An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations

Abstract

An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research's (NCAR's) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)–NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.–Canada region as well as in summer and winter daily precipitation over the middle and eastern United States.

Dynamical downscaling simulation and future projection of precipitation over China

“…Colette et al [2012] revealed that the biases of the downscaled fields with bias correction were lower compared to those without bias correction. Xu and Yang [2012] suggested that an improved dynamical downscaling method with GCM bias correction prior to the simulation greatly improved the simulations in both climatological means and extreme events relative to the traditional downscaling simulation without correction. While Ehret et al view that bias correction methods impaired the GCM advantages by altering spatiotemporal field consistency and relations among variables, they largely neglected the feedback mechanisms.…”

A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability

“…LBCs, and driven fields (e.g., Davis and Turner, 1977;von Storch et al, 2000;Kanamaru and Kanamitsu, 2007;Cha et al, 2011;Xu and Yang, 2012;Yoshimura and Kanamitsu, 2013;Omrani et al, 2013; and numerous others). Because this approach substantially changes the RCM's internal variability and affects every factor/process that we review here, a comprehensive discussion on this issue is out of the scope of this paper.…”

Bias corrections of global models for regional climate simulations of high-impact weather

“…Bias correction of the CCSM3 boundary conditions uses the approach in Holland et al (2010) (see also Xu and Yang 2012;Done et al 2013), which can be applied consistently across variables and times. This corrects the mean bias from the GCM, but allows synoptic and climate variability to change and is similar to the approach used in Maraun (2012).…”

“…Unfortunately, biases that may be acceptable at global scales can be problematic for these downscaling applications to regional and extreme weather climate scales (e.g. Liang et al 2008;Ehret et al 2012;Xu and Yang 2012;Done et al 2013).…”

“…Variance is free to change into the future (within the resolution constraints of the driving GCM) and the RCM has the freedom to develop its own interior solution within the bias corrected boundary data. A number of variations on this theme have been attempted including; correcting bias in the mean and variance (Xu and Yang 2012), quantile-quantile mapping (Colette et al 2012), and feature location correction (Levy et al 2012). White and Toumi (2013) tested both the mean bias correction and quantile-quantile mapping methods, and found that the mean bias correction method is a more reliable and accurate method compared to the quantile-quantile mapping method.…”