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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Cited articles

Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, 1989. a
Angeline, P., Saunders, G., and Pollack, J.: An evolutionary algorithm that constructs recurrent neural networks, IEEE T. Neural Networ., 5, 54–65, https://doi.org/10.1109/72.265960, 1994. a
Baker, B., Gupta, O., Naik, N., and Raskar, R.: Designing Neural Network Architectures using Reinforcement Learning, ArXiv [preprint], https://doi.org/10.48550/arXiv.1611.02167, 2017. a
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.: Bounding global aerosol radiative forcing of climate change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b
Bergstra, J., Yamins, D., and Cox, D.: Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures, in: Proceedings of the 30th International Conference on Machine Learning, edited by: Dasgupta, S. and McAllester, D., Proceedings of Machine Learning Research, Atlanta, Georgia, USA, Vol. 28, 115–123, https://doi.org/10.5555/3042817.3042832, 2013. a
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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.
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