Detecting forced change within combined climate fields using explainable neural networks
| dc.contributor.author | Rader, Jamin, author | |
| dc.contributor.author | Barnes, Elizabeth, advisor | |
| dc.contributor.author | Rugenstein, Maria, committee member | |
| dc.contributor.author | Witt, Jessica, committee member | |
| dc.date.accessioned | 2022-01-07T11:28:35Z | |
| dc.date.available | 2022-01-07T11:28:35Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single- and multi-variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer-wise relevance propagation, a neural network visualization tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These "indicator patterns" vary in time and between climate models, providing a template for investigating inter-model differences in the time evolution of the forced response. This work demonstrates how neural networks and their visualization tools can be harnessed to identify patterns of the forced signal within combined fields. | |
| dc.format.medium | born digital | |
| dc.format.medium | masters theses | |
| dc.identifier | Rader_colostate_0053N_16859.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/234163 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright 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.subject | patterns | |
| dc.subject | forced signal | |
| dc.subject | forced climate change | |
| dc.subject | neural networks | |
| dc.subject | visualization | |
| dc.title | Detecting forced change within combined climate fields using explainable neural networks | |
| dc.type | Text | |
| dcterms.rights.dpla | This 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.discipline | Atmospheric Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
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