  Many problems in climate science require the identification of signals
obscured by both the "noise" of internal climate variability and differences
across models. Following previous work, we train an artificial neural network
(ANN) to identify the year of input maps of temperature and precipitation from
forced climate model simulations. This prediction task requires the ANN to
learn forced patterns of change amidst a background of climate noise and model
differences. We then apply a neural network visualization technique (layerwise
relevance propagation) to visualize the spatial patterns that lead the ANN to
successfully predict the year. These spatial patterns thus serve as "reliable
indicators" of the forced change. The architecture of the ANN is chosen such
that these indicators vary in time, thus capturing the evolving nature of
regional signals of change. Results are compared to those of more standard
approaches like signal-to-noise ratios and multi-linear regression in order to
gain intuition about the reliable indicators identified by the ANN. We then
apply an additional visualization tool (backward optimization) to highlight
where disagreements in simulated and observed patterns of change are most
important for the prediction of the year. This work demonstrates that ANNs and
their visualization tools make a powerful pair for extracting climate patterns
of forced change.
