  Aerosols sourced from combustion such as black carbon (BC) are important
short-lived climate forcers whose direct radiative forcing and atmospheric
lifetime depend on their morphology. These aerosols' complex morphology makes
modeling their optical properties difficult, contributing to uncertainty in
both their direct and indirect climate effects. Accurate and fast calculations
of BC optical properties are needed for remote sensing inversions and for
radiative forcing calculations in atmospheric models, but current methods to
accurately calculate the optical properties of these aerosols are
computationally expensive and are compiled in extensive databases off-line to
be used as a look-up table. Recent advances in machine learning approaches have
shown the potential of graph neural networks (GNN's) for various physical
science applications, demonstrating skill in generalizing beyond initial
training data by learning internal properties and small-scale interactions
defining the emergent behavior of the larger system. Here we demonstrate that a
GNN trained to predict the optical properties of numerically-generated BC
fractal aggregates can accurately generalize to arbitrarily shaped particles,
even over much larger (10x) aggregates than in the training dataset, providing
a fast and accurate method to calculate aerosol optical properties in models
and for observational retrievals. This zero-shot learning approach could be
integrated into atmospheric models or remote sensing inversions to predict the
physical properties of realistically-shaped aerosol and cloud particles. In
addition, GNN's can be used to gain physical intuition on the relationship
between small-scale interactions (here of the spheres' positions and
interactions) and large-scale properties (here of the radiative properties of
aerosols).
