• Berg, P., , J. Haerter, , P. Thejll, , C. Piani, , S. Hagemann, , and J. Christensen, 2009: Seasonal characteristics of the relationship between daily precipitation intensity and surface temperature. J. Geophys. Res., 114, D18102, doi:10.1029/2009JD012008.

    • Search Google Scholar
    • Export Citation
  • Berg, P., , C. Moseley, , and J. O. Haerter, 2013: Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci., 6, 181185, doi:10.1038/ngeo1731.

    • Search Google Scholar
    • Export Citation
  • Bernard, E., , P. Naveau, , M. Vrac, , and O. Mestre, 2013: Clustering of maxima: Spatial dependencies among heavy rainfall in France. J. Climate, 26, 79297937, doi:10.1175/JCLI-D-12-00836.1.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., and et al. , 2013: Climate phenomena and their relevance for future regional climate change. Climate Change 2013: The Physical Science Basis, T. F. Stocker et al., Eds., Cambridge University Press, 1217–1308.

  • Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics, Vol. 208, Springer, 209 pp., doi:10.1007/978-1-4471-3675-0.

  • Cooley, D., 2009: Extreme value analysis and the study of climate change. Climatic Change, 97, 7783, doi:10.1007/s10584-009-9627-x.

  • Deser, C., , A. Phillips, , V. Bourdette, , and H. Teng, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, doi:10.1007/s00382-010-0977-x.

    • Search Google Scholar
    • Export Citation
  • Dominguez, F., , E. Rivera, , D. P. Lettenmaier, , and C. L. Castro, 2012: Changes in winter precipitation extremes for the western United States under a warmer climate as simulated by regional climate models. Geophy. Res. Lett., 39, L05803, doi:10.1029/2011GL050762.

    • Search Google Scholar
    • Export Citation
  • d’Orgeville, M., , W. R. Peltier, , A. R. Erler, , and J. Gula, 2014: Climate change impacts on Great Lakes Basin precipitation extremes. J. Geophys. Atmos., 119, 10 79910 812, doi:10.1002/2014JD021855.

    • Search Google Scholar
    • Export Citation
  • Efron, B., , and R. J. Tibshirani, 1994: An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability, Vol. 57, Chapman & Hall/CRC, 456 pp.

  • Eggert, B., , P. Berg, , J. Haerter, , D. Jacob, , and C. Moseley, 2015: Temporal and spatial scaling impacts on extreme precipitation. Atmos. Chem. Phys., 15, 59575971, doi:10.5194/acp-15-5957-2015.

    • Search Google Scholar
    • Export Citation
  • Endo, H., , A. Kitoh, , T. Ose, , R. Mizuta, , and S. Kusunoki, 2012: Future changes and uncertainties in Asian precipitation simulated by multiphysics and multi–sea surface temperature ensemble experiments with high-resolution Meteorological Research Institute atmospheric general circulation models (MRI-AGCMS). J. Geophys. Res., 117, D16118, doi:10.1029/2012JD017874.

    • Search Google Scholar
    • Export Citation
  • Erler, A. R., , and W. R. Peltier, 2015: Clustering of station observations for extreme value analysis. Proc. Fifth Int. Workshop on Climate Informatics (CI 2015), Boulder, CO, NCAR, 16. [Available online at https://www2.cisl.ucar.edu/sites/default/files/16-%20Erler.pdf.]

  • Erler, A. R., , W. R. Peltier, , and M. d’Orgeville, 2015: Dynamically downscaled high-resolution hydroclimate projections for western Canada. J. Climate, 28, 423450, doi:10.1175/JCLI-D-14-00174.1.

    • Search Google Scholar
    • Export Citation
  • Gent, P. R., and et al. , 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., , and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, 1693, doi:10.1029/2002GL015311.

    • Search Google Scholar
    • Export Citation
  • Gula, J., , and W. R. Peltier, 2012: Dynamical downscaling over the Great Lakes Basin of North America using the WRF Regional Climate Model: The impact of the Great Lakes system on regional greenhouse warming. J. Climate, 25, 77237742, doi:10.1175/JCLI-D-11-00388.1.

    • Search Google Scholar
    • Export Citation
  • Hartigan, J. A., , and M. A. Wong, 1979: Algorithm AS 136: A K-means clustering algorithm. J. Roy. Stat. Soc., 28C, 100108.

