A tropical Atlantic dynamics analysis by combining machine learning and satellite data

Abstract

The western tropical Atlantic Ocean is a very energetic and highly variable region. It is one of the main contributors to the inter-hemispheric mass and heat transports. This study aim is to give a new picture of the space and time variability of this region using statistical tools applied to five different satellite measurements (Sea Surface Temperature, Sea Surface Salinity, ocean topography, wind stress vectors). We first processed each data set by using a Self-Organizing Maps (SOM), which is an efficient clustering methodology based on non-linear artificial neural networks to compress the information embedded in the data. The SOM was then combined with a Hierarchical Ascendant Classification (HAC) to cluster the different phenomena in a small number of classes whose physical characteristics are easy to identify. Three classes were identified which allowed us to analyse the dynamics of the North Brazil Current, and the North Equatorial Countercurrent, respectively, and their links with the Inter-Tropical Convergence Zone and the Amazon and Orinoco river runoffs. The SOM + HAC analysis gave a coherent picture of the concomitant seasonal variability of the variables. Furthermore, we were able to point out the correlations existing between salinity features recently discovered and wind, temperature, and dynamic topography structures. Applying our method to the interannual signals, we showed a year to year variability which deserves further analysis.