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Climate–carbon cycle uncertainties and the Paris Agreement

An Author Correction to this article was published on 04 July 2018

This article has been updated


The Paris Agreement1 aims to address the gap between existing climate policies and policies consistent with “holding the increase in global average temperature to well below 2 C”. The feasibility of meeting the target has been questioned both in terms of the possible requirement for negative emissions2 and ongoing debate on the sensitivity of the climate–carbon-cycle system3. Using a sequence of ensembles of a fully dynamic three-dimensional climate–carbon-cycle model, forced by emissions from an integrated assessment model of regional-level climate policy, economy, and technological transformation, we show that a reasonable interpretation of the Paris Agreement is still technically achievable. Specifically, limiting peak (decadal) warming to less than 1.7 °C, or end-of-century warming to less than 1.54 °C, occurs in 50% of our simulations in a policy scenario without net negative emissions or excessive stringency in any policy domain. We evaluate two mitigation scenarios, with 200 gigatonnes of carbon and 307 gigatonnes of carbon post-2017 emissions respectively, quantifying the spatio-temporal variability of warming, precipitation, ocean acidification and marine productivity. Under rapid decarbonization decadal variability dominates the mean response in critical regions, with significant implications for decision-making, demanding impact methodologies that address non-linear spatio-temporal responses. Ignoring carbon-cycle feedback uncertainties (which can explain 47% of peak warming uncertainty) becomes unreasonable under strong mitigation conditions.

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Fig. 1: Summary time series of the 69-member CP, 2P0C and 1P5C E3ME-FTT-GENIE emissions-forced PLASIM-GENIE ensembles.
Fig. 2: DJF surface air temperature scaling patterns and uncertainty.
Fig. 3: JJA precipitation scaling patterns and uncertainty.
Fig. 4: Ocean stressor scaling patterns and uncertainty.

Change history

  • 04 July 2018

    In the version of this Article originally published, H. Pollit’s name was incorrectly listed as H. E. Pollit (H.E.P.) throughout the paper, this has been corrected to H. Pollitt (H.P.) in the online versions of this Article.


  1. Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015);

  2. Anderson, K. & Peters, G. The trouble with negative emissions. Science 354, 182–183 (2016).

    Article  CAS  Google Scholar 

  3. Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).

    Article  Google Scholar 

  4. Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458, 1163–1166 (2009).

    Article  CAS  Google Scholar 

  5. Ehlert, D. & Zickfeld, K. What determines the warming commitment after cessation of CO2 emissions? Environ. Res. Lett. 12, 015002 (2017).

    Article  CAS  Google Scholar 

  6. Steinacher, M., Joos, F. & Stocker, T. F. Allowable carbon emissions lowered by multiple climate targets. Nature 499, 197–201 (2013).

    Article  CAS  Google Scholar 

  7. Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017).

    Article  CAS  Google Scholar 

  8. Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5° C. Nat. Clim. Change 8, 325–332 (2018).

    Article  CAS  Google Scholar 

  9. Holden, P. B. et al. PLASIM–GENIEv1.0: a new intermediate complexity AOGCM. Geosci. Mod. Dev. 9, 3347–3361 (2016).

    Article  Google Scholar 

  10. Geoffroy, O. et al. Transient climate response in a two-layer energy-balance model. Part I: Analytical solution and parameter calibration using CMIP5 AOGCM experiments. J. Clim. 26, 1841–1857 (2013).

    Article  Google Scholar 

  11. Gregory, J. M., Andrews, T. & Good, P. The inconstancy of the transient climate response parameter under increasing CO2. Phil. Trans. R. Soc. A 373, 20140417 (2015).

  12. Winton, M., Takahashi, K. & Held, I. M. Importance of ocean heat uptake efficacy to transient climate change. J. Clim. 23, 2333–2344 (2010).

    Article  Google Scholar 

  13. Williamson, D. et al. History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble. Clim. Dynam. 41, 1703–1729 (2013).

    Article  Google Scholar 

  14. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ.Press, 2013).

  15. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213–241 (2011).

