This article examines initial-condition dependence and initial-condition uncertainty for climate projections and predictions. The first contribution is to provide a clear conceptual characterization of predictions and projections. Concerning initial-condition dependence, projections are often described as experiments that do not depend on initial conditions. Although prominent, this claim has not been scrutinized much and can be interpreted differently. If interpreted as the claim that projections are not based on estimates of the actual initial conditions of the world or that what (...) makes projections true are conditions in the world, this claim is true. However, it can also be interpreted as the claim that simulations used to obtain projections are independent of initial-condition ensembles. This article argues that evidence does not support this claim. Concerning initial-condition uncertainty, three kinds of initial-condition uncertainty are identified. The first is the uncertainty associated with the spread of the ensemble simulations. The second arises because the theoretical initial ensemble cannot be used in calculations and has to be approximated by finitely many initial states. The third uncertainty arises because it is unclear how long the model should be run to obtain potential initial conditions at pre-industrial times. Overall, the discussion shows that initial-condition dependence and uncertainty in climate science are more complex and important issues than usually acknowledged. 1Introduction2Projections and Predictions 2.1Predictions2.2Projections3Initial-Condition Dependence 3.1Projections 3.1.1Dynamical conditions justifying independence of initial conditions?3.1.2What projections can and cannot provide3.2Predictions4Initial-Condition Uncertainty 4.1Projections4.2Predictions5ConclusionAppendix. (shrink)

This article argues that, in public and policy contexts, the ways in which many scientists talk about uncertainty in simulations of future climate change not only facilitates communications and cooperation between scientific and policy communities but also affects the perceived authority of science. Uncertainty tends to challenge the authority of chmate science, especially if it is used for policy making, but the relationship between authority and uncertainty is not simply an inverse one. In policy contexts, many scientists (...) are compelled to talk about uncertainty but do not wish to imply that uncertainty is a serious challenge to the authority of scientific knowledge or to its substantial use in policy making. "Boundary-ordering devices, " the contextual discursive attempts to reconcile uncertainty and authority in science, depend critically for their success on their "dual" interpretation: at a general level across a boundary and differently on either side of it. The authors empirically identify a range of such boundary-ordering devices in the climate field. (shrink)

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)

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: “How can ensemble studies be designed so that they probe uncertainty in desired ways?” This paper offers two interpretations of what General Circulation Models (GCMs) are and how (...) MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by “plugging and playing” with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs. (shrink)

The paper addresses the evaluation of climate models and gives an overview of epistemic uncertainties in climate modeling; the uncertainties concern the data situation as well as the causal behavior of the climate system. In order to achieve reasonable results nonetheless, multimodel ensemble studies are employed in which diverse models simulate the future climate under different emission scenarios. The models jointly deliver a robust range of climate prognoses due to a broad plurality of (...) theories, techniques, and methods in climate research; the range reliably indicates the future development of the global climate. Nevertheless, the uncertainties are widely used by skeptics to challenge the IPCC’s prognoses. Such skeptical allegations can well be distinguished from points of fruitful epistemological criticism: in spite of the enduring range of prognoses, the epistemic uncertainties should not play a role in finding agreements on climate change mitigation. (shrink)

Like any science marked by high uncertainty, climate science is characterized by a widespread use of expert judgment. In this paper, we first show that, in climate science, expert judgment is used to overcome uncertainty, thus playing a crucial role in the domain and even at times supplanting models. One is left to wonder to what extent it is legitimate to assign expert judgment such a status as an epistemic superiority in the climate context, (...) especially as the production of expert judgment is particularly opaque. To begin answering this question, we highlight the key components of expert judgment. We then argue that the justification for the status and use of expert judgment depends on the competence and the individual subjective features of the expert producing the judgment since expert judgment involves not only the expert's theoretical knowledge and tacit knowledge, but also their intuition and values. This goes against the objective ideal in science and the criteria from social epistemology which largely attempt to remove subjectivity from expertise. (shrink)

