Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Jun, Mikyoung; Knutti, Reto; Nychka, Douglas W. (2008)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 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.
    • Torgerson, W. S. 1952. Multidimensional scaling, 1: theory and methods. Psychometrika 17, 401-419.
    • Young, G. and Householder, A. S. 1938. Discussion of a set of points in terms of their mutual distances. Psychometrika 3, 19-22.
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

  • NSF | A Statistics Program at the...
  • NSF | CMG: Non-Gaussian Statistic...

Cite this article

Collected from