural processes but also enhances our knowledge of global climate change and marine ecosystems. However, existing methods are often susceptible to noise and overlook dynamic spatial correlations, making it difficult for them to capture complex spatiotemporal dependencies. To address these issues, we propose a Dynamic Graph Contrastive Learning Network (DGCLN) for daily mean SST spatiotemporal forecasting. Specifically, we improve robustness to noise through topological data augmentation and generate both local and global augmented graphs using predefined adjacency matrices and self-optimized matrices. In addition, a dual-branch shared network that integrates multi-head self-attention with dynamic graphs is employed to fully exploit the dynamic spatiotemporal dependencies in SST sequences. Furthermore, a spatiotemporal contrastive learning auxiliary task is introduced to alleviate the problem of sparse supervision and extract critical spatiotemporal information. Experiments conducted on the Bohai Sea and South China Sea datasets demonstrate that DGCLN significantly outperforms existing state-of-the-art methods. Specifically, on the South China Sea dataset, the model achieves an MAE of 0.1506 for 1-day prediction, while outperforming the best-performing baseline by an average of 3.3% across all evaluated horizons.