A number of authors, including me, have argued that the output of our most complex climate models, that is, of global climate models and Earth system models, should be assessed possibilistically. Worries about the viability of doing so have also been expressed. I examine the assessment of the output of relatively simple climate models in the context of discovery and point out that this assessment is of epistemic possibilities. At the same time, I show that the concept (...) of epistemic possibility used in the relevant studies does not fit available analyses of this concept. Moreover, I provide an alternative analysis that does fit the studies and broad climate modelling practices as well as meshes with my existing view that climate model assessment should typically be of real possibilities. On my analysis, to assert that a proposition is epistemically possible is to assert that it is not known to be false and is consistent with at least approximate knowledge of the basic way things are. I, finally, consider some of the implications of my discussion for available possibilistic views of climate model assessment and for worries about such views. I conclude that my view helps to address worries about such assessment and permits using the full range of climate models in it. (shrink)

Non-epistemic values pervade climate modelling, as is now well documented and widely discussed in the philosophy of climate science. Recently, Parker and Winsberg have drawn attention to what can be termed “epistemic inequality”: this is the risk that climate models might more accurately represent the future climates of the geographical regions prioritised by the values of the modellers. In this paper, we promote value management as a way of overcoming epistemic inequality. We argue that value management (...) can be seriously considered as soon as the value-free ideal and inductive risk arguments commonly used to frame the discussions of value influence in climate science are replaced by alternative social accounts of objectivity. We consider objectivity in Longino's sense as well as strong objectivity in Harding's sense to be relevant options here, because they offer concrete proposals that can guide scientific practice in evaluating and designing so-called multi-model ensembles and, in fine, improve their capacity to quantify and express uncertainty in climate projections. (shrink)

Over the last several years, there has been an explosion of interest and attention devoted to the problem of Uncertainty Quantification (UQ) in climate science—that is, to giving quantitative estimates of the degree of uncertainty associated with the predictions of global and regional climate models. The technical challenges associated with this project are formidable, and so the statistical community has understandably devoted itself primarily to overcoming them. But even as these technical challenges are being met, a number (...) of persistent conceptual difficulties remain. So why is UQ so important in climate science? UQ, I would like to argue, is first and foremost a tool for communicating knowledge from experts to policy makers in a way that is meant to be free from the influence of social and ethical values. But the standard ways of using probabilities to separate ethical and social values from scientific practice cannot be applied in a great deal of climate modeling, because the roles of values in creating the models cannot be discerned after the fact—the models are too complex and the result of too much distributed epistemic labor. I argue, therefore, that typical approaches for handling ethical/social values in science do not work well here. (shrink)

Integrated assessment models play a major role in the science and policy of climate change. Similarly to other widely used computational tools for addressing socially relevant problems, IAMs need to account for the key uncertainties characterizing processes and socio-economic responses. In the case of climate change, these are particularly complex given the very long-term nature of climate and the deep uncertainty characterizing technological and human systems. Here we draw from philosophical discussion of mathematical modeling of (...) social problems and review the role of uncertainty in climate-economic modeling. In agreement with the literature, we highlight the crucial role of epistemic uncertainty in IAMs. We posit that the normative components of models, more than the physical and socio-techno-economic ones, are the most fraught by uncertainty and yet the least understood. We suggest a research agenda to explore uncertainties of evaluation frameworks, transcending the current implicit normativity of IAMs. (shrink)

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the “brute force” strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited (...) by computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins’ paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. (shrink)

ABSTRACT This paper reflects on how the issue of climate change and the general state of our planet is, among other causes, a main factor in the paralyzing divisions ailing Western societies. This situation, while unsettling to democracies, is promoting a kind of education in and through fear and I question if education can succeed under these circumstances without becoming indoctrination. This paper does not try to diminish the urgency and the importance of current environmental problems but rather expands (...) today´s perspectives and incorporates research in more constructive ways of thinking and doing. I use scientific contributions in climatology, evolution, environmental conservation, economics, and neuroscience to bring new light to today’s investigations about the human and the non-human world. Finally, I propose Deep Ecology’s principles of deep questioning, deep experience and deep commitment, as a guide for new educational and ecological practices. (shrink)

