Optimal choice of proxy for cloud condensation nuclei reduces uncertainty in aerosol–cloud–climate forcing

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.