EGU22-4305, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Providing useful local climate information through statistical bias correction

Muralidhar Adakudlu1, Elena Xoplaki1, Heiko Paeth2, Chibuike Ibebuchi2, and Daniel Schoenbein2
Muralidhar Adakudlu et al.
  • 1Justus Liebig University Giessen, Giesen, Germany
  • 2University of Wuerzburg

Daily precipitation and temperature simulated by regional climate models carry large systematic biases owing to multiple factors including inadequate model resolution and limitations in the parameterization of important processes. Reduction of these biases is a crucial process in rendering the model information more reliable for climate change and hydrological assessments. We present an evaluative study of bias correction of daily precipitation and temperature from an ensemble of regional climate models from the EUR-11 CORDEX domain (CLMCOM-CCLM4, GERICS-REMO15, SMHI-RCA4, DMI-HIRHAM5, and CanRCM4 driven by MPI-ESM). This is an important milestone within a larger framework of the RegiKlim consortium towards generating high-resolution bias corrected and statistically downscaled fields for providing useful climate information in specific areas in Germany. A quantile delta mapping (QDM) approach is applied to adjust the biases in the distribution characteristics of precipitation and temperature. The delta factor, derived from the ratio of the projected value of a given quantile to that of the present value, is applied to the standard transfer function so that the modelled climate change signal can be preserved. High-resolution (0.1°) gridded dataset from the German Weather Service, DWD-HYRAS, is used as the reference for bias correcting the variables. The impact of the bias adjustment on important parameters such as the number and frequency of wet/dry and cold/hot spells are quantified. The response of the quantile mapping method to the seasonal variations in the dominant driving processes is further investigated. 

How to cite: Adakudlu, M., Xoplaki, E., Paeth, H., Ibebuchi, C., and Schoenbein, D.: Providing useful local climate information through statistical bias correction, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4305,, 2022.