Abstract
Two different bias correction methods, the quantile mapping (QM) and quantile delta mapping (QDM), are applied to simulated daily temperature and precipitation over China from a set of 21st century regional climate model (the ICTP RegCM4) projections. The RegCM4 is driven by five different general circulation models (GCMs) under the representative concentration pathway RCP4.5 at a grid spacing of 25 km using the CORDEX East Asia domain. The focus is on mean temperature and precipitation in December–January–February (DJF) and June–July–August (JJA). The impacts of the two methods on the present day biases and future change signals are investigated. Results show that both the QM and QDM methods are effective in removing the systematic model biases during the validation period. For the future changes, the QDM preserves the temperature change signals well, in both magnitude and spatial distribution, while the QM artificially modifies the change signal by decreasing the warming and modifying the patterns of change. For precipitation, both methods preserve the change signals well but they produce greater magnitude of the projected increase, especially the QDM. We also show that the effects of bias correction are variable- and season-dependent. Our results show that different bias correction methods can affect in different way the simulated change signals, and therefore care has to be taken in carrying out the bias correction process.
Highlights
While general circulation models (GCMs) produce important climate change information on the global scale, they are still characterized by an excessively coarse resolution to provide information for impact studies
While an overall lowering of the regional mean temperatures over China is found in all simulations, a larger change in ensR, ensR_QM and ensR_QDM at the end of 21st century over the 10 major river basins in China: a temperature in DJF; b temperature in JJA; c precipitation in DJF; d precipitation in JJA
Regional mean over the whole of China for a to c and the CORs with original model projection for b to c are provided in the lower left corner of the panels is found in HdR_QM, a simulation for which we find the smallest COR with the original model projection (0.68)
Summary
While general circulation models (GCMs) produce important climate change information on the global scale, they are still characterized by an excessively coarse resolution to provide information for impact studies. Regional climate models (RCMs) with higher horizontal resolution have been widely used in recent decades (Giorgi 2019) to downscale GCM simulations and provide fine scale regional climate information. They can be especially useful for the East Asia. Systematic biases of climate model simulations relative to observations widely exist due to various reasons It can be very difficult and sometimes even not possible to use model outputs directly in impact assessment studies, e.g., as forcings for hydrological and agricultural models. Bias correction has been widely used to postprocess the climate model output prior to application for impact studies (Wood et al 2004; Boé et al 2007; Ashfaq et al 2010; Piani et al 2010; Yang et al 2010; Hagemann et al 2011; Teutschbein and Seibert 2012; Eden et al 2012)
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