Ambient air pollution remains a major public health concern, contributing to millions of premature deaths worldwide according to the World Health Organization. Regional air quality assessments are commonly performed using chemical-transport models that require substantial computational resources due to their detailed representation of atmospheric processes. This study explores the feasibility of applying the second-generation dispersion model URBAIR^® as a computationally efficient alternative for long-term regional air quality simulations. URBAIR^® was implemented for three European case studies within the DISTENDER project to simulate particulate matter (PM₁₀) and nitrogen dioxide (NO₂) concentrations for 2018 under different spatial and temporal resolutions. Model performance was assessed against background monitoring stations and compared across grid configurations. The results show that the model successfully reproduces annual mean concentration patterns, particularly in urban areas, with R² values ranging mostly between 0.2–0.6, RMSE between 16–36 µg.m⁻³, and mean bias from −8 to 5 µg.m⁻³, indicating overall acceptable statistical performance. Within the specific configurations evaluated in this study, increasing spatial resolution was not consistently associated with improved model performance. However, because spatial resolution covaried with other factors including meteorological temporal resolution, domain characteristics, and monitoring station density, the present analysis does not allow the independent effect of spatial resolution to be isolated. Moreover, a key limitation of the modeling approach is the absence of chemical transformation processes, which may affect the representation of secondary pollutants. Overall, the dispersion-based modeling framework substantially reduces computational demand and input complexity, proving suitable for long-term exposure and climate-related applications when annual average concentrations are the primary objective. In future studies, the modeling approach should be applied to other case studies to consolidate the findings of this exploratory work so that it may contribute to sustainability-oriented decision making by facilitating regional assessments of air quality and potential health impacts related to climate change.

Insect-driven forest disturbances are important contributors to tree mortality and biomass losses in temperate and boreal regions. With the rising temperatures and shifting precipitation patterns, insect induced tree mortality is expected to increase in many regions. Insect outbreaks not only influence tree cover and carbon stocks, but , through their impact on tree functioning, also influence land-atmosphere exchanges of water and energy, which in turn can impact atmospheric properties. While insect outbreaks can impact very large regions, most observational studies focus on small regions and individual events. Here, we aim to provide an observation-based regional synthesis of the impact of insect-driven tree mortality on land-atmosphere water and energy exchanges, focusing on western USA. For this, we analyse satellite-based data (MODIS) on evapotranspiration (ET), albedo, land-surface temperature (LST) and snow cover for insect-affected regions between 2001-2022. Preliminary results indicate an increase in summer LST in areas affected by more severe insect-driven tree mortality, along with a decrease in ET, compared to the years before the mortality events. These differences can be partly explained by reduced snow cover in winter, which contributes to decreased winter albedo in insect-affected areas. These effects are not only limited to the outbreak event, but also show persistent trends in the subsequent years.

Climate extremes have direct impacts on ecosystems, for example reduced productivity during heat-drought events, but often their impact is amplified by compounding ecosystem disturbances, such as wildfires or insect outbreaks. Through their impact on ecosystem functioning and structure, compound climate extremes and ecosystem disturbances modulate land-atmosphere exchanges of water, energy, and greenhouse-gases, which in turn influence atmospheric properties from local to global scales, thus feeding-back to climate change. Recent observations indicate that such feedbacks are, however, non-negligible and might result in a much weaker role of the biosphere in climate change mitigation, especially under high emission scenarios.Currently, ecosystem disturbances are not appropriately represented in most Earth System Models, which implies that extreme-event induced climate-biosphere feedbacks are likely overlooked in future climate simulations. Here, we will examine observation-based evidence for extreme-event induced climate-biosphere feedbacks through CO2 and land-atmosphere water and energy exchanges at different scales. We will then showcase recent developments in simulating some of these feedbacks in a global land-surface model and discuss the resulting implications for climate change adaptation and mitigation.