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Climate change is among the principal threats to global terrestrial biodiversity, especially to megadiverse ecosystems such as the Amazon rainforest. In this study, we investigate how it could affect an iconic forest speciesBertholletia excelsa(the Brazil nut) which has values in multiple dimensions in an Amazonian context. We used an ensemble from various distribution modeling methods designed for four different climate scenarios from CMIP6 by the end of the century. Then, we simulate how spatial dynamics under climate change, including explicitly dispersal events, can affect the persistence, colonization, and potential extinction of Bertholletia excelsa in the future. Our results show that by the end of the century there would be a generalized loss of suitability on the Amazon biome, regardless of the climate scenario evaluated, which could promote a significant loss (up to 94%) of the area available for the species via extinction. Our results also show that, in the future, the species would colonize higher altitudes in search of favorable conditions for its survival. Finally, we detected that areas that had previously become unsuitable because of climate change would have favorable conditions by the end of the century. Such an outcome could be useful in fostering an active restoration agenda that can mitigate the negative effects of climate change on species in this study.

Pantanal fires have a significant impact on the environment. Anthropogenic emissions of residual gases have changed the tropospheric composition in this region due to burning. This study aims to analyze the spatial patterns of atmospheric pollutants (including carbon monoxide (CO), nitrogen dioxide (NO2), black carbon (BC), organic carbon (OC) and sulfur dioxide (SO2) and aerosol optical depth, along with fire outbreaks across the Pantanal biome from 2016 to 2021. The data collected is based on remote sensing data. The fire outbreaks peaked pollutant concentrations reached their highest between June to November, particularly during the drier months of August to October. This increase was even greater during the last three years (2019-2021), especially in 2020, when the average CO, NO2, SO2, BC, and OC concentrations increased by 29%, 31%, 50%, 52%, and 50%, respectively. The rainfall values do not justify the increase in the number of fire outbreaks between 2019 and 2021, indicating that the rise is likely due to increased burning. In 2021, the average monthly rainfall was 48% greater than that in 2016-2020 but it had the highest FRP value and the second highest fire outbreak number and pollutant concentration. The 2020 year experienced a record number of fire outbreaks and the highest levels of pollutants in the atmosphere in the region for this period.

Under global warming, extreme events have been increasing in the last decade and are projected to increase in the future with every increment of global warming. The potential increase in compound drought and hot events may induce a complex web of impacts on societies, ecosystems, and economies, including crop failure, wildfires, and water scarcity. This is particularly concerning for Brazil, where it has been demonstrated to be vulnerable to recent extreme climate events. Using an ensemble of CORDEX-CORE simulations over Tropical Brazil, we investigate changes in compound events in response to changes in radiative forcing and their impact on climate extreme events, including drought and extreme heat. The simulations are conducted at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Each model covers the period from 1980 to 2100 under two Radiative Concentration Pathways (RCP2.6 and RCP8.5) in the 21st-century projection period. We used observed data from the Brazilian Daily Weather Gridded Data (BR-DWGD) to evaluate the simulations and perform a quantitative assessment of areas affected by these compound events during the present day. The study finds a generally good agreement between RCM simulations and observed data, with moderate to high correlation coefficients for precipitation, though the strength of these correlations varies across different regions and seasons. The analysis emphasizes the prevalence of compound climate events during the Austral summer season and projects a significant increase in both extreme heat and drought events in the coming decades. These findings underscore Brazils vulnerability to compound climate events, highlighting the need for adaptive strategies and policy interventions to mitigate the socio-economic and environmental impacts across various sectors.

Integrating Light Detection And Ranging (LiDAR) and Hyperspectral Imaging (HSI) enhances the assessment of tropical forest degradation and regeneration, which is crucial for conservation and climate mitigation strategies. This study optimized procedures using combined airborne LiDAR, HSI data, and machine learning algorithms across 12 sites in the Brazilian Amazon, covering various environmental and anthropogenic conditions. Four forest classes (undisturbed, degraded, and two stages of second-growth) were identified using Landsat time series (19842017) and auxiliary data. Metrics from 600 samples were analyzed with three classifiers: Random Forest, Stochastic Gradient Boosting, and Support Vector Machine. The combination of LiDAR and HSI data improved classification accuracy by up to 12% compared with single data sources. The most decisive metrics were LiDAR-based upper canopy cover and HSI-based absorption bands in the near-infrared and shortwave infrared. LiDAR produced significantly fewer errors for discriminating second-growth from old-growth forests, while HSI had better performance to discriminate degraded from undisturbed forests. HSI-only models performed similarly to LiDAR-only models (mean F1 of about 75% for both data sources). The results highlight the potential of integrating LiDAR and HSI data to improve our understanding of forest dynamics in the context of nature-based solutions to mitigate climate change impacts.

