Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach

Abstract. It is necessary to accurately determine the optical properties of highly absorbing black carbon (BC) aerosols to estimate their climate impact. In the past, there has been hesitation about using realistic fractal morphologies when simulating BC optical properties due to the complexity involved in the simulations and the cost of the computations. In this work, we demonstrate that the predictions of optical properties like single scattering albedo (ω) and mass absorption cross-section (MAC) can be improved compared to the conventional Mie-based predictions using a highly accurate benchmark machine learning algorithm. Unlike the computationally intensive simulations of complex scattering models, the ML-based approach accurately predicts optical properties in a fraction of a second. There has been an extensive evaluation procedure carried out in this study. Based on comparisons with laboratory measurements, it was demonstrated that incorporating realistic morphologies of BC significantly improved their optical properties. The results indicate that it is possible to generate optical properties in the visible spectrum using BC fractal aggregates with any desired physicochemical properties, such as size, morphology, or organic coating. Based on these findings, climate models can improve their radiative forcing estimates using such comprehensive parameterizations for the optical properties of BC based on their aging stages.