Filters: partyWithName: South Central CASC (X)
Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > South Central CASC > FY 2013 Projects ( Show direct descendants )
3 results (9ms)
Location
Folder
ROOT _ScienceBase Catalog __National and Regional Climate Adaptation Science Centers ___South Central CASC ____FY 2013 Projects
Filters
Date Range
Extensions Types Contacts Categories Tag Types
|
Abstract: The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated...
Categories: Publication;
Types: Citation;
Tags: Data Visualization & Tools,
Rivers, Streams and Lakes,
Science Tools For Managers,
South Central CASC,
Water, Coasts and Ice
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...
|
