Changes in Temperature and Precipitation Extremes in the IPCC Ensemble of Global Coupled Model Simulations

Viatcheslav V. Kharin Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, British Columbia, Canada

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Francis W. Zwiers Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, British Columbia, Canada

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Xuebin Zhang Climate Data and Analysis Section, Environment Canada, Toronto, Ontario, Canada

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Gabriele C. Hegerl Nicholas School for the Environment and Earth Science, Duke University, Durham, North Carolina

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Abstract

Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios.

Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipitation extremes are plausible in the extratropics, but uncertainties in extreme precipitation in the Tropics are very large, both in the models and the available observationally based datasets.

Changes in warm extremes generally follow changes in the mean summertime temperature. Cold extremes warm faster than warm extremes by about 30%–40%, globally averaged. The excessive warming of cold extremes is generally confined to regions where snow and sea ice retreat with global warming. With the exception of northern polar latitudes, relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation, particularly in tropical and subtropical regions. Consistent with the increased intensity of precipitation extremes, waiting times for late-twentieth-century extreme precipitation events are reduced almost everywhere, with the exception of a few subtropical regions. The multimodel multiscenario consensus on the projected change in the globally averaged 20-yr return values of annual extremes of 24-h precipitation amounts is that there will be an increase of about 6% with each kelvin of global warming, with the bulk of models simulating values in the range of 4%–10% K−1. The very large intermodel disagreements in the Tropics suggest that some physical processes associated with extreme precipitation are not well represented in models. This reduces confidence in the projected changes in extreme precipitation.

Corresponding author address: Viatcheslav Kharin, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC, Canada. Email: slava.kharin@ec.gc.ca

Abstract

Temperature and precipitation extremes and their potential future changes are evaluated in an ensemble of global coupled climate models participating in the Intergovernmental Panel on Climate Change (IPCC) diagnostic exercise for the Fourth Assessment Report (AR4). Climate extremes are expressed in terms of 20-yr return values of annual extremes of near-surface temperature and 24-h precipitation amounts. The simulated changes in extremes are documented for years 2046–65 and 2081–2100 relative to 1981–2000 in experiments with the Special Report on Emissions Scenarios (SRES) B1, A1B, and A2 emission scenarios.

Overall, the climate models simulate present-day warm extremes reasonably well on the global scale, as compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes, especially in sea ice–covered areas. Simulated present-day precipitation extremes are plausible in the extratropics, but uncertainties in extreme precipitation in the Tropics are very large, both in the models and the available observationally based datasets.

Changes in warm extremes generally follow changes in the mean summertime temperature. Cold extremes warm faster than warm extremes by about 30%–40%, globally averaged. The excessive warming of cold extremes is generally confined to regions where snow and sea ice retreat with global warming. With the exception of northern polar latitudes, relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation, particularly in tropical and subtropical regions. Consistent with the increased intensity of precipitation extremes, waiting times for late-twentieth-century extreme precipitation events are reduced almost everywhere, with the exception of a few subtropical regions. The multimodel multiscenario consensus on the projected change in the globally averaged 20-yr return values of annual extremes of 24-h precipitation amounts is that there will be an increase of about 6% with each kelvin of global warming, with the bulk of models simulating values in the range of 4%–10% K−1. The very large intermodel disagreements in the Tropics suggest that some physical processes associated with extreme precipitation are not well represented in models. This reduces confidence in the projected changes in extreme precipitation.

Corresponding author address: Viatcheslav Kharin, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC, Canada. Email: slava.kharin@ec.gc.ca

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  • Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419 , 224232.

  • Coles, S. G., and M. J. Dixon, 1999: Likelihood-based inference for extreme value models. Extremes, 2 , 523.

  • Collins, W. D., and Coauthors, 2006: The Community Climate System Model version 3 (CCSM3). J. Climate, 19 , 21222143.

  • Cubasch, U., and Coauthors, 2001: Projections of future climate change. Climate Change 2001: The Scientific Basis, J. T. Houghton et al., Eds., Cambridge University Press, 525–582.

    • Search Google Scholar
    • Export Citation
  • Delworth, T. L., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part I: Formulation and simulation characteristics. J. Climate, 19 , 643674.

    • Search Google Scholar
    • Export Citation
  • Diansky, N. A., and E. M. Volodin, 2002: Simulation of present-day climate with a coupled atmosphere-ocean general circulation model. Izv. Atmos. Oceanic Phys., 38 , 732747.

