Examining the Impact of Bias Correction on the Prediction Skill of Regional Climate Projections

Abstract

Rainfall is crucial for many applications e.g. agriculture, health, water resources, energy among many others. However, quantitative rainfall estimation is normally a challenge especially in areas with sparse rain gauge network. This has introduced uncertainties in rainfall projections by climate models. This study evaluates the performance of three representative concentration pathways, RCP i.e. 4.5, 6.0 and 8.5 over Uganda using the Weather Research and Forecasting (WRF) model. It evaluates the model output using observed daily rain gauge data over the period 2006-2018 using Pearson correlation; relative root mean square error; relative mean error and skill scores (accuracy). It also evaluates the potential improvement in the performance of the WRF model with respective RCPs by applying bias correction. The bias correction is carried out using the quantile mapping method. A poor correlation with observed rainfall is generally found (-0.4 to +0.4); error magnitudes in the ranges of 1 to 3.5 times the long-term mean are observed. The RCPs presented different performances over different areas suggesting that no one RCP is universally valid. Application of bias correction did not produce realistic improvement in performance. Largely, the RCPs underestimated rainfall over the study area suggesting that the projected rainfall cases under these RCPs could be seriously underestimated. However, the study found RCP8.5 with slightly better performance and is thus recommended. Due to the general weak performance of the RCPs, the study recommends re-evaluating the assumptions under the RCPs for different regions or attempt to improve them using data assimilation.