• Abiodun, B. J., , J. M. Prusa, , and W. J. Gutowski, 2008: Implementation of a non-hydrostatic, adaptive-grid dynamics core in CAM3. Part I: Comparison of dynamics cores in aqua-planet simulations. Climate Dyn., 31, 795810, doi:10.1007/s00382-008-0381-y.

    • Search Google Scholar
    • Export Citation
  • 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
  • Bader, D. C., , C. Covey, , W. J. Gutkowski Jr., , I. M. Held, , K. E. Kunkel, , R. L. Miller, , R. T. Tokmakian, , and M. H. Zhang, 2008: Climate models: An assessment of strengths and limitations. U.S. Climate Change Science Program Synthesis and Assessment Product 3.1, Department of Energy, Office of Biological and Environmental Research, 124 pp.

  • Caya, D., , and R. LaPrise, 1999: A semi-Lagrangian semi-implicit regional climate model: The Canadian RCM. Mon. Wea. Rev., 127, 341362.

    • Search Google Scholar
    • Export Citation
  • Crichton, D. J., and Coauthors, 2012: Software and architecture for sharing satellite observations with the climate modeling community. IEEE Software, 29, 7381.

    • Search Google Scholar
    • Export Citation
  • Dosio, A., , and P. Paruolo, 2011: Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. J. Geophys. Res., 116, D16106, doi:10.1029/2011JD015934.

    • Search Google Scholar
    • Export Citation
  • Gangopadhyay, S., , and T. Pruitt, 2011: West-wide climate risk assessments: Bias-corrected and spatially downscaled surface water projections. Tech. Memo. 86-68210–2011-01, U.S. Department of the Interior, Bureau of Reclamation, 122 pp.

  • Giorgi, F., , M. R. Marinucci, , and G. T. Bates, 1993a: Development of a second-generation regional climate model (RegCM2). Part I: Boundary-layer and radiative transfer processes. Mon. Wea. Rev., 121, 27942813.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , M. R. Marinucci, , G. de Canio, , and G. T. Bates, 1993b: Development of a second-generation regional climate model (RegCM2). Part II: Convective processes and assimilation of lateral boundary conditions. Mon. Wea. Rev., 121, 28142832.

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

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., , C. Jones, , and G. Asrar, 2009: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175183.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P., , R. Ferraro, , and D. Waliser, 2008a: Improving use of satellite data in evaluating climate models. Eos, Trans. Amer. Geophys. Union, 92, 172, doi:10.1029/2011EO200005.

    • Search Google Scholar
    • Export Citation
  • Gleckler, P., , K. E. Taylor, , and C. Doutriaux, 2008b: Performance metrics for climate models. J. Geophys. Res., 113, D06104, doi:10.1029/2007JD008972.

    • Search Google Scholar
    • Export Citation
  • Grell, G., , J. Dudhia, , and D. R. Stauffer, 1993: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note NCAR/TN-398-STR, 107 pp.

  • Grigory, N., and Coauthors, 2012: Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. J. Climate, 25, 60576078.

    • Search Google Scholar
    • Export Citation
  • Hart, A. F., , C. E. Goodale, , C. A. Mattmann, , P. Zimdars, , D. Crichton, , P. Lean, , J. Kim, , and D. E. Waliser, 2011: A cloud-enabled regional climate model evaluation system. Proc. SECLOUD'11, Honolulu, HI, ICSE, 43–49, doi:10.1145/1985500.1985508.

  • Higgins, R. W., , Y. Yao, , and X. Wang, 1997: Influence of the North American monsoon system on the U.S. summer precipitation regime. J. Climate, 10, 26002622.

    • Search Google Scholar
    • Export Citation
  • Higgins, R. W., , Y. Chen, , and A. V. Douglas, 1999: Interannual variability of the North American warm season precipitation regime. J. Climate, 12, 653680.

    • Search Google Scholar
    • Export Citation
  • Higgins, R. W., , W. Shi, , and E. Yarosh, 2000: Improved United States precipitation quality control system and analysis. NCEP/Climate Prediction Center Atlas 7, 40 pp.

  • Hinkelman, L. M., , P. W. Stackhouse Jr., , B. A. Wielicki, , T. Zhang, , and S. R. Wilson, 2009: Surface insolation trends from satellite and ground measurements: Comparisons and challenges. J. Geophys. Res., 114, D00D20, doi:10.1029/2008JD011004.

