The computer models central to today’s climate research bring together techniques discovered by scientists in the 19th century
without greenhouse gases in its atmosphere is to turn the familiar blue marble into a barren lump of rock and ice on which the average surface temperature hovers around -18ºC. Such a planet would not receive less of the sunlight which is the ultimate source of all Earth’s warmth. But when the energy it absorbed from the sunlight was re-emitted as infrared radiation, as the laws of physics require, it would head unimpeded back out into space.
Doing so required him to tackle a problem of the sort that most frustrates and most delights scientists who study the Earth system: a feedback loop through which a change in one factor affects another factor which, in turn, affects the first factor more. Around the same time as Arrhenius was pondering the climate, a Norwegian scientist called Vilhelm Bjerknes was working on the physics of how heat drives fluid flow. His students applied these insights to large scale flows in the atmosphere and the oceans, laying the foundations of 20th-century weather forecasting. In 1950 one of those students’ students, Ragnar Fjørtoft, was part of the team which first programmed a computer to forecast the weather by solving such equations.
Dozens of teams at meteorological and research organisations around the world run such models, each using different code to capture the climate’s underlying mechanisms and study everything from future peak rainfall to the tracks of storms to shifts in seasonality. Since 1995 the Coupled Model Intercomparison Project, or, has brought these teams together by providing standardised tasks for their models and then looking at the range of results.
The problem is that the processes which control a cloud’s thickness, lifetime and other qualities work on pretty small scales. The models do not. Even if every layer of the atmosphere is represented by hundreds of thousands of grid cells, they still end up being hundreds of kilometres on a side—much too large to capture the processes responsible for individual clouds.
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