Information about the vertical distribution of cloud condensation nuclei (CCN) concentrations (N_CCN) is necessary for accurately quantifying aerosol-cloud interactions (ACI), as is constraining environmental conditions to separate aerosol effects from meteorological influences on clouds. Motivated by previous findings from the Southeast Atlantic, we investigate ACI and their dependence on lower tropospheric stability (LTS) using a remote sensing-based data set. Utilizing a new machine learning (ML) method for retrieving N_CCN from High Spectral Resolution Lidar 2 (HSRL-2) observables, we assess the simultaneous impact of above- and below-cloud N_CCN on cloud microphysical properties via clear-sky, cloud-adjacent lidar profiles and collocated polarimetric retrievals of cloud properties. 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. Additionally, we find that the magnitude of these ACI are strongly dependent on LTS. We calculate ACI_REFF = -∂ln(R_eff)/∂ln(N_CCN) and ACI_CDNC = dln(N_d)/dln(N_CCN) and find that ACI_REFF decreases from 0.161 to 0.042 (-73.9 %) and ACI_CDNC decreases from 0.452 to 0.116 (-74.3 %) as LTS increases from 10 to 22 K. Additionally, we find that the relationship between below-cloud N_CCN and cloud top properties is weak and that above-cloud N_CCN – cloud property relationships are similar for cloud edge and cloud center observations. These findings demonstrate the importance of vertically resolved N_CCN and consideration of LTS in ACI studies and establish a remote sensing-based analysis method with which future satellite studies can investigate ACI.