IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Impact of bias-corrected RCM lateral boundary conditions on precipitation extremes

Youngil Kim1, Jason Evans2, and Ashish Sharma1
Youngil Kim et al.
  • 1University of New South Wales, School of Civil and Environmental Engineering, Australia
  • 2University of New South Wales, Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, Australia

Hydro-climatological applications often require global climate models (GCMs) outputs to assess the impacts of climate change. However, it is well known that the direct use of GCM simulations is limited as their spatial and temporal resolution are insufficient to provide output at the regional scale required in assessing changes in extreme rainfall. Although regional climate models (RCMs) forced with GCM data are widely used to resolve finer resolutions, their application is hindered by systematic biases contained in large-scale circulation patterns from driving GCM data. To deal with these considerable biases, recent studies have suggested the bias correction of the input boundary conditions of RCM.

This study focuses on the impact of bias corrections in the input boundary conditions of RCM on extreme rainfall events. Three bias correction methods are used: mean, mean and variance, and nested bias correction (NBC) that corrects lag-1 autocorrelations. RCM used here is the Weather Research and Forecasting model (WRF), and the European Center for Medium-Range Weather Forecast’s (ECMWF) ERA-Interim (ERA-I) reanalysis model is used as an “observational” reference for bias correction. The downscaling is performed over the Australasian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain.

Two quantitative measures are used to evaluate the impact of bias correction on the RCM output: root-mean-square errors (RMSE) and bias. Indices from the World Meteorological Organization (WMO) Expert Team on Climate Risk and Sectoral Climate Indicators (ET-CRSCI) are used to evaluate bias correction performance on extreme rainfall.

It is clear from the statistics used here that bias correction on the input boundary condition produces a noticeable improvement in daily precipitation percentile indices. The results also show that the sophisticated method representing rainfall variability and long-term persistence corrects details in simulating extreme rainfall.

How to cite: Kim, Y., Evans, J., and Sharma, A.: Impact of bias-corrected RCM lateral boundary conditions on precipitation extremes, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-312, https://doi.org/10.5194/iahs2022-312, 2022.