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Robust abatement pathways to tolerable climate futures require immediate global action

Nature Climate Change volume 9, pages 290–294 (2019)Cite this article

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A Publisher Correction to this article was published on 16 April 2019

Abstract

Disentangling the relative importance of climate change abatement policies from the human–Earth system (HES) uncertainties that determine their performance is challenging because the two are inexorably linked, and the nature of this linkage is dynamic, interactive and metric specific1. Here, we demonstrate an approach to quantify the individual and joint roles that diverse HES uncertainties and our choices in abatement policy play in determining future climate and economic conditions, as simulated by an improved version of the Dynamic Integrated model of Climate and the Economy2,3. Despite wide-ranging HES uncertainties, the growth rate of global abatement (a societal choice) is the primary driver of long-term warming. It is not a question of whether we can limit warming but whether we choose to do so. Our results elucidate important long-term HES dynamics that are often masked by common time-aggregated metrics. Aggressive near-term abatement will be very costly and do little to impact near-term warming. Conversely, the warming that will be experienced by future generations will mostly be driven by earlier abatement actions. We quantify probabilistic abatement pathways to tolerable climate/economic outcomes4,5, conditional on the climate sensitivity to the atmospheric CO2 concentration. Even under optimistic assumptions about the climate sensitivity, pathways to a tolerable climate/economic future are rapidly narrowing.

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Fig. 1: Joint sampling of HES uncertainties and the policy space.
Fig. 2: Time-aggregated and time-varying sensitivities for climate damages, abatement costs and warming.
Fig. 3: Abatement pathways with associated probabilities of achieving tolerable climate/economic conditions for an assumed climate sensitivity.

Code availability

The CDICE2013 model2 was updated to be consistent with DICE-2016R3, and is available at: https://github.com/JRLamontagne/cdice_sa

Data availability

The data that were used in this analysis are available at the GitHub repository: https://github.com/JRLamontagne/cdice_sa

References

  1. Butler, M. P., Reed, P. M., Fisher-Vanden, K., Keller, K. & Wagener, T. Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis. Environ. Model. Software 59, 10–29 (2014).

    Article  Google Scholar 

  2. Garner, G., Reed, P. & Keller, K. Climate risk management requires explicit representation of societal trade-offs. Clim. Change 134, 713–723 (2016).

    Article  Google Scholar 

  3. Nordhaus, W. D. Revisiting the social cost of carbon. Proc. Natl Acad. Sci. USA 114, 1518–1523 (2017).

    Article  CAS  Google Scholar 

  4. Yohe, G. W. Uncertainty, short-term hedging and the tolerable window approach. Global Environ. Change 7, 303–315 (1997).

    Article  Google Scholar 

  5. Bruckner, T. et al. Climate change decision-support and the tolerable windows approach. Environ. Model. Assess. 4, 217–234 (1999).

    Article  Google Scholar 

  6. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 63, 747–756 (2010).

    Article  Google Scholar 

  7. Weyant, J. Integrated assessment of climate change: state of the literature. J. Benefit–Cost Anal. 5, 377–409 (2014).

    Article  Google Scholar 

  8. Vuuren, D. Pv. et al. The representative concentration pathways: an overview. Clim. Change 109, 5 (2011).

    Article  Google Scholar 

  9. Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environ. Change 2, 153–168 (2017).

    Article  Google Scholar 

  10. Marangoni, G. et al. Sensitivity of projected long-term CO2 emissions across the shared socioeconomic pathways. Nat. Clim. Change 7, 113–117 (2017).

    Article  CAS  Google Scholar 

  11. Mathias, J.-D., Anderies, J. M. & Janssen, M. A. On our rapidly shrinking capacity to comply with the planetary boundaries on climate change. Sci. Rep. 7, 42061 (2017).

    Article  CAS  Google Scholar 

  12. Petschel-Held, G., Schellnhuber, H.-J., Bruckner, T., Tóth, F. L. & Hasselmann, K. The tolerable windows approach: theoretical and methodological foundations. Clim. Change 41, 303–331 (1999).

    Article  CAS  Google Scholar 

  13. Nordhaus, W. Estimates of the social cost of carbon: concepts and results from the DICE-2013r model and alternative approaches. J. Assn Environ. Resource Econom. 1, 273–312 (2014).

    Google Scholar 

  14. Weyant, J. P. Some contributions of integrated assessment models of global climate change. Rev. Environ. Econ. Policy 11, 115–137 (2017).

    Article  Google Scholar 

  15. Goes, M., Tuana, N. & Keller, K. The economics (or lack thereof) of aerosol geoengineering. Clim. Change 109, 719–744 (2011).

    Article  Google Scholar 

  16. Kriegler, E. Imprecise Probability Analysis For Integrated Assessment Of Climate Change. PhD thesis, Univ. Potsdam (2005).

  17. Lamontagne, J. et al. Large ensemble analytic framework for consequence‐driven discovery of climate change scenarios. Earth’s Future 6, 488–504 (2018).

    Article  Google Scholar 

  18. Nordhaus, W. Strategies for the Control of Carbon Dioxide Cowles Foundation Discussion Paper 443 (Cowles Foundation for Research in Economics, Yale University, 1977); https://econpapers.repec.org/paper/cwlcwldpp/443.htm

  19. Leimbach, M. & Bruckner, T. Influence of economic constraints on the shape of emission corridors. Comput. Econ. 18, 173–191 (2001).

