Climatic Change

, Volume 102, Issue 1–2, pp 51–75 | Cite as

Regional climate model projections for the State of Washington

  • Eric P. SalathéJr.Email author
  • L. Ruby Leung
  • Yun Qian
  • Yongxin Zhang
Open Access


Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970–1999) to the mid twenty-first century (2030–2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations.


Global Climate Model Snow Water Equivalent Statistical Downscaling Regional Simulation Windward Slope 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Eric P. SalathéJr.
    • 1
    Email author
  • L. Ruby Leung
    • 2
  • Yun Qian
    • 2
  • Yongxin Zhang
    • 3
  1. 1.JISAO/CSES Climate Impacts GroupUniversity of WashingtonSeattleUSA
  2. 2.Atmospheric Science and Global Change DivisionPacific Northwest National LaboratoryRichlandUSA
  3. 3.Research Applications LaboratoryNational Center for Atmospheric ResearchBoulderUSA

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