  We explore the use of deep neural networks for nonlinear dimensionality
reduction in climate applications. We train convolutional autoencoders (CAEs)
to encode two temperature field datasets from pre-industrial control runs in
the CMIP5 first ensemble, obtained with the CCSM4 model and the IPSL-CM5A-LR
model, respectively. With the later dataset, consisting of 36500 96$\times$96
surface temperature fields, the CAE out-performs PCA in terms of mean squared
error of the reconstruction from a 40 dimensional encoding. Moreover, the noise
in the filters of the convolutional layers in the autoencoders suggests that
the CAE can be trained to produce better results. Our results indicate that
convolutional autoencoders may provide an effective platform for the
construction of surrogate climate models.
