Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change
In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art climate prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. T...
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Online Access: | http://www.osti.gov/servlets/purl/1782577 https://www.osti.gov/biblio/1782577 https://doi.org/10.1016/j.cam.2021.113451 |
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ftosti:oai:osti.gov:1782577 2023-07-30T04:01:15+02:00 Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change Nichol, J. Jake Peterson, Matthew G. Peterson, Kara J. Fricke, G. Matthew Moses, Melanie E. 2022-03-29 application/pdf http://www.osti.gov/servlets/purl/1782577 https://www.osti.gov/biblio/1782577 https://doi.org/10.1016/j.cam.2021.113451 unknown http://www.osti.gov/servlets/purl/1782577 https://www.osti.gov/biblio/1782577 https://doi.org/10.1016/j.cam.2021.113451 doi:10.1016/j.cam.2021.113451 54 ENVIRONMENTAL SCIENCES 2022 ftosti https://doi.org/10.1016/j.cam.2021.113451 2023-07-11T10:03:32Z In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art climate prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. The issue is grave because accurate modeling is critical for economic, ecological, and geopolitical planning. We use machine learning techniques, including random forest regression and Gini importance, to show that the Energy Exascale Earth System Model (E3SM) relies too heavily on just one of the ten chosen climatological quantities to predict September sea ice averages. Furthermore, E3SM gives too much importance to six of those quantities when compared to observed data. Finally, identifying the features that climate models incorrectly rely on should allow climatologists to improve prediction accuracy. Other/Unknown Material Arctic Climate change Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Journal of Computational and Applied Mathematics 395 113451 |
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Open Polar |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
op_collection_id |
ftosti |
language |
unknown |
topic |
54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Nichol, J. Jake Peterson, Matthew G. Peterson, Kara J. Fricke, G. Matthew Moses, Melanie E. Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
In September of 2020, Arctic sea ice extent was the second-lowest on record. State of the art climate prediction uses Earth system models (ESMs), driven by systems of differential equations representing the laws of physics. Previously, these models have tended to underestimate Arctic sea ice loss. The issue is grave because accurate modeling is critical for economic, ecological, and geopolitical planning. We use machine learning techniques, including random forest regression and Gini importance, to show that the Energy Exascale Earth System Model (E3SM) relies too heavily on just one of the ten chosen climatological quantities to predict September sea ice averages. Furthermore, E3SM gives too much importance to six of those quantities when compared to observed data. Finally, identifying the features that climate models incorrectly rely on should allow climatologists to improve prediction accuracy. |
author |
Nichol, J. Jake Peterson, Matthew G. Peterson, Kara J. Fricke, G. Matthew Moses, Melanie E. |
author_facet |
Nichol, J. Jake Peterson, Matthew G. Peterson, Kara J. Fricke, G. Matthew Moses, Melanie E. |
author_sort |
Nichol, J. Jake |
title |
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change |
title_short |
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change |
title_full |
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change |
title_fullStr |
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change |
title_full_unstemmed |
Machine learning feature analysis illuminates disparity between E3SM climate models and observed climate change |
title_sort |
machine learning feature analysis illuminates disparity between e3sm climate models and observed climate change |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1782577 https://www.osti.gov/biblio/1782577 https://doi.org/10.1016/j.cam.2021.113451 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1782577 https://www.osti.gov/biblio/1782577 https://doi.org/10.1016/j.cam.2021.113451 doi:10.1016/j.cam.2021.113451 |
op_doi |
https://doi.org/10.1016/j.cam.2021.113451 |
container_title |
Journal of Computational and Applied Mathematics |
container_volume |
395 |
container_start_page |
113451 |
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1772812003677569024 |