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)

In the philosophy of climate science, debate surrounding the issue of variety of evidence has mostly taken the form of attempting to connect these issues in climate science and climate modeling with philosophical accounts of what has come to be known as “robustness analysis.” I argue that an “explanatory” conception of robustness is the best candidate for understanding variety of evidence in climate science. I apply the analysis to both examples of (...) model agreement, as well at to the convergence of evidence from both model and non-model sources. (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)

In this paper, I provide policymakers, who rely on science to address their missions, with two arguments for improving science for social benefits. I argue for a refined concept of social robustness that can distinguish socially appropriate cases of political reliance on science from inappropriate ones. Both of the constituents are essential for evaluating the social suitability of science-relevant policy or action. Using four cases of population control, I show that socially inappropriate political reliance on (...) science can make science epistemically and socially harmful. Moreover, I argue that such an evaluation process should be supported by a political culture of mutual criticism within science and society. This demands freedom of expression and critical examination of science-relevant policy consequences in view of a sufficiently wide range of social value commitments. This paper suggests that the same considerations of social robustness and political culture of mutual criticism should be of general relevance for policymakers engaging in debates about science and values such as those regarding climate change. (shrink)

Robustness with respect to climate models is often considered by philosophers of climate science to be a crucial issue in determining whether and to what extent the projections of the Earth’s future climate that models yield should be trusted. Parker (2011) and Lloyd (2009, 2015) have introduced influential accounts of robustness for climate models with seemingly conflicting conclusions. I argue that Parker and Lloyd are characterizing distinct notions of robustness and providing complementary (...) insights. Confidence, if warranted, need be by virtue of causally consistent climate models rather than by agreement upon projections by a diverse range of models. (shrink)

Climate scientists have proposed two methods to link extreme weather events and anthropogenic climate forcing: the probabilistic and the storyline approach. Proponents of the first approach have raised the criticism that the storyline approach could be overstating the role of anthropogenic climate change. This issue has important implications because, in certain contexts, decision-makers might seek to avoid information that overstates the effects of anthropogenic climate change. In this paper, we explore two research questions. First, whether and (...) to what extent the storyline approach overstates the effects of anthropogenic climate change. Second, whether the objections offered against the storyline approach constitute good reasons to prefer the probabilistic approach. Concerning the first question, we show that the storyline approach does not necessarily overstate the effects of climate change, and particularly not for the reasons offered by proponents of the probabilistic approach. Concerning the second question, we show, independently, that the probabilistic approach faces the same or very similar objections to those raised against the storyline approach due to the lack of robustness of climate models and the way events are commonly defined when applying the probabilistic approach. These results suggest that these objections might not constitute good reasons to prefer the probabilistic approach over the storyline approach. (shrink)

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independentlysupported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I (...) present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists’ discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. (shrink)

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modelling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of “model (...) fit”; i.e., how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model’s adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model’s adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model’s adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. (shrink)

Convergence of model projections is often considered by climate scientists to be an important objective in so far as it may indicate the robustness of the models’ core hypotheses. Consequently, the range of climate projections from a multi-model ensemble, called “model spread”, is often expected to reduce as climate research moves forward. However, the successive Assessment Reports of the Intergovernmental Panel on Climate Change indicate no reduction in model spread, whereas it is indisputable that (...) class='Hi'>climate science has made improvements in its modelling. In this paper, after providing a detailed explanation of the situation, we describe an epistemological setting in which a steady model spread is not doomed to be seen as negative, and is indeed compatible with a desirable evolution of climate models taken individually. We further argue that, from the perspective of collective progress, as far as the improvement of the products of a multi-model ensemble is concerned, reduction of model spread is of lower priority than model independence. (shrink)

In an influential paper, Wendy Parker argues that agreement across climate models isn’t a reliable marker of confirmation in the context of cutting-edge climate science. In this paper, I argue that while Parker’s conclusion is generally correct, there is an important class of exceptions. Broadly speaking, agreement is not a reliable marker of confirmation when the hypotheses under consideration are mutually consistent—when, e.g., we’re concerned with overlapping ranges. Since many cutting-edge questions in climate modeling require making (...) distinctions between mutually consistent hypotheses, agreement across models will be generally unreliable in this domain. In cases where we are only concerned with mutually exclusive hypotheses, by contrast, agreement across climate models is plausibly a reliable marker of confirmation. (shrink)

Robustness analysis (RA) is the prescription to consider a diverse range of evidence and only regard a hypothesis as well-supported if all the evidence agrees on it. In contexts like climate science, the evidence in support of a hypothesis often comes from scientific models. This leads to model-based RA (MBRA), whose core notion is that a hypothesis ought to be regarded as well-supported on grounds that a sufficiently diverse set of models agrees on the hypothesis. This chapter, (...) which is the second part of a two-part review of MBRA, addresses the thorny issue of justifying the inferential steps taking us from the premises to the conclusions. The chapter begins by making explicit what exactly the problem is. It then turns to a discussion of two broad families of justificatory strategies, namely top-down and bottom-up justifications. In the latter group, one can distinguish between the likelihood approach, independence approaches, and the explanatory approach. This discussion leads to the sober conclusion that multimodel situations raise issues that are not yet fully understood and that MBRA has not yet reached a stage of maturity. Important questions remain open, and these will have to be addressed in future research. (shrink)

Robustness analysis (RA) is the prescription to consider a diverse range of evidence and only regard a hypothesis as well-supported if all the evidence agrees on it. In contexts like climate science, the evidence in support of a hypothesis often comes in the form of model results. This leads to model-based RA (MBRA), whose core notion is that a hypothesis ought to be regarded as well-supported on grounds that a sufficiently diverse set of models agrees on the (...) hypothesis. This chapter, which is the first part of a two-part review of MBRA, begins by providing a detailed statement of the general structure of MBRA. This statement will make visible the various parts of MBRA and will structure the discussion in the remainder of the chapter. The chapter explicates the core concepts of independence and agreement, and it discusses what they mean in the context of climate modeling. The statement shows that MBRA is based on three premises, which concern robust properties, common structures, and so-called robust theorems. The chapter analyzes what these involve and what problems they raise in the context of climate science. In the next chapter, which is the second part of the review, an analysis of how the conclusions of MBRA can be justified is provided. (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)

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 to have evidence for the hypothesis is now more secure. (shrink)

ABSTRACT ‘Facts remain robust only when … supported by a common culture,’ observes Bruno Latour. Current debates over the veracity of climate change are, in actuality, crises of facts. Questions of facticity have, moreover, precipitated a deeper issue – the prospects of unshared, ‘alternative’ worlds. Climate science believers have one world, climate change deniers another, creating what Latour calls ‘epistemological delirium.’ Following Latour, the paper turns to Pierre Hadot’s description of Stoic physics and understanding of philosophy (...) as spiritual exercise. Finally, taking up both Latour’s claims of ‘alternative worlds’ and Hadot’s notion of spiritual exercise, this paper explores the possibilities of shared practices in light of unshared realities. (shrink)

The National Science Foundation (NSF) in the United States, like many other funding agencies all over the globe, has made large investments in interdisciplinary research in the sciences and engineering, arguing that interdisciplinary research is an essential resource for addressing emerging problems, resulting in important social benefits. Using NSF as a case study for problem that might be relevant in other contexts as well, I argue that the NSF itself poses a significant barrier to such research in not sufficiently (...) appreciating the value of the humanities as significant interdisciplinary partners. This essay focuses on the practices of philosophy as a highly valuable but currently under-appreciated partner in achieving the goals of interdisciplinary research. This essay advances a proposal for developing deeper and wider interdisciplinary research in the sciences through coupled ethical-epistemological research. I argue that this more robust model of interdisciplinary practice will lead to better science by providing resources for understanding the types of value decisions that are entrenched in research models and methods, offering resources for identifying the ethical implications of research decisions, and providing a lens for identifying the questions that are ignored, under-examined, and rendered invisible through scientific habit or lack of interest. In this way, we will have better science both in the traditional sense of advancing knowledge by building on and adding to our current knowledge as well as in the broader sense of science for the good of, namely, scientific research that better benefits society. (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)

Climate science has been deeply affected by social and political values in the last fifty years [1]. If we focus on climate denial and obfuscation, we might see the influence of values as wholly negative and aim instead for objective, value-free climate science. But, perhaps surprisingly, this is at odds with the view of many philosophers who study the influence of values on science. Science cannot and should not be free from values, they (...) argue. Rather, we should be transparent about our values, study their influence, and ensure that the right values play the right roles. But philosophers alone cannot determine what the right values are or what their appropriate use is. Close collaboration between climate scientists and philosophers is required for values in climate science to be accurately studied and appropriately managed. (shrink)

