Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

• Published in: Climatic change 2016-12, Vol.139 (3-4), p.551-564
• Main Authors: Wallach, Daniel, Mearns, Linda O., Ruane, Alexander C., Roetter, Reimund P., Asseng, Senthold
• Format: Article
• Language: English
• Subjects: Agricultural production; Agricultural sciences; Atmospheric Sciences; Biodiversity and Ecology; Climate change; Climate Change/Climate Change Impacts; Climate models; Collaboration; Criteria; crop models; Crops; Earth and Environmental Science; Earth Sciences; Environmental Sciences; Life Sciences; Mathematical models; Meteorology And Climatology; Modelling; prediction; Sampling; Statistical analysis; Statistical models; Uncertainty
• ISSN: 0165-0009; 1573-1480
• DOI: 10.1007/s10584-016-1803-1

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.