General Circulation Models of Climate
This essay is partly based on an essay by Paul Edwards (University of Michigan)(1) rewritten and expanded by Spencer Weart. For additional information and references on this topic, see Edwards' site.
The climate system is too complex for the human brain to grasp with simple insight. No scientist managed to devise a page of equations that explained the global atmosphere's operations. With the coming of digital computers in the 1950s, a small American team set out to model the atmosphere as an array of thousands of numbers. The work spread during the 1960s as computer modelers began to make decent short-range predictions of regional weather. Modeling long-term climate change for the entire planet, however, was held back by lack of computer power, ignorance of key processes such as cloud formation, inability to calculate the crucial ocean circulation, and insufficient data on the world's actual climate. By the mid 1970s, enough had been done to overcome these deficiencies so that Syukuro Manabe could make a quite convincing calculation. He reported that the Earth's average temperature should rise a few degrees if the level of carbon dioxide gas in the atmosphere doubled. This was confirmed in the following decade by increasingly realistic models. Skeptics dismissed them all, pointing to dubious technical features and the failure of models to match some kinds of data. By the late 1990s these problems were largely resolved, and most experts found the predictions of overall global warming plausible. Yet modelers could not be sure that the real climate, with features their equations still failed to represent, would not produce some big surprise. (The history of rudimentary physical models without extensive calculations is told in a separate essay on Simple Models of Climate, and there is a supplementary essay for the Basic Radiation Calculations that became part of the technical foundation of comprehensive calculations. For a brief technical introduction to current climate modeling see Schmidt, Physics World, Feb. 2007).
"Dr. Richardson said that the atmosphere resembled London for in both there were always far more things going on than anyone could properly attend to."(2)
| Subsections: Numerical Weather Prediction (1945-1965), Credible Climate Prediction (1965-1979), Ocean Circulation and Real Climates (1969-1988), After 1988 |
| Climate is made by the general circulation of the atmosphere — the global pattern of air movements, with its semi-tropical trade winds, its air masses rising in the tropics to descend farther north, its cyclonic storms that carry energy and moisture through middle latitudes, and so forth. Many meteorologists suspected that shifts in this pattern were a main cause of climate change. They could only guess about such shifts, for the general circulation was poorly mapped before the 1940s (even the jet streams remained to be discovered). The Second World War and its aftermath brought a phenomenal increase in observations from ground level up to the stratosphere, which finally revealed all the main features. Yet up to the 1960s, the general circulation was still only crudely known, and this knowledge was strictly observational. | - LINKS - |
| From the 19th century forward, many scientists had attempted to explain the general pattern by applying the laws of the physics of gases to a heated, rotating planet. All their ingenious efforts failed to derive a realistic mathematical solution. The best mathematical physicists could only offer simple arguments for the character of the circulation, arguments which might seem plausible but in fact were mere hand-waving.(3) And with the general global circulation not explained, attempts to explain climate change in terms of shifts of the pattern were less science than story-telling. | Full discussion in
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| The solution would come by taking the problem from the other end. Instead of starting with grand equations for the planet as a whole, one might seek to find how the circulation pattern was built up from the local weather at thousands of points. But the physics of local weather was also a formidable problem. | |
| Early in the 20th century a Norwegian meteorologist, Vilhelm Bjerknes, argued that weather forecasts could be calculated from the basic physics of the atmosphere. He developed a set of seven "primitive equations" describing the behavior of heat, air motion, and moisture. The solution of the set of equations would, in principle, describe and predict large-scale atmospheric motions. Bjerknes proposed a "graphical calculus," based on weather maps, for solving the equations. His methods were used and developed until the 1950s, but the slow speed of the graphical calculation methods sharply limited their success in forecasting. Besides, there were not enough accurate observational data to begin with.(4) | |
| In 1922, the British mathematician and physicist Lewis Fry Richardson published a more complete numerical system for weather prediction. His idea was to divide up a territory into a grid of cells, each with its own set of numbers describing its air pressure, temperature, and the like, as measured at a given hour. He would then solve the equations that told how air behaved (using a method that mathematicians called finite difference solutions of differential equations). He could calculate wind speed and direction, for example, from the difference in pressure between two adjacent cells. These techniques were basically what computer modelers would eventually employ. Richardson used simplified versions of Bjerknes's "primitive equations," reducing the necessary arithmetic computations to a level where working out solutions by hand seemed feasible. Even so, "the scheme is complicated," he admitted, "because the atmosphere itself is complicated." | |
| The number of required computations was so great that Richardson scarcely hoped his idea could lead to practical weather forecasting. Even if someone assembled a "forecast-factory" employing tens of thousands of clerks with mechanical calculators, he doubted they would be able to compute weather faster than it actually happens. But if he could make a model of a typical weather pattern, it could show meteorologists how the weather worked. | |
| So Richardson attempted to compute how the weather over Western Europe had developed during a single eight-hour period, starting with the data for a day when scientists had coordinated balloon-launchings to measure the atmosphere simultaneously at various levels. The effort cost him six weeks of pencil-work Perhaps never has such a large and significant set of calculations been carried out under more arduous conditions: a convinced pacifist, Richardson had volunteered to serve as an ambulance-driver on the Western Front. He did his arithmetic as a relief from the surroundings of battle chaos and dreadful wounds. The work ended in complete failure. At the center of Richardson's simulacrum of Europe, the computed barometric pressure climbed far above anything ever observed in the real world. "Perhaps some day in the dim future it will be possible to advance the calculations faster than the weather advances," he wrote wistfully. "But that is a dream." Taking the warning to heart, meteorologists gave up any hope of numerical modeling.(5) |
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| Numerical Weather Prediction (1945-1965) TOP OF PAGE | |
| The alternative to the failed numerical approach was to keep trying to find a solution in terms of mathematical functions — a few pages of equations that an expert might comprehend as easily as a musician reads music. Through the 1950s, some leading meteorologists tried a variety of such approaches, working with simplified forms of the physics equations that described the entire global atmosphere. They managed to get mathematical models that reproduced some features of atmospheric layers, but they were never able to convincingly show the features of the general circulation — not even something as simple and important as the trade winds. The proposed solutions had instabilities. They left out eddies and other features that evidently played crucial roles. In short, the real atmosphere was too complex to pin down in a few hundred lines of mathematics. "There is very little hope," the leading climatologist Bert Bolin declared as early as 1952, "for the possibility of deducing a theory for the general circulation of the atmosphere from the complete hydrodynamic and thermodynamic equations."(6) |
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| That threw people back on Richardson's program of numerical computation. What had been hopeless with pencil and paper might possibly be made to work with the new digital computers. A handful of extraordinary machines, feverishly developed during the Second World War to break enemy codes and to calculate atomic bomb explosions, were leaping ahead in power as the Cold War demanded ever more calculations. In the lead, energetically devising ways to simulate nuclear weapons explosions, was the Princeton mathematician John von Neumann. Von Neumann saw parallels between his explosion simulations and weather prediction (both are problems of non-linear fluid dynamics). In 1946, soon after his pioneering computer ENIAC became operational, he began to advocate using computers for numerical weather prediction.(7) |
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| This was a subject of keen interest to everyone, but particularly to the military services, who well knew how battles could turn on the weather. Von Neumann, as a committed foe of Communism and a key member of the American national security establishment, was also concerned about the prospect of "climatological warfare." It seemed likely that the U.S. or the Soviet Union could learn to manipulate weather so as to harm their enemies. | |
| Under grants from the Weather Bureau, the Navy, and the Air Force, von Neumann assembled a small group of theoretical meteorologists at Princeton's Institute for Advanced Study. (Initially the group was at the Army's Aberdeen Proving Grounds, and later it also got support from the U.S. Atomic Energy Commission.) If regional weather prediction proved feasible, the group planned to move on to the extremely ambitious problem of modeling the entire global atmosphere. Von Neumann invited Jule Charney, an energetic and visionary meteorologist, to head the new Meteorology Group. Charney came from Carl-Gustaf Rossby's pioneering meteorology department at the University of Chicago, where the study of weather maps and fluids had developed a toolkit of sophisticated mathematical techniques and an intuitive grasp of basic weather processes. |
