Projeções de alterações climáticas na região Nordeste do Brasil

Abstract

Changes in climate can occur due to natural or man-made consequences, however, as described in the report of the Intergovernmental Panel on Climate Change, human actions are the main cause of recent global warming. Seeking to understand climate behavior, global climate models (GCM’s) and regional climate models (RCM’s) were developed. In these models, parameters of temperature, precipitation, relative humidity of the air, among others are used, and with that it is possible to project future climatic characteristics. The general objective of the work was to project future climate conditions for the Northeast region of Brazil, using a set of global climate models and a regional climate model associated with two scenarios of the IPCC (Intergovernmental Panel on Climate Change). For this, a bibliographic review was first carried out from two databases (Science Direct and Web of Science), using a set of pre-defined keywords. In addition, climatic projections were made for average, maximum and minimum air temperatures, precipitation, relative air humidity, wind speed at 10 m and 100 m, short-wave radiation incident to the surface and cloudiness from a set of climate models (BESM, HadGEM2-ES, CanESM2 and MIROC5, only for precipitation) associated with the Eta regional climate model for the period 2041-2069 and 2070-2099 based on the scenarios RCP4.5 and RCP8.5 for the Northeast region of Brazil. The results showed the importance of using more than one climate model for the projections, in order to give more reliability to the results. In addition, the data projected for the Northeast region indicated increases in future temperature and a reduction in precipitation for most models. This decrease was also observed for the relative humidity of the air. In the case of wind speed, although some locations show possible reductions, the scenario of increase is predominant in the Northeast region. Finally, radiation and cloudiness showed different results between the models applied. In this way, it is possible to predict, for example, whether the Northeast region of Brazil will be a favorable area for renewable energy production and whether the population will suffer from a more arid scenario.