• Andronova, N. G., , and M. E. Schlesinger, 2001: Objective estimation of the probability density function for climate sensitivity. J. Geophys. Res., 106 , 2260522611.

    • Search Google Scholar
    • Export Citation
  • Carter, T. R., , M. Hulme, , and D. Viner, Eds.,. 1999: Representing uncertainty in climate change scenarios and impact studies. Proc. ECLAT-2 Helsinki Workshop, Norwich, United Kingdom, CRU, 128 pp.

    • Search Google Scholar
    • Export Citation
  • Dai, A., , T. M. L. Wigley, , B. Boville, , J. T. Kiehl, , and L. Buja, 2001: Climates of the twentieth and twenty-first centuries simulated by the NCAR climate system model. J. Climate, 14 , 485519.

    • Search Google Scholar
    • Export Citation
  • Flato, G. M., , and G. J. Boer, 2001: Warming asymmetry in climate change simulations. Geophys. Res. Lett., 28 , 195198.

  • Fyfe, J. C., , and G. M. Flato, 1999: Enhanced climate change and its detection over the Rocky Mountains. J. Climate, 12 , 230243.

  • Giorgi, F., , and L. O. Mearns, 1991: Approaches to regional climate change simulation: A review. Rev. Geophys., 29 , 191216.

  • Giorgi, F., , and R. Francisco, 2000a: Uncertainties in regional climate change predictions. A regional analysis of ensemble simulations with the HADCM2 GCM. Climate Dyn., 16 , 169182.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , and R. Francisco, . 2000b: Evaluating uncertainties in the prediction of regional climate change. Geophys. Res. Lett., 27 , 12951298.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , J. W. Hurrell, , M. R. Marinucci, , and M. Beniston, 1997: Elevation signal in surface climate change: A model study. J. Climate, 10 , 288296.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and Coauthors. 2001a: Emerging patterns of simulated regional climatic changes for the 21st century due to anthropogenic forcings. Geophys. Res. Lett., 28 , 33173320.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and Coauthors. 2001b: Regional climate information—Evaluation and projections. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 583–638.

    • Search Google Scholar
    • Export Citation
  • Gordon, H. B., , and S. P. O'Farrell, 1997: Transient climate change in the CSIRO coupled model with dynamic sea ice. Mon. Wea. Rev., 125 , 875907.

    • Search Google Scholar
    • Export Citation
  • Hulme, M., , and T. R. Carter, 2000: The changing climate of Europe. Assessment of the Potential Effects of Climate Change in Europe, M. L. Parry, Ed., ACACIA Concerted Action Report, UEA, 350 pp.

    • Search Google Scholar
    • Export Citation
  • Jones, R. N., 2000a: Managing uncertainty in climate change projections—Issues for impact assessment. Climate Change, 45 , 403419.

  • Jones, R. N., . 2000b: Analysing the risk of climate change using an irrigation demand model. Climate Res., 14 , 89100.

  • Kattenberg, G. A., and Coauthors. 1996: Climate models—Projections of future climate. Climate Change 1995: The Science of Climate Change, J. T. Houghton et al., Eds., Cambridge University Press, 285–358.

    • Search Google Scholar
    • Export Citation
  • Katz, R. W., 2001: Techniques for estimating uncertainty in climate change scenarios and impact studies. Climate Res., in press.

  • Kittel, T. G. F., , F. Giorgi, , and G. A. Meehl, 1998: Intercomparison of regional biases and doubled CO2 sensitivity of coupled atmosphere–ocean general circulation model experiments. Climate Dyn., 14 , 115.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., , T. L. Delworth, , K. W. Dixon, , and R. J. Stouffer, 1999: Model assessment of regional surface temperature trends (1949–97). J. Geophys. Res., 104 , 3098130996.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., , C. M. Kishtawal, , T. LaRow, , D. Bachiochi, , Z. Zhang, , C. E. Williford, , S. Gadgil, , and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285 , 15481550.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., , C. M. Kishtawal, , Z. Zhang, , T. LaRow, , D. Bachiochi, , C. E. Williford, , S. Gadgil, , and S. Surendran, 2000: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13 , 41964216.

    • Search Google Scholar
    • Export Citation
  • Lambert, S. J., , and G. J. Boer, 2001: CMIP1 evaluation and intercomparison of coupled climate models. Climate Dyn., 17 , 83106.

  • Machenhauer, B., , J. Windelband, , M. Botzet, , J. H. Christensen, , M. Deque, , R. G. Jones, , P. M. Ruti, , and G. Visconti, 1998: Validation and analysis of regional present-day climate and climate change simulations over Europe. Max-Planck-Institut für Meteorologie Rep. 275, 78 pp.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., , M. Hulme, , T. R. Carter, , R. Leemans, , M. Lal, , and P. Whetton, 2001: Climate scenario development. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 739–768.

    • Search Google Scholar
    • Export Citation
  • Mitchell, J. F. B., , T. C. Johns, , M. Eagles, , W. J. Ingram, , and R. A. Davis, 1999: Towards the construction of climate change scenarios. Climatic Change, 41 , 547581.

    • Search Google Scholar
    • Export Citation
  • New, M., , and M. Hulme, 2000: Representing uncertainty in climate change scenarios: A Monte-Carlo approach. Integr. Assess., 1 , 203214.

    • Search Google Scholar
    • Export Citation
  • New, M., , M. Hulme, , and P. D. Jones, 2000: Representing twentieth-century space time climate fields. Part II: Development of a 1901–1996 mean monthly terrestrial climatology. J. Climate, 13 , 22172238.

