Mapping ocean salinity data using Gaussian Mixture Modeling.

Understanding ocean salinity is crucial for tracking changes in the Earth's water cycle and climate. However, collecting accurate salinity data has been challenging due to limited observations, especially in certain regions. This study focuses on the development of a method to create 2-dimensional maps of ocean salinity and its trends on pressure surfaces from sparse observations. An unsupervised classification technique called Gaussian Mixture Modeling (GMM) is used to identify coherent regions where temperature and salinity are tightly related at constant pressure. By grouping similar ocean regions using GMM, we are able to predict missing salinity data and fill gaps in historical salinity records from 1970 to 2014. The results show that this approach effectively estimates past salinity data. In the South Atlantic, at a pressure of 539 dbar, the root mean square error of salinity and of the linear trend of salinity are 0.040 g kg⁻¹ and 2.1 10⁻³g kg⁻¹ yr⁻¹. The method could help fill in missing salinity observations and thus improve our understanding of the intensification of the global water cycle in response to climate change.