Peeking Beneath Clouds: An Investigation of Aerosol-Cloud Interactions over the Southeast Atlantic

The contribution to effective radiative forcing (ERF) of climate due to interactions between clouds and atmospheric aerosols remains highly uncertain after decades of research. One key piece of information needed to reduce this uncertainty and better understand such aerosol-cloud interactions (ACI) is knowledge about the vertical distribution of cloud condensation nuclei (CCN), or the subset of aerosols that activate into cloud droplets and directly impact cloud microphysical properties. Recently, many studies have taken advantage of lidar observations to glean information about the vertical distribution of aerosols and CCN. Specifically, Redemann & Gao (2024) developed a machine learning (ML) technique that uses lidar observables to predict CCN concentration (N_CCN) with mean relative errors of about 15% for the most complete sets of lidar observables.

In this study, we take advantage of the high vertical resolution of this ML-derived N_CCN dataset to investigate ACI over the Southeast Atlantic (SEA), where a seasonal biomass burning aerosol plume resides atop a semi-permanent deck of marine stratocumulus clouds. We assess the simultaneous impact of above- and below-cloud N_CCN on cloud top microphysical properties via clear-sky, cloud-adjacent lidar profiles and collocated polarimetric retrievals of cloud properties. Through this method we observe a decrease in cloud droplet effective radius (R_eff) and an increase in cloud droplet number concentration (N_d) associated with an increase in above-cloud N_CCN concentration within 100 m of the cloud top, which aligns well with previous in situ-based results. We find that the relationship between below-cloud N_CCN and cloud top microphysical properties is weaker than those with above-cloud N_CCN. Additionally, we find that the magnitude of these ACI are strongly dependent on lower tropospheric stability (LTS), with ACI_REFF = -∂ln(R_eff)/∂ln(N_CCN) and ACI_CDNC = dln(N_d)/dln(N_CCN) both decreasing by approximately 74% as LTS increases from 10 to 22 K. These findings demonstrate the importance of vertically resolved N_CCN in ACI studies and establish a remote sensing-based analysis method which future satellite-based studies can employ to investigate ACI.