When do probability distribution functions (PDFs) about future climate misrepresent uncertainty? How can we recognise when such misrepresentation occurs and thus avoid it in reasoning about or communicating our uncertainty? And when we should not use a PDF, what should we do instead? In this paper we address these three questions. We start by providing a classification of types of uncertainty and using this classification to illustrate when PDFs misrepresent our uncertainty in a way that may adversely affect decisions. (...) We then discuss when it is reasonable and appropriate to use a PDF to reason about or communicate uncertainty about climate. We consider two perspectives on this issue. On one, which we argue is preferable, available theory and evidence in climate science basically excludes using PDFs to represent our uncertainty. On the other, PDFs can legitimately be provided when resting on appropriate expert judgement and recognition of associated risks. Once we have specified the border between appropriate and inappropriate uses of PDFs, we explore alternatives to their use. We briefly describe two formal alternatives, namely imprecise probabilities and possibilistic distribution functions, as well as informal possibilistic alternatives. We suggest that the possibilistic alternatives are preferable. -/- . (shrink)

While the foundations of climate science and ethics are well established, fine-grained climate predictions, as well as policy-decisions, are beset with uncertainties. This chapter maps climate uncertainties and classifies them as to their ground, extent and location. A typology of uncertainty is presented, centered along the axes of scientific and moral uncertainty. This typology is illustrated with paradigmatic examples of uncertainty in climate science, climate ethics and climate economics. Subsequently, the (...) chapter discusses the IPCC’s preferred way of representing uncertainties and evaluates its strengths and weaknesses from a risk management perspective. Three general strategies for decision-makers to cope with climate uncertainty are outlined, the usefulness of which largely depends on whether or not decision-makers find themselves in a context of deep uncertainty. The chapter concludes by offering two recommendations to ease the work of policymakers, faced with the various uncertainties engrained in climate discourse. (shrink)

We report the results of an exploratory study that examines the judgments of climate scientists, climate policy experts, astrophysicists, and non-experts (N = 3367) about the factors that contribute to the creation and persistence of disagreement within climate science and astrophysics and about how one should respond to expert disagreement. We found that, as compared to non-experts, climate experts believe that within climate science (i) there is less disagreement about climate change, (ii) (...) methodological factors play less of a role in generating disagreements, (iii) fewer personal or institutional biases influence climate research, and (iv) there is more agreement about which methods should be used to examine relevant phenomena we also observed that the uniquely American political context predicted experts’ judgments about some of these factors. We also found that, in regard to disagreements concerning cosmic ray physics, and commensurate with the greater inherent uncertainty and data lacunae in their field, astrophysicists working on cosmic rays were generally more willing to acknowledge expert disagreement, more open to the idea that a set of data can have multiple valid interpretations, and generally less quick to dismiss someone articulating a non-standard view as non-expert, than climate scientists were in regard to climate science. (shrink)

This is the second of three parts of an introduction to the philosophy of climate science. In this second part about modelling climate change, the topics of climate modelling, confirmation of climate models, the limits of climate projections, uncertainty and finally model ensembles will be discussed.

Decisions in the environmental and in particular the climate domain are burdened with uncertainty. Here, we focus on uncertainties faced by individuals when making decisions about environmental behavior, and we use the statistical sampling framework to develop a classification of different sources of uncertainty they encounter. We then map these sources to different public policy strategies aiming to help individuals cope with uncertainty when making environmental decisions.

To study Earth’s climate, scientists now use a variety of computer simulation models. These models disagree in some of their assumptions about the climate system, yet they are used together as complementary resources for investigating future climatic change. This paper examines and defends this use of incompatible models. I argue that climate model pluralism results both from uncertainty concerning how to best represent the climate system and from difficulties faced in evaluating the relative merits of complex (...) models. I describe how incompatible climate models are used together in ‘multi-model ensembles’ and explain why this practice is reasonable, given scientists’ inability to identify a ‘best’ model for predicting future climate. Finally, I characterize climate model pluralism as involving both an ontic competitive pluralism and a pragmatic integrative pluralism. (shrink)

