J1.4 A Probabilistic Approach to Climate Downscaling: Flexibility, and Realistic Representation of Extremes

Tuesday, 24 January 2017: 9:15 AM
602 (Washington State Convention Center )
Daniel J. Vimont, University of Wisconsin, Madison, WI; and D. J. Lorenz and M. Kirchmeier

Adaptation to climate variability and climate change is a complex problem that requires both understanding of global- and regional-scale climate, and an appreciation of the social setting within which the adaptation is occurring. On the physical side, developing climate information on a regional scale requires understanding and representation of both global and regional processes that contribute to producing regional responses to climate variability and change. Production of relevant climate information also requires an appreciation of the interdisciplinary social setting within which adaptation is occurring. Unfortunately, the complexity of the problem precludes a priori knowledge of what information is necessary from decision-making communities. As such, downscaling methods should be flexible enough to adapt to a wide range of potential applications.

The University of Wisconsin Probabilistic Downscaled data (UWPD) provides a very flexible downscaling product that can easily be integrated into existing research and assessment activities. The methodology was designed after conversations with a wide range of interdisciplinary researchers who identified characteristics of climate data that were needed for climate impact assessment. In particular, the data set retains probabilistic information about daily precipitation and temperature variations, which allows realistic representation of variance and extremes. The methodology will be briefly described, and a diverse set of examples of how the data have been used will be presented.

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