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Author Wallach, D.; Mearns, L.O.; Ruane, A.C.; Rötter, R.P.; Asseng, S.
Title (up) Lessons from climate modeling on the design and use of ensembles for crop modeling Type Journal Article
Year 2016 Publication Climatic Change Abbreviated Journal Clim. Change
Volume 139 Issue 3-4 Pages 551-564
Keywords change projections; elevated CO2; uncertainty; wheat; water; soil; simulations; yield; rice; 21st-century; Model ensembles; Crop models; Climate models; Model weighting; Super ensembles
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.
Address 2017-01-06
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ISSN 0165-0009 ISBN Medium Article
Area Expedition Conference
Notes CropM, ft_MACSUR Approved no
Call Number MA @ admin @ Serial 4933
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Author Foskolis, A.; Moorby, J.
Title (up) Lifetime nitrogen efficiency of dairy cattle: Model description and sensitivity analysis Type Conference Article
Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 8 Issue Pages SP8-9
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Abstract Conference poster PDF
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Area Expedition Conference LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change
Notes Approved no
Call Number MA @ admin @ Serial 4842
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Author Foskolos, A.; Moorby, J.M.
Title (up) Lifetime nitrogen use efficiency of dairy cattle: model description and sensitivity analysis Type Journal Article
Year 2016 Publication Advances in Animal Biosciences Abbreviated Journal Advances in Animal Biosciences
Volume 7 Issue 03 Pages 256-258
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ISSN 2040-4700 ISBN Medium
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Notes LiveM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4864
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Author Kipling, R.
Title (up) LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change Type Report
Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 8 Issue Pages L0.1-D1
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Notes Approved no
Call Number MA @ admin @ Serial 4841
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Author Semenov, M.A.; Stratonovitch, P.
Title (up) Local-scale CMIP5-based climate scenarios for MACSUR2 Type Report
Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 8 Issue Pages C2.2-D
Keywords CropM
Abstract Climate sensitivity of GCMs was used to select 5 GCMs from the CMIP5 ensemble for impact studies in MACSUR2. Selected GCMs for MACSUR2 are EC-EARTH (7), GFDL-CM3 (8) HadGEM2-ES (10), MIROC5 (13), and MPI-ESM-MR (15). These GCMs are evenly distributed among CMIP5 (Fig 1) and should capture, in principal, climate uncertainty of the CMIP5 ensemble. Using 5 GCMs will enable us to assess uncertainties in impacts related to uncertainty in climate projections. The selection of GCMs in MACSUR2 has a good overlap with selections of GCMs used in CORDEX and AgMIP projects.  We used the LARS-WG generator to construct local-scale CMIP5-based climate scenarios for Europe (Semenov & Stratonovitch, 2015). Fifteen sites were selected in Europe for MACSUR2. For each site and each selected GCM, 100 yrs climate daily data were generated by LARS-WG for RCP4.5 and RCP8.5 emission scenarios and for baseline and 3 future periods: near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100).
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Publisher Place of Publication Editor
Language Summary Language Original Title
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Area Expedition Conference
Notes Approved no
Call Number MA @ admin @ Serial 2270
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