Implementation of a regional model for oceanic climatic studies in Tropical and Western South Atlantic Ocean

Luciano Ponzi Pezzi – National Institute for Space Research (INPE)
Ricardo de Camargo – University of São Paulo (USP)

This research is running under an international project, called Global Networking to Improve Marine Prediction of Extreme Events. This is an international project funded by the Lloyd’s Register Educational Trust (LRET), with four member countries participating. Overall coordination is done by Dr. Jinyu Sheng and Dr. Keith Thompson at Dalhousie University in Canada with the participation of Dr. Mike Tsimplis from the National Oceanographic Centre in the UK, Dr. Gary Brassington University of Melbourne in Australia and, Dr. Ricardo de Camargo at IAG/USP Brazil. The project’s main goal is to establish an international network of researchers in the physical oceanography and climate of the four countries, in order to increase the ability to predict the impacts of extreme weather events over the oceans as well as prepare estimates of frequency of occurrence of these extremes in future decades with realistic estimates of uncertainties.

Through this network, it is possible to integrate the participating researchers and their groups in order to promote the development and improvement of numerical models as well as the use of statistical analysis methods. There is also an educational outreach component to include graduate students and postdoctoral fellows. Objectively, the activities assigned to the Brazilian node are: (i) identification of extreme events with emphasis on the South Atlantic Ocean and (ii) use of numerical models and statistical analysis of collected observational data.

This study intends to show the preliminary results and advances obtained during the first project year, like the setting and adjusting of a regional ocean model for the Tropical and Western South Atlantic (with a spatial resolution on the order of a few tens of kilometres). Several short experiments were carried out to fine tune model options and physical parameterizations for the period 1980-2007. High temporal frequency, large-scale atmospheric forcing was used. The simulated climate mean features were analyzed and compared with observed climatologies (satellite and in situ). The results presented here are preliminary.