  Convolutional neural networks (CNNs) can potentially provide powerful tools
for classifying and identifying patterns in climate and environmental data.
However, because of the inherent complexities of such data, which are often
spatio-temporal, chaotic, and non-stationary, the CNN algorithms must be
designed/evaluated for each specific dataset and application. Yet to start,
CNN, a supervised technique, requires a large labeled dataset. Labeling demands
(human) expert time, which combined with the limited number of relevant
examples in this area, can discourage using CNNs for new problems. To address
these challenges, here we (1) Propose an effective auto-labeling strategy based
on using an unsupervised clustering algorithm and evaluating the performance of
CNNs in re-identifying these clusters; (2) Use this approach to label thousands
of daily large-scale weather patterns over North America in the outputs of a
fully-coupled climate model and show the capabilities of CNNs in re-identifying
the 4 clustered regimes. The deep CNN trained with $1000$ samples or more per
cluster has an accuracy of $90\%$ or better. Accuracy scales monotonically but
nonlinearly with the size of the training set, e.g. reaching $94\%$ with $3000$
training samples per cluster. Effects of architecture and hyperparameters on
the performance of CNNs are examined and discussed.
