PNNL

Abstract

Upper-ocean stratification strongly impacts vertical mixing and the associated momentum, heat, freshwater, and nutrient fluxes between the thermocline and mixed layer. Near-surface stratification is particularly strong in tropical regions and is crucial for studying coupled phenomena across multiple time scales, including tropical cyclones, MJO, IOD, and ENSO. Upper-ocean stratification is mainly determined by temperature except in areas with strong precipitation, such as the Inter-Tropical Convergence Zone, or in river plumes. 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 dataset from 2004-2017, we investigate the possibility of retrieving upper-ocean stratification from surface data 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 the reanalysis. Sea surface temperature (SST) and sea surface salinity (SSS) are the most important predictors for temperature and salinity stratification, respectively. Simple regression can be used with satellite surface observations to create a high-resolution, near-real-time gridded ocean stratification dataset with meaningful applications