• Adler, R. F., and Coauthors, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167.

    • Search Google Scholar
    • Export Citation
  • Forgy, E. W., 1965: Cluster analysis of multivariate data: Efficiency vs. interpretability of classifications. Biometrics, 21, 768769.

    • Search Google Scholar
    • Export Citation
  • Fovell, R. G., , and M.-Y. C. Fovell, 1993: Climate zones of the conterminous United States defined using cluster analysis. J. Climate, 6, 21032135.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P. J., , K. E. Taylor, , and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res., 113, D06104, doi:10.1029/2007JD008972.

    • Search Google Scholar
    • Export Citation
  • Iizumi, T., , M. Nishimori, , and M. Yokozawa, 2010: Diagnostics of climate model biases in summer temperature and warm-season insolation for the simulation of regional paddy rice yield in Japan. J. Appl. Meteor. Climatol., 49, 574591.

    • Search Google Scholar
    • Export Citation
  • Josey, S. A., , E. C. Kent, , and P. K. Taylor, 1999: New insights into the ocean heat budget closure problem from analysis of the SOC air–sea flux climatology. J. Climate, 12, 28562880.

    • Search Google Scholar
    • Export Citation
  • K-1 Model Developers, 2004: K-1 coupled model (MIROC) description. K-1 Tech. Rep., H. Hasumi and S. Emori, Eds., 34 pp. [Available online at http://www.ccsr.u-tokyo.ac.jp/~agcmadm/.]

    • Search Google Scholar
    • Export Citation
  • Kawase, H., , T. Yoshikane, , M. Hara, , F. Kimura, , T. Yasunari, , B. Ailikun, , H. Ueda, , and T. Inoue, 2009: Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method. J. Geophys. Res., 114, D24110, doi:10.1029/2009JD011803.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., , R. Furrer, , C. Tebaldi, , J. Cermak, , and G. A. Meehl, 2010: Challenges in combining projections from multiple climate models. J. Climate, 23, 27392758.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , C. Covey, , T. Delworth, , M. Latif, , B. McAvaney, , J. F. B. Mitchell, , R. J. Stoufeer, , and K. E. Taylor, 2007a: The WCRP CMIP3 multimodel dataset. Bull. Amer. Meteor. Soc., 88, 13831394.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2007b: Global climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 747–846.

    • Search Google Scholar
    • Export Citation
  • Murphy, J. M., , D. M. H. Sexton, , D. N. Barnett, , G. S. Jones, , M. J. Webb, , M. Collins, , and D. A. Stainforth, 2004: Quantification of modeling uncertainties in a large ensemble of climate change simulations. Nature, 430, 768772.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., , C. P. Batstone, , R. J. P. Hofmann, , K. E. Taylor, , and P. J. Gleckler, 2008: Evaluating the present-day simulation of clouds, precipitation, and radiation in climate models. J. Geophys. Res., 113, D14209, doi:10.1029/2007JD009334.

    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., , P. Brohan, , D. E. Parker, , C. K. Folland, , J. J. Kennedy, , M. Vanicek, , T. Ansell, , and S. F. B. Tett, 2006: Improved analyses of changes and uncertainties in sea surface temperature measured in situ since the mid-nineteenth century: The HadSST2 dataset. J. Climate, 19, 446469.

    • Search Google Scholar
    • Export Citation
  • Reichler, T., , and J. Kim, 2008: How well do coupled models simulate today’s climate? Bull. Amer. Meteor. Soc., 89, 303311.

  • Rossow, W. B., , and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612287.

  • Santer, B. D., and Coauthors, 2009: Incorporating model quality information in climate change detection and attribution studies. Proc. Natl. Acad. Sci. USA, 106, 14 77814 783.

    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012.

  • Ward, J. H. Jr., 1963: Hierarchical grouping to optimize an objective function. J. Amer. Stat. Assoc., 58, 236244.

  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 63126335.

    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., , B. R. Barkstrom, , E. F. Harrison, , R. B. Lee III, , G. L. Smith , , and J. E. Cooper, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System experiment. Bull. Amer. Meteor. Soc., 77, 853868.

    • Search Google Scholar
    • Export Citation
  • Williams, K. D., , and G. Tselioudis, 2007: GCM intercomparison of global cloud regimes: Present-day evaluation and climate change response. Climate Dyn., 29, 231250.

    • Search Google Scholar
    • Export Citation
  • Williams, K. D., , and M. J. Webb, 2009: A quantitative performance assessment of cloud regimes in climate models. Climate Dyn., 33, 141157.

    • Search Google Scholar
    • Export Citation
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Application of Cluster Analysis to Climate Model Performance Metrics

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  • 1 Atmosphere and Ocean Research Institute, The University of Tokyo, Kashiwa, Japan
  • 2 Graduate School of Science, The University of Tokyo, Tokyo, Japan
  • 3 Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
  • 4 Meteorological Research Institute, Tsukuba, Japan
  • 5 Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
  • 6 Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
  • 7 Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan
  • 8 Tokyo Gakugei University, Tokyo, Japan
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Abstract

The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.

Corresponding author address: Satoru Yokoi, Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. E-mail: yokoi@aori.u-tokyo.ac.jp

Abstract

The overall performance of general circulation models is often investigated on the basis of the synthesis of a number of scalar performance metrics of individual models that measure the reproducibility of diverse aspects of the climate. Because of physical and dynamic constraints governing the climate, a model’s performance in simulating a certain aspect of the climate is sometimes related closely to that in simulating another aspect, which results in significant intermodel correlation between performance metrics. Numerous metrics and intermodel correlations may cause a problem in understanding the evaluation and synthesizing the metrics. One possible way to alleviate this problem is to group the correlated metrics beforehand. This study attempts to use simple cluster analysis to group 43 performance metrics. Two clustering methods, the K-means and the Ward methods, yield considerably similar clustering results, and several aspects of the results are found to be physically and dynamically reasonable. Furthermore, the intermodel correlation between the cluster averages is considerably lower than that between the metrics. These results suggest that the cluster analysis is helpful in obtaining the appropriate grouping. Applications of the clustering results are also discussed.

Corresponding author address: Satoru Yokoi, Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8568, Japan. E-mail: yokoi@aori.u-tokyo.ac.jp
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