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Characterizing Sources of Uncertainty from Global Climate Models and Downscaling Techniques

Authors :
Adrienne Wootten
Fred Semazzi
Ryan Boyles
Adam J. Terando
Brian J. Reich
Source :
Journal of Applied Meteorology and Climatology. 56:3245-3262
Publication Year :
2017
Publisher :
American Meteorological Society, 2017.

Abstract

In recent years, climate model experiments have been increasingly oriented toward providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here a method is presented, on the basis of a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. The method is applied to the southeastern United States using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios is typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast for precipitation and ~30% for extreme heat days (>35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a subsample of all models is available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. This article concludes with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Details

ISSN :
15588432 and 15588424
Volume :
56
Database :
OpenAIRE
Journal :
Journal of Applied Meteorology and Climatology
Accession number :
edsair.doi...........020d4cf2d2daa028227c61e3dc5e937b
Full Text :
https://doi.org/10.1175/jamc-d-17-0087.1