We isolate frontal liquid clouds using a deep learning model which is trained on carefully selected SEVIRI satellite images using cloud optical depth and phase. We use simulations and observations from the UHSAS instrument at the ARM site to infer aerosol concentration at the cloud base, and satellite and surface remote sensing observations of cloud droplet concentrations. We then compare the Twomey effects in frontal and non-frontal clouds in observations and simulations.
We find that pristine frontal clouds in the North Atlantic are in some ways more sensitive to aerosol concentrations than non-frontal clouds. We expect aerosol effects on cloud fraction and cloud liquid water path to be muted in the strongly dynamically forced frontal clouds. However, the high updraft speeds in frontal clouds mean the albedo of these clouds is expected to be more sensitive to the number concentration of aerosols. We quantify these sensitivities, and we find Aitken-mode aerosols contribute up to ~40% of the total activated fraction in frontal clouds. Climate models generally simulate Aitken mode aerosol number concentrations inaccurately, and some widely used reanalyses consider only aerosol mass concentration as a prognostic variable. Our results suggest such models may not accurately represent how cloud albedo changes with changes in aerosol concentration, an important component of effective radiative forcing of climate. These results underscore the importance of aerosol activation diameter and Aitken-mode aerosols in aerosol-cloud interactions in frontal clouds, enhancing our understanding of their implications for climate.

