Skilful prediction of mid-term sea surface temperature using 3D self-attention-based neural network

Sea surface temperature (SST) is a critical parameter in the global ocean-atmospheric system, exerting a substantial impact on climate change and extreme weather events like droughts and floods. The precise forecasting of future SSTs is thus vital for identifying such weather anomalies. Here we present a novel three-dimensional (3D) neural network model based on self-attention mechanisms and Swin-Transformer for mid-term SST predictions. This model, integrating both climatic and temporal features, employs self-attention to proficiently capture the temporal dynamics and global patterns in SST. This approach significantly enhances the model's capability to detect and analyze spatiotemporal changes, offering a more nuanced understanding of SST variations. Trained on 59 years of global monthly ERA5-Land reanalysis data, our model demonstrates strong deterministic forecast capabilities in the test period. It employs a convolution strategy and global attention mechanism, resulting in faster and more accurate training compared to traditional methods, such as Convolutional Neural Network with Long short-term memory (CNN-LSTM). The effectiveness of this SST prediction model highlights its potential for extensive multidimensional modelling applications in geosciences.