The role of ‘complex’ empiricism in the debates about satellite data and climate models

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.     

Holism, physical theories and quantum mechanics

Motivated by the question what it is that makes quantum mechanics a holistic theory (if so), I try to define for general physical theories what we mean by `holism'. For this purpose I propose an epistemological criterion to decide whether or not a physical theory is holistic, namely: a physical theory is holistic if and only if it is impossible in principle to infer the global properties, as assigned in the theory, by local resources available to an agent. I propose that these resources include at least all local operations and classical communication. This approach is contrasted with the well-known approaches to holism in terms of supervenience. The criterion for holism proposed here involves a shift in emphasis from ontology to epistemology. I apply this epistemological criterion to classical physics and Bohmian mechanics as represented on a phase and configuration space respectively, and for quantum mechanics (in the orthodox interpretation) using the formalism of general quantum operations as completely positive trace non-increasing maps. Furthermore, I provide an interesting example from which one can conclude that quantum mechanics is holistic in the above mentioned sense, although, perhaps surprisingly, no entanglement is needed.     

Model robustness as a confirmatory virtue: The case of climate science

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 independently-supported 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.     

Research on quantitative assessment of climate change risk at an urban scale: Review of recent progress and outlook of future direction

Cities are not only major contributors to global climate change but also stand at the forefront of climate change impact. Quantifying and assessing the risk potentially induced by climate change has great significance for cities to undertake positive climate adaptation and risk prevention. However, most of the previous studies focus on global, national or regional dimensions, only a few have attempted to examine climate change risk at an urban scale and even less in the case of a recent literature review. As a result, a quantitative assessment of climate change risk for cities remains highly challenging. To fill this gap, the article makes a critical review of the recent literature on urban-scale climate change risk assessment, and classifies them into four major categories of studies which jointly constitute a stepwise modelling chain from global climate change towards urban-scale risk assessment. On this basis, the study summarizes the updated research progresses and discusses the major challenges to be overcome for the seamless coupling of climate simulation between different scales, the reproduction of compound climate events, the incorporation of non-market and long-lasting impacts and the representation of risk transmission insides or beyond a city. Furthermore, future directions to advance quantitative assessment of urban-scale climate change risk are highlighted, with fresh insights into improving study methodology, enriching knowledge of climate change impact on city, enhancing abundance and accessibility to data, and exploring the best practice to provide city-specific climate risk service.     

The Duhem-Quine problem for equiprobable conjuncts

In this paper, we distinguish Quine’s thesis of holism from the related Duhem-Quine problem. We discuss the construal of holism which claims that the effect of falsification is felt on a conjunction of hypotheses. The Duhem-Quine problem claims that there is no principled way of knowing how falsification affects individual conjuncts. This latter claim relies on holism and an additional commitment to the hypothetico-deductive model of theory confirmation such that it need not arise in non-deductive accounts. While existing personalist Bayesian treatments of the problem make this point by assuming values of priors for the conjuncts, we arrive at the same conclusion without invoking such assumptions. Our discussion focuses on the falsification of equiprobable conjuncts and highlights the role played by their alternatives in ascertaining their relative disconfirmation. The equiprobability of conjuncts is discussed alongside a historical case study     

Computer models and the evidence of anthropogenic climate change: An epistemology of variety-of-evidence inferences and robustness analysis

To study climate change, scientists employ computer models, which approximate target systems with various levels of skill. Given the imperfection of climate models, how do scientists use simulations to generate knowledge about the causes of observed climate change? Addressing a similar question in the context of biological modelling, Levins (1966) proposed an account grounded in robustness analysis. Recent philosophical discussions dispute the confirmatory power of robustness, raising the question of how the results of computer modelling studies contribute to the body of evidence supporting hypotheses about climate change. Expanding on Staley’s (2004) distinction between evidential strength and security, and Lloyd’s (2015) argument connecting variety-of-evidence inferences and robustness analysis, I address this question with respect to recent challenges to the epistemology robustness analysis. Applying this epistemology to case studies of climate change, I argue that, despite imperfections in climate models, and epistemic constraints on variety-of-evidence reasoning and robustness analysis, this framework accounts for the strength and security of evidence supporting climatological inferences, including the finding that global warming is occurring and its primary causes are anthropogenic.     

Understanding climate phenomena with data-driven models

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding, specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent.     

Seepage,objectivity, and climate science

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.     