  • Herring, S. C., , M. P. Hoerling, , T. C. Peterson, , and P. A. Stott, 2014: Explaining extreme events of 2013 from a climate perspective. Bull. Amer. Meteor. Soc., 95 (9), S1S104, doi:10.1175/1520-0477-95.9.S1.1.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., , and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129151.

  • Hosking, J. R. M., , and J. R. Wallis, 1997: Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, 240 pp.

  • Iacono, M. J., , J. S. Delamere, , E. J. Mlawer, , M. W. Shephard, , S. A. Clough, , and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, doi:10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. C. B. Field et al., Eds., Cambridge University Press, 582 pp.

  • Jones, E., and et al. , 2001: SciPy: Open source scientific tools for Python. Accessed 9 June 2015. [Available online at http://www.scipy.org/.]

  • Jones, M. R., , S. Blenkinsop, , H. J. Fowler, , and C. G. Kilsby, 2014: Objective classification of extreme rainfall regions for the UK and updated estimates of trends in regional extreme rainfall. Int. J. Climatol., 34, 751765, doi:10.1002/joc.3720.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Katz, R. W., 2010: Statistics of extremes in climate change. Climatic Change, 100, 7176, doi:10.1007/s10584-010-9834-5.

  • Katz, R. W., , M. B. Parlange, , and P. Naveau, 2002: Statistics of extremes in hydrology. Adv. Water Resour., 25, 12871304, doi:10.1016/S0309-1708(02)00056-8.

    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., , F. Zwiers, , X. Zhang, , and M. Wehner, 2013: Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119, 345357, doi:10.1007/s10584-013-0705-8.

    • Search Google Scholar
    • Export Citation
  • Köppen, W., 1936: Das Geographische System der Climate. Vol. 1, Handbuch der Klimatologie, Gebruder Borntraeger, 44 pp.

  • Livezey, R. E., , and W. Chen, 1983: Statistical field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 111, 4659, doi:10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lucas-Picher, P., , D. Caya, , R. de Elía, , and R. Laprise, 2008: Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain. Climate Dyn., 31, 927940, doi:10.1007/s00382-008-0384-8.

    • Search Google Scholar
    • Export Citation
  • Massey, F. J., Jr., 1951: The Kolmogorov-Smirnov test for goodness of fit. J. Amer. Stat. Assoc., 46, 6878, doi:10.1080/01621459.1951.10500769.

    • Search Google Scholar
    • Export Citation
  • Mekis, É., , and L. A. Vincent, 2011: An overview of the second generation adjusted daily precipitation dataset for trend analysis in Canada. Atmos.–Ocean, 49, 163177, doi:10.1080/07055900.2011.583910.

    • Search Google Scholar
    • Export Citation
  • Mladjic, B., , L. Sushama, , M. Khaliq, , R. Laprise, , D. Caya, , and R. Roy, 2011: Canadian RCM projected changes to extreme precipitation characteristics over Canada. J. Climate, 24, 25652584, doi:10.1175/2010JCLI3937.1.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., , G. Thompson, , and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev., 137, 9911007, doi:10.1175/2008MWR2556.1.

    • Search Google Scholar
    • Export Citation
  • Pedregosa, F., and et al. , 2011: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 12, 28252830.

  • Schindler, A., , A. Toreti, , M. Zampieri, , E. Scoccimarro, , S. Gualdi, , S. Fukutome, , E. Xoplaki, , and J. Luterbacher, 2015: On the internal variability of simulated daily precipitation. J. Climate, 28, 36243630, doi:10.1175/JCLI-D-14-00745.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, doi:10.1175/MWR2830.1.

    • Search Google Scholar
    • Export Citation
  • Smirnov, N. V., 1939: On the estimation of the discrepancy between empirical curves of distribution for two independent samples. Moscow Univ. Math. Bull., 2, 314.

    • Search Google Scholar
    • Export Citation
  • Smirnov, N., 1948: Table for estimating the goodness of fit of empirical distributions. Ann. Math. Stat., 19, 279281, doi:10.1214/aoms/1177730256.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and et al. , 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Stott, P., 2015: Attribution: Weather risks in a warming world. Nat. Climate Change, 5, 517518, doi:10.1038/nclimate2640.

  • Tewari, M., and et al. , 2004: Implementation and verification of the unified Noah land surface model in the WRF model. 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., 14.2a. [Available online at https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm.]

  • Torma, C., , F. Giorgi, , and E. Coppola, 2015: Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps. J. Geophys. Res. Atmos., 120, 39573972, doi:10.1002/2014JD022781.

    • 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, doi:10.1175/BAMS-84-9-1205.

