Abstract The primary uncertainty in anthropogenic climate forcing arises from a limited understanding of aerosol effects on cloud albedo, which in combination with other effects, is termed the radiative forcing from aerosol‐cloud interactions (RF aci ). Although climate models provide estimates of RF aci , observational constraints remain critical for reducing its uncertainty. Observationally based estimates of RF aci traditionally have been inferred from large‐scale satellite relationships between aerosol and cloud properties, but these approaches rely on substantial assumptions. Here, we develop a novel framework that investigates cloud responses to aerosol variability using Machine Learning (ML) derived cloud condensation nuclei (CCN) profiles from lidar, combined with polarimetric cloud retrievals. Our results demonstrate that the ML‐CCN product consistently improves estimates of CCN‐cloud relationships. By providing vertically resolved CCN information and avoiding complications from aerosol humidification and vertical heterogeneity, this approach yields tighter and more physically plausible constraints on aerosol‐cloud interactions than conventional methods based on aerosol optical properties.
Gao et al. (Sat,) studied this question.