Aerosols play a large role in the Earth-Atmosphere system through their complex interactions with clouds, radiation, precipitation, air quality and ecosystems. Accurately representing these interactions is critical, as aerosols drive feedbacks that span weather to climate timescales. Achieving this requires models that robustly capture the full aerosol lifecycle— from emissions and transport to chemical transformation and removal— across diverse aerosol types, including dust, sea salt, volcanic ash, biomass burning smoke, and anthropogenic pollutants.

Modeling aerosols on a global scale, with a growing demand for city-scale information, increases the complexity, requiring high-resolution representations while managing significant computational constraints. Advances in data assimilation, parameterization, and machine learning offer promising pathways to bridge these gaps.

This session encourages contributions highlighting novel techniques that improve the representation of aerosol species or processes within global models, innovative assimilation of observational datasets, as well as status updates from operational aerosol prediction systems and reanalysis activities.

• GC - Global Environmental Change