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Byline: Eric P. Salathe (1), L. Ruby Leung (2), Yun Qian (2), Yongxin Zhang (3) Abstract: 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. Author Affiliation: (1) JISAO/CSES Climate Impacts Group, University of Washington, Seattle, WA, USA (2) Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA (3) Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA Article History: Registration Date: 24/03/2010 Received Date: 04/06/2009 Accepted Date: 23/03/2010 Online Date: 05/05/2010

Snow albedo plays an important role in land models for weather, climate, and hydrometeorological studies, but its treatment in various land models still contains significant deficiencies. Complementary to previous studies that evaluated the snow albedo as part of an overall land model study, the snow albedo formulations as used in four major weather forecasting and climate models [European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) 'Noah' land model, National Center for Atmospheric Research (NCAR) Community Land Model (CLM3), and NCEP global model] were directly evaluated here using multiyear Boreal Ecosystem--Atmosphere Study (BOREAS) in situ data over grass and forest sites. First, four idealized cases over grass and forest sites were designed to understand better the different albedo formulations in these models. Then the BOREAS data were used to evaluate snow albedo and relevant formulations and to identify deficiencies of each model. Based on these analyses, suggestions that involve only minor changes in parameters or formulations were made to significantly reduce these deficiencies of each model. For the ECMWF land model, using the square root of snow water equivalent (SWE), rather than SWE itself, in the computation of snow fraction would significantly reduce the underestimation of albedo over grass. For the NCEP Noah land model, reducing (increasing) the critical SWE for full snow cover over short (tall) vegetation would reduce the underestimate (overestimate) of snow albedo over the grass (forest) site. For the NCAR CLM3, revising the coefficient used in the ground snow-fraction computation would substantially reduce the albedo underestimation over grass. For the albedo formulations in the NCEP global model, replacing the globally constant fresh snow albedo by the vegetation-type-dependent Moderate-Resolution Imaging Spectroradiometer (MODIS) maximum snow albedo would significantly improve the overestimation of model albedo over forest. DOI: 10.1175/2009JAMC2134.1