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In the present study two extreme events that occurred in the East Coast of Northeast Brazil (ENEB) during 2022 and 2023 were evaluated. These events are becoming increasingly frequent in all regions of Brazil, associated with significant material and human losses, emphasizing the significance of a deeper comprehension of these events. ERA5 global reanalysis data, GOES-16 satellite imagery and pluviometric stations were used for the analysis. Model simulations were also conducted using the Model for Prediction Across Scales (MPAS) with variable resolution (603 km). Both events corresponded to Easterly Wave Disturbances (EWDs) that occurred under opposite large-scale conditions of the ENSO cycle, since extreme events are becoming increasingly frequent in all regions of Brazil and could be responsible for significant material and human losses. Thus, an emphasis was given to characterize the synoptic conditions. Both analyzed cases occurred along the ENEB, specifically over the Alagoas state. The trough axis penetrating the studied area was observed on both examined dates, with a very characteristic relative vorticity of this tropical disturbance. In general, moisture convergence resulted from the high flow of moisture prevailing over the region combined with upward movements caused by the trough present at low levels, which combined with local factors in the region such as topography, contributed to the increase in rainfall over the study area in both analyzed cases. The MPAS showed excellent spatial representation when compared to station data, highlighting intense precipitation over parts of Alagoas.

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

This study aims to identify the capacity of intact forests to maintain Above Ground Biomass (AGB) under new climate conditions. Using a predictive approach under different climate scenarios (SSP12.6 and SSP58.5), and considering the mean of General Circulation Models (GCMs) for the period (20212040), it was revealed that the regions with the greatest capacity for AGB in the optimistic scenario are concentrated in the northwest and southeast regions, covering the basins of the Negro, Xingu and Tapajós rivers. In the SSP58.5 scenario, although potential AGB gains are concentrated in the same regions, the spatial distribution is more restricted and dispersed. Indigenous Landsemerge as areas with the greatest potential AGB gains in both climate scenarios. However, these predictions do not include the impact of extreme climate events, such as storms, severe droughts, and wildfires, which could lead to an underestimation of actual biomass variations.

In the present study, we propose a transdisciplinary investigation aimed at developing an index to assess the vulnerability to landslides in the Brazilian municipality of São José dos Campos. The proposed Socio-Geoenvironmental Vulnerability Index (SGeoVI) was developed using a hybrid modeling approach that integrates socioeconomic data with landslide susceptibility mapping. Landslide susceptibility was derived from the FS FIORI deterministic model, which calculates the Safety Factor (FS) using the concept of limit equilibrium. The mapping of land use and land cover enabled the inclusion of new parameters related to certain anthropogenic conditions and vegetation cover, such as the overabundance of buildings and the presence or absence of tree vegetation, which are significant factors influencing landslide occurrence. Socioeconomic indicators were extracted from Brazilian Institute of Geography and Statistics (IBGE) census data, and variables were selected and spatially represented for each of São José dos Camposs 1073 census tracts. Socioeconomic indicators were taken into account for the SGeoVI proposal and categorized into five thematic groups: economic; educational; housing infrastructure; social dependency; e family structure. Based on the Socio-geoenvironmental vulnerability indicators, several neighborhoods in São José dos Campos, SP, were selected for a detailed SGeoVI assessment. In Pinheirinhos, a neighborhood situated in the southernmost part of São José dos Campo, the highest SGeoVI value reached to 0.91. In contrast, Jardim Apolo I, an established neighborhood located in the Central Region of São José dos Campos, exhibited a considerably lower SGeoVI value of only 0.04, for instance. The verification results of SGeoVI revealed socioeconomic disparities that align with geomorphological variations. The northern and southern parts of the municipality exhibit high susceptibility to landslides, coupled with low-income communities and inadequate housing structures, contributing to elevated socio-geoenvironmental vulnerability. In contrast, central regions feature flat terrain, reduced natural susceptibility, higher income levels, and improved access to housing infrastructure, resulting in lower vulnerability values. Given its ability to assess intra-municipal socio-geoenvironmental vulnerability, SGeoVI holds potential for extrapolation to other municipalities. Consequently, it can serve as a valuable tool for municipal authorities in formulating public policies aimed at landslide risk management.

