B. Geerts and E. Linacre
The atmosphere is essentially chaotic because the processes involved in its evolution, in particular advection, are non-linear. It is said that the flap of the wings of a butterfly may trigger a thunderstorm. But no particular butterfly’s wings fix the following weather, since there are untold numbers of other butterflys’ wings of equal importance. A chaotic system is not random. It is deterministic, but in a way that is too complicated to ascertain. In weather forecasting Newton’s laws of motion can be applied to a grid that is many orders of magnitude more coarse than the actual atmosphere (e.g. 100 km vs 1 mm). But such forecasts become inaccurate (not unstable) after a number of days, by which time the conversion of kinetic energy to heat becomes large.
An increasingly used method of weather forecasting allows for the uncertainties concerning initial conditions and the role of chance. It is called ‘ensemble forecasting’. The idea is that repeated forecasts are made from the same initial time, with the initial conditions varied by an error whose magnitude reflects the degree of uncertainty of the observations. In 1965, MIT meteorology professor Ed Lorenz (1) noted that forecast errors are due to at least three causes:
The first two sources of error have become smaller since the days of Lorenz, but the fundamental reasons for forecast failure are still there. Ensemble forecasting yields a range of prognoses. The breadth of this range indicates the possible error in any forecast, and their consensus provides the best estimate. Sometimes the atmosphere behaves more chaotically, and small errors amplify rapidly. At other times the various forecasts stay within a narrow range, therefore they can be treated with more confidence. Various operational medium-range forecasting centers, including the ECMWF, use the ensemble technique. It can also be used in short-range forecasting, in particular to predict the evolution and movement of tropical cyclones (3).