P17  Dynamical Downscaling for Climate Impacts

Harkey, Monica and Tracey Holloway, University of Wisconsin-Madison

To assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban-rural gradients, complex topography, and sub-synoptic atmospheric phenomena. With current GCM resolution too coarse for this work, the resolution needed is often achieved by employing dynamical downscaling. However, dynamical downscaling can result in conditions departing greatly from the ÒparentÓ data, making model inter-comparisons and attribution of outcomes difficult. Conditions simulated by employing spectral nudging have smaller departures from the parent data than conditions simulated without nudging. However, the differences are small when different physical parameterizations are used in the downscaling model and parent model: mean errors in 2-meter temperature range within 1 C and within 7.5 mm of monthly accumulated precipitation among our experiments. We have also found that a comparison of nudged to non-nudged simulations is a means of quantifying any biases that result from the parameterizations of the downscaling model. Our results indicate that spectral nudging is a useful tool for consistent, comparable studies of downscaling climate for regional and local impacts.