Assessment of Climate Variability in Summer Over the Northeast Region of Brazil Simulated By the Global BAM Model

Objective: The objective of this study is to evaluate the performance of the model (BAM - Brazilian Atmospheric Model) in simulating regional climate variability in the NEB and the associated atmospheric circulation characteristics during the austral summers of 2011 (La Niña), 2014 (Normal), and 2016 (El Niño). Method: For this study, climate simulation data from the BAM model were used, along with Precipitation data from the Global Precipitation Climatology Project (GPCP). Data on zonal and meridional components from the MERRA2 reanalysis were used, and in addition to the zonal and meridional components, air temperature at 850 hPa from ERA5 was also utilized. A comparison was made between the BAM data and the ERA-5, MERRA2, and GPCP data. To assess the model's efficiency, the following statistical methods were used: Pearson Correlation (r), Mean Bias (BIAS), and Root Mean Square Error (RMSE). Results and Discussion: The analyses show that the BAM model was skillful in simulating atmospheric circulation patterns and air temperature at lower and upper levels of the atmosphere over the NEB, as well as interannual variability during the aforementioned summers, when compared to the ERA5 and MERRA2 reanalyses, and for precipitation when compared to the GPCP data. The best performance was observed for the air temperature field across all investigated areas, relative to the precipitation field, during the three austral summers. Research Implications: Based on the analyses of the BAM model, we can assist both in diagnosing and forecasting the impacts of climate variability in specific regions of the NEB, leading to more effective action policies to mitigate the impacts caused by droughts affecting this region. Originality/Value: The study presents the initial analyses and validations of the BAM model data, demonstrating the model's efficiency and skill in improving studies and forecasts in the NEB region, particularly in future scenarios affected by climate change.