Authors
Wendy Parker
Virginia Tech
Abstract
Simulation-based weather and climate prediction now involves the use of methods that reflect a deep concern with uncertainty. These methods, known as ensemble prediction methods, produce multiple simulations for predictive periods of interest, using different initial conditions, parameter values and/or model structures. This paper provides a non-technical overview of current ensemble methods and considers how the results of studies employing these methods should be interpreted, paying special attention to probabilistic interpretations. A key conclusion is that, while complicated inductive arguments might be given for the trustworthiness of probabilistic weather forecasts obtained from ensemble studies, analogous arguments are out of reach in the case of long-term climate prediction. In light of this, the paper considers how predictive uncertainty should be conveyed to decision makers.
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DOI 10.1016/j.shpsb.2010.07.006
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References found in this work BETA

A Material Theory of Induction.John D. Norton - 2003 - Philosophy of Science 70 (4):647-670.
Deterministic Nonperiodic Flow.Edward Lorenz - 1963 - Journal of Atmospheric Sciences 20 (2):130-148.

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Distinguishing Between Legitimate and Illegitimate Values in Climate Modeling.Kristen Intemann - 2015 - European Journal for Philosophy of Science 5 (2):217-232.
Models in the Geosciences.Alisa Bokulich & Naomi Oreskes - 2017 - In Lorenzo Magnani & Tommaso Wayne Bertolotti (eds.), Springer Handbook of Model-Based Science. Springer. pp. 891-911.

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