Improving future temperature projections with bias correction methods in Lake of Guiers/Senegal

Climate models are generally used to evaluate the climate change impacts. However, they have important biases at the regional or local scales. This study evaluates the future temperature projections in Lake of Guiers/Senegal. For this, the daily maximum and minimum temperature from the ensemble mean of five (5) Coordinated Regional climate Downscaling Experiment (CORDEX) regional climate models (RCMs) under the greenhouse gas scenarios RCP4.5 and RCP8.5 and three (3) bias correction methods (Linear scaling, variance scaling and quantile mapping methods) were used. The performance of raw ensemble mean of the models was first evaluated against the WFDEI data. The results show that this latter exhibits some limitations to reproduce the minimum and the maximum temperature at the Lake scale. In order to make temperature data more accurate, the three bias correction methods were used. Results show that bias correction methods improve well the simulated minimum and maximum temperature. The future temperature projections show an increase of temperature which are faster in bias-corrected data. From the results it is indicated that it is necessary to implement appropriate adaptation measures to address these climate changes.