  Determining changes in global temperature and precipitation that may indicate
climate change is complicated by annual variations. One approach for finding
potential climate change indicators is to train a model that predicts the year
from annual means of global temperatures and precipitations. Such data is
available from the CMIP6 ensemble of simulations. Here a two-hidden-layer
neural network trained on this data successfully predicts the year. Differences
among temperature and precipitation patterns for which the model predicts
specific years reveal changes through time. To find these optimal patterns, a
new way of interpreting what the neural network has learned is explored.
Alopex, a stochastic correlative learning algorithm, is used to find optimal
temperature and precipitation maps that best predict a given year. These maps
are compared over multiple years to show how temperature and precipitations
patterns indicative of each year change over time.