    Article  Google Scholar 

  20. Azar, C. & Schneider, S. H. Are the economic costs of stabilising the atmosphere prohibitive? Ecol. Econ. 42, 73–80 (2002).

    Article  Google Scholar 

  21. Paris Agreement (UNFCCC, 2016); https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf

  22. Sobol, I. Sensitivity estimates for nonlinear mathematical models and their Monte Carlo estimation. Math. Model. Comput. Exp. 1, 407–417 (1993).

    Google Scholar 

  23. Beck, M. & Krueger, T. The epistemic, ethical, and political dimensions of uncertainty in integrated assessment modeling. Wiley Interdiscip. Rev. Clim. Change 7, 627–645 (2016).

    Article  Google Scholar 

  24. Adler, M. et al. Priority for the worse-off and the social cost of carbon. Nat. Clim. Change 7, 443–449 (2017).

    Article  Google Scholar 

  25. Shue, H. Mitigation gambles: uncertainty, urgency and the last gamble possible. Phil. Trans. R. Soc. A 376, 20170105 (2018).

    Article  Google Scholar 

  26. Raftery, A. E., Zimmer, A., Frierson, D. M. W., Startz, R. & Liu, P. Less than 2 °C warming by 2100 unlikely. Nat. Clim. Change 7, 637–641 (2017).

    Article  CAS  Google Scholar 

  27. Butler, M. P., Reed, P. M., Fisher-Vanden, K., Keller, K. & Wagener, T. Inaction and climate stabilization uncertainties lead to severe economic risks. Clim. Change 127, 463–474 (2014).

    Article  CAS  Google Scholar 

  28. Fuss, S. et al. Betting on negative emissions. Nat. Clim. Change 4, 850–853 (2014).

  29. Vuuren, D. Pv, Hof, A. F., Sluisveld, M. A. Ev & Riahi, K. Open discussion of negative emissions is urgently needed. Nat. Energy 2, 902–904 (2017).

    Article  Google Scholar 

  30. Luderer, G. et al. Residual fossil CO2 emissions in 1.5–2 °C pathways. Nat.Clim. Change 8, 626–633 (2018).

  31. Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change. Rev. Geophys. 48 (2010); https://doi.org/10.1029/2010RG000345

  32. Hansen, J., Sato, M., Kharecha, P. & von Schuckmann, K. Earth’s energy imbalance and implications. Atmos. Chem. Phys. 11, 13421–13449 (2011).

    Article  CAS  Google Scholar 

  33. Sobol, I. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55, 271–280 (2001).

    Article  Google Scholar 

  34. Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Comp. Phys. Commun. 145, 280–297 (2002).

    Article  CAS  Google Scholar 

  35. Saltelli, A. et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun. 181, 259–270 (2010).

    Article  CAS  Google Scholar 

  36. Archer, G., Saltelli, A. & Sobol, I. Sensitivity measures, ANOVA-like techniques and the use of bootstrap. J. Statist. Comput. Simul. 58, 99–120 (1997).

    Article  Google Scholar 

  37. Herman, J. & Usher, W. SALib: an open-source Python library for Sensitivity Analysis. J Open Source Software 2, 97 (2017).

    Article  Google Scholar 

  38. Wilks, D. Statistical Methods in the Atmospheric Sciences 2nd edn (Academic Press, New York, 2006).

    Google Scholar 

  39. Bryant, B. P. & Lempert, R. J. Thinking inside the box: a participatory, computer-assisted approach to scenario discovery. Technol. Forecast. Soc. Change 77, 34–49 (2010).

    Article  Google Scholar 

  40. Quinn, J. D. et al. Exploring how changing monsoonal dynamics and human pressures challenge multireservoir management for flood protection, hydropower production, and agricultural water supply. Water Resour. Res. 54, 4683–4662 (2018).

    Google Scholar 

  41. Herman, J, D., Reed, P, M., Zeff, H, B. & Characklis W. How should robustness be defined for water systems planning under change?. J. Water Res. Plan. Man. 141, 04015012 (2015).

  42. McPhail, C. et al. Robustness metrics: how are they calculated, when should they be used and why do they give different results?. Earth’s Future 6, 169–191 (2018).

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Science Foundation (NSF) through the Network for Sustainable Climate Risk Management under NSF cooperative agreement GEO-1240507 as well as the Penn State Center for Climate Risk Management. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Author information

Author notes

  1. G. Marangoni

    Present address: Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Tufts University, Medford, MA, USA

    J. R. Lamontagne

  2. School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA

    P. M. Reed

  3. Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA, USA

    G. Marangoni & K. Keller

  4. Department of Geosciences, The Pennsylvania State University, University Park, PA, USA

    K. Keller

  5. Department of Earth and Planetary Sciences, Rutgers University, Piscataway, NJ, USA

    G. G. Garner

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Contributions

J.R.L., G.G.G. and G.M. prepared the computer models. J.R.L. conducted the simulation. J.R.L. and G.M. performed the data analysis with help from G.G.G. P.M.R. and K.K. supervised the project. All authors wrote the manuscript.

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Correspondence to J. R. Lamontagne.

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The authors declare no competing interests.

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Journal peer review information: Nature Climate Change thanks Jan Kwakkel, Francesca Pianosi and Matthias Weitzel for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Tables 1–15, Supplementary Figures 1–7, Supplementary References

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Lamontagne, J.R., Reed, P.M., Marangoni, G. et al. Robust abatement pathways to tolerable climate futures require immediate global action. Nat. Clim. Chang. 9, 290–294 (2019). https://doi.org/10.1038/s41558-019-0426-8

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