Automated Detection Algorithm for SACZ, Oceanic SACZ, and Their Climatological Features

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Abstract

The South Atlantic Convergence Zone (SACZ) is responsible for a large amount of the total summer precipitation over Brazil and is related to severe droughts and extreme floods over the southeast of Brazil. This paper aims to demonstrate the feasibility of an objective, simplified and automated method based on satellite outgoing longwave radiation (OLR) for South Atlantic Convergence Zone (SACZ) and oceanic SACZ (SACZOCN) detection, and characterize their climatological features. Here we developed an automated algorithm and made available the SACZ and SACZOCN dates and characteristics (intensity and size) for the first time in the literature. The method agreed with 77% of SACZ occurrences compared with 21 years of SACZ observations. The temporal criterion of permanency of the SACZ convective activity for at least 4 days was essential to differentiate the SACZ from the transient frontal systems over the Brazilian Southeast. About 30% of the SACZ days occurred in November and March, therefore the December to February period is not sufficient to fully represent its activity. A barotropic trough near the Uruguay coast influences the intensity and position of the coastal and oceanic SACZ portions. When this trough closes into a cyclonic vortex Southwest of the SACZ (CVSS) cloud band it characterizes a SACZOCN episode. SACZOCN episodes were objectively identified, being characterized by a more intense convective activity and shifted to the north. We show that some oceanic SACZ episodes are associated with extreme floods and severe droughts over Brazil, therefore its identification is important to the Brazilian society. Besides, oceanic surface currents and temperature over the Southwestern Atlantic Ocean are modified during the SACZOCN active phase. The method presented here is a viable alternative to objectively classify SACZ and SACZOCN episodes, it can be implemented operationally and used to SACZ studies in the context of climate change.

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Rosa, E. B., Pezzi, L. P., Quadro, M. F. L. de, & Brunsell, N. (2020). Automated Detection Algorithm for SACZ, Oceanic SACZ, and Their Climatological Features. Frontiers in Environmental Science, 8. https://doi.org/10.3389/fenvs.2020.00018

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