Philosophy of Earth Sciences

We explore three questions about Earth system modeling that are of both scientific and philosophical interest: What kind of understanding can be gained via complex Earth system models? How can the limits of understanding be bypassed or managed? How should the task of evaluating Earth system models be conceptualized?

When we look at 800,000 year ice core data CO2 levels since 1950 have risen at a rate of 123-fold faster than the fastest rate in 800,000 years. When we see that this rise is precisely correlated with global carbon emissions the human link to climate change seems certain and any rebuttal becomes ridiculously implausible. The 800,000 year correlation between CO2 and global temperatures seems to be predicting at least 9 degrees C of more warming based on current CO2 levels.

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include data conversion, data correction, data interpolation, (...) data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

A review of Thomas Moynihan's Spinal Catastrophism: A Secret History (2019).

In 2012 the Geological Time Scale, which sets the temporal framework for studying the timing and tempo of all major geological, biological, and climatic events in Earth's history, had one-quarter of all its boundaries moved in a prima facie puzzling, widespread revision of radiometric dates. In this paper I show how recent work in the philosophy of metrology can help us understand this episode, and in turn how a careful consideration of this case elucidates the distinct, but related, notions of (...) calibration, coherence, and consilience. In particular, I argue that coherence testing should be recognized as a distinct activity that precedes both calibration and consilience arguments, and I highlight the tradeoffs scientists often face between intercalibrating two measurement methods or keeping them independent for arguments of consilience. Finally, I show how the iterative nature of calibration leads to the problem of legacy data, which has been inadequately appreciated. (shrink)

For computer simulation models to usefully inform climate risk management decisions, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many times over, and because computing resources are finite, uncertainty assessment is more feasible using models that need less computer processor time. Such models are generally simpler in the sense of being more idealized, or less realistic. So modelers face a trade-off between realism and extent of uncertainty quantification. Seeing this trade-off for the (...) important epistemic issue that it is requires a shift in perspective from the established simplicity literature in philosophy of science. (shrink)

Can we attribute individual extreme weather events to human-induced climate change? In this chapter I will be turning a philosophical eye on this question, asking what concept (or concepts) of causation are being employed by scientists and asking which concept of causation is most appropriate. I will show that scientists, politicians, and journalists have made a number of mistakes in their thinking about the causal links between individual extreme events and climate change, and argue that scientists should be less hesitant (...) when it comes to attributing such events to climate change. (shrink)

William Whewell’s work on historical science has received some attention from historians and philosophers of science. Whewell’s own work on the history of German Gothic church architecture has been touched on within the context of the history of architecture. To a large extent these discussions have been conducted separately. I argue that Whewell intended his work on Gothic architecture as an attempt to (help) found a science of historical architecture, as an exemplar of historical science. I proceed by analyzing the (...) key features of Whewell’s philosophy of historical science. I then show how his architectural history exemplifies this philosophy. Finally, I show how Whewell’s philosophy of historical science matches some developments in a science (biological systematics) that, in the mid-to latenineteenth century, came to be reinterpreted as a historical science. I comment briefly on Whewell as a potential influence on nineteenth century biology and in particular on Darwin. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: First, the 'purity' of a data model is not a measure of its epistemic reliability. (...) Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved 'vicariously', such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate (or inadequate) for particular purposes. (shrink)

