by Sarah Dry
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The search for simplicity is a recurring theme in much scientific effort, not least when facing the confounding complexities of swirling air and water. If the grail of simplicity had a home, it would be a wooden cabin at the tip of Cape Cod, on the campus of the Woods Hole Oceanographic Institution. There, every summer since 1959, a group of scientists have gathered to hash out the simplest ways to describe the motions of fluids on planetary scales. This seminar is called GFD, for geophysical fluid dynamics, and it is an approach to understanding the motions of planetary fluids that has influenced (and been influenced by) much of the science described in this book.7 It is significant that this conceptually reductive approach to earth science has been nurtured in an un-insulated wooden shack into which can fit no more than two dozen researchers crammed in a motley assemblage of folding chairs and surrounded on three sides by chalk boards. The size of the cabin is important because it has constrained the size of the community. There are not too many scientists who focus on geophysical fluid dynamics, compared, say, to the number of people involved in computer modeling or the even larger numbers of field scientists studying the multifarious aspects of climate. Most have attended the GFD summer school, which has been running for fifty-nine years. The walls of the cabin can remain uninsulated because GFD is only a summer school. It exists from June to August each year and goes quiescent, save for a minimum of administrative functions, until the next year. The aim (and result) is that GFD sits between a variety of disciplines—the very disciplines described in this book. Oceanographers, meteorologists, atmospheric physicists, and glaciologists are among those who apply to study or lecture here. They leave having absorbed a particular way of seeing the planet, which they apply in the course of their doctoral work, postdocs, and subsequent careers.
The GFD seminar emerged out of a joint seminar series hosted in the fall of 1956 by WHOI and MIT. On the MIT side, the seminar was attended by Norman Phillips and Jule Charney, both of whom had recently moved to MIT from the Institute for Advanced Study in Princeton, where Charney had been running the numerical weather prediction work that John von Neumann had championed. Ed Lorenz, a meteorologist, also attended. On the WHOI side, seminar attendees included Henry Stommel, Joanne Malkus, her then-husband Willem Malkus, and Fritz Fuglister (who did the early observations on eddies in the Gulf Stream). Carl-Gustaf Rossby, then visiting WHOI, also participated. Thus a high concentration of the mathematically inclined oceanographic and meteorological community (who might be called, for convenience’s sake, theoreticians) spent two hours together every two weeks, alternating between Woods Hole and Cambridge, MA, not including the dinner afterward and the car ride between the venues.
At these seminars, a shared language and shared set of interests in understanding the fluid dynamics of the atmosphere and oceans emerged. So too did the idea for a summer school to train graduate students in this way of thinking. In the fall of 1958, George Veronis, Henry Stommel, and Willem Malkus drafted a proposal for a GFD summer school on the topic of “Theoretical studies in geophysical hydrodynamics.” Both Joanne Malkus and Henry Stommel were early advisors, though Joanne stopped attending after her divorce from Willem Malkus, who remained closely involved. Stommel attended for several years. Both exemplified the ethos of GFD. They sought physical insights into the motions of air and water that could explain the complexities of the world in the simplest terms possible.
The first program consisted of four students and six invited staff, in addition to WHOI members. Rather than teaching a set curriculum, the program consisted largely in seminars describing the work currently being tackled by the staff. Questions were not merely tolerated but encouraged, and the emphasis was not so much on conveying a set body of knowledge but on students and staff together exploring interesting research questions. Stommel and Alan Robinson talked about their recently developed theory of the so-called thermocline, or strata of the ocean in which the temperature dropped dramatically. Joanne Malkus gave a talk on cloud physics. The presiding ethos was of equality. This egalitarian spirit remains as strong as ever as the seminar completes its sixtieth anniversary, with constructive interruptions welcomed at the seminars and a spirit of constant questioning that breaks down barriers between students and faculty.
