Journal article
The Art and Science of Climate Model Tuning
- Hourdin, Frédéric Laboratoire de Météorologie Dynamique, IPSL, CNRS, UPMC, Paris, France
- Mauritsen, Thorsten Max Planck Institute for Meteorology, Hamburg, Germany
- Gettelman, Andrew National Center for Atmospheric Research,+ Boulder, Colorado
- Golaz, Jean-Christophe National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
- Balaji, Venkatramani Cooperative Institute for Climate Science, Princeton University, Princeton, New Jersey
- Duan, Qingyun Beijing Normal University, Beijing, China
- Folini, Doris Eidgenössische Technische Hochschule, Zurich, Switzerland
- Ji, Duoying Beijing Normal University, Beijing, China
- Klocke, Daniel Deutscher Wetterdienst, Offenbach, Germany
- Qian, Yun Pacific Northwest National Laboratory, Richland, Washington
- Rauser, Florian Max Planck Institute for Meteorology, Hamburg, Germany
- Rio, Catherine Laboratoire de Météorologie Dynamique, IPSL, CNRS, UPMC, Paris, France
- Tomassini, Lorenzo Max Planck Institute for Meteorology, Hamburg, Germany
- Watanabe, Masahiro University of Tokyo, Tokyo, Japan
- Williamson, Daniel University of Exeter, Exeter, United Kingdom
- 2017-3-31
Published in:
- Bulletin of the American Meteorological Society. - American Meteorological Society. - 2017, vol. 98, no. 3, p. 589-602
English
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.
- Language
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- English
- Open access status
- hybrid
- Identifiers
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- DOI 10.1175/bams-d-15-00135.1
- ISSN 0003-0007
- Persistent URL
- https://folia.unifr.ch/global/documents/187849
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