Abstract
Between the fourth and the recent fifth IPCC report, science as well as policy making have made great advances in dealing with uncertainties in global climate models. However, the uncertainties public decision making has to deal with go well beyond what is currently addressed by policy makers and climatologists alike. It is shown in this paper that within an anthropocentric framework, a whole hierarchy of models from various scientific disciplines is needed for political decisions as regards climate change. Via what is sometimes referred to as ‘uncertainty cascade’ in the technical literature, the uncertainties of various models accumulate in the input to political decision making. This paper aims to chart the uncertainties in the multi-disciplinary enterprise of current climate modeling in broad strokes. It is shown that the uncertainty cascade makes it impossible to quantify uncertainties in the form of probability estimates. Moreover the paper highlights how global climate models fail to provide probability estimates. A better treatment of the uncertainties of climate predictions on the political level would require an overhaul of the current IPCC practice that separates the various scientific fields in various working groups.
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Notes
There have been various attempts recently to better address conceptualization uncertainties (e.g. Moss and Schneider 2000), but these all remain rather fragmentary.
References
Benestad, R. E., Hanssen-Baur, I., & Chen, D. (2008). Empirical-statistical downscaling. Singapore: World Scientific Publishing.
Betz, G. (2009). Underdetermination, model-ensembles and surprises—on the epistemology of scenario-analysis in climatology. Journal for General Philosophy of Science, 40, 3–21.
Biddle, J., & Winsberg, E. (2010). Value judgements and the estimation of uncertainty in climate modeling. In P. D. Magnus & J. Busch (Eds.), New waves in philosophy of science. New York: Palgrave Macmillan.
Castles, I., & Henderson, D. (2003). Economics, emission scenarios and the work of the IPCC. Energy and Environment, 14(4), 415–435.
Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A.. Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W. T., Laprise, R., Magaña Rueda, V., Mearns, L., Menéndez, C. G., Räisänen, J., Rinke, A., Sarr, A., & Whetton, P. (2007). Regional climate projections. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, H. L. Miller (Hg.) (Eds.), Climate change 2007: The scientific basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
Churchman, C. W. (1948). Theory of experimental inference. New York: Macmillan.
Churchman, C. W. (1956). Science and decision making. Philosophy of Science, 22, 247–249.
Collins, M. (2007). Ensembles and Probabilities: A New Era in the Prediction of Climate Change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, 1957–1970. doi: 10.1098/rsta.2007.2068
Douglas, H. (2000). Inductive risk and values in science. Philosophy of Science, 67, 559–579.
Frisch, M. (2013). Modeling climate policies: A critical look at integrated assessment models. Philosophy of Technology, 26, 117–137.
Giere, R. N. (1988). Explaining science. A cognitive approach. Chicago: The University of Chicago Press.
Gleckler, P. J., Taylor, K. E., & Doutriaux, C. (2008). Performance metrics for climate models. Journal of Geophysical Research: Atmospheres, 113(D6).http://onlinelibrary.wiley.com/doi/10.1029/2007JD008972/full.
Grübler, A., et al. (2004). Emission scenarios: A final response. Energy and Environment, 15, 11–24.
Hannart, A., Ghil, M., Dufresne, J. L., & Naveau, P. (2013). Disconcerting learning on climate sensitivity and the uncertain future of uncertainty. Climatic Change, 119(3–4), 585–601.
Hillerbrand, R., & Ghil, M. (2008). Anthopogenic climate change: Scientific uncertainties and moral dilemmas. Physica D, 237, 2132–2138.
Hillerbrand, R. (2010). On non-propositional aspects in modelling complex systems. Analyse und Kritik, 32, 107–120.
Hillerbrand, R., & Schneider, C. (2013). Unwissenschaftlich weil unsicher? Unsicher weil wissenschaftlich! Szenarien, Modelle und Projektionen in den Klimawissenschaften. In S. Jeschke, E.-M. Jakobs, & A. Dröge (Eds.), Exploring uncertainty (pp. 151–177). Berlin: Springer.
Holtsmark, B., & Alfsen, K. (2005). PPP Correction of the IPCC Emission Scenarios: Does It Matter?. Climatic Change, 68(1–2), 11–19.
Katzav, J. (2013). Severe testing of climate change hypotheses. Studies in History and Philosophy of Modern Physics, 44(4), 433–441.
Maslin, M., & Austin, P. (2012). Climate models at their limit? Nature, 486, 184.
Mayo, D. (1983). An objective theory of statistical testing. Synthese, 57, 297–340.
Mayo, D. (1996). Error and the growth of experimental knowledge. Chicago: The University of Chicago Press.
Meinshausen, M., et al. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109, 213–241.
Morgan, M. S., & Morrison, M. (Eds.). (1999). Models as mediators. Perspectives on natural and social science. Cambridge, MA: Cambridge University Press.
