Aerosols can strongly influence atmospheric circulation, and categorizing it into circulation types (CTs) helps in understanding the relationship between atmospheric forcing and surface conditions. However, few studies have considered the impact of interactive aerosols on atmospheric dynamics from a climatic perspective. This contribution aims to assess whether simulations with interactive aerosols (online solving of aerosol–radiation interactions, ARI, and aerosol–radiation–cloud interactions, ARCI) significantly impact atmospheric dynamics over Europe during winter compared to conventional regional climate models with prescribed aerosols. For that, Principal Component Analysis (PCA) has been applied to reduce the dimensionality of the problem in order to cluster different weather patterns. Results showed significant differences in the two predominant patterns, characterized by a western zonal flow (CT1) and a low-pressure system centered in Italy (CT2). The ARI experiment revealed a substantial reduction of surface level pressure over central-eastern Europe for CT1, resulting in a southward shift of the flux direction, and an increase in pressure over Scandinavia for CT2. The ARCI experiment exhibited a similar, but weaker effect. Furthermore, the study demonstrated the impact of aerosols on the frequency of different CTs and on the concentration of black and white aerosols. The findings of this study emphasize the significant role of aerosols in the atmospheric system and the need for further research to reduce uncertainty in meteorological and climatic experiments, particularly in the context of mitigating climate change.

Extreme fire weather (hot, dry, and windy conditions) has intensified globally, yet formally attributing this trend to anthropogenic climate change remains challenging. Here, we analyze global trends in extreme fire weather days (FWI95d, annual count of days with Fire Weather Index above the 95th percentile) over 1980–2023, using climate model ensembles, observational data, and fingerprint detection techniques. We find that the observed increase in extreme fire weather bears a clear externally forced signal, detectable at 99% confidence above natural variability and attributable to human-induced climate change. This emerging human-induced fingerprint on extreme fire weather highlights a benchmark for climate science and underscores the urgency of integrating these insights into wildfire risk management and adaptation strategies.

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity.

Compound climate events involving concurrent heatwaves and air pollution episodes represent a growing threat to public health. This study identifies the large-scale synoptic patterns most frequently associated with such events and their impact on summertime excess mortality in mainland Spain from 2015 to 2022. Using a combination of principal component analysis and cluster techniques applied to ERA5 reanalysis data, we classify 12 circulation types (CTs). Two of them, associated with Iberian thermal lows and upper-level ridges (CT1 and CT4), are linked to simultaneous anomalies in maximum temperature (+4-6 °C), ozone (+10-25μg m^-3) and PM₁₀ (+10-40μg m^-3), together accounting for 25% of summer days. To assess the impacts on mortality, the index M5d is proposed, representing the 5-day forward sum of all-cause deaths. CT1 and CT4 are associated with standardised M5d anomalies exceeding 1.5-2.0 standard deviations in several provinces, indicating a strong health response. Subtypes with Omega block configurations (e.g., CT1.3, CT4.4) show the highest efficiencies, with 70%-80% of days exceeding extreme thresholds for multiple variables and up to 50% efficiency in triggering excess mortality. These findings strengthen the scientific understanding of compound weather-health interactions in Southern Europe.

Although the atmospheric boundary layer height (ABLH) is a highly relevant parameter for various meteorological studies, the analysis of its behavior remains undersampled in South America, especially in Brazil. In this context, this work presents a monthly characterization of the ABLH during the convective period (Convective Boundary Layer Height-CBLH) using radiosonde data and a comparison between the monthly patterns obtained from ERA5 and COSMIC-2 data. The results demonstrate that, based on radiosonde data, the CBLH can be grouped into six regions (Northern Amazon, North, Northeast, Midwest, Southeast, and South), with seasonality varying according to the continentality and the climate to which they are exposed. The ERA5 and COSMIC-2 data show considerable agreement for most of the year [average absolute difference of [362 ± 182] m] and demonstrate the same seasonality observed in radiosondes for the North Amazon, North, Northeast, Southeast, and South regions. The highest discrepancies between ERA5 and COSMIC-2 occur during the fire season, mainly at Midwest region, reaching 802 m in July, likely linked to the sensitivity of the COSMIC-2 to fire plumes.

Extreme weather events, such as stagnation conditions and heatwaves, are known to exacerbate hazardous air quality situations by promoting the accumulation and persistence of pollutants like ozone (O3) in the near-surface environment. In particular, during the summer over the Iberian Peninsula (IP), extreme O3 values often exceed the 180 µg/m3 threshold, significantly impacting air quality and public health. While meteorological factors like high temperatures and radiation are important drivers of these events, classifying them based solely on synoptic weather patterns fails to capture the full scope of the risks involved. Even when events are classified under the same synoptic category, O3 concentration can vary greatly. This implies that variability is not only due to direct meteorological influences, but also other factors related to transport, previous concentrations or processes linked to surface conditions can modify emissions of compouds that affect O3 formation. In this regard, biogenic volatile organic compound (BVOC) emissions from vegetation can significantly influence O3 formation in a complex and non-linear way. The amount and type of vegetation, as well as the available soil moisture, modulate these emissions.In this study, we conducted sensitivity experiments using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem, version 4.6.0) to explore how factors like soil moisture and vegetation amount influence extreme O3 events over the IP. We first performed simulations varying leaf area index (LAI) and soil moisture (SM) by multiplying the original fields of SM (all layers) and LAI by a factor ranging from 0.25 to 2. The results indicate that decreasing the available soil water (-50%) increases BVOCs emission rates (20% spatial and temporal average), which is reflected in an increase in daily maximum O3 concentrations (10%). On the other hand, increasing the vegetation (50 %) leads to an increase in BVOC emission rates (10%), as well as in O3 concentrations (up to 10 %). The combined experiments exacerbated the changes, although most of the time they are smaller than the sum of the isolated experiments. Regional Climate Models (RCMs) often use climatological variables to characterize vegetation. Some studies show that differences in vegetation fraction with respect to climatological values can reach up to 40% over the IP. We performed a series of simulations of extreme O3 occurrences, employing both observed and climatological values of vegetation. Preliminary results indicate that such differences in vegetation moderately modify final O3 concentrations, in most cases obtaining better agreement with observations. Acknowledgements: Project PID2020-115693RB-I00 funded by MCIN/ AEI /10.13039/501100011033