• Allen, M. R., and W. J. Ingram, 2002: Constraints on future changes in climate and the hydrologic cycle. Nature, 419, 224232, doi:10.1038/nature01092.

    • Search Google Scholar
    • Export Citation
  • Assunção, R., N. Cressie, S. H. Holan, M. Levine, O. Nicolis, J. Zhang, and J. Zou, 2012: Dynamical random-set modeling of concentrated precipitation in North America. Stat. Interface, 5, 169182, doi:10.4310/SII.2012.v5.n2.a3.

    • Search Google Scholar
    • Export Citation
  • Baigorria, G. A., J. W. Jones, D.-W. Shin, A. Mishra, and J. J. O. Brien, 2007: Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Climate Res., 34, 211222, doi:10.3354/cr00703.

    • Search Google Scholar
    • Export Citation
  • Baldwin, M. E., J. S. Kain, and S. Lakshmivarahan, 2005: Development of an automated classification procedure for rainfall systems. Mon. Wea. Rev., 133, 844862, doi:10.1175/MWR2892.1.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., and Coauthors, 2008: Human-induced changes in the hydrology of the western United States. Science, 319, 10801083, doi:10.1126/science.1152538.

    • 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
  • Bukovsky, M. S., and D. J. Karoly, 2009: Precipitation simulations using WRF as a nested regional climate model. J. Appl. Meteor., 48, 21522159, doi:10.1175/2009JAMC2186.1.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher, 2008: On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys. Res. Lett., 35, L20709, doi:10.1029/2008GL035694.

    • Search Google Scholar
    • Export Citation
  • Christensen, N. S., A. W. Wood, N. Voisin, D. P. Lettenmaier, and R. N. Palmer, 2004: The effects of climate change on the hydrology and water resources of the Colorado River basin. Climatic Change, 62, 337363, doi:10.1023/B:CLIM.0000013684.13621.1f.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., W. A. Gallus Jr., M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles. Wea. Forecasting, 24, 11211140, doi:10.1175/2009WAF2222222.1.

    • Search Google Scholar
    • Export Citation
  • Davis, C., B. Brown, and R. Bullock, 2006a: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 17721784, doi:10.1175/MWR3145.1.

    • Search Google Scholar
    • Export Citation
  • Davis, C., B. Brown, and R. Bullock, 2006b: Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Wea. Rev., 134, 17851795, doi:10.1175/MWR3146.1.

    • Search Google Scholar
    • Export Citation
  • Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Eddins, S., 2010: Almost-connected-component-labeling. [Available online at http://blogs.mathworks.com/steve/2010/09/07/almost-connected-component-labeling/.]

  • Fox, N. I., and C. K. Wikle, 2005: A Bayesian quantitative precipitation nowcast scheme. Wea. Forecasting, 20, 264275, doi:10.1175/WAF845.1.

    • Search Google Scholar
    • Export Citation
  • Gelfand, A. E., P. Diggle, P. Guttorp, and M. Fuentes, Eds., 2010: Handbook of Spatial Statistics. CRC Press, 619 pp.

  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 49734991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., and X. Bi, 2005: Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys. Res. Lett., 32, L21715, doi:10.1029/2005GL024288.

    • Search Google Scholar
    • Export Citation
  • Guinard, K., A. Mailhot, and D. Caya, 2015: Projected changes in characteristics of precipitation spatial structures over North America. Int. J. Climatol., 35, 596612, doi:10.1002/joc.4006.

    • Search Google Scholar
    • Export Citation
  • Han, L., S. Fu, L. Zhao, Y. Zheng, H. Wang, and Y. Lin, 2009: 3D convective storm identification, tracking, and forecasting—An enhanced TITAN algorithm. J. Atmos. Oceanic Technol., 26, 719732, doi:10.1175/2008JTECHA1084.1.

    • 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, doi:10.2307/2346830.

    • Search Google Scholar
    • Export Citation
  • Hawkins, E., T. M. Osborne, C. K. Ho, and A. J. Challinor, 2013: Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agric. For. Meteor., 170, 1931, doi:10.1016/j.agrformet.2012.04.007.

    • Search Google Scholar
    • Export Citation
  • Hay, L. E., R. L. Wilby, and G. H. Leavesley, 2000: A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Amer. Water. Resour. Assoc., 36, 387397, doi:10.1111/j.1752-1688.2000.tb04276.x.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, doi:10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation
  • Hennessy, K., J. Gregory, and J. Mitchell, 1997: Changes in daily precipitation under enhanced greenhouse conditions. Climate Dyn., 13, 667680, doi:10.1007/s003820050189.

    • Search Google Scholar
    • Export Citation
  • Ho, C. K., D. B. Stephenson, M. Collins, C. A. Ferro, and S. J. Brown, 2012: Calibration strategies: A source of additional uncertainty in climate change projections. Bull. Amer. Meteor. Soc., 93, 2126, doi:10.1175/2011BAMS3110.1.