This paper argues that besides mechanistic explanations, there is a kind of explanation that relies upon “topological” properties of systems in order to derive the explanandum as a consequence, and which does not consider mechanisms or causal processes. I first investigate topological explanations in the case of ecological research on the stability of ecosystems. Then I contrast them with mechanistic explanations, thereby distinguishing the kind of realization they involve from the realization relations entailed by mechanistic explanations, and explain how both (...) kinds of explanations may be articulated in practice. The second section, expanding on the case of ecological stability, considers the phenomenon of robustness at all levels of the biological hierarchy in order to show that topological explanations are indeed pervasive there. Reasons are suggested for this, in which “neutral network” explanations are singled out as a form of topological explanation that spans across many levels. Finally, I appeal to the distinction of explanatory regimes to cast light on a controversy in philosophy of biology, the issue of contingence in evolution, which is shown to essentially involve issues about realization. (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)

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)

Precaution is a relevant and much-invoked value in environmental risk analysis, as witnessed by the ongoing vivid discussion about the precautionary principle (PP). This article argues (i) against purely decision-theoretic explications of PP; (ii) that the construction, evaluation, and use of scientific models falls under the scope of PP; and (iii) that epistemic and decision-theoretic robustness are essential for precautionary policy making. These claims are elaborated and defended by means of case studies from climate science and conservation (...) biology. (shrink)

Worldwide there are substantial differences within and between countries in aggression and violence. Although there are various exceptions, a general rule is that aggression and violence increase as one moves closer to the equator, which suggests the important role of climate differences. While this pattern is robust, theoretical explanations for these large differences in aggression and violence within countries and around the world are lacking. Most extant explanations focus on the influence of average temperature as a factor that triggers (...) aggression, or the notion that warm temperature allows for more social interaction situations in which aggression is likely to unfold. We propose a new model, CLimate, Aggression, and Self-control in Humans, that helps us to understand differences within and between countries in aggression and violence in terms of differences in climate. Lower temperatures, and especially larger degrees of seasonal variation in climate, call for individuals and groups to adopt a slower life history strategy, a greater focus on the future, and a stronger focus on self-control. The CLASH model further outlines that slow life strategy, future orientation, and strong self-control are important determinants of inhibiting aggression and violence. We also discuss how CLASH differs from other recently developed models that emphasize climate differences for understanding conflict. We conclude by discussing the theoretical and societal importance of climate in shaping individual and societal differences in aggression and violence. (shrink)

Climate policy decisions are decisions under uncertainty and are, therefore, based on a range of future climate scenarios, describing possible consequences of alternative policies. Accordingly, the methodology for setting up such a scenario range becomes pivotal in climate policy advice. The preferred methodology of the Intergovernmental Panel on Climate Change will be characterised as ,,modal verificationism"; it suffers from severe shortcomings which disqualify it for scientific policy advice. Modal falsificationism, as a more sound alternative, would radically (...) alter the way the climate scenario range is set up. Climate science's inability to find robust upper bounds for future temperature rise in line with modal falsificationism does not disprove that methodology, rather, this very fact prescribes even more drastic efforts to curb CO2 emissions than currently proposed. (shrink)

Historians of the sciences have paid great attention to the ways that faith in what has been called the quantitative spirit emerged as a dominant feature of the politics of science, a theme of obvious salience in current epidemiological and climate crises. There are instructive connexions between measurement practices and orientation towards other cultures—as though scientific modernity somehow appeared through the primacy of robust quantification over subaltern, past, and exotic worlds, where merely provisional judgment allegedly still operated. This (...) highly simplistic Orientalist distinction accompanied assumptions that the remote was best understood as the ancient, a viewpoint common at the same late Enlightenment moment as the apparent institutionalisation of the regime of the exact sciences within European polities. Under this regime, precision surveys—the way the state saw—have often been understood as integral for European societies and even more so in colonised territories. This version of what might be called metrological Orientalism can be disoriented through excellent recent scholarship that explores complex entanglements of measurement practices circulating across very different scientific cultures, which shows how precision devices that claimed merely to represent phenomena often helped produce them. Studies of select cases of relations between European practitioners and indigenous experts, in fields such as Egyptian hydraulics or South Pacific surveys, can reveal even more: the role that judgment and exactitude played in forging very different, politically significant versions of the past history of the sciences. These disorientations can aid novel forms of historical understanding of the politics of science. (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)

