Editorial Type:
Article
Article Type:
Research Article

A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation over Complex Terrain

Ethan D. Gutmann Research Applications Laboratory, and Advanced Studies Program, National Center for Atmospheric Research,* Boulder, Colorado

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Roy M. Rasmussen Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado

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Changhai Liu Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado

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Kyoko Ikeda Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado

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David J. Gochis Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado

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Martyn P. Clark Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado

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Jimy Dudhia Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research,* Boulder, Colorado

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Gregory Thompson Research Applications Laboratory, National Center for Atmospheric Research,* Boulder, Colorado

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Print Publication:
01 Jan 2012
DOI:
https://doi.org/10.1175/2011JCLI4109.1
Page(s):
262–281
Restricted access

Abstract

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Ethan Gutmann, RAL, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: gutmann@ucar.edu

Abstract

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Ethan Gutmann, RAL, NCAR, P.O. Box 3000, Boulder, CO 80307-3000. E-mail: gutmann@ucar.edu
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  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569–585.

    • Search Google Scholar
    • Export Citation

  • Colle, B., C. Mass, and K. Westrick, 2000: MM5 precipitation verification over the Pacific Northwest during the 1997–99 cool seasons. Wea. Forecasting, 15, 730–744.

    • Search Google Scholar
    • Export Citation

  • Collins, W. D., and Coauthors, 2004: Description of the NCAR Community Atmosphere Model (CAM 3.0). NCAR Tech. Note NCAR/TN-464+STR, 214 pp.

    • Search Google Scholar
    • Export Citation

  • Collins, W. D., and Coauthors, 2006: Radiative forcing by well-mixed greenhouse gases: Estimates from climate models in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). J. Geophys. Res., 111, D14317, doi:10.1029/2005JD006713.

    • Search Google Scholar
    • Export Citation

  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158.

    • Search Google Scholar
    • Export Citation

  • Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 2031–2064.

    • Search Google Scholar
    • Export Citation

  • Gangopadhyay, S., M. Clark, and B. Rajagopalan, 2005: Statistical downscaling using K-nearest neighbors. Water Resour. Res., 41, W02024, doi:10.1029/2004WR003444.

    • Search Google Scholar
    • Export Citation

  • Garvert, M. F., C. P. Woods, B. A. Colle, C. F. Mass, P. V. Hobbs, M. T. Stoelinga, and J. B. Wolfe, 2005: The 13–14 December 2001 IMPROVE-2 event. Part II: Comparisons of MM5 model simulations of clouds and precipitation with observations. J. Atmos. Sci., 62, 3520–3534.

    • Search Google Scholar
    • Export Citation

  • Garvert, M. F., B. Smull, and C. Mass, 2007: Multiscale mountain waves influencing a major orographic precipitation event. J. Atmos. Sci., 64, 711–737.

    • Search Google Scholar
    • Export Citation

  • Guan, H., J. Wilson, and O. Makhnin, 2005: Geostatistical mapping of mountain precipitation incorporating autosearched effects of terrain and climatic characteristics. J. Hydrometeor., 6, 1018–1031.

    • Search Google Scholar
    • Export Citation

  • Hara, M., T. Yoshikane, H. Kawase, and F. Kimura, 2008: Estimation of the impact of global warming on snow depth in Japan by the pseudo-global-warming method. Hydrol. Res. Lett., 2, 61–64.

    • Search Google Scholar
    • Export Citation

  • Hayhoe, K., and Coauthors, 2004: Emissions pathways, climate change, and impacts on California. Proc. Natl. Acad. Sci. USA, 101, 12 422–12 427.

    • Search Google Scholar
    • Export Citation

  • Hewitson, B., and R. Crane, 1996: Climate downscaling: Techniques and application. Climate Res., 7, 85–95.

  • Hewitt, C. D., and D. J. Griggs, 2004: Ensembles-based predictions of climate changes and their impacts (ENSEMBLES). Eos, Trans. Amer. Geophys. Union, 85, doi:10.1029/2004EO520005.

    • Search Google Scholar
    • Export Citation

  • Hidalgo, H. G., M. D. Dettinger, and D. R. Cayan, 2008: Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States. PIER Final Project Rep., California Energy Commission Rep. CEC-500-2007-123, 62 pp.

    • Search Google Scholar
    • Export Citation

  • Hughes, J., and P. Guttorp, 1994: A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour. Res., 30, 1535–1546.