Putting the Earth System in a numerical box? The evolution from climate modeling toward global change

Since the 1980s, climate modeling has undergone major transformations. The most prominent of these are the proliferation of coupled models and the integration within models of a growing number of environments and feedbacks. Climate modelers now increasingly define their object in terms of an “Earth System” instead of a “climate system”. In addition to this proliferation of coupled models, the carbon cycle and its feedback on various environments, from the atmosphere to the ocean and to vegetation cover, has become a prominent component of climate modeling. These transformations derive from the IPCC’s overall methodology, and are closely bound up with both a heightened awareness of the risks of climate change, as well as an issue of crucial political importance: the question of socio-economic/climate integration. In this article I follow, from a roughly chronological point of view, the major steps of this evolution and its links with the evolution of the political agenda. What can we say about this seemingly irreversible tendency to incorporate everything into models and to take account of everything that influences the Earth’s climate? Could we correlate it to the strong tendency toward globalization? How is the notion of climate itself affected? These are the main questions of the paper.     

Making the abstract concrete: The role of norms and values in experimental modeling

Experimental modeling is the construction of theoretical models hand in hand with experimental activity. As explained in Section 1, experimental modeling starts with claims about phenomena that use abstract concepts, concepts whose conditions of realization are not yet specified; and it ends with a concrete model of the phenomenon, a model that can be tested against data. This paper argues that this process from abstract concepts to concrete models involves judgments of relevance, which are irreducibly normative. In Section 2, we show, on the basis of several case studies, how these judgments contribute to the determination of the conditions of realization of the abstract concepts and, at the same time, of the quantities that characterize the phenomenon under study. Then, in Section 3, we compare this view on modeling with other approaches that also have acknowledged the role of relevance judgments in science. To conclude, in Section 4, we discuss the possibility of a plurality of relevance judgments and introduce a distinction between locally and generally relevant factors.     

Styles of reasoning: A pluralist view

Styles of reasoning are important devices to understand scientific practice. As I use the concept, a style of reasoning is a pattern of inferential relations that are used to select, interpret, and support evidence for scientific results. In this paper, I defend the view that there is a plurality of styles of reasoning: different domains of science often invoke different styles. I argue that this plurality is an important source of disunity in scientific practice, and it provides additional arguments in support of the disunity claim. I also contrast Ian Hacking’s broad characterization of styles of reasoning with a narrow understanding that I favor. Drawing on examples from molecular biology, chemistry and mathematics, I argue that differences in style of reasoning lead to differences in the way the relevant results are obtained and interpreted. The result is a pluralist view about styles of reasoning that is sensitive to nuances of inferential relations in scientific activity.     

Gauge theories and holisms

Those looking for holism in contemporary physics have focused their attention primarily on quantum entanglement. But some gauge theories arguably also manifest the related phenomenon of nonseparability. While the argument is strong for the classical gauge theory describing electromagnetic interactions with quantum “particles”, it fails in the case of general relativity even though that theory may also be formulated in terms of a connection on a principal fiber bundle. Anandan has highlighted the key difference in his analysis of a supposed gravitational analog to the Aharonov–Bohm effect. By contrast with electromagnetism in the original Aharonov–Bohm effect, gravitation is separable and exhibits no novel holism in this case. Whether the nonseparability of classical gauge theories of nongravitational interactions is associated with holism depends on what counts as the relevant part–whole relation. Loop representations of quantized gauge theories of nongravitational interactions suggest that these conclusions about holism and nonseparability may extend also to quantum theories of the associated fields.     

升高CO2浓度和温度对针叶林的影响以及在川西亚高山针叶林研究中的展望

以CO2浓度和温度升高为主要特征的全球气候变化受到了世界各国科学家们的普遍关注。目前已经有大量关于森林生态系统对全球变化响应的研究报道和综述性文章,涉及分子、生理生态、种群和群落生态以及区域和全球尺度等微观和宏观领域的研究内容,因而要想对森林生态系统对全球变化的响应研究进行全面的综述是相当困难的。因此,本文只简要评述了森林生态系统对升高CO2浓度和温度响应研究中的诸如CO2浓度和温度升高对森林生长和生产力的影响、森林生态系统物种组成和多样性对升高CO2浓度和温度的响应、植被变化与全球碳循环的互动以及气候变化与森林生态系统生物地球化学循环的关系等几个热点问题。川西亚高山针叶林区为我国的第二大林区,地处高山峡谷地带,是全球变化的敏感地带。相对简单的植物群落结构、较低的物种丰富度和多样性、巨大的冷冻土壤碳库、不同演替阶段和功能的植物群落组合以及分布于林线附近的邻接效应等为研究针叶林系统对升高CO2浓度和温度的响应提供了良好的天然实验室。针叶林生产力、植物群落物种组成和多样性以及土壤碳氮过程等对升高CO2浓度和温度的响应对于预测未来气候变化下针叶林系统的生态过程具有重要意义。     