Uncertainty is recognized as a key issue in water resources research, amongst other sciences. Discussions of uncertainty typically focus on tools and techniques applied within an analysis, e.g. uncertainty quantification and model validation. But uncertainty is also addressed outside the analysis, in writing scientific publications. The language that authors use conveys their perspective of the role of uncertainty when interpreting a claim —what we call here “framing” the uncertainty. This article promotes awareness of uncertainty framing in four ways. 1) It (...) proposes a typology of eighteen uncertainty frames, addressing five questions about uncertainty. 2) It describes the context in which uncertainty framing occurs. This is an interdisciplinary topic, involving philosophy of science, science studies, linguistics, rhetoric, and argumentation. 3) We analyze the use of uncertainty frames in a sample of 177 abstracts from the Water Resources Research journal in 2015. This helped develop and tentatively verify the typology, and provides a snapshot of current practice. 4) Provocative recommendations promote adjustments for a more influential, dynamic science. Current practice in uncertainty framing might be described as carefully-considered incremental science. In addition to uncertainty quantification and degree of belief (present in ~5% of abstracts), uncertainty is addressed by a combination of limiting scope, deferring to further work (~25%) and indicating evidence is sufficient (~40%) – or uncertainty is completely ignored (~8%). There is a need for public debate within our discipline to decide in what context different uncertainty frames are appropriate. Uncertainty framing cannot remain a hidden practice evaluated only by lone reviewers. (shrink)

Reports of the Intergovernmental Panel on Climate Change (IPCC) employ an evolving framework of calibrated language for assessing and communicating degrees of certainty in findings. A persistent challenge for this framework has been ambiguity in the relationship between multiple degree-of-certainty metrics. We aim to clarify the relationship between the likelihood and confidence metrics used in the Fifth Assessment Report (2013), with benefits for mathematical consistency among multiple findings and for usability in downstream modeling and decision analysis. We discuss how our (...) proposal meshes with current and proposed practice in IPCC uncertainty assessment. (shrink)

In this paper we take a close look at current interdisciplinary modeling practices in the environmental sciences, and suggest that closer attention needs to be paid to the nature of scientific practices when investigating and planning interdisciplinarity. While interdisciplinarity is often portrayed as a medium of novel and transformative methodological work, current modeling strategies in the environmental sciences are conservative, avoiding methodological conflict, while confining interdisciplinary interactions to a relatively small set of pre-existing modeling frameworks and strategies (a process we (...) call crystallization). We argue that such practices can be rationalized as responses in part to cognitive constraints which restrict interdisciplinary work. We identify four salient integrative modeling strategies in environmental sciences, and argue that this crystallization, while contradicting somewhat the novel goals many have for interdisciplinarity, makes sense when considered in the light of common disciplinary practices and cognitive constraints. These results provide cause to rethink in more concrete methodological terms what interdisciplinarity amounts to, and what kinds of interdisciplinarity are obtainable in the environmental sciences and elsewhere. (shrink)

The present paper draws on climate science and the philosophy of science in order to evaluate climate-model-based approaches to assessing climate projections. We analyze the difficulties that arise in such assessment and outline criteria of adequacy for approaches to it. In addition, we offer a critical overview of the approaches used in the IPCC working group one fourth report, including the confidence building, Bayesian and likelihood approaches. Finally, we consider approaches that do not feature in the IPCC reports, including three (...) approaches drawn from the philosophy of science. We find that all available approaches face substantial challenges, with IPCC approaches having as a primary source of difficulty their goal of providing probabilistic assessments. (shrink)

Primarily between 1833 and 1840, William Whewell attempted to accomplish what natural philosophers and scientists since at least Galileo had failed to do: to provide a systematic and broad-ranged study of the tides and to attempt to establish a general scientific theory of tidal phenomena. I document the close interaction between Whewell’s philosophy of science and his scientific practice as a tidologist. I claim that the intertwinement between Whewell’s methodology and his tidology is more fundamental than has hitherto been documented.Keywords: (...) William Whewell; Tidology; History of HPS; Scientific methodology; Whewell papers. (shrink)

In the spirit of explanatory pluralism, this chapter argues that causal and noncausal explanations of a phenomenon are compatible, each being useful for bringing out different sorts of insights. After reviewing a model-based account of scientific explanation, which can accommodate causal and noncausal explanations alike, an important core conception of noncausal explanation is identified. This noncausal form of model-based explanation is illustrated using the example of how Earth scientists in a subfield known as aeolian geomorphology are explaining the formation of (...) regularlyspaced sand ripples. The chapter concludes that even when it comes to everyday "medium-sized dry goods" such as sand ripples, where there is a complete causal story to be told, one can find examples of noncausal scientific explanations. (shrink)

