Multiscale Periodic Spatiotemporal Graph Convolutional Network for Sea Surface Temperature Prediction

Abstract

Sea surface temperature (SST), as a key variable in the ocean-climate system, plays a crucial role in global climate evolution, the occurrence of extreme weather events, and changes in marine ecosystems. Accurate SST prediction is of great significance for improving medium- and long-term climate forecasting capabilities and supporting effective marine environmental management. In recent years, deep learning methods have been widely applied to SST prediction tasks and have achieved promising results. However, existing approaches often suffer from inadequate modeling of temporal periodic structures and multilevel spatial features, making it difficult to effectively capture the multiscale dynamic variations of SST under complex spatiotemporal contexts. To address this issue, this article proposes a multiscale periodic spatiotemporal graph convolutional network (MPSGCN) to model the multiscale periodic spatiotemporal dependencies of SST. The proposed model explicitly captures periodic patterns in SST sequences through a periodic modeling mechanism, integrates multiscale adaptive graph convolution to dynamically learn regional dependencies at varying spatial scales, and employs multilayer spatial convolutions to mitigate the over-smoothing issue introduced by spectral-based graph convolution. Extensive experiments conducted on three representative marine regions with distinct climatic characteristics demonstrate that the proposed model significantly outperforms existing state-of-the-art methods across multiple evaluation metrics, validating the effectiveness and robustness of MPSGCN in modeling the complex spatiotemporal evolution of SST. Code available at https://github.com/xiaoling-0621/MPSGCN