    • Search Google Scholar
    • Export Citation
  • Houghton, J. T., , L. G. Meira Filho, , B. A. Callander, , N. Harris, , A. Kattenberg, , and K. Maskell, Eds., 1996: Climate Change 1995: The Science of Climate Change. Cambridge University Press, 572 pp.

  • Houghton, J. T., , Y. Ding, , D. J. Griggs, , M. Noguer, , P. J. van der Linden, , X. Dai, , K. Maskell, , and C. A. Johnson, Eds., 2001: Climate Change 2001: The Scientific Basis. Cambridge University Press, 881 pp.

  • Jiang, X., , N.-C. Lau, , and S. A. Klein, 2006: Role of eastward propagating convective episodes in the diurnal cycle and seasonal mean of summertime rainfall over the U.S. Great Plains. Geophys. Res. Lett., 33, L19809, doi:10.1029/2006GL027022.

    • Search Google Scholar
    • Export Citation
  • Jones, R. G., , D. C. Hassell, , D. Hudson, , S. S. Wilson, , G. J. Jenkins, , and J. F. B. Mitchell, 2004: Workbook on Generating High Resolution Climate Change Scenarios Using PRECIS. Hadley Centre for Climate Prediction and Research, 39 pp.

  • Juang, H., , S. Hong, , and M. Kanamitsu, 1997: The NMC nested regional spectral model: An update. Bull. Amer. Meteor. Soc., 78, 21252143.

    • Search Google Scholar
    • Export Citation
  • Kim, J., 2001: A nested modeling study of elevation-dependent climate change signals in California induced by increased atmospheric CO2. Geophys. Res. Lett., 28 (15), 29512954.

    • Search Google Scholar
    • Export Citation
  • Kim, J., 2002: Precipitation variability associated with the North American monsoon in the 20th century. Geophys. Res. Lett., 29, 1650, doi:10.1029/2001GL014316.

    • Search Google Scholar
    • Export Citation
  • Kim, J., , and J.-E. Lee, 2003: A multiyear regional climate hindcast for the western United States using the Mesoscale Atmospheric Simulation model. J. Hydrometeor., 4, 878890.

    • Search Google Scholar
    • Export Citation
  • Kim, J., , J. Kim, , J. D. Farrara, , and J. O. Roads, 2005: The effects of the Gulf of California SSTs on warm-season rainfall in the southwestern United States and northwestern Mexico: A regional model study. J. Climate, 18, 49704992.

    • Search Google Scholar
    • Export Citation
  • Kim, J., , D. E. Waliser, , P. J. Neiman, , B. Guan, , J.-M. Ryoo, , and G. A. Wick, 2013a: Effects of atmospheric river landfalls on the cold season precipitation in California. Climate Dyn., 40, 465474.

    • Search Google Scholar
    • Export Citation
  • Kim, J., and Coauthors, 2013b: Evaluation of the CORDEX-Africa multi-RCM hindcast: Systematic model errors. Climate Dyn., doi:10.1007/s00382-013-1751-7, in press.

    • Search Google Scholar
    • Export Citation
  • Matsuura, K., , and C. Willmott, 2009: Terrestrial air temperature and precipitation: 1900–2008 gridded monthly time series (V2.01). Center for Climatic Research, Department of Geography, University of Delaware. [Available online at http://climate.geog.udel.edu/~climate/html_pages/archive.html.]

  • Mattmann, C. A., , D. J. Crichton, , N. Medvidovic, , and J. S. Hughes, 2006: A software architecture-based framework for highly distributed and data intensive scientific applications. Proc. 28th Intl. Conf. on Software Engineering, ACM, 721–730.

  • Mattmann, C. A., and Coauthors, 2009: A reusable process control system framework for the Orbiting Carbon Observatory and NPP Sounder PEATE Missions. Proc. Third IEEE Int. Conf. on Space Mission Challenges for Information Technology, Pasadena, CA, IEEE, 165–172.

  • Mearns, L. O., , W. J. Gutowski, , R. Jones, , R. Leung, , S. McGinnis, , A. Nunes, , and Y. Qian, 2009: A regional climate change assessment program for North America. Eos, Trans. Amer. Geophys. Union, 90, 311312.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., and Coauthors, 2012a: The North American Regional Climate Change Assessment Program: Overview of phase I results. Bull. Amer. Meteor. Soc., 93, 13371362.

