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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Cited
28 citations as recorded by crossref.
- On the thermodynamic invariance of fine‐grain and coarse‐grain fluid models T. Dubos 10.1002/qj.4830
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- WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer X. Zhong et al. 10.5194/gmd-16-199-2023
- Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
- An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration Y. Han et al. 10.1029/2022MS003508
- Deep Learning Based Cloud Cover Parameterization for ICON A. Grundner et al. 10.1029/2021MS002959
- Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
- Projections of Global Land Runoff Changes and Their Uncertainty Characteristics During the 21st Century C. Miao et al. 10.1029/2022EF003286
- Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0 X. Zhong et al. 10.5194/gmd-17-3667-2024
- Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306
- Confronting uncertainty: The future of hydropower in the himalayan region amidst climate ambiguity N. Devkota et al. 10.1016/j.esd.2025.101657
- Deep Learning Parameterization of the Tropical Cyclone Boundary Layer L. Wang & Z. Tan 10.1029/2022MS003034
- Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung 10.3390/atmos16010060
- Shallow and deep learning of extreme rainfall events from convective atmospheres G. Bürger & M. Heistermann 10.5194/nhess-23-3065-2023
- Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0 P. Ukkonen & R. Hogan 10.5194/gmd-16-3241-2023
- Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model L. Wang et al. 10.3390/atmos15101219
- Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
- On fast simulation of dynamical system with neural vector enhanced numerical solver Z. Huang et al. 10.1038/s41598-023-42194-y
- Technical note: Emulation of a large-eddy simulator for stratocumulus clouds in a general circulation model K. Nordling et al. 10.5194/acp-24-869-2024
- 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
- TorchClim v1.0: a deep-learning plugin for climate model physics D. Fuchs et al. 10.5194/gmd-17-5459-2024
- Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes X. Wang et al. 10.5194/gmd-15-3923-2022
- 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
- Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
- Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations C. Bretherton et al. 10.1029/2021MS002794
23 citations as recorded by crossref.
- On the thermodynamic invariance of fine‐grain and coarse‐grain fluid models T. Dubos 10.1002/qj.4830
- Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
- A Fortran–Python interface for integrating machine learning parameterization into earth system models T. Zhang et al. 10.5194/gmd-18-1917-2025
- WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer X. Zhong et al. 10.5194/gmd-16-199-2023
- Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
- An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration Y. Han et al. 10.1029/2022MS003508
- Deep Learning Based Cloud Cover Parameterization for ICON A. Grundner et al. 10.1029/2021MS002959
- Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
- Projections of Global Land Runoff Changes and Their Uncertainty Characteristics During the 21st Century C. Miao et al. 10.1029/2022EF003286
- Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0 X. Zhong et al. 10.5194/gmd-17-3667-2024
- Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306
- Confronting uncertainty: The future of hydropower in the himalayan region amidst climate ambiguity N. Devkota et al. 10.1016/j.esd.2025.101657
- Deep Learning Parameterization of the Tropical Cyclone Boundary Layer L. Wang & Z. Tan 10.1029/2022MS003034
- Utilization of a Lightweight 3D U-Net Model for Reducing Execution Time of Numerical Weather Prediction Models H. Park & S. Chung 10.3390/atmos16010060
- Shallow and deep learning of extreme rainfall events from convective atmospheres G. Bürger & M. Heistermann 10.5194/nhess-23-3065-2023
- Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0 P. Ukkonen & R. Hogan 10.5194/gmd-16-3241-2023
- Stochastic Parameterization of Moist Physics Using Probabilistic Diffusion Model L. Wang et al. 10.3390/atmos15101219
- Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
- On fast simulation of dynamical system with neural vector enhanced numerical solver Z. Huang et al. 10.1038/s41598-023-42194-y
- Technical note: Emulation of a large-eddy simulator for stratocumulus clouds in a general circulation model K. Nordling et al. 10.5194/acp-24-869-2024
- 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
- TorchClim v1.0: a deep-learning plugin for climate model physics D. Fuchs et al. 10.5194/gmd-17-5459-2024
5 citations as recorded by crossref.
- Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes X. Wang et al. 10.5194/gmd-15-3923-2022
- 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
- Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives B. Bochenek & Z. Ustrnul 10.3390/atmos13020180
- Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations C. Bretherton et al. 10.1029/2021MS002794
Latest update: 01 Apr 2025
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...