This edited volume looks at whether it is possible to be more transparent about uncertainty in scientific evidence without undermining public understanding and trust. With contributions from leading experts in the field, this book explores the communication of risk and decision-making in an increasingly post-truth world. Drawing on case studies from climate change to genetic testing, the authors argue for better quality evidence synthesis to cut through the noise and highlight the need for more structured public dialogue. For uncertainty (...) in scientific evidence to be communicated effectively, they conclude that trustworthiness is vital: the data and methods underlying statistics must be transparent, valid, and sound, and the numbers need to demonstrate practical utility and add social value to people's lives. Presenting a conceptual framework to help navigate the reader through the key social and scientific challenges of a post-truth era, this book will be of great relevance to students, scholars and policy makers with an interest in risk analysis and communication. (shrink)

While it is widely acknowledged that science is not “free” of non-epistemic values, there is disagreement about the roles that values can appropriately play. Several have argued that non-epistemic values can play important roles in modeling decisions, particularly in addressing uncertainties ; Risbey 2007; Biddle and Winsberg 2010; Winsberg : 111-137, 2012); van der Sluijs 359-389, 2012). On the other hand, such values can lead to bias ; Bray ; Oreskes and Conway 2010). Thus, it is important to (...) identify when it is legitimate to appeal to non-epistemic values in modeling decisions. An approach is defended here whereby such value judgments are legitimate when they promote democratically endorsed epistemological and social aims of research. This framework accounts for why it is legitimate to appeal to non-epistemic values in a range of modeling decisions, while addressing concerns that the presence of such values will lead to bias or give scientists disproportionate power in deciding what values ought to be endorsed. (shrink)

While the foundations of climate science and ethics are well established, fine-grained climate predictions, as well as policy-decisions, are beset with uncertainties. This chapter maps climate uncertainties and classifies them as to their ground, extent, and location. A typology of uncertainty is presented, centered along the axes of scientific and moral uncertainty. This typology is illustrated with paradigmatic examples of uncertainty in climate science, climate ethics, and climate economics. The chapter (...) discusses the IPCC’s preferred way of representing uncertainties and evaluates its strengths and weaknesses from a risk management perspective. Three general strategies for decision-makers to cope with climate uncertainty are outlined, the usefulness of which largely depends on whether decision-makers find themselves in a context of “deep uncertainty.” The chapter concludes that various uncertainties engrained in climate discourse cannot be overcome. It offers two recommendations to ease the work of policymakers, given this predicament. (shrink)

We argue that for scientists and science communicators to build usable knowledge for various publics, they require social and political capital, skills in boundary work, and ethical acuity. Drawing on the context of communicating seasonal climate predictions to farmers in Australia, we detail four key issues that scientists and science communicators would do well to reflect upon in order to become effective and ethical intermediaries. These issues relate to the boundary work used to link science and (...) values and thereby construct public identities, emplacement, that is, the importance of situating knowledge in relation to the places with which people identify, personal and organizational processes of reflexivity, and the challenges of developing and maintaining the social and political capital necessary to simultaneously represent people’s identities and lifeworlds and the climate systems that affect them. Through a discourse analysis of in-depth interviews with Australian agro-climatologists, we suggest that three distinct “modes of extension” are apparent, namely, discursive, conceptual, and contextual. Our participants used these three modes interdependently to create knowledge that has salience, credibility, and legitimacy. They thereby generated new narratives of place, practice, and identity for Australian agriculture. (shrink)

Lingering skepticism about climate change might be due in part to the way climate projections are perceived by members of the public. Variability between scientists’ estimates might give the impression that scientists disagree about the fact of climate change rather than about details concerning the extent or timing. Providing uncertainty estimates might clarify that the variability is due in part to quantifiable uncertainty inherent in the prediction process, thereby increasing people's trust in climate projections. This hypothesis (...) was tested in two experiments. Results suggest that including uncertainty estimates along with climate projections leads to an increase in participants’ trust in the information. Analyses explored the roles of time, place, demographic differences, and initial belief in climate change. Implications are discussed in terms of the potential benefit of adding uncertainty estimates to public climate projections. (shrink)

