Heatwaves (HWs) are among the most impactful climate extremes affecting tropical urban environments, yet local‑scale assessments of their future characteristics remain scarce in the Amazon. Here, we quantify projected changes in key HW indicators for the Belém urban core, eastern Amazon, using a station‑referenced framework based on a carefully evaluated and bias‑corrected ensemble of CMIP6 global models. HWs are identified from daily maximum air temperature (Tmax) using a smoothed day‑of‑year 95th percentile threshold calculated over the 1994-2023 baseline period. Changes in annual frequency, event duration, and thermal intensity are assessed for near‑future (2025-2049) and far‑future (2050-2074) periods under four Shared Socioeconomic Pathways (SSP1‑2.6, SSP2‑4.5, SSP3‑7.0, and SSP5‑8.5). A multi‑metric performance evaluation is first applied to select the best‑performing CMIP6 models for Tmax over Belém, followed by bias correction using Quantile Delta Mapping calibrated against local observations. Results show a clear, scenario‑dependent intensification of HW hazards. In the current climate, 126 HW events were detected, with pronounced interannual variability and a marked increase during the last decade. Near‑future changes are modest and variable across scenarios. In contrast, far‑future projections under the high‑emission SSP5‑8.5 pathway reveal a substantial transformation of the HW regime: annual frequency more than doubles relative to the baseline, and the 99th percentile of event duration increases from approximately 19 to 65 days (Δ = +46 days), with a 14.5% probability that any given HW will exceed the current extreme threshold. Peak Tmax during HWs increases from 37.0 °C to 37.55 °C (Δ = +0.55 °C), thus indicating an upper-tail amplification. Under the strong mitigation pathway SSP1‑2.6, changes in HW duration and intensity remain statistically indistinguishable from present‑day conditions. While this study does not assess impacts or adaptation capacity, the results provide a hazard‑based, scenario‑dependent characterization of future heatwave behavior in an Amazonian urban context, offering a quantitative foundation for subsequent assessments of climate risk in tropical cities.

Dengue outbreaks in Sao Paulo State are expanding, driven by a complex interplay of environmental and demographic factors. This study employs an ecological niche model (ENM) to investigate the drivers of dengue suitability across three major epidemic years: 2011, 2015, and 2019. Utilising a robust methodological framework, including semi-annual environmental data and spatially explicit cross-validation, it was found that population density was the most influential predictor of outbreak suitability in all three years (63.4 % contribution in 2011, 91.6 % in 2015, and 48.6 % in 2019). Secondary drivers shifted between epidemics, with maximum temperature being most relevant in 2011 (26.3 %) and the Normalized Difference Vegetation Index (NDVI) gaining importance in 2019 (18.5 %). Spatially, the analysis revealed a highly dynamic risk landscape, characterised by a massive statewide expansion of high-suitability zones in 2015, followed by a significant contraction and northward shift in 2019. The model's predictive performance was strong for 2011 and 2015, but declined in 2019, suggesting a less distinct environmental signature for that year's outbreak. These findings indicate that urban structure is the primary determinant of dengue suitability, while climatic and vegetation factors modulate risk differently across epidemics with varying characteristics.

Extratropical cyclones play a critical role in midlatitude climate dynamics, but their accurate simulation remains a challenge in climate models. While enhanced resolution simulations have been widely used to study these systems in the Northern Hemisphere, their performance in the Southern Hemisphere, particularly for intense cyclones, remains less explored. This study evaluates the impact of increased horizontal resolution on the simulation of extratropical cyclones over a key hotspot in southeastern South America, comparing HighResMIP models (25–50 km) with standard CMIP6 models (100–250 km). Using an objective feature-tracking algorithm applied to T42 vorticity at 850 hPa, we analyze spatial and frequency distributions, structure, and life cycle of extratropical cyclones, comparing against ERA5 reanalysis. Results show that finer resolution models reduce biases in cyclone number and track density, significantly improving the detection of intense cyclones, whereas coarser resolution models underestimate their frequency by more than 50

