Local eigenvalue analysis of CMIP3 climate model errors

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Jun, Mikyoung ; Knutti, Reto ; Nychka, Douglas W. (2008)

Of the two dozen or so global atmosphere–ocean general circulation models (AOGCMs), many share parameterizations, components or numerical schemes, and several are developed by the same institutions. Thus it is natural to suspect that some of the AOGCMs have correlated error patterns. Here we present a local eigenvalue analysis for the AOGCM errors based on statistically quantified correlation matrices for these errors. Our statistical method enables us to assess the significance of the result based on the simulated data under the assumption that all AOGCMs are independent. The result reveals interesting local features of the dependence structure of AOGCM errors. At least for the variable and the timescale considered here, the Coupled Model Intercomparison Project phase 3 (CMIP3) model archive cannot be treated as a collection of independent models. We use multidimensional scaling to visualize the similarity of AOGCMs and all-subsets regression to provide subsets of AOGCMs that are the best approximation to the variation among the full set of models.
  • References (12)
    12 references, page 1 of 2

    Cox, T. F. and Cox, M. A. A. 2001. Multidimensional Scaling 2nd Edition. Chapman & Hall/CRC Boca Raton, London, New York, Washington, D.C.

    Furrer, R., Sain, S. R., Nychka, D. W. and Meehl, G. A. 2007. Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophys. Res. Lett. 34, (doi:10.1029/2006GL027754).

    Jones, P. D., New, M., Parker, D. E., Martin, S. and Rigor, I. G. 1999. Surface air temperature and its variations over the last 150 years. Rev. Geophys. 37, 173-199.

    Jong, J.-C. and Kotz, S. 1999. On a relation between principal components and regression analysis. Am. Stat. 53, 349-351.

    Jun, M., Knutti, R. and Nychka, D. W. 2008. Spatial analysis to quantify numerical model bias and dependence: how many climate models are there? J. Am. Stat. Assoc. In press.

    Lambert, S. J. and Boer, G. J. 2001. CMIP1 evaluation and intercomparison of coupled climate models. Clim. Dyn. 17, 83-106.

    Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B. and co-authors. 2007a. The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull. Ame. Meteorol. Soc. 88, 1383- 1394.

    Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T. and co-authors. 2007b. Global climate projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, (eds. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, and co-editors). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

    Rayner, N. A., Brohan, P., Parker, D. E., Folland, C. K., Kennedy, J. J., and co-authors. 2006. Improved analyses of changes and uncertainties in marine temperature measured in situ since the mid-nineteenth century: the HadSST2 dataset. J. Clim. 19, 446-469.

    Tebaldi, C., Smith, R. L., Nychka, D. W. and Mearns, L. O. 2005. Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multimodel ensembles. J. Clim. 18, 1524-1540.

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