scholarly journals Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue

2013 ◽  
Vol 26 (6) ◽  
pp. 2137-2143 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Temporal Structure ◽  
Regional Climate ◽  
Quantile Mapping ◽  
Model Simulations ◽  
Scale Mismatch ◽  
Climate Model Simulations

Abstract Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. However, if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here, it is shown for daily precipitation that such quantile mapping–based downscaling is not feasible but introduces similar problems as inflation of perfect prognosis (“prog”) downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is overcorrected, area-mean extremes are overestimated, and trends are affected. To overcome these problems, stochastic bias correction is required.

scholarly journals Can Bias Correction of Regional Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall Variability?

2017 ◽  
Vol 30 (24) ◽  
pp. 9785-9806 ◽  
Author(s):  
Eytan Rocheta ◽  
Jason P. Evans ◽  
Ashish Sharma
Keyword(s):  
Boundary Conditions ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Low Frequency ◽  
Lateral Boundary ◽  
Model Simulations ◽  
Low Frequency Variability ◽  
Climate Model Simulations

Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.

scholarly journals Bias Correction of Regional Climate Model Simulations in a Region of Complex Orography

2013 ◽  
Vol 52 (1) ◽  
pp. 82-101 ◽  
Author(s):  
Roger Bordoy ◽  
Paolo Burlando
Keyword(s):  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Temporal Stability ◽  
Correction Method ◽  
Monthly Basis ◽  
Climate Model Simulations ◽  
Precipitation And Temperature

AbstractThis study presents a method to correct regional climate model (RCM) outputs using observations from automatic weather stations. The correction applies a nonlinear procedure, which recently appeared in the literature, to both precipitation and temperature on a monthly basis in a region of complex orography. To assess the temporal stability of such a correction, the correcting parameters of each variable are investigated using different time periods within the observational record. The RCM simulations used in this study to evaluate the bias-correction method are the publicly available “Reg-CM3” experiments from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project. They provide daily precipitation and temperature time series on a raster with spatial resolution of 0.22°. The analysis is performed in the Rhone catchment, located in southwestern Switzerland and characterized by highly complex orography. The results show that the nonlinear bias correction increases dramatically the accuracy not only of the RCM mean daily precipitation and temperature but also of values across the entire domain of the probability distribution. Moreover, the correction parameters seem to be reasonably independent from the sample used for their calibration, especially in the case of temperature. The good performance of the method over the considered mountainous region during the evaluation period points to the suitability of this technique for correcting RCM biases regardless of the stationarity of the climate and, therefore, also for future climate and in regions characterized by marked orography.

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