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

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.