Statistical Bias Correction of Fifth Coupled Model Intercomparison Project Data from the CGIAR Research Program on Climate Change, Agriculture and Food Security - Climate Portal for Mount Makulu, Zambia

Although Global Climate Models (GCMs) are regarded as the best tools available for future climate projections, there are biases in simulating precipitation and temperature due to their coarse spatial resolution and cannot be used directly to assess the impact of projected climate change. The study objective was to investigate how bias correction methods impact the modelled future climate change under Representative Concentration Pathway 8.5 (RCP8.5) for 2020-2050.Reanalysisdata (1980-2000) and bias correction approaches (change factor [CF], nudging and Quantile Mapping [QM]) were used to calibrate GCMs [GFDL-ESM2M, MIROC-MIROC5, MPI-ESM-MR, and NCAR-CCSM4] data under RCP8.5 scenarios (2020-2050) for Mount Makulu, Zambia (latitude: 15.550° S, longitude: 28.250° E, altitude: 1200 m). Bias correction methods enable the comparison of observed and modelled impacts between the future climate scenarios and the baseline. A widely used bias correction method is the QM. QM adjusts a GCM value by mapping quantiles of the model’s distribution onto quantiles of the observed time series data. In spite of nudging being robust and easy to implement, it suppresses high-frequency variability and introduces artificial phase shifts. CF cannot provide information on future climate changes in high frequency variability that may be critical for specific impact applications such as estimates of peak discharge in hydrological catchments or inputs for crop models. Future climate signals shows that the number of days with and the amount of precipitation (mm/year) for 2020-2050 would range from 62 - 92 days and 211.9 - 906 mm/year, respectively. On the other hand, maximum and minimum temperature would increase in the in the range of 1.23 - 1.97°C and 1.45 - 2.68°C, respectively. QM can be used for precipitation while the CF can be used for temperature. Nudging is a widely used technique for online bias reduction, where modelled fields are continuously forced toward observed climatology