Title: The Art and Science of Climate Model Tuning

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. Here, we discuss how tuning methodologies may affect fundamental results of climate models, suchmore » as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.« less

Authors:

Hourdin, Frederic ^[1]; Mauritsen, Thorsten ^[2]; Gettelman, Andrew ^[3]; Golaz, Jean -Christophe ^[4]; Balaji, Venkatramani ^[5]; Duan, Qingyun ^[6]; Folini, Doris ^[7]; Ji, Duoying ^[6]; Klocke, Daniel ^[8]; Qian, Yun ^[9]; Rauser, Florian ^[2]; Rio, Catherine ^[1]; Tomassini, Lorenzo ^[2]; Watanabe, Masahiro ^[10]; Williamson, Daniel ^[11]

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+ Show Author Affiliations

1. Lab. de Meteorologie Dynamique, Paris (France)
2. Max Planck Institute for Meteorology, Hamburg (Germany)
3. National Center for Atmospheric Research, Boulder, CO (United States)
4. National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Lab., Princeton, NJ (United States)
5. Princeton Univ., Princeton, NJ (United States)
6. Beijing Normal Univ., Beijing (China)
7. Eidgenossische Technische Hochschule, Zurich (Switzerland)
8. Deutscher Wetterdienst, Offenbach (Germany)
9. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
10. Univ. of Tokyo, Tokyo (Japan)
11. Univ. of Exeter, Exeter (United Kingdom)

Publication Date:

Fri Mar 31 00:00:00 EDT 2017

Research Org.:

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

1366951

Report Number(s):

LLNL-JRNL-680477
Journal ID: ISSN 0003-0007

Grant/Contract Number:  

AC52-07NA27344

Resource Type:

Journal Article: Accepted Manuscript

Journal Name:

Bulletin of the American Meteorological Society

Additional Journal Information:

Journal Volume: 98; Journal Issue: 3; Journal ID: ISSN 0003-0007

Publisher:

American Meteorological Society

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE