Deep Learning–based Eddy-resolving Reconstruction of Subsurface Temperature and Salinity in the South China Sea

The inversion of ocean subsurface temperature and salinity (TS) is a hot topic and challenging problem in the oceanic sciences. In this study, a new method for the inversion of underwater TS in the South China Sea is proposed based on an improved generative adversarial network (GAN). The proposed model can derive the underwater TS from sea surface data (specifically, sea surface temperature and the sea surface height anomalies) with an eddy-resolving horizontal resolution of (1/12)°. For comparison, a robust statistics-based model, the Modular Ocean Data Assimilation System (MODAS), is also used to invert the subsurface TS in this study. Results show that the root-mean-square errors (RMSEs) of the TS inversions from the GAN-based model are significantly smaller than those from MODAS, especially in the thermocline of the South China Sea, where the RMSE of temperature can be reduced by up to 21.7% and the subsurface salinity RMSE is smaller than 0.32. In particular, the inversion results obtained using the proposed model are more accurate in either the seasonal-scale or the synoptic-scale analysis. Firstly, the GAN-based model is more effective for the seasonal-scale extraction and diagnosis of the subsurface stratification, especially in the Luzon Strait and coastal shelf sea areas, in which stronger nonlinearities arise from the Kuroshio intrusion or complex coastal processes dominate the ocean subsurface dynamics. Secondly, the vertical heat pump and cold suction effects in the ocean’s upper layers induced by the passage of a typhoon can be reflected more reasonably based on the synoptic-scale analysis with the proposed model. Furthermore, the underwater 3D structure of mesoscale eddies can be skillfully captured by AIGAN (Attention and Inception GAN), which can extract more refined eddy patterns with stronger recognition capability compared with the statistics-based MODAS. The present study can be extended to further explore the subsurface characteristics of the internal variability in the South China Sea.