An Interpretable Model of Climate Change Using Correlative Learning
This work addresses the challenge of interpreting climate change signals for researchers, though it is incremental in applying existing methods to new data.
The paper tackled the problem of identifying climate change indicators by training a neural network to predict years from global temperature and precipitation data, using the CMIP6 ensemble, and developed an interpretable method to reveal changing patterns over time.
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.