Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3923-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3923-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 May 2022
Development and technical paper |
| 16 May 2022
| 16 May 2022
Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes
Department of Computer Science and Technology, Tsinghua University,
Beijing, 100084, China
Yilun Han
CORRESPONDING AUTHOR
Department of Earth System Science, Tsinghua University, Beijing,
100084, China
Wei Xue
CORRESPONDING AUTHOR
Department of Computer Science and Technology, Tsinghua University,
Beijing, 100084, China
Guangwen Yang
Department of Computer Science and Technology, Tsinghua University,
Beijing, 100084, China
Guang J. Zhang
Scripps Institution of Oceanography, La Jolla, CA, USA
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- Improving the Reliability of ML‐Corrected Climate Models With Novelty Detection C. Sanford et al. 10.1029/2023MS003809
- Projected increase in the frequency of extremely active Atlantic hurricane seasons H. Lopez et al. 10.1126/sciadv.adq7856
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- Deep Learning Parameterization of the Tropical Cyclone Boundary Layer L. Wang & Z. Tan 10.1029/2022MS003034
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- Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations S. Clark et al. 10.1029/2022MS003219
- Quantifying the Uncertainty Sources of Future Climate Projections and Narrowing Uncertainties With Bias Correction Techniques Y. Wu et al. 10.1029/2022EF002963
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Latest update: 22 Nov 2024
Short summary
This study uses a set of deep neural networks to learn a parameterization scheme from a superparameterized general circulation model (GCM). After being embedded in a realistically configurated GCM, the parameterization scheme performs stably in long-term climate simulations and reproduces reasonable climatology and climate variability. This success is the first for long-term stable climate simulations using machine learning parameterization under real geographical boundary conditions.
This study uses a set of deep neural networks to learn a parameterization scheme from a...
