Advertisement

Establishing causation in climate litigation: admissibility and reliability

  • Tobias Pfrommer
  • Timo Goeschl
  • Alexander Proelss
  • Martin Carrier
  • Johannes Lenhard
  • Henrike Martin
  • Ulrike Niemeier
  • Hauke Schmidt
Article

Abstract

Climate litigation has attracted renewed interest as a governance tool. A key challenge in climate litigation is to assess the factual basis of causation. Extreme weather attribution, specifically the Fraction of Attributable Risk (FAR), has been proposed as a way to tackle this challenge. What remains unclear is how attribution science interacts with the legal admissibility of evidence based on climate models. While evidence has to be legally admissible in order to be considered in a trial, it has to be reliable in order for the court to arrive at a legally correct conclusion. Since parties to the trial have incentives to produce evidence favorable to their case, admissibility requirements and the reliability of the evidence brought forward are linked. We provide a specific proposal for how to accommodate FAR estimates in admissibility standards by modifying an existing set of admissibility criteria, the Daubert criteria. We argue that two of the five Daubert criteria are unsuitable for dealing with such evidence and that replacing those criteria with ones directly addressing the reliability of FAR estimates is adequate. Lastly, we highlight the dependence of courts on both the existence and accessibility of a framework to determine the reliability of FAR estimates in executing such criteria.

Notes

Author contributions

TP developed the basic idea and concept underlying the study. All of the authors have contributed to the development of idea and concept of this paper and to the writing. TP, TG, and AP have drafted large parts of the manuscript. TP coordinated the writing process and calculated the FAR estimates. UN has performed the climate model simulations and contributed to their analysis.

Funding

The authors gratefully acknowledge funding by the German Research Foundation DFG under grant numbers CA120/19-2 (MC, JL), GO1604/3-2 (TG, TP), PR1323/2-2 (AP, HM), and SCHM2158/4-2 (HS, UN).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10584_2018_2362_MOESM1_ESM.docx (423 kb)
ESM 1 (DOCX 423 kb)

