Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model
<p>The ability of machine-learning-based (ML-based) model components to generalize to the previously unseen inputs and its impact on the stability of the models that use these components have been receiving a lot of recent attention, especially in the context of ML-based parameterizations. At...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-12-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/14/7425/2021/gmd-14-7425-2021.pdf |
| Summary: | <p>The ability of machine-learning-based (ML-based) model
components to generalize to the previously unseen inputs and its impact on
the stability of the models that use these components have been receiving a lot
of recent attention, especially in the context of ML-based
parameterizations. At the same time, ML-based emulators of existing
physically based parameterizations can be stable, accurate, and fast when
used in the model they were specifically designed for. In this work we show
that shallow-neural-network-based emulators of radiative transfer
parameterizations developed almost a decade ago for a state-of-the-art
general circulation model (GCM) are robust with respect to the substantial
structural and parametric change in the host model: when used in two 7-month-long experiments with a new GCM, they remain stable and generate
realistic output. We concentrate on the stability aspect of the emulators'
performance and discuss features of neural network architecture and training
set design potentially contributing to the robustness of ML-based model
components.</p> |
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| ISSN: | 1991-959X 1991-9603 |