Abstract
Light-absorbing organic matter, known as brown carbon (BrC), has previously been found to significantly enhance the absorption of solar radiation by biomass burning (BB) aerosol. Previous studies have also proposed methods aimed at constraining the BrC contribution to the overall aerosol absorption using the absorption angstrom ngstrom exponents (AAEs) derived from the multi-wavelength remote observations at Aerosol Robotic Network (AERONET). However, representations of the BrC absorption in atmospheric models remain uncertain, particularly due to the high variability in the absorption properties of BB organic aerosol (OA). As a result, there is a need for stronger observational constraints on these properties. We extend the concept of the established AAE-based methods in the framework of our Bayesian method, which combines remote optical observations with Monte Carlo simulations of the aerosol absorption properties. We propose that the observational constraints on the absorption properties of BB OA can be enhanced by using the single-scattering albedo (SSA) as part of the observation vector. The capabilities of our method were first examined by using synthetic data, which were intended to represent the absorption properties of BB aerosol originating from wildfires in Siberia. We found that observations of AAEs and SSA can provide efficient constraints not only on the BrC contribution to the total absorption but also on both the imaginary part of the refractive index and the mass absorption efficiency of OA. The subsequent application of our method to the original multi-annual data from Siberian AERONET sites, along with the supplementary analysis of possible biases in the a posteriori estimates of the inferred absorption properties, indicates that the contribution of BrC to the overall light absorption by BB aerosol in Siberia at the 440 nm wavelength is most likely to range, on average, from about 15 % to 21 %, although it is highly variable and, in some cases, can exceed 40 %. Based on the analysis of the AERONET data, we also derived simple nonlinear parameterizations for the absorption characteristics of BB OA in Siberia as functions of the AAE.