Meta-learning-based Estimation of the Barrier Layer Thickness in the Tropical Indian Ocean

Abstract Accurately estimating the barrier layer thickness (BLT) is crucial for enhancing our understanding of ocean’s role in climate variability on both regional and global scales. Here, we propose a meta-learning-based ensemble model to estimate BLT using satellite observations tropical Indian Ocean. The results show that outperforms three individual models terms spatial distribution accuracy, with significantly reduced root mean square errors Southeast Arabian Sea, Bay Bengal, eastern equatorial Furthermore, found sea surface salinity plays most significant estimation BLT, highlighting dominant influence stratification. These preliminary provide an insight into feasibility predicting have implications studying upper ocean dynamics machine learning techniques.