Hailing Jia

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. Using optimal proxies for cloud condensation nuclei reduces the uncertainty in aerosol-cloud forcing estimates by one-third.