Values in early-stage climate engineering: The ethical implications of “doing the research”

Calls for research on climate engineering have increased in the last two decades, but there remains widespread agreement that many climate engineering technologies (in particular, forms involving global solar radiation management) present significant ethical risks and require careful governance. However, proponents of research argue, ethical restrictions on climate engineering research should not be imposed in early-stage work like in silico modeling studies. Such studies, it is argued, do not pose risks to the public, and the knowledge gained from them is necessary for assessing the risks and benefits of climate engineering technologies. I argue that this position, which I call the “broad research-first” stance, cannot be maintained in light of the entrance of nonepistemic values in climate modeling. I analyze the roles that can be played by nonepistemic political and ethical values in the design, tuning, and interpretation of climate models. Then, I argue that, in the context of early-stage climate engineering research, the embeddedness of values will lead to value judgments that could harm stakeholder groups or impose researcher values on non-consenting populations. I conclude by calling for more robust reflection on the ethics and governance of early-stage climate engineering research.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Cointegration rank switching model: an application to forecasting interest rates

This paper proposes a new forecasting method in which the cointegration rank switches at unknown times. In this method, time series observations are divided into several segments, and a cointegrated vector autoregressive model is fitted to each segment. The goodness of fit of the global model, consisting of local models with different cointegration ranks, is evaluated using the information criterion (IC). The division that minimizes the IC defines the best model. The results of an empirical application to the US term structure of interest rates and a Monte Carlo simulation suggest the efficacy as well as the limitations of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

The development of general circulation models of climate

With the coming of digital computers in the 1950s, a small American team set out to model the weather, followed by attempts to represent the entire general circulation of the atmosphere. The work spread during the 1960s, and by the 1970s a few modelers had produced somewhat realistic looking models of the planet’s regional climate pattern. The work took on wider interest when modelers tried increasing the level of greenhouse gases, and invariably found serious global warming. Skeptics pointed to dubious technical features, but by the late 1990s these problems were largely resolved—thanks to enormous increases in computer power, the number and size of the closely interacting teams that now comprised the international modeling community, and the crucial availability of field experiments and satellite data to set against the models’ assumptions and outputs. By 2007 nearly all climate experts accepted that the climate simulations represented reality well enough to impel strong action to restrict gas emissions.     

Forecast Combination for Euro Area Inflation: A Cure in Times of Crisis?

The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue for forecasts from a range of short‐term forecasting models. Our analysis shows that there is considerable variation of the relative performance of the different models over time. To take that into account we suggest employing performance‐based forecast combination methods—in particular, one with more weight on the recent forecast performance. We compare such an approach with equal forecast combination that has been found to outperform more sophisticated forecast combination methods in the past, and investigate whether it can improve forecast accuracy over the single best model. The time‐varying weights assign weights to the economic interpretations of the forecast stemming from different models. We also include a number of benchmark models in our analysis. The combination methods are evaluated for HICP headline inflation and HICP excluding food and energy. We investigate how forecast accuracy of the combination methods differs between pre‐crisis times, the period after the global financial crisis and the full evaluation period, including the global financial crisis with its extraordinary volatility in inflation. Overall, we find that forecast combination helps hedge against bad forecast performance and that performance‐based weighting outperforms simple averaging. Copyright © 2017 John Wiley & Sons, Ltd.     

‘Lord only of the ruffians and fiends’? William Whewell and the plurality of worlds debate

By the middle of the nineteenth century, the opinion of science, as well as of philosophy and even religion, was, at least in Britain, firmly in the camp of the plurality of worlds, the view that intelligent life exists on other celestial bodies. William Whewell, considered an expert on science, philosophy and religion (among other areas), would have been expected to support this position. Yet he surprised everyone in 1853 by publishing a work arguing strongly against the plurality view. This was even stranger given that he had endorsed pluralism twenty years earlier in his contribution to the Bridgewater Treatises. In this paper I show that the shift in Whewell’s view was motivated by three factors: the influence of Richard Owen’s theory of archetypes on Whewell’s view of the argument from design, and Whewell’s perception of the need to strengthen such arguments in light of evolutionary accounts of human origins; important developments in his view of philosophy and his role as a scientific expert; and new findings in astronomy. An examination of the development of Whewell’s position provides a lens through which we can view the interplay of theology, philosophy and science in the plurality of worlds debate.