3D characterisation of the thermohaline structure in the southwestern tropical Atlantic derived from functional data analysis of in situ profiles

Abstract

The dynamic of the thermohaline structure of the upper ocean, which depends on ocean–atmosphere interactions, drives most near surface oceanic processes, including the control of gases and heat fluxes, and nutrient availability in the photic layer. The thermohaline structure of the southwestern tropical Atlantic (SWTA), a key region for diagnosing variation of the Atlantic Meridional Overturning Circulation, has prime impact on global climate. Characterising the thermohaline structure is typically based on the application of classical statistical methods on vertical profiles. Such approach has important limitations since classical methods do not explicitly contemplate the vertical nature of the profiles. Functional Data Analysis (FDA) is a new alternative to solve such drawbacks. Here, we apply an FDA approach to characterise the 3D canonical thermohaline structure of the SWTA in austral spring and fall. Our results reveal a clear spatial pattern with the presence of three areas with significantly different thermohaline structure. Area 1, mostly located along the continental slope, reflects the western boundary current system, with low static stability and high frequency of occurrence of barrier layer (BL). Conversely, Area 2, located along the Fernando de Noronha chain, presents strong static stability with a well-marked thermocline. This area, under the influence of the eastern Atlantic, is characterised by a low BL frequency, which is seasonally modulated by the latitudinal oscillation of the Intertropical Convergence Zone, controlling the regime of precipitation. In turn, Area 3 behaves as a transition zone between A1 and A2 with the presence of the water core of maximum salinity in subsurface, and therefore presence of strong-moderate BL. Beyond this study, FDA approach emerges as a powerful way to describe, characterise, classify and compare ocean patterns and processes. It can be applied to in situ data but could also be used to deeply and comprehensively explore ocean model output.