An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations

作者：Mehrotra Rajeshwar^*; Sharma Ashish

来源：Water Resources Research, 2012, 48: W12601.

DOI：10.1029/2012WR012446

The quality of the absolute estimates of general circulation models (GCMs) calls into question the direct use of GCM outputs for climate change impact assessment studies, particularly at regional scales. Statistical correction of GCM output is often necessary when significant systematic biases occur between the modeled output and observations. A common procedure is to correct the GCM output by removing the systematic biases in low-order moments relative to observations or to reanalysis data at daily, monthly, or seasonal timescales. In this paper, we present an extension of a recently published nested bias correction (NBC) technique to correct for the low- as well as higher-order moments biases in the GCM-derived variables across selected multiple time-scales. The proposed recursive nested bias correction (RNBC) approach offers an improved basis for applying bias correction at multiple timescales over the original NBC procedure. The method ensures that the bias-corrected series exhibits improvements that are consistently spread over all of the timescales considered. Different variations of the approach starting from the standard NBC to the more complex recursive alternatives are tested to assess their impacts on a range of GCM-simulated atmospheric variables of interest in downscaling applications related to hydrology and water resources. Results of the study suggest that three to five iteration RNBCs are the most effective in removing distributional and persistence related biases across the timescales considered. Citation: Mehrotra, R., and A. Sharma (2012), An improved standardization procedure to remove systematic low frequency variability biases in GCM simulations, Water Resour. Res., 48, W12601, doi:10.1029/2012WR012446.

• 出版日期2012-12-15