Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 9
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.
https://doi.org/10.5194/gmd-15-3923-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 9
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

Viewed

Total article views: 7,447 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,361 1,922 164 7,447 174 189 205
  • HTML: 5,361
  • PDF: 1,922
  • XML: 164
  • Total: 7,447
  • Supplement: 174
  • BibTeX: 189
  • EndNote: 205
Views and downloads (calculated since 29 Sep 2021)
Cumulative views and downloads (calculated since 29 Sep 2021)

Viewed (geographical distribution)

Total article views: 7,447 (including HTML, PDF, and XML) Thereof 7,068 with geography defined and 379 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Jan 2026
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.
Read more
Share