Aerosols and their interactions with clouds and radiation remain some of the largest sources of uncertainty in Earth’s climate forcing. Earth system models (ESMs) continue to showcase systematic process biases, and recent advances in ESMs and machine learning (AI/ML) techniques have significantly enhanced our ability to analyze and interpret aerosol and cloud microphysics. This session aims to highlight new insights into aerosol-cloud-radiation interactions modeling across a wide range of spatial and temporal scales. Relevant topics include but are not limited to:

(1) modeling of aerosol processes, such as cloud condensation and ice nucleation, new particle formation, and chemical ageing, which can affect clouds and radiative forcing,

(2) covariability between aerosols, liquid, mixed-, and ice phase clouds, and radiation,

(3) theoretical and modeling efforts related to the development of microphysical parameterizations,

(4) uncertainty quantification for aerosol and cloud microphysical processes, and

(5) development and application of AI/ML techniques advancing aerosol-cloud-radiation research.