4.2.1 Climate Models and Complexity
Earlier in the history of climate science, even textbooks within the field were willing to attempt to rank various climate models in terms of ascending “complexity[1].” While the sense of the term ‘complexity’ doesn’t exactly mirror the concept of dynamical complexity developed in Chapter Three, there are enough parallels to be worth remarking on, and I shall argue that the important aspects of the climate modeler’s sense are, like the various approaches to complexity surveyed in Chapter Two, well-captured by the notion of dynamical complexity. Interestingly, there’s at least some evidence that more recent work in climatology has backed off from the attempt to rank models by complexity. While the hierarchical “climate pyramid” reproduced below appears in all editions of McGuffie & Herderson-Sellers’ work on climate modeling, by 2005 (and the publication of the third edition of the work), they had introduced a qualification to its presentation:
This constructed hierarchy is useful for didactic purposes, but does not reflect all the uses to which models are put, nor the values that can be derived from them. The goal of developers of comprehensive models is to improve performance by including every relevant process, as compared to the aim of [EMIC] modelers who try to capture and understand processes in a restricted parameter space. Between these two extremes there is a large territory populated, in part, by leakage from both ends. This intermediate area is a lively and fertile ground for modeling innovation. The spectrum of models [included in EMICs] should not be viewed as poor cousins to the coupled models[2].
It is worth emphasizing that this egalitarian perspective on climate science—in which a multitude of perspectives (encoded in a multitude of models) are included without prejudice—fits nicely with the account of science in general we explored in Chapter One, and only serves to reinforce the view that contemporary scientific practice requires this multifarious
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