Aerosol‐cloud interactions: overcoming a barrier to projecting near‐term climate evolution and risk
Author(s)
Type
Journal Article
Abstract
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. These interactions influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. This uncertainty 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. In many cases, the influence of ACI is either underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification. This perspective 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.
Date Issued
2026-02-01
Date Acceptance
2026-01-20
Citation
AGU Advances, 2026, 7 (1)
ISSN
2576-604X
Publisher
American Geophysical Union (AGU)
Journal / Book Title
AGU Advances
Volume
7
Issue
1
Copyright Statement
© 2026. The Author(s). This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Publication Status
Published
Article Number
e2025AV001872
Date Publish Online
2026-02-10