Over the last few decades climate change has been gaining importance in international scientific and political debates. However, the social sciences, especially in Latin America, have only lately become interested in the subject and their approach is still vague. Scientific understanding of global environmental change and the process of designing public policies to face them are characterized by their complexity as well as by epistemic and normative uncertainties. This makes it necessary to problematize the way in which research (...) efforts understand ‘the social’ of climate change. How do ‘the climate’ and ‘the social’ interpenetrate as scientific objects? What does the resulting field look like? Is the combination capable of promoting reflexivity and collaboration on the issue, or does it merely become dispersed with diffuse boundaries? Our paper seeks to answer these and other related questions using Chile as a case study and examining peer-reviewed scientific research on the topic. By combining in-depth qualitative content analysis of each paper with a statistical meta-analysis, we were able to: characterize the key content and forms of such literature; identify divisions and patterns within it; and, discuss some factors and trends that may help explain these. We conclude that the literature displays two competing trends: while it is inclined to become fragmented beyond the scope of the ‘mitigation’ black box, it also tends to cluster along the lines of methodological distinctions traditionally contested within the social sciences. This, in turn, highlights the persistence of disciplinary divisions within an allegedly interdisciplinary field. (shrink)

Scientific knowledge is the most solid and robust kind of knowledge that humans have because of its inherent self-correcting character. Nevertheless, anti-evolutionists, climate denialists, and anti-vaxxers, among others, question some of the best-established scientific findings, making claims unsupported by empirical evidence. A common aspect of these claims is reference to the uncertainties of science concerning evolution, climate change, vaccination, and so on. This is inaccurate: whereas the broad picture is clear, there will always exist uncertainties (...) about the details of the respective phenomena. This book shows that uncertainty is an inherent feature of science that does not devalue it. In contrast, uncertainty advances science because it motivates further research. This is the first book on this topic that draws on philosophy of science to explain what uncertainty in science is and how it makes science advance. It contrasts evolution, climate change, and vaccination, where the uncertainties are exaggerated, and genetic testing and forensic science, where the uncertainties are usually overlooked. The goal is to discuss the scientific, psychological, and philosophical aspects of uncertainty in order to explain what it really is, what kinds of problems it actually poses, and why in the end it makes science advance. Contrary to public representations of scientific findings and conclusions that produce an intuitive but distorted view of science as certain, people need to understand and learn to live with uncertainty in science. This book is intended for anyone who wants to get a clear view of the nature of science. (shrink)

This article critically examines a recent philosophical debate on the role of values in climate change forecasts, such as those found in assessment reports of the Intergovernmental Panel on Climate Change. On one side, several philosophers insist that the argument from inductive risk, as developed by Rudner and Douglas among others, applies to this case. AIR aims to show that ethical value judgments should influence decisions about what is sufficient evidence for accepting scientific hypotheses that have implications for (...) policy issues. Advocates of extending AIR to climate science claim that values are deeply... (shrink)

For decades, cigarette companies helped to promote the impression that there was no scientific consensus concerning the safety of their product. The appearance of controversy, however, was misleading, designed to confuse the public and to protect industry interests. Created scientific controversies emerge when expert communities are in broad agreement but the public perception is one of profound scientific uncertainty and doubt. In the first book-length analysis of the concept of a created scientific controversy, David Harker explores issues including climate (...) change, Creation science, the anti-vaccine movement and genetically modified crops. Drawing on work in cognitive psychology, social epistemology, critical thinking and philosophy of science, he shows readers how to better understand, evaluate, and respond to the appearance of scientific controversy. His book will be a valuable resource for students of philosophy of science, environmental and health sciences, and social and natural sciences. (shrink)

We are facing uncertainties regarding climate change and its impacts. To conceptualize and communicate these uncertainties to policy makers, the Intergovernmental Panel on Climate Change (IPCC) has introduced an uncertainty framework. In this paper, I assess the latest, most developed, version of this framework. First, I provide an interpretation of this framework, which draws from supporting documents and the practice of its users. Second, I argue that even a charitable interpretation exhibits three substantial conceptual problems. These (...) problems point towards untenable assumptions regarding evidence aggregation in the context of climate scientific findings. Third, I put forward a tentative roadmap for improving the uncertainty framework. (shrink)

