Aerosol-cloud Interactions: Overcoming a Barrier to Projecting Near-term Climate Evolution and Risk

Aerosol–cloud interactions (ACI) are a major source of uncertainty in climate science, critically affecting our ability to project near-term climate evolution and assess societal risks. ACI influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. Uncertainty from ACI hampers robust policy advice across multiple domains—from estimating remaining carbon budgets and climate sensitivity, to anticipating regional extreme events and evaluating climate interventions such as solar radiation modification. Despite these important issues, ACI is often underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification.

This talk outlines a path forward to overcome these barriers by leveraging emerging opportunities in satellite remote sensing, ground-based and airborne observations, high resolution climate modeling, and machine learning. We identify key areas where rapid progress is feasible, including improved retrievals of cloud microphysical properties, better representation of natural aerosols in a warming world, and enhanced integration of observational and modeling communities. Even as anthropogenic aerosol and its impacts on clouds is reducing owing to emissions controls, addressing ACI uncertainties remains essential for refining climate projections, supporting effective mitigation and adaptation strategies, and delivering actionable science to policymakers in a rapidly changing climate system.