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
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 observations lie within the
bounds of the ship-based data uncertainty, validating existing quality
control processes for Argo. However, salinity data from seal-mounted
sensors has a salty bias of up to 0.13 g/kg below 250m, which peaks at 5
months since sensor deployment. Our results showcase a new, flexible and
computationally efficient way to assess in-situ ocean data, with
potential for global implementation.