Correction of atmospheric variables to remove systematic biases in global climate model (GCM) simulations before downscaling offers a means of improving climate simulation accuracy in climate change impact assessments. Various mathematical approaches have been used to correct the lateral and lower boundary conditions of regional climate models (RCMs). Most of these techniques correct only the magnitude of each variable individually over time without regard to spatial and multivariate bias. Here, we investigate how well an RCM is able to reproduce the dependence of an observed variable based on three aspects: temporal, spatial, and multivariate. Results show that the RCM simulations with univariate bias-corrected GCM boundary conditions perform well in capturing both temporal and spatial dependence. However, all RCM simulations do not show improvement in the representation of dependence between variables, indicating the need for alternatives that correct systematic biases in multivariate dependence in both lateral and lower boundary conditions.
Figure 1. Temporal correlation length (CL) as a percentage change with ERA-I-driven RCM simulation as the reference of three atmospheric variables
(P: precipitation (mm), T2: 2-m temperature (K), and q2: 2-m specific humidity (kg/kg)) for a 14-day lag truncated from the 30 years covering Australasian
CORDEX domain for RCM(CSIRO), RCM(M), and RCM(NBC). The number in the lower right of each map represents mean percentage change. NBC, Nested
Bias Correction; RCM, regional climate model.
This page is maintained by Jason Evans |
Last updated 23 January 2018