Machine learning and the quest for objectivity in climate model parameterization

Julie Jebeile (), Vincent Lam (), Mason Majszak () and Tim Räz ()
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Julie Jebeile: University of Bern
Vincent Lam: University of Bern
Mason Majszak: University of Bern
Tim Räz: University of Bern

Climatic Change, 2023, vol. 176, issue 8, No 3, 19 pages

Abstract: Abstract 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.

Keywords: Climate modeling; Parameterizations; Parameter tuning; Objectivity; Subjectivity; Expert judgement; Machine learning; Deep neural networks; Gaussian processes (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10584-023-03532-1

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