摘要：This study assesses the accuracy of state-of-the-art regional climate models for
agriculture applications in West Africa. A set of nine regional configurations with eight
regional models from the ENSEMBLES project is evaluated. Although they are all
based on similar large-scale conditions, the performances of regional models in
reproducing the most crucial variables for crop production are extremely variable.
This therefore leads to a large dispersion in crop yield prediction when using
regional models in a climate/crop modelling system. This dispersion comes from the
different physics in each regional model and also the choice of parametrizations for a
single regional model. Indeed, two configurations of the same regional model are
sometimes more distinct than two different regional models. Promising results are
obtained when applying a bias correction technique to climate model outputs.
Simulated yields with bias corrected climate variables show much more realistic means
and standard deviations. However, such a bias correction technique is not able
to improve the reproduction of the year-to-year variations of simulated yields. This study confirms the importance of the multi-model approach for quantifying
uncertainties for impact studies and also stresses the benefits of combining both regional
and statistical downscaling techniques. Finally, it indicates the urgent need to address the
main uncertainties in atmospheric processes controlling the monsoon system and to
contribute to the evaluation and improvement of climate and weather forecasting models in
that respect.