We are facing uncertainties regarding climate change and its impacts. To conceptualize and communicate these uncertainties to policy makers, the Intergovernmental Panel on Climate Change (IPCC) has introduced an uncertainty framework. In this paper, I assess the latest, most developed, version of this framework. First, I provide an interpretation of this framework, which draws from supporting documents and the practice of its users. Second, I argue that even a charitable interpretation exhibits three substantial conceptual problems. These (...) problems point towards untenable assumptions regarding evidence aggregation in the context of climate scientific findings. Third, I put forward a tentative roadmap for improving the uncertainty framework. (shrink)

When it comes to climate change, the greatest difficulty we face is that we do not know the likely degree of change or its cost, which means that environmental policy decisions have to be made under uncertainty. This book offers an accessible philosophical treatment of the broad range of ethical and policy challenges posed by climate change uncertainty. Drawing on both the philosophy of science and ethics, Martin Bunzl shows how tackling climate change revolves around weighing (...) up our interests now against those of future generations, which requires that we examine our assumptions about the value of present costs versus future benefits. In an engaging, conversational style, Bunzl looks at questions such as our responsibility towards non-human life, the interests of the developing and developed worlds, and how the circumstances of poverty shape the perception of risk, ultimate developing and defending a view of humanity and its place in the world that makes sense of our duty to Nature without treating it as a rights bearer. This book will be of interest to students and scholars of environmental studies, philosophy, politics and sociology as well as policy makers. (shrink)

Increasing societal concerns regarding the potential deleterious effects of future climate change have galvanized efforts to manage the problem both through reduction of greenhouse gases and through development of plans to reduce the impacts of climate change that cannot be avoided. These critical activities require making decisions under conditions of considerable uncertainty regarding future conditions in physical and human systems. As the focus on providing information about future climate for taking actions to cope with climate change, (...) the science of uncertainty of climate change will develop more and more with the needs of decision makers in mind. (shrink)

Using past episodes of climate change as a source of evidence to inform our projections about contemporary climate change requires establishing the extent to which episodes in the deep past are analogous to the current crisis. However, many scientists claim that contemporary rates of climate change (e.g., rates of carbon emissions or temperature change) are unprecedented, including compared to episodes in the deep past. If so, this would limit the utility of paleoclimate analogues. In this paper, I (...) show how a data adjustment procedure called “temporal scaling,” which must be applied to both contemporary and past rate data, complicates the claim that contemporary rates are truly unprecedented. On top of giving actionable recommendations to scientists, this paper advances the philosophical literature concerning the use of models that are known to be somewhat disanalogous to their target systems. (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)

1. Introduction; Elisabeth A. Lloyd and Eric Winsberg.- Section 1: Confirmation and Evidence.- 2. The Scientific Consensus on Climate Change: How Do We Know We’re Not Wrong?; Naomi Oreskes.- 3. Satellite Data and Climate Models Redux.- 3a. Introduction to Chapter 3: Satellite Data and Climate Models; Elisabeth A. Lloyd.- Ch. 3b Fact Sheet to "Consistency of Modelled and Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- Ch. 3c Reprint of "Consistency of Modelled and (...) Observed Temperature Trends in the Tropical Troposphere"; Benjamin D. Santer et al..- 4. The Role of ’Complex’ Empiricism in the Debates about Satellite Data and Climate Models; Elisabeth A. Lloyd.- 5. Reconciling Climate Model/Data Discrepancies: The Case of the Trees That Didn’t Bark; Michael Mann.- 6. Downscaling of Climate Information; Linda O. Mearns et al..- Section 2: Uncertainties and Robustness.- 7. The Significance of Robust Model Projections; Wendy S. Parker.- 8. Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling; Jay Odenbaugh.- Section 3: Climate Models as Guides to Policy.- 9. Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World; Reto Knutti.- 10. Uncertainty in Climate Science and Climate Policy; Jonathan Rougier and Michel Crucifix.- 11. Communicating Uncertainty to Policy Makers: The Ineliminable Role of Values; Eric Winsberg.- 12. Modeling Climate Policies: A Critical Look at Integrated Assessment Models; Mathias Frisch.- 13. Modelling Mitigation and Adaptation Policies to Predict their Effectiveness: The Limits of Randomized Controlled Trials; Alexandre Marcellesi and Nancy D. Cartwright. (shrink)