A global target of stabilizing greenhouse-gas concentrations at between 450 and 550 parts per million carbon-dioxide equivalent has proven robust to recent developments in the science and economics of climate change. Retrospective analysis of the Stern Review suggests that the risks were underestimated, indicating a stabilization target closer to 450 ppm CO2e. Climate policy at the international level is now moving rapidly towards agreeing an emissions pathway, and distributing responsibilities between countries. A feasible framework can be constructed (...) in which each country takes on its own responsibilities and targets, based on a shared understanding of the risks and the need for action and collaboration on climate change. The global deal should contain six key features: a pathway to achieve the world target of 50 per cent reductions by 2050, where rich countries contribute at least 75 per cent of the reductions; global emissions trading to reduce costs; reform of the clean development mechanism to scale up emission reductions on a sectoral or benchmark level; scaling up of R&D funding for low-carbon energy; an agreement on deforestation; and adaptation finance. (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)

Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better (...) understand the nature of complex systems in general. -/- Questions surrounding climate science can be divided into three rough categories: foundational, methodological, and evaluative questions. "How do we know that we can trust science?" is a paradigmatic foundational question (and a surprisingly difficult one to answer). Because the global climate is so complex, questions like "what makes a system complex?" also fall into this category. There are a number of existing definitions of `complexity,' and while all of them capture some aspects of what makes intuitively complex systems distinctive, none is entirely satisfactory. Most existing accounts of complexity have been developed to work with information-theoretic objects (signals, for instance) rather than the physical and social systems studied by scientists. -/- Dynamical complexity, a concept articulated in detail in the first third of the dissertation, is designed to bridge the gap between the mathematics of contemporary complexity theory (in particular the formalism of "effective complexity" developed by Gell-Mann and Lloyd [2003]) and a more general account of the structure of science generally. Dynamical complexity provides a physical interpretation of the formal tools of mathematical complexity theory, and thus can be used as a framework for thinking about general problems in the philosophy of science, including theories, explanation, and lawhood. -/- Methodological questions include questions about how climate science constructs its models, on what basis we trust those models, and how we might improve those models. In order to answer questions about climate modeling, it's important to understand what climate models look like and how they are constructed. Climate model families are significantly more diverse than are the model families of most other sciences (even sciences that study other complex systems). Existing climate models range from basic models that can be solved on paper to staggeringly complicated models that can only be analyzed using the most advanced supercomputers in the world. I introduce some of the central concepts in climatology by demonstrating how one of the most basic climate models might be constructed. I begin with the assumption that the Earth is a simple featureless blackbody which receives energy from the sun and releases it into space, and show how to model that assumption formally. I then gradually add other factors (e.g. albedo and the greenhouse effect) to the model, and show how each addition brings the model's prediction closer to agreement with observation. After constructing this basic model, I describe the so-called "complexity hierarchy" of the rest of climate models, and argue that the sense of "complexity" used in the climate modeling community is related to dynamical complexity. -/- With a clear understanding of the basics of climate modeling in hand, I then argue that foundational issues discussed early in the dissertation suggest that computation plays an irrevocably central role in climate modeling. "Science by simulation" is essential given the complexity of the global climate, but features of the climate system--the presence of non-linearities, feedback loops, and chaotic dynamics--put principled limits on the effectiveness of computational models. This tension is at the root of the staggering pluralism of the climate model hierarchy, and suggests that such pluralism is here to stay, rather than an artifact of our ignorance. Rather than attempting to converge on a single "best fit" climate model, we ought to embrace the diversity of climate models, and view each as a specialized tool designed to predict and explain a rather narrow range of phenomena. Understanding the climate system as a whole requires examining a number of different models, and correlating their outputs. This is the most significant methodological challenge of climatology. -/- Climatology's role contemporary political discourse raises an unusually high number of evaluative questions for a physical science. The two leading approaches to crafting policy surrounding climate change center on mitigation (i.e. stopping the changes from occurring) and adaptation (making post hoc changes to ameliorate the harm caused by those changes). Crafting an effective socio-political response to the threat of anthropogenic climate change, however, requires us to integrate multiple perspectives and values: the proper response will be just as diverse and pluralistic as the climate models themselves, and will incorporate aspects of both approaches. I conclude by offering some concrete recommendations about how to integrate this value pluralism into our socio-political decision making framework. (shrink)

Many examples of calibration in climate science raise no alarms regarding model reliability. We examine one example and show that, in employing Classical Hypothesis-testing, it involves calibrating a base model against data that is also used to confirm the model. This is counter to the "intuitive position". We argue, however, that aspects of the intuitive position are upheld by some methods, in particular, the general Cross-validation method. How Cross-validation relates to other prominent Classical methods such as the Akaike (...) Information Criterion and Bayesian Information Criterion is also discussed. (shrink)