    • Search Google Scholar
    • Export Citation
  • Hodges, K., 1994: A general-method for tracking analysis and its application to meteorological data. Mon. Wea. Rev., 122, 25732586, doi:10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ines, A. V., and J. W. Hansen, 2006: Bias correction of daily GCM rainfall for crop simulation studies. Agric. For. Meteor., 138, 4453, doi:10.1016/j.agrformet.2006.03.009.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 1535 pp., doi:10.1017/CBO9781107415324.

  • Jankov, I., W. A. Gallus Jr., M. Segal, B. Shaw, and S. E. Koch, 2005: The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 20, 10481060, doi:10.1175/WAF888.1.

    • Search Google Scholar
    • Export Citation
  • Johnson, J., P. L. MacKeen, A. Witt, E. D. W. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263276, doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., Ed., 2009: Global Climate Change Impacts in the United States. Cambridge University Press, 188 pp.

  • Knutson, T. R., and S. Manabe, 1995: Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean–atmosphere model. J. Climate, 8, 21812199, doi:10.1175/1520-0442(1995)008<2181:TMROTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., K. Hondl, and R. Rabin, 2009: An efficient, general-purpose technique for identifying storm cells in geospatial images. J. Atmos. Oceanic Technol., 26, 523537, doi:10.1175/2008JTECHA1153.1.

    • Search Google Scholar
    • Export Citation
  • Leeds, W., E. Moyer, M. Stein, T. Doan, J. Haslett, A. Parnell, R. Philbin, and M. Jun, 2015: Simulation of future climate under changing temporal covariance structures. Adv. Stat. Climatol. Meteor. Oceanogr., 1, 114, doi:10.5194/ascmo-1-1-2015.

    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP Stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Lo, J. C.-F., Z.-L. Yang, and R. A. Pielke, 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res., 113, D09112, doi:10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation
  • Ma, J., H. Wang, and K. Fan, 2015: Dynamic downscaling of summer precipitation prediction over China in 1998 using WRF and CCSM4. Adv. Atmos. Sci., 32, 577584, doi:10.1007/s00376-014-4143-y.

    • Search Google Scholar
    • Export Citation
  • Meinshausen, M., and Coauthors, 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213241, doi:10.1007/s10584-011-0156-z.

    • Search Google Scholar
    • Export Citation
  • Morel, C., and S. Senesi, 2002a: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. I: Methodology. Quart. J. Roy. Meteor. Soc., 128, 19531971, doi:10.1256/003590002320603485.

    • Search Google Scholar
    • Export Citation
  • Morel, C., and S. Senesi, 2002b: A climatology of mesoscale convective systems over Europe using satellite infrared imagery. II: Characteristics of European mesoscale convective systems. Quart. J. Roy. Meteor. Soc., 128, 19731995, doi:10.1256/003590002320603494.

    • Search Google Scholar
    • Export Citation
  • Muerth, M., and Coauthors, 2013: On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff. Hydrol. Earth Syst. Sci., 17, 11891204, doi:10.5194/hess-17-1189-2013.

    • Search Google Scholar
    • Export Citation
  • Murthy, C. R., B. Gao, A. R. Tao, and G. Arya, 2015: Automated quantitative image analysis of nanoparticle assembly. Nanoscale, 7, 97939805, doi:10.1039/C5NR00809C.

    • Search Google Scholar
    • Export Citation
  • Nelson, G. C., and Coauthors, 2009: Climate change: Impact on agriculture and costs of adaptation. Food Policy Rep., Vol. 21, International Food Policy Research Institute, 19 pp.

  • Piani, C., J. Haerter, and E. Coppola, 2010a: Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol., 99, 187192, doi:10.1007/s00704-009-0134-9.

    • Search Google Scholar
    • Export Citation
  • Piani, C., G. Weedon, M. Best, S. Gomes, P. Viterbo, S. Hagemann, and J. Haerter, 2010b: Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol., 395, 199215, doi:10.1016/j.jhydrol.2010.10.024.

    • Search Google Scholar
    • Export Citation
  • Piao, S., and Coauthors, 2010: The impacts of climate change on water resources and agriculture in China. Nature, 467, 4351, doi:10.1038/nature09364.

    • Search Google Scholar
    • Export Citation
  • Poppick, A., D. J. McInerney, E. J. Moyer, and M. L. Stein, 2016: Temperatures in transient climates: Improved methods for simulations with evolving temporal covariances. Ann. Appl. Stat., 10, 477505, doi:10.1214/16-AOAS903.

    • Search Google Scholar
    • Export Citation
  • Prat, O., and B. Nelson, 2015: Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012). Hydrol. Earth Syst. Sci., 19, 20372056, doi:10.5194/hess-19-2037-2015.

    • Search Google Scholar
    • Export Citation
  • Räisänen, J., and O. Räty, 2013: Projections of daily mean temperature variability in the future: Cross-validation tests with ensembles regional climate simulations. Climate Dyn., 41, 15531568, doi:10.1007/s00382-012-1515-9.

