CA-ConvLSTM: A Cross-Attention-Based Deep Learning Framework for Joint Prediction of Sea Surface Temperature and Sea Level Anomaly

Accurate prediction of sea surface temperature (SST) and sea level anomaly (SLA) is essential for numerical weather prediction, climate monitoring, and ocean state estimating, as both variables are key indicators of the thermal and dynamic conditions of the upper ocean. However, effectively capturing the complex dynamic coupling between SST and SLA, along with their spatiotemporal dependencies and mutual influences, remains a significant challenge. This study proposes a unified deep learning framework, termed CA-ConvLSTM, which jointly predicts SST and SLA by integrating a cross-attention mechanism with a Convolutional Long Short-Term Memory (ConvLSTM) architecture. The cross-attention module enables dynamic feature interaction between SST and SLA, allowing to effectively capture complex spatiotemporal dependencies. A key advantage of CA-ConvLSTM lies in its ability to simultaneously learn from SST and SLA through a unified training process, thus improving the prediction accuracy of both variables. Numerical experiments conducted in the South China Sea demonstrate that the proposed CA-ConvLSTM consistently outperforms several state-of-the-art baseline models, including ConvLSTM, PredRNN, TCTN, SwinLSTM, and UniOcean, in the joint prediction of SST and SLA. These results underscore the effectiveness of incorporating multivariate oceanic information through attention-based mechanisms and highlight the potential of such frameworks for advancing spatiotemporal marine prediction capabilities.