    Article  CAS  Google Scholar 

  16. Cheng, W., Chiang, J. C. H. & Zhang, D. Atlantic Meridional Overturning Circulation (AMOC) in CMIP5 models: RCP and historical simulations. J. Clim. 26, 7187–7197 (2013).

    Article  Google Scholar 

  17. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Article  Google Scholar 

  18. Mercure, J.-F. et al. Environmental impact assessment for climate change policy with the simulation-based integrated assessment model E3ME-FTT-GENIE. Energy Strategy Rev. 20, 195–208 (2018).

    Article  Google Scholar 

  19. Pollitt, H. & Mercure, J.-F. The role of money and the financial sector in energy-economy models used for assessing climate and energy policy. Clim. Policy 18, 184–197 (2017).

    Article  Google Scholar 

  20. Mercure, J.-F. et al. Macroeconomic impact of stranded fossil-fuel assets. Nat. Clim. Change (2018).

  21. Yamamoto, A., Kawamiya, M., Ishida, A., Yamanaka, Y. & Watanabe, S. Impact of rapid sea-ice reduction in the Arctic Ocean on the rate of ocean acidification. Biogeosciences 9, 2365–2375 (2012).

    Article  CAS  Google Scholar 

  22. Ridgwell, A. et al. Marine geochemical data assimilation in an efficient Earth System Model of global biogeochemical cycling. Biogeosciences 4, 87–104 (2007).

    Article  CAS  Google Scholar 

  23. Steinacher, M. et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences 7, 979–1005 (2010).

    Article  CAS  Google Scholar 

  24. Rykaczewski, R. R. & Dunne, J. P. Enhanced nutrient supply to the California Current Ecosystem with global warming and in creased stratification in an Earth system model. Geophys. Res. Lett. 37, L21606 (2010) .

  25. Santner, B. D., Wigley, T. M. L., Schlesinger, M. E. & Mitchell, J. F. B. Developing Climate Scenarios from Equilibrium GCM Results (Max-Planck-Institut fuer Meteorologie, 1990).

  26. Tebaldi, C. & Arblaster, J. M. Pattern scaling: its strengths and limitations, and an update on the latest model simulations. Climatic Change 122, 459–471 (2014).

    Article  CAS  Google Scholar 

  27. Wu, P., Wood, R., Ridley, J. & Lowe, J. Temporary acceleration of the hydrological cycle in response to a CO2 rampdown. Geophys. Res. Lett. 37, L12705 (2010).

    Google Scholar 

  28. Deser, C., Knutti, R., Solomon, S. & Phillips, A. S. Communication of the role of natural variability in future North American climate. Nat. Clim. Change 2, 775–779 (2012).

    Google Scholar 

  29. Holden, P. B., Edwards, N. R., Garthwaite, P. H. & Wilkinson, R. D. Emulation and interpretation of high-dimensional climate outputs. J. Appl. Stat. 42, 2038–2055 (2015).

    Article  Google Scholar 

  30. Fraedrich, K. A suite of user-friendly climate models: hysteresis experiments. Eur. Phys. J. Plus 127, 53 (2012).

    Article  CAS  Google Scholar 

  31. Lenton, T. M. et al. Millennial timescale carbon cycle and climate change in an efficient Earth system model. Clim. Dynam 26, 687–711 (2006).

    Article  Google Scholar 

  32. Zickfeld, K. et al. Long-term climate change commitment and reversibility: an EMIC intercomparison. J. Clim. 26, 5782–5809 (2013).

    Article  Google Scholar 

  33. Joos, F. et al. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis. Atmos. Chem. Phys. 13, 2793–2825 (2013).

    Article  CAS  Google Scholar 

  34. Jain, A. K., Meiyappan, P., Song, Y. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land cover data. Glob. Change Biol. 19, 2893–2906 (2013).

    Article  Google Scholar 

  35. Ramankutty, M. et al. Challenges to estimating carbon emissions from tropical deforestation. Glob. Change Biol. 13, 51–66 (2007).