On Saturday, August 26, 1893, thirteen-year-old Edith Low Babson was swimming in her favorite swimming hole on the Annisquam river in her home town of Gloucester, Massachusetts. Though she was a strong swimmer, something went wrong, and she drowned. A tragedy like all such. But this drowning had unusual consequences. Edith’s older brother was Roger W. Babson, who grew up to become one of America’s most prominent businessmen of the early twentieth century. A statistician, prolific author, philanthropist, founder of Babson (...) College, in Wellesley, Massachusetts, and the Prohibition Patry’s Presidential candidate in 1940, Roger Babson was deeply affected by his sister’s death, as he was again many years later, in 1947, by the death of his grandson, Michael, who drowned while saving the life of a companion who had been knocked off of a sailboat in Lake Sunapee, New Hampshire. But Roger Babson was a man of action, not one quietly to acquiesce when confronted by suffering inflicted by a seemingly impersonal and uncaring nature. One year after his grandson’s death, Babson dedicated a significant part of his vast personal wealth to the establishment of the Gravity Research Foundation in New Boston, New Hampshire, which thereafter awarded an annual prize for theoretical research on gravitation, a prize whose winners include the likes of Stephen Hawking. Why? As Babson explained in a pamphlet published by the new foundation, “Gravity: Our Enemy Number One” (Babson 1948), the goal was to alleviate the suffering for which gravity was responsible, the gravity that seized his sister “like a dragon and.. (shrink)

Keywords: geology instrument probe Earth interior crust magma core crack underground nuclear explosion molten iron China Syndrome melting rock Published in the December-2003 issue of Analog Science Fiction & Fact Magazine ; This column was written and submitted 06/22/2003 and is copyrighted ©2003 by John G. Cramer. All rights reserved. No part may be reproduced in any form without the explicit permission of the author.

This essay furthers debate about the burgeoning science of Probabilistic Event Attribution (PEA) and its relevance to imminent climate policy decisions. It critically examines Allen Thompson and Friederike Otto’s recent arguments concerning the implications of PEA studies for how the United Nations Framework Convention on Climate Change (UNFCCC) policy framework should be revised during the 2016 ‘review and decision.’ I show that their contention that PEA studies cannot usefully inform decision-making about adaptation policies and strategies is misguided and argue that (...) the current UNFCCC treaty, the “Paris Agreement,” supersedes their proposed revision. (shrink)

Between the fourth and the recent fifth IPCC report, science as well as policy making have made great advances in dealing with uncertainties in global climate models. However, the uncertainties public decision making has to deal with go well beyond what is currently addressed by policy makers and climatologists alike. It is shown in this paper that within an anthropocentric framework, a whole hierarchy of models from various scientific disciplines is needed for political decisions as regards climate change. Via what (...) is sometimes referred to as ‘uncertainty cascade’ in the technical literature, the uncertainties of various models accumulate in the input to political decision making. This paper aims to chart the uncertainties in the multi-disciplinary enterprise of current climate modeling in broad strokes. It is shown that the uncertainty cascade makes it impossible to quantify uncertainties in the form of probability estimates. Moreover the paper highlights how global climate models fail to provide probability estimates. A better treatment of the uncertainties of climate predictions on the political level would require an overhaul of the current IPCC practice that separates the various scientific fields in various working groups. (shrink)