    • Search Google Scholar
    • Export Citation

  • Hurrell, J. W., G. J. Holland, and W. G. Large, 2008: The nested regional climate model: An approach toward prediction across scales. Eos, Trans. Amer. Geophys. Union, 89 (Fall Meeting Suppl.), Abstract PA13C-1350.

    • Search Google Scholar
    • Export Citation

  • Ikeda, K., and Coauthors, 2010: Simulation of seasonal snowfall over Colorado. Atmos. Res., 97, 462–477.

  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181.

  • Kawase, H., T. Yoshikane, M. Hara, B. Ailikun, F. Kimura, and T. Yasunari, 2008: Downscaling of the climatic change in the mei-yu rainband in East Asia by a pseudo climate simulation method. SOLA, 4, 73–76.

    • Search Google Scholar
    • Export Citation

  • Klemp, J. B., W. C. Skamarock, and J. Dudhia, 2007: Conservative split-explicit time integration methods for the compressible nonhydrostatic equations. Mon. Wea. Rev., 135, 2897–2913.

    • Search Google Scholar
    • Export Citation

  • Liu, C., K. Ikeda, G. Thompson, R. Rasmussen, and J. Dudhia, 2011: High-resolution simulations of wintertime precipitation in the Colorado Headwaters region: Sensitivity to physics parameterizations. Mon. Wea. Rev., 139, 3533–3553.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., A. Wood, J. Adam, D. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 3237–3251.

    • Search Google Scholar
    • Export Citation

  • Maurer, E. P., L. Brekke, T. Pruitt, and P. B. Duffy, 2007: Fine-resolution climate projections enhance regional climate change impact studies. Eos, Trans. Amer. Geophys. Union, 88, doi:10.1029/2007EO470006.

    • Search Google Scholar
    • Export Citation

  • Mearns, L. O., W. 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, doi:10.1029/2009EO360002.

    • Search Google Scholar
    • Export Citation

  • Medina, S., B. Smull, R. Houze, and M. Steiner, 2005: Cross-barrier flow during orographic precipitation events: Results from MAP and IMPROVE. J. Atmos. Sci., 62, 3580–3598.

    • Search Google Scholar
    • Export Citation

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360.

  • Rasmussen, R. M., B. C. Bernstein, M. Murakami, G. Stossmeister, J. Reisner, and B. Stankov, 1995: The 1990 Valentine’s Day arctic outbreak. Part I: Mesoscale and microscale structure and evolution of a Colorado Front Range shallow upslope cloud. J. Appl. Meteor., 34, 1481–1511.

    • Search Google Scholar
    • Export Citation

  • Rasmussen, R. M., and Coauthors, 2011: High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of current and warmer climate. J. Climate, 24, 3015–3048.

    • Search Google Scholar
    • Export Citation

  • Schar, C., C. Frei, D. Luthi, and H. Davies, 1996: Surrogate climate-change scenarios for regional climate models. Geophys. Res. Lett., 23, 669–672.

    • Search Google Scholar
    • Export Citation

  • Serreze, M., M. Clark, R. Armstrong, D. McGinnis, and R. Pulwarty, 1999: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resour. Res., 35, 2145–2160.

    • 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, 3465–3485.

    • Search Google Scholar
    • Export Citation

  • Stern, P. C., and W. E. Easterling, Eds., 1999: Making Climate Forecasts Matter. National Academy Press, 175 pp.

  • Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132, 519–542.

    • Search Google Scholar
    • Export Citation

  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 5095–5115.

    • Search Google Scholar
    • Export Citation

  • Thornton, P., S. Running, and M. White, 1997: Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol., 190, 214–251.

    • 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, 169–184.

    • Search Google Scholar
    • Export Citation

  • Wang, S.-Y., R. R. Gillies, E. S. Takle, and W. J. Gutowski Jr., 2009: Evaluation of precipitation in the intermountain region as simulated by the NARCCAP regional climate models. Geophys. Res. Lett., 36, L11704, doi:10.1029/2009GL037930.

    • Search Google Scholar
    • Export Citation

  • Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks, 1998: Statistical downscaling of general circulation model output: A comparison of methods. Water Resour. Res., 34, 2995–3008.

    • Search Google Scholar
    • Export Citation

  • Wood, A., L. Leung, V. Sridhar, and D. Lettenmaier, 2004: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189–216.

    • Search Google Scholar
    • Export Citation

  • Yang, D., B. Goodison, S. Ishida, and C. Benson, 1998: Adjustment of daily precipitation data at 10 climate stations in Alaska: Application of World Meteorological Organization intercomparison results. Water Resour. Res., 34, 241–256.

    • Search Google Scholar
    • Export Citation
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