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The impact of the GFD seminar on shaping our understanding of how oceans, ice, and atmospheres move has been large. But the history of climate science has been just as much a story of increasing complexity as it has been of the simplified visions of the GFDers. As important as the Bretherton diagram was, it has long since been surpassed in importance by another kind of global vision in which the values of both simplicity and complexity can be inscribed. It is this global vision—even more than the glamorous image of our “blue marble” against the inky black of space—which has been responsible for shaping how we think about the earth’s climate. This is the General Circulation Model (GCM), a complex simulation that tries to reproduce the dynamics of the earth system by calculating how a grid of data points respond to a set of physical equations. Like Jorge Luis Borges’s ironically “perfected” art of cartography in which a map of an empire occupies the entirety of an empire, these GCMs aim to cover the globe as completely as possible. Instead of paper, they use imaginary grids whose resolution improves with each rise in computer processing power.8 Time is another factor in climate models. While it would be possible to run a more highly spatially resolved climate model by taking bigger increments of time, scientists have generally used time steps of thirty minutes to run models over a century or more. This translates into 1,753,152 steps at each of the grid points of any given model. For each grid point, a series of what are called model parameters—values for temperature, wind speed, pressure, humidity, and so on—would also need to be calculated. Multiplying these three sets of numbers by each other—the number of time steps, the number of points on a grid, and the number of values for each point—quickly generates an almost unimaginably huge number of calculations. For the most finely resolved GCMs currently in use, the number of calculations needed to run the model for a century taxes even the fastest and most powerful computers in the world. As a rule, doubling the resolution of the model results in ten times more calculations being required.9 Like thirsty behemoths, these models suck up all the available computing power with each advance of Moore’s famous law.
GCMs have had notable success in reproducing certain aspects of the climate system, such as the great ocean and atmospheric currents, the pulsing growth and decay of ice caps, and the distribution of atmospheric carbon dioxide. Other features—particularly those operating on small spatial or temporal scales—are harder to capture, even using the largest computers available. The resolution of these kinds of GCMs is currently some 100 kilometers. Anything smaller—a cloud or a small ocean eddy—gets missed out. (Since clouds are key aspects of the global climate system, scientists have worked hard to find other ways of including them. They do this by parameterizing—by finding mathematical shorthands for summarizing the effects of clouds. These are useful tools and better than ignoring such small-scale features altogether, but they are also limited.) The complexity of these model worlds (and there are dozens of them, to complicate matters even more) is such that climate scientists now worry that they are in danger of forgetting that they are not studying the actual earth but a model version of it. Getting lost in the byways of a GCM, they are at risk of losing sight of the real aim of these models—to understand our own planet.10
There are other climate models that sit at the other end of an imaginary spectrum of models. These simple models seek not so much to simulate climate as to provide a useful medium for exploring it. A good example of one of these is the energy–balance model of the kind Joanne Simpson and Herbert Riehl used to “discover” hot towers. By eliminating as much detail as possible, these models obey an opposite epistemology to the GCMs. Taking away as much as can be taken away to leave the essential features of
the climate system intact offers a powerful kind of vision. This tradition is an old one—stretching back to the work of men such as Croll, Ferrell, and James Thomson. It often has a counterfactual quality, playing around not with the aim of approximating the earth but of imagining alternative earths. Oceanographer John Marshall’s aqua planet models do this well.11 Marshall asks what the earth’s climate would be like if its entire surface were covered with water. Letting this model spin out through 5,000 years, he finds that it eventually falls into a settled climate regime—an ice cap forms on both of the poles. Marshall runs the experiment four times, each time adding a line to represent the simplest possible landmass, which serves to interrupt the flow of water around the planet. With four simple variations, Marshall is able to test the importance of landmass distribution on ocean circulation and climate regime—to better understand whether a planet will experience fixed ice ages, oscillating ice ages, or descend into a permanent snowball state.