When attendant to the agency of models and the general context in which they perform, climate models can be seen as instrumental policy tools that may be evaluated in terms of their adequacy for purpose. In contrast, when analysed independently of their real-world usage for informing decision-making, the tendency can be to prioritise their representative role rather than their instrumental role. This paper takes as a case study the development of the UK Climate Projections 2009 in relation to its probabilistic (...) treatment of uncertainties and the implications of this approach for adaptation decision-making. It is considered that the move towards ensemble-based probabilistic climate projections has the benefit of encouraging organisations to reshape their adaptation strategies and decisions towards a risk-based approach, where they are confronted definitively with climate modelling uncertainties and drawn towards a more nuanced understanding of how climate impacts could affect their operations. This is further illustrated through the example of the built environment sector, where it can be seen that the probabilistic approach may be of limited salience for the urban heat island in the absence of a corresponding effort towards a more place-based analysis of climate vulnerabilities. Therefore, further assessment of the adequacy-for-purpose of climate models might also consider the usability of climate projections at the urban scale. (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)

The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...) our knowledge of near-Earth space. To facilitate data-sharing interoperability among distinct orbital debris and space object catalogs, and SSA information systems, I proposed ontology in (Rovetto, 2015) and (Rovetto and Kelso, 2016). I continue this effort toward formal representations and models of the overall domain that may serve to improve peaceful SSA and increase our scientific knowledge. This paper explains the project concept introduced in those publications, summarizing efforts to date as well as the research field of ontology development and engineering. I describe concepts for an ontological framework for the orbital space environment, near-Earth space environment and SSA domain. An ontological framework is conceived as a part of a potential international information system. The purpose of such a system is to consolidate, analyze and reason over various sources and types of orbital and SSA data toward the mutually beneficial goals of safer space navigation and scientific research. Recent internationals findings on the limitations of orbital data, in addition to existing publications on collaborative SSA, demonstrate both the overlap with this project and the need for data-sharing and integration. (shrink)

Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (...) (i) represent SSA general knowledge, data, and entities/objects, (ii) clearly express the meaning of SSA data, and (iii) foster SSA data-sharing. The overall goal and motivation is to (iv) improve our capacity for planetary defense, e.g., from near- or deep-space objects and phenomena, and (v) facilitate safer and peaceful space access, navigation and travel, by improving global SSA. This research is thereby intended only for peaceful space-domain applications and uses, with particular interests in orbital debris. There is little application of ontology to the space domain as compared with other disciplines and little if any ontological development of SSA and related domains. In this respect, this paper offers novel concepts. (shrink)

This paper develops the ontology of space objects for theoretical and computational ontology applied to the space (astronautical/astronomical) domain. It follows “An ontological architecture for Orbital Debris Data” (Rovetto, 2015) and “Preliminaries of a Space Situational Awareness Ontology” (Rovetto, Kelso, 2016). Important considerations for developing a space object ontology, or more broadly, a space domain ontology are presented. The main category term ‘Space Object’ is analyzed from a philosophical perspective. The ontological commitments of legal definitions for artificial space objects are (...) also discussed. Space object taxonomies are offered and space object terms are defined. (shrink)

A short summary paper of my Orbital Space Domain Ontology project (purl.org/space-ontology), originally conceived in 2011. Since then I've sought (without success) opportunities to realize it (either as a PhD or other degree thesis; or in an employment position) toward my original passion of entering the space sector and gaining further space education. Since then persons in the relevant space disciplines have seen the potential in it, and unfortunately some have taken advantage of my ideas yet excluded me from work. (...) I continue to struggle to fight for my own ideas as I see others professionally and financially benefit at my expense. References and documentation are available upon request to confirm my early ideation and origination on this topic. Please contact me if you have opportunities. Thank you. (shrink)

The question is: should the United States and nations at large pursue a human spaceflight program (and if so, why)? I offer an unwavering positive answer to this question, and state the reasons for it while considering the broad challenges and benefits of (human) spaceflight. Space exploration is a human activity that is intrinsically forward-looking, and as such, has positive potential. Both national and international space programs can galvanize the population, inspire the youth, foster job-creation, and motivate the existing workforce. (...) The nature of the enterprises involved—their scale, novelty, and complexity—requires a steady and continuous upward progression toward greater societal, scientific and technological development. That is, in order to overcome the challenges of human spaceflight, progress is required. More to the point, the survival of humanity depends on expanding beyond the confines of our planet. Human spaceflight, in short, presents us with an opportunity to significantly advance the nation and the global community. (shrink)

