Articles | Volume 15, issue 9
https://doi.org/10.5194/gmd-15-3923-2022
https://doi.org/10.5194/gmd-15-3923-2022
Development and technical paper
 | 
16 May 2022
Development and technical paper |  | 16 May 2022

Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes

Xin Wang, Yilun Han, Wei Xue, Guangwen Yang, and Guang J. Zhang

Related authors

HOPE: An Arbitrary-Order Non-Oscillatory Finite-Volume Shallow Water Dynamical Core with Automatic Differentiation
Lilong Zhou and Wei Xue
EGUsphere, https://doi.org/10.5194/egusphere-2025-1889,https://doi.org/10.5194/egusphere-2025-1889, 2025
Short summary
Covariability of dynamics and composition in the Asian monsoon tropopause layer from satellite observations and reanalysis products
Shenglong Zhang, Jiao Chen, Jonathon S. Wright, Sean M. Davis, Jie Gao, Paul Konopka, Ninghui Li, Mengqian Lu, Susann Tegtmeier, Xiaolu Yan, Guang J. Zhang, and Nuanliang Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2025-543,https://doi.org/10.5194/egusphere-2025-543, 2025
Short summary
Evaluating reanalysis representations of climatological trace gas distributions in the Asian monsoon tropopause layer
Jonathon S. Wright, Shenglong Zhang, Jiao Chen, Sean M. Davis, Paul Konopka, Mengqian Lu, Xiaolu Yan, and Guang J. Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-135,https://doi.org/10.5194/egusphere-2025-135, 2025
Short summary
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024,https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024,https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary

Related subject area

Climate and Earth system modeling
FINAM is not a model (v1.0): a new Python-based model coupling framework
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025,https://doi.org/10.5194/gmd-18-4483-2025, 2025
Short summary
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025,https://doi.org/10.5194/gmd-18-4399-2025, 2025
Short summary
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025,https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025,https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025,https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary

Cited articles

Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. 
Brenowitz, N. D. and Bretherton, C. S.: Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-Graining, J. Adv. Model. Earth Syst., 11, 2728–2744, https://doi.org/10.1029/2019ms001711, 2019. 
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection, J. Atmos. Sci., 77, 4357–4375, https://doi.org/10.1175/jas-d-20-0082.1, 2020. 
Bretherton, C. S., Blossey, P. N., and Stan, C.: Cloud feedbacks on greenhouse warming in the superparameterized climate model SP-CCSM4, J. Adv. Model. Earth Syst., 6, 1185–1204, https://doi.org/10.1002/2014MS000355, 2014. 
Cao, G. and Zhang, G. J.: Role of Vertical Structure of Convective Heating in MJO Simulation in NCAR CAM5.3, J. Climate, 30, 7423–7439, https://doi.org/10.1175/jcli-d-16-0913.1, 2017. 
Download
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.
Share