Indicator patterns of forced change learned by an artificial neural network

TL;DR

This paper demonstrates how artificial neural networks can identify and visualize spatial patterns of forced climate change signals from model simulations, outperforming traditional methods and revealing evolving regional indicators.

Contribution

The study introduces a neural network approach with visualization techniques to detect and interpret climate change signals amidst noise and model differences.

Findings

ANN accurately predicts the year of climate model input maps.

Visualization reveals spatial patterns as reliable indicators of forced change.

ANN outperforms traditional signal-to-noise and regression methods.

Abstract

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.