DDA-BNN v1.0: A Morphology-Aware Surrogate Model for the Optical Properties of Black Carbon–Containing Particles

Abstract. Black carbon (BC) is the most strongly absorbing component of atmospheric aerosol and significantly impacts Earth's energy balance. The optical properties of BC-containing particles depend on particle-level variability in size, chemical composition, and internal morphology. Such particle-level details are not easily represented in large-scale atmospheric models. Existing parameterizations typically assume idealized particle geometries (e.g., homogeneous spheres or concentric core–shell spheres) and homogeneous mixing, which can yield biased predictions and provide no quantitative estimate of model-form uncertainty at the single-particle level. In this work, we present a probabilistic framework for predicting the optical properties of individual BC-containing particles using a hybrid Bayesian neural network (BNN) model trained on numerically exact discrete dipole approximation (DDA) simulations. The hybrid BNN is a flexible combination of deterministic and Bayesian layers allowing for more realistic treatment of particle optical properties and quantification of uncertainty. The hybrid BNN model predicts extinction efficiency, single-scattering albedo, and asymmetry parameter and returns predictive uncertainty that can be decomposed into aleatoric (data-driven variability) and epistemic (uncertainty due to limited training coverage) components. We show that the hybrid BNN outperforms homogeneous-sphere and core–shell Mie approximations for calculating extinction and scattering-sensitive quantities (Q_ext, SSA and g), while maintaining comparable accuracy for absorption-related metrics. We further demonstrate how epistemic uncertainty highlights under-sampled regions of particle parameter space, enabling targeted design of future DDA simulations that most effectively reduce model uncertainty. This uncertainty-aware surrogate provides a practical pathway for incorporating realistically complex particle morphologies into parameterizations of aerosol optical properties, which will ultimately improve the reliability of model-based assessments of BC impacts on the atmosphere.