    • Search Google Scholar
    • Export Citation
  • Mearns, L. O., and Coauthors, cited 2012b: The North American Regional Climate Change Assessment Program dataset. NCAR Earth System Grid data portal. Data downloaded 18 January 2012. [Available online at http://www.earthsystemgrid.org/project/NARCCAP.html.]

  • Meehl, G. A., , C. Covey, , K. E. Taylor, , T. Delworth, , R. J. Stouffer, , M. Latif, , B. McAvaney, , and J. F. B. Mitchell, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394.

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

    • Search Google Scholar
    • Export Citation
  • Nature, 2010: Validation required. Nature, 463, 849, doi:10.1038/463849a.

  • Overpeck, J. T., , G. A. Meehl, , S. Bony, , and D. R. Easterling, 2011: Climate data challenges in the 21st century. Science, 331, 700702, doi:10.1126/science.1197869.

    • Search Google Scholar
    • Export Citation
  • Pollard, D., , and S. L. Thompson, 1992: Interdisciplinary climate systems. Users' Guide to the GENESIS Global Climate Model Version 1.02, Climate and Global Dynamics Division, NCAR, 58 pp.

  • Redmond, K. T., , and R. W. Koch, 1991: Surface climate and streamflow variability in the western United States and their relationship to large-scale circulation indices. Water Resour. Res., 27, 23812399.

    • 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.

  • Shige, S., , H. Sasaki, , K. Okamoto, , and T. Iguchi, 2006: Validation of rainfall estimates from the TRMM precipitation radar and microwave imager using a radiative transfer model: 1. Comparison of the version-5 and -6 products. Geophys. Res. Lett., 33, L13803, doi:10.1029/2006GL026350.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., , J. B. Klemp, , J. Dudhia, , D. O. Gill, , D. M. Barker, , W. Wang, , and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp.

  • Solomon, S., , D. Qin, , M. Manning, , M. Marquis, , K. Averyt, , M. M. B. Tignor, , H. L. Miller Jr., , and Z. Chen, Eds., 2007: Climate Change 2007: The Physical Science Basis. Cambridge University Press, 996 pp.

  • Stackhouse, P. W., Jr., , S. K. Gupta, , S. J. Cox, , T. Zhang, , J. C. Mikovitz, , and L. M. Hinkelman, 2011: 24.5-year surface radiation budget data set released. GEWEX News, No. 1, International GEWEX Project Office, Silver Spring, MD, 10–12.

  • Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106 (D7), 71837192.

  • Waliser, D. E., , Z. Shi, , J. R. Lanzante, , and A. H. Oort, 1999: The Hadley circulation: Assessing NCEP/NCAR reanalysis and sparse in-situ estimates. Climate Dyn., 15, 719735.

    • Search Google Scholar
    • Export Citation
  • Waliser, D. E., and Coauthors, 2011: Simulating the Sierra Nevada snowpack: The impact of snow albedo and multi-layer snow physics. Climatic Change, 109 (Suppl. 1), S95S117, doi:10.1007/s10584-011-0312-5.

    • Search Google Scholar
    • Export Citation
  • White, T., 2009: Hadoop: The Definitive Guide. O'Reilly, 528 pp.

  • Willmott, C. J., , and K. Matsuura, 1995: Smart interpolation of annually averaged air temperature in the United States. J. Appl. Meteor., 34, 25772586.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 15 15 3
PDF Downloads 8 8 4

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

View More View Less
  • 1 * Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California
  • 2 Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 3 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • 4 Institute for Mathematical Applications to the Geosciences, National Center for Atmospheric Research, Boulder, Colorado
© Get Permissions
Restricted access

Abstract

Surface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation.

Corresponding author address: Jinwon Kim, JIFRESSE, UCLA, 607 Charles E Young Drive East, Young Hall, Room 4242, Los Angeles, CA 90095-7228. E-mail: jkim@atmos.ucla.edu

Abstract

Surface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the eastern United States. Negative correlation between the biases in insolation and precipitation suggest that these two fields are related, likely via clouds. Systematic variations in biases for regions, seasons, variables, and metrics suggest that the bias correction in applying climate model data to assess the climate impact on various sectors must be performed accordingly. Precipitation evaluation with multiple observations reveals that observational data can be an important source of uncertainties in model evaluation; thus, cross examination of observational data is important for model evaluation.

Corresponding author address: Jinwon Kim, JIFRESSE, UCLA, 607 Charles E Young Drive East, Young Hall, Room 4242, Los Angeles, CA 90095-7228. E-mail: jkim@atmos.ucla.edu
Save