One of the most important anthropogenic influences on climate is land use change (LUC). In particular, the Amazon (AMZ) basin is a highly vulnerable area to climate change due to substantial modifications of the hydroclimatology of the region expected as a result of LUC. However, both the magnitude of these changes and the physical process underlying this scenario are still uncertain. This work aims to analyze the simulated Amazon deforestation and its impacts on local mean climate. We used the Common Land Model (CLM) version 4.5 coupled with the Regional Climate Model (RegCM4) over the Coordinated Regional Climate Downscaling Experiment (CORDEX) South America domain. We performed one simulation with the RegCM4 default land cover map (CTRL) and one simulation under a scenario of deforestation (LUC), i.e., replacing broadleaf evergreen trees with C₃ grass over the Amazon basin. Both simulations were driven by ERA Interim reanalysis from 1979 to 2009. The climate change signal due to AMZ deforestation was evaluated by comparing the climatology of CTRL with LUC. Concerning the temperature, the deforested areas are about 2 °C warmer compared to the CTRL experiment, which contributes to decrease the surface pressure. Higher air temperature is associated with a decrease of the latent heat flux and an increase of the sensible heat flux over the deforested areas. AMZ deforestation induces a dipole pattern response in the precipitation over the region: a reduction over the west (about 7.9%) and an increase over the east (about 8.3%). Analyzing the water balance in the atmospheric column over the AMZ basin, the results show that under the deforestation scenario the land surface processes play an important role and drive the precipitation in the western AMZ; on the other hand, on the east side, the large scale circulation drives the precipitation change signal. Dipole patterns over scenarios of deforestation in the Amazon was also found by other authors, but the precipitation decrease on the west side was never fully explained. Using budget equations, this work highlights the physical processes that control the climate in the Amazon basin under a deforestation scenario.

Since 2020, southern Brazil’s Rio Grande do Sul (RS) State has been affected by extreme precipitation episodes caused by different atmospheric systems. However, the most extreme was registered between the end of April and the beginning of May 2024. This extreme precipitation caused floods in most parts of the state, affecting 2,398,255 people and leading to 183 deaths and 27 missing persons. Due to the severity of this episode, we need to understand its drivers. In this context, the main objective of this study is a multi-scale analysis of the extreme precipitation between 26 April and 5 May, i.e., an analysis of the large-scale patterns of the atmosphere, a description of the synoptic environment, and an analysis of the mesoscale viewpoint (cloud-top features and lightning). Data from different sources (reanalysis, satellite, radar, and pluviometers) were used in this study, and different methods were applied. The National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN) registered accumulated rainfall above 400 mm between 26 April and 5 May using 27 pluviometers located in the central-northern part of RS. The monthly volumes reached 667 mm and 803 mm, respectively, for April and May 2024, against a climatological average of 151 mm and 137 mm for these months. The maximum precipitation recorded was 300 mm in a single day on 30 April 2024. From a large-scale point of view, an anomalous heat source in the western Indian Ocean triggered a Rossby wave that contributed to a barotropic anticyclonic anomalous circulation over mid-southeastern Brazil. While the precipitant systems were inhibited over this region (the synoptic view), the anomalous stronger subtropical jet southward of the anticyclonic circulation caused uplift over RS State and, consequently, conditions leading to mesoscale convective system (MCS) development. In addition, the low-level jet east of the Andes transported warm and moist air to southern Brazil, which also interacted with two cold fronts that reached RS during the 10-day period, helping to establish the precipitation. Severe deep MCSs (with a cloud-top temperature lower than −80 °C) were responsible for a high lightning rate (above 10 flashes km⁻² in 10 days) and accumulated precipitation (above 600 mm in 10 days), as observed by satellite measurements. This high volume of rainfall caused an increase in soil moisture, which exceeded a volume fraction of 0.55, making water infiltration into the soil difficult and, consequently, favoring flood occurrence.

The Northeast of Brazil (NEB) is the region with the highest number of municipal decrees of emergency situation declaration caused by weather events in the period from 2013 to 2022 and with the highest rate of natural disasters per risk area. In the NEB, the city of Recife and its metropolitan region are the biggest localities with populations in risk areas. Focusing on this region, five events of extreme precipitation were chosen for simulations using the WRF model and diagnostics analyses. First, a set of configurations of the model was tested, including 11 microphysics (MPH) schemes, 9 planetary boundary layer (PBL) schemes, 5 cumulus (CUM), and 7 surface layer (SFC) schemes. Then, through diagnostic analysis, the conditional instability, the moisture supply at low levels, and the support of the medium and high levels in storm formation were verified. The model’s configurations were verified by 298 rain gauges with hourly registrations through statistical metrics such as bias, MSE, standard deviation, and Pearson’s correlation, and demonstrated that the MPH schemes of Thompson Aerosol-Aware and NSSL + CCM, ACM2, MYJ for the PBL, KFCuP for CUM, and RUC for SFC were considered the best. All the cases were better with CUM parametrizations turned on. In all cases, diagnostics analyses highlighted the strong moisture flux convergence at the low levels, the presence of wind shear on the middle layer, weak cyclonic vorticity advection at high levels, and CAPE values around 1500 J/kg, in addition to an inverse relationship between wind shear action and CAPE values. This work is part of the national strategy for monitoring, diagnosis, and modeling of information that can minimize or even prevent damage caused by severe precipitation events.

This study evaluated the atmospheric pattern precursors to the occurrence of natural disasters (ND) in the southern region of Brazil (SRB) due to the passage of frontal systems (FS). The results can be used as prognostics to assist in risk management with a set of preventive and mitigating actions in order to minimize the impact of natural disasters suffered by the population. The natural disasters data were provided by the Centro de monitoramento e alertas de desastres naturais (Cemaden). For atmospheric analysis we used ERA5 reanalysis data, and the precipitation dataset was estimated from Integrated Multi-satellitE Retrievals for GPM (IMERG) from the Global Precipitation Measurement (GPM) mission. The most affected regions are the coast of Santa Catarina and the central-eastern region of Rio Grande do Sul. The results indicate that FS associated with ND are different from the other FS that affect the SRB. The observations were: a pattern of increase and accumulation of available convective potential energy west of the SRB before the event, especially in spring; a considerable increase in specific humidity at low levels associated with runoff east of the Andes; and an anticyclonic circulation at high levels similar to the Bolivian High. Analysis of rainfall behavior indicates that it is highest in the two days preceding the disaster. The mean precipitation values identified, together with atmospheric behavior observed in this study, allow us to identify the potential occurrence of a disaster in the cities of SRB in the passage of a frontal system.

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below −65 °C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day⁻¹ and 48 mm h⁻¹, combined with the region’s soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters.