Philosophy of Climate Science

This paper discusses ethical issues surrounding Integrated Assessment Models (IAMs) of the economic effects of climate change, and how climate economists acting as policy advisors ought to represent the uncertain possibility of catastrophe. Some climate economists, especially Martin Weitzman, have argued for a precautionary approach where avoiding catastrophe should structure climate economists’ welfare analysis. This paper details ethical arguments that justify this approach, showing how Weitzman’s “fat tail” probabilities of climate catastrophe pose ethical problems for widely used IAMs. The main claim is that economists who ignore or downplay catastrophic risks in their representations of uncertainty likely fall afoul of ethical constraints on scientists acting as policy advisors. Such scientists have duties to honestly articulate uncertainties and manage (some) inductive risks, or the risks of being wrong in different ways.

This is the first of three parts of an introduction to the philosophy of climate science. In this first part about observing climate change, the topics of definitions of climate and climate change, data sets and data models, detection of climate change, and attribution of climate change will be discussed.

This article debunks Bruno Latour's seemingly pro-scientific and well-intentioned (in particular, environment-friendly) posture. I briefly summarize Latour's constructivist, relativist, hybridist, and mystic philosophy, insisting on his radicalization in his last two books (Face à Gaïa and Où atterrir?). I show that Latour's conception is akin to "pseudo-profound bullshit" (Frankfurt, Pennycook et al.), inasmuch as he tries to hide his mysticism behind the invocation of scientific facts. I then concentrate on Latour's politicization of climate science, showing that it is: self-contradictory from an epistemological point of view, since it presupposes scientifically established facts (such as anthropogenic climate change) while at the same time undermining their objectivity; counterproductive, and even dangerous, from the political point of view, since it recommends a full politicization of climate science and ignores its harmful effects. I conclude by advocating a distinction between science and politics, and by showing that Latour's philosophy fosters our current post-truth predicament.

This article discusses various dangers that
accompany the supposedly benign methods in behavioral
evolutionary biology and evolutionary psychology that fall
under the framework of ‘‘methodological adaptationism.’’
A ‘‘Logic of Research Questions’’ is proposed that aids in
clarifying the reasoning problems that arise due to the
framework under critique. The live, and widely practiced,
‘‘evolutionary factors’’ framework is offered as the key
comparison and alternative. The article goes beyond the
traditional critique of Stephen Jay Gould and Richard C.
Lewontin, to present problems such as the disappearance of
evidence, the mishandling of the null hypothesis, and
failures in scientific reasoning, exemplified by a case from
human behavioral ecology. In conclusion the paper shows
that ‘‘methodological adaptationism’’ does not deserve its
benign reputation.

The theoretical foundations of climate science have received little attention from philosophers thus far, despite a number of outstanding issues. We provide a brief, non-technical overview of several of these issues – related to theorizing about climates, climate change, internal variability and more – and attempt to make preliminary progress in addressing some of them. In doing so, we hope to open a new thread of discussion in the emerging area of philosophy of climate science, focused on theoretical foundations.

Climate scientists have been engaged in a decades-long debate over the standing of satellite measurements
of the temperature trends of the atmosphere above the surface of the earth. This is especially significant
because skeptics of global warming and the greenhouse effect have utilized this debate to spread
doubt about global climate models used to predict future states of climate. I use this case from an understudied
science to illustrate two distinct philosophical approaches to the relations among data, scientist,
measurement, models, and theory. I argue that distinguishing between ‘direct’ empiricist and ‘complex’
empiricist approaches helps us understand and analyze this important scientific episode. I also introduce
a complex empiricist account of testing and evaluation, and contrast it with the basic Hypothetico-
Deductive approach to the climate models used by the direct empiricists. This more developed complex
empiricist approach will serve philosophy of science well, as computational models become more widespread
in the sciences.