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| Richardson's equations were the necessary starting-point, but Charney had to simplify them if he hoped to run large-scale calculations in weeks rather than centuries. Solutions for the atmosphere equations were only too complete. They even included sound waves (random pressure oscillations, amplified through the computations, were a main reason Richardson's heoric attempt had failed). Charney explained that it would be necessary to "filter out" these unwanted solutions, as one might use an electronic filter to remove noise from a signal, but mathematically. |
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| Charney began with a set of simplified equations that described the flow of air along a narrow band of latitude. By 1949, his group had results that looked fairly realistic — sets of numbers that you could almost mistake for real weather diagrams, if you didn't look too closely. In one characteristic experiment, they modeled the effects of a large mountain range on the air flow across a continent. Modeling was taking the first steps toward the computer games that would come a generation later, in which the player acts as a god: raise up a mountain range and see what happens! Soon the group proceeded to fully three-dimensional models for a region.(8) | <=Radiation math |
| All this was based on a few equations that could be written on one sheet of paper. It would be decades before people began to argue that modelers were creating an entirely new kind of science; to Charney, it was just an extension of normal theoretical analysis. "By reducing the mathematical difficulties involved in carrying a train of physical thought to its logical conclusion," he wrote, "the machine will give a greater scope to the making and testing of physical hypotheses." Yet in fact he was not using the computer just as a sort of giant calculator representing equations. With hindsight we can see that computer models conveyed insights in a way that could not come from physics theory, nor a laboratory setup, nor the data on a weather map, but in an altogether new way.(9) | |
| The big challenge was still what it had been in the traditional style of physics theory: to combine and simplify equations until you got formulas that gave sensible results with a limited amount of computation. To be sure, the new equipment could handle an unprecedented volume of computations. However, the most famous computers of the 1940s and 1950s were dead slow by comparison with a simple laptop computer of later years. Moreover, a team had to spend a good part of its time just fixing the frequent breakdowns. A clever system of computation could be as helpful as a computer that ran five times faster. Developing usable combinations and approximations of meteorological variables took countless hours of work, and a rare combination of mathematical ingenuity and physical insight. And that was only the beginning. | |
| To know when you were getting close to a realistic model, you had to compare your results with the actual atmosphere. To do that you would need an unprecedented number of measurements of temperature, moisture, wind speed, and so forth for a large region — indeed for the whole planet, if you wanted to check a global model. During the war and after, networks had been established to send up thousands of balloons that radioed back measurements of the upper air. This was largely to meet military needs, and later to help civilian aviation. For the first time the atmosphere was seen not as a single layer, as represented by a surface map, but in its full three dimensions. By the 1950s, the weather over continental areas, up to the lower stratosphere, was being mapped well enough for comparison with results from rudimentary models.(10) | |
| The first serious weather simulation that Charney's team completed was two-dimensional. They ran it on the ENIAC in 1950. Their model, like Richardson's, divided the atmosphere into a grid of cells; it covered North America with 270 points about 700 km apart. Starting with real weather data for a particular day, the computer solved all the equations for how the air should respond to the differences in conditions between each pair of adjacent cells. Taking the outcome as a new set of weather data, it stepped forward in time (using a step of three hours) and computed all the cells again. The authors remarked that between each run it took them so long to print and sort punched cards that "the calculation time for a 24-hour forecast was about 24 hours, that is, we were just able to keep pace with the weather." The resulting forecasts were far from perfect, but they turned up enough features of what the weather had actually done on the chosen day to justify pushing forward.(11) | |
| The Weather Bureau and units of the armed forces established a Joint Numerical Weather Prediction Unit, which in May 1955 began issuing real-time forecasts in advance of the weather.(12) They were not the first: since December 1954 a meteorology group at the University of Stockholm had been delivering forecasts to the Royal Swedish Air Force Weather Service, sometimes boasting better accuracy than traditional methods.(13) At their best, these models could give fairly good forecasts up to three days ahead. Yet with the limited computing power available, they had to use simplifying assumptions, not the full "primitive equations" of Bjerknes and Richardson. Even with far faster computers, the teams would have been limited by their ignorance about many features of weather, such as how clouds are formed. It would be well over a decade before the accuracy of computer forecasts began to reliably outstrip the subjective guesswork of experienced human forecasters.(14) | |
| These early forecasting models were regional, not global in scale. Calculations for numerical weather prediction were limited to what could be managed in a few hours by the rudimentary digital computers — banks of thousands of glowing vacuum tubes that frequently burned out, connected by spaghetti-tangles of wiring. Real-time weather forecasting was also limited by the fact that a computation had to start off with data that described the actual weather at a given hour at every point in a broad region. That was always far from perfect, for the instruments that measured weather were often far apart and none too reliable. Besides, the weather had already changed by the time you could bring the data together and convert it to a digital form that the computers could chew on. It was not for practical reasons that meteorologists wanted to push on to model the entire general circulation of the global atmosphere. Their interest was theoretical, a hope of understanding at last how the climate system worked — why there are deserts in some places, wet zones in others, and so forth. | |
| That was a fundamentally different type of problem from forecasting. Weather prediction is what physicists and mathematicians call an "initial value" problem, where you start with the particular set of conditions found at one moment and compute how the system evolves, getting less and less accurate results as you push forward in time. Calculating the climate is a "boundary value" problem, where you define a set of unchanging conditions, the physics of air and sunlight and the geography of mountains and oceans, and compute the unchanging average of the weather that these conditions determine. (To see how climate might change, modelers would eventually have to combine these two approaches, but that would have to wait until they could compute something resembling the present climate.) That computation, the general circulation model, became a holy grail for theoretical meteorologists. | |
| Norman Phillips in Princeton took up the challenge. He was encouraged by "dishpan" experiments carried out in Chicago, where patterns resembling weather had been modeled in a rotating pan of water that was heated at the edge. For Phillips this proved that "at least the gross features of the general circulation of the atmosphere can be predicted without having to specify the heating and cooling in great detail." If such an elementary laboratory system could model a hemisphere of the atmosphere, shouldn't a computer be able to do as well? To be sure, the computer at Phillips's disposal was as primitive as the dishpan (its RAM held all of five kilobytes of memory and its magnetic drum storage unit held ten). So his model had to be extremely simple. By mid-1955 Phillips had developed improved equations for a two-layer atmosphere. To avoid mathematical complexities, his grid covered not a hemisphere but a cylinder, 17 cells high and 16 in circumference. He drove circulation by putting heat into the lower half, somewhat like the dishpan experimenters only with numbers rather than an electrical coil. The calculations turned out a plausible jet stream and the evolution of a realistic-looking weather disturbance over as long as a month. |
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| This settled an old controversy over what processes built the pattern of circulation. For the first time scientists could see, among other things, how giant eddies spinning through the atmosphere played a key role in moving energy and momentum from place to place. Phillips's model was quickly hailed as a "classic experiment" — the first true General Circulation Model (GCM).(15) | |
| Von Neumann immediately called a conference to publicize Phillips's triumph, drumming up government funding for a long-term project. The effort got underway that same year, 1955, under the direction of Joseph Smagorinsky at the U.S. Weather Bureau near Washington, DC. Smagorinsky's goal was the one first envisaged by von Neumann and Charney: a general circulation model of the entire three-dimensional global atmosphere built directly from the primitive equations.(16) In 1958, Smagorinsky invited Syukuro ("Suki") Manabe to join the lab. Manabe was one of a group of young men who had studied physics at Tokyo University in the difficult years following the end of the Second World War. These ambitious and independent-minded students had few opportunities for advancement in Japan, and several wound up as meteorologists in the United States. With Smagorinsky and others, Manabe built one of the most vigorous and long-lasting GCM development programs in the world. |