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)

Scientific inquiry is typically focused on particular questions about particular objects and properties. This leads to a multiplicity of models which, even when they draw on a single, consistent body of concepts and principles, often employ different methods and assumptions to model different systems. Pluralists have remarked on how scientists draw on different assumptions to model different systems, different aspects of systems and systems under different conditions and defended the value of distinct, incompatible models within science at any given (...) time. Paraconsistentists have proposed logical strategies to avoid trivialization when inconsistencies arise by a variety of means. Here we examine how chunk and permeate, a simple approach to paraconsistent reasoning which avoids heterodox logic by confining commitments to separate contexts in which reasoning with them is taken to be reliable while allowing ‘permeation’ of some conclusions into other contexts, can help to systematize pluralistic reasoning across the boundaries of plural contexts, using regional climate models as an example. The result is a kind of unity for science—but a unity achieved by the constrained exchange of specified information between different contexts, rather than the closure of all commitments under some paraconsistent consequence relation. (shrink)

Climate change fingerprint studies investigate the causes of recent climate change. I argue that these studies have much in common with Steel’s (2008) streamlined comparative process tracing, illustrating a mechanisms-based approach to extrapolation in which the mechanisms of interest are simulated rather than physically instantiated. I then explain why robustness and variety-of-evidence considerations turn out to be important for understanding the evidential value of climate change fingerprint studies.

Muitos termos possuem um sentido técnico sem que ele seja evidente para todos, por exemplo, a "governança ambiental", termo que remete no contexto atual a uma participação cidadã nesse tipo de questão, por exemplo, da saúde de um ecossistema específico, tal como uma floresta ou um vale agrícola, a partir de preocupações partilhadas e não a partir de uma problemática de controle organizacional. Após ter tornado preciso o que é a expertise e quais são os principais problemas postos pelo recurso (...) à expertise nos contextos da ação cidadã, proponho que as expertises técnicas podem ser postas no mesmo patamar que outros tipos de expertise, referindo principalmente aos saberes comuns, à prática e à experiência concreta, dita "de campo", o que corresponde a uma ampliação. Sugere-se, então, o modelo de fórum híbrido como quadro teórico possível dessa reformulação da concepção de expertise. Many terms have a technical meaning although it is not evident for everyone, for example, the "environmental governance", term that refers in the present context to citizen participation on this type of question, for example, of the health of a specific ecosystem, such as a forest or an agricultural field, from shared preoccupations and not from a problematic of organizational control. After having made precise what is the expertise and what are the main problems posed by the recourse to the expertise in the contexts of citizen action, I propose that technical expertise can be tantamount to other types of expertise, referring mainly to profane knowledge, to the practice and concrete experience called "of field", which corresponds to an enlargement. Then it is suggested the model of an hybrid forum as a possible theoretical frame of this reformulation of the conception of expertise. (shrink)

In this article, I discuss calls for access to empirical data within controversies about climate science, as revealed and highlighted by the publication of the e-mail correspondence involving scientists at the Climatic Research Unit at the University of East Anglia in 2009. I identify several arguments advanced for and against the sharing of scientific data. My conclusions are that, whereas transparency in science is to be valued, appeals to an unproblematic category of ‘empirical data’ in climate (...) science do not reflect the complexities of scientific practice in this field. (shrink)

Climate science denial results from ignorance and perpetuates ignorance about scientific facts and methods of inquiry. In this paper, I explore climate science denial as a type of active ignorance...

Scientists and decision makers rely on climate models for predictions concerning future climate change. Traditionally, physical processes that are key to predicting extreme events are either directly represented or indirectly represented. Scientists are now replacing physically based parameterizations with neural networks that do not represent physical processes directly or indirectly. I analyze the epistemic implications of this method and argue that it undermines the reliability of model predictions. I attribute the widespread failure in neural network generalizability to the (...) lack of process representation. The representation of climate processes adds significant and irreducible value to the reliability of climate model predictions. (shrink)

