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...
| Published in: | Journal of Computational and Applied Mathematics |
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| Main Authors: | , , , , |
| Language: | unknown |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://www.osti.gov/servlets/purl/1782577 https://www.osti.gov/biblio/1782577 https://doi.org/10.1016/j.cam.2021.113451 |
| Summary: | 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. |
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