Investigate Uncertainties in Quantifying the Aerosol–Cloud Interaction for Shallow Warm Clouds

The aerosol indirect effect continues to be the largest uncertainty in climate prediction. The fundamental issues causing this large uncertainty stem from a myriad of challenges related to artifactual retrieval in satellite products and simplified or missing processes in models. Aerosol-cloud interaction (ACI) estimated from observations and models at the microphysical scale suggests that the aerosol indirect radiative effect is much higher than estimates from observations comparable to GCM grid cell sizes and GCM models. For future climate projections, it is critical to close this gap so we can understand to what extent anthropogenic aerosols have cooled, and continue to cool the climate system. Despite decades of effort, satellite-based remote sensing as the primary source of global data for aerosol-cloud interactions remains big concern regarding how measurement artefacts affect retrievals of both aerosol and cloud properties. For example, higher AOD retrieval due to humidification from aerosol hygroscopic growth near humid cloud field. Radiation scattered by clouds that illuminates the nearby clear sky enhancing the AOD retrieval. However, these are exactly what are used in generating satellite estimates of aerosol-cloud interaction induced radiative forcing. With the availability of high-resolution remote sensing data (eMAS data from SEAC4RS campaign), aircraft in situ data and a Monte Carlo Radiative Transfer model developed by authors, we have the ability to evaluate those artifacts mentioned above and to investigate the uncertainties of aerosol indirect radiative forcing. Three specific questions are addressed in this study: 1) how does ACI depend on sampling resolution; 2) to what extent does artefactual retrieval from remote sensing affect calculated ACI; 3) what contribution does each factor (aerosol humidification and 3-D radiative transfer) make to affect calculated ACI.