Changes in United States summer temperatures revealed by explainable
neural networks
Abstract
To better understand the regional changes in summertime temperatures
across the conterminous United States (CONUS), we adopt a recently
developed machine learning framework that can be used to reveal the
timing of emergence of forced climate signals from the noise of internal
climate variability. Specifically, we train an artificial neural network
(ANN) on seasonally-averaged temperatures across the CONUS and then task
the ANN to output the year associated with an individual map. In order
to correctly identify the year, the ANN must therefore learn
time-evolving patterns of climate change amidst the noise of internal
climate variability. The ANNs are first trained and tested on data from
large ensembles and then evaluated using observations from a
station-based dataset. To understand how the ANN is making its
predictions, we leverage a collection of ad hoc feature attribution
methods from explainable artificial intelligence (XAI). We find that
anthropogenic signals in seasonal mean minimum temperature have emerged
by the early 2000s for the CONUS, which occurred earliest in the Eastern
United States. While our observational timing of emergence estimates are
not as sensitive to the spatial resolution of the training data, we find
a notable improvement in ANN skill using a higher resolution climate
model, especially for its early 20th century predictions. Composites of
XAI maps reveal that this improvement is linked to temperatures around
higher topography. We find that increases in spatial resolution of the
ANN training data may yield benefits for machine learning applications
in climate science.