NARCCAP Model Skill and Bias for the Southeast United States

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DOI: 10.4236/ajcc.2015.41009    2,564 Downloads   2,850 Views   Citations


This paper investigates dynamically downscaled regional climate model (RCM) output from the North American Regional Climate Change Assessment Program (NARCCAP) for two sub-regions of the Southeast United States. A suite of four statistical measures were used to assess model skill and biases were presented in hindcasting daily minimum and maximum temperature and mean precipitation during a historical reference period, 1970-1999. Most models demonstrated high skill for temperature during the historical period. Two outliers included two RCMs run using the Geophysical Fluids Dynamics Lab (GFDL) model as their lateral boundary conditions; these models suffered from a cold maximum temperature bias. Improvement with GFDL-based projections of maximum temperature was noted from May through November when they ran with observed seasurface conditions (GFDL-timeslice), particularly for the east sub-region. Precipitation skill proved mixed-relatively high when measured using a probability density function overlap measurement or the index of agreement, but relatively low when measured with root-mean square error or mean absolute error, because several models overestimated the frequency of extreme precipitation events.

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Kabela, E. and Carbone, G. (2015) NARCCAP Model Skill and Bias for the Southeast United States. American Journal of Climate Change, 4, 94-114. doi: 10.4236/ajcc.2015.41009.


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