European Journal for Philosophy of Science

, Volume 5, Issue 2, pp 171–190

Predictivism and old evidence: a critical look at climate model tuning

Original paper in Philosophy of Science

Abstract

Many climate scientists have made claims that may suggest that evidence used in tuning or calibrating a climate model cannot be used to evaluate the model. By contrast, the philosophers Katie Steele and Charlotte Werndl have argued that, at least within the context of Bayesian confirmation theory, tuning is simply an instance of hypothesis testing. In this paper I argue for a weak predictivism and in support of a nuanced reading of climate scientists’ concerns about tuning: there are cases, model-tuning among them, in which predictive successes are more highly confirmatory of a model than accommodation of evidence.

Keywords

Climate models Bayesian confirmation theory Tuning Problem of old evidence 

References

  1. Barnes, E. C. (2008). The paradox of predictivism. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  2. Brush, S. G. (1994). Dynamics of theory change: the role of predictions. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 1994(January), 133–45.Google Scholar
  3. Cartwright, N. (1983). How the laws of physics lie. London: Oxford University Press.CrossRefGoogle Scholar
  4. Douglas, H., & Magnus, P. D. (2013). State of the field: why novel prediction matters. Studies in History and Philosophy of Science Part A, 44(4), 580–89.CrossRefGoogle Scholar
  5. Earman, J., & Glymour, C. (1978). Einstein and Hilbert: two months in the history of general relativity. Archive for History of Exact Sciences, 19(3), 291–308.CrossRefGoogle Scholar
  6. Flato, G., Marotzke J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., et al. (2013) Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. In T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex & P.M. Midgley (Eds.), Evaluation of Climate Models. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.Google Scholar
  7. Frisch, M. (2013). Modeling climate policies: a critical look at integrated assessment models. Philosophy and Technology, 26, 117–137.CrossRefGoogle Scholar
  8. Gleckler, P. J., Taylor, K. E., & Doutriaux, C. (2008). Performance metrics for climate models, Journal of Geophysical Research, 113, D06104. doi:10.1029/2007JD008972.
  9. Glymour, C. (2010). Why I Am Not a Bayesian. In Philosophy of Probability: Contemporary Readings. Routledge.Google Scholar
  10. Golaz, J.-C., Salzmann, M., Donner, L. J., Horowitz, L. W., Ming, Y., & Zhao, M. (2010). Sensitivity of the aerosol indirect effect to subgrid variability in the cloud parameterization of the GFDL atmosphere general circulation model AM3. Journal of Climate, 24(13), 3145–60. doi:10.1175/2010JCLI3945.1.CrossRefGoogle Scholar
  11. Golaz, J.-C., Horowitz, L. W., & Levy, H. (2013). Cloud tuning in a coupled climate model: impact on 20th century warming. Geophysical Research Letters, 40(10), 2246–51. doi:10.1002/grl.50232.CrossRefGoogle Scholar
  12. Howson, C., & Franklin, A. (1991). Maher, Mendeleev and Bayesianism. Philosophy of Science, 58(4), 574--585.Google Scholar
  13. Humphreys, P. (2009). The philosophical novelty of computer simulation methods. Synthese, 169(3), 615–26. doi:10.1007/s11229-008-9435-2.CrossRefGoogle Scholar
  14. Katzav, J. (2013). Hybrid models, climate models, and inference to the best explanation. The British Journal for the Philosophy of Science, 64(1), 107–29. doi:10.1093/bjps/axs002.CrossRefGoogle Scholar
  15. Katzav, J. (2014). The epistemology of climate models and some of its implications for climate science and the philosophy of science. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 46(Part B), 228–38. doi:10.1016/j.shpsb.2014.03.001.CrossRefGoogle Scholar
  16. Knutti, R. (2008). Should we believe model predictions of future climate change? Philosophical Transactions of the Royal Society A, 366:4647–4664Google Scholar
  17. Knutti, R. (2010). The end of model democracy? Climatic Change, 102(3–4), 395–404. doi:10.1007/s10584-010-9800-2.CrossRefGoogle Scholar
  18. Knutti, R., Allen, M. R., Friedlingstein, P., Gregory, J. M., Hegerl, G. C., Meehl, G. A., Meinshausen, M., et al. (2008). A review of uncertainties in global temperature projections over the twenty-first century. Journal of Climate, 21(11), 2651–63. doi:10.1175/2007JCLI2119.1.CrossRefGoogle Scholar
  19. Lenhard, J., & Winsberg, E. (2010). Holism, entrenchment, and the future of climate model pluralism. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, Special Issue: Modelling and Simulation in the Atmospheric and Climate Sciences, 41(3), 253–62. doi:10.1016/j.shpsb.2010.07.001.CrossRefGoogle Scholar
  20. Maher, P. (1988). Prediction, accommodation, and the logic of discovery. PSA: Proceedings of the biennial meeting of the philosophy of science association 1988, 273--285.Google Scholar
  21. Masson, D., & Knutti, R. (2013). Predictor screening, calibration, and observational constraints in climate model ensembles: An illustration using climate sensitivity. Journal of Climate, 26:887–898.Google Scholar
  22. Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., Haak, H., et al. (2012). Tuning the climate of a global model. Journal of Advances in Modeling Earth Systems, 4(3), M00A01. doi:10.1029/2012MS000154.CrossRefGoogle Scholar
  23. Parker, W. S. (2009). II—Confirmation and adequacy-for-purpose in climate modelling. Aristotelian Society Supplementary Volume, 83(1), 233–49. doi:10.1111/j.1467-8349.2009.00180.x.CrossRefGoogle Scholar
  24. Randall, D.A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., J. Fyfe, et al. (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. In Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., and Miller, H.L. (Eds.), Climate models and their evaluation. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.Google Scholar
  25. Stainforth, D.A., Allen, M.R., Tredger, E.R. & Smith, L.A. (2007). Confidence, uncertainty and decision-support relevance in climate predictions. Philosophical Transactions of the Royal Society A, 365:2145–2161.Google Scholar
  26. Werndl, C., & Steele, K. (2013). Climate models, calibration, and confirmation. British Journal for the Philosophy of Science, 64(3), 609–35.CrossRefGoogle Scholar
  27. Worrall, J. (2014). Prediction and accommodation revisited. Studies in History and Philosophy of Science Part A, 45(March), 54–61. doi:10.1016/j.shpsa.2013.10.001.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.University of MarylandCollege ParkUSA

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