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.