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| Smagorinsky and Manabe put into their model the way rain fell on the surface and evaporated, they put in how radiation passing through the atmosphere was impeded not only by water vapor but also by ozone and carbon dioxide gas (CO2), they put in how the air exchanged water and heat with simplified ocean, land, and ice surfaces, and much more. Manabe spent many hours in the library studying such esoteric topics as how various types of soil absorbed water. The huge complexities of the modeling required contributions from several others. "This venture has demonstrated to me," Smagorinsky wrote, "the value if not the necessity of a diverse, imaginative, and dedicated working group in large research undertakings." | |
| By 1965 they had a reasonably complete three-dimensional model that solved the basic equations for an atmosphere divided into nine levels. This was still highly simplified, with no geography — land and ocean were blended into a single damp surface, which exchanged moisture with the air but could not take up heat. Nevertheless, the way the model moved water vapor around the planet looked gratifyingly realistic. The printouts showed a stratosphere, a zone of rising air near the equator (creating the doldrums, a windless zone that becalmed sailors), a subtropical band of deserts, and so forth. Many details came out wrong, however.(17) | |
| From the early 1960s on, modeling work interacted crucially with fields of geophysics such as hydrology (soil moisture and runoff), glaciology (ice sheet formation and flow), meteorological physics (cloud formation and precipitation, exchanges between winds and waves, and so forth). Studies of local small-scale phenomena — often stimulated by the needs of modelers — provided basic parameters for GCM's. Those developments are not covered in these essays. | <=External input |
| In the late 1950s, as computer power grew and the need for simplifying assumptions diminished, other scientists around the world began to experiment with fully three-dimensional, many-leveled models based on the primitive equations of Bjerknes and Richardson. An outstanding case was the work of Yale Mintz in the Department of Meteorology of the University of California, Los Angeles. Already in the early 1950s Mintz had been trying to use the temperamental new computers to understand the circulation of air — "heroic efforts" (as a student recalled) "during which he orchestrated an army of student helpers and amateur programmers to feed a prodigious amount of data through paper tape to SWAC, the earliest computer on campus."(18) Phillips’s pioneering 1956 paper convinced Mintz that numerical models would be central to progress in meteorology. He embarked on an ambitious program (far too ambitious for one junior professor, grumbled some of his colleagues). Unlike Smagorinsky's team, Mintz sometimes had to scramble to get access to enough computer time.(19) But like Smagorinsky, Mintz had the rare vision and drive necessary to commit himself to a research program that must take decades to reach its goals. And like Smagorinsky, Mintz recruited a young Tokyo University graduate, Akio Arakawa, to help design the mathematical schemes for a general circulation model. In the first of a number of significant contributions, Arakawa devised a novel and powerful way to represent the flow of air on a broad scale without requiring an impossibly large number of computations. |
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| A supplementary essay on Arakawa's Computation Device describes his scheme for computing fluid flow, a good example of how modelers developed important (but sometimes controversial) techniques. | |
| From 1961 on, Mintz and Arakawa worked away at their problem, constructing a series of increasingly sophisticated GCMs. By 1964 they had produced a climate computed for an entire globe, with only a two-layer atmosphere but including realistic geography — the topography of mountain ranges was there, and a rudimentary treatment of oceans and ice cover. Although the results missed some features of the real world's climate, the basic wind patterns and other features came out more or less right.(20*) The model, packed with useful techniques, had a powerful influence on other groups. Arakawa was becoming especially interested in a problem that was emerging as a main barrier to progress — accounting for the effects of cumulus clouds. The smallest single cell in a global model that a computer can handle, even today, is far larger than an individual cloud. Thus the computer calculates none of the cloud's details. Models have had to get by with a "parameterization," a set of numbers (parameters) that represent the net behavior of all the clouds in a cell under given conditions. Through the decades, Arakawa and others would spend countless hours developing and exchanging ways to attack this critical problem.(21) |
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| Modeling techniques and entire GCMs spread by a variety of means. In the early days, as Phillips recalled, modelers had been like "a secret code society." The machine-language computer programs were "an esoteric art which would be passed on in an apprentice system."(22) Over the years, programming languages became more transparent and codes were increasingly well documented. Yet there were so many subtleties that a real grasp still required an apprenticeship on a working model. Commonly, a new modeling group began with some version of another group's model. A post-doctoral student might take a job at another institution, bringing along his old team's computer code. The new team he assembled would start off working with the old code and then set to modifying it. Others built new models from scratch. Through the 1960s and 1970s, important GCM groups appeared at institutions from New York to Australia. |
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| Americans dominated the field during the first postwar decades. That was assured by the government funding that flowed into almost anything related to geophysics, computers, and other subjects likely to help in the Cold War. The premier group was Smagorinsky's Weather Bureau unit (renamed the Geophysical Fluid Dynamics Laboratory in 1963), with Manabe's groundbreaking models. In 1968, the group moved from the Washington, DC area to Princeton, and it eventually came under the wing of the U.S. National Oceanic and Atmospheric Administration. Almost equally influential were the Mintz-Arakawa group at UCLA. Another major effort got underway in 1964 at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado under Warren Washington and yet another Tokyo University graduate, Akira Kasahara. The framework of their first model was quite similar to Richardson’s pioneering attempt, but without the instability that had struck him down, and incorporating additional features such as the transfer of radiation up and down through the atmosphere — or rather between the two vertical layers that was all their computer could handle. Less visible was a group at RAND Corporation, a defense think-tank in Santa Monica, California. Their studies, based on the Mintz-Arakawa model, were driven by the Department of Defense's concern about possibilities for deliberately changing a region's climate. Although the RAND results were published only in secret "gray" reports, the work produced useful techniques that became known to other modelers.(23) —For a list and a "family tree" of models, see Paul Edwards's GCM Web site. |
<=Government |
| Credible Climate Prediction (1965-1979) TOP OF PAGE | |
| Although the modelers of the 1950s and early 1960s got results good enough to encourage them to persevere, they were still a long way from reproducing the details of the Earth's actual circulation patterns and climate zones. In 1965, a blue-ribbon panel of the U.S. National Academy of Sciences reported on where GCMs stood that year. The panel reported that the best models (like Mintz-Arakawa and Smagorinsky-Manabe) calculated simulated atmospheres with gross features "that have some resemblance to observation." There was still much room for improvement in converting equations into systems that a computer could work through within a few weeks. To do much better, the panel concluded, modelers would need computers that were ten or even a hundred times more powerful.(24) | |
| Yet even if the computers had been vastly faster, the simulations would still have been unreliable. For they were running up against that famous limitation of computers, "garbage in, garbage out." Some sources of error were known but hard to drive out, such as getting the right parameters for factors like convection in clouds. To diagnose the failings that kept GCMs from being more realistic, scientists needed an intensified effort to collect and analyze aerological data — the actual profiles of wind, heat, moisture, and so forth, at every level of the atmosphere and all around the globe. The data in hand were still deeply insufficient. Continent-scale weather patterns had been systematically recorded only for the Northern Hemisphere's temperate and arctic regions and only since the 1940s. Through the 1960s, the actual state of the entire general circulation remained unclear. For example, the leisurely vertical movements of air had not been measured at all, so the large-scale circulation could only be inferred from the horizontal winds. As for the atmosphere's crucial water and energy balances, one expert estimated that the commonly used numbers might be off by as much as 50%.(25) Smagorinsky put the problem succinctly in 1969: "We are now getting to the point where the dispersion of simulation results is comparable to the uncertainty of establishing the actual atmospheric structure."(26) |
