Optimal choice of proxy for cloud condensation nuclei reduces uncertainty in aerosol-cloud-climate forcing
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Aerosol-cloud interactions (ACI) remain the largest uncertainty in anthropogenic climate forcings. Observation-based estimates of instantaneous radiative forcing from ACI (RFaci; the Twomey effect) rely on the choice of aerosol quantities as proxies for cloud condensation nuclei (CCN) concentrations, which differ in their ability to represent cloud-base CCN and data accuracy. Using diverse observations and aerosol-climate models, we evaluate the utility of different proxies with two independent approaches. Both approaches reveal that surface CCN exhibits the smallest bias in predicting RFaci (+5%), followed by aerosol index, surface sulfate and column CCN with similar biases of +25%, while aerosol optical depth and column sulfate show the largest biases (−60% and +92%). Constraining RFaci with the optimal proxy reduces uncertainty from 66 to 43%, yielding a less negative RFaci (−1.0 W m−2) than the unconstrained case (−1.2 W m−2). Our findings highlight the crucial role of proxy constraint in reconciling and improving RFaci estimates.
Date Issued
2026-02-20
Date Acceptance
2026-01-14
Citation
Science Advances, 2026, 12 (8)
ISSN
2375-2548
Publisher
American Association for the Advancement of Science (AAAS)
Journal / Book Title
Science Advances
Volume
12
Issue
8
Copyright Statement
© 2026 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41706857
Publication Status
Published
Coverage Spatial
United States
Article Number
eaea4828
Date Publish Online
2026-02-18