Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2303-2020
https://doi.org/10.5194/acp-20-2303-2020
Technical note
 | 
26 Feb 2020
Technical note |  | 26 Feb 2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Theodore Weber, Austin Corotan, Brian Hutchinson, Ben Kravitz, and Robert Link

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Cited articles

Arora, V. K. and Boer, G. J.: Uncertainties in the 20th century carbon budget associated with land use change, Glob. Change Biol., 16, 3327–3348, https://doi.org/10.1111/j.1365-2486.2010.02202.x, 2011. a
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L., Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805, https://doi.org/10.1029/2010GL046270, 2011. a
Bellucci, A., Haarsma, R., Bellouin, N., Booth, B., Cagnazzo, C., van den Hurk, B., Keenlyside, N., Koenigk, T., Massonnet, F., Materia, S., and Weiss, M.: Advancements in decadal climate predictability: The role of nonoceanic drivers, Rev. Geophys., 53, 165–202, https://doi.org/10.1002/2014RG000473, 2015. a
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks, in: Advances in Neural Information Processing Systems, NIPS Proceedings, 1171–1179, 2015. a, b
Bengio, Y.: Practical recommendations for gradient-based training of deep architectures, in: Neural networks: Tricks of the trade, 437–478, Springer, 2012. a
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Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
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