Today’s most sophisticated simulation studies of future climate employ not just one climate model but a number of models. I explain why this “ensemble” approach has been adopted—namely, as a means of taking account of uncertainty—and why a comprehensive investigation of uncertainty remains elusive. I then defend a middle ground between two camps in an ongoing debate over the transformation of ensemble results into probabilistic predictions of climate change, highlighting requirements that I refer to as ownership, justification, and robustness.

Philosophers continue to debate both the actual and the ideal roles of values in science. Recently, Eric Winsberg has offered a novel, model-based challenge to those who argue that the internal workings of science can and should be kept free from the influence of social values. He contends that model-based assignments of probability to hypotheses about future climate change are unavoidably influenced by social values. I raise two objections to Winsberg’s argument, neither of which can wholly undermine its conclusion but (...) each of which suggests that his argument exaggerates the influence of social values on estimates of uncertainty in climate prediction. I then show how a more traditional challenge to the value-free ideal seems tailor-made for the climate context. (shrink)

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. (shrink)

In 1995, the Intergovernmental Panel on Climate Change announced that anthropogenic climate change had become discernible. Since then, numerous independent studies have affirmed that anthropogenic climate change is underway, and the meta-conclusion that there is a broad expert consensus on this point. It has also been demonstrated that most of the challenges to this claim come from interested parties outside the scientific community. But even if we allow that the challenges to climate science are politically or economically motivated, it does (...) not prove that the scientific consensus is correct. In other words, even if we accept the fact of scientific consensus, how do we know that this consensus is not wrong? This chapter addresses this question by examining a set of criteria that philosophers have traditionally or recently identified as possible bases for trust in scientific conclusions, and shows that climate science meets all of these criteria. Thus, while there is no way to know for sure that scientists are correct in their conclusions, the various means we have to test and evaluate scientific claims lead to the conclusion that, so far as we are able to tell, it is most likely that scientists are not wrong about the reality of anthropogenic climate change. (shrink)

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 (...) to “downscale” climate models are still in the early stages of development. Because we do not know what adaptations will be required, we cannot say whether they will be harder or easier—more expensive or less—than emissions control. Whatever improvements in regional predictive capacity may come about in the future, the lack of current predictive capacity on the relevant scale is a strong argument for why we must both control greenhouse gas emissions and prepare to adapt. (shrink)

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 (...) by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC. (shrink)

A reprint of the Prentice-Hall edition of 1992. Prepared by nine distinguished philosophers and historians of science, this thoughtful reader represents a cooperative effort to provide an introduction to the philosophy of science focused on cultivating an understanding of both the workings of science and its historical and social context. Selections range from discussions of topics in general methodology to a sampling of foundational problems in various physical, biological, behavioral, and social sciences. Each chapter contains a list of suggested readings (...) and study questions. (shrink)

The environmental modeller faces a dilemma. Science often demands that more and more process representations are incorporated into models. Testing the causal representations in environmental models then depends on specifying boundary conditions and model parameters adequately. This will always be difficult in applications to a real system because of the heterogeneities, non-stationarities, complexities and epistemic uncertainties inherent in environmental prediction. Thus, it can be difficult to define the information content of a data set used in model evaluation and any consequent (...) measures of belief or verisimilitude. A limit of acceptability approach to model evaluation is suggested as a way of testing models, implying that thought is required to define critical experiments that will allow models as hypotheses to be adequately differentiated. 2012 Published by Elsevier Masson SAS on behalf of Academie des sciences. (shrink)