A new dynamical downscaling approach with GCM bias corrections and spectral nudging

Abstract

To improve confidence in regional projections of future climate, a new dynamical downscaling (NDD) approach with both general circulation model (GCM) bias corrections and spectral nudging is developed and assessed over North America. GCM biases are corrected by adjusting GCM climatological means and variances based on reanalysis data before the GCM output is used to drive a regional climate model (RCM). Spectral nudging is also applied to constrain RCM-based biases. Three sets of RCM experiments are integrated over a 31 year period. In the first set of experiments, the model configurations are identical except that the initial and lateral boundary conditions are derived from either the original GCM output, the bias-corrected GCM output, or the reanalysis data. The second set of experiments is the same as the first set except spectral nudging is applied. The third set of experiments includes two sensitivity runs with both GCM bias corrections and nudging where the nudging strength is progressively reduced. All RCM simulations are assessed against North American Regional Reanalysis. The results show that NDD significantly improves the downscaled mean climate and climate variability relative to other GCM-driven RCM downscaling approach in terms of climatological mean air temperature, geopotential height, wind vectors, and surface air temperature variability. In the NDD approach, spectral nudging introduces the effects of GCM bias corrections throughout the RCM domain rather than just limiting them to the initial and lateral boundary conditions, thereby minimizing climate drifts resulting from both the GCM and RCM biases.

Climatic features of the Red Sea from a regional assimilative model

“…However, it may also degrade global predictions over certain areas in certain situations by amplifying biases in the global fields due to insufficient resolution and/or errors from inappropriate physics (Srinivas et al , ). To demonstrate the added values at the different stages of the adopted methodology, we computed various error statistics following Xu and Yang () and presented them in Table . The error statistics are computed between the observations and NCEP FNL data; WRF 30 km downscaled data; 10 km downscaled data and the 10 km assimilative data for the year 2009.…”

Application of the Bias Correction and Spatial Downscaling Algorithm on the Temperature Extremes From CMIP5 Multimodel Ensembles in China

“…However, several studies revealed that noticeable biases still exist in the GCM products due to their relatively coarse spatial resolution, particularly at regional or local scale (Ines & Hansen, 2006;Xu & Yang, 2015). However, several studies revealed that noticeable biases still exist in the GCM products due to their relatively coarse spatial resolution, particularly at regional or local scale (Ines & Hansen, 2006;Xu & Yang, 2015).…”

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

“…As a result of these deficiencies, the dynamical downscaling technique has been widely used in regional climate studies (Oh et al, 2014;Wang and Kotamarthi, 2015;Xu and Yang, 2015). Dynamical downscaling uses initial conditions (ICs) and boundary conditions (BCs) from global models to drive regional models for high-resolution simulations.…”