The large extension and diversity of the Brazilian Amazon biome hampers the assessment of the regional-scale carbon budget based solely on local observations. Considering the shortage of observations, this study aims to examine the carbon fluxes throughout the Brazilian Amazon biome using a process-based model (JULES, Joint UK land environment simulator). A sensitivity analysis detected five critical model parameters for the Amazon tropical broadleaf evergreen forest, optimized using carbon flux and meteorological data from four forest sites. The simulations with the new parametrization were compared with JULES default parameter values and with simulations of the Vegetation Photosynthesis and Respiration Model (VPRM). Net ecosystem exchange (NEE) and gross primary production (GPP) estimates were improved at all sites, reaching a Root Mean Squared Error (RMSE) about 30 % lower in comparison to the default version. The optimized parameter values varied among the four sites, indicating that a single parameterization for the whole Amazonia may not be adequate. JULES model parameters were spatialized for the Brazilian Amazonia, based on canopy height and leaf area index gridded data. Applying JULES with spatially dependent parameterization for the year 2021 resulted in a carbon sink of -1.34 Pg C yr-1. Regional differences were observed in the carbon fluxes, with a carbon source of 0.75 kg C m-2 yr-1 in the southwest and north, likely explained by increased ecosystem respiration in older and taller forests.

This study identified four hidden rainfall states in Northeast Brazil (NEB) during the austral autumn (March-May) from 1981 to 2015 using a Hidden Markov Model (HMM). Two states correspond to widespread dry (State 1) and wet (State 2) conditions, both exhibiting strong daily persistence and associations with large-scale atmospheric features, including the Intertropical Convergence Zone (ITCZ), the South American Monsoon System (SAMS), and the South Atlantic Subtropical High (SASH). The remaining two states are characterized by localized rainfall over the northwestern sector (State 3) and the coastal areas (State 4) of the NEB. Running correlation analyses revealed that the interannual frequency of these states is modulated by oceanic modes, whose influences exhibit non-stationary behaviour over time. The Extreme Gradient Boosting (XGBoost) algorithm showed a good fit for predicting state frequencies, with particular emphasis on State 1. SHapley Additive exPlanations (SHAP) values highlighted the dominant role of Atlantic variability modes, with a strong negative Atlantic Meridional Mode (AMM) phase and positive South Atlantic Ocean Dipole (SAOD) phase tending to modulate the frequency of wet and dry conditions. The El Ni & ntilde;o-Southern Oscillation (ENSO) shows a stronger association with State 3 and acts as a conditional modulator of State 1, becoming dominant during strong El Ni & ntilde;o events. State 4 reflects a more complex inter-basin structure, with alternating Atlantic and Pacific influences and a secondary contribution from the Indian Ocean. These findings enhance our understanding of the non-stationary and non-linear ocean-atmosphere interactions that shape seasonal precipitation variability in the NEB and provide valuable insights for improving seasonal climate assessments and climate risk management in the region.

This study investigates the mesoscale convective systems (MCSs) over South America in a future climate using two distinct modeling approaches: the Weather Research and Forecasting (WRF) regional model with dynamical downscaling and pseudoglobal warming, and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) global cloud-resolving model within the Coupled Model Intercomparison Project (CMIP6) High Resolution Model Intercomparison Project (HighResMIP) framework. Results are analyzed in terms of changes in MCS occurrence, precipitation contribution, maximum precipitation rates, and precipitation volume across subregions of South America. MCSs in the future climate tend to exhibit higher maximum precipitation rates and larger precipitation volumes across South America. This tendency toward MCS intensification with increasing temperatures is consistent with previous studies and represents a key aspect of future climate projections. The spatial extent of statistically significant changes is limited, indicating that robust signals common to both models are restricted to specific regions and seasons. Overall, increases in MCS occurrence and precipitation proportion are mainly projected over Northwest South America (NWS) and North South America (NSA), particularly during the austral summer (DJF). Over southern Brazil, they are projected to increase during the austral winter (JJA), suggesting that warmer temperatures may influence the winter climate at the mesoscale level over that region. In contrast, decreases in both variables are found over the South America Monsoon region (SAM), especially during the transition months from dry to wet season (SON) and wet to dry season (MAM).

