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Machine learning and the quest for objectivity in climate model parameterization

Author

Listed:
  • Julie Jebeile

    (University of Bern
    University of Bern
    CNRM UMR 3589, Météo-France/CNRS, Centre National de Recherches Météorologiques)

  • Vincent Lam

    (University of Bern
    University of Bern
    The University of Queensland, School of Historical and Philosophical Inquiry)

  • Mason Majszak

    (University of Bern
    University of Bern)

  • Tim Räz

    (University of Bern)

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.

Suggested Citation

  • Julie Jebeile & Vincent Lam & Mason Majszak & Tim Räz, 2023. "Machine learning and the quest for objectivity in climate model parameterization," Climatic Change, Springer, vol. 176(8), pages 1-19, August.
    • Handle: RePEc:spr:climat:v:176:y:2023:i:8:d:10.1007_s10584-023-03532-1
    DOI: 10.1007/s10584-023-03532-1
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    References listed on IDEAS

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    2. Carlo Martini, 2015. "Expertise and institutional design in economic committees," Journal of Economic Methodology, Taylor & Francis Journals, vol. 22(3), pages 391-409, September.
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