Many studies examining shortwave-absorbing aerosol-cloud interactions over the southeast Atlantic apply a seasonal averaging. This disregards a meteorology that raises the mean altitude of the smoke layer from July to October. This study details the month-by-month changes in cloud properties and the large-scale environment as a function of the biomass-burning aerosol loading from July to October, based on measurements from Ascension Island (8°S, 14.5°W), satellite retrievals and reanalysis. In July and August, variability in the smoke loading predominantly occurs in the boundary layer. During both months, the low-cloud fraction is less and is increasingly cumuliform when more smoke is present, with the exception of a late morning boundary layer deepening that encourages a short-lived cloud development. September marks a transition month during which mid-latitude disturbances can intrude into the Atlantic subtropics, constraining the land-based anticyclonic circulation transporting free-tropospheric aerosol to closer to the coast, and resulting deeper, drier, and cooler boundary layers with strongly reduced cloud cover near the main stratocumulus deck. The October meteorology is more singularly dependent on the strength of the free-tropospheric winds advecting aerosol offshore. Low-level clouds increase and are more stratiform, when the smoke loadings are higher. The increased cloud-top moisture and cloud droplet number concentrations can help sustain a thinner stratiform cloud layer through microphysical interactions. Overall the monthly changes in the large-scale circulation and aerosol/moisture vertical structure act to amplify the seasonal cycle in low-cloud amount and morphology, raising a climate importance as cloudiness changes dominate the top-of-atmosphere radiation budget.

Marine low clouds are one of the greatest sources of uncertainty for climate projection. We present an observed climatology of cloud albedo susceptibility to cloud droplet number concentration perturbations (S0) with changing sea surface temperature (SST) and estimated inversion strength for single-layer warm clouds over the North Atlantic Ocean, using eight years of satellite and reanalysis data. The key findings are that SST has a dominant control on S0 in the presence of co-varying synoptic conditions and aerosol perturbations. Regions conducive to aerosol-induced darkening (brightening) clouds occur with high (low) local SST. Higher SST significantly hastens cloud-top evaporation with increasing aerosol loading, by accelerating entrainment and facilitating entrainment drying. In a global-warming-like scenario, cloud darkening is expected, mainly as a result of increased entrainment drying via Clausius-Clapeyron scaling. Our results imply a more (less) positive low-cloud liquid water path feedback in a warmer climate with increasing (decreasing) aerosol loading.

Aerosol-cloud interactions are both uncertain and important in global and regional climate models, and especially in the southeast Atlantic Ocean. This uncertainty in the region is largely due to two correlated factors---the expansive, bright, semi-permanent stratocumulus cloud deck and the fact that southern Africa is the largest source of biomass-burning aerosols in the world. We study this region using the WRF-Chem model with CAM5 aerosols and in situ observations from the ORACLES and LASIC field campaigns in August-October of 2016 through 2018. We compare aerosol and cloud properties to measure and improve model performance and expand upon observational findings of aerosol-cloud effects. Relevant comparison variables include aerosol number concentration, mean particle diameter and spread, CCN activation tendency, hygroscopicity, and cloud droplet number concentrations. Specifically, our approach is to analyze colocated model data along flight tracks to resolve aerosol-cloud interactions. Within and between single-day flights, there is high spatiotemporal variability that can get lost to large-scale averaging analyses. We have found that CCN is substantially under-represented in the model compared to observations. For a given aerosol number concentration, size, supersaturation and hygroscopicity, the model will consider fewer particles as CCN than observations indicate. We plan to explore this result further, diagnosing the model-observation differences more consistently and updating the model with more physically accurate values of aerosol size, concentration, or hygroscopicity based on observations. We will also intercompare multiple instrument platforms involved with the ORACLES and LASIC campaigns. With improved small-scale aerosol-cloud interactions, this work also shows promise to substantially improve that representation in climate models.