In theory, between simple models like these aqua planets and complex GCMs lies a series of intermediate models of increasing complexity. The climate system is so complex, according to those who promote the so-called “hierarchy of models” approach, that we need a system of nested models to understand the many scales on which energy flows through the system. The “answer” to the big questions of climate science, according to this view, lies not in any particular model but in the different understandings that each of these hierarchically arranged models enable.12
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Today, climate scientists are intensely self-conscious about not only their disciplinary but also their epistemic identities—how they know what they know. At conferences which aim for interdisciplinary thinking, it is normal for scientists to preface comments by saying “as a modeler” or “as a theoretician,” and so forth. Such thinking was also present, in a different guise, in the dispute between Tyndall and Forbes over the nature of glacier motion. Walker struggled with the limitations of statistics to generate physical understanding. And so too was it present in the concerns of Stommel and Simpson to find the correct balance between observing the complex phenomena of the ocean and atmosphere and finding ways to describe it using the special pithiness of math and physics. The interplay between people “doing” observing, people “doing” theory, and people “doing” modeling has been a central theme to this book. A self-consciousness of the need for balance (where what counts as balance is itself a moving target), rather than a recipe for precise ratios, is a consistent feature of the 150 years of history covered here.
While it is tempting to assert an increasing mathematization of the earth sciences—along the lines of GFD—it seems more accurate to say instead that the need for iteration between theory, observation, and modeling has intensified, and the cycle has sped up. Theorists need data, as much if not more of it than they ever did. And those who generate data—through observations or through modeling—need theory with which to shape their research focus and even, as Paul Edwards has convincingly explained, to see their data at all.
Historians tend to be jumpy about the risks of something that goes by the name of presentism. The tendency to see the past in light of the present is seen as a Bad Thing—blinding us to the truth of the past by seeing it with our foreign eyes. But presentism is inevitable. We cannot escape from the perspective from which we view the past—that is, right now. Rather than struggling to deny this perspective, we need to face it head-on. In light of the environmental challenges facing the world today, we urgently need to think hard about the relationship between the present and the past. Any fears about how we are blinded by our present prejudices seem increasingly less significant than the risk of depriving ourselves of the best tools we can use for imagining the future.
The uses of history to imagine possible futures can go by many names. The past is sometimes looked to as a source of lessons, or case studies, like the climate analogues sought by paleoclimate scientists or old weather patterns used to make new forecasts. We can learn from equivalent moments in the past and (the implicit suggestion seems to be) avoid making the same mistakes. A somewhat more nuanced approach seeks to use the past not as a cheat sheet for future events but as an exercise in imaginative stretching—a way to prepare us to see differently, to use the difference of the past to help us conceive of the future with more options in mind. Anticipatory history is one term for this, developed by those engaged in thinking about how to manage real things—often heritage sites—whose location in the landscape makes them literally susceptible to imminent change.13 This forces the mind to focus on the issue when it might otherwise skirt it. When it comes to history of climate, the problem and the application are less clear—our scientific practice doesn’t feel under threat by climate change in the same way that our landscape does.
But if we think about this harder, perhaps science is under threat. Not, I think, only from those who seek to undermine its authority to speak, though this threat is real and stubbornly hard to dispel. Instead, climate science may be at risk from a lack of self-consciousness. What climate science seems to need is a vocabulary for making explicit what are usually implicit assumptions about the values that inform it. There are many such values embedded in the doing of science, but the historian in me would like to make a special plea for examining the nature of the histories implicit in our view of climate.14 What, for example, counts as history in climate? Which tools, both conceptual and material, are used to generate such histories? What moments do they render meaningful, and which are ignored or erased? These questions—which we’ve only just begun to ask—are essential to determining what we care about when it comes to climate, and the answers to them will form the basis—whether we realize it or not—of our responses to the changes we face.
