Multimodel ensembles of wheat growth: many models are better than one

Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rötter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide; Hatfield, Jerry L.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Robert K.; Grant, R. F.; Heng, Lee; Hooker, Josh; Hunt, L. A.; Ingwersen, Joachim; Izaurralde, R. C.; Kersebaum, Kurt Christian; Müller, Christoph; Kumar, Soora Naresh; Nendel, Claas; O’Leary, G.; Olesen, Jørgen E.; Osborne, Thomas M.; Palosuo, Taru; Priesack, Eckart; Ripoche, Dominique; Semenov, Mikhail A.; Shcherbak, Iurii; Steduto, Pasquale; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Travasso, Maria; Waha, Katharina; White, Jeffrey W.; Wolf, J.

doi:10.1111/gcb.12768

Cited by 433 publications

(290 citation statements)

References 42 publications

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“…This was especially evident for NEE and LE for rain-fed maize at US-NE3, rain-fed soybean LE, and overall diurnal cycles of H. Unlike in some other previous studies (e.g. Asseng et al 2013Asseng et al , 2015Bassu et al 2014;Martre et al 2015) the ensemble mean did not generally outperform the individual models, which was especially true for the sites with maize and soybean crops, which had few models with crop specificity that had better skills compared to the ensemble mean.…”

contrasting

confidence: 71%

“…Ryan et al 1996;Amthor et al 2001;Grant et al 2005), and agricultural (e.g. Semenov et al 1996;Frolking et al 1998;Ciais et al 2010;Asseng et al 2013Asseng et al , 2015Bassu et al 2014;Martre et al 2015) ecosystems. Semenov et al (1996) compared the performance of five wheat models at two sites in Europe: Rothamsted, United Kingdom, and Seville, Spain.…”

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confidence: 99%

“…Recent studies by Asseng et al (2013Asseng et al ( , 2015 and Martre et al (2015) compared the performance of multiple wheat models (27-30 models) in simulating crop yields and growth variables and highlighted the better performance shown by the ensemble mean or median compared to individual models. Similarly, Bassu et al (2014) highlighted the better accuracy of the ensemble mean in evaluating the performance of 23 models simulating maize yields at four sites.…”

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confidence: 99%

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Carbon and energy fluxes in cropland ecosystems: a model-data comparison

et al. 2016

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Croplands are highly productive ecosystems that contribute to land-atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization Author's personal copy for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.Keywords Carbon and energy fluxes Á Cropland ecosystems Á Land-atmosphere exchange Á Modeldata comparison Á Cropland carbon and energy exchange

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confidence: 71%

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confidence: 99%

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confidence: 99%

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Carbon and energy fluxes in cropland ecosystems: a model-data comparison

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“…One approach with crop models has been to identify models that have similar equations for underlying processes, such as photosynthesis. In general, however, it has not been found that structural similarity leads to similarity in simulated values in crop MMEs (Palosuo et al 2011;Martre et al 2015;Li et al 2015). An alternative approach, proposed for climate models (Bishop and Abramowitz 2013), is to examine the covariance in model errors as the measure of model dependence.…”

Section: Evaluating the Degree Of Relatedness Of The Models In A Mmementioning

confidence: 98%

“…Ensembles allow one to obtain a probability distribution instead of a point prediction (Harris et al 2010). Furthermore, it has been empirically observed in many fields that ensemble averages or medians often better reproduce observations than even the best individual model (Hagedorn et al 2005;Tebaldi and Knutti 2007;Palosuo et al 2011;Martre et al 2015). Additional benefits from working with ensembles of models arise from the closer collaboration between modeling groups.…”

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confidence: 99%

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

et al. 2016

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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.

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The effects of teleconnections on carbon fluxes of global terrestrial ecosystems

Zhu

Piao

et al. 2017

Geophysical Research Letters

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Large‐scale atmospheric circulation patterns (i.e., teleconnections) influence global climate variability patterns and can be studied to provide a simple framework for relating the complex response of ecosystems to climate. This study analyzes the effects of 15 major teleconnections on terrestrial ecosystem carbon fluxes during 1951–2012 using an ensemble of nine dynamic global vegetation models. We map the global pattern of the dominant teleconnections and find that these teleconnections significantly affect gross primary productivity variations over more than 82.1% of the global vegetated area, through mediating the global temperature and regional precipitation and cloud cover. The El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation are strongly correlated with global, hemispherical, and continental carbon fluxes and climatic variables, while the Northern Hemisphere teleconnections have only regional influences. Further research regarding the interactions among the teleconnections and the nonstationarity of the relationship between teleconnections and carbon fluxes is needed.

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Multimodel ensembles of wheat growth: many models are better than one

Cited by 433 publications

References 42 publications

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

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

The effects of teleconnections on carbon fluxes of global terrestrial ecosystems

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