Abstract
Upper‐ocean stratification strongly impacts vertical mixing and the heat flux between the ocean and atmosphere, especially under extreme conditions of tropical cyclones (TCs). Knowledge of prestorm stratification is important for accurate TC intensity prediction. In situ observations of the tropical ocean have significantly increased in the past decade. However, they are still too sparse to resolve ocean stratification variability in near‐real time and on small spatial scales. In this study, based on long‐term observations and an ocean reanalysis data set from 2004–2017, we investigate the possibility of retrieving upper‐ocean stratification from sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) using a simple regression method. It is found that more than 90% of the mean seasonal cycle and about 30% to 80% of temperature and salinity stratification anomalies can be reconstructed using surface data from either observations or an ocean reanalysis. Simple regression can be used with satellite observations to create a high‐resolution, near‐real‐time‐gridded ocean stratification data set that successfully reproduces both the large and mesoscale variability of ocean stratification. When used in a simple expression for TC‐induced SST cooling, the satellite‐derived stratification shows improvements over an ocean analysis in terms of variance explained of SST cooling, offering promise as a near‐real‐time indicator of the ocean's impact on TC intensification. Key Points Inferring upper‐ocean stratification from surface data is feasible with simple regression Stratification data set created using satellite data and a statistical model can realistically reproduce large and mesoscale features An example of the potential usefulness of the stratification data set is illustrated for tropical cyclone intensity prediction