Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios

Mearns, Linda O.; Mavromatis, T.; Tsvetsinskaya, E.; Hays, C.; Easterling, William E.

doi:10.1029/1998jd200061

Cited by 117 publications

(72 citation statements)

References 25 publications

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“…300 km in the atmospheric module of HadCM3). However, this resolution is generally insufficient to serve climate impact applications (Hostetler 1994;Mearns et al 2001), many of which require information at much finer scales to predict impacts in, say, river flow (Bell et al 2006) or crop growth (Mearns et al 1999). A method is therefore needed of obtaining realizations of fine-scale climate physically consistent with the simulations of large-scale climate obtained from GCMs.…”

Section: (I ) Downscaling Transient Changes For Impact Assessments (Bmentioning

confidence: 99%

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

Murphy

Booth

Collins

et al. 2007

Phil. Trans. R. Soc. A.

289

254

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A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales.The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

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Section: (I ) Downscaling Transient Changes For Impact Assessments (Bmentioning

confidence: 99%

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

Murphy

Booth

Collins

et al. 2007

Phil. Trans. R. Soc. A.

289

254

View full text Add to dashboard Cite

show abstract

“…These include multiple crop models with multiple climate models (Mearns et al 1999;Asseng et al 2013;Li et al 2015), multiple crop and climate models with multiple parameters (Tao et al 2009) and multiple parameter values with multiple values for inputs (Aggarwal 1995).…”

Section: Super Ensemblesmentioning

confidence: 99%

“…It is therefore not surprising that both fields have major programs related to model intercomparison and the construction of multi-model ensembles (MMEs). However, the climate modeling community began working with MMEs with the Atmospheric Model Intercomparison project in 1989 (Gates et al 1999), whereas global collaboration to create crop multi-model ensembles began in 2011 with the Agricultural Modeling Intercomparison and Improvement project ), though more limited intercomparison studies predated that project (e.g., Jamieson et al 1998;Mearns et al 1999). Progress in the use of ensembles of both climate and crop models in studies of climate change is discussed in (Challinor et al 2013).…”

Section: Introductionmentioning

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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“…It has been shown to improve weather and climate variability, especially over complex terrain (see ref. 16 For agricultural impacts, initial studies suggested that dynamical downscaling may improve projections, because the use of RCMs altered modeled crop yields by up to 20% (22)(23)(24)(25)(26)(27)(28)(29). These studies were not definitive, however; they covered limited areas and times, which can increase GCM-RCM differences, and most used the delta method to remove climate model bias, which can reduce differences.…”

mentioning

confidence: 99%

Evaluating the utility of dynamical downscaling in agricultural impacts projections

Glotter¹,

Elliott

McInerney³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscalingnested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output-to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO 2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM-and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.agriculture | food security | NARCCAP | CORDEX

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Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios

Cited by 117 publications

References 25 publications

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

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

Evaluating the utility of dynamical downscaling in agricultural impacts projections

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