  On average once every four years, the Tropical Pacific warms considerably
during events called El Ni\~no, leading to weather disruptions over many
regions on Earth. Recent machine-learning approaches to El Ni\~no prediction,
in particular Convolutional Neural Networks (CNNs), have shown a surprisingly
high skill at relatively long lead times. In an attempt to understand this high
skill, we here use data from distorted physics simulations with an intermediate
complexity El Ni\~no model to determine what aspects of El Ni\~no physics are
represented in a specific CNN-based classification method. We find that the CNN
can adequately correct for distortions in the ocean adjustment processes, but
that the machine-learning method has far more trouble to deal with distortions
in upwelling feedback strength.