    • Search Google Scholar
    • Export Citation
  • Räty, O., J. Räisänen, and J. S. Ylhäisi, 2014: Evaluation of delta change and bias correction methods for future daily precipitation: Intermodel cross-validation using ensembles simulations. Climate Dyn., 42, 22872303, doi:10.1007/s00382-014-2130-8.

    • Search Google Scholar
    • Export Citation
  • Rosenzweig, C., and M. L. Parry, 1994: Potential impact of climate change on world food supply. Nature, 367, 133138, doi:10.1038/367133a0.

    • Search Google Scholar
    • Export Citation
  • Sapiano, M., and P. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149166, doi:10.1175/2008JHM1052.1.

    • Search Google Scholar
    • Export Citation
  • Semenov, V., and L. Bengtsson, 2002: Secular trends in daily precipitation characteristics: Greenhouse gas simulation with a coupled AOGCM. Climate Dyn., 19, 123140, doi:10.1007/s00382-001-0218-4.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and J. B. Klemp, 2008: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J. Comput. Phys., 227, 34653485, doi:10.1016/j.jcp.2007.01.037.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and T. D. Ellis, 2008: Controls of global-mean precipitation increases in global warming GCM experiments. J. Climate, 21, 61416155, doi:10.1175/2008JCLI2144.1.

    • Search Google Scholar
    • Export Citation
  • Tebaldi, C., L. O. Mearns, D. Nychka, and R. L. Smith, 2004: Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations. Geophys. Res. Lett., 31, L24213, doi:10.1029/2004GL021276.

    • Search Google Scholar
    • Export Citation
  • Teutschbein, C., and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. J. Hydrol., 456–457, 1229, doi:10.1016/j.jhydrol.2012.05.052.

    • Search Google Scholar
    • Export Citation
  • Vörösmarty, C. J., P. Green, J. Salisbury, and R. B. Lammers, 2000: Global water resources: Vulnerability from climate change and population growth. Science, 289, 284288, doi:10.1126/science.289.5477.284.

    • Search Google Scholar
    • Export Citation
  • Vrac, M., and P. Friederichs, 2015: Multivariate—intervariable, spatial, and temporal—bias correction. J. Climate, 28, 218237, doi:10.1175/JCLI-D-14-00059.1.

    • Search Google Scholar
    • Export Citation
  • Vrac, M., M. Stein, and K. Hayhoe, 2007: Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing. Climate Res., 34, 169184, doi:10.3354/cr00696.

    • Search Google Scholar
    • Export Citation
  • Wang, J., and V. R. Kotamarthi, 2015: High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America. Earth’s Future, 3, 268288, doi:10.1002/2015EF000304.

    • Search Google Scholar
    • Export Citation
  • Wang, J., F. N. U. Swati, M. L. Stein, and V. R. Kotamarthi, 2015: Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model. J. Geophys. Res. Atmos., 120, 12391259, doi:10.1002/2014JD022434.

    • Search Google Scholar
    • Export Citation
  • Wang, K., and R. E. Dickinson, 2012: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Rev. Geophys., 50, RG2005, doi:10.1029/2011RG000373.

    • Search Google Scholar
    • Export Citation
  • WCRP, 2010: Coupled Model Intercomparison Project–Phase 5–CMIP5. CLIVAR Exchanges, No. 56, International CLIVAR Project Office, Southampton, United Kingdom. [Available online at www.clivar.org/sites/default/files/documents/Exchanges56.pdf.]

  • Willett, K. M., N. P. Gillett, P. D. Jones, and P. W. Thorne, 2007: Attribution of observed surface humidity changes to human influence. Nature, 449, 710712, doi:10.1038/nature06207.

    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon, 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79, 20792099, doi:10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xu, K., C. K. Wikle, and N. I. Fox, 2005: A kernel-based spatio-temporal dynamical model for nowcasting weather radar reflectivities. J. Amer. Stat. Assoc., 100, 11331144, doi:10.1198/016214505000000682.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1064 690 83
PDF Downloads 864 543 62

Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions

View More View Less
  • 1 Department of Statistics, University of Chicago, Chicago, Illinois
  • | 2 Environmental Science Division, Argonne National Laboratory, Lemont, Illinois
  • | 3 Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%–7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%–2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics—including starting location, intensity, spatial extent, duration, and trajectory—that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model–observation biases, implying that the best means of incorporating them into impact assessments is via “data-driven simulations” that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0844.s1.

Current affiliation: Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio.

Corresponding author address: Won Chang, Department of Mathematical Sciences, University of Cincinnati, 4199 French Hall West, Cincinnati, OH 45221. E-mail: changwn@uc.edu

Abstract

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%–7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%–2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics—including starting location, intensity, spatial extent, duration, and trajectory—that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model–observation biases, implying that the best means of incorporating them into impact assessments is via “data-driven simulations” that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0844.s1.

Current affiliation: Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio.

Corresponding author address: Won Chang, Department of Mathematical Sciences, University of Cincinnati, 4199 French Hall West, Cincinnati, OH 45221. E-mail: changwn@uc.edu
Save