Whether on personal health, politics, or climate change, we are constantly bombarded with more numerous 'breaking news' articles than we have time for. In such an environment, how can we tell which to read, or which is even true. Science of the Earth, Climate and Energy helps readers understand major issues that affect us individually and the world as a whole. In language that a non-scientist can follow easily, the book first explains the general principles of (...) class='Hi'>science, its nature and how it works, with a certain degree of emphasis on the meaning of the words "uncertainty" and "fact, before it goes into the related topics of the earth, its climate and energy sources at a level that does not require a background in science. Finally, the book addresses what individuals and societies can do to mitigate problems associated with both climate change and limited resources. (shrink)

There are different kinds of uncertainty. I outline some of the various ways that uncertainty enters science, focusing on uncertainty in climate science and weather prediction. I then show how we cope with some of these sources of error through sophisticated modelling techniques. I show how we maintain confidence in the face of error.

The Intergovernmental Panel on Climate Change has developed a novel framework for assessing and communicating uncertainty in the findings published in their periodic assessment reports. But how should these uncertainty assessments inform decisions? We take a formal decision-making perspective to investigate how scientific input formulated in the IPCC’s novel framework might inform decisions in a principled way through a normative decision model.

In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that (...) arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles — parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. (shrink)

The use of evidence to resolve uncertainties is key to many endeavours, most conspicuously science and law. Despite this, the logic of uncertainty is seldom taught explicitly, and often seems misunderstood. Traditional educational practice even fails to encourage students to identify uncertainty when they express knowledge, though mark schemes that reward the identification of reliable and uncertain responses have long been shown to encourage more insightful understanding. In our information-rich society the ability to identify uncertainty is often more (...) important than the possession of knowledge itself. In both science and law there are fundamentally different kinds of uncertainty at issue. There is uncertainty whether a particular hypothesis is correct, and there is uncertainty about observable data that may be generated if a particular hypothesis is correct. Both are expressed in terms of probabilities. Each has its own domain of application and its own logic, but the inter-relationship is complex and sometimes misunderstood. Hypothesis probabilities are always open to error through possible failure to take account of realistic alternatives, while the proper inferences that can be drawn from data probabilities (often in the context of significance testing) are quite limited and easily over-interpreted. When considering these two kinds of probability in a court of law it is possible to interpret the phrase 'reasonable doubt' in different ways. It can be seen as addressing data uncertainty: whether such incriminating evidence might with reasonable probability arise to confront an innocent person. Or (the more conventional view) it can be seen as some sort of threshold level on the probability that the defendant is guilty (a hypothesis probability). Each typically involves elements of subjective judgement, but fewer issues and uncertainties arise when considering the data probability and it is argued that this is often the more critical and proper issue for a jury to address. This has particular repercussions for cases involving identification of a suspect through trawl of a DNA or other database. (shrink)

Despite recognizing many adverse impacts, the climate science literature has had little to say about the conditions under which climate change might threaten civilization. Discussions of the mechanisms whereby climate change might cause the collapse of current civilizations has mostly been the province of journalists, philosophers, and novelists. We propose that this situation should change. In this opinion piece, we call for treating the mechanisms and uncertainties associated with climate collapse as a critically important (...) topic for scientific inquiry. Doing so requires clarifying what "civilization collapse" means and explaining how it connects to topics addressed by climate scientists. [Open access] -/- . (shrink)

