Comparison results for the CFSv2 hindcasts and statistical downscaling over the northeast of Brazil

Abstract

An Artificial Neural Networks (ANNs) approach was used to reproduce the precipitation anomalies for the rainy seasons over the south and north parts of the Northeast of Brazil (NEB) during 1982–2009 period. The seasonal hindcasts of precipitation anomalies from Climate Forecast System v2 (CFSv2) model and the observed dominant modes of anomalous Sea Surface Temperature over the South and North Atlantic Ocean were used as explanatory variables separately. The reduction of dispersion between the explanatory and dependent variables after the fit of the networks suggest the ANN as an important complementary technique for the climate studies over the NEB. However, a large dataset are required to the models capture the non-linear process in more details. The practical implication of the results is that ANNs constructed here could be applied in further analyses, for example, to explore the ANN's ability in improving the seasonal climate forecasts considering that the numerical and statistical methods must be complementary tools.