For applied geologists working in the minerals industry the tasks of problem formulation, observation and data collection, interpretation and modelling invoke various philosophical considerations whether the practitioner is aware of them or not. A primary goal of applied geologists is to build models that accurately predict reality to an acceptable degree. In this paper, we describe the key philosophical frameworks proposed for conducting scientific investigations and relate them to the field of applied geology. We consider the very important differences in (...) the types of problem confronted in experimental sciences compared to the historical sciences, such as geology, where the processes studied are unique and only evidential traces of past events are available. The prediction quality of models is likely to be materially improved if the geologist is firmly and consciously practiced in the scientific method. In addition, if the predictions are framed and presented in terms of the underlying science, the quality of decisions made based on those predictions will likewise be improved. The implications for creating additional value to a project or operation can be very significant when geological models are constructed and used by a practitioner with an understanding of the philosophical basis of the activities constituting a scientific investigation. The method of multiple working hypotheses is particularly important when working in historical sciences. We argue that working within the framework of multiple working hypotheses can provide a valuable insurance against the adoption of, or persistence with, flawed models. (shrink)

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

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)

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)

As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a (...) sound methodological basis would imply that climate simulations which are based on complex climate models had, in stark contrast to their current hegemony, hardly an epistemic role to play in climate scenario analysis at all. Their main function might actually consist in ‘foreseeing future ozone-holes’. In order to argue for these theses, I explain first of all the plurality of climate models used in climate science by the failure to avoid the problem of underdetermination. As a consequence, climate simulation results have to be interpreted as modal sentences, stating what is possibly true of our climate system. This indicates that climate policy decisions are decisions under uncertainty. Two general methodological principles which may guide the construction of the scenario range are formulated and contrasted with each other: modal inductivism and modal falsificationism. I argue that modal inductivism, being the methodology implicitly underlying the third IPCC report, is severely flawed. Modal falsificationism, representing the sound alternative, would in turn require an overhaul of the IPCC practice. (shrink)

Reference models of the earth’s interior play an important role in the acquisition of knowledge about the earth’s interior and the earth as a whole. Such models are used as a sort of standard reference against which data are compared. I argue that the use of reference models merits more attention than it has gotten so far in the literature on models, for it is an example of a method of doing science that has a long and significant history, and (...) a study of reference models could increase our understanding of this methodology. (shrink)

Darcy's law is a phenomenological relationship for fluid flow rate that finds one of its principle applications in hydrology. Theoretical hydrologists rely upon a multiplicity of conceptual models to carry out approximate derivations of Darcy's law. These derivations provide structural explanations of the law; they require the application of fundamental principles, such as conservation of momentum, to idealized models of the porous media within which the flow occurs. In practice, recognition of the idealized conditions incorporated into models facilitates the empirical (...) clarification of the domain within which the law remains accurate. Structural explanations also contribute to the physical interpretation of phenomenological parameters. (shrink)

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

Recent work by Frigg et. al. and Mayo-Wilson have called attention to a particular sort of error associated with attempts to model certain complex systems: structural modeling error. The assessment of the degree of SME in a model presupposes agreement between modelers about the best way to individuate natural systems, an agreement which can be more problematic than it appears. This problem, which we dub “the system individuation problem” arises in many of the same contexts as SME, and the two (...) often compound one another. This paper explores the common roots of the two problems in concerns about the precision of predictions generated by scientific models, and discusses how both concerns bear on the study of complex natural systems, particularly the global climate. (shrink)

Climate change adaptation is largely a local matter, and adaptation planning can benefit from local climate change projections. Such projections are typically generated by accepting climate model outputs in a relatively uncritical way. We argue, based on the IPCC’s treatment of model outputs from the CMIP5 ensemble, that this approach is unwarranted and that subjective expert judgment should play a central role in the provision of local climate change projections intended to support decision-making.

