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Using neural networks to learn the forced response of the jet-stream to tropospheric temperature tendencies

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dc.contributor.authorConnolly, Charlotte, author
dc.contributor.authorBarnes, Elizabeth, advisor
dc.contributor.authorRandall, David, committee member
dc.contributor.authorAnderson, Chuck, committee member
dc.date.accessioned2023-06-01T17:26:56Z
dc.date.available2023-06-01T17:26:56Z
dc.date.issued2023
dc.description.abstractTwo distinct features of anthropogenic climate change, warming in the tropical upper troposphere and warming at the Arctic surface, have competing effects on the midlatitude jet-stream's latitudinal position, often referred to as a "tug-of-war". Many previous studies have investigated the strength of the jet response to these thermal forcings, as well as many others, and have shown that the jet response is sensitive to model type, season, initial atmospheric conditions, and the shape and magnitude of the forcing. Here, we explore the potential for training a convolutional neural network (CNN) on internal variability alone to examine possible nonlinear jet responses to a variety of thermal forcings. Our approach thus makes use of the fluctuation-dissipation theorem, which relates the internal variability of a system to its forced response. We train a CNN on data from a long control run of the CESM dry dynamical core, thereby providing it with ample data to learn relationships between the temperature forcing and the jet movement over the coming days. Then, we use the CNN to explore the jet response to a wide range of tropospheric temperature tendencies. Despite being trained on the jet-stream response to internal variability alone, we show that the trained CNN is able to skillfully predict the nonlinear response of the jet-stream 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.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierConnolly_colostate_0053N_17590.pdf
dc.identifier.urihttps://hdl.handle.net/10217/236549
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectclimate change
dc.subjectmachine learning
dc.subjectarctic amplification
dc.subjecttropical hot spot
dc.subjectjet stream
dc.titleUsing neural networks to learn the forced response of the jet-stream to tropospheric temperature tendencies
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineAtmospheric Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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