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| In the absence of a good match between atmospheric data and GCM calculations, many researchers continued through the 1960s to experiment with simple models for climate change. A few equations and some hand-waving gave a variety of fairly plausible descriptions for how one or another factor might cause an ice age or global warming. There was no way to tell which of these models was correct, if any. As for the present circulation of the atmosphere, some continued to work on pencil-and-paper mathematical models that would represent the planet's shell of air with a few fundamental physics equations, seeking an analytic solution that would bypass the innumerable mindless computer operations. They made little headway. In 1967, Edward Lorenz, an MIT professor of meteorology, cautioned that "even the trade winds and the prevailing westerlies at sea level are not completely explained." Another expert more bluntly described where things stood for an explanation of the general circulation: "none exists." Lorenz and a few others began to suspect that the problem was not merely difficult, but impossible in principle. Climate was apparently not a well-defined system, but only an average of the ever-changing jumble of daily thunderstorms and storm fronts.(27) |
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| Would computer modelers ever be able to say they had "explained" the general circulation? Many scientists looked askance at the new method of numerical simulation as it crept into more and more fields of research. This was not theory, and it was not observation either; it was off in some odd new country of its own. People were attacking many kinds of scientific problems by taking a set of basic equations, running them through hundreds of thousands of computations, and publishing a result that claimed to reflect reality. Their results, however, were simply stacks of printout with rows of numbers. That was no "explanation" in the traditional sense of a model in words or diagrams or equations, something you could write down on a few pages, something your brain could grasp intuitively as a whole. The numerical approach "yields little insight," Lorenz complained. "The computed numbers are not only processed like data but they look like data, and a study of them may be no more enlightening than a study of real meteorological observations."(28) | |
| Yet the computer scientist could "experiment" in a sense, by varying the parameters and features of a numerical model. You couldn't put a planet on a laboratory bench and vary the sunlight or the way clouds were formed, but wasn't playing with computer models functionally equivalent? In this fashion you could make a sort of "observation" of almost anything, such as the effect of doubling the level of greenhouse gases. Through many such trials you might eventually come to understand how the real world operated. Indeed you might be able to observe the planet more clearly in graphs printed out from a model than in the clutter of real-world observations, so woefully inaccurate and incomplete. As one scientist put it, "in many instances large-scale features predicted by these models are beyond our intuition or our capability to measure in the real atmosphere and oceans."(29) | |
| Sophisticated computer models were gradually displacing the traditional hand-waving models where each scientist championed some particular single "cause" of climate change. Such models had failed to come anywhere near to explaining even the simplest features of the Earth's climate, let alone predicting how it might change. A new viewpoint was spreading along with digital computing. Climate was not changed by any single cause, the modelers said, but was the outcome of a staggeringly intricate complex of interactions, which could only be comprehended in the working-through of the numbers themselves. |
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| GCMs were not the only way to approach this problem. Scientists were developing a rich variety of computer models, for there were many ways to slice up the total number of arithmetic operations that a computer could run through in whatever time you could afford to pay for. You could divide up the geography into numerous cells, each with numerous layers of atmosphere; you could divide up the time into many small steps, and work out the dynamics of air masses in a refined way; you could make complex calculations of the transfer of radiation through the air; you could construct detailed models for surface effects such as evaporation and snow cover... but you could not do all these at once. Different models intended for different purposes made different trade-offs. | |
| One example was the work of Julian Adem in Mexico City, who sought a practical way to predict climate anomalies a few months ahead. He built a model that had low geographical resolution but incorporated a large number of land and ocean processes. John Green in London pursued a wholly different line of attack, aimed at shorter-term weather prediction. His analysis concentrated on the actions of large eddies in the atmosphere and was confined to idealized mathematical equations. It proved useful to computer modelers who had to devise numerical approximations for the effects of the eddies. Other groups chose to model the atmosphere in one or two dimensions rather than all three.(30) The decisions such people made in choosing an approach involved more than computer time. They also had to allocate another commodity in short supply — the time they could spend thinking. This essay does not cover the entire range of models, but concentrates on those which contributed most directly to greenhouse effect studies. For models in one or two dimensions, see the article on Basic Radiation Calculations. |
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| None of the concepts of the 1960s inspired confidence. The modelers were missing some essential physics, and their computers were too slow to perform the millions of computations needed for a satisfactory solution. But as one scientist explained, where the physics was lacking, computers could do schematic "numerical experiments" directed toward revealing it.(31) By the time modelers got their equations and parameters right, surely not many years off, the computers would have grown faster by another order of magnitude or so and would be able to handle the necessary computations. In 1970, a report on environmental problems by a panel of top experts declared that work on computer models was "indispensable" for the study of climate change.(32*) |
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| The growing community of climate modelers was strengthened by the progress of computer systems that carried out detailed calculations on short timescales for weather prediction. This progress required much work on parameterization — schemes for representing cloud formation, interactions between waves and winds, and so forth. Such studies accelerated as the 1970s began.(33) The weather forecasting models also required data on conditions at every level of the atmosphere at thousands of points around the world. Such observations were now being provided by the balloons and sounding rockets of an international World Weather Watch, founded in the mid 1960s. The volume of data was so great that computers had to be pressed into service to compile the measurements. Computers were also needed to check the measurements for obvious errors (sometimes several percent of the thousands of observations needed to be adjusted). Finally, computers would massage the data with various smoothing and calibration operations to produce a unified set of numbers suitable for calculations. Global data and global models were no longer distinct entities, but parts of a single system for representing the world.(34) The weather predictions became accurate enough — looking as far as three days ahead — to be economically important. That built support for the meteorological measurement networks and computer studies necessary for climate work. |
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| An example of the crossover could be found at NASA's Goddard Institute for Space Studies in New York City. A group there under James (Jim) Hansen had been developing a weather model as a practical application of its mission to study the atmospheres of planets. For one basic component of this model, Hansen developed a set of equations for the transfer of radiation through the atmosphere, based on work he had originally done for studies of the planet Venus. The same equations could be used for a climate model, by combining them with the elegant method for computing fluid dynamics that Arakawa had developed. |
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| In the 1970s, Hansen assembled a team to work up schemes for cloud physics and the like to put into a model that would be both fast-running and realistic. An example of the kind of detail they pursued was a simple equation they devised to represent the reflection of sunlight from snow. They included the age of the snow layer (as it gradually melted away) and the "masking" by vegetation (snowy forests are darker than snowy tundra). To do the computations within a reasonable time, they had to use a grid with cells a thousand kilometers square, averaging over all the details of weather. Eventually they managed to get a quite realistic-looking climate. It ran an order of magnitude faster than some rival GCMs, permitting the group to experiment with multiple runs, varying one factor or another to see what changed.(35*) In such studies, the global climate was beginning to feel to researchers like a comprehensible physical system, akin to the systems of glassware and chemicals that experimental scientists manipulated on their laboratory benches. |
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| Meanwhile the community of modelers devised more realistic parameters for various physical processes, and sharpened their mathematical techniques. A major innovation that spread during the 1970s took a new approach to the basic architecture of models. Some groups, instead of dividing the planet's surface into a grid of thousands of square cells, took to dividing it into a tier of segments — hemispheres, quadrants, eighths, sixteenths, etc. ("spherical harmonics"). After doing a calculation on this abstracted system, they could combine and transform the numbers back into a geographical map. This "spectral transform" technique simplified many of the computations, but it was feasible only with the much faster new computers. For decades afterward, physicists who specialized in other fields of fluid dynamics were startled when they saw a climate model that did not divide up the atmosphere into millions of boxes, but used the refined abstraction of spherical harmonics. The method worked only because the Earth's atmosphere has an unusual property for a fluid system — it is in fact quite nearly spherical. | |
