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of identifying new ways to carve up the world such that different patterns in how the world changes over time become salient—then dynamical complexity is a concept that should be of great interest to working scientists, since it captures (in a sense) how fruitful (and how difficult) scientific inquiry into the behavior of a given system is likely to be. Finally, we saw how the formalism of effective complexity very naturally dove-tails with the intuitive conceptual machinery developed here and in Chapter One. I argued that summing over the effective complexities of different representations of the same system offers a way to quantify the dynamical complexity of the system. This value will be a moving target, and will be observer (and goal) relative to some degree. This should concern us no more than the observation that the choice of what patterns we pay attention to in science is goal-relative should trouble us, as they stem from precisely the same features of the scientific project.

In Chapter Four, we will leave foundational questions behind and move on to considering some methodological questions relevant to climate science. We’ll introduce the basics of climatology and atmospheric science, and examine the difficulties involved in creating a working model of the Earth’s climate. From there, we will consider the particular challenges that climate science faces, given that it explicitly deals with a system of high dynamical complexity, and think about and how have those challenges been met in different fields. We’ll examine why it is that scientists care about dynamical complexity, and what can be learned by assessing the dynamical complexity of a given system. In Chapter Five, I’ll synthesize the two threads that have, up to that point, been pursued more-or-less in parallel and argue the global climate is a paradigmatic dynamically complex system. We’ll examine how that fact has shaped the methodology of climate science, as well as how it has given rise to a number of unique problems