The east coast of Northeast Brazil (ENEB) has experienced an increase in extreme precipitation events in recent years because of climate change caused by human activities. ENEB is characterized by the influence of several synoptic-scale systems throughout the year that contribute to the development of extreme precipitation events, emphasizing that the region needs actions to increase its capacity to predict the threat of natural hazards with significant material and human losses. Several studies have shown an increase in the frequency and intensity of these events, accenting the growing importance of accurate numerical weather prediction models has become increasingly critical in aiding decision-makers and governments to plan effective actions that can prevent or mitigate catastrophic consequences. In the present study, one extreme precipitation event developed over the Alagoas state (located at the ENEB) occurred in July 01, 2022 was analyzed. The accumulated rainfall of 185 mm/day was recorded, triggering landslides, floods and river overflows in different cities. The main objective of this study was to apply the Model for Prediction Across Scales Atmosphere (MPAS) model with a variable-resolution mesh to perform the simulation for both extreme precipitation events. A 60-3 km variable resolution mesh with refinement centered on the city of Maceió was configured. The high resolution (3 km) can resolve the known finer-scale mechanisms responsible for cloud formation in the region. Numerical simulations were driven by ERA5 reanalysis to investigate sensitivity to initial conditions. Gridded data from MERGE/CPTEC (0.1°) for hourly accumulated precipitation were used to evaluate the simulated hourly accumulated precipitation. Through the results obtained in the analysis, it was possible to observe a significant variation in the correlations between MPAS and MERGE, with values above 0.67 for some cities affected by heavy rainfall (Quebrangulo, Paulo Jacinto, and Cajueiro), indicating that the model was able to track the temporal variability in these cities. On the other hand, in cities where lower correlations were observed, there might have been some displacement of the rain relative to the observed MERGE data. The BIAS analysis indicated that MPAS tends to underestimate precipitation at all evaluated points, with the highest intensities occurring in locations with higher correlations, suggesting that the model captured the position of the rainfall well but did not accurately represent the volumes. This becomes evident when evaluating the RMSE, MAE, and BIAS, where the values were more pronounced in locations with higher rainfall volumes, reaching up to 7.78 mm/h. These results provide important information into the performance of MPAS, highlighting the need for an extended evaluation with other physical parameterizations and model configurations.

The moisture transport from Amazon forest is a great moisture source for the regional water cycle in South America (SA), especially at southeast and southern part including Parana and La Plata Basins. Several previous studies have shown this importance and how anthropic processes of either global warming and/or Amazon deforestation may affect this Amazonian moisture by the aerial rivers. The new advances in the parameterization of the microphysics (for instance using the convective permitting models) allow improvements in the water representation in varying timescales. So, in order to do this study, we have used 2 data-set from Amazonia: a) the Meteorological Office Unified Model (UM) simulations for 4.5 and 25 km horizontal resolutions; b) the ERA5 reanalysis (vertical profiles of windspeed and specific humidity). The period used in these analysis were from 1998-2007 during the austral summer (months from December up to February). The moisture transport was computed from the surface up to middle troposphere (500 hPa) using IVT definitions. We selected 2 boxs domains: one in the Amazon basin and the other in the Brazilian southeast region (named Sao Paulo) in order to identify the moistures output and input for water budget. The results showed some precipitation bias, mainly in the central/south Amazon basin (~ +2.5 mm.day-1) and in Sao Paulo +1.5 mm.day-1). Some similarities were identified between 4.5 km and observations (ERA5) vertically integrated moisture flow patterns for Amazon basin as well as northern Sao Paulo area (-7% and -13%, respectively). On the other hand, the 25 km simulation underestimated the intensity of fluxes in the whole pathway by 4.5%, which represents -6.9 kg m-1 s-1. The low water transport was explained by some reduction on the vertical moisture transport, mainly between 700 and 500hPa (~ -3.2 kg m-1 s-1 in the Amazon southern area and -2.5 kg m-1 s-1 in Sao Paulo area). Despite some biases identified, the differences compared to ERA5 data are small, indicating that convection-permitting models offer valuable insights for better understanding regional climate connections, albeit their elevated computational costs.