    • Search Google Scholar
    • Export Citation
  • Dupuis, D. J., and M. Tsao, 1998: A hybrid estimator for the Generalized Pareto and Extreme-Value Distributions. Comm. Stat. Theory Methods, 27 , 925941.

    • Search Google Scholar
    • Export Citation
  • Efron, B., and R. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman and Hall, 436 pp.

  • Emori, S., and S. J. Brown, 2005: Dynamic and thermodynamic changes in mean and extreme precipitation under changed climate. Geophys. Res. Lett., 32 .L17706, doi:10.1029/2005GL023272.

    • Search Google Scholar
    • Export Citation
  • Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. M. G. Klein Tank, and T. Peterson, 2002: Observed coherent changes in climate extremes during the second half of the twentieth century. Climate Res., 19 , 193212.

    • Search Google Scholar
    • Export Citation
  • Gibson, J. K., P. Kalberg, S. Uppala, A. Hernandes, A. Nomura, and E. Serrano, 1997: ERA Description. ECMWF Reanalysis Report Series 1, ECMWF, Reading, United Kingdom, 72 pp.

  • Gnanadesikan, A., and Coauthors, 2006: GFDL’s CM2 global coupled climate models. Part II: The baseline ocean simulation. J. Climate, 19 , 675697.

    • Search Google Scholar
    • Export Citation
  • Gregory, J. M., and J. F. B. Mitchell, 1995: Simulation of daily variability of surface temperature and precipitation over Europe in the current and 2xCO2 climate using the UKMO high-resolution climate model. Quart. J. Roy. Meteor. Soc., 121 , 14511476.

    • Search Google Scholar
    • Export Citation
  • Hasumi, H., and S. Emori, 2004: K-1 coupled model (MIROC) description. K-1 Tech. Rep. 1, Center for Climate System Research, University of Tokyo, 34 pp.

  • Hosking, J. R. M., 1990: L-moments: Analysis and estimation of distributions using linear combinations of order statistics. J. Roy. Stat. Soc. (Ser. A), B52 , 105124.

    • Search Google Scholar
    • Export Citation
  • Hosking, J. R. M., 1992: Moments or L-moments? An example comparing the two measures of distributional shape. Amer. Stat., 46 , 186189.

    • Search Google Scholar
    • Export Citation
  • Hosking, J. R. M., J. R. Wallis, and E. F. Wood, 1985: Estimation of the generalized extreme-value distribution by the method of probability-weighted moments. Technometrics, 27 , 251261.

    • Search Google Scholar
    • Export Citation
  • International Ad Hoc Detection and Attribution Group, 2005: Detecting and attributing external influences on the climate system: A review of recent advances. J. Climate, 18 , 12911314.

    • Search Google Scholar
    • Export Citation
  • Jenkinson, A. F., 1955: The frequency distribution of the annual maximum (or minimum) values of meteorological elements. Quart. J. Roy. Meteor. Soc., 81 , 158171.

    • Search Google Scholar
    • Export Citation
  • Jungclaus, J. H., and Coauthors, 2006: Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. J. Climate, 19 , 39523972.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77 , 437471.

  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83 , 16311643.

    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., and F. W. Zwiers, 2000: Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere–ocean GCM. J. Climate, 13 , 37603788.

    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., and F. W. Zwiers, 2005: Estimating extremes in transient climate change simulations. J. Climate, 18 , 11561173.

  • Kharin, V. V., F. W. Zwiers, and X. Zhang, 2005: Intercomparison of near surface temperature and precipitation extremes in AMIP-2 simulations, reanalyses, and observations. J. Climate, 18 , 52015223.

    • Search Google Scholar
    • Export Citation
  • Martins, E. S., and J. R. Stedinger, 2000: Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrological data. Water Resour. Res., 36 , 737744.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., and Coauthors, 2006: Climate change projections for the twenty-first century and climate change commitment in the CCSM3. J. Climate, 19 , 25972616.

    • Search Google Scholar
    • Export Citation
  • Min, S-K., S. Legutke, A. Hense, and W-T. Kwon, 2005: Internal variability in a 1000-year control simulation with the coupled climate model ECHO-G. Part I: Near surface temperature, precipitation, and mean sea level pressure. Tellus, 57A , 605621.

