An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Journal Article · 09 October 2023 · Journal of Advances in Modeling Earth Systems

DOI: https://doi.org/10.1029/2022MS003508 · OSTI ID:2067628

Han, Yilun  ^[1]; Zhang, Guang J.  ^[2]; Wang, Yong  ^[1]

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1. Department of Earth System Science Tsinghua University Beijing China
2. Scripps Institution of Oceanography La Jolla CA USA

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

• Research Organization: Univ. of California, San Diego, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC)
• Grant/Contract Number: SC0022064
• OSTI ID: 2067628
• Journal Information: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 10 Vol. 15; ISSN 1942-2466
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English