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Machine learning and the quest for objectivity in climate model parameterization.

Julie Jebeile1,2,3, Vincent Lam1,2,4, Mason Majszak1,2

  • 1Institute of Philosophy, University of Bern, Länggassstrasse 49a, 3012 Bern, Switzerland.

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This summary is machine-generated.

Machine learning can aid climate model parameterization, but expert judgment remains crucial. Automating climate model tuning still requires subjective insights, blending art and science.

Keywords:
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Area of Science:

  • Climate Science
  • Computational Science
  • Artificial Intelligence

Background:

  • Parameterization and tuning are essential but subjective in climate modeling.
  • Automated methods like machine learning offer potential improvements.

Purpose of the Study:

  • To investigate the role and limitations of machine learning in climate model parameterization.
  • To assess whether machine learning truly removes subjectivity from climate model development.

Main Methods:

  • Analysis of case studies involving machine learning in climate model parameterization.
  • Qualitative assessment of the subjective elements in machine learning-assisted tuning.

Main Results:

  • Machine learning techniques show promise for enhancing climate model parameterization.
  • Subjective expert judgment remains indispensable even with machine learning integration.

Conclusions:

  • Machine learning in climate modeling is a hybrid of art and science.
  • Careful expert supervision is necessary for effective machine learning application in parameterization.