  The ability of Machine-Learning (ML) based model components to generalize to
the previously unseen inputs, and the resulting stability of the models that
use these components, has been receiving a lot of recent attention, especially
when it comes to ML-based parameterizations. At the same time, ML-based
emulators of existing 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 GCM are
robust with respect to the substantial structural and parametric change in the
host model: when used in the AMIP-like experiment with the new model, they not
only remain stable, but generate realistic output. Aspects of neural network
architecture and training set design potentially contributing to stability of
ML-based model components are discussed.
