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442 assessment of climate change in the southwest united statesfeet. In regional climate models (such as the Weather Research and Forecasting Model,or WRF ii ) the mountains are represented as over 10,000 feet. The difference is becausethe topography must be simplified for global models and because of different modelresolutions. iii Although the mountains are better represented in the RCMs, their higherresolution requires more intensive computational resources, which, in a practical sense,means that the RCMs are only able to utilize the inputs from a subset of the twenty-twoavailable GCMs. Clearly, more data would be gained by using a larger suite (number) ofGCMs, yet GCMs alone cannot account adequately for the important role of topographyin the Intermountain West. The GCMs used in the IPCC’s Fourth Assessment Reporthave a weak but systematic bias for overestimating the speed of upper-level westerlywinds near 30°N and November-to-April precipitation in the Southwest. Of relevanceis that the wettest models project the greatest drying in this region with climate change.As it turns out, all of these models have “subdued” topography that may contribute tothe zonal wind bias and may also underestimate rain shadow effects, producing wetbiases on the lee side of the mountains (McAfee, Russell, and Goodman 2011). Thus, inthis case, the tradeoff between statistical and dynamical downscaling involves either agreater range of potential futures (which is valuable in planning and risk-based management)or potentially more accurate representation of climate.Direct IMPACTS. Regional climate projections from downscaling are in turn usedto drive other models of the physical environment. In the Southwest, water is a criticalcomponent of climate. Therefore, assessments typically must translate future climateprojections into impacts on the region’s hydrologic processes (such as precipitation,snowmelt runoff, streamflow, infiltration, groundwater recharge and discharge, evapotranspiration,and so on). Simulation models are often used for this task, with most ofthe effort spent characterizing future weather conditions that are consistent with climateprojections. Those weather conditions are then used to simulate hydrologic processes.The hydrologic model itself is typically developed and verified under historical climateand watershed conditions. Uncertainty in projecting hydrologic processes arises fromhow the hydrologic model is structured, the way future weather over the watershed ischaracterized (which often requires some blending of historical weather observationsand projected changes in climate), and assumptions about other features of a watershedthat might change as climate changes and affects runoff. (See also the discussion presentedin Chapter 10, especially in Section 10.3 and in “Planning Techniques and Stationarity”in Section 10.5.)Despite limitations associated with such hydrologic models, outputs from thesemodels are most influenced by the choice of GCM used to provide input, followed bythe type of downscaling method used, then by the hydrologic model chosen (Wilby andHarris 2006; Crosbie, McCallum and Walker 2011). This suggests that GCMs and thelevel of understanding of large-scale processes are the largest source of uncertainties inthe model uncertainty typology continuum discussed earlier. Given that outputs basedon the averaging of results of numerous models are better than those based on the resultsof an individual model (Reichler and Kim 2008), impact studies that are informedby multiple global climate models will have a greater certainty than those based on asingle global model.