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The Art and Science of Climate Model Tuning

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  • 1 Laboratoire de Météorologie Dynamique, IPSL, CNRS, UPMC, Paris, France
  • 2 Max Planck Institute for Meteorology, Hamburg, Germany
  • 3 National Center for Atmospheric Research, Boulder, Colorado
  • 4 National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
  • 5 Cooperative Institute for Climate Science, Princeton University, Princeton, New Jersey
  • 6 Beijing Normal University, Beijing, China
  • 7 Eidgenössische Technische Hochschule, Zurich, Switzerland
  • 8 Beijing Normal University, Beijing, China
  • 9 Deutscher Wetterdienst, Offenbach, Germany
  • 10 Pacific Northwest National Laboratory, Richland, Washington
  • 11 Max Planck Institute for Meteorology, Hamburg, Germany
  • 12 Laboratoire de Météorologie Dynamique, IPSL, CNRS, UPMC, Paris, France
  • 13 Max Planck Institute for Meteorology, Hamburg, Germany
  • 14 University of Tokyo, Tokyo, Japan
  • 15 University of Exeter, Exeter, United Kingdom
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Abstract

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

Current affiliations: TomassiniMet Office, Exeter, United Kingdom; GolazLawrence Livermore National Laboratory, Livermore, California

The National Center for Atmospheric Research is supported by the National Science Foundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR E-MAIL: Frédéric Hourdin, frederic.hourdin@lmd.jussieu.fr

A supplement to this article is available online (10.1175/BAMS-D-15-00135.2)

Abstract

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

Current affiliations: TomassiniMet Office, Exeter, United Kingdom; GolazLawrence Livermore National Laboratory, Livermore, California

The National Center for Atmospheric Research is supported by the National Science Foundation.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR E-MAIL: Frédéric Hourdin, frederic.hourdin@lmd.jussieu.fr

A supplement to this article is available online (10.1175/BAMS-D-15-00135.2)

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