A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations

作者：Johnson Fiona^*; Sharma Ashish

来源：Water Resources Research, 2012, 48(1): W01504.

DOI：10.1029/2011WR010464

Climate change impact assessments of water resources systems require simulations of precipitation and evaporation that exhibit distributional and persistence attributes similar to the historical record. Specifically, there is a need to ensure general circulation model (GCM) simulations of rainfall for the current climate exhibit low-frequency variability that is consistent with observed data. Inability to represent low-frequency variability in precipitation and flow leads to biased estimates of the security offered by water resources systems in a warmer climate. This paper presents a method to postprocess GCM precipitation simulations by imparting correct distributional and persistence attributes, resulting in sequences that are representative of observed records across a range of time scales. The proposed approach is named nesting bias correction (NBC), the rationale being to correct distributional and persistence bias from fine to progressively longer time scales. In the results presented here, distributional attributes have been represented by order 1 and 2 moments with persistence represented by lag 1 autocorrelation coefficients at monthly and annual time scales. The NBC method was applied to the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Mk3.5 and MIROC 3.2 hires rainfall simulations for Australia. It was found that the nesting method worked well to correct means, standard deviations, and lag 1 autocorrelations when the biases in the raw GCM outputs were not too large. While the bias correction improves the representation of distributional and persistence attributes at the time scales considered, there is room for representation of longer-term persistence by extending to time scales longer than a year.

• 出版日期2012-1-6