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. The realistic representation of aerosol–cloud interactions is of primaryimportance for accurate climate model projections. The investigation of these interactions in strongly contrasting clean and polluted atmospheric conditionsin the Amazon region has been one of the motivations for several fieldcampaigns, including the airborne “Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (Global Precipitation Measurement) (ACRIDICON-CHUVA)” campaign based in Manaus, Brazil, in September 2014.In this work we combine in situ and remotely sensed aerosol, cloud, andatmospheric radiation data collected during ACRIDICON-CHUVA with regional,online-coupled chemistry-transport simulations to evaluate the model's abilityto represent the indirect effects of biomass burning aerosol on cloudmicrophysical and optical properties (droplet number concentration and effective radius). We found agreement between the modeled and observed median cloud droplet numberconcentration (CDNC) for low values of CDNC, i.e., low levels of pollution. Ingeneral, a linear relationship between modeled and observed CDNC with a slopeof 0.3 was found, which implies a systematic underestimation of modeled CDNCwhen compared to measurements. Variability in cloud condensation nuclei (CCN)number concentrations was also underestimated, and cloud droplet effective radii(reff) were overestimated by the model. Modeled effective radiusprofiles began to saturate around 500 CCN cm−3 at cloud base,indicating an upper limit for the model sensitivity well below CCNconcentrations reached during the burning season in the Amazon Basin. AdditionalCCN emitted from local fires did not cause a notable change in modeled clouddroplet effective radii. Finally, we also evaluate a parameterization of CDNC atcloud base using more readily available cloud microphysical properties, showingthat we are able to derive CDNC at cloud base from cloud-side remote-sensingobservations.

The challenge of simulating the sensitivity of the Amazonian cloud microstructure to cloud condensation nuclei number concentrations