Ocean eddies contribute significantly to the transfer of heat and energy throughout the world’s oceans, playing a key role in regulating climate. Eddies are observed predominantly through Earth-orbiting satellites that collect data on sea surface height (SSH), a metric that can be used to estimate eddies on a global scale. Historically, satellites could only capture point-wise measurements, resulting in low-resolution SSH maps, which led to underestimations of small-scale eddy strength. Launched in 2022, NASA’s Surface Water and Ocean Topography (SWOT) satellite now provides groundbreaking 2D SSH imagery with higher resolution relative to existing SSH products. However, there are only two years of SWOT data available, unlike other satellites with decades-long records. Here, we considered how the recent SWOT data could be deployed to improve the spatial resolution of SSH products from the preceding 30 years. To achieve this, we trained an image-to-image U-Net neural network to predict the high-resolution SSH from an existing low-resolution product (NeurOST). We used SWOT high-resolution data as a ground truth to train this neural network and minimize the mean squared error of the SSH output with respect to the SWOT data. By evaluating the accuracy of the SSH output maps against an independent withheld satellite, we demonstrated that our method improves the spatial resolution of the SSH product compared to the NeurOST dataset. We next plan to test the accuracy of our method when applied to years before SWOT was launched. Overall, our research highlighted how leveraging deep learning and SWOT can enhance the spatial resolution of a decades-long eddy observation time series, enabling more accurate studies of eddies and their climate impact.

Understanding and predicting changes in primary productivity depend on both upper ocean warming and nutrient supply from the ocean interior. Fronts, where distinct water masses converge, are hotspots for these vertical exchanges, transporting nutrients upward and supporting diverse ecosystems. These fronts create sharp gradients in temperature and salinity, generating strong vertical velocities that upwell nutrients and biomass. However, the exact dynamics of frontogenesis (the formation of fronts) remain poorly understood. Additionally, these processes occur at scales too fine to be resolved in global climate models and are only marginally captured by high-resolution ocean simulations. This underscores the need for observational studies to characterize frontogenesis and test existing theoretical frameworks. In this study, we diagnose frontal dynamics using Petterson’s frontogenesis function, which quantifies the roles of divergence and strain. Using NcCut, a GUI developed by our group, we compiled a unique dataset capturing the full life cycle of numerous ocean fronts in front-following coordinates from a state-of-the-art ocean simulation. Our results indicate that for mesoscale (~100 km) fronts, strain dominates over divergence, aligning with classical theories. In contrast, submesoscale (~10 km) fronts exhibit shorter life cycles and no clear dominant driver of frontogenesis within the Petterson framework. We also identified key limitations in conventional diagnostics and improved our analysis by masking the front from its surrounding environment before diagnosing its drivers. This enhancement provides a more accurate representation of frontogenesis dynamics. In the future, we plan to apply our method to satellite observations to study real-world ocean fronts, validate ocean models, and improve predictions of primary productivity changes. Our findings highlight the importance of refining frontogenesis diagnostics to better capture the small-scale dynamics critical to ocean biogeochemistry and climate predictions.