| The new technique was especially prized because it got around the trouble computers had with the Earth's poles, where all the lines of longitude come together in a point and the mathematics gets weird. (The earliest models had avoided the poles altogether and computed climate on a cylinder, but that wouldn’t take you very far.) Spherical harmonics did not exhaust the ingenuity of climate modelers. For example, in the late 1990s, when people had begun to run separate computations for the atmospheric circulation and the equally important circulation of ocean currents, many groups introduced new coordinate schemes for their ocean models. They avoided problems with the North and South Poles simply by shifting the troublesome points onto a land mass.(36) | |
| Groups continued to proliferate, borrowing ideas from earlier models and devising new techniques of their own. Here as in most fields of science, Europeans had recovered from the war's devastation and were catching up with the Americans. In particular, during the mid-1970s a consortium of nations set up a European Centre for Medium-Range Weather Forecasts and began to contribute to climate modeling. | |
| For further technical and institutional developments and references, see Paul Edwards' GCM Web site.(37) | |
| In their first decade or so of work the GCM modelers had treated climate as a given, a static condition. They had their hands full just trying to understand one year's average weather. Typical was a list that Mintz made in 1965 of possible uses for his and Arakawa's computer model. Mintz showed an interest mainly in answering basic scientific questions. He also listed long-range forecasting and "artificial climate control" — but not greenhouse effect warming or other possible causes of long-term climate change.(38) Around this time, however, a few modelers began to take an interest in global climate change as a problem over the long term. The discovery that the level of CO2 in the atmosphere was rising fast prompted hard thinking about greenhouse warming, prompting conferences and government panels in which GCM experts like Smagorinsky participated.(39*) Computer modelers began to interact with the community of carbon researchers. Another stimulus was Fritz Möller's discovery in 1963 that simple models built out of a few equations — the only models available for long-term climate change — showed grotesque instabilities. Everyone understood that Möller's model was unrealistic (in fact it had fundamental flaws). Nevertheless it raised a nagging possibility that mild perturbations, such as humanity itself might bring about, could trigger an outright global catastrophe.(40) |
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| Manabe took up the challenge. He had a long-standing interest in the effects of CO2, not because he was worried about the future climate, but simply because the gas at its current level was a significant factor in the planet's heat balance. But when Möller visited Manabe and explained his bizarre results, Manabe decided to look into how the climate system might change. He and his colleagues were already building a model that took full account of water in all its forms. This was crucial. For it realistically included the feedback between the air's temperature and the amount of moisture the air would hold — and water vapor was the most powerful greenhouse gas. In particular, Manabe's group calculated the way rising columns of moisture-laden air conveyed heat from the surface into the upper atmosphere. To get a really sound answer, the entire atmosphere had to be studied as a tightly interacting system. The required computations were so extensive, however, that Manabe stripped down the model to a single one-dimensional column, which represented the atmosphere averaged over the globe (or in some runs, averaged over a particular band of latitude). His aim was to get a system that could be used as a basic building-block for a full three-dimensional GCM.(41) |
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| In 1967, Manabe and a collaborator, Richard Wetherald, used the one-dimensional model to test what would happen if the level of CO2 changed. Their target was something that would eventually become a central preoccupation of modelers: the climate's "sensitivity." Just how much would temperature be altered when something affected incoming and outgoing radiation (a change in the Sun's output of sunlight, say, or a change in CO2)? The method was transparent. Run a model with one value of the something (say, of CO2 concentration), run it again with a new value, and compare the answers. Researchers since Arrhenius had pursued this with highly simplified models. They used as a benchmark the difference if the CO2 level doubled.(42) That not only made comparisons between results easier, but seemed like a good number to look into. For it seemed likely that the level would in fact double before the end of the 21st century. The answer Manabe's group came up with was that global temperature would rise roughly 2°C (around 3-4°F).(43) | |
| This was the first time a greenhouse warming calculation included enough of the essential factors — especially the correct accounting for water vapor feedback — to seem reasonable to many experts. For instance, Wallace Broecker, who would later play a major role in climate change studies, recalled that it was the 1967 paper "that convinced me that this was a thing to worry about."(44). The work drew on all the experience and insights accumulated in the labor to design GCMs, but it was no more than a first baby step toward a realistic three-dimensional model of the changing climate. | |
| The next important step was taken in the late 1960s by Manabe’s group, now at Princeton. Their GCM was still highly simplified. In place of actual land and ocean geography they pictured a geometrically neat planet, half damp surface (land) and half wet (a "swamp" ocean). Worse, they could not predict cloudiness but just held it unchanged at the present level when they calculated the warmer planet with doubled CO2. However, they did incorporate the movements of water, predicting changes in soil moisture and snow cover on land, and they calculated sea surface temperatures well enough to show the extent of sea ice. The results, published in 1975, looked quite realistic overall (link from below). |
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| The model with increased CO2 had more moisture in the air, with an intensified hydrological cycle of evaporation and precipitation. That was what physicists might have expected for a warmer atmosphere on elementary physical grounds (if they had thought about it, which few had). Actually, with so many complex interactions between soil moisture, cloudiness, and so forth, a simple argument could be in error. It took the model computation to show that this accelerated cycle really could happen, as hot soil dried out in one region and more rain came down elsewhere. The Manabe-Wetherald model also showed greater warming in the Arctic than in the tropics. This too could be predicted from simple reasoning. Not only did a more active circulation carry more heat poleward, but less snow and ice meant more absorption of sunlight by ground and sea. Again it took a calculation to show that what sounded reasonable on elementary principles was indeed likely in the real world.(45*) | |
| Averaged over the entire planet, for doubled CO2 the computer predicted a warming of around 3.5°C. It all looked plausible. The results made a considerable impact on scientists, and through them on policy-makers and the public. | =>Government =>Public opinion = Milestone |
| Manabe and Wetherald warned that "it is not advisable to take too seriously" the specific numbers they published.(46) They singled out the way the model treated the oceans as a simple wet surface. On our actual planet, the oceans absorb large quantities of heat from the atmosphere, move it around, and release it elsewhere.(47) Another and more subtle problem was that Manabe and Wetherald had not actually computed a climate change. Instead they had run their model twice to compute two equilibrium states, one with current conditions and one with doubled CO2. In the real world, the atmosphere would pass through a series of changes as the level of the gas rose, and there were hints that the model could end up in different states depending on just what route it took. | |
| Even if those uncertainties could be cleared up, there remained the old vexing problem of clouds. As the planet got warmer the amounts of cloudiness would probably change at each level of the atmosphere in each zone of latitude, but change how? There was no reliable way to figure that out. Worse, it was not enough to have a simple number for cloud cover. Scientists were beginning to realize that clouds could either tend to cool a region (by reflecting sunlight) or warm it (by trapping heat radiation from below, especially at night). The net effect depended on the types of cloud and how high they floated in the atmosphere. A better prediction of climate change would have to wait on general improvements. |
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| Progress was steady, thanks to the headlong advance of electronic computers. From the mid 1950s to the mid 1970s, the power available to modelers increased by a factor of thousands. That meant modelers could put in more factors in more complex ways, they could divide the planet into more segments to get higher resolution of geographical features, and they could run models to represent longer periods of time. The models no longer had gaping holes that required major innovations, and the work settled into a steady improvement of existing techniques. At the foundations, modelers devised increasingly more sophisticated and efficient schemes of computation. As input for the computations they worked endlessly to improve parameterizations describing various processes. From around 1970 on, many journal articles appeared with ideas for dealing with convection, evaporation of moisture, reflection from ice, and so forth.(48) | <=External input |
