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A temperatura do planeta vem apresentando aumento desde o início da revolução industrial, conforme documentado nos relatórios do Painel Intergovernamental sobre Mudanças Climáticas (IPCC). Isso afeta os diferentes componentes do sistema climático, que, por sua vez, influenciam o clima (mecanismo de retroalimentação). Portanto, para a compreensão dos processos físicos associados às mudanças climáticas é necessário um conhecimento básico sobre o sistema climático, o que é o foco deste estudo. Apresentam-se aqui os conhecimentos necessários, em uma linguagem simples, para que o entendimento das mudanças climáticas seja adquirido por todos aqueles que tenham interesse.

Accurate and complete global solar radiation (Hs) data at a specific region are crucial for regional climate assessment, crop growth modeling, and all operations that use solar energy. However, in the Minas Gerais state, Southeastern Brazil (SEB), the number of weather stations that measure global solar radiation is scarce, and when it is available, it presents gaps in the time series. An attractive alternative to solve the data gap problem is to estimate global solar radiation using empirical models. In this study, thirteen models based on maximum and minimum air temperatures, precipitation, sunshine duration, and extraterrestrial solar radiation were compared in the daily estimation of Hs. Data from 10 weather stations, from 1999 to 2017, located in Minas Gerais were used. Also, cluster analysis was used to group the localities (weather stations) with similar patterns of model performance, climatic classification (Köppen–Geiger and Thornthwaite), and seasonal data variability, considering minimum and maximum air temperatures, precipitation, sunshine duration, and global solar radiation. Although it is apparently simple, studies on this subject are scarce and the few existing ones in Minas Gerais have flaws, which justifies this study. The models were evaluated by root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (BIAS), Willmott’s index of agreement (d), and performance index (c-index). Models based on sunshine duration, such as those proposed by Ertekin and Yaldiz and by Newland, showed the best performance (average c-index = 0.71). Models based on temperature and precipitation showed the worst results (average c-index = 0.41). Cluster analysis showed that there is a similar pattern between the performance of the models, climatic classification, and seasonal variability of data among the localities of Minas Gerais. In general, models that presented extremely poor performance were formed with weather stations located in the dry zone, but with different climate classification, and models that presented very good (and good) performance were composed by weather stations located in the humid zone (dry subhumid) with the same climate classification and similar seasonal variability. Furthermore, the models based on temperature have a tendency to overestimate radiation values below 10 MJ·m−2 day−1 and to underestimate values higher than 25 MJ·m−2 day−1. This point is a limitation of the model for estimating global solar radiation below and above these levels, showing the influence of atmospheric systems and atmospheric attenuation mechanisms of global solar radiation.

A large part of Brazil is highly vulnerable to climate changes projected for the end of the 21st century. Analyzing these vulnerabilities is particularly important for agriculture, since the country is one of the largest agricultural commodity producers in the world. Changes in the reference evapotranspiration (ETo) can impact crops and make cultivation unfeasible. However, studies on ETo patterns under climate change scenarios for Brazil have been restricted to regional scales and use too few climate models or too simplified water balance models for their analysis. This can lead to uncertainties in assessing the impacts of climate change on ETo. Therefore, this study seeks to analyze ETo patterns in Brazil towards the end of the 21st century using two methods that are better at estimating regional ETo, i.e., the Turc and Abtew methods, under two radiative forcing scenarios (RCPs 4.5 and 8.5). Daily data on near surface air temperature (mean and maximum), global solar radiation, and near surface relative humidity from six General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to analyze the simulations and projections for climate change. The performance of climate simulations is heterogeneous among the GCMs, with overestimations (~ 2.5 mm day-1) in some models, and underestimations (~ 1.5 mm day-1) in others. In general, climate change projections indicate increases of up to 1 mm day-1 in ETo, mainly in the North, Northeast, and Center-West regions of Brazil. Both estimation methods showed similar spatial patterns, however the Turc method projected lower intensity changes compared to the Abtew method.