    Article  Google Scholar 

  36. Jackson, R. B. et al. Reaching peak emissions. Nat. Clim. Change 6, 7–10 (2016).

    Article  Google Scholar 

  37. Craig, P. S., Goldstein, M., Seheult, A. H. & Smith, J. A. in Case Studies in Bayesian Statistics (Lecture Notes in Statistics, Springer, New York, 1997).

  38. Edwards, N. R., Cameron, D. & Rougier, J. Precalibrating an intermediate complexity climate model. Clim. Dynam. 37, 1469–1482 (2011).

    Article  Google Scholar 

  39. Williamson, D. B., Blaker, A. T. & Sinha, B. Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model. Geosci. Model Dev. 10, 1789–1816 (2017).

    Article  Google Scholar 

  40. Holden, P. B., Edwards, N. R., Oliver, K. I. C., T. Lenton, T. M. & Wilkinson, R. D. A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1. Clim. Dynam. 35, 785–806 (2010).

    Article  Google Scholar 

  41. Sacks, J., Welch, W. J., Mitchell, T. J. & Wynn, H. P. Design and analysis of computer experiments. Stat. Sci. 4, 409–23 (1989).

    Article  Google Scholar 

  42. O’Hagan, A. Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Saf. 91, 1290–1300 (2006).

    Article  Google Scholar 

  43. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267–88 (1996).

    Google Scholar 

  44. Rasmussen, C. E. Gaussian processes in machine learning. In Advanced Lectures on Machine Learning (eds Bousquet, O., von Luxburg, U. & Rätsch, G.) 63–71 (Springer, Berlin, 2004).

  45. Marin, J.-M., Pudlo, P., Robert, C. P. & Ryder, R. J. Approximate Bayesian Computational Methods. Stat. Comput. 22, 1167–1180 (2012).

    Article  Google Scholar 

  46. World Energy Outlook 2015 (OECD/IEA, 2015);

  47. World Energy Investment Outlook (OECD/IEA, 2014);

  48. Mercure, J. F., Pollitt, H., Bassi, A. M., Viñuales, J. E. & Edwards, N. R. Modelling complex systems of heterogeneous agents to better design sustainability transitions policy. Glob. Environ. Change 37, 102–115 (2016).

    Article  Google Scholar 

  49. Mercure, J. F., Lam, A., Billington, S. & Pollitt, H. Integrated assessment modelling as a positive science: private passenger road transport policies to meet a climate target well below 2 degrees C. Preprint at (2018).

  50. World Energy Outlook 2014 (OECD/IEA, 2014);

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We acknowledge C-EERNG and Cambridge Econometrics for support, and funding from EPSRC (to J.-F.M., fellowship number EP/ K007254/1); the Newton Fund (to J.-F.M., P.S. and J.E.V., EPSRC grant number EP/N002504/1 and ESRC grant number ES/N013174/1), NERC (to N.R.E., P.H. and H.P., grant number NE/P015093/1), CONICYT (to P.S.), the Philomathia Foundation (to J.E.V.) and Horizon 2020 (to H.P. and J.-F.M., the Sim4Nexus project).

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Authors and Affiliations



P.B.H., N.R.E. and R.D.W. designed and coordinated the Earth system modelling. H.P., J.-F.M. and N.R.E. designed and coordinated the energy-economy modelling. P.B.H., N.R.E., R.D.W. and H.P. wrote the article with contributions from all. P.B.H. performed the PLASIM-GENIE simulations. U.C. performed the E3ME-FTT simulations. All authors developed model components and/or provided scientific support: P.B.H. (ESM coupling), K.F. and F.L. (atmosphere), N.R.E. (ocean), A.R. (biogeochemistry), H.P. and J.F.M. (energy-economic), P.S. and J.-F.M. (power sector), A.L. and J.-F.M. (transport sector), F.K. and J.-F.M. (household heating), J.E.V. (geopolitics) and R.D.W. (statistics).

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Correspondence to P. B. Holden.

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Holden, P.B., Edwards, N.R., Ridgwell, A. et al. Climate–carbon cycle uncertainties and the Paris Agreement. Nature Clim Change 8, 609–613 (2018).

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