Abstract
As climate policy decisions are decisions under uncertainty, being based on a range of future climate change scenarios, it becomes a crucial question how to set up this scenario range. Failing to comply with the precautionary principle, the scenario methodology widely used in the Third Assessment Report of the International Panel on Climate Change (IPCC) seems to violate international environmental law, in particular a provision of the United Nations Framework Convention on Climate Change. To place climate policy advice on a sound methodological basis would imply that climate simulations which are based on complex climate models had, in stark contrast to their current hegemony, hardly an epistemic role to play in climate scenario analysis at all. Their main function might actually consist in ‘foreseeing future ozone-holes’. In order to argue for these theses, I explain first of all the plurality of climate models used in climate science by the failure to avoid the problem of underdetermination. As a consequence, climate simulation results have to be interpreted as modal sentences, stating what is possibly true of our climate system. This indicates that climate policy decisions are decisions under uncertainty. Two general methodological principles which may guide the construction of the scenario range are formulated and contrasted with each other: modal inductivism and modal falsificationism. I argue that modal inductivism, being the methodology implicitly underlying the third IPCC report, is severely flawed. Modal falsificationism, representing the sound alternative, would in turn require an overhaul of the IPCC practice.
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Notes
I would like to thank the two anonymous referees of this journal for their distinct and critical remarks. Their comments helped me to improve upon a previous version of this paper. Yet, it goes without saying that I take responsibility for its residual shortcomings.
By using computer models to simulate the climate system we clearly “extend ourselves” (Humphreys 2004): Nobel prize winner Svante Arrhenius was the first who, towards the end of the nineteenth century, attempted to calculate the effects of a doubling of the atmospheric CO2-concentration (see Crawford 1997). In spite of several months of “laborious pencil work” and results that came close to the current estimates, Weart (2003) underlines that “neither Arrhenius nor anyone for the next half-century had the tools to show what an increase of CO2 would really do to climate”.
A similar explanation of climate model pluralism can be found in Parker (2006).
Brackets contain the arguments’ and theses’ titles as in Fig. 1.
These standards comprise, for example, explanatory power (see Laudan 1991).
In another paper, Winsberg (2006) argues that the successful usage of so-called contrary-to-fact principles in complex modelling and simulation represents an example for reliability without truth. Truth, on this account, is no prerequisite for reliability. Truth and reliability, in other words, can be conflicting epistemic aims.
E.g. Bayesian Confirmation Theory, see Howson and Urbach (1993); for a critical assessment compare Mayo (1996). Thus, Bayesian Theory can help to explain and justify the methodological rule that a hypothesis which explains an otherwise puzzling fact is, ceteris paribus, better confirmed than a hypothesis which explains a well-understood fact. See also Carrier (2006, pp. 104–129).
See also Morgan and Morrison (1999).
See also IPCC (2001, p. 474f).
For if two contrary theories were derivable from some body of knowledge claims (including empirical evidence), that very body of beliefs were contradictory in the first place and would not represent knowledge at all.
For a detailed account of climate model construction compare Shackley (2001).
Winsberg’s own pragmatic methodology of model simulations which partly reduces the credibility of simulation results to the credibility of the model-building techniques—namely insofar as these have proven to yield reliable and successful models in the past—does not avoid underdetermination in climate science, either. The techniques currently applied by climatologists underdetermine choices during model construction, as the variety of climate models shows.
Being incompatible, climate models display "ontic competitive pluralism" (Parker 2006, p. 362).
I make use of the terminological distinction between “risk” and “uncertainty” introduced by Knight (1921).
In this paper, "climate scenario" refers to a prediction of some regional or global variable(s) describing the climate system, e.g. a forecast of global mean temperature change in the twenty-first century. Thus, climate scenarios are distinguished from the so-called SRES emission scenarios which define boundary conditions for climate forecasting.
While it figures as a major premiss in Rawls thought-experiment that justifies the Difference Principle (Rawls 1971), it has been criticised by Harsanyi (1975). For attempts to reformulate and implement the precautionary principle see for example Gardiner (2006) and European Environmental Agency (2001).
Or, more precisely: might prevent.
