Many details came out wrong, however. From the early s 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. Those developments are not covered in these essays.
In the late s, as computer power grew and the need for simplifying assumptions diminished, other scientists around the world began to experiment with many-leveled models based on the primitive equations of Bjerknes and Richardson. 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.
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. 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 on, Mintz and Arakawa worked away at their problem, constructing a series of increasingly sophisticated GCMs. 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. The model, packed with useful techniques, had a powerful influence on other groups.
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. 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 especially from the influential UCLA group 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. 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 , with Manabe's groundbreaking models. In , the group moved from the Washington, DC area to Princeton, and it eventually came under the wing of the U. National Oceanic and Atmospheric Administration.
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.
Although the modelers of the s and early s 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 , a blue-ribbon panel of the U. 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. To do much better, the panel concluded, modelers would need computers that were ten or even a hundred times more powerful. 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. 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 s. Through the s, 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.
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In the absence of a good match between atmospheric data and GCM calculations, many researchers continued through the s 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 , Edward Lorenz, an MIT professor of meteorology, cautioned that "even the trade winds and the prevailing westerlies at sea level are not completely explained.
Climate was apparently not a well-defined system, but only an average of the ever-changing jumble of daily thunderstorms and storm fronts. 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.
- General circulation model!
- Atmospheric Circulation Dynamics and General Circulation Models | Masaki Satoh | Springer.
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. 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, for example, the effect of changing the amount of moisture or CO 2 in the atmosphere.
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.
General circulation model
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. 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 regulated 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. 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 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.
How do General Circulation models work ?
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. This essay does not cover the entire range of models, but concentrates on those which contributed most directly to greenhouse effect studies.
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For models in one or two dimensions, see the article on Basic Radiation Calculations. None of the concepts of the s 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. In , a report on environmental problems by a panel of top experts declared that work on computer models was "indispensable" for progress in the study of climate change.
The growing community of climate modelers was strengthened by the advance of computer systems that carried out detailed calculations on short timescales for weather prediction. Such studies accelerated as the s began. Such observations were now being provided by the balloons and sounding rockets of an international World Weather Watch, founded in the mid s. 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 to feed into calculations. The instrumental systems were increasingly oriented toward producing numbers meaningful to the models, and vice-versa; global data and global models were no longer distinct entities, but parts of a single system for representing the world.
That built support for the meteorological measurement networks and computer studies necessary for climate work. 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.
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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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