An Improved Dynamical Downscaling for the Western United States

Abstract

A quantitative assessment of climate change impacts on water management depends heavily on the knowledge of basic climate variables, such as precipitation and temperature, and how they might change over time. The approach of dynamical downscaling – nesting regional climate models (RCMs) within general circulation models (GCMs) – has shown promise in producing climate information at scales useful to e.g. water managers (Leung et al. 2006). Organized efforts such as the European project PRUDENCE (Christensen et al. 2007) and the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2009) have demonstrated the value of dynamical downscaling on regional climate projections. However, a significant degree of uncertainty in regional downscaling still exists. The uncertainties are more so in mountainous and drought-prone regions such as the western United States (U.S.) (Lo et al. 2008), as this region of the U.S. is projected to experience significant warming and precipitation reduction that portend a drying climate scenario (IPCC 2007). Hence, an assessment of climate projection uncertainties is paramount. The western U.S. relies both economically and socially on the development of winter mountain snowpack and the timely release of its retained water (Gleick and Chalecki 1999). Decreasing and early melting of the snowpack across the western U.S. have occurred during the past century (Cayan et al. 2001; Pierce et al. 2008) and are expected to continue due to a warming climate (McCabe and Wolock 1999; Leung et al. 2004). RCMs are envisaged to be a crucial tool to simulate future projections at finer scales. However, a recent analysis on change in snow property (Gillies et al. 2011) have noted that most NARCCAP models tend to produce persistent cold biases in the surface over the western U.S., thus leading to an overestimation of the snowfall and the snow depth. Analyzing several mesoscale forecast models, Coniglio et al. (2010) have observed similar cold biases in daily minimum temperature, which are attributable to the models’ inability to break down the morning inversion layer quickly enough. Such cold biases are most serious in the interior West. While temperature biases alone may be corrected by statistical methods, these documented cold biases in RCMs can and do alter the climate projections; this is because the amount of available water in the atmosphere is also a function of evapotranspiration, which changes exponentially with temperature variations (Nash and Gleick 1993). Moreover, the impacts