Weather is the state of the atmosphere at any given moment, which is chaotic and difficult to predict, while climate is the state of weather parameters averaged over several decades (usually thirty years). As students of atmospheric physics learn: “Climate is what you expect; weather is what you get”. It can be said that weather is like the result of a dice throw, and climate is like the average and distribution from the results of the throws (the incidence of each result). While without detailed data on the dice throwing process itself (initial position, forces, moments, properties of the dice and the surface on which they fall) it is not possible to calculate the result of a single dice throw, predicting the statistical result of a large number of throws is easy: the average and scatter of the results can be determined easily, in advance and with a good approximation. And so it is with climate: although the development of atmospheric eddies – highs and lows – cannot be predicted exactly, the average temperatures and precipitation over a long period of time in a given region can be given quite accurately.

The farmer decides whether to harvest the next day’s grain by looking at the weather forecast, but in turn, the decision on what crops to grow – cassava, rice or rye – depends on the local climate. As far as the weather is concerned, a 4°C change from one day to the next does not make much difference. However, when it comes to climate, 4°C makes a dramatic difference. However, when it comes to climate, 4°C makes a dramatic difference.

Why don’t we bother with chaos theory in climate prediction? Here we are not interested in whether or not it will rain at noon on 1 July 2100, and we do not expect the model to correctly determine such details. We ask what conditions will prevail, on average, in July 2100. Whether our simulation predicts a big downpour at the beginning or the end of the month, the average rainfall will come out similar. Here, the initial conditions lose their importance, while the so-called boundary conditions, i.e. changes in greenhouse gas concentrations, for example, which are ignored in the weather forecast for the coming days, come to the fore. The problem of climate prediction is therefore called the ‘boundary conditions problem’ – we look at how climate statistics change in response to their changes.

This article was written with the help of Marcina Popkiewicza.