    • Search Google Scholar
    • Export Citation
  • van der Ent, R. J., , H. H. Savenije, , B. Schaefli, , and S. C. Steele-Dunne, 2010: Origin and fate of atmospheric moisture over continents. Water Resour. Res., 46, W09525, doi:10.1029/2010WR009127.

    • Search Google Scholar
    • Export Citation
  • Vincent, L. A., , and E. Mekis, 2006: Changes in daily and extreme temperature and precipitation indices for Canada over the twentieth century. Atmos.–Ocean, 44, 177193, doi:10.3137/ao.440205.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., , and F. W. Zwiers, 2002: Statistical Analysis in Climate Research.Cambridge University Press, 496 pp.

  • Wang, W., and et al. , 2012: ARW version 3 modeling system user’s guide. Version 3.3, Tech. Rep., Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, 371 pp. [Available online http://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V3.3/ARWUsersGuideV3.pdf.]

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 39 39 7
PDF Downloads 58 58 9

Projected Changes in Precipitation Extremes for Western Canada based on High-Resolution Regional Climate Simulations

View More View Less
  • 1 Department of Physics, University of Toronto, Toronto, Ontario, Canada
© Get Permissions
Restricted access

Abstract

An analysis of changes in precipitation extremes in western Canada is presented, based upon an ensemble of high-resolution regional climate projections. The ensemble is composed of four independent, identically configured Community Earth System Model (CESM) integrations that were dynamically downscaled to 10-km resolution, using the WRF Model in two different configurations. Only the representative concentration pathway 8.5 (RCP8.5) scenario is considered. Changes in extremes are found to generally follow changes in the (seasonal) mean, but changes in mean and extreme precipitation differ strongly between seasons and regions (where extremes are defined as the seasonal maximum of daily precipitation). At the end of the twenty-first century, the highest projected increase in precipitation extremes is approximately 30% in winter away from the coast and in fall at the coast. Changes in winter are consistent between models; however, changes in summer are not: CESM is characterized by a decrease in summer precipitation (and extremes), while one WRF configuration shows a significant increase and another no statistically significant change. Nevertheless, the fraction of convective precipitation (extremes) in summer increases by 20%–30% in all models. There is also evidence that the climate change signal in summer is sensitive to the choice of the convection scheme. A comparison of CESM and WRF shows that higher resolution clearly improves the representation of winter precipitation (extremes), while summer precipitation does not appear to be sensitive to resolution (convection is parameterized in both models). To increase the statistical power of the extreme value analysis that has been performed, a novel method for combining data from climatologically similar stations was employed.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0530.s1.

Corresponding author address: Andre R. Erler, Department of Physics, University of Toronto, 60 St. George St., Toronto ON M5S 1A7, Canada. E-mail: aerler@atmosp.physics.utoronto.ca

Abstract

An analysis of changes in precipitation extremes in western Canada is presented, based upon an ensemble of high-resolution regional climate projections. The ensemble is composed of four independent, identically configured Community Earth System Model (CESM) integrations that were dynamically downscaled to 10-km resolution, using the WRF Model in two different configurations. Only the representative concentration pathway 8.5 (RCP8.5) scenario is considered. Changes in extremes are found to generally follow changes in the (seasonal) mean, but changes in mean and extreme precipitation differ strongly between seasons and regions (where extremes are defined as the seasonal maximum of daily precipitation). At the end of the twenty-first century, the highest projected increase in precipitation extremes is approximately 30% in winter away from the coast and in fall at the coast. Changes in winter are consistent between models; however, changes in summer are not: CESM is characterized by a decrease in summer precipitation (and extremes), while one WRF configuration shows a significant increase and another no statistically significant change. Nevertheless, the fraction of convective precipitation (extremes) in summer increases by 20%–30% in all models. There is also evidence that the climate change signal in summer is sensitive to the choice of the convection scheme. A comparison of CESM and WRF shows that higher resolution clearly improves the representation of winter precipitation (extremes), while summer precipitation does not appear to be sensitive to resolution (convection is parameterized in both models). To increase the statistical power of the extreme value analysis that has been performed, a novel method for combining data from climatologically similar stations was employed.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0530.s1.

Corresponding author address: Andre R. Erler, Department of Physics, University of Toronto, 60 St. George St., Toronto ON M5S 1A7, Canada. E-mail: aerler@atmosp.physics.utoronto.ca

Supplementary Materials

    • Supplemental Materials (PDF 5.90 MB)
Save