Forecasting urban air quality is important for protecting public health, but current model forecasts are often limited by an inaccurate prescription of pollutant emissions from human activities. We developed a new approach that improves air quality forecasts by adjusting emission prescription based on observed concentrations in urban agglomerations for key pollutants such as nitrogen oxides, sulfur dioxide, carbon monoxide, particulate matter, and volatile organic compounds. Applying this new approach to the São Paulo metropolitan area, Brazil, we compared forecasted and observed pollutant concentrations (from 6 February to 17 April 2023). Using adjusted emission significantly improved air quality forecasts for São Paulo, especially for ozone levels after adjusting estimates of volatile organic compound emissions. However, the forecast of particulate matter concentrations remained challenging due to their links with gaseous pollutants. Our study demonstrates the potential of using observed concentrations in urban agglomerations to improve air quality forecasts. Extending this approach to other urban agglomerations can help refine emission estimates and improve regional air quality forecasts, enabling better decision making for health protection.

Black carbon (BC) and brown carbon (BrC) are light-absorbing aerosols with significant climate impacts, but their absorption properties and direct radiative effect (DRE) remain uncertain. We simulated BC and BrC absorption during the intense Canadian boreal wildfires in June 2023 using an enhanced version of CHIMERE model. The study focused on a domain extending from North America to Eastern Europe, including a significant portion of the Arctic up to 85°N. The enhanced model includes an updated treatment for the BC absorption enhancement and a BrC ageing scheme accounting for both browning and blanching through oxidation. When compared to observations, the updated model accurately captured aerosol optical depth (AOD) at multiple wavelengths, both near the wildfires and during transoceanic transport to Europe. Improvements were observed in the simulation of absorbing aerosol optical depth (AAOD) compared to the control model. The all-sky regional direct radiative effect (DRE) for June 2023 attributed to the intense Canadian wildfires, was reduced from -2.1 W/m² in the control model to -1.9 W/m² (-2.0/-1.8 W/m², ±5%), in the enhanced model, indicating an additional warming effect of +0.2 W/m² (about +10%) due to advanced schemes used for the BC and BrC absorption. The results indicate the importance of an accurate simulation of aerosol absorption in regional climate predictions, especially during large-scale biomass burning events. They also suggest that traditional models could overestimate the cooling effect of boreal wildfires, highlighting the need for improvement of aerosol parameterization to better predict the DRE and develop effective mitigation strategies.

A key challenge in accurate simulations of desert dust emission is the parameterization of the threshold wind speed above which dust emission occurs. However, the existing parameterizations yield a unrealistically low dust emission threshold in some climate models such as the Community Earth System Model (CESM), leading to higher simulated dust source activation frequencies than observed and requiring global tuning constants to scale down dust emissions. Here we develop a more realistic parameterization for the dust emission threshold in CESM. In particular, we account for the dissipation of surface wind momentum by surface roughness elements such as vegetation, rocks, and pebbles, which reduce the wind momentum exerted on the bare soil surface. We achieve this by implementing a dynamic wind drag partition model by considering the roughness of the time-varying vegetation as quantified by the leaf area index (LAI), as well as the time-invariant rocks and pebbles using satellite-derived aeolian roughness length. Furthermore, we account for the effect of soil size on dust emission threshold by replacing the currently used globally constant soil median diameter with a spatially varying soil texture map. Results show that with the new parameterization dust emissions decrease by 20–80% over source regions such as Africa, Middle East, and Asia, thereby reducing the need for the global tuning constant. Simulated dust emissions match better in both spatiotemporal variability and emission frequency when compared against satellite observed dust activation frequency data. Our results suggest that including more physical dust emission parameterizations into climate models can lessen bias and improve simulation results, possibly eliminate the use of empirical source functions, and reduce the need for tuning constants. This development could improve assessments of dust impacts on the Earth system.