We discuss uncertainty quantification for aerosol type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses pre-calculated aerosol microphysical models stored in look-up tables (LUTs) and top of atmosphere spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modeled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval. <br><br> This paper focuses on the aerosol microphysical model selection and characterization of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach where all uncertainties are described as a posterior probability distribution. When there is no single best matching aerosol microphysical model we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best matching models of a certain main aerosol type in order to quantify how plausible each main aerosol type is in representing the underlying atmospheric aerosol conditions. <br><br> The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using multi-wavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterization of the uncertainty in this kind of problem for aerosol type selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.