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Lightning in a Bottle

机译:瓶中的闪电

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摘要

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.
机译:气候学是复杂系统科学的范式。了解全球气候涉及解决物理,化学,经济学和许多其他学科的问题。我认为像全球气候这样的复杂系统具有某些动态特征,这些动态特征解释了这些系统如何随时间变化。复杂系统的动力学是由在许多不同的时空尺度上运行的许多不同组件的相互作用所决定的。研究气候学的多学科和整体方法可以帮助我们更好地了解复杂系统的本质。围绕气候科学的问题可以分为三大类:基础性,方法性和评估性问题。 “我们怎么知道我们可以相信科学?”是一个范式的基础问题(也是一个非常难以回答的问题)。因为全球气候是如此复杂,所以诸如“什么使系统变得复杂?”之类的问题出现了。也属于这一类。现有许多关于“复杂性”的定义,尽管它们都涵盖了使直观复杂的系统与众不同的某些方面,但没有一个是完全令人满意的。已经开发出大多数现有的复杂性说明,以用于信息理论对象(例如信号),而不是用于科学家研究的物理和社会系统。论文的前三分之一详细阐述了动态复杂性这一概念,旨在填补当代复杂性理论数学之间的空白(特别是由Gell-Mann和Lloyd [2003]开发的“有效复杂性”形式主义)。以及对科学结构的更一般的解释。动态复杂性为数学复杂性理论的形式工具提供了物理解释,因此可以用作思考科学哲学中的一般问题(包括理论,解释和法律)的框架。方法论问题包括以下问题:气候科学如何构建模型,我们在何种基础上信任这些模型以及如何改进这些模型。为了回答有关气候模型的问题,重要的是要了解气候模型的外观以及其构建方式。与大多数其他科学(甚至是研究其他复杂系统的科学)的模型族相比,气候模型族的多样性要大得多。现有的气候模型包括可以在纸上解决的基本模型,以及只能使用世界上最先进的超级计算机进行分析的极其复杂的模型。通过演示如何构建最基本的气候模型之一,我介绍了一些气候学的中心概念。我首先假设地球是一个简单的无特征的黑体,它从太阳吸收能量并将其释放到太空中,并展示如何正式地对该假设建模。然后,我逐渐将其他因素(例如反照率和温室效应)添加到模型中,并展示每次添加如何使模型的预测与观察更接近。构建此基本模型后,我将描述其余气候模型的所谓“复杂性层次结构”,并指出气候建模社区中使用的“复杂性”感与动态复杂性有关。在清楚地掌握了气候建模的基础知识之后,我认为,本文前面讨论的基础问题表明,计算在气候建模中起着不可撤销的核心作用。考虑到全球气候的复杂性,“模拟科学”是必不可少的,但是气候系统的特性(非线性,反馈回路和混沌动力学的存在)对计算模型的有效性提出了原则性的限制。这种紧张关系是气候模型层次结构错综复杂的多元性的根源,表明这种多元性将继续存在,而不是我们无知的产物。与其试图收敛于单一的“最佳拟合”气候模型,不如让我们接受气候模型的多样性,并将每种模型视为旨在预测和解释范围很窄的现象的专门工具。要从整体上了解气候系统,需要检查许多不同的模型,并关联其输出。这是气候学最重大的方法论挑战。气候学的作用当代政治话语对物理科学提出了异常大量的评估问题。围绕气候变化制定政策的两种主要方法集中在缓解(即阻止变化的发生)和适应(进行事后变化以减轻这些变化所造成的危害)上。然而,针对人为气候变化的威胁制定有效的社会政治应对措施,要求我们整合多种观点和价值观:正确的应对措施将与气候模型本身一样多样化和多元,并将两种方法的各个方面都纳入其中。最后,我就如何将这种价值多元化纳入我们的社会政治决策框架提出了一些具体建议。

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    Lawhead Jonathan James;

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  • 年度 2014
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