• 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
  • Barnett, T., and Coauthors, 2005: Detecting and attributing external influences on the climate system: A review of recent advances. J. Climate, 18, 12911314.

    • Search Google Scholar
    • Export Citation
  • Boé, J. L., A. Hall, and X. Qu, 2009: September sea-ice cover in the Arctic Ocean projected to vanish by 2100. Nat. Geosci., 2, 341343.

    • Search Google Scholar
    • Export Citation
  • Buser, C., H. Künsch, D. Lüthi, M. Wild, and C. Schär, 2009: Bayesian multi-model projection of climate: Bias assumptions and interannual variability. Climate Dyn., 33, 849868, doi:10.1007/s00382-009-0588-6.

    • Search Google Scholar
    • Export Citation
  • Christensen, J., and Coauthors, 2007: Regional climate projections. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 847–940.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: A regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Climate Dyn., 16, 169182.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and L. O. Mearns, 2002: Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the Reliability Ensemble Averaging (REA) method. J. Climate, 15, 11411158.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and L. O. Mearns, 2003: Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method. Geophys. Res. Lett., 30, 1629, doi:10.1029/2003GL017130.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from radiative forcing and observed temperature change. J. Geophys. Res., 113, D23105, doi:10.1029/2008JD010405.

    • Search Google Scholar
    • Export Citation
  • Jun, M., R. Knutti, and D. W. Nychka, 2008: Spatial analysis to quantify numerical model bias and dependence: How many climate models are there? J. Amer. Stat. Assoc., 103, 934947.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., 2008a: Should we believe model predictions of future climate change? Philos. Trans. Roy. Soc., A366, 46474664.

  • Knutti, R., 2008b: Why are climate models reproducing the observed global surface warming so well? Geophys. Res. Lett., 35, L18704, doi:10.1029/2008GL034932.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., 2010: The end of model democracy? Climatic Change, 102, 395404, doi:10.1007/s10584-010-9800-2.

  • Knutti, R., G. A. Meehl, M. R. Allen, and D. A. Stainforth, 2006: Constraining climate sensitivity from the seasonal cycle in surface temperature. J. Climate, 19, 42244233.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., and Coauthors, 2008: A review of uncertainties in global temperature projections over the twenty-first century. J. Climate, 21, 26512663.

    • Search Google Scholar
    • Export Citation
  • Knutti, R., G. Abramowitz, M. Collins, V. Eyring, P. J. Gleckler, B. Hewitson, and L. Mearns, 2010a: Good practice guidance paper on assessing and combining multimodel climate projections. Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, Stocker et al., Eds., IPCC Working Group I Technical Support Unit, 13 pp. [Available online at https://www.ipcc-wg1.unibe.ch/guidancepaper/IPCC_EM_MME_GoodPracticeGuidancePaper.pdf.]

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

    • Search Google Scholar
    • Export Citation
  • Mahlstein, I., and R. Knutti, 2009: Regional climate change patterns identified by cluster analysis. Climate Dyn., 35, 587600, doi:10.1007/s00382-009-0654-0.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., W. M. Washington, W. D. Collins, J. M. Arblaster, A. X. Hu, L. E. Buja, W. G. Strand, and H. Y. Teng, 2005: How much more global warming and sea level rise? Science, 307, 17691772.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor, 2007a: The WCRP CMIP3 multimodel dataset—A new era in climate change research. 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, 747–785.

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

    • Search Google Scholar
    • Export Citation
  • Nakicenovic, N., and Coauthors, 2000: IPCC Special Report on Emissions Scenarios. Cambridge University Press, 570 pp.

  • Räisänen, J., 2001: CO2-induced climate change in CMIP2 experiments: Quantification of agreement and role of internal variability. J. Climate, 14, 20882104.

    • Search Google Scholar
    • Export Citation
  • Räisänen, J., 2007: How reliable are climate models? Tellus, 59A, 229.

  • Randall, D., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

    • 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.

  • 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
  • Stainforth, D. A., M. R. Allen, E. R. Tredger, and L. A. Smith, 2007: Confidence, uncertainty and decision-support relevance in climate predictions. Philos. Trans. Roy. Soc., A365, 21452161.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., and J. A. Kettleborough, 2002: Origins and estimates of uncertainty in predictions of twenty-first century temperature rise. Nature, 416, 723726.

    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., and R. Knutti, 2007: The use of the multi-model ensemble in probabilistic climate projections. Philos. Trans. Roy. Soc., A365, 20532075.

    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., R. L. Smith, D. Nychka, and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles. J. Climate, 18, 15241540.

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

  • von Storch, H., and F. Zwiers, 2004: Statistical Analysis in Climate Research. Cambridge University Press, 485 pp.

  • Washington, W. M., R. Knutti, G. A. Meehl, H. Y. Teng, C. Tebaldi, D. Lawrence, L. Buja, and W. G. Strand, 2009: How much climate change can be avoided by mitigation? Geophys. Res. Lett., 36, L08703, doi:10.1029/2008GL037074.

    • Search Google Scholar
    • Export Citation
  • Weigel, A., R. Knutti, M. Liniger, and C. Appenzeller, 2010: Risks of model weighting in multimodel climate projections. J. Climate, 23, 41754191.

    • Search Google Scholar
    • Export Citation
  • Xie, P. P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558.

    • Search Google Scholar
    • Export Citation
  • Zhang, X. B., F. W. Zwiers, G. C. Hegerl, F. H. Lambert, N. P. Gillett, S. Solomon, P. A. Stott, and T. Nozawa, 2007: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461466.

    • Search Google Scholar
    • Export Citation
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Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble

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  • 1 Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
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Abstract

About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

Corresponding author address: David Masson, ETH Zurich, Universitaetstr. 16, CH-8092 Zurich, Switzerland. E-mail: david.masson@env.ethz.ch

Abstract

About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

Corresponding author address: David Masson, ETH Zurich, Universitaetstr. 16, CH-8092 Zurich, Switzerland. E-mail: david.masson@env.ethz.ch
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