CAS-Canglong: A skillful 3D Transformer model for sub-seasonal to seasonal global sea surface temperature prediction

TL;DR

This paper introduces CAS-Canglong, a 3D deep learning model with self-attention that significantly improves sub-seasonal to seasonal sea surface temperature predictions over traditional physics-based models, enhancing forecast accuracy and efficiency.

Contribution

The paper presents a novel 3D transformer neural network that models complex climate systems, outperforming physics-based models in S2S sea surface temperature prediction.

Findings

Improves prediction skill by 13.7% to 77.1% across seven ocean regions.

Enhances computational efficiency over traditional physics models.

Demonstrates deep learning's potential in climate forecasting.

Abstract

Accurate prediction of global sea surface temperature at sub-seasonal to seasonal (S2S) timescale is critical for drought and flood forecasting, as well as for improving disaster preparedness in human society. Government departments or academic studies normally use physics-based numerical models to predict S2S sea surface temperature and corresponding climate indices, such as El Ni\~no-Southern Oscillation. However, these models are hampered by computational inefficiencies, limited retention of ocean-atmosphere initial conditions, and significant uncertainty and biases. Here, we introduce a novel three-dimensional deep learning neural network to model the nonlinear and complex coupled atmosphere-ocean weather systems. This model incorporates climatic and temporal features and employs a self-attention mechanism to enhance the prediction of global S2S sea surface temperature pattern. Compared to the physics-based models, it shows significant computational efficiency and predictive capability, improving one to three months sea surface temperature predictive skill by 13.7% to 77.1% in seven ocean regions with dominant influence on S2S variability over land. This achievement underscores the significant potential of deep learning for largely improving forecasting skills at the S2S scale over land.