This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g. energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyse such fluctuations and their spatio-temporal dependencies, time series of wind speeds at an hourly or higher frequency are needed. We provide a comprehensive evaluation of the global and mesoscale-model-derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-spatial-resolution simulations often lower the correlations of wind time series with measurements. Thus, we recommend finer-spatial-resolution simulations for modelling power ramp or voltage stability studies but ERA5 rather than mesoscale simulations for studies where correlations with measurements are essential. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed as estimated from observations. Still, the spatial correlations and the wind speed distributions are insensitive to the mesoscale model configuration tested in this study. Regarding these two metrics, mesoscale model simulations present more favourable results than ERA5.

As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g., energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyze such fluctuations and their spatio-temporal dependencies, time series of wind speeds at hourly time-frequency or higher are needed. We provide a comprehensive evaluation of the global and mesoscale-model derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-resolution simulations often lower the correlations of wind time series with measurements. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed with respect to their observed values. Still, the spatial correlations and the wind speed distributions are insensitive to the model configuration tested in this study.

As the share of variable renewable energy (VRE) such as wind and solar power continues to grow across Europe, understanding how climate change may affect their short-term variability and extreme events is critical for energy planning, system stability, and resilience. This study investigates projected changes in sub-daily variability and the frequency of extreme conditions in wind and solar energy resources using regional climate model simulations over Europe.We employ a suite of features to characterize the short-term dynamics of VRE resources, including ramp rates, lag-1 autocorrelation, and event-based metrics such as the duration and persistence of power drops (e.g., time below the P10 threshold). These features are computed from 3-hourly wind speed and surface solar radiation data, covering both historical and future periods from the Euro-CORDEX ensemble under the RCP8.5 scenario. To support the robustness of the analysis, model outputs are compared against long-term observations from tall towers and surface radiation stations at selected European sites.The results suggest that future climate conditions could lead to both beneficial and adverse changes in VRE variability: increased persistence may enhance predictability in some regions, while intensified ramp events and prolonged low-production periods may pose challenges for grid management and storage needs elsewhere. The spatially diverse impacts highlight the need for regional-scale assessments.These findings demonstrate the importance of integrating short-term variability and extremes into long-term energy system modeling and risk assessments, and they provide actionable insights for improving the resilience of future low-carbon energy systems.