    • Search Google Scholar
    • Export Citation
  • Palutikof, J. P., B. B. Brabson, D. H. Lister, and S. T. Adcock, 1999: A review of methods to calculate extreme wind speeds. Meteor. Appl., 6 , 119132.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., P. J. Crutzen, J. T. Kiehl, and D. Rosenfeld, 2001: Atmosphere—Aerosols, climate, and the hydrological cycle. Science, 294 , 21192124.

    • Search Google Scholar
    • Export Citation
  • Russell, G. L., J. R. Miller, and D. Rind, 1995: A coupled atmosphere–ocean model for transient climate change studies. Atmos.–Ocean, 33 , 683730.

    • Search Google Scholar
    • Export Citation
  • Russell, G. L., J. R. Miller, D. Rind, R. A. Ruedy, G. A. Schmidt, and S. Sheth, 2000: Comparison of model and observed regional temperature changes during the past 40 years. J. Geophys. Res., 105 , 1489114898.

    • Search Google Scholar
    • Export Citation
  • Schmidt, G. A., and Coauthors, 2006: Present-day atmospheric simulations using GISS ModelE: Comparison to in situ, satellite, and reanalysis data. J. Climate, 19 , 153192.

    • Search Google Scholar
    • Export Citation
  • Scinocca, J. F., and N. A. McFarlane, 2004: The variability of modeled tropical precipitation. J. Atmos. Sci., 61 , 19932015.

  • Semenov, V. A., and L. Bengtsson, 2002: Secular trends in daily precipitation characteristics: Greenhouse gas simulation with a coupled AOGCM. Climate Dyn., 19 , 123140.

    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and J. K. Gibson, 2000: The ERA-40 project plan. ECMWF ERA-40 Project Report Series 1, 63 pp.

  • Stephens, M. A., 1970: Use of the Kolmogorov-Smirnov, Cramer-von-Mises and related statistics without extensive tables. J. Roy. Stat. Soc. (Ser. A), 32B , 115122.

    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., K. Hayhoe, J. M. Arblaster, and G. A. Meehl, 2006: An intercomparison of model-simulated historical and future changes in extreme events. Climatic Change, 79 , 185211.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84 , 12051217.

    • Search Google Scholar
    • Export Citation
  • Voss, R., W. May, and E. Roeckner, 2002: Enhanced resolution modelling study on anthropogenic climate change: Changes in extremes of the hydrological cycle. Int. J. Climatol., 22 , 755777.

    • Search Google Scholar
    • Export Citation
  • Washington, W. M., and Coauthors, 2000: Parallel climate model (PCM) control and transient simulations. Climate Dyn., 16 , 755774.

  • Wehner, M. F., 2004: Predicted twenty-first-century changes in seasonal extreme precipitation events in the Parallel Climate Model. J. Climate, 17 , 42814290.

    • Search Google Scholar
    • Export Citation
  • Wilby, R. L., and T. M. L. Wigley, 2002: Future changes in the distribution of daily precipitation totals across North America. Geophys. Res. Lett., 29 .1135, doi:10.1029/2001GL013048.

    • Search Google Scholar
    • Export Citation
  • Wilcoxon, F., 1945: Individual comparisons by ranking methods. Biometrics, 1 , 8083.

  • Xie, P., J. E. Janowiak, P. A. Arkin, R. Adler, A. Gruber, R. Ferraro, G. J. Huffman, and S. Curtis, 2003: GPR pentad precipitation analyses: An experimental dataset based on gauge observations and satellite estimates. J. Climate, 16 , 21972214.

    • Search Google Scholar
    • Export Citation
  • Yukimoto, S., and Coauthors, 2001: The new Meteorological Research Institute coupled GCM (MRI-CGCM2)—Model climate and variability. Pap. Meteor. Geophys., 51 , 4788.

    • Search Google Scholar
    • Export Citation
  • Yukimoto, S., A. Noda, T. Uchiyama, and S. Kusunoki, 2006: Climate change of the twentieth through twenty-first centuries simulated by the MRI-CGCM2.3. Pap. Meteor. Geophys., 56 , 924.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., F. W. Zwiers, and G. Li, 2004: Monte Carlo experiments on the detection of trends in extreme values. J. Climate, 17 , 19451952.

    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., and V. V. Kharin, 1998: Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling. J. Climate, 11 , 22002222.

    • Search Google Scholar
    • Export Citation
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