The historical senses that are embedded in climate help determine what counts as normal when it comes to climate. Determining what is a natural climate is a key focus within climate science and policy today. As we learn more about how the climate has changed in the past and consider how it might change in the future, we rely on assumptions about what a “good” or “natural” climate might be. These assumptions have so far been defined by those who study the earth’s past. These are not historians but scientists who look to past records of climate change to determine both what we can expect and what we should be happy to accept. How much change is acceptable may be a partly scientific question, but it is not only that. What counts as acceptable changes depends signally on where you draw your frame.15 The past 12,000 years of history, called the Holocene, have been, in the context of the preceding millions, both unusually stable and unusually warm. This happens to be the period in which human beings evolved. Do we have a responsibility to maintain this particular climate? There are even more versions of normal if we broaden our population of those who have the right to determine the answer to this question beyond climate scientists alone.
Stating that there are many different kinds of knowledge would seem to be a recipe only for dispute of the partisan kind that has caused a crisis of trust, or in some cases an outright rejection of the values of science. Science, in this point of view, is under threat and must be saved by its own methods—by proving with evidence that it “works,” which is to say that it can make meaningful predictions. In another sense, understanding the many-strandedness of science can offer a way toward understanding something even more fundamental—the limitations of science. To acknowledge the limits of science need not be an exercise in abnegation. It can open up new ways forward. Identifying the presence and necessity of various values within science, such as the importance of interestedness, commitment, emotional connection, and self-determination, provides a better understanding of what science is. This clears the way to recognizing that the decisions which we make as a society about how we live on the planet can be informed by scientific values without being determined by them. Our choices about how we use energy, how we dispose of our goods, how we live with and in the landscap
e, have always been about so much more than, for example, our understanding of the ice ages, or our ability to predict the weather.
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The nature of the relationship of climate and history has become both a key political issue and an unresolved scientific question. The earth is now self-evidently a planet of change. The past is now always a resource for the future. Scientists now seek to understand the dynamics of the climate in the past, based on paleo records, in order to better understand how it might change in the future. Politically, the question of what futures are available to us—of what futures we imagine—is also partly constrained by this scientific understanding of climate dynamics in the past and future.
The dance goes on. The Bretherton diagram, influential as it once was, now looks dated and clunky. From a focus on understanding the components of the earth “system,” as the NASA engineers considered it, a new way of looking at the planet emphasizes not the boxes, so to speak, but the arrows between them.16 Feedback loops and their associated tipping points have come to the fore. The artificiality of distinctions between elements of the system have given way to a new sense that there can be no substantive distinctions between aspects of the whole. It is all connected in such deep and complex ways that only by studying the connections can any sense of it be made. Some may argue that a study of connections implies also a study of discrete elements, but the emphasis seems to have shifted. The “essential oneness” which Victor Starr drew attention to some sixty years ago is a recurrent theme, but one which, it seems, every generation must arrive at independently.
The scientists whose work I’ve described in this book were each, in their own ways, playful in their approaches to knowledge—they treated the planet as an arena for exploration. Walker’s throwing of the boomerang is the most literal form of play described here, and all the more striking against the austere backdrop of his mathematical intensity. Tyndall was playful, too, in ways that were always testing—his own appetite for risk, the forbearance of his peers with his tendency to dispute, the delicacy of his apparatus as he asked it to answer increasingly difficult questions. Piazzi Smyth played with authority—his own, his instruments’, and what we might now call the “truthiness” of images. In seeking knowledge in the most evanescent of phenomena, he played with his own desire for understanding, challenging himself ultimately to accept on faith what he could not prove. Joanne Simpson played in the arena of the sky, using whatever tools she needed—from airplanes to hand-calculated models to her own cloud photographs—to get to the physics she sought. Henry Stommel was playful in his making and his thinking, tinkering with things as he tinkered with ideas, using his mind as a telephoto lens that zoomed in and out, and across the oceanic landscape, seeking problems he thought worth thinking about. For Willi Dansgaard, the icy landscape of Greenland hid a frozen past across which his mind could wander at will, thanks to his one “really good idea.”
