  Aerosols influence weather and climate by interacting with radiation through
absorption and scattering. These effects heavily rely on the optical properties
of aerosols, which are mainly governed by attributes such as morphology, size
distribution, and chemical composition. These attributes undergo continuous
changes due to chemical reactions and aerosol micro-physics, resulting in
significant spatio-temporal variations. Most atmospheric models struggle to
incorporate this variability because they use pre-calculated tables to handle
aerosol optics. This offline approach often leads to substantial errors in
estimating the radiative impacts of aerosols alongwith posing significant
computational burdens. To address this challenge, we introduce a novel and
computationally efficient machine learning ap proach called MieAI. It allows
for online calculation of the optical properties of internally mixed aerosols
with a log-normal size distribution. Importantly, MieAI fully incorporates the
variability in aerosol chemistry and microphysics. Our evaluation of MieAI
against traditional Mie calculations, using number concentrations from
ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases
(ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive
accuracy for aerosol optical properties. MieAI achieves this with errors well
within 10%, and it operates more than 1000 times faster than the benchmark
approach of Mie calculations. Due to its generalized nature, the MieAI approach
can be implemented in any chemistry transport model which represents aerosol
size distribution in the form of log-normally distributed internally mixed
modes. This advancement has the potential to replace frequently employed lookup
tables and play a substantial role in the ongoing attempts to reduce
uncertainties in estimating aerosol radiative forcing.