At this point, the argumentation is touching another substantial debate in the philosophy of science, namely the question of value-free science. Irrespective of whether science is necessarily value laden in some sense (as for different reasons, Putnam (2002) and Kitcher (2001) have argued recently), my argument rests on the modest idea that avoidable value-ladenness should be avoided in scientific policy advice (for democratic reasons).
Which ranks the alternative policy measures according to a weighted sum of best and worst possible outcome (Shackle 1949).
That is no role in the process of justification.
See, for instance, Edwards (2001). Edwards, moreover, claims that computer models “are, and will remain the historical, social, and epistemic core of the climate science/policy community” (p. 64). Yet, he reaches this conclusion by implicitly assuming that the epistemic role of climate science is to predict the consequences of alternative policies. If one conceives climate policy as decision making under uncertainty, however, identification of possible scenarios instead of (deterministic) prediction becomes climatology’s main goal. How this can be accomplished without the use of GCMs will be discussed below.
Likewise, Norton and Suppe (2001) stress that without computer models “we would be unable to understand the climate system as a single, integrated whole […]” (ibid., p. 67). The arguments put forward in this paper do not contradict that thesis. I would merely add that understanding a complex system ought to be distinguished from constructing possible future scenarios. Referring to underdetermination, Oreskes et al. (1994), however, argue in favour of a more far-reaching thesis, namely that GCMs have no epistemic role to play in science at all. While accepting the model-underdetermination thesis, Norton and Suppe (2001) criticise their reasoning by stressing that non-uniqueness poses no problem as long as scientific results are restricted to common features of all models that have been set up. Yet, this last reasoning apparently rests on the idea that the set of all models covers the entire space of physical possibilities—a seemingly unwarranted assumption.
References
Albert, M. (2003). Bayesian rationality and decision making: A critical review. Analyse & Kritik, 25, 101–117.
Beck, U. (1986). Risikogesellschaft-Auf dem Weg in die Moderne. Frankfurt am Main: Suhrkamp.
Betz, G. (2006). Prediction or prophecy? The boundaries of economic foreknowledge and their socio-political consequences. Wiesbaden: DUV.
Betz, G. (2007). Probabilities in climate policy advice: A critical comment. Climatic Change, 85(1–2), 1–9.
Carrier, M. (2006). Wissenschaftstheorie zur Einführung. Hamburg: Junius.
Cartwright, N. (1999). The dappled world: A study of the boundaries of science. Cambridge: Cambridge University Press.
Crawford, E. (1997). Arrhenius’ 1896 model of the greenhouse effect in context. Ambio, 26(1), 6–11.
Dessai, S., & Hulme, M. (2003). Does climate policy need probabilities? Working Paper 34. Tyndall Centre for Climate Change Research.
Edwards, P. N. (2001). Representing the global atmosphere: Computer models, data, and knowledge about climate change. In Miller and Edwards (2001), pp. 31–66.
European Environmental Agency. (2001). Late lessons from early warnings: the precautionary principle 1896–2000. Office for Official Publications of the European Communities.
Gardiner, S. M. (2006). A core precautionary principle. The Journal of Political Philosophy, 14(1), 33–60.
Gillies, D. (2000). Philosophical theories of probability. London: Routledge.
Harsanyi, J. C. (1975). Can the maximin principle serve as a basis for morality? A critique of John Rawls’ theory. The American Political Science Review, 69, 594–606.
Howson, C., & Urbach, P. (1993). Scientific reasoning: The Bayesian approach (Vol. 2). Chicago: Open Court.
Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. New York: Oxford University Press.
IPCC. (2001). Climate Change 2001: The scientific basis; contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press.
IPCC. (2007). Climate Change 2007: The physical science basis; contribution of working group i to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, New York: Cambridge University Press.
Kitcher, P. (2001). Science, truth, and democracy. Oxford: Oxford University Press.
Knight, F. (1921). Risk, uncertainty and profit. Boston, New York: Houghton Mifflin.
Kuhn, T. S. (1977). Objectivity, value judgement, and theory choice. In T. S. Kuhn (Ed.), The essential tension: Selected studies in scientific tradition and change (pp. 320–329). Chicago: Chicago University Press.
Laudan, L. (1991). Empirical equivalence and underdetermination. The Journal of Philosophy, LXXXVIII(9), 449–472.