The most essential element for progress, however, was better data on the real world. Strong efforts were rapidly extending the observing systems. For example, in 1959 the physicist Lewis Kaplan found an ingenious way to use measurements of infrared radiation from satellites to find the temperature at different levels of the atmosphere, all around the world. During the 1960s satellite data began to provide heat budgets by zones of latitude, which gave a measure of transport of heat toward the poles. "It is a warmer and darker planet than we previously believed," one report announced. "More solar energy is being absorbed, primarily in the tropics... The trend toward departure from the earlier computation studies of the radiation budget seems irreversible." In 1969 NASA's Nimbus 3 satellite began to broadcast measurements designed explicitly to provide a fundamental check on model results. The reflection of sunlight at each latitude from Manabe's 1975 model planet agreed pretty well with the actual numbers for the Earth, as measured by the Nimbus 3 (see above).(48a) |
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| Encouragement came from a 1972 model by Mintz and Arakawa (unpublished, like much of their work), which managed to simulate in a rough way the huge changes as the sunlight shifted from season to season. During the next few years, Manabe and collaborators published a model that produced entirely plausible seasonal variations. To modelers, the main point of such work was gaining insight into the dynamics of climate through close inspection of their printouts. (They could study, for example, just what role the ocean surface temperature played in driving the tropical rain belt from one hemisphere to the other as the seasons changed.) To everyone else, seasons were a convincing test of the models' validity. It was almost as if a single model worked for two quite different planets — the planets Summer and Winter. A 1975 review panel felt that with this success, realistic numerical climate models "may be considered to have begun."(49*) | |
| Basic problems remained unsolved, such as predicting cloudiness. And new difficulties appeared. For example, scientists began to realize that the way clouds formed, and therefore how much they helped to warm or cool a region, could be strongly affected by the haze of dust and chemical particles floating in the atmosphere. Little was known about how these aerosols helped or hindered the formation of different types of clouds. Another surprise came when two scientists pointed out that the reflectivity of clouds and snow depends on the angle of the sunlight — and in polar regions the Sun always struck at a low angle.(50) Figuring how sunlight might warm an ice cap was as complicated as the countless peculiar forms taken by snow and ice themselves. Little of this had been explored through physics theory. Nor had it been measured in the field, for it was only gradually that model-makers realized how much they suffered from the absence of reliable measurements of the parameters they needed to describe the action of dust particles, snow surfaces, and so forth. Overall, as Smagorinsky remarked in 1972, modelers still needed "to meet standards of simulation fidelity considerably beyond our present level."(51) |
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| Modelers felt driven to do better, for people had begun to demand much more than a crude reproduction of the present climate. Weather disasters and the energy crisis of the early 1970s had put greenhouse warming on the political agenda (at least for the more well-informed people). It was now a matter of public debate whether the computer models were correct in their predictions of how CO2 emissions would raise global temperatures. Newspapers reported disagreements among prominent scientists. Some experts suspected that factors overlooked in the models might keep the climate system from warming at all, or might even bring on cooling instead. "Meteorologists still hold out global modeling as the best hope for achieving climate prediction," a senior scientist observed in 1977. "However, optimism has been replaced by a sober realization that the problem is enormously complex."(52*) |
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| The problem was so vexing that the President's Science Adviser (who happened to be a geophysicist) asked the National Academy of Sciences to study the issue. The Academy appointed a panel, chaired by Jule Charney and including other respected experts who had been distant from the recent climate debates. They convened at Woods Hole in the summer of 1979. Charney’s group compared two independent GCMs, one constructed by Manabe and the other by Hansen — elaborate three-dimensional models that used different physical approaches and different computational methods for many features. The panel found differences in detail but solid agreement for the main point: the world would get warmer as CO2 levels rose. |
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| But might both GCMs share some fundamental unrecognized flaw? As a basic check, the Charney panel went back to the models of one-dimensional and two-dimensional slices of atmosphere, which various groups were using to explore a wider range of possibilities than the GCMs could handle. These models showed crudely but directly the effects of adding CO2 to the atmosphere. All the different approaches, simplified in very different ways, were in rough overall agreement. They came up with figures that were at least in the same ballpark for the temperature in an atmosphere with twice as much CO2 (the level projected for around the middle of the 21st century).(53*) | <=Radiation math |
| To make their conclusion more concrete, the Charney panel decided to announce a specific range of numbers. They argued out among themselves a rough-and-ready compromise. Hansen's GCM predicted a 4°C rise for doubled CO2, and Manabe's latest figure was around 2°C. Splitting the difference, the panel said they had rather high confidence that as CO2 reached this level the planet would warm up by about three degrees, plus or minus fifty percent: in other words, 1.5-4.5°C (2.7-8°F). They concluded dryly, "We have tried but have been unable to find any overlooked or underestimated physical effects" that could reduce the warming.(54) | =>CO2 greenhouse =>Public opinion = Milestone =>Government =>International |
| Ocean Circulation and Real Climates
(1969-1988) TOP OF PAGE |
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| In the early 1980s, several groups pressed ahead toward more realistic models. They put in a reasonable facsimile of the Earth's actual geography, and replaced the wet "swamp" surface with an ocean that could exchange heat with the atmosphere. Thanks to increased computer power the models were now able to handle seasonal changes as a matter of course. It was also reassuring when Hansen's group and others got a decent match to the rise-fall-rise curve of global temperatures since the late 19th century, once they put in not only the rise of CO2 but also changes in emissions of volcanic dust and solar activity. | |
| Adding a solar influence was a stretch, for nobody had figured out any plausible way that the superficial variations seen in numbers of sunspots could affect climate. To arbitrarily adjust the strength of the presumed solar influence in order to match the historical temperature curve was guesswork, dangerously close to fudging. But many scientists suspected there truly was a solar influence, and adding it did improve the match. Sometimes a scientist must "march with both feet in the air," assuming a couple of things at once in order to see whether it all eventually works out.(55) Reassured that they might be on the right track, in the 1980s climate modelers increasingly looked toward the future. When they introduced a doubled CO2 level into their improved models, they consistently found the same few degrees of warming.(56*) | <=Aerosols
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| The skeptics were not persuaded. The Charney panel itself had pointed out that much more work was needed before models would be fully realistic. The treatment of clouds remained a central uncertainty. Another great unknown was the influence of the oceans. Back in 1979 the Charney panel had warned that the oceans' enormous capacity for soaking up heat could delay an atmospheric temperature rise for decades. Global warming might not become obvious until all the surface waters had warmed up, which would be too late to take timely precautions.(57) This time lag was not revealed by the existing GCMs, for these computed only equilibrium states. The models, lacking nearly all the necessary data and thwarted by formidable calculational problems, simply did not account for the true influence of the oceans. | |
| Oceanographers were coming to realize that large amounts of energy were carried through the seas by a myriad of whorls of various types, from tiny convection swirls up to sluggish eddies a thousand kilometers wide. Calculating these whorls, like calculating all the world's individual clouds, was beyond the reach of the fastest computer. Again parameters had to be devised to summarize the main effects, only this time for entities that were far worse observed and understood than clouds. Modelers could only put in average numbers to represent the heat that they knew somehow moved vertically from layer to layer in the seas, and the energy somehow carried from warm latitudes toward the poles. They suspected that the actual behavior of the oceans might work out quite differently from their models. And even with the simplifications, to get anything halfway realistic required a vast number of computations, indeed more than for the atmosphere. |
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| Manabe was keenly aware that if the Earth's future climate were ever to be predicted, it was "essential to construct a realistic model of the joint ocean-atmosphere system."(58) He shouldered the task in collaboration with Kirk Bryan, an oceanographer with meteorological training, who had been brought into the group back in 1961 to build a stand-alone numerical model of an ocean. The two got together to construct a computational system that coupled together their separate models. Manabe's winds and rain would help drive Bryan's ocean currents, while in return Bryan's sea-surface temperatures and evaporation would help drive the circulation of Manabe's atmosphere. At first they tried to divide the work: Manabe would handle matters from the ocean surface upward, while Bryan would take care of what lay below. But they found things just didn't work that way for studying a coupled system. They moved into one another's territory, aided by a friendly personal relationship. Bryan and Manabe were the first to put together in one package approximate calculations for a wide variety of important features. They not only incorporated both oceans and atmosphere, but added into the bargain feedbacks from changes in sea ice. Moreover, they included a detailed scheme that represented, region by region, how moisture built up in the soil, or evaporated, or ran off in rivers to the sea. |