In the year 2023, the Earth experienced the highest near-surface temperature anomalies ever recorded until then. In addition, several extreme weather and climate events occurred around the world, including in Brazil. In this context, the primary objective of this study is to analyze the anomalous temperature and precipitation patterns observed in Brazil during 2023, along with the most significant extreme events. Different datasets and methodologies were applied. The north coast of the state of São Paulo had the highest accumulation of rainfall recorded in Brazil in a single day. September, October, and November 2023 experienced the large precipitation deficits over the Amazon region, leading to a very intense and prolonged drought. The south of Brazil was affected by a large amount of precipitation in a short time, associated with cyclones, resulting in fatalities and economic losses. Southeast and Central-West Brazil experienced two intense heatwaves in the austral spring, breaking daily temperature records in major cities like São Paulo and Rio de Janeiro. Overall, this study describes the main physical processes responsible for these extremes, along with the socioenvironmental impacts caused by most of them.

Abstract Mesoscale convective systems (MCSs) are organized complexes of thunderstorms that significantly influence atmospheric energy redistribution and precipitation patterns globally. Characterized by extensive cloud formations, including cumulonimbus and stratiform clouds, MCSs are responsible for various weather phenomena such as heavy rainfall, lightning, and severe weather events including damaging winds and tornadoes. These systems are particularly prominent in South America, where they are critical to the region’s hydrological cycle, contributing substantially to annual precipitation in areas such as the La Plata basin, the Amazon, and Colombia. MCSs are sustained by dynamic atmospheric processes that involve warm air rising in updrafts and cool air descending in downdrafts, enabling their persistence over extended periods. MCSs encompass various types, including mesoscale convective complexes (MCCs) and squall lines. MCCs, which are more frequent in southeast South America, are larger and last longer compared to those typically observed in other regions, while squall lines consist of elongated clusters of storms with a leading edge of intense convective activity followed by a broad stratiform area. The detection and analysis of MCSs often rely on satellite remote sensing techniques due to limited availability of in-situ radar data. These systems can generate extreme weather events such as derechos and bow echoes, significantly impacting regional weather patterns. The formation of Amazon squall lines is influenced by several synoptic-scale factors, including easterly waves, sea breezes, and low-level jets, which facilitate their movement, particularly from March to May. Although many squall lines dissipate near coastal areas, some can extend considerable distances inland. Conversely, the frequency of MCSs diminishes during the South American monsoon system, particularly from October to November. Distinct characteristics of MCSs in subtropical regions, influenced by the South American Low-Level Jet, often result in larger and more enduring systems than those in tropical areas. Climate variability, notably the El Niño-Southern Oscillation and the Madden-Julian Oscillation, also modulates MCS frequency and intensity. Projections related to climate change suggest potential decreases in MCS activity over the Amazon while indicating possible increases in subtropical regions. Understanding MCSs occurrences and behavior is essential for grasping their impacts on regional weather dynamics and precipitation distribution.

Cut-off low (COL) pressure systems significantly influence local weather in regions with high COL frequency, particularly in western North America. Nonetheless, future changes in COL frequency, intensity, and precipitation patterns remain uncertain. This study examines projected COL changes and their drivers in western North America under a high greenhouse gas concentration pathway (SSP585) using a multi-model ensemble from CMIP6 and a feature-tracking algorithm. We compare historical simulations (1980–2009) and future projections (2070–2099), revealing a marked increase in COL track density during summer in the northeast Pacific and western United States, while a strong decrease is projected for winter, associated with shifts in jet streams. Climate models project an increase in COL-related precipitation in future climate, with winter and spring experiencing more intense and localized precipitation, while autumn showing a more widespread precipitation pattern. Additionally, there is an increased frequency of extreme precipitation events, though accompanied by large uncertainties. The projected increase in extreme precipitation highlights the need to understand COL dynamics for effective climate adaptation in affected areas. Further research should aim to refine projections and reduce uncertainties, supporting better-informed policy and decision-making.

This project aims to investigate anomalies in Earth's gravity field associated with ice loss in Antarctica and its relationship with the Antarctic Oscillation Index (AAO). The research will explore these phenomena and their implications for the South Atlantic region through a study of teleconnections. The methodology will include a linear regression analysis to evaluate the relationship between Bouguer anomalies and the topography of the Antarctic continent, aiming to identify representative locations of mass change potentially linked to ice melt. At these sites, data from the GRACE and GRACE-FO missions will be used to conduct an analysis of temporal variations in the gravity field, gravity anomalies, and their relationship with the AAO index. Satellite observations will be employed to assess interannual variations in sea surface temperature (SST), enabling the identification of teleconnections in the South Atlantic.

