# Lightning in a Bottle

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Lightning in a Bottle
Jonathan Lawhead
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences
COLUMBIA UNIVERSITY
2014
2014
To the extent possible under law, Jonathan Lawhead has waived all copyright and related or neighboring rights to Lightning in a Bottle.
No rights reserved.
ABSTRACT
Lightning in a Bottle
Jonathan Lawhead
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.
Table of Contents :
| List of Figures | ii |
| Acknowledgements | iii-iv |
| Dedication | v |
| Prelude - Doing Better | 1-14 |
| Chapter One - Who Are You, and What Are You Doing Here? | 15-50 |
| Chapter Two - What’s the Significance of Complexity? | 51-77 |
| Chapter Three - Dynamical Complexity | 78-101 |
| Chapter Four - A Philosopher’s Introduction to Climate Science | 102-146 |
| Chapter Five - Complexity, Chaos, and Challenges in Modeling Complex Systems | 147-186 |
| Chapter Six - Why Bottle Lightning? | 187-219 |
| Coda - Modeling and Public Policy | 220-232 |
| Works Cited | 233-240 |
LIST OF FIGURES
| Fig. 2.1 | 70 |
| Fig. 2.2 | 71 |
| Fig. 4.1 | 120 |
| Fig. 4.2 | 123 |
| Fig. 5.1 | 167 |
| Fig. 5.2 | 176 |
| Fig. 6.1 | 190 |
ACKNOWLEDGEMENTS
This work is the sum result of the effort of an innumerable list of people, only one of which (me) actually wrote the thing. There are too many contributors to mention, but I’ll highlight a few of the most significant, in order of the product of contribution and temporal appearance.
First, my mother Peggy Polczinski who taught me the value of education and the value of humor, and who has been my biggest supporter and cheerleader since the day I was born. In a very literal sense, I would not be here without her, and she deserves top billing. Second, my stepfather Peter Polczinski, who taught me the value of hard work and perseverance, despite my innate aversion to both those virtues. Long days and pleasant nights to both of you. I get by with a little help from my friends.
Philip Kitcher, who has been a tireless, patient, kind, and inspirational advisor during my time at Columbia. Professor Kitcher published five books (and counting!) while I was his graduate student, and was responsible for suggesting the topic of this work to me one rainy December New York morning on the steps of Low Library on the Columbia University campus. Every aspiring thinker should be so lucky as to have a mentor like you, Philip, and your encouragement, prodding, criticism, and advice have been indispensable and unforgettable. It’s a debt and an honor that I will never be able to repay.
Allegra Pincus who has been along for the last and possibly most bumpy part of this project, and who has been a steadfast partner, friend, companion, and supporter. I love you very much, my dear. Thank you for being in my life.
I’ve always been the kind of person who really only produces anything of value when forced into argument with someone else, so all of the various people who have spoken to and debated with me over the years deserve a mention. Chief among these are two individuals: Daniel Estrada and Quee Nelson. Dan and Quee (who would be horrified at being included in the same exclusive set) have both functioned as steadfast critics and interlocutors over the course of years. They’ve both read early drafts of things I’ve written, offered arguments that challenged my assumptions, and stayed up late into the night debating with me. Dan and Quee, the friendship of both of you has been invaluable, and you’ve both functioned as mentors in your own way. I can’t wait to keep working with you both.
​The members of my dissertation examination committee: David Albert, John Collins, Mark Cane, and Michael Strevens, who pushed me to produced a stronger final draft of this work, and who provided excellent, insightful, and appreciated criticism, deserve special mention. In particular, Mark and John did their best to ensure that I wasn’t totally uninformed about my topics. I owe them a huge debt.
Finally, all of my other family, friends, and colleagues at Columbia University (and elsewhere) who have read my work, spoken with me about my ideas, and influenced me in ways they’ll never know deserve a mention. There are far too many to list, but some of the most significant (still in order of appearance) are: Chelsea Canon (who taught me to sing out and be free), Matt Royal and Jason Euren (who have been better brothers than I could have ever gotten by blood), Adam See (who is one of the kindest people I’ve ever known), Katie MacIntyre (who taught me that maybe Foucault isn’t that bad), Mark Berger (who dogged me about social responsibility), Porter Williams (who could beat me up if he wanted to), Timothy Ignaffo (who has supported this project in ways that defy mentioning here), and Rebecca Spizzirri, (who has suffered living with me and hearing about these ideas for two years). Thanks to all of you, and to those whom I haven’t specifically mentioned; this meatbrain is fallible and weak.
Thank you, everyone.
DEDICATION
To Peggy
(of course)
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