Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability

TL;DR

This paper demonstrates that a convolutional neural network trained on internal climate variability can effectively predict the nonlinear response of the jet stream to tropospheric thermal forcing, offering a new tool for climate response analysis.

Contribution

It introduces a CNN-based approach trained on internal variability data to predict nonlinear jet stream responses to thermal forcings, extending beyond traditional linear methods.

Findings

CNN accurately predicts nonlinear jet responses

Method enables rapid sensitivity analysis

Applicable to models with long control runs

Abstract

Two distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the mid-latitude jet stream's latitudinal position, often referred to as a "tug-of-war". Studies that investigate the jet's response to these thermal forcings show that it is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Much of this past work focuses on studying a simulation's response to external manipulation. In contrast, we explore the potential to train a convolutional neural network (CNN) on internal variability alone and then use it to examine possible nonlinear responses of the jet to tropospheric thermal forcing that more closely resemble anthropogenic climate change. Our approach leverages the idea behind the fluctuation-dissipation theorem, which relates the internal variability of a system to its forced response but so far has been only used to quantify linear responses. We train a CNN on data from a long control run of the CESM dry dynamical core and show that it is able to skillfully predict the nonlinear response of the jet to sustained external forcing. The trained CNN provides a quick method for exploring the jet stream sensitivity to a wide range of tropospheric temperature tendencies and, considering that this method can likely be applied to any model with a long control run, could lend itself useful for early stage experiment design.