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more-or-less purely descriptive, consisting of the collection and analysis of statistical information about weather trends over long time-scales, and relying almost exclusively on graphical presentation. Although some inroads were being made in theoretical meteorology at the same time—mostly by applying cutting-edge work in fluid dynamics to the flow of air in the upper atmosphere—it wasn’t until the advent of the electronic computer in the 1950s and 1960s, which made numerical approximation of the solutions to difficult-to-solve equations finally feasible on a large scale, that forecasting and climatology moved away from this purely qualitative approach. Today, the three fields are more tightly integrated, though differences in the practical goals of weather and climate forecasting—most significantly, the need for weather forecasts to be generated quickly enough to be of use in (say) deciding whether or not to take an umbrella to work tomorrow—still give rise to somewhat different methods. We will return to these issues in Chapter Five when we discuss the role of computer models in climate science.

We can think of the relationship between weather and climate as being roughly analogous to the relationship between (say) the Newtonian patterns used to predict the behavior of individual atoms, and thermodynamics, which deals with the statistical behavior of collections of atoms. The question of exactly how many atoms we need before we can begin to sensibly apply patterns that make reference to average behavior—patterns like temperature, pressure, and so on—just isn’t one that needs a clear answer (if this dismissive shrug of an answer bothers you, review the discussion of the structure of the scientific project in Chapter One). When we apply the patterns of thermodynamics and when we apply the dynamics of Newtonian mechanics to individual atoms is a matter of our goals, not a matter of deep metaphysics. Precisely the same is true of the line between weather forecasting and climatology: which set of patterns we choose to

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