Amazon forests are the largest forests in the tropics and play a fundamental role for regional and global ecosystem service provision. However, they are under threat primarily from deforestation. Amazonia’s carbon balance trend reflects the condition of its forests. There are different approaches to estimate large-scale carbon balances, including top-down (e.g., CO2 atmospheric measurements combined with atmospheric transport information) and bottom-up (e.g., land use and cover change (LUCC) data based on remote sensing methods). It is important to understand their similarities and differences. Here we provide bottom-up LUCC estimates and determine to what extent they are consistent with recent top-down flux estimates during 2010 to 2018 for the Brazilian Amazon. We combine LUCC datasets resulting in annual LUCC maps from 2010 to 2018 with emissions and removals for each LUCC, and compare the resulting CO2 estimates with top-down estimates based on atmospheric measurements. We take into account forest carbon stock maps for estimating loss processes, and carbon uptake of regenerating and mature forests. In the bottom-up approach total CO2 emissions (2010 to 2018), deforestation and degradation are the largest contributing processes accounting for 58% (4.3 PgCO2) and 37% (2.7 PgCO2) respectively. Looking at the total carbon uptake, primary forests play a dominant role accounting for 79% (−5.9 PgCO2) and secondary forest growth for 17% (−1.2 PgCO2). Overall, according to our bottom-up estimates the Brazilian Amazon is a carbon sink until 2014 and a source from 2015 to 2018. In contrast according to the top-down approach the Brazilian Amazon is a source during the entire period. Both approaches estimate largest emissions in 2016. During the period where flux signs are the same (20152018) top-down estimates are approximately 3 times larger in 20152016 than bottom-up estimates while in 20172018 there is closer agreement. There is some agreement between the approachesnotably that the Brazilian Amazon has been a source during 20152018 however there are also disagreements. Generally, emissions estimated by the bottom-up approach tend to be lower. Understanding the differences will help improve both approaches and our understanding of the Amazon carbon cycle under human pressure and climate change.

Climate change can modify the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In Brazil, an example of a location with a high occurrence of landslides and with few studies on the subject is the municipality of Cunha, located in the State of São Paulo. In this way, the main aims of this study are twofold: 1) to identify and analyze areas susceptible to landslides and rainfall-induced landslide hazard in the municipality of Cunha; and 2) by using climate projections from the regional model Eta-HadGEM2-ES under RCP 4.5, to predict new extreme events and possible new landslides in this municipality, from 2024 to 2040. The susceptibility map has been prepared using selected physical-biotic environment elements whereas the hazard map has been produced using precipitation data. Using Eta-HadGEM2-ES climate projections under RCP 4.5, for precipitation events, results project the occurrence of 49 days with landslides for the period between 2024 and 2040. The outcomes of this study are considered of extreme relevance for the local community, public authorities, and decision-makers, to raise awareness regarding future urban planning strategies to prevent and mitigate the effects of catastrophes arising from natural hazard-induced disasters.

Most of the world’s nations (around 130) have committed to reaching net-zero carbon dioxide or greenhouse gas (GHG) emissions by 2050, yet robust policies rarely underpin these ambitions. To investigate whether existing and expected national policies will allow Brazil to meet its net-zero GHG emissions pledge by 2050, we applied a detailed regional integrated assessment modelling approach. This included quantifying the role of nature-based solutions, such as the protection and restoration of ecosystems, and engineered solutions, such as bioenergy with carbon capture and storage. Our results highlight ecosystem protection as the most critical cost-effective climate mitigation measure for Brazil, whereas relying heavily on costly and not-mature-yet engineered solutions will jeopardise Brazil’s chances of achieving its net-zero pledge by mid-century. We show that the full implementation of Brazil’s Forest Code (FC), a key policy for emission reduction in Brazil, would be enough for the country to achieve its short-term climate targets up to 2030. However, it would reduce the gap to net-zero GHG emissions by 38% by 2050. The FC, combined with zero legal deforestation and additional large-scale ecosystem restoration, would reduce this gap by 62% by mid-century, keeping Brazil on a clear path towards net-zero GHG emissions by around 2040. While some level of deployment of negative emissions technologies will be needed for Brazil to achieve and sustain its net-zero pledge, we show that the more mitigation measures from the land-use sector, the less costly engineered solutions from the energy sector will be required. Our analysis underlines the urgent need for Brazil to go beyond existing policies to help fight climate emergency, to align its short- and long-term climate targets, and to build climate resilience while curbing biodiversity loss.

