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 Ångström 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.