Morrison, M. (2009). Models, measurement and computer simulation: The changing face of experimentation. Philosophical Studies, 143, 33–57.
Moss, R. H., & Schneider, S. H. (2000). Uncertainties in the IPCC TAR: Recommendation to lead authors for more consistent assessment and reporting. In R. Pachauri, T. Taniguchi, & T. Tanaka (Eds.), Third assessment report. Geneva: Cross Cutting Issues Guidance Papers.
Nakicenovic, N., Davidson, O., Davis, G., Grubler, A., Kram, T., Lebre La Rovere, E., Metz, B., Morita, T., Pepper, W., Pitcher, H., Sankovski, A., Shukla, P., Swart, R., Watson, R., Dadi, Z. (2000). IPCC Special Report: Emission Scenarios–Summary for Policymakers. http://www.ipcc.ch/pdf/special-reports/spm/sresen.pdf.
Nordhaus, W. (2008). A question of balance. Weighing the options on global warming policies. Yale: Yale University Press.
Norton, S. D. & Suppe, F. (2001). “Why atmospheric modeling is good science” in Changing the atmosphere: Expert knowledge and environmental governances. In C. Miller & P. N. Edwards (Eds.) (pp. 67–105). Cambridge: MIT press.
Oreskes, N., Stainforth, D. A., & Smith, L. A. (2010). Adaptation to global warming: Do climate models tell us what we need to know? Philosophy of Science, 77, 1012–1028.
Parker, W. (2009). Does matter really matter? Computer simulations, experiments, and materiality. Synthese, 169(3), 483–496.
Parker, W. (2010). Predicting weather and climate: Uncertainty, ensembles and probability. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 41(3), 263–272.
Polanyi, M. (1958). Personal knowledge: Towards a post-critical philosophy. Chicago: University of Chicago Press.
Pope, S. B. (2001). Turbulent flows. Cambridge, MA: Cambridge University Press.
Poznic, M. (2013). Misrepresentation, similarity, and indirect modeling. Presentation at OZSW Conference, November 2013, Rotterdam, NL, (pp. 15–16).
Reiss, J. (2013). Philosophy of economics. A contemporary introduction. London: Routledge.
Roundtree, A. K. (2010). The rhetoric of computer simulations in astrophysics: A case study. Journal of Science Communication, 9, A02.
Rudner, R. (1953). The scientist qua scientist makes value judgments. Philosophy of Science, 20(1), 1–6.
Sauter, T., Weitzenkamp, B., & Schneider, C. (2010). Spatio-temporal prediction of snow cover in the Black Forest mountain range using remote sensing and a recurrent neural network. International Journal of Climatology, 30(15), 2330–2341. doi:10.1002/joc.2043.
Smith, L. A., & Petersen, A. (2014). Variations on reliability: Connecting climate predictions to climate policy. In M. Boumans, A. Petersen, G. Hon (Eds.), Error and uncertainty in scientific practice. History and philosophy of technoscience (1) (pp. 137–156). London, UK: Pickering and Chatto Publishers. ISBN 9781848934160.
Solomon, S. et al. (Hg.) (2007). Climate change 2007: The scientific basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, MA: Cambridge University Press.
Spash, C. (2002). Greenhouse economics: Value and ethics. London: Routledge.
Stern, N. (2007). The economics of climate change. In The stern review. Cambridge, MA: Cambridge University Press.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M. M. B., Allen, S. K., Boschung, J., et al. (2013). Climate Change 2013: The physical science basis. Working group I contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. http://www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.
Sundberg, M. (2011). The dynamics of coordinated comparisons: How simulationists in astrophysics, oceanography and meteorology create standards for results. Social Studies of Science, 41, 107.
Suppes, P. (1962). Models of data. In Proceedings of the 1960 international congress of logic methodology and philosophy of science. http://suppes-corpus.stanford.edu/articles/mpm/41.
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: an overview. In: Climatic Change. doi: 10.1007/s10584-011-0148-z.
Winsberg, E. (2013). Computer simulations in science. In E. N. Zalta (Ed.) The Stanford Encyclopedia of Philosophy (Summer 2013 Edition). http://plato.stanford.edu/archives/sum2013/entries/simulations-science/.
Winsberg, E. (2003). Simulated experiments: Methodology for a virtual world. Philosophy of Science, 70, 105–125.
Winsberg, E. (2012). Values and uncertainties in the predictions of global climate models. Kennedy Institute of Ethics Journal, 22(2), 111–137.
Wittgenstein, L. (2001). Philosophical investigations. Oxford: Blackwell Publishing.
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Hillerbrand, R. Climate Simulations: Uncertain Projections for an Uncertain World. J Gen Philos Sci 45 (Suppl 1), 17–32 (2014). https://doi.org/10.1007/s10838-014-9266-4
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DOI: https://doi.org/10.1007/s10838-014-9266-4