A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies

The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950–2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders. It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century