How accurate are salinity measurements around Antarctica? A machine learning based approach

Abstract

The Antarctic margin is a critically under-observed region despite its importance to the global climate. Here, in-situ ocean observations are difficult to obtain and clustered in easier-to-access regions. In addition, autonomous salinity measurements have to be corrected for drift or bias after collection. In this work, we introduce a new method that uses neural networks to identify and correct errors in ocean salinity observations. Salinity estimates from a neural network trained on ship-based data are evaluated against Argo and seal measurements around Antarctica. We find that Argo salinity observational errors lie within the bounds of the method uncertainty, validating existing quality co..