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

Related authors

G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024,https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Hemispherically symmetric strategies for stratospheric aerosol injection
Yan Zhang, Douglas G. MacMartin, Daniele Visioni, Ewa M. Bednarz, and Ben Kravitz
Earth Syst. Dynam., 15, 191–213, https://doi.org/10.5194/esd-15-191-2024,https://doi.org/10.5194/esd-15-191-2024, 2024
Short summary
Injection strategy – a driver of atmospheric circulation and ozone response to stratospheric aerosol geoengineering
Ewa M. Bednarz, Amy H. Butler, Daniele Visioni, Yan Zhang, Ben Kravitz, and Douglas G. MacMartin
Atmos. Chem. Phys., 23, 13665–13684, https://doi.org/10.5194/acp-23-13665-2023,https://doi.org/10.5194/acp-23-13665-2023, 2023
Short summary
Opinion: The scientific and community-building roles of the Geoengineering Model Intercomparison Project (GeoMIP) – past, present, and future
Daniele Visioni, Ben Kravitz, Alan Robock, Simone Tilmes, Jim Haywood, Olivier Boucher, Mark Lawrence, Peter Irvine, Ulrike Niemeier, Lili Xia, Gabriel Chiodo, Chris Lennard, Shingo Watanabe, John C. Moore, and Helene Muri
Atmos. Chem. Phys., 23, 5149–5176, https://doi.org/10.5194/acp-23-5149-2023,https://doi.org/10.5194/acp-23-5149-2023, 2023
Short summary
Climate response to off-equatorial stratospheric sulfur injections in three Earth system models – Part 1: Experimental protocols and surface changes
Daniele Visioni, Ewa M. Bednarz, Walker R. Lee, Ben Kravitz, Andy Jones, Jim M. Haywood, and Douglas G. MacMartin
Atmos. Chem. Phys., 23, 663–685, https://doi.org/10.5194/acp-23-663-2023,https://doi.org/10.5194/acp-23-663-2023, 2023
Short summary

Related subject area

Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Above-cloud concentrations of cloud condensation nuclei help to sustain some Arctic low-level clouds
Lucas J. Sterzinger and Adele L. Igel
Atmos. Chem. Phys., 24, 3529–3540, https://doi.org/10.5194/acp-24-3529-2024,https://doi.org/10.5194/acp-24-3529-2024, 2024
Short summary
Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study
Andreas Bier, Simon Unterstrasser, Josef Zink, Dennis Hillenbrand, Tina Jurkat-Witschas, and Annemarie Lottermoser
Atmos. Chem. Phys., 24, 2319–2344, https://doi.org/10.5194/acp-24-2319-2024,https://doi.org/10.5194/acp-24-2319-2024, 2024
Short summary
Effects of intermittent aerosol forcing on the stratocumulus-to-cumulus transition
Prasanth Prabhakaran, Fabian Hoffmann, and Graham Feingold
Atmos. Chem. Phys., 24, 1919–1937, https://doi.org/10.5194/acp-24-1919-2024,https://doi.org/10.5194/acp-24-1919-2024, 2024
Short summary
Cloud properties and their projected changes in CMIP models with low to high climate sensitivity
Lisa Bock and Axel Lauer
Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024,https://doi.org/10.5194/acp-24-1587-2024, 2024
Short summary
Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 2: The imprint of the atmospheric circulation at different scales
Leonie Villiger and Franziska Aemisegger
Atmos. Chem. Phys., 24, 957–976, https://doi.org/10.5194/acp-24-957-2024,https://doi.org/10.5194/acp-24-957-2024, 2024
Short summary

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
Download
Short summary
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.
Altmetrics
Final-revised paper
Preprint