Geodetic landmarks (GLs) are essential for obtaining precise height, horizontal coordinates, and the Earth’s gravity field. This study aims to assess the soil susceptibility of GLs for past, present, and future scenarios, considering the projected anthropogenic effect from Coupled Model Intercomparison Project, Version 6 (CMIP6). Therefore, the soil loss estimations were made for all the GLs in the southern region of Santa Catarina state in Brazil using the Revised Soil Loss Equation (RUSLE). Our results showed average soil loss decreasing from 1985 to 2020. There was an increase in GLs in the null class (soil loss = 0 t/ha/year), mainly caused by urban growth. A decrease occurred in the low (0 t/ha/year < soil loss ≤10 t/ha/year), very severe (soil loss >200 t/ha/year), severe (50 t/ha/year < soil loss ≤200 t/ha/year), and moderate classes (10 t/ha/year < soil loss ≤50 t/ha/year). In addition, most future scenarios projected an increase in soil loss susceptibility, which also increased the GLs’ susceptibility to soil loss from 2020 to 2100, albeit with lower values than the historical series. The soil loss remained stable in ssp126, slightly increased in ssp245 and ssp370, and increased in ssp585. The future scenarios only take into account changes in rainfall. Thus, the land cover change forecast would also be necessary for better analysis for future studies. Therefore, climate simulations can be used to understand the effects of climate change on soil erosion to support decision-making regarding GLs maintenance and the construction of new ones.

Little is known about the impact of hypoxia and anoxia during mycelial growth on tolerance to different stress conditions of developing fungal conidia. Conidia of the insect-pathogenic fungus Metarhizium robertsii were produced on potato dextrose agar (PDA) medium under normoxia (control = normal oxygen concentrations), continuous hypoxia, and transient anoxia, as well as minimal medium under normoxia. The tolerance of the conidia produced under these different conditions was evaluated in relation to wet heat (heat stress), menadione (oxidative stress), potassium chloride (osmotic stress), UV radiation, and 4-nitroquinoline-1-oxide (=4-NQO genotoxic stress). Growth under hypoxic condition induced higher conidial tolerance of M. robertsii to menadione, KCl, and UV radiation. Transient anoxic condition induced higher conidial tolerance to KCl and UV radiation. Nutritional stress (i.e., minimal medium) induced higher conidial tolerance to heat, menadione, KCl, and UV radiation. However, neither of these treatments induced higher tolerance to 4-NQO. The gene hsp30 and hsp101 encoding a heat shock protein was upregulated under anoxic condition. In conclusion, growth under hypoxia and anoxia produced conidia with higher stress tolerances than conidia produced in normoxic condition. The nutritive stress generated by minimal medium, however, induced much higher stress tolerances. This condition also caused the highest level of gene expression in the hsp30 and hsp101 genes. Thus, the conidia produced under nutritive stress, hypoxia, and anoxia had greater adaptation to stress.