Promoting sustainable development depends on addressing the challenges generated by climate change. The nature of these complex and interconnected challenges requires knowledge generation and the strengthening of interdisciplinary research networks. Research developed by the National Institute for Science and Technology for Climate Change Phase 2 (INCT-MC2) project, with its six thematic lines (or subcomponents) and three integrative (crosscutting) topics, provides a deep understanding of the interactions between the different aspects of climate change and sustainable development. Emphasis is placed on the impacts on water, energy, food security, health, ecosystems, and disaster risk reduction under urban development. These core topics are transversally integrated by climate modeling, economics, education, and communication research. The article analyzes the contribution of the INCT-MC2 scientific production and knowledge towards a better understanding of climate change and its impacts on various strategic sectors in Brazil. The results of the INCT-MC2 go along with the Sustainable Development Goals. Our analysis is based on a conceptual model that argues that the economy and society depend on the biosphere and ecosystem services and that their integrity is fundamental for global sustainability.

O desenvolvimento humano, iniciado há cerca de 300 mil anos, sempre ocorreu em paralelo às variações naturais do clima. No entanto, desde a Revolução Industrial, a ação antrópica intensificou o efeito estufa, elevando a temperatura média global e alterando os padrões climáticos, resultando em impactos severos, como o aumento das temperaturas, alterações nos padrões de precipitação e aumento na frequência de eventos severos de tempo, como tempestades, tornados e furacões. Este artigo explora as principais mudanças climáticas atuais e como elas são monitoradas por meio da análise de dados históricos. Além disso, apresenta projeções de modelos climáticos que permitem antecipar cenários futuros, incluindo o aumento das temperaturas e mudanças nos padrões de precipitação, especialmente na América do Sul. Por fim, o texto destaca a importância de ações concretas para mitigar as emissões de gases de efeito estufa, responsáveis pelas mudanças climáticas atuais, utilizando tecnologias já disponíveis. Concluímos que, para enfrentar a emergência climática, é fundamental integrar os recursos humanos e naturais do Brasil, aliados ao conhecimento científico, a fim de implementar soluções eficazes para reduzir os impactos e proteger as populações mais vulneráveis.

The Amazon biome is one of the largest carbon reservoirs, a relevant carbon sink in the world. The large extension and diversity of the Amazon biome hampers the assessment of regional-scale carbon budget based solely on local observations. Land surface models can provide carbon flux estimates, but they require proper calibration to represent the dynamics of the different ecosystems, abiotic conditions and vegetation characteristics in the Amazon Basin. One of the most important land surface model is JULES being increasingly used in tropical forests to estimate carbon fluxes. However, there is a lack of parameterization information that can be applied to the Amazon biome. Thus, this study presents an optimization of JULES main sensitivities parameters for different sites of the Amazon biome. For this attempt, we selected four Eddy-covariance flux towers as a reference based on different regions of the Amazon biome: K34 (Manaus, 2.614S/60.12W); K67 (Santarem, 2.85S/54.97W); RJA (Reserva Jaru, 10.08S/61.93W and ATTO (São Sebastião do Uatumã, 2.15S/59.03W). The variables analyzed to reproduce the carbon dynamics were the Net Ecosystem Exchange (NEE), Gross Primary Production (GPP) and eutrophic respiration (RESP) during one year of analysis. JULES most sensitivities parameters adjusted were related to the Upper-temperature threshold for photosynthesis (tupp_io); Scale factor for dark respiration (fd_io); The maximum ratio of internal to external CO2 (f0_io) and Quantum efficiency (alpha_io). The optimization was made using the Nelder-Mead method and after a leave-one-out cross-validation method was implemented to evaluate the simulation efficiency in each site. Also, the new parametrization in each site was compared with the default version of JULES and with another model Vegetation Photosynthesis and Respiration Model (VPRM). We selected the Wilmott index of agreement (d) and the Root Mean Square Error (RMSE) to analyze simulation efficiency. The Nelder-Mead optimization method reduced the error in GPP simulations in each Tower in comparison to the two models evaluated however the new parametrization of JULES was not able to improve RESP in these sites. However, the optimization procedure presented better results in NEE in each tower evaluated in the Amazon biome being the ATTO tower that demonstrated the most efficient simulations (d =0.60; RMSE = 2.03 g C m-2 day-1) in comparison to the default version (d= 0.52; 3.09 g C m-2 day-1) and VPRM (d = 0.58; 2.29 g C m-2 day-1). In general, results demonstrated that the new parametrization of JULES reduced the error of simulation compared to the last version of JULES for tropical forests and better represented the seasonality compared to the VPRM model.

Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10−11 K m−2; AdvT < −1 × 10−4 K s−1), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences.

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s−1), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes.

Based on statistical analyses applied to official data from the Digital Atlas of Disasters in Brazil over the last 25 years, we evidenced a consistent intensification in the annual occurrence of natural disasters in the state of Pará, located in the eastern Brazilian Amazon. The quantitative comparison between the averages of the most intense period of disasters (2017 to 2023) and the earlier years (1999 to 2016) revealed a remarkable percentage increase of 473%. Approximately 81% of the state’s municipalities were affected, as indicated by disaster mapping. A clear seasonal pattern was observed, with Hydrological disasters (Inundations, Flash floods, and Heavy rainfall) peaking between February and May, while Climatological disasters (Droughts and Forest fires) were most frequent from August to October. The catastrophic impacts on people and the economy were documented, showing a significant rise in the number of homeless individuals and those directly affected, alongside considerable material damage and economic losses for both the public and private sectors. Furthermore, we conducted a comprehensive composite analysis on the tropical ocean–atmosphere dynamic structure that elucidated the various triggering mechanisms of disasters arising from Inundations, Droughts, and Forest fires (on seasonal scale), and Flash floods and Heavy rainfall (on sub-monthly scale) in Pará. The detailed characterization of disasters on a municipal scale is relevant in terms of the scientific contribution applied to the strategic decision-making, planning, and implementation of public policies aimed at early risk management (rather than post-disaster response), which is critical for safeguarding human well-being and strengthening the resilience of Amazonian communities vulnerable to climate change.

The study of Rossby wave propagation in strong jet stream waveguides is essential, as extreme weather events are associated with persistent atmospheric patterns at the surface which may be favored by quasi stationary Rossby waves in the upper troposphere through these pathways. But so far, all the studies are mostly for winter and summer seasons. Therefore, in the present study, we extended earlier works to the transition seasons. The waveguide patterns in both hemispheres during the spring and autumn transition seasons are explored using numerical simulations from a baroclinic model with six selected forcings in the 1979-2016 period. The results show that stronger subtropical jet streams are found in boreal and austral spring associated with stronger wave propagation. Particularly, stronger eddy kinetic energy and wave activity flux are found in boreal spring from the north of Middle East to eastern North Pacific, associated with stronger subtropical Asian jet, and in austral autumn in western Pacific region, associated with greater extension of polar jet. Interhemispheric propagation is verified in spring season in both hemispheres, through the equatorial eastern Pacific and Atlantic ducts, with a northwest-southeast orientation.

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.

In this study we defined the association of SACZ episodes with ENOS phases during the 2000–2021 summer seasons, considering November to March months, and the circulation associated patterns. Each SACZ episode was classified when OLR values were below 220 W m− 2 and precipitable water values above 45 kg m− 2 for more than three consecutive days. The association between ONI and the annual number of days with SACZ and the number of SACZ episodes shows linear correlation of -0.44 and − 0.34, respectively, showing the prevalence of SACZ episodes during the La Niña phase. Analysis of the entire series shows a linear annual mean increase of 16 days with SACZ episodes from 2000 up to 2021. Sea level pressure anomalies between El Niño and La Niña periods present meridional dipole patterns between northern and southern South Atlantic, including the southamerican continent. OLR anomalies fields present negative (positive) values during LN (EN) periods over the northern South America extending to SACZ areas, helping to explain the higher number of SACZ episodes in LN (63 episodes) than in EN (29 episodes) periods. Both SLP and OLR anomalous patterns are associated with higher moisture convergence in the SACZ area in LN than in EN periods. Analysis of very dry (2014–2015) and very rainy (2020–2021) summer seasons over southeastern South America, illustrating El Niño and La Niña periods, respectively, shows strengthened upward movement over southwestern South America and weakened upward movement over northeastern in the former summer season and the opposite signals in the second one. These patterns were associated with typical circulation at low and high tropospheric levels: the upper level cyclonic vortex and the subtropical South Atlantic high pressure displacement to continental areas during the very dry period, 2014–2015, and the displacement of both systems, in upper and low levels, to the ocean during the very rainy period, 2020–2021.