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Ahmed, K.F., Wang, G., Silander, J., Wilson, A.M., Allen, J.M., Horton, R., et al. (2013) Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast. Global and Planetary Change, 100, 320-332.
http://dx.doi.org/10.1016/j.gloplacha.2012.11.003

has been cited by the following article:

• TITLE: Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

  AUTHORS: Sohom Mandal, Patrick A. Breach, Slobodan P. Simonovic

  KEYWORDS: Climate Change, Downscaling, Multivariate Regression, Uncertainty

  JOURNAL NAME: American Journal of Climate Change, Vol.5 No.1, March 31, 2016

  ABSTRACT: This study investigates different sources of uncertainty in the assessment of the climate change impacts on total monthly precipitation in the Campbell River basin, British Columbia, Canada. Four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs) and six downscaling methods (DSMs) are used in the assessment. These sources of uncertainty are analyzed separately for two future time periods (2036 to 2065 and 2066 to 2095). An uncertainty metric is calculated based on the variation in simulated precipitation due to choice of GCMs, emission scenarios and downscaling models. The results show that the selection of a downscaling method provides the largest amount of uncertainty when compared to the choice of GCM and/or emission scenario. However, the choice of GCM provides a significant amount of uncertainty if downscaling methods are not considered. This assessment work is conducted at ten different locations in the Campbell River basin.