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Article overview
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Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability | | Charlotte Connolly
; Elizabeth A. Barnes
; Pedram Hassanzadeh
; Mike Pritchard
; | | Date: |
2 Jan 2023 | | 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. | | Source: | arXiv, 2301.00496 | | Services: | Forum | Review | PDF | Favorites | |
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