Recent advancements in computational technologies have enhanced the importance of meteorological modeling, driven by an increased reliance on weather-dependent systems. This research implemented a lightning data assimilation technique to improve short-term weather forecasts in South America, potentially refining initialization methods used in meteorological operation centers. The main goal was to implement and enhance a data assimilation algorithm integrating lightning data into the WRF model, assessing its impact on forecast accuracy. Focusing on southern Brazil, a region with extensive observational infrastructure and frequent meteorological activity, this research utilized several data sources: precipitation data from the National Institute of Meteorology (INMET), lightning data from the Brazilian Lightning Detection Network (BrasilDAT), GOES-16 satellite images, synoptic weather charts from the National Institute for Space Research (INPE), and initial conditions from the GFS model. Employing the WRF-ARW model version 3.9.1.1 and WRFDA system version 3.9.1 with 3DVAR methodology, the study conducted three experimental setups during two meteorological events to evaluate the assimilation algorithm. These included a control (CTRL) without assimilation, a lightning data assimilation (LIGHT), and an adaptive humidity threshold assimilation (ALIGHT). Results showed that the lightning data assimilation system enhanced forecasts for large-scale systems, especially with humidity threshold adjustments. While it improved squall line timing and positioning, it had mixed effects when convection was thermally driven. The lightning data assimilation methodology represents a significant contribution to the field, indicating that using such alternative data can markedly improve short-term forecasts, benefiting various societal sectors.

O crescimento urbano acelerado e a consequente expansão da frota veicular têm exacerbado a poluição atmosférica em grandes cidades, tornando-se um problema de importância socioeconômica, ambiental e de saúde pública. O monitoramento da qualidade do ar desempenha um papel crucial nas políticas de controle ambiental, permitindo avaliar os níveis de poluentes e desenvolver tecnologias sustentáveis. Um estudo na Região Metropolitana do Vale do Paraíba, São Paulo, entre 2018 e 2022, analisou as variações anuais, sazonais e mensais de poluentes como CO, NO, NO2, O3, MP2.5 e MP10 em quatro estações, Guaratinguetá (1), Taubaté (1) e São José dos Campos (2), de monitoramento da qualidade do ar a partir do sistema QUALAR da CETESB. Para elaboração dos gráficos de médias anuais, mensais e médias horárias por estação do ano (sazonal), foram utilizadas as médias dos dados horários de cada estação de monitoramento durante o período de cinco anos. O tratamento dos dados dos poluentes do sistema Qualar da CETESB foi realizado usando a linguagem Python e os gráficos foram feitos usando a linguagem R. Houve aumento na concentração de NO em 2021 e 2022, enquanto o NO2 manteve praticamente os mesmos valores. São José dos Campos registrou as maiores concentrações de NO, NO2 e MP10. No outono e inverno, todos os poluentes, exceto O3, atingiram picos de concentração, devido às condições atmosféricas. Em geral, os poluentes ficaram dentro dos limites do CONAMA, mas NOx, MP2.5 e MP10 ultrapassaram os padrões da OMS, indicando desafios contínuos na gestão da qualidade do ar e a necessidade de medidas adicionais para proteger a saúde pública e o meio ambiente.

The focus of this work is on small municipalities (population below 50 thousand inhabitants) that cover around 87% of the territory of the Brazilian Legal Amazon (BLA). Based on a comprehensive integrated analysis approach using the three components hazard (climate extremes from CMIP6 future scenarios), exposure (directly affected population), and vulnerability (subdimensions of susceptibility and coping/adaptive capacity by using multidimensional indicators), the latter two using current datasets provided by the official Census IBGE 2022, we document a quantitative assessment of the risk R of natural disasters in the BLA region. We evidenced a worrying and imminent intensification of the curve of R in most Amazonian municipalities over the next two 25-year periods. The overall results of the highest proportions of R (total municipalities affected) pointed out the Amazonas, Roraima, Pará, and Maranhão as the main states, presenting projected categories of R high in the near future (2015 to 2039) and very high in the far future (2040 to 2064). The detailed assessment of the susceptibility and coping/adaptive capacity allowed us to elucidate the principal indicators that aggravate the degree of vulnerability: economy, the precariousness of urban infrastructure, medical services, communication, and urban mobility, whose combined factors, unfortunately, reveal a widespread poverty profile along the small Amazonian municipalities. Our scientific findings can assist decision makers in targeted strategies planning and public policies to minimize and mitigate ongoing and future climate change.