We applied the Goddard Earth Observing System for subseasonal to seasonal climate prediction to assess the impact of inclusion biomass burning (BB) aerosols over South America (SA) during the austral winter. We also evaluated the model sensitivity to the BB emissions prescription using no emissions, monthly climatological, and daily emissions. Each hindcast consisted of four members running from June to November of each year between 2000 and 2015. Our results indicated that interactive BB aerosols improve the seasonal climate prediction performance over SA. More realistic daily based emissions significantly further improve the performance in comparison with the climatological ones. Therefore, improvements in the BB emissions representation are urged to represent the aerosol impacts on seasonal climate prediction performance adequately.

Solar energy has presented itself as a valid and sustainable alternative for generating electricity. However, its viability depends on the accuracy of the solar irradiance prediction and on meteorological parameters that influence the amount of solar radiation that reaches the surface. Numerical forecasting has become one of the main tools for estimating solar irradiance in advance. The objective of this work is to evaluate the prediction of solar Global Horizontal Irradiance (GHI) and temperature near the surface (T2M) through numerical modeling of the atmosphere, for the region of Ilha Solteira. Two numerical experiments with different atmospheric conditions (clear and cloudy sky) were designed, using Weather Research and Forecasting (WRF), to evaluate the potential of this model to simulate the variables GHI and T2M. The WRF was configured with a horizontal resolution of 5 km for 72 hours of forecast for the months of September/2021 and October/2021. The average diurnal cycle of GHI and T2M was relatively well represented by the model in the 3 forecast days. For the GHI the model tries to overestimate the measured values in both experiments. The largest errors were observed for the experiment with cloud cover and rain condition. For T2M the model tends to underestimate the observed values. Additional studies with different sets of parameterizations are needed to adjust the model to local conditions, as well as to evaluate a larger set of experiments. A energia solar tem se apresentado com uma alternativa válida e sustentável para a geração de eletricidade. No entanto, sua viabilidade depende de acurácia da previsão da irradiância solar e de parâmetros meteorológicos que influenciam na quantidade de radiação solar que chega à superfície. A previsão numérica vem se tornando uma das principais ferramentas para estimar com determinada antecedência a irradiância solar. O objetivo desse trabalho é avaliar a previsão da irradiância solar (GHI) e da temperatura próximo da superfície (T2M) através da modelagem numérica da atmosfera, para a região de Ilha Solteira. Dois experimentos numéricos com diferentes condições atmosféricas (céu claro e nublado) foram projetados, utilizando o Weather Research and Forecasting (WRF), para avaliar o potencial deste modelo em simular as variáveis GHI e T2M. O WRF foi configurado com uma resolução horizontal de 5 km para 72 horas de previsão para os meses de setembro/2021 e outubro/2021. O ciclo diurno médio da GHI e T2M foi relativamente bem representado pelo modelo nos 3 dias de previsão. Para a GHI o modelo tende a superestimar os valores medidos, em ambos os experimentos. Sendo os maiores erros observados para o experimento com nebulosidade e condição de chuva. Para a T2M o modelo tende a subestimar os valores observados. Estudos adicionais com diferentes conjuntos de parametrizações são necessários para ajustar o modelo as condições locais, assim como, avaliar um conjunto maior de experimentos.

High-resolution climate projection datasets for South America require careful model selection, since regional climate depends on interacting atmospheric systems across multiple scales. Consequently, evaluations based only on statistical metrics may fail to determine whether models realistically reproduce the key phenomena organizing climate over the continent. As the first step in building the HiClim-SA dataset, this study proposes a novel framework for selecting models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Evaluation metrics are derived from objective criteria defined for major atmospheric phenomena over South America and integrated through a multi-criteria ranking applied to 19 CMIP6 Earth System Models. The results revealed substantial differences in model skill across the evaluated phenomena, indicating that no single model performs best for all aspects of South American climate. Instead, only a limited subset of models emerges as consistently more robust across precipitation, convection, and atmospheric circulation, whereas the lowest-ranked models exhibit broader and more persistent biases over tropical and central South America. Comparisons with baseline rankings showed partial agreement for the best- and worst-performing models, but also important discrepancies for several intermediate models. These findings demonstrate the added value of the proposed framework by showing that process-oriented model evaluation can identify robust model results beyond what is captured by conventional climatological metrics alone. The framework therefore provides a physically grounded basis for model selection and establishes the methodological foundation for the subsequent bias correction, downscaling, and future projection stages of the HiClim-SA dataset.