Regarding climate models, physician and science fiction writer Michael Crichton had this to say:

“Since climate may be a chaotic system—no one is sure—these predictions are inherently doubtful, to be polite.” (Aliens Cause Global Warming)

What does he mean when he says that climate may be chaotic, and what impact does this have on climate modelling?

Flash back to the early 1960s. Meteorologist Edward Lorenz was studying a bare-bones weather model, consisting of three differential equations. Give the model an initial state and the differential equations would describe how the state changes over time, in much the same way that you can predict where Johnny will be in three hours’ time, given that he starts in Chicago and is driving west at 60 miles per hour. The hope was that with a big enough computer, a powerful enough model, and an accurately measured state of the atmosphere, the weather could one day be predicted far in advance.

Lorenz, the story goes, found a run of the model which interested him, and sat down to replay the simulation. He entered the initial conditions and set the model in motion, only to watch in bewilderment as the replay rapidly diverged from the original simulation.

"From nearly the same starting point, Edward Lorenz saw his computer weather produce patterns that grew farther and farther apart until all resemblance disappeared" (Image and caption from Chaos: Making a New Science, by James Gleick, 1987, p.17)

Lorenz tore his code apart looking for the error, only to realise that the error had been in his assumptions. In a distinctly Crichtonesque twist, the computer worked with numbers to six decimal places (0.123456) but only printed out values to three decimal places (0.123) in order to save space. It was these shortened number which Lorenz entered as the initial conditions for his model. Surely those last digits were inconsequential; after all, they were but a few hundred parts per million, comparable to the atmospheric concentration of the trace gas carbon dioxide.

Oh, but the consequences! Its roots stretched back to earlier anomalies and the term ‘chaos’ would not be introduced for another decade, but it was Lorenz’s observation which heralded the beginnings of chaos theory.

Lorenz had discovered that even very small changes in the state of a chaotic system can quickly and radically change the way that the system develops over time. This property is known as extreme sensitivity to initial conditions, also called the ‘Butterfly Effect’ because it suggested neglecting an event as small as the flapping of a butterfly’s wings could be enough to derail a weather forecast. There is more to chaotic systems than the Butterfly Effect, but this characteristic is one of their best know properties. Lorenz’s work put and end to hopes of long-term weather forecasting. The state of the atmosphere could only be known so well, and even the smallest of imprecisions would lead the simulations to catastrophic failure.

‘Nobody believes a weather prediction twelve hours ahead. Now we’re asked to believe a prediction that goes out 100 years into the future? And make financial investments based on that prediction? Has everybody lost their minds?’ – Crichton

But does chaos theory signal doom for climate modelling? Stay tuned for part II….

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