References

  1. Allen M (2003) Liability for climate change. Nature 421(6926):891CrossRefGoogle Scholar
  2. Allen M et al (2007) Scientific challenges in the attribution of harm to human influence on climate. Univ Pa Law Rev:1353–1400Google Scholar
  3. Angélil O et al (2017) An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events, Journal of Climate. 30.1:5–16Google Scholar
  4. Berger MA (2005) What has a decade of Daubert wrought? Am J Public Health 95(S1):S59–S65CrossRefGoogle Scholar
  5. Carmines, Edward G., and Richard A. Zeller (1979) Reliability and validity assessment. Vol. 17. Sage publicationsGoogle Scholar
  6. Carrier M (2011) Underdetermination as an epistemological test tube: expounding hidden values of the scientific community. Synthese 180(2):189–204CrossRefGoogle Scholar
  7. Christidis N et al (2013) A new HadGEM3-A-based system for attribution of weather-and climate-related extreme events. Journal of Climate 26(9):2756–2783CrossRefGoogle Scholar
  8. Coumou D, Rahmstorf S (2012) A decade of weather extremes. Nat Clim Chang 2(7):491CrossRefGoogle Scholar
  9. Diffenbaugh NS, Swain DL, Touma D (2015) Anthropogenic warming has increased drought risk in California. Proceedings of the National Academy of Sciences112 13:3931–3936Google Scholar
  10. Diffenbaugh NS et al (2017) Quantifying the influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci 114(19):4881–4886CrossRefGoogle Scholar
  11. Dole R et al (2011) Was there a basis for anticipating the 2010 Russian heat wave? Geophysical Research Letters38:6Google Scholar
  12. Goldman AI (1986) Epistemology and cognition. Harvard University PressGoogle Scholar
  13. Haack S (2008) What's wrong with litigation-driven science-an essay in legal epistemology. Seton Hall L Rev 38:1053Google Scholar
  14. Haack S (2010) Federal philosophy of science: a deconstruction-and a reconstruction. NYUJL & Liberty 5:394Google Scholar
  15. Hannart A et al (2016) Causal counterfactual theory for the attribution of weather and climate-related events. Bulletin of the American Meteorological Society 97(1):99–110CrossRefGoogle Scholar
  16. Hauser, Mathias, et al. (2017) "Methods and model dependency of extreme event attribution: the 2015 European drought." Earth's Future 5(10): 1034–1043Google Scholar
  17. Heinzerling L (2006) Doubting Daubert. JL & Pol'y 14:65Google Scholar
  18. Herring SC et al (2016) Explaining extreme events of 2015 from a climate perspective. Bull Am Meteorol Soc 97(12):1–145Google Scholar
  19. Herring SC et al (2018) Explaining extreme events of 2016 from a climate perspective. Bull Am Meteorol Soc 99(1):1–157Google Scholar
  20. Horton JB, Parker A, Keith D (2014) Liability for solar geoengineering: historical precedents, contemporary innovations, and governance possibilities. NYU Envtl LJ 22:225Google Scholar
  21. Jasanoff S (2005) Law’s knowledge: science for justice in legal settings. Am J Public Health 95(S1):S49–S58CrossRefGoogle Scholar
  22. Kravitz B et al (2011) The geoengineering model intercomparison project (GeoMIP). Atmos Sci Lett 12(2):162–167CrossRefGoogle Scholar
  23. Lott FC, Stott PA (2016) Evaluating simulated fraction of attributable risk using climate observations. J Clim 29(12):4565–4575CrossRefGoogle Scholar
  24. Lusk G (2017) The social utility of event attribution: liability, adaptation, and justice-based loss and damage. Clim Chang 143(1–2):201–212CrossRefGoogle Scholar
  25. Mann ME, Lloyd EA, Oreskes N (2017) Assessing climate change impacts on extreme weather events: the case for an alternative (Bayesian) approach. Clim Chang 144(2):131–142CrossRefGoogle Scholar
  26. Marjanac S, Patton L, Thornton J (2017) Acts of God, human influence and litigation. Nat Geosci 10(9):616–619CrossRefGoogle Scholar
  27. Marjanac S, Patton L (2018) Extreme weather event attribution science and climate change litigation: an essential step in the causal chain? J Energy Nat Resour Law:1–34Google Scholar
  28. McAvaney, Bryant J., et al. Model evaluation. Climate change 2001: the scientific basis. Contribution of WG1 to the Third Assessment Report of the IPCC (TAR). Cambridge University Press, 2001. 471–523Google Scholar
  29. McCormick S et al (2017) Science in litigation, the third branch of US climate policy. Science 357(6355):979–980CrossRefGoogle Scholar
  30. McGarity TO (2004) Our science is sound science and their science is junk science: science-based strategies for avoiding accountability and responsibility for risk-producing products and activities. U Kan L Rev 52:897Google Scholar
  31. National Academies of Sciences, Engineering, and Medicine. Attribution of extreme weather events in the context of climate change. National Academies Press, 2016Google Scholar
  32. Niemeier U, Tilmes S (2017) Sulfur injections for a cooler planet. Science 357(6348):246–248CrossRefGoogle Scholar
