This study quantitatively assesses the sensitivity of cloud droplet number (CDNC) to errors in cloud condensation nuclei (CCN) predictions that arise from application of K¿hler theory. The CDNC uncertainty is assessed by forcing a droplet activation parameterization with a comprehensive dataset of CCN activity and aerosol size and chemical composition obtained during the ICARTT field campaign in August 2004. Our analysis suggests that, for a diverse range of updraft velocity, droplet growth kinetics and airmass origin, the error in predicted CDNC is (at most) half of the CCN prediction error. This means that the typical 20--50% error in ambient CCN closure studies would result in a 10--25% error in CDNC. For the first time, a quantitative link between aerosol and CDNC prediction errors is available, and can be the basis of a robust uncertainty analysis of the first aerosol indirect effect.