    • Search Google Scholar
    • Export Citation
  • Noda, A., , K. Yoshimatsu, , S. Yukimoto, , K. Yamaguchi, , and S. Yamaki, 1999: Relationship between natural variability and CO2-induced warming pattern: MRI AOGCM experiment. Preprints, 10th Symp. on Global Change Studies, Dallas, TX, Amer. Meteor. Soc., 359–362.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., , C. Brankovic, , and D. C. Richardson, 2000: A probability and decision model analysis of PROVOST seasonal multi-model ensemble integrations. Quart. J. Roy. Meteor. Soc., 126 , 20132033.

    • Search Google Scholar
    • Export Citation
  • Schneider, S. H., 2001: What is dangerous climate change? Nature, 411 , 1719.

  • Smart, R., , N. Nakicenovic, , and S. Nakicenovic, Eds.,. 2000: Emission Scenarios. Cambridge University Press, 599 pp.

  • Stendel, M., , T. Schmith, , E. Roeckner, , and U. Cubasch, 2000: The climate of the 21st century: Transient dimulations with a coupled atmosphere–ocean general circulation model. Danmarks Klimacenter Rep. 00-6.

    • Search Google Scholar
    • Export Citation
  • Stott, P. A., , and S. F. B. Tett, 1998: Scale-dependent detection of climate change. J. Climate, 11 , 32823294.

  • Visser, H., , R. J. M. Folkert, , J. Hoekstra, , and J. J. deWolff, 2000: Identifying key sources of uncertainty in climate change projections. Climatic Change, 45 , 421457.

    • Search Google Scholar
    • Export Citation
  • Whetton, P. H., , M. H. England, , S. P. O'Farrell, , I. G. Watterson, , and A. B. Pittock, 1996: Global comparison of the regional rainfall results of enhanced greenhouse coupled and mixed layer ocean experiments: Implications for climate change scenario development. Climatic Change, 33 , 497519.

    • Search Google Scholar
    • Export Citation
  • Wigley, T. M. L., , and S. C. B. Raper, 2001: Interpretation of high projections for global-mean warming. Science, 293 , 451454.

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Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method

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  • 1 Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
  • | 2 NCAR, Boulder, Colorado
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Abstract

The “reliability ensemble averaging” (REA) method for calculating average, uncertainty range, and a measure of reliability of simulated climate changes at the subcontinental scale from ensembles of different atmosphere–ocean general circulation model (AOGCM) simulations is introduced. The method takes into account two “reliability criteria”: the performance of the model in reproducing present-day climate (“model performance” criterion) and the convergence of the simulated changes across models (“model convergence” criterion). The REA method is applied to mean seasonal temperature and precipitation changes for the late decades of the twenty-first century, over 22 land regions of the world, as simulated by a recent set of nine AOGCM experiments for two anthropogenic emission scenarios (the A2 and B2 scenarios of the Intergovernmental Panel for Climate Change). In the A2 scenario the REA average regional temperature changes vary between about 2 and 7 K across regions and they are all outside the estimated natural variability. The uncertainty range around the REA average change as measured by ± the REA root-mean-square difference (rmsd) varies between 1 and 4 K across regions and the reliability is mostly between 0.2 and 0.8 (on a scale from 0 to 1). For precipitation, about half of the regional REA average changes, both positive and negative, are outside the estimated natural variability and they vary between about −25% and +30% (in units of percent of present-day precipitation). The uncertainty range around these changes (± rmsd) varies mostly between about 10% and 30% and the corresponding reliability varies widely across regions. The simulated changes for the B2 scenario show a high level of coherency with those for the A2 scenario. Compared to simpler approaches, the REA method allows a reduction of the uncertainty range in the simulated changes by minimizing the influence of “outlier” or poorly performing models. The method also produces a quantitative measure of reliability that shows that both criteria need to be met by the simulations in order to increase the overall reliability of the simulated changes.

Corresponding author address: Dr. Filippo Giorgi, Abdus Salam International Centre for Theoretical Physics, P.O. Box 586, Trieste 34100, Italy. Email: giorgi@ictp.trieste.it

Abstract

The “reliability ensemble averaging” (REA) method for calculating average, uncertainty range, and a measure of reliability of simulated climate changes at the subcontinental scale from ensembles of different atmosphere–ocean general circulation model (AOGCM) simulations is introduced. The method takes into account two “reliability criteria”: the performance of the model in reproducing present-day climate (“model performance” criterion) and the convergence of the simulated changes across models (“model convergence” criterion). The REA method is applied to mean seasonal temperature and precipitation changes for the late decades of the twenty-first century, over 22 land regions of the world, as simulated by a recent set of nine AOGCM experiments for two anthropogenic emission scenarios (the A2 and B2 scenarios of the Intergovernmental Panel for Climate Change). In the A2 scenario the REA average regional temperature changes vary between about 2 and 7 K across regions and they are all outside the estimated natural variability. The uncertainty range around the REA average change as measured by ± the REA root-mean-square difference (rmsd) varies between 1 and 4 K across regions and the reliability is mostly between 0.2 and 0.8 (on a scale from 0 to 1). For precipitation, about half of the regional REA average changes, both positive and negative, are outside the estimated natural variability and they vary between about −25% and +30% (in units of percent of present-day precipitation). The uncertainty range around these changes (± rmsd) varies mostly between about 10% and 30% and the corresponding reliability varies widely across regions. The simulated changes for the B2 scenario show a high level of coherency with those for the A2 scenario. Compared to simpler approaches, the REA method allows a reduction of the uncertainty range in the simulated changes by minimizing the influence of “outlier” or poorly performing models. The method also produces a quantitative measure of reliability that shows that both criteria need to be met by the simulations in order to increase the overall reliability of the simulated changes.

Corresponding author address: Dr. Filippo Giorgi, Abdus Salam International Centre for Theoretical Physics, P.O. Box 586, Trieste 34100, Italy. Email: giorgi@ictp.trieste.it

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