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Ensemble modeling, uncertainty and robust predictions

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  • Wendy S. Parker

Abstract

Many studies of future climate change take an ensemble modeling approach in which simulations of future conditions are produced with multiple climate models (or model versions), rather than just one. These ensemble studies are of two main types—perturbed‐physics and multimodel—which investigate different sources of uncertainty about future climate change. Increasingly, methods are being applied which assign probabilities to future changes in climate on the basis of the set of projections (the ensemble) produced in a perturbed‐physics or multimodel study. This has prompted debate over both the appropriate interpretation of ensembles as well as how best to communicate uncertainty about future climate change to decision makers; such communication is a primary impetus for ensemble studies. The intuition persists that agreement among ensemble members about the extent of future climate change warrants increased confidence in the projected changes, but in practice the significance of this robustness is difficult to gauge. Priority topics for future research include how to design ensemble studies that take better account of structural uncertainty, how to weight ensemble members and how to improve the process by which ensemble studies are synthesized with other information in expert assessments. WIREs Clim Change 2013, 4:213–223. doi: 10.1002/wcc.220 This article is categorized under: Climate, History, Society, Culture > Ideas and Knowledge Climate Models and Modeling > Knowledge Generation with Models

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  • Wendy S. Parker, 2013. "Ensemble modeling, uncertainty and robust predictions," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 4(3), pages 213-223, May.
  • Handle: RePEc:wly:wirecc:v:4:y:2013:i:3:p:213-223
    DOI: 10.1002/wcc.220
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    Cited by:

    1. Rutger Dankers & Zbigniew W. Kundzewicz, 2020. "Grappling with uncertainties in physical climate impact projections of water resources," Climatic Change, Springer, vol. 163(3), pages 1379-1397, December.
    2. Veronica Villani & Elvira Romano & Giuliana Barbato & Paola Mercogliano, 2021. "Selecting and correcting RCM models ensemble: a case study for the evaluation of thermal discomfort for the city of Prato," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1541-1557, June.
    3. Arnald Puy & Razi Sheikholeslami & Hoshin V. Gupta & Jim W. Hall & Bruce Lankford & Samuele Lo Piano & Jonas Meier & Florian Pappenberger & Amilcare Porporato & Giulia Vico & Andrea Saltelli, 2022. "The delusive accuracy of global irrigation water withdrawal estimates," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    4. Alexandra M. Schmidt & Marco A. Rodríguez, 2022. "Discussion on “A combined estimate of global temperature”," Environmetrics, John Wiley & Sons, Ltd., vol. 33(3), May.
    5. Marius Zumwald & Benedikt Knüsel & Christoph Baumberger & Gertrude Hirsch Hadorn & David N. Bresch & Reto Knutti, 2020. "Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 11(5), September.

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