The role of values in scientific research has become an important topic of discussion in both scholarly and popular debates. Pundits across the political spectrum worry that research on topics like climate change, evolutionary theory, vaccine safety, and genetically modified foods has become overly politicized. At the same time, it is clear that values play an important role in science by limiting unethical forms of research and by deciding what areas of research have the greatest relevance for society. (...) Deciding how to distinguish legitimate and illegitimate influences of values in scientific research is a matter of vital importance.Recently, philosophers of science have written a great deal on this topic, but most of their work has been directed toward a scholarly audience. This book makes the contemporary philosophical literature on science and values accessible to a wide readership. It examines case studies from a variety of research areas, including climate science, anthropology, chemical risk assessment, ecology, neurobiology, biomedical research, and agriculture. These cases show that values have necessary roles to play in identifying research topics, choosing research questions, determining the aims of inquiry, responding to uncertainty, and deciding how to communicate information. Kevin Elliott focuses not just on describing roles for values but also on determining when their influences are actually appropriate. He emphasizes several conditions for incorporating values in a legitimate fashion, and highlights multiple strategies for fostering engagement between stakeholders so that value influences can be subjected to careful and critical scrutiny. (shrink)

Introduction 1 P. D. Magnus and Jacob Busch 1. Form-driven vs. Content-driven Arguments for Realism 8 Juha Saatsi 2. Optimism about the Pessimistic Induction 29 Sherrilyn Roush 3. Metaphysics between the Sciences and Philosophies of Science 59 Anjan Chakravartty 4. Nominalism and Inductive Generalizations 78 Jessica Pfeifer 5. Models and Scientific Representations 94 Otávio Bueno 6. The Identical Rivals Response to Underdetermination 112 Gregory Frost-Arnold and P. D. Magnus 7. Scientific Representation and the Semiotics of Pictures 131 Laura Perini (...) 8. Philosophy of the Environmental Sciences 155 Jay Odenbaugh 9. Value Judgements and the Estimation of Uncertainty in Climate Modeling 172 Justin Biddle and Eric Winsberg 10. Feminist Standpoint Empiricism: Rethinking the Terrain in Feminist Philosophy of Science 198 Kristen Intemann 11. Naturalism and the Enlightenment Ideal: Rethinking a Central Debate in the Philosophy of Social Science 226 Daniel Steel 12. New Approaches to the Division of Cognitive Labor 250 Michael Weisberg. (shrink)

Computer simulation modeling is an important part of contemporary scientific practice but has not yet received much attention from philosophers. The present project helps to fill this lacuna in the philosophical literature by addressing three questions that arise in the context of computer simulation of Earth's climate. Computer simulation experimentation commonly is viewed as a suspect methodology, in contrast to the trusted mainstay of material experimentation. Are the results of computer simulation experiments somehow deeply problematic in ways that the (...) results of material experiments are not? I argue against categorical skepticism toward the results of computer simulation experiments by revealing important parallels in the epistemologies of material and computer simulation experimentation. It has often been remarked that simple computer simulation models---but not complex ones---contribute substantially to our understanding of the atmosphere and climate system. Is this view of the relative contributions of simple and complex models tenable? I show that both simple and complex climate models can promote scientific understanding and argue that the apparent contribution of simple models depends upon whether a causal or deductive account of scientific understanding is adopted. When two incompatible scientific theories are under consideration, they typically are viewed as competitors, and we seek evidence that refutes at least one of the theories. In the study of climate change, however, logically incompatible computer simulation models are accepted as complementary resources for investigating future climate. How can we make sense of this use of incompatible models? I show that a collection of incompatible climate models persists in part because of difficulties faced in evaluating and comparing climate models. I then discuss the rationale for using these incompatible models together and argue that this climate model pluralism has both competitive and integrative components. (shrink)

This paper explores what kind of uncertainty a research program governing solar climate engineering through Stratospheric Aerosol Injection (SAI) needs to account for. Specifically, it tries to answer two central issues with regards to SAI research and it’s ethical evaluation: One, what irreducible uncertainties remain throughout the decision-process, and, two, how do these remaining uncertainties affect the ethical evaluation of SAI research. The main assumption is that decisions on SAI research governance will be made under normative uncertainty, (...) i.e. situations under irreducible knowledge-constraints that arise in concrete, practical decision-contexts. These decision-contexts are multi-lateral and empirically ambiguous, and the decision-makers need to reconcile a plurality of values. While normative uncertainty complicates the ethical evaluation of policy decisions, I argue that moral considerations can be accommodated through the inclusion of recognitional, participatory justice approaches, as well as adaptive and anticipatory governance methods. (shrink)