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  1. When Climate Models Agree: The Significance of Robust Model Predictions.Wendy S. Parker - 2011 - Philosophy of Science 78 (4):579-600.
    This article identifies conditions under which robust predictive modeling results have special epistemic significance---related to truth, confidence, and security---and considers whether those conditions hold in the context of present-day climate modeling. The findings are disappointing. When today’s climate models agree that an interesting hypothesis about future climate change is true, it cannot be inferred---via the arguments considered here anyway---that the hypothesis is likely to be true or that scientists’ confidence in the hypothesis should be significantly increased or that a claim (...)
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  • Adaptation to Global Warming: Do Climate Models Tell Us What We Need to Know?Naomi Oreskes, David A. Stainforth & Leonard A. Smith - 2010 - Philosophy of Science 77 (5):1012-1028.
    Scientific experts have confirmed that anthropogenic warming is underway, and some degree of adaptation is now unavoidable. However, the details of impacts on the scale of climate change at which humans would have to prepare for and adjust to them are still the subject of considerable research, inquiry, and debate. Planning for adaptation requires information on the scale over which human organizations and institutions have authority and capacity, yet the general circulation models lack forecasting skill at these scales, and attempts (...)
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  • Laplace's demon and the adventures of his apprentices.Roman Frigg, Seamus Bradley, Hailiang Du & Leonard A. Smith - 2014 - Philosophy of Science 81 (1):31-59.
    The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account (...)
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  • The Scientific Consensus on Climate Change: How Do We Know We 're Not Wrong?'.Naomi Oreskes - 2007 - In Joseph F. DiMento & Pamela Doughman (eds.), Climate Change: What It Means for Us, Our Children, and Our Grandchildren. MIT Press. pp. 65.
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