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Aerosols influence weather and climate by interacting with radiation throughabsorption and scattering. These effects heavily rely on the optical propertiesof aerosols, which are mainly governed by attributes such as morphology, sizedistribution, and chemical composition. These attributes undergo continuouschanges due to chemical reactions and aerosol micro-physics, resulting insignificant spatio-temporal variations. Most atmospheric models struggle toincorporate this variability because they use pre-calculated tables to handleaerosol optics. This offline approach often leads to substantial errors inestimating the radiative impacts of aerosols alongwith posing significantcomputational burdens. To address this challenge, we introduce a novel andcomputationally efficient machine learning ap proach called MieAI. It allowsfor online calculation of the optical properties of internally mixed aerosolswith a log-normal size distribution. Importantly, MieAI fully incorporates thevariability in aerosol chemistry and microphysics. Our evaluation of MieAIagainst traditional Mie calculations, using number concentrations fromICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases(ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictiveaccuracy for aerosol optical properties. MieAI achieves this with errors wellwithin 10%, and it operates more than 1000 times faster than the benchmarkapproach of Mie calculations. Due to its generalized nature, the MieAI approachcan be implemented in any chemistry transport model which represents aerosolsize distribution in the form of log-normally distributed internally mixedmodes. This advancement has the potential to replace frequently employed lookuptables and play a substantial role in the ongoing attempts to reduceuncertainties in estimating aerosol radiative forcing.

MieAI: A neural network for calculating optical properties of internally mixed aerosol in atmospheric models