Abstract
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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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.
References
Adoption of the Paris Agreement FCCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015);http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf
Anderson, K. & Peters, G. The trouble with negative emissions. Science 354, 182–183 (2016).
Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).
Allen, M. R. et al. Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458, 1163–1166 (2009).
Ehlert, D. & Zickfeld, K. What determines the warming commitment after cessation of CO2 emissions? Environ. Res. Lett. 12, 015002 (2017).
Steinacher, M., Joos, F. & Stocker, T. F. Allowable carbon emissions lowered by multiple climate targets. Nature 499, 197–201 (2013).
Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017).
Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5° C. Nat. Clim. Change 8, 325–332 (2018).
Holden, P. B. et al. PLASIM–GENIEv1.0: a new intermediate complexity AOGCM. Geosci. Mod. Dev. 9, 3347–3361 (2016).
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).
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).
Winton, M., Takahashi, K. & Held, I. M. Importance of ocean heat uptake efficacy to transient climate change. J. Clim. 23, 2333–2344 (2010).
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).
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ.Press, 2013).
Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change 109, 213–241 (2011).
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).
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
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).
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).
Mercure, J.-F. et al. Macroeconomic impact of stranded fossil-fuel assets. Nat. Clim. Change https://doi.org/10.1038/s41558-018-0182-1 (2018).
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).
Ridgwell, A. et al. Marine geochemical data assimilation in an efficient Earth System Model of global biogeochemical cycling. Biogeosciences 4, 87–104 (2007).
Steinacher, M. et al. Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences 7, 979–1005 (2010).
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) .
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).
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).
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).
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).
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).
Fraedrich, K. A suite of user-friendly climate models: hysteresis experiments. Eur. Phys. J. Plus 127, 53 (2012).
Lenton, T. M. et al. Millennial timescale carbon cycle and climate change in an efficient Earth system model. Clim. Dynam 26, 687–711 (2006).
Zickfeld, K. et al. Long-term climate change commitment and reversibility: an EMIC intercomparison. J. Clim. 26, 5782–5809 (2013).
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).
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).
Ramankutty, M. et al. Challenges to estimating carbon emissions from tropical deforestation. Glob. Change Biol. 13, 51–66 (2007).
Jackson, R. B. et al. Reaching peak emissions. Nat. Clim. Change 6, 7–10 (2016).
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).
Edwards, N. R., Cameron, D. & Rougier, J. Precalibrating an intermediate complexity climate model. Clim. Dynam. 37, 1469–1482 (2011).
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).
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).
Sacks, J., Welch, W. J., Mitchell, T. J. & Wynn, H. P. Design and analysis of computer experiments. Stat. Sci. 4, 409–23 (1989).
O’Hagan, A. Bayesian analysis of computer code outputs: a tutorial. Reliab. Eng. Syst. Saf. 91, 1290–1300 (2006).
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267–88 (1996).
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).
Marin, J.-M., Pudlo, P., Robert, C. P. & Ryder, R. J. Approximate Bayesian Computational Methods. Stat. Comput. 22, 1167–1180 (2012).
World Energy Outlook 2015 (OECD/IEA, 2015); https://www.iea.org/publications/freepublications/publication/WEO2015.pdf
World Energy Investment Outlook (OECD/IEA, 2014); https://www.iea.org/publications/freepublications/publication/WEIO2014.pdf
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).
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 https://arxiv.org/abs/1702.04133 (2018).
World Energy Outlook 2014 (OECD/IEA, 2014); https://www.iea.org/publications/freepublications/publication/WEO2014.pdf
Acknowledgements
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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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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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). https://doi.org/10.1038/s41558-018-0197-7
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DOI: https://doi.org/10.1038/s41558-018-0197-7