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The Role of “Complex” Empiricism in the Debates About Satellite Data and Climate Models

Abstract

Climate scientists have been engaged in a decades-long debate over the standing of satellite measurements of the temperature trends of the atmosphere above the surface of the earth. This is especially significant because skeptics of global warming and the greenhouse effect have utilized this debate to spread doubt about global climate models used to predict future states of climate. I use this case from an understudied science to illustrate two distinct philosophical approaches to the relations among data, scientist, measurement, models, and theory. I argue that distinguishing between “direct” empiricist and “complex” empiricist approaches helps us understand and analyze this important scientific episode. I introduce a complex empiricist account of testing and evaluation, and contrast it with the basic hypothetico-deductive approach to the climate models used by the direct empiricists. This more developed complex empiricist approach will serve philosophy of science well, as computational models become more widespread in the sciences.

Keywords

  • Empirical Direction
  • Empirical Complexity
  • Radiosonde Dataset
  • Satellite Datasets
  • Radiosonde Data

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Much of the content of this chapter was taken from an article with the same title in Studies in History and Philosophy of Science 43: 390–401, 2012.

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Notes

  1. 1.

    John Christy, Written testimony, US Senate Committee on Commerce, Science, and Transportation, Subcommittee on Space, Science, and Competitiveness, January 8, 2015, convened by Chairman US Senator Ted Cruz.

  2. 2.

    This includes Peter Thorne, John Lanzante , Thomas Peterson , Dian Seidel , and Keith Shine , data analysts involved in the debate, who offer a new detailed, technical review of the decades of debate, which balances my focus on the satellites with that on the radiosonde datasets, which remain relatively neglected in my study (Thorne et al. 2011). Their study offers independent support for my analysis regarding the philosophical approaches to the satellite and radiosonde datasets.

  3. 3.

    R. Raff, Pers. comm. February 14, 2010.

  4. 4.

    In climate science, what philosophers such as Patrick Suppes call “models of data” are called “datasets (1962).”

  5. 5.

    Ricky Rood , Pers. comm. August 30, 2010.

  6. 6.

    When I talk of “construction” of datasets, this is not meant to imply anything arbitrary or fanciful about the process. Rather, it refers to the necessary, rational, and scientific decision processes that are required in the production of the final measurements that make up the datasets.

  7. 7.

    Paul Edwards gives a detailed account of this process in his book, A Vast Machine (2010).

  8. 8.

    Van Fraassen himself does not use the name “complex empiricism” for his view but has agreed to my attribution.

  9. 9.

    For a contemporary review of some of these ways of analyzing data, see Thorne et al. (2011) and Edwards (2010, esp. pp. 256–273).

  10. 10.

    As a coauthor, Christy admitted that the question of which dataset—RSS, UAH, or UMd—is closest to the true tropospheric temperature was unknown (Karl et al. 2006).

  11. 11.

    The Intergovernmental Panel on Climate Change (IPCC) is a United Nations panel , part of which is dedicated to summarizing the state of climate science at a given time. The conclusion of the reports of the IPCC had been that global warming existed, and was significantly affected by human causes (Houghton et al. 2001; IPCC 2007).

  12. 12.

    The NAS panel itself was split on the importance of the radiosonde data. It noted that “[t]hose more inclined to take the MSU [satellite] measurements at face value cite the high degree of consistency with radiosonde measurements (Figs. 2.3, 9.2, and 9.3) [based on data from Christy et al. 2000], whereas those less inclined to do so note the retreat of the tropical glaciers and the increasing burden of water vapor” (Wallace et al. 2000, p. 65).

  13. 13.

    See, for another example, Jeffrey Kiehl et al. (2005), “On using global climate model simulations to assess the accuracy of MSU retrieval methods for tropospheric warming trends.”

  14. 14.

    This attitude is also reflected in the IPCC 2007 report when discussing the radiosonde and satellite datasets. There, the authors of the chapter on climate observations write: “It is difficult to make quantitatively defensible judgments as to which, if any, of the multiple, independently derived estimates is closer to the true climate evolution. This. . . points to the need for future network design that provides the reference sonde-based ground truth” (Solomon et al. IPCC 2007, p. 265).

  15. 15.

    Spencer et al. (2006) subsequently attacked the Fu and Johanson methods, and rejected their conclusions about radiosonde temperature trends. Johanson and Fu (2006) addressed the issues raised in the Spencer et al. (2006) paper.

  16. 16.

    Ben Santer, pers. comm. March 10, 2011.

  17. 17.

    John Christy , Written testimony, Subcommittee on Energy and Power, Committee on Energy and Commerce, March 8, 2011.

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Acknowledgment

I am indebted to climate scientists Caspar Ammann, Jim Hurrell, Jeffrey Kiehl, Ricky Rood, Ben Santer, Peter Thorne, Kevin Trenberth, and Tom Wigley for their assistance. I thank philosophers Ron Giere, Peter Gildenhuys, Alex Klein, Steven Lawrie, Helen Longino, Gordon McOuat, Rudy Raff, Paul Teller, Trin Turner, Sean Valles, and Eric Winsberg. Bas van Fraassen and Isabelle Peschard organized “The Experimental Side of Modeling” workshop at San Francisco State University (March 2010), where this paper got its start, and I thank them, as well as the attendees, for their guidance and comments on this project and its issues.

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Lloyd, E.A. (2018). The Role of “Complex” Empiricism in the Debates About Satellite Data and Climate Models. In: A. Lloyd, E., Winsberg, E. (eds) Climate Modelling. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65058-6_6

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