A linear decomposition of the Southern Ocean thermohaline structure

• Published in: Journal of Physical Oceanography
• Main Authors: Pauthenet, Etienne, Roquet, Fabien, Madec, Gurvac, Nerini, David
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: Antarctic; Southern Ocean; The Antarctic; Antarc*
• Online Access: https://eprints.soton.ac.uk/407130/; https://eprints.soton.ac.uk/407130/1/JPO_D_16_0083.1.pdf

The thermohaline structure of the Southern Ocean is deeply influenced by the presence of the Antarctic Circumpolar Current (ACC), where water masses of the World Ocean are advected, transformed, and redistributed to the other basins. It remains a challenge to describe and visualize the complex 3D pattern of this circulation and its associated tracer distribution. Here, a simple framework is presented to analyze the Southern Ocean thermohaline structure. A functional principal component analysis (PCA) is applied to temperature θ and salinity S profiles to determine the main spatial patterns of their variations. Using the Southern Ocean State Estimate (SOSE), this study determines the vertical modes describing the Southern Ocean thermohaline structure between 5 and 2000 m. The first two modes explain 92% of the combined θ–S variance, thus providing a surprisingly good approximation of the thermohaline properties in the Southern Ocean. The first mode (72% of total variance) accurately describes the north–south property gradients. The second mode (20%) mostly describes salinity at 500 m in the region of Antarctic Intermediate Water formation. These two modes present circumpolar patterns that can be closely related with standard frontal definitions. By projecting any given hydrographic profile onto the SOSE-based modes, it is possible to determine its position relative to the fronts. The projection is successfully applied on the hydrographic profiles of the WOCE SR3 section. The Southern Ocean thermohaline decomposition provides an objective way to define water mass boundaries and their spatial variability and has useful application for comparing model output with observations.