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

Supplement

https://doi.org/10.5194/gmd-15-3923-2022-supplement

Data sets

Data for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes" X. Wang and Y. Han https://doi.org/10.5281/zenodo.5625616

Model code and software

Codes for "Stable climate simulations using a realistic GCM with neural network parameterizations for atmospheric moist physics and radiation processes" X. Wang and Y. Han https://doi.org/10.5281/zenodo.5596273

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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.
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