TITLE:
NARCCAP Model Skill and Bias for the Southeast United States
AUTHORS:
Erik D. Kabela, Gregory J. Carbone
KEYWORDS:
NARCCAP, Model Skill, Model Bias
JOURNAL NAME:
American Journal of Climate Change,
Vol.4 No.1,
March
23,
2015
ABSTRACT: 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during a historical reference period, 1970-1999.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.