  While previous works have shown that machine learning (ML) can improve the
prediction accuracy of coarse-grid climate models, these ML-augmented methods
are more vulnerable to irregular inputs than the traditional physics-based
models they rely on. Because ML-predicted corrections feed back into the
climate model's base physics, the ML-corrected model regularly produces out of
sample data, which can cause model instability and frequent crashes. This work
shows that adding semi-supervised novelty detection to identify out-of-sample
data and disable the ML-correction accordingly stabilizes simulations and
sharply improves the quality of predictions. We design an augmented climate
model with a one-class support vector machine (OCSVM) novelty detector that
provides better temperature and precipitation forecasts in a year-long
simulation than either a baseline (no-ML) or a standard ML-corrected run. By
improving the accuracy of coarse-grid climate models, this work helps make
accurate climate models accessible to researchers without massive computational
resources.
