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Adrienne Wootten

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In recent years climate model experiments have been increasingly oriented towards 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 we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply...
Future climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving...
Categories: Publication; Types: Citation
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Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM...
The Rio Grande is a vital water source for the southwestern States of Colorado, New Mexico, and Texas and for northern Mexico. The river serves as the primary source of water for irrigation in the region, has many environmental and recreational uses, and is used by more than 13 million people including those in the Cities of Albuquerque and Las Cruces, New Mexico; El Paso, Texas; and Ciudad Juárez, Chihuahua, Mexico. However, concern is growing over the increasing gap between water supply and demand in the Upper Rio Grande Basin. As populations increase and agricultural crop patterns change, demands for water are increasing, at the same time the region is undergoing a decrease in supply due to drought and climate...
Categories: Publication; Types: Citation
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