disciplines until fairly recently. Increasingly, though, the question of whether there might be general principles underlying these cases—principles that deal with how systems of many highly connected interactive parts behave, regardless of the nature of those parts—has started to surface in these discussions. This is precisely the question that complexity theory aims to explore: what are the general features of systems for which the decompositionist approach fails to capture the whole story? What rigorous methods might we adopt to augment traditional approaches to science? How can we integrate holistic and analytic understanding into a unified scientific whole? These are, I suspect, the questions that will come to define scientific progress in the 21st century, and they are questions that climate science—perhaps more than anything else—urgently needs to consider.
The contribution of EMICs shouldn’t be underestimated: they are very important tools in their own right, and they have much to contribute to our understanding of the climate. Still, though, they’re highly specific tools, deliberately designed to apply to a very narrow range of circumstances. EMICs are intentionally limited in scope, and while this limitation can take different forms (e.g. spatio-temporal restriction vs. restriction to a single climate sub-system considered more-or-less in isolation), it is a defining characteristic of the class of models—perhaps the only defining characteristic. Such a narrow focus is a double-edged sword; it makes EMICs far easier to work with than their monstrously complicated big brothers, but it also limits the class of predictions that we can reasonably expect to get out of applying them. If we’re going to get as complete a picture of the patterns underlying the time-evolution of the Earth’s climate as possible, then we’ll need as many tools as possible at our disposal: low-level energy balance models, EMICs, and high-level holistic models.