Modelling black carbon absorption of solar radiation : combining external and internal mixing assumptions
An accurate simulation of the absorption properties is key for assessing the radiative effects of aerosol on meteorology and climate. The representation of how chemical species are mixed inside the particles (the mixing state) is one of the major uncertainty factors in the assessment of these effects. Here we compare aerosol optical properties simulations over Europe and North America, coordinated in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII), to 1 year of AERONET sunphotometer retrievals, in an attempt to identify a mixing state representation that better reproduces the observed single scattering albedo and its spectral variation. We use a single post-processing tool (FlexAOD) to derive aerosol optical properties from simulated aerosol speciation profiles, and focus on the absorption enhancement of black carbon when it is internally mixed with more scattering material, discarding from the analysis scenes dominated by dust. We found that the single scattering albedo at 440nm (omega(0,440)) is on average overestimated (underestimated) by 3-5% when external (core-shell internal) mixing of particles is assumed, a bias comparable in magnitude with the typical variability of the quantity. The (unphysical) homogeneous internal mixing assumption underestimates omega(0,440) by 14%. The combination of external and core-shell configurations (partial internal mixing), parameterized using a simplified function of air mass aging, reduces the omega(0,440) bias to-1/-3%. The black carbon absorption enhancement (E-abs) in core-shell with respect to the externally mixed state is in the range 1.8-2.5, which is above the currently most accepted upper limit of 1.5. The partial internal mixing reduces E-abs to values more consistent with this limit. However, the spectral dependence of the absorption is not well reproduced, and the absorption Angstrom exponent AAE(675)(440) is overestimated by 70-120%. Further testing against more comprehensive campaign data, including a ...