Publicações

Dissolved organic carbon (DOC) is a key component of the biogeochemical carbon cycle in the Southern Ocean. However, there are still significant gaps in understanding the role of DOC in polar environments, due to the limitations of spatiotemporal sampling. In this study, we investigated the regional aspects controlling the distribution and diffusive and advective fluxes of DOC along the northern Antarctic Peninsula (NAP) during austral late summers of 1995 and between 2015 and 2019. DOC concentrations ranged from 33.1 to 157.6 μmol kg1. The NAP showed regional differences in both its hydrographic conditions and DOC distribution. The regional variability reflected the main biogeochemical sources and fates of DOC associated with the Antarctic Circumpolar Current inflows, the Weddell Gyre transport and the meltwater input. The intensity of the advective fluxes of DOC was 106 times greater than the diffusive fluxes. However, ocean fronts along NAP environments are mesoscale structures for observations of downward and upward diffusive fluxes of DOC. This study adds insights on the role of DOC as a proxy for a better understanding of the coupling between physical and biogeochemical processes over time in an environment sensitive to climate change.

Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.

The native vegetation removal (NVR) of Caatinga Brazilian biome has been mapped by Prodes Project coordinated by National Institute for Spatial Research (INPE) between 2000 to 2023 year. Prodes is considered an official government product for achieving Brazilian and international targets for the conservation of natural vegetation. Therefore, Prodes is committed to achieving the highest possible accuracy associated with the history of mapping already carried out, by visual interpretation of satellite images and the use of past mapping masks techniques. Mapping NVR in Caatinga biome is a major challenge compared to biomes where the tree canopy with native vegetation prevails. Caatinga is one of the least protected of Brazilian biomes and is in one of the most pressured semi-arid regions on the planet. This work aims to present the NVR in Caatinga biome from Prodes data, in terms of its trajectory and main geographical patterns, concerning the municipalities, and areas that are the focus of monitoring, conservation or restoration. According to Prodes results, by 2023, the cumulative NVR in the entire Caatinga reached 43,05%; in 2000, this figure was 29,54%. The major NVR occurred in the easternmost areas of Caatinga and regions of Serrinha and Feira de Santana in Bahia State. Also, there are areas with considerable NVR further inside the biome, such as, Irecê and Guanambi region in Bahia, west of Pernambuco and Paraíba States and South and North of Ceará State. Areas prioritized for conservation actions showed high NVR values, indicating the urgent need for implementation of such actions. The fact that Desertification Cores showed low NVR values is important, and these regions should be closely monitored to prevent uncontrolled NVR rates. The two regions with high anthropic pressure or speculation showed contrasting behaviors; while the Araripe Gypsum Pole exhibited higher NVR levels compared to the control area, the São Francisco River integration project (PISF) so far does not appear significantly different from the rest of the biome. Overall, this study clearly demonstrates the potential of monitoring the Caatinga through Prodes in understanding and integrating the various factors influencing changes in the biome’s native vegetation cover. Future approaches should move beyond descriptive studies to increasingly contribute to the territorial management of this important Brazilian region.

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 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.