The downward global horizontal irradiance (GHI) modulates various physical processes on Earth and is essential in climate, ecology, and renewable resources studies. In recent years, the use of geostationary satellites for estimating downward global solar irradiance (GHI) has gained increasing interest due to their regional coverage, lower maintenance, and calibration requirements. The GHI estimates using satellite-based models rely on calculating the cloud cover index (Ceff) that depends on the assumed reflectance for totally overcast condition (ρcloud). This work investigates how the spatial resolution of satellite imagery and ρcloud variability affect Ceff evaluation and the skill metrics of satellite-based models to estimate GHI over a tropical region. The results show the robustness of the cloud index parameterization against pixel resolution and solar geometry. A small detachment was observed when the resolution was reduced to 1 km. Zenith and satellite phase angles induced no bias change in GHI estimates. On low cloud cover conditions, models overestimated GHI while underestimating otherwise, suggesting an internal compensation to minimize the error on all cloud cover conditions. Ranging ρcloud from 0.3 to 1.0 led to a bias deviation from 200 Wm−2 (−40%) to 40 Wm−2 (8%), recalling the importance of adequately evaluating ρcloud from satellite imagery to calculate Ceff. The clear sky model and the overcast-representative cloud reflectance, ρcloud, were the most sensitive parameters affecting model performance. In summary, when calibrating the cloud index model for a specific region, it is essential to consider both the clear sky model and the maximum cloud reflectance to achieve optimal results.

Payment for Ecosystem Services (PES) globally addresses land use-tenure-restoration tradeoffs. The Atlantic Forest Connection Projetc, a Brazilian collaborative initiative, aimed to preserve and restore the Brazilian Atlantic Forest fostering and rewarding sustainable land use that could generate biodiversity, climate, and socioeconomic benefits through Nature-based Solutions (NbS). However, comprehensive assessments of these strategic implementations are still lacking. To validate them and enhance overall effectiveness, we have developed the PES-Catchment protocol, a watershed-based framework that targets strategic areas, and propose metrics for evaluating water and soil conservation services. This methodology seeks to optimize the impact of existing environmental and socioeconomic public policies and governance, by directing the efficient implementation of NbS in the restoration of agroecosystems. Priority areas were identified using a multicriteria decision analysis in GIS software, by merging key factors (watershed management, landscape vulnerability, water availability, land degradation) for improving landscape restoration and ecosystem services through agroforestry implementation. Three reference scenarios were created to identify where the benefits can be maximized: SI) multiple ecosystem services conservation (water and soil); SII) hydrological services provision; and SIII) land degradation neutrality. The extent of high levels of degradation across scenarios ranges from 22 to 26%, with scenario SI being the most responsive to high-priority areas. In contrast, low priority areas were more frequently observed under SII scenario, followed by SIII and SI, respectively. The projections prioritized 25% of the area along the most critical catchments and subbasins, exposing a significant level of land degradation and risks to hydrological services. Furthermore, around 50% of the private rural properties registered in the programs are distributed in medium priority areas, and 21% on average are in high-priority areas, which represents an alert to redirect targets and intensify the efforts in resource allocation (implementing/monitoring). The PES – Catchment outcomes offer insights and structural support to adjust governance strategies, optimizing payments for ecosystem services aimed at landscape restoration and watershed conservation.

Linkages have been established between the Atlantic Multidecadal Oscillation (AMO) and surface air temperature variations, low-level jet streams, and precipitation trends in both northeastern Brazil and southeastern South America. Previous studies have discerned distinct wet-season (MarchMay) precipitation responses in northeastern Brazil, with cold (warm) AMO phases triggering increased (decreased) precipitation. Findings from various records indicate that the AMO’s variability extends for thousands of years. A recent reconstruction suggests a significant AMO role in the shift from the Medieval Climate Anomaly (MCA) to the Little Ice Age (LIA) and reveals the LIA as the longest period with a persistent cold anomaly in the North Atlantic over the past ∼3 millennia. Stable oxygen isotope records from South America show typical AMO periodicities (∼65 years), however, despite increased paleo precipitation data, the AMO’s role in South American precipitation during the LIA and MCA remains unclear. In this study, the influence of AMO phases on atmospheric dynamics, precipitation patterns, and stable oxygen isotope composition (δ18O) of precipitation over South America is assessed using the water isotope-enabled version of the Community Earth System Model version 1.2 (iCESM1.2). This research sheds light on the connection between AMO-induced precipitation anomalies and isotopic signals observed in paleoclimate records and emphasizes the significance of isotope-enabled climate models in unraveling the mechanisms behind past variations. The analysis involves comparing δ18O simulations with published reconstructions from South America. By utilizing climate models that incorporate isotopes, we can delve deeper into understanding the impact of the AMO on precipitation patterns and isotopic ratios. Contrary to expectations, the simulated δ18Op signal differ from speleothem records over the western Amazon and Andes during the LIA. That is, the simulated significant total precipitation amounts change over the western Amazon and Andes are not reflected in δ18Op depletion.

