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Randall, D.A., Wood, R.A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinvasan, J., Stouffer, R. J., Sumi, A. and Taylor, K.E. (2007) Climate Models and Their Evaluation. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.

has been cited by the following article:

• 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skill and biases were presented in hindcasting daily minimum and maximum temperature and mean precipitation during a historical reference period, 1970-1999.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.