Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator approach

Using climate scenarios from only 1 or a small number of global models (GCMs) in change impact studies may lead to biased assessment due large uncertainty projections. Ensemble means projections derived multi-GCM ensemble are often used as best estimates reduce bias. However, it is time consuming run process-based (e.g. hydrological and crop models) using numerous scenarios. It would be interesting investigate if reduced could reasonable estimate the mean. In this study, we generated single ensemble-mean scenario (En-WG scenario) factors 20 GCMs included CMIP5 perturb parameters weather generator, LARS-WG, for selected locations across Canada. We En-WG drive growth DSSAT ver. 4.7 simulate yields canola spring wheat under RCP4.5 RCP8.5 emission evaluated potential by comparing them with simulated LARS-WG based on each (WG scenarios). Our results showed that were close WG high probability outperforming simulations randomly GCM. Further required, proposed approach influenced types, models, generators, GCM ensembles.