Filters: partyWithName: Keith Dixon (X) > Categories: Publication (X)
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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
Categories: Publication;
Types: Citation;
Tags: Climate and Ecosystem Modeling,
Daily precipitation,
South Central CASC,
maximum and minimum temperature,
statistical downscaling
Categories: Publication;
Types: Citation;
Tags: Aneides hardii,
Birds,
Chiricahua Leopard Frog,
Jemez Mountains Salamander,
Lithobates chiricahuensis,
Abstract (from RMetS): The cumulative distribution function transform (CDFt) downscaling method has been used widely to provide local‐scale information and bias correction to output from physical climate models. The CDFt approach is one from the category of statistical downscaling methods that operates via transformations between statistical distributions. Although numerous studies have demonstrated that such methods provide value overall, much less effort has focused on their performance with regard to values in the tails of distributions. We evaluate the performance of CDFt‐generated tail values based on four distinct approaches, two native to CDFt and two of our own creation, in the context of a “Perfect Model”...
Categories: Publication;
Types: Citation;
Tags: Birds,
Mammals,
Other Wildlife,
South Central CASC,
Wildlife and Plants
Abstract: Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using...
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