Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2355-2023
https://doi.org/10.5194/gmd-16-2355-2023
Development and technical paper
 | 
05 May 2023
Development and technical paper |  | 05 May 2023

Emulating aerosol optics with randomly generated neural networks

Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin

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Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.