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Ensemble modeling, uncertainty and robust predictions
Author(s) -
Parker Wendy S.
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: climate change
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.678
H-Index - 75
eISSN - 1757-7799
pISSN - 1757-7780
DOI - 10.1002/wcc.220
Subject(s) - climate change , ensemble forecasting , robustness (evolution) , intuition , ensemble learning , uncertainty analysis , climate model , computer science , ensemble average , climate science , econometrics , climatology , machine learning , mathematics , ecology , epistemology , biochemistry , chemistry , biology , gene , geology , simulation , philosophy
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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