TS-Cast: deep learning for subsurface ocean reconstruction from satellite observations in the northwestern Pacific

Abstract

Since the 1990s, satellite observations have been providing reliable estimates of ocean surface state, including absolute dynamic topography (ADT), sea surface temperature (SST), and sea surface salinity (SSS) at sufficient space and time resolution to characterize ocean dynamics. Together with the extensive hydrographic dataset from Argo and ship-based hydrographic profiles, these measurements provide a comprehensive view of oceanic conditions. While ADT reflects full ocean dynamics, its steric component represents the integrated information for subsurface water properties. However, relating surface variables to subsurface profiles remains challenging because surface signatures are often non-linearly related to interior structures, and satellite data contain inherent non-steric signals. To address these limitations, we introduce TS-Cast, a novel uncertainty-aware deep neural network. Unlike direct regression models, TS-Cast is designed to adjust monthly climatological profiles as a physical prior and learns to dynamically adjust them. By using a 31 s sequence of satellite inputs (SST, SSS, and ADT) and quantifying prediction uncertainty, the model effectively captures the temporal variation of mesoscale dynamics. It was trained on approximately 155 000 Argo and ship-based thermohaline profiles in the northwestern Pacific. TS-Cast’s capability is demonstrated by comparisons with independent time-series data from moorings that measured temperature and salinity or vertical acoustic travel time. The network significantly improves upon the climatological baseline, achieving an overall Root Mean Square Error (RMSE) of <1° C for temperature and <0.1 psu for salinity in the upper 500 m depths at the Kuroshio Extension region. This performance is comparable to or surpasses that of data-assimilating numerical and statistical models, validating TS-Cast as a powerful tool for ocean monitoring. Critically, this framework reveals not only TS-Cast's high fidelity but also demonstrates that the limitations of the input satellite data fundamentally constrain its predictive skill.