Lessons from climate modeling on the design and use of ensembles for crop modeling

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 Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. 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.

Higher contributions of uncertainty from global climate models than crop models in maize‐yield simulations under climate change

“…Variance analysis is an important and popular approach to disaggregate total uncertainty among the various contributions (Diniz‐Filho et al ., ; Yip et al ., ; Ceglar and Kajfež‐Bogataj, ; Wallach et al ., ). A random effects analysis of variance (ANOVA) was performed to separate the uncertainties in the yield simulations attributable to the crop model, GCM, RCP and error (inter‐annual variability).…”

Genetics of barley tiller and leaf development

“…Recently, crop modelling revealed its potential as a tool to support ideotype design for crop breeding (Li et al ; Rötter et al ; Gouache et al ). Simulation testing within a series of environments through an ensemble of models was proposed as a promising way to investigate ideotype design and reduce uncertainties in the simulations (Wallach et al ; Tao et al ).…”

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

“…The simulations were usually driven by climate projections from global climate models (GCMs) downscaled by statistical methods or regional climate models (RCMs) (White et al., ). The climate change impact assessments are plagued with uncertainties from many physical, biological and socioeconomic processes involved (Asseng et al., , ; Challinor et al., ; Lobell & Burke, ; Rötter, ; Rötter, Carter, Olesen, & Porter, ; Tao, Yokozawa, & Zhang, ; Tao, Zhang et al., ; Wallach, Mearns, Ruane, Rötter, & Asseng, ; Wallach et al., ). Among others, uncertainties can originate from greenhouse gas emission scenarios, climate projections of GCMs and their downscaling, crop model structure (different crop models or model equations), input data and parameters (Challinor, Smith, & Thornton, ; Wallach et al., ; White et al., ).…”

“…Recent studies have made great progress in dealing with these uncertainties (Asseng et al., , ; Challinor et al., ; Iizumi, Yokozawa, & Nishimori, ; Palosuo et al., ; Porter et al., ; Rötter, Carter et al., , ; Tao et al., ; Tao, Yokozawa et al., ; Tao, Zhang et al., ; Wallach et al., ). For example a probabilistic assessment approach was developed for assessing future climate impact on rice productivity and water use in China, based on 20 climate change scenarios and a Monte Carlo technique, to account for the uncertainty from climate projections (Tao et al., ).…”