Abstract
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.Electronic supplementary materialThe online version of this article (doi:10.1007/s10584-016-1803-1) contains supplementary material, which is available to authorized users.
Highlights
In modeling complex systems, it is important to have a measure of uncertainty in simulated values (Tebaldi and Knutti 2007)
The objective of this paper is to identify questions and approaches related to developing and using model ensembles that have been studied in the climate modeling literature and are relevant to crop models
The major advantage of doing so is that model ensembles (MMEs) provide information on uncertainty in crop model estimations and projections related to model structure, which has been shown to be a major source of uncertainty
Summary
It is important to have a measure of uncertainty in simulated values (Tebaldi and Knutti 2007). Climate modeling and crop modeling are two fields in which multiple groups have developed different models to represent the same complex system In both fields there is major interest in the uncertainty of simulations and the potential of ensemble statistics to improve predictions or projections. The objective of this paper is to identify questions and approaches related to developing and using model ensembles that have been studied in the climate modeling literature and are relevant to crop models This should hopefully accelerate the progression of the crop model community in taking advantage of this useful diagnostic approach
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