Deep Learning-Based 3D Ocean Current Reconstruction Improved by Vertical Temperature and Salinity

The ocean circulation in the Western Pacific is crucial for climate regulation and marine ecosystems, but reconstructing 3D subsurface currents remains challenging due to limited observations. This study presents SpadeUp, a novel deep learning model that fuses surface data (wind fields, sea surface height, and surface currents) with subsurface thermohaline data to achieve high-precision 3D ocean current reconstruction. We systematically compared SpadeUp against DiSpade (using only surface data through knowledge distillation) and U-Net (benchmark model). SpadeUp achieved superior performance with average root-mean-square error below 0.05 m/s, representing over 30% improvement compared to U-Net while using fewer parameters. The model successfully reproduced subsurface-intensified eddy in the South China Sea, and accurately captured complex vertical structures of the Kuroshio. Variable importance analysis confirmed that subsurface thermohaline information, especially temperature, is decisive for enhancing reconstruction accuracy, particularly below the thermocline.