Science strives for coherence. For example, the findings from climate science form a highly coherent body of knowledge that is supported by many independent lines of evidence: greenhouse gas emissions from human economic activities are causing the global climate to warm and unless GHG emissions are drastically reduced in the near future, the risks from climate change will continue to grow and major adverse consequences will become unavoidable. People who oppose this scientific body of knowledge (...) because the implications of cutting GHG emissions—such as regulation or increased taxation—threaten their worldview or livelihood cannot provide an alternative view that is coherent by the standards of conventional scientific thinking. Instead, we suggest that people who reject the fact that the Earth’s climate is changing due to greenhouse gas emissions oppose whatever inconvenient finding they are confronting in piece-meal fashion, rather than systematically, and without considering the implications of this rejection to the rest of the relevant scientific theory and findings. Hence, claims that the globe “is cooling” can coexist with claims that the “observed warming is natural” and that “the human influence does not matter because warming is good for us.” Coherence between these mutually contradictory opinions can only be achieved at a highly abstract level, namely that “something must be wrong” with the scientific evidence in order to justify a political position against climate change mitigation. This high-level coherence accompanied by contradictory subordinate propositions is a known attribute of conspiracist ideation, and conspiracism may be implicated when people reject well-established scientific propositions. (shrink)

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. (shrink)

. In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth’s climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model. I argue that as a result of system complexity, climate modelers use general circulation models (...) to perform model dynamic exploration and climate dynamic exploration. MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively. (shrink)

Based on the disproportionate amount of attention paid by climate scientists to the supposed global warming hiatus, it has recently been argued that contrarian discourse has “seeped into” climate science. While I agree that seepage has occurred, its effects remain unclear. This lack of clarity may give the impression that climate science has been compromised in a way that it hasn't—such a conclusion should be defended against. To do this I argue that the effects of (...) seepage should be analyzed in terms of objectivity. I use seven meanings of objectivity to analyze contrarian discourse's impact on climate science. The resulting account supports the important point that climate science has not been compromised in a way that invalidates the conclusions its scientists have drawn, despite the reality of seepage having occurred. (shrink)

Climate science denial has a perhaps surprisingly strong standing in anthroposophy. Anthroposophical deniers of climate science usually do not contest the existence of global warming, but they ascribe it to “cosmic” processes that are largely described in astrological terms. Thoroughly refuted claims that ongoing global warming depends on variations in solar activity have been adopted by anthroposophists. This article proposes three major explanations for the persistence of climate science denial in the anthroposophical movement: Anthroposophists (...) constantly look for guidance on scientific issues in the writings of their founder Rudolf Steiner, who made claims far off from the mainstream science of his time. They consider the material world to be constantly influenced by “spiritual” factors, including astrological constellations and a host of supernatural beings. Finally, they cherish ideas on a predetermined, largely cyclic, cosmic plan, of which humanity is a part. (shrink)

Recent philosophical attention to climate models has highlighted their weaknesses and uncertainties. Here I address the ways that models gain support through observational data. I review examples of model fit, variety of evidence, and independent support for aspects of the models, contrasting my analysis with that of other philosophers. I also investigate model robustness, which often emerges when comparing climate models simulating the same time period or set of conditions. Starting from Michael Weisberg’s analysis of robustness, (...) I conclude that his approach involves a version of reasoning from variety of evidence, enabling this robustness to be a confirmatory virtue.. (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)

Recent discoveries have revealed that there is intellectual bias in the field of climate science; the present paper makes the case that this moral and intellectual rot has also affected the field of economics.

Stephen Schneider’s perspective on climate change communication is distinguished by its longevity, a keen anticipation of research findings, historical understanding, and grounding in first-person experience. In this article, the author elaborates Schneider’s work in terms of its key claims, suggestive research directions, and lessons for scientists, journalists, and citizens. This article also evaluates his “double ethical bind” formulation to discuss potential limitations regarding precautionary policy. In conclusion, the author suggests that Schneider’s work has been important for advancing a robust (...) precautionary perspective for climate change, but that it may be insufficient for carrying this perspective through to fuller expression. (shrink)

In an era when public faith in politicians is dwindling, yet trust in scientists remains relatively high, governments are increasingly emphasizing the role of science based policy-making in response to challenges such as climate change and global pandemics. In this paper we question the quality of some scientific advice given to governments and the robustness and transparency of the entire framework which envelopes such advice, all of which raise serious ethical concerns. In particular we focus on the (...) so-called Imperial Model which heavily influenced the government of the United Kingdom in devising its response to the COVID-19 crisis. We focus on and highlight several fundamental methodological flaws of the model, raise concerns as to the robustness of the system which permitted these to remain unchallenged, and discuss the relevant ethical consequences. (shrink)