Lempert, R., Nakicenovic, N., et al. (2004). Characterizing climate-change uncertainties for decision-makers—An editorial essay. Climatic Change, 65(1–2), 1–9.
Lenhard, J. (2007). Computer simulation: The cooperation between experimenting and modeling. Philosophy of Science, 74, 176–194.
Lorius, C., Jouzel, J., Raynaud, D., Hansen, J., & Le Treut, H. (1990). The ice-core record: Climate sensitivity and future greenhouse warming. Nature, 347, 139–145.
Mayo, D. (1996). Error and the growth of experimental knowledge. Chicago: Chicago University Press.
McGuffie, K., & Henderson-Sellers, A. (2001). Forty years of numerical climate modelling. International Journal of Climatology, 21, 1067–1109.
Miller, C. A., & Edwards, P. N. (Eds.). (2001). Changing the atmosphere: Expert knowledge and environmental governance. Cambridge: MIT Press.
Morgan, M. G., & Keith, D. W. (1995). Climate-change—Subjective judgments by climate experts. Environmental Science and Technology, 29, A468–A476.
Morgan, M., & Morrison, M. (Eds.). (1999). Models as mediators. Cambridge: Cambridge University Press.
Norton, S. D., & Suppe, F. (2001). Why atmospheric modeling is good science. In Miller and Edwards (2001), pp. 67–106.
Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in earth sciences. Science, 263, 641–646.
Parker, W. S. (2006). Understanding pluralism in climate modeling. Foundations of Science, 11, 349–368.
Putnam, H. (2002). The collapse of the fact/value dichotomy. Cambridge: Harvard University Press.
Quine, W. V. O. (1953). Two dogmas of empiricism. In W. Van Orman Quine (Ed.), From a logical point of view, chapter 2 (pp. 20–46). Cambridge: Harvard University Press.
Rawls, J. (1971). A theory of justice. Cambridge: Harvard University Press.
Shackle, G. L. S. (1949). Expectations in economics. Cambridge: Cambridge University Press.
Shackley, S. (2001). Epistemic lifestyles in climate change modeling. In Miller and Edwards (2001), pp. 107–134.
Siegenthaler, U., Stocker, T. F., Monnin, E., Lüthi, D., Schwander, J., Stauffer, B., et al. (2005). Stable carbon cycle? Climate relationship during the late Pleistocene. Science, 310(5752), 1313–1317.
Stainforth, D. A., Aina, T., et al. (2005). Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature, 433(7024), 403–406.
Tetens, H. (2006). Selbstreflexive Physik. Transzendentale Begründungen am Beispiel des Strukturenrealismus. Deutsche Zeitschrift für Philosophie, 54(3), 431–448.
Tetens, H. (2007). Einstein als Philosoph. In P. W. Balsinger & R. Kötter (Eds.), Die Kultur moderner Wissenschaft am Beispiel Albert Einsteins. Heidelberg: Spektrum Akademischer Verlag.
United Nations. (1992). Report of the United Nations Conference on Environment and Development (A/CONF.151/26).
Weart, S. R. (2003). The discovery of global warming. Cambridge, MA: Harvard University Press.
Webster, M. D., Forest, C. E., Reilly, J., Babikerand, M., Kicklighter, D., Mayer, M., et al. (2003). Uncertainty analysis of climatic change and policy responses. Climatic Change, 61(3), 295–320.
Winsberg, E. (2003). Simulated experiments: Methodology for a virtual world. Philosophy of Science, 70, 105–125.
Winsberg, E. (2006). Models of success vs. the success of models: Reliability without truth. Synthese, 152, 1–19.
Zickfeld, K., Levermann, A., Granger Morgan, M., Kuhlbrodt, T., Rahmstorf, S., & Keith, D. W. (2007). Present state and future fate of the Atlantic meridional overturning circulation as viewed by experts. Climatic Change, 82, 235–265.
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Betz, G. Underdetermination, Model-ensembles and Surprises: On the Epistemology of Scenario-analysis in Climatology. J Gen Philos Sci 40, 3–21 (2009). https://doi.org/10.1007/s10838-009-9083-3
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DOI: https://doi.org/10.1007/s10838-009-9083-3