Jebeile, Julie; Lam, Vincent; Majszak, Mason Meyer; Räz, Tim (2023). Machine learning and the quest for objectivity in climate model parameterization. Climatic change, 176(8), p. 101. Springer 10.1007/s10584-023-03532-1
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Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve certain subjective elements. Even if the use of these subjective elements is not necessarily epistemically problematic, there is an intuitive appeal for replacing them with more objective (automated) methods, such as machine learning. Relying on several case studies, we argue that, while machine learning techniques may help to improve climate model parameterization in several ways, they still require expert judgment that involves subjective elements not so different from the ones arising in standard parameterization and tuning. The use of machine learning in parameterizations is an art as well as a science and requires careful supervision.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
06 Faculty of Humanities > Department of Art and Cultural Studies > Institute of Philosophy |
UniBE Contributor: |
Jebeile, Julie Alia Nina, Lam, Vincent Minh Duc, Majszak, Mason Meyer, Räz, Tim |
Subjects: |
100 Philosophy |
ISSN: |
0165-0009 |
Publisher: |
Springer |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
24 Jul 2023 15:31 |
Last Modified: |
20 Aug 2023 02:36 |
Publisher DOI: |
10.1007/s10584-023-03532-1 |
PubMed ID: |
37476487 |
Uncontrolled Keywords: |
Climate modeling Deep neural networks Expert judgement Gaussian processes Machine learning Objectivity Parameter tuning Parameterizations Subjectivity |
BORIS DOI: |
10.48350/184987 |
URI: |
https://boris.unibe.ch/id/eprint/184987 |
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