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| Their big problem was that from a standing start it took several centuries of simulated time for an ocean model to settle into a realistic state. After all, that was how long it would take the surface currents of the real ocean to establish themselves from a random starting-point. The atmosphere, however, readjusts itself in a matter of weeks. After about 50,000 time steps of ten minutes each, Manabe's model atmosphere would approach equilibrium. The team could not conceivably afford the computer time to pace the oceans through decades in ten-minute steps. Their costly Univac 1108, a supercomputer by the standards of the time, needed 45 minutes to compute the atmosphere through a single day. Bryan's ocean could use longer time steps, say a hundred minutes, but the simulated currents would not even begin to settle down until millions of these steps had passed. | |
| The key to their success was a neat trick for matching the different timescales. They ran their ocean model with its long time steps through twelve days. They ran the atmosphere model with its short time-steps through three hours. Then they coupled the atmosphere and ocean to exchange heat and moisture. Back to the ocean for another twelve days, and so forth. They left out seasons by using annual average sunlight to drive the system. | |
| Manabe and Bryan were confident enough of their model to undertake a heroic computer run, some 1100 hours long (more than 12 full days of computer time devoted to the atmosphere and 33 to the ocean). In 1969, they published the results in an unusually short paper, as Manabe recalled long afterward — "and still I am very proud of it."(59) | |
| Bryan wrote modestly at the time that "in one sense the... experiment is a failure." For even after a simulated century, the deep ocean circulation had not nearly reached equilibrium. It was not clear what the final climate solution would look like.(60) Yet it was a great success just to carry through a linked ocean-atmosphere computation that was at least starting to settle into equilibrium. The result looked like a real planet — not our Earth, for in place of geography there was only a radically simplified geometrical sketch, but in its way realistic. It was obviously only a first draft with many details wrong, yet there were ocean currents, trade winds, deserts, rain belts, and snow cover, all in roughly the right places. Unlike our actual Earth, so poorly observed, in the simulation one could see every detail of how air, water, and energy moved about. |
<=>The oceans |
| Following up, in 1975 Manabe and Bryan published results from the first coupled ocean-atmosphere GCM that had a roughly Earth-like geography. Looking at their crude map, one could make out continents like North America and Australia, although not smaller features like Japan or Italy. The supercomputer ran for fifty straight days, simulating movements of air and sea over nearly three centuries. "The climate that emerges," they wrote, "includes some of the basic features of the actual climate." For example, it showed the Sahara and the American Southwest as deserts, but plenty of rain in the Pacific Northwest and Brazil. Manabe and Bryan had not shaped their equations deliberately to bring forth such features. These were "emergent features," emerging spontaneously out of the computations. The computer’s output looked roughly like the actual climate only because the modelers had succeeded in roughly representing the actual operations of the atmosphere upon the Earth’s geography. |
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| "However," Manabe and Bryan admitted, their model had "many unrealistic features." For example, it still failed to show the full oceanic circulation. After all, the inputs had not been very realistic — for one thing, the modelers had not put in the seasonal changes of sunlight. Still, the results were getting close enough to reality to encourage them to push ahead.(61) By 1979, they had mobilized enough computer power to run their model through more than a millennium while incorporating seasons.(62) |
<=>The oceans |
| Meanwhile a team headed by Warren Washington at NCAR in Colorado developed another ocean model, based on Bryan's, and coupled it to their own quite different GCM. Since they had begun with Bryan's ocean model it was not surprising that their results resembled Manabe and Bryan's, but it was still a gratifying confirmation. Again the patterns of air temperature, ocean salinity, and so forth came out roughly correct overall, albeit with noticeable deviations from the real planet, such as tropics that were too cold. As Washington's team admitted in 1980, the work "must be described as preliminary."(63) Through the 1980s, these and other teams continued to refine coupled models, occasionally checking how they reacted to increased levels of CO2. These were not so much attempts to predict the real climate as experiments to work out methods for doing so. | |
| The results, for all their limitations, said something about the predictions of the atmosphere-only GCMs. As the Charney panel had pointed out, the oceans would delay the appearance of global warming for decades by soaking up heat. Hansen's group therefore warned in 1985 that a policy of "wait and see" might be wrongheaded. A mild temperature rise in the atmosphere might not become apparent until much worse greenhouse warming was inevitable. Also as expected, complex feedbacks showed up in the ocean circulation, influencing just how the weather would change in a given region. Aside from that, including a somewhat realistic ocean did not turn up anything that would alter the basic prediction of future warming. Once again it was found that simple models had pointed in the right direction.(64) |
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| A few of the calculations showed a disturbing new feature — a possibility that the ocean circulation was fragile. Signs of rapid past changes in circulation had been showing up in ice cores and other evidence that had set oceanographers to speculating. In 1985, Bryan and a collaborator tried out a coupled atmosphere-ocean model with a CO2 level four times higher than at present. They found signs that the world-spanning "thermohaline" circulation, where differences in heat and salinity drove a vast overturning of sea water in the North Atlantic, could come to a halt. Three years later Manabe and another collaborator produced a simulation in which, even at present CO2 levels, the ocean-atmosphere system could settle down in one of two states — the present one, or a state without the overturning.(66*) Some experts worried that global warming might indeed shut down the circulation. Halting the steady flow of warm water into the North Atlantic would bring devastating climate changes in Europe and perhaps beyond. |
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| As oceanographer Wallace Broecker remarked, the GCMs had been designed to come to equilibrium, giving a stability that might be illusory. As scientists got better at modeling ocean-atmosphere interactions, they might find that the climate system was liable to switch rapidly from one state to another. On the other hand, since the cold oceans would soak up heat for many decades before they reached an equilibrium, a climate that was computed for an atmosphere with doubled CO2 would not show what the planet would look like immediately after a doubling took place, but only what it would look like many decades later. Acknowledging these criticisms, Hansen's group and a few others undertook protracted computer runs to find what would actually happen while the CO2 level rose. Instead of separately computing "before" and "after" states, they computed the entire "transient response," plodding through a century or more simulating from one day to the next. Hansen's coupled ocean-atmosphere model, which incorporated the rise not only of CO2 but also other greenhouse gases, plus volcanic aerosols, turned out a fair approximation to the observed global temperature trend of the previous half century. Pushed into the future, the model showed sustained global warming. By 1988 Hansen had enough confidence to issue a strong public pronouncement, warning of an imminent threat.(67) |
<=>Rapid change
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| This was pushing the state of the art to its limit, however. Most model groups could barely handle the huge difficulties of constructing three-dimensional models of both ocean circulation and atmospheric circulation, let alone link the two together and run the combination through a century or so. |
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| The climate changes that different GCMs computed for doubled CO2, reviewers noted in 1987, "show many quantitative and even qualitative differences; thus we know that not all of these simulations can be correct, and perhaps all may be wrong."(68) Skeptics pointed out that GCMs were unable to represent even the present climate successfully from first principles. Anything slightly unrealistic in the initial data or equations could be amplified a little at each step, and after thousands of steps the entire result usually veered off into something impossible. To get around this, the modelers had kept one eye over their shoulder at the real world. They adjusted various parameters (for example, the numbers describing cloud physics), "tuning" the models and running them again and again until the results looked like the real climate. These adjustments were not calculated from physical principles, but were fiddled until the model became stable. It was possible to get a crude climate representation without the tuning, but the best simulations relied on this back-and-forth between model and observation. If models were tuned to match current climate, the critics asked, how reliably could they calculate a future, different state? | |