Computation of seismic parameters and its interpretation from the recorded earthquake signal is empowered by digital data acquisition systems. This enables seismologist to automatically compute all the relevant parameters. Futuristic applications require extensive software development to implement seismic prediction and forecasting models. While developing such models, software developer prefer to write their own in-house analysis & modeling software with complete control over the required computations and models. This paper presents simplified mathematical framework of the seismic events and back-end computational software (...) logic & algorithm to provide a simple framework to software engineers develop customized seismic analysis & computation software. (shrink)

Level measurements, thickness measurement or remote surface detection using ultrasonic pulse transit method require that the target surface be at 90 O to the incident beam so that reflected beam comes back at 180-degree angle to effectively use this method. This is perfectly true in case of flat, solid surface at right angle to the incident beam. But surface irregularities of a porous, non-smooth, uneven material such as snow cause penetration of incident wave into the surface, absorption of the incident (...) energy, scatter of energy in many directions and further attenuation of reflected signal making it difficult to detect the reflected echo. Such a received reflected echo is very low in amplitude and is heavily noise-ridden. The successful surface detection with excellent repeatability and accuracy without any false measurement in this case requires combination of physical acoustic, mechanical, hardware and software techniques together. In this paper we discuss the suitable physical, mechanical, electronics design to physically implement the theory of Arrayed Ultrasonic transducers to shape up the directional response, beam width and avoid interference to improve the chances of proper and sufficient reflection from the non-smooth, highly porous, uneven, non-planar irregular surface. (shrink)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...) use models for possibilistic prediction and salvage widespread scientific practice. (shrink)

There are many parts of science in which a certain sort of underdetermination of theory by evidence is known to be common. It is argued that reflection on this fact should serve to shift the burden of proof from scientific anti-realists to scientific realists at a crucial point in the debate between them.

Summary Analogue models are actual physical setups used to model something else. They are especially useful when what we wish to investigate is difficult to observe or experiment upon due to size or distance in space or time: for example, if the thing we wish to investigate is too large, too far away, takes place on a time scale that is too long, does not yet exist or has ceased to exist. The range and variety of analogue models is too (...) extensive to attempt a survey. In this article, I describe and discuss several different analogue model experiments, the results of those model experiments, and the basis for constructing them and interpreting their results. Examples of analogue models for surface waves in lakes, for earthquakes and volcanoes in geophysics, and for black holes in general relativity, are described, with a focus on examining the bases for claims that these analogues are appropriate analogues of what they are used to investigate. A table showing three different kinds of bases for reasoning using analogue models is provided. Finally, it is shown how the examples in this article counter three common misconceptions about the use of analogue models in physics. (shrink)

The state of geoscience education, in terms of numbers of teachers, students taught, and perceived importance, has been lagging behind the other science disciplines for decades. Part of the reason for this is that geology is seen as a “derivative” science as compared to its “experimental” counterparts (for instance, physics and chemistry). However, with current global issues facing the populations of the world (climate change, scarcity of clean water, increasing fossil fuel usage), being geoscience literate is a must. We will (...) show that, in fact, the geological sciences have their own philosophical structure, being both historical and hermeneutic, and it is the structure that makes the teaching of the geosciences for addressing such global issues advantageous. In addition, we will explore the use of historical controversies as a pedagogical tool for geoscience instruction. The history of geology is rife with controversy and the use of such a strategy has been shown to be effective for developing students’ interest in the content, sharpening critical thinking skills, as well as emphasizing the nature of science. This chapter consolidates the knowledge base by describing the structure of the geosciences in terms of its philosophical, theoretical, and cognitive frameworks. It highlights four geoscience controversies in terms of these frameworks, all the while reviewing the literature for the use of HPS in geoscience teaching. Finally it contains recommendations for possible future directions for geoscience education research within this context. (shrink)