An essay exploring the ethical philosophy behind Guidelines for Considering Traditional Knowledges in Climate Change Initiatives. The essay can be found here, http://earthzine.org/2015/07/31/the-ethics-of-traditional-knowledge-exchange-in-climate-change-initiatives/. Climate scientists, policymakers and the growing community of citizens engaged in observing global change are increasingly turning to traditional knowledges of indigenous peoples to improve understanding of and strategies for adaptation and mitigation. Indigenous peoples are also recognizing the value of methods and information from western climate science, such as models, risk and vulnerability assessments and monitoring strategies. Unfortunately, policymakers who design and implement climate change initiatives frequently overlook indigenous peoples. While they call for access to traditional knowledge to help inform choices for preparation, adaptation or mitigation in response to climate change, they have little awareness of real risks of harm when indigenous peoples share their traditional knowledges. Currently, there are few protections to ensure that traditional knowledges will remain the property of the indigenous peoples or knowledge holders who choose to share traditional knowledges. These Guidelines are intended to promote the use of traditional knowledges in climate change initiatives in such a way as to protect the rights and interests of indigenous peoples, promote greater collaboration with scientists and government professionals and increase indigenous representation in climate change initiatives such as those of the U.S. federal government.

When do probability distribution functions (PDFs) about future 21 climate misrepresent uncertainty? How can we recognise 22 when such misrepresentation occurs and thus avoid it in 23 reasoning about or communicating our uncertainty? And when 24 we should not use a PDF, what should we do instead? In this 25 paper we address these three questions. We start by providing 26 a classification of types of uncertainty and using this 27 classification to illustrate when PDFs misrepresent our 28 uncertainty in a way that may adversely affect decisions. We 29 then discuss when it is reasonable and appropriate to use a 30 PDF to reason about or communicate uncertainty about 31 climate. We consider two perspectives on this issue. On one, 32 which we argue is preferable, available theory and evidence in 33 climate science basically excludes using PDFs to represent 34 our uncertainty. On the other, PDFs can legitimately be 35 provided when resting on appropriate expert judgement and 36 recognition of associated risks. Once we have specified the 37 border between appropriate and inappropriate uses of PDFs, 38 we explore alternatives to their use. We briefly describe two 39 formal alternatives, namely imprecise probabilities and 40 possibilistic distribution functions, as well as informal 41 possibilistic alternatives. We suggest that the possibilistic 42 alternatives are preferable.

There continues to be a vigorous public debate in our society about the status of climate science. Much of the skepticism voiced in this debate suffers from a lack of understanding of how the science works - in particular the complex interdisciplinary scientific modeling activities such as those which are at the heart of climate science. In this book Eric Winsberg shows clearly and accessibly how philosophy of science can contribute to our understanding of climate science, and how it can also shape climate policy debates and provide a starting point for research. Covering a wide range of topics including the nature of scientific data, modeling, and simulation, his book provides a detailed guide for those willing to look beyond ideological proclamations, and enriches our understanding of how climate science relates to important concepts such as chaos, unpredictability, and the extent of what we know.

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.

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.

Richard Jeffrey (1956) responded to Richard Rudner’s (1953) argument from inductive risk by maintaining that scientists could produce explicit representations of uncertainty and present these to decision-makers, who could then combine these “value-free” epistemic assessments with utilities (including all non-epistemic values) as in decision theory.1 Heather Douglas (2000, 2009), Kevin Elliott (2011), Daniel Steel (2015), and others have responded to Jeffrey’s strategy by arguing that decisions involving non- epistemic values affect both the content and interpretation of probabilities. On this view, problems of inductive risk arise in methodological decisions that produce probabilities (or other representations of risk or uncertainty) and in the interpretation of these estimates for policymakers.2 This chapter will focus on articulating the Jeffreyan value-free ideal and its limits, and clarifying and exploring the problem of inductive risk due to higher-order uncertainty.

In the first part of the next section, I review Jeffrey’s decision-theoretic response to Rudner and some of its main problems. In the second part, I characterize the problem of inductive risk due to higher-order uncertainty in decision-theoretic terms, using decision trees whose nodes represent methodological choices affecting both the content and interpretation of uncertainty estimates. In the third section, I argue that while it is unrealistic to expect to insulate these decisions from the influence of all non-epistemic values, the Jeffreyan decision-theoretic version of the value-free ideal may be useful in some limited contexts. These are exactly contexts in which methodological decisions introduce minimal higher-order uncertainty, and in which communications of uncertainty are unlikely to be manipulated or misunderstood by decision-makers or scientists themselves. In the fourth section, I illustrate the limitations of the Jeffreyan ideal with reference to climate science. I discuss Gregor Betz’s (2013) recent Jeffreyan response to the inductive risk consider- ations raised by Justin Biddle and Eric Winsberg (2010) and Winsberg (2012) for climate modeling. I argue that the context of policy interactions with regard to climate science are far from the Jeffreyan ideal, thus the argument that climate modeling is value-laden due to inductive risks withstands Betz’s criticisms. I conclude that while the Jeffreyan value-free ideal deserves to be further discussed and debated by philosophers of science, this chapter shows that it faces problems in complex and policy-relevant scientific contexts due to higher-order uncertainty.

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.

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 modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of 'model fit'; that is, 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.

Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research.Big data is increasingly popular in many research domains. This Perspective discusses where elements of big data approaches have been employed in climate research and where combining big data with theory-driven research can be most fruitful.

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. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change K E Y W O R D S climate datasets, dataset ensembles, framework, model evaluation, uncertainty

This article was published on the Blog of the APA (edited by David V. Johnson), having previously received Honorable Mention in the APA Blog's first Public Philosophy Award for Undergraduates (2019). It is a narrative-style public philosophy piece which tries to understand the motivating factors behind the rejection of scientific claims - specifically, the denial by members of the public of scientific claims about climate change. It argues that, at least in some cases, this rejection is motivated by a perception of 'science' as a uni-vocal behemoth which spits out opaque claims, rather than a dynamic poly-vocal system involved in an ongoing process of explaining.

The issue of climate change provides a drastic example of the importance, but also the precariousness of public trust in science. Climate science almost unanimously warns that global warming requires governments to effectively pursue long-term agendas of mitigation, adaptation and compensation without further delay. In democracy, however, this requires of citizens a long-term commitment to prioritizing the issue of climate change in their voting decisions. Recent developments prove such prioritizing unlikely as long as scientists think of themselves primarily as political consultants. In fact, climate scientists have long amplified their direct communication with the public, gaining additional support for climate policies, but also aggravating in the eyes of some (what sociologists call) the politicization of science. Against the background of these developments, the article asks what kind of normative model should henceforth govern the relationship of (climate) experts and citizens. Two families of proposals are considered. Scientistic models typically assume that citizens ought to base their autonomous political decisions on scientific facts and that the primary goal of scientists in dealing with the public should hence be to provide detailed, reliable scientific information. Proponents of the model of practical judgment, by contrast, criticize scientistic models for being overly demanding, and suggest the cultivation of practical judgment as an alternative. The article argues in favor of a scientistic model, but shifts the focus from the proliferation of information toward the cultivation of trust in science. According to the suggested account, citizens are obligated to make an effort to correctly identify the community of scientific experts, and to give in their own deliberations appropriate weight to identified expert recommendations.

Climate change has been the subject of increasing efforts by scientists to understand its causes and implications; it has been of growing interest to policymakers, international bodies, and a variety of nongovernment organizations; and it has attracted varied amounts of attention from traditional and, increasingly, online media. These developments have been aligned with shifts in the nature of climate change communication, with changes in how researchers study it and how a variety of actors try to influence it. This article situates the theory and practice of climate change communication within developments that have taken place since we first reviewed the field in 2009. These include the rise of new social media conduits for communication, research, and practice aimed at fine tuning communication content, and the rise to prominence of scientific consensus as part of that content. We focus in particular on continuing tensions between a focus on the part of communicators to inform the public and more dialogic strategies of pubic engagement.Wealso consider the tension between efforts to promote consensus and certainty in climate science and approaches that attempt to engage with uncertainty more fully. We explore the lessons to be learnt from climate communication since 2009, highlighting how the field remains haunted by the deficit model of science communication. Finally, we point to more fruitful future directions for climate change communication, including more participatory models that acknowledge, rather than ignore, residual uncertainties in climate science in order to stimulate debate and deliberation.