| One way to check that was to see whether models could make a reasonable facsimile of the Earth during a glacial period — virtually a different planet. If you could reproduce a glacial climate with the same physical parameters for clouds and so forth that you used for the current planet, that would be evidence the models were not arbitrarily trimmed just to reproduce the present. But first you would need to know what the conditions had actually been around the world during an ice age. That required far more data than paleoclimatologists had turned up. Already in 1968 a meteorologist warned that henceforth reconstructing past climate would not be limited by theory so much as by "the difficulty of establishing the history of paleoenvironment." Until data and models were developed together, he said, atmospheric scientists could only gaze upon the ice ages with "a helpless feeling of wonderment."(69) | |
| To meet the need, a group of oceanographers persuaded the U.S. government to fund a large-scale project to analyze ooze extracted from the sea bottom at numerous locations. The results, combined with terrestrial data from fossil pollen and other evidence, gave a world map of temperatures at the peak of the last ice age. As soon as this CLIMAP project began publishing its results in 1976, modelers began trying to make a representation for comparison. The first attempts showed only a very rough agreement, although good enough to reproduce essential features such as the important role played by the reflection of sunlight from ice.(70*) |
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| At first the modelers simply worked to reproduce the ice age climate over land by using the CLIMAP figures for sea surface temperatures. But when they tried to push on and use models to calculate the sea surface temperatures, they ran into trouble. The CLIMAP team had reported that in the middle of the last ice age, tropical seas had been only slightly cooler than at present, a difference of barely 1°C. That raised doubts about whether the climate was as sensitive to external forces (like greenhouse gases) as the modelers thought. Moreover, while the tropical seas had stayed warm during the last ice age, the air at high elevations had certainly been far colder. That was evident in lower altitudes of former snowlines detected by geologists on the mountains of New Guinea and Hawaii. No matter how much the GCMs were fiddled, they could not be persuaded to show such a large difference of temperature with altitude. A few modelers contended that the tropical sea temperatures must have varied more than CLIMAP said. But they were up against an old and strongly held scientific conviction that the lush equatorial jungles had changed little over millions of years, testifying to a stable climate. (This was an echo of traditional ideas that the entire planet's climate was fundamentally stable, with ice ages no more than regional perturbations at high latitudes and elevations.)(71*) | <=Uses of shells |
| On the other hand, by 1988 modelers had passed a less severe test. Some 8,000 years ago the world had gone through a warm period — presumably like the climate that the greenhouse effect was pushing us toward. One modeling group managed to compute a fairly good reproduction of the temperature, winds, and moisture in that period. (The comparison of model results with the past was only possible, of course, thanks to many geologists who worked with the modelers to assemble and interpret data on ancient climates.)(72) |
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| Meanwhile all the main models had been developed to a point where they could reliably reproduce the enormously different climates of summer and winter. That was a main reason why a review panel of experts concluded in 1985 that "theoretical understanding provides a firm basis" for predictions of several degrees of warming in the next century.(73) So why did the models fail to match the relatively mild sea-surface temperatures along with cold mountains reported for the tropics in the previous ice age? Experts could only say that the discrepancies "constitute an enigma."(74) | |
| A more obvious and annoying problem was the way models failed to tell how global warming would affect a particular region. Policy-makers and the public were less interested in the planet as a whole than in how much warmer their own particular locality would get, and whether to expect wetter or dryer conditions. Already in 1979, the Charney panel's report had singled out the absence of local climate predictions as a weakness. At that time the modelers who attacked climate change had only tried to make predictions averaged over entire zones of latitude. They might calculate a geographically realistic model through a seasonal cycle, but nobody had the computer power to drive one through centuries. In the mid 1970s, when Manabe and Wetherald had introduced a highly simplified geography that divided the globe into land and ocean segments without mountains, they had found, not surprisingly, that the model climate's response to a raised CO2 level was "far from uniform geographically."(75) | |
| During the 1980s, modelers got enough computer power to introduce much more realistic geography into their climate change calculations. They began to grind out maps in which our planet's continents could be recognized, showing climate region by region in a world with doubled CO2. However, for many important regions the maps printed out by different groups turned out to be incompatible. Where one model predicted more rainfall in the greenhouse future, another might predict less. That was hardly surprising, for a region's climate depended on particulars like the runoff of water from its type of soil, or the way a forest grew darker as snow melted. Modelers were far from pinning down such details precisely. A simulation of the present climate was considered excellent if its average temperature for a given region was off by only a few degrees and its rainfall was not too high or too low by more than 50% or so. On the positive side, the GCMs mostly did agree fairly well on global average predictions. But the large differences in regional predictions emboldened skeptics who cast doubt on the models' fundamental validity.(76) | |
| A variety of other criticisms were voiced. The most prominent came from Sherwood Idso. In 1986 he calculated that for the known increase of CO2 since the start of the century, models should predict something like 3°C of warming, which was far more than what had been observed. Idso insisted that something must be badly wrong with the models' sensitivity, that is, their response to changes in conditions.(77) Other scientists gave little heed to the claim. It was only an extension of a long and sometimes bitter controversy in which they had debated Idso's arguments and rejected them as shamefully oversimplified. | <=Radiation math |
| Setting Idso's criticisms aside, there undeniably remained points where the models stood on shaky foundations. Researchers who studied clouds, the transfer of radiation through the atmosphere and other physical features warned that more work was needed before the fundamental physics of GCMs would be entirely sound. For some features, no calculation could be trusted until more observations were made. And even when the physics was well understood, it was no simple task to represent it properly in the computations. "The challenges to be overcome through the use of mathematical models are daunting," a modeler remarked, "requiring the efforts of dedicated teams working a decade or more on individual aspects of the climate system."(78) As Manabe regretfully explained, so much physics was involved in every raindrop that it would never be possible to compute absolutely everything. "And even if you have a perfect model which mimics the climate system, you don't know it, and you have no way of proving it."(79) | |
| Indeed philosophers of science explained to anyone who would listen that a computer model, like any other embodiment of a set of scientific hypotheses, could never be "proved" in the absolute sense one could prove a mathematical theorem. What models could do was help people sort through countless ideas and possibilities, offering evidence on which were most plausible. Eventually the models, along with other evidence and other lines of reasoning, might converge on an explanation of climate which — if necessarily imperfect, like all human knowledge — could be highly reliable.(80) | |
| Through the 1980s and beyond, however, different models persisted in coming up with noticeably different numbers for climate in one place or another. These qualitative divergences could be significant for anyone trying to plan for change, at least when one looked closely at particular phenomena such as the projected changes in soil moisture for a given region. Worse, some groups suspected that even apparently correct results were sometimes generated for the wrong reasons. Above all, they recognized that their modeling of cloud formation was still scarcely justified by the little that was known about cloud physics. Even the actual cloudiness of various regions of the world had been measured in only a sketchy fashion. (Until satellite measurements became available later in the decade, most models used data from the 1950s that only gave averages by zones of latitude, and only forthe Northern Hemisphere. Modelers mirrored the set to represent clouds in the Southern Hemisphere, with the seasons reversed — although of course the distribution of land, sea, and ice is very different in the two halves of the planet.) Having come this far, many modelers felt a need to step back from the global calculations. Reliable progress would require more work on fundamental elements, to improve the sub-models that represented clouds, snow, vegetation, and so forth.(81) Modelers settled into a long grind of piecemeal improvements. | |
| After 1988 | =>after88 |
| "There has been little change over the last 20 years or so in the approaches of the various modeling groups," an observer remarked in 1989. He thought this was partly due to a tendency "to fixate on specific aspects of the total problem," and partly to limited resources. "The modeling groups that are looking at the climate change process," he noted, "are relatively small in size compared to the large task."(82) The limitations not only in resources but in computer power, global data, and plain scientific understanding kept the groups far from their goal of precisely reproducing all the features of climate. Yet under any circumstances it would be impossible to compute the current cl |
