Climate data uncertainty for agricultural impact assessments in West Africa

Reliable information on climate impacts can support planning processes to make the agricultural sector-which has cascading effects on food security, livelihoods and the security situation-more resilient. Subsequently, uncertainties in past and future climate data need to be decreased and better understood. In this study, we analysed the quality and limitations of different past and future climate data sets to be used for agricultural impact assessments in West Africa. The high differences between the three analysed past climate data sets underline the high observational uncertainty in West Africa and show the influence of selecting the observational data set for the bias-adjustment of climate model data. The ten CMIP6 (Coupled Model Inter-comparison Project Phase 6) models show regional and model-dependent biases with similar systematic biases as have been observed in earlier CMIP versions. Although the bias-adjusted version of this data (ISIMIP3b-Inter-Sectoral Impact Model Intercomparison Project) aligns overall well with observations, we could detect some regional strong deviations from observations for some agroclimatological indices. The use of the multi-model ensemble mean has resulted in an improved agreement of CMIP6 and the bias-adjusted ISIMIP3b data with observations. Choosing a sub-ensemble of bias-adjusted models could only improve the performance of the ensemble mean locally but not over the whole region. Therefore, our results suggest the use of the whole model ensemble for agricultural impact assessments in West Africa. While averaging the impact results over all climate models can serve as a best guess, the spread of the results over all models should be considered to give insights into the uncertainties. This study can support agricultural impact modelling in quantifying climate risk hotspots as well as suggesting suitable adaptation measures to increase the resilience of the agricultural sector in West Africa.