Integrated assessment models (IAMs) treat the global climate and the global economy as two coupled parts of a single hybrid system, and can be used to construct policy portfolios that reflect a wide variety of social and scientific values and priorities.  However, there is a high degree of uncertainty endemic to both climatological and economic models, which is inherited by IAMs.  In addition, the decision of which models to use, how to prioritize future outcomes, and how to delineate relevantly similar states of the hybrid system are deeply value-laden and perspectival choices, complicating the task of constructing policies based on IAMs.  In this paper, we examine the conceptual and technical challenges associated with IAM-based policy and argue that the best way to meet these challenges is to use IAMs to construct policies that maximize the range of future policy options available, rather than to pursue specific outcomes.  By optimizing our policy choice to produce a maximally wide range of future choice, we both mitigate the practical impact of structural model uncertainty and minimize the extent to which individual evaluative choices are irreversibly enshrined in policy decisions.  This approach greatly enhances the potential utility of IAMs, and has concrete, specific implications for climate policy deliberations.

This paper argues that common intuitions regarding a) the specialness of "use-novel" data for confirmation, and b) that this specialness implies the "no-double-counting rule", which says that data used in "constructing" (calibrating) a model cannot also play a role in confirming the model's predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in light of prominent accounts of confirmation of model predictions. We show that, on the Bayesian account of confirmation, and also on the standard Classical hypothesis-testing account, claims a) and b) are not generally true, but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialised Classical model-selection methods, on the other hand, uphold a nuanced version of claim a), but this comes apart from b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised.

Climate change assessments rely upon scenarios of socioeconomic developments
to conceptualize alternative outcomes for global greenhouse gas emissions.
These are used in conjunction with climate models to make projections of future
climate. Specifically, the estimations of greenhouse gas emissions based on socioeconomic
scenarios constrain climate models in their outcomes of temperatures, precipitation,
etc. Traditionally, the fundamental logic of the socioeconomic scenarios—that
is, the logic that makes them plausible—is developed and prioritized using methods
that are very subjective. This introduces a fundamental challenge for climate change
assessment: The veracity of projections of future climate currently rests on subjective
ground. We elaborate on these subjective aspects of scenarios in climate change
research. We then consider an alternative method for developing scenarios, a systems
dynamics approach called ‘Cross-Impact Balance’ (CIB) analysis. We discuss
notions of ‘objective’ and ‘objectivity’ as criteria for distinguishing appropriate scenario
methods for climate change research. We distinguish seven distinct meanings
of ‘objective,’ and demonstrate that CIB analysis is more objective than traditional
subjective approaches. However, we also consider criticisms concerning which of the
seven meanings of ‘objective’ are appropriate for scenario work. Finally, we arrive
at conclusions regarding which meanings of ‘objective’ and ‘objectivity’ are relevant for climate change research. Because scientific assessments uncover knowledge relevant
to the responses of a real, independently existing climate system, this requires
scenario methodologies employed in such studies to also uphold the seven meanings
of ‘objective’ and ‘objectivity.’

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. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change K E Y W O R D S climate datasets, dataset ensembles, framework, model evaluation, uncertainty

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. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change K E Y W O R D S climate datasets, dataset ensembles, framework, model evaluation, uncertainty

This paper examines initial conditions dependence and initial conditions uncertainty for climate projections and predictions. The first contribution is to provide a clear conceptual characterisation of predictions and projections. Concerning initial conditions 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, what is often meant is that the simulations used to obtain projections are independent of initial conditions. This paper argues that evidence does not support this claim. Concerning initial conditions uncertainty, three kinds of initial conditions uncertainty are identified (two have received little attention from philosophers so far). The first (the one usually discussed) 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 conditions dependence and uncertainty in climate science are more complex and important issues than usually acknowledged.

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 modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of 'model fit'; that is, 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.

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 MichaelWeisberg’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

Syllabus for an upper-division undergraduate or introductory graduate seminar on the philosophy of climate science.  Issues covered include: the structure and function of climate models; the difficulty of evaluating the claims of scientific experts; the role of models and computation in science, and particular issues in modeling complex systems; the relationship between public policy and scientific work; decision making under risk and uncertainty about the future; environmental ethics and our responsibility to future generations; evaluating climate policies, including adaptation, mitigation, and geoengineering.