Abstract
In this paper I argue that the appropriate analogy for “understanding what makes simulation results reliable” in global climate modeling is not with scientific experimentation or measurement, but—at least in the case of the use of global climate models for policy development—with the applications of science in applied design problems. The prospects for using this analogy to argue for the quantitative reliability of GCMs are assessed and compared with other potential strategies.
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Notes
A climate scenario is, roughly, a possible future trajectory of the planet’s greenhouse gas emissions. The scenarios are chosen to span the range of considered policy interventions and political developments.
Though the Working Group 1 Contribution to the IPCC Fifth Assessment Report was still in un-quotable draft at the time of this writing, the central role of models in facilitating the detection, attribution and projection of climate change is, if anything, emphasized even more than it was in earlier reports.
For instance Oreskes (2007, 84–89) gives broadly Popperian reasons for thinking that climate models should be used as heuristic devices (as opposed to accurate quantitative predictors of the “tempo and mode” of climate change). She concludes: “[i]f model results were the only basis for current scientific understanding, they would be grounds for some healthy skepticism.”
Some scientists have taken up broadly philosophical objections to climate modeling as well, see Curry and Webster (2011) for example. Evidence for a broad consensus is presented in Oreskes (2004). The IPCC reports are intended to present or summarize the current state of knowledge in the field. In the face of this apparent consensus, one line of argument suggests that some of the scientists who drafted and ratified the IPCC report misrepresent their true understanding of the state of climate science in order, perhaps, to further their own political agenda. Because these climate scientists (it is alleged) desire restrictions on greenhouse gas emissions, they overstate the confidence of the climate science community in the quantitative reliability of climate models. While it may be true that some climate scientists have doubts about the quantitative reliability of global climate models, it is also been suggested that some think their reliability was downplayed in the IPCC report in response to pressure from representatives of high greenhouse gas emission nations. It is hard to know what to make of such conflicting anecdotal reports on the actual state of mind of climate scientists. Without substantial evidence to the contrary, it seems sensible to take the ratified statement about the state of their understanding as expressed in the IPCC reports to be the best indication of the consensus opinion of climate scientists.
It is evidently tempting for environmentally friendly philosophers and scientists who have concerns about the quantitative reliability of GCMs to think that basic climate science and the accumulated climate data are enough to establish the reality of climate change, diagnose its source(s), and differentiate between policy options (e.g. would halving our emissions make any difference?). Not only is this position different from that of the IPCC, but there are also systematic reasons to suspect that it is wrong. Simple physics may establish how greenhouse gases and changes in solar output can result in global warming, but it also establishes the possibility of global cooling due to both sulfate emissions and surface albedo changes resulting from deforestation. Analyzing the significance of measured climate data requires a quantitative resolution of these various causal factors and the capacity to contrast them with natural variation—this is what the climate models provide. Without any way to compare the quantitative contributions of these various effects, the measured warming over the twentieth century cannot be unambiguously explained, nor can possible future changes be compared. This should be expected because the climate is a complex system to which many fundamental laws apply, but for which there is no ‘super covering law’ indicating how all of these factors are to be put together. This idea will be addressed in more detail in the last section of the paper. Also, see Cartwright (1983, 100–128).
This is the approach taken in my previous work (Goodwin 2009).
See Lenhard and Winsberg (2011) on using ‘generative entrenchment’ as a way of understanding the epistemological situation of climate models.
This is, I think, a variation on Winsberg’s criticism of Norton and Suppe’s way of conceptualizing climate modeling. Winsberg claims, “the real problem with this sort of story…is that it begs the question of whether or not…and under what conditions, a simulation reliably mimics the system of interest” (Winsberg 2003, 115). A climate model is just a form of empirical data collection, only if you assume, antecedently, that it “realizes” the system of interest—but the potential for such a realization is exactly what a global modeling skeptic denies!
There are two parts to Norton and Suppe’s argument, and though I have doubts about the analogy between GCMs and experimental probes, I am wholly convinced by the first part of their argument. In this first part they argue that the global temperature data sets that are generated by elaborate modeling from a complex range of data sources, including satellites and water buckets, are epistemologically no different from the vast majority of measured scientific data. This is a relative argument for the reliability of global data sets, which I think is compelling and useful.
For interesting recent work, see the collection of articles in the symposium “Applying Science,” which is reproduced in International Studies in the Philosophy of Science, Volume 20, Number 1, March 2006. For a classic formulation of the problem, see Cartwright (1976).
I certainly do not mean to suggest that GCMs are sufficient for policy design, only that they supply information necessary to sensible policy design.
Engineers have already developed this analogy to a certain extent. Simulations of the atmosphere typically employ “implicit large eddy modeling” (Cullen and Brown 2009, 2947) and so are analogous to other forms of applied large eddy simulation, which is “now seen more and more as a viable alternative to current industrial practice” (Tucker and Lardeau 2009, 2809).
More specifically, the relative consistency of the projections of global mean temperature change as the grid sizes of the GCMs get smaller and smaller is a reason (not the only one) for trusting the projections of global mean temperature. This, it might be argued, is similar in kind to the sorts of grid size robustness testing that CFD simulations undergo.
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Goodwin, W.M. Global Climate Modeling as Applied Science. J Gen Philos Sci 46, 339–350 (2015). https://doi.org/10.1007/s10838-015-9301-0
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DOI: https://doi.org/10.1007/s10838-015-9301-0