Incremental Stable/Dynamic Disentanglement Learning for Ocean Subsurface Temperature Prediction

Predicting subsurface temperatures is critical for comprehending ocean dynamics and climate shifts. This study presents an incremental stable/dynamic (SD) disentanglement learning framework merging data-driven methods with physics-based insights. It separates stable and dynamic temperature modes to untangle intricate spatiotemporal interactions. To accommodate ongoing data influx, we introduce a recursive evolution approach for updating stable representations, employing orthogonal-triangular decomposition (QR) to capture incremental information. Moreover, a retrospective learning algorithm, guided by temporal changes and ocean temperature correlations, is employed to track the dynamic behavior adaptively. The subsurface temperature fields can be efficiently reconstructed and predicted after model convergence. Extensive experiments validate the model across various depths (−2.5 to −800 m) and times (from May 1964 to December 2021), achieving robust performance metrics: root mean square error (RMSE) of 0.1362, mean absolute error (MAE) of 0.0901, accuracy (ACC) of 0.9911, and coefficient of determination (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) between predictions and observations of 0.9998. Comparative analysis underscores the proposed method’s interpretability, adaptability, and overall performance superiority. Temperature anomaly analysis accurately identifies subsurface decadal oscillations in the low- and mid-latitude Pacific regions.