  33. Ocean Studies Board and National Research Council (2015) Climate intervention: reflecting sunlight to cool earth. National Academies PressGoogle Scholar
  34. Otto FEL et al (2012) Reconciling two approaches to attribution of the 2010 Russian heat wave. Geophys Res Lett 39:4CrossRefGoogle Scholar
  35. Otto, Friederike EL (2012) Modelling the earth’s climate-an epistemic perspective. Diss. Freie Universität BerlinGoogle Scholar
  36. Otto FEL et al (2017) Assigning historic responsibility for extreme weather events. Nature Climate Change 7(11):757CrossRefGoogle Scholar
  37. Palmer TN et al (2008) Toward seamless prediction: calibration of climate change projections using seasonal forecasts. Bull Am Meteorol Soc 89(4):459–470CrossRefGoogle Scholar
  38. Parker, Wendy S. (2009) "II—confirmation and adequacy-for-purpose in climate modelling." Aristotelian Society Supplementary Volume. Vol. 83. No. 1. Oxford, UK: Blackwell Publishing LtdGoogle Scholar
  39. Petersen, Arthur C (2012) Simulating nature: a philosophical study of computer-simulation uncertainties and their role in climate science and policy advice. CRC PressGoogle Scholar
  40. Popper KR (1959) The logic of scientific discovery. RoutledgeGoogle Scholar
  41. Rahmstorf S, Coumou D (2011) Increase of extreme events in a warming world. Proc Natl Acad Sci 108(44):17905–17909CrossRefGoogle Scholar
  42. Reynolds JL (2015) An economic analysis of liability and compensation for harm from large-scale field research in solar climate engineering. Climate Law 5(2–4):182–209CrossRefGoogle Scholar
  43. Saxler B, Siegfried J, Proelss A (2015) International liability for transboundary damage arising from stratospheric aerosol injections. Law Innov Technol 7(1):112–147CrossRefGoogle Scholar
  44. Schäfer S et al (2015) The European transdisciplinary assessment of climate engineering (EuTRACE): removing greenhouse gases from the atmosphere and reflecting sunlight away from. EarthGoogle Scholar
  45. Seager R, et al. (2015) "Causes of the 2011–14 California drought." J Clim 28.18: 6997–7024Google Scholar
  46. Shepherd TG (2014) Atmospheric circulation as a source of uncertainty in climate change projections. Nat Geosci 7(10):703CrossRefGoogle Scholar
  47. Shepherd TG (2016) A common framework for approaches to extreme event attribution. Curr Clim Chang Rep 2(1):28–38CrossRefGoogle Scholar
  48. Shiogama H et al (2013) An event attribution of the 2010 drought in the South Amazon region using the MIROC5 model. Atmos Sci Lett 14(3):170–175CrossRefGoogle Scholar
  49. Sillmann J et al (2013) Climate extremes indices in the CMIP5 multimodel ensemble: part 1. Model evaluation in the present climate. J Geophys Res: Atmospheres 118(4):1716–1733Google Scholar
  50. Stott, Peter A., et al. "Attribution of extreme weather and climate-related events." Wiley Interdisciplinary Reviews: Climate Change 7.1 (2016): 23–41Google Scholar
  51. Stott PA, Karoly DJ, Zwiers FW (2017) Is the choice of statistical paradigm critical in extreme event attribution studies? Clim Chang 144(2):143–150CrossRefGoogle Scholar
  52. Stott PA et al (2018) Future challenges in event attribution methodologies. Bull Am Meteorol Soc 99(1):S155–S157CrossRefGoogle Scholar
  53. Swinehart MW (2007) Remedying Daubert’s inadequacy in evaluating the admissibility of scientific models used in environmental-tort litigation. Tex L Rev 86:1281Google Scholar
  54. Thornton J, Covington H (2016) Climate change before the court. Nat Geosci 9(1):3CrossRefGoogle Scholar
  55. Trenberth KE, Fasullo JT, Shepherd TG (2015) Attribution of climate extreme events. Nat Clim Chang 5(8):725CrossRefGoogle Scholar
  56. Wagner W (2005) The perils of relying on interested parties to evaluate scientific quality. Am J Public Health 95(S1):S99–S106CrossRefGoogle Scholar
  57. Weisheimer A, Palmer TN (2014) On the reliability of seasonal climate forecasts. J Royal Soc Interface 11(96):20131162CrossRefGoogle Scholar
  58. Wilhite DA, Glantz MH (1985) Understanding: the drought phenomenon: the role of definitions. Water Int 10(3):111–120CrossRefGoogle Scholar
  59. Williams AP et al (2015) Contribution of anthropogenic warming to California drought during 2012–2014. Geophys Res Lett 42(16):6819–6828CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Tobias Pfrommer
    • 1
  • Timo Goeschl
    • 1
  • Alexander Proelss
    • 2
  • Martin Carrier
    • 3
  • Johannes Lenhard
    • 3
  • Henrike Martin
    • 4
  • Ulrike Niemeier
    • 5
  • Hauke Schmidt
    • 5
  1. 1.Department of EconomicsHeidelberg UniversityHeidelbergGermany
  2. 2.Faculty of LawUniversity of HamburgHamburgGermany
  3. 3.Philosophy Department and Institute for Interdisciplinary Studies of ScienceBielefeld UniversityBielefeldGermany
  4. 4.Institute of Environmental Law of Trier University (IUTR)Trier UniversityTrierGermany
  5. 5.Max-Planck-Institute for MeteorologyHamburgGermany

Personalised recommendations