This study assesses the performance of the latest phase of Coupled Model Intercomparison Project (CMIP6) models in simulating easterly wave disturbances (EWD) over the tropical South Atlantic (TSA) impacting northeast Brazil (NEB). Initially, we evaluate simulated precipitation from 17 historical CMIP, 16 AMIP, 7 hist-1950, and 10 highresSST-present models against the Global Precipitation Climatology Project (GPCP) dataset to identify models that accurately reproduce the spatial and temporal precipitation patterns in the study region. The ensemble’s spatial analysis demonstrates their capability in reproducing annual and seasonal precipitation climatology. However, models underestimate precipitation intensity along NEB’s coast while overestimating it in TSA and NEB’s north. Model uncertainties tend to be greater with higher latitudes. The models represented the annual cycle in all subareas within the study region, particularly from July to October, albeit with a greater spread in the first half of the year, especially over the Intertropical Convergence Zone (ITCZ). Based on it, three top-performing models from each ensemble were selected for EWD evaluation. The automatic tracking algorithm for EWDs showed the model’s ability to represent mean values of EWD lifetime (~ 6 days) and phase speed (~ 7 m s−1) as found in ERA5 reanalysis. However, they failed to capture EWD’s interannual variability or climatological mean frequency. Despite CMIP6 model weaknesses, they accurately identified two primary EWD genesis regions: one over the TSA and another near the West African coast. Overall, CMIP6 models, particularly atmospheric and high-resolution models (HighResMIP), effectively captured precipitation climatology and EWD characteristics over NEB and the adjacent TSA.

Semi-arid regions worldwide face critical challenges related to prolonged droughts and limited water availability, which significantly impact ecosystem dynamics and plant productivity. This study investigates the energy balance and the phenomenon of hysteresis between actual evapotranspiration (ET) and environmental variables in areas of Dense Caatinga (DC) and Sparse Caatinga (SC) using micrometeorological data collected between 2013 and 2015. It was observed that sensible heat flux (H) was the main component of the energy balance, representing more than 50% of the available energy. In comparison, latent heat flux (LE) varied between 20% annually and 40% during the rainy season. The energy balance closure was satisfactory, with closure coefficients ranging from 65% to 88% depending on the season and vegetation type. In SC, with a more exposed surface, the conversion of net radiation (Rn) at the surface into H is increased. In both regions, during dry periods, LE decreases mirroring the scarcity of water availability. The hysteresis analysis revealed complex cycles of ET in relation to net radiation (Rn), air temperature (Temp), and vapor pressure deficit (VPD), with significant interannual variations. The correlation between ET and gross primary productivity (GPP) indicated counterclockwise hysteresis patterns in DC, suggesting that changes in GPP precede and influence ET, reflecting a rapid stomatal response to increased photosynthetic activity. In SC, the hysteresis cycles between ET and GPP were more variable, displaying both counterclockwise and clockwise loops, especially during 2015, strongly influenced by the El Niño event, indicating lower efficiency in the ET response to GPP due to water constraints. Canopy conductance (Gc) and the decoupling coefficient (Ω) also showed seasonal variations, reflecting the ecophysiological responses to environmental conditions. The correlation between these two variables suggests that Gc in SC was more influenced by external factors, like radiation and water availability. These results demonstrate that the dynamics of the Caatinga Biome are strongly tied to seasonal rainfall patterns, as the ecosystem’s physiological and metabolic traits are primarily regulated by water availability. The variations observed across years and vegetation types underscore the importance of considering interannual variability and the distinctive characteristics of the ecosystem when analyzing climate relationships.

We examined the surface total alkalinity (AT) and salinity (SSS) distributions and their relationship along the southwestern South Atlantic Ocean margin off the Brazilian coast. In situ hydrographic data from shelf and slope stations were measured during three oceanographic cruises in October 2014 (austral spring), April 2018 (austral autumn) and June 2019 (austral winter). The northern portion of the study area lies within the Brazil Current domain, transporting warm and salty Tropical Water, while further south the Subtropical Shelf Water and the freshwater plume derived from the Patos Lagoon and Plata River influence the temperature, salinity, and AT distribution. The present study aimed to determine the relationship between AT and SSS to establish a model for reconstructing a surface time series for the marine carbonate system in the undersampled region of the subtropical southwestern South Atlantic Ocean margin. The AT – SSS relationship showed a significant positive linear relationship (r2=0.76), reflecting both current-dominated (i.e., outer and northernmost stations) and freshwater mixing processes (i.e., inner and southernmost stations and continental inputs). The observed AT values were well correlated with the reported ranges for the tropical and subtropical South Atlantic Ocean, and the predicted AT obtained from the Global Ocean Data Analysis Project and the World Ocean Database 2013 SSS data confirmed lower AT in shelf waters under the influence of continental inputs, which increased towards the outer shelf and slope. Finally, the newly proposed regional AT – SSS relationship improves previous linear regressions for the southwestern Atlantic Ocean margin.