Effective climate policy requires information from various scientific disciplines. Here, we construct a metamodel from climate and integrated assessment models that assesses the emissions budget, costs and uncertainty sources of achieving temperature targets. By calibrating to the model-based literature range, the metamodel goes beyond the parametric uncertainty of individual models. The resulting median estimates for the cumulative abatement costs (at 5% discount rate) for 2 °C and 1.5 °C targets are around US$15 trillion and US$30 trillion, but estimates vary over a wide range (US$10–100 trillion for the 1.5 °C target). The sources determining this uncertainty depend on the climate target stringency. Climate system uncertainty dominates at high warming levels, but uncertainty in emissions reductions costs dominates for the Paris Agreement targets. In fact, costs differences between different socio-economic development paths can be larger than the difference in median estimates for the 2 °C and 1.5 °C targets. This simple metamodel helps to explore implications of scenario uncertainty and identify research priorities.
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The data used to assess the impact of non-CO2 emissions and the mitigation costs that support the findings of this study are publicly available online at the scenario databases hosted by IIASA: https://tntcat.iiasa.ac.at/SspDb/dsd (ref. 4), https://tntcat.iiasa.ac.at/AR5DB/dsd (ref. 2) and https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/workspaces (ref. 18). The data for each figure are available at the PBL/IMAGE website.
Model codes are available at https://github.com/kvanderwijst/variancedecomposition-IAM (https://doi.org/10.5281/zenodo.3633944).
IPCC. Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).
Clarke, L. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 414–510 (Cambridge Univ. Press, 2014).
Edenhofer, O. et al. The economics of low stabilization: model comparison of mitigation strategies and costs. Energy J. 31, 11–48 (2010).
Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Global Environ. Chang. 42, 153–168 (2017).
Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).
Friedlingstein, P. et al. Climate–carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Clim. 19, 3337–3353 (2006).
Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (Cambridge Univ. Press, 2013).
Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017).
Schweizer, V. A few scenarios still do not fit all. Nat. Clim. Change 8, 361–362 (2018).
Rozenberg, J., Guivarch, C., Lempert, R. & Hallegatte, S. Building SSPs for climate policy analysis: a scenario elicitation methodology to map the space of possible future challenges to mitigation and adaptation. Clim. Change 122, 509–522 (2014).
Guivarch, C., Lempert, R. & Trutnevyte, E. Scenario techniques for energy and environmental research: an overview of recent developments to broaden the capacity to deal with complexity and uncertainty. Environ. Modell. Softw. 97, 201–210 (2017).
Van Vuuren, D. P. et al. Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Clim. Change 81, 119–159 (2007).
Rogelj, J., McCollum, D. L., Reisinger, A., Meinshausen, M. & Riahi, K. Probabilistic cost estimates for climate change mitigation. Nature 493, 79–83 (2013).
Meinshausen, M. et al. Greenhouse-gas emission targets for limiting global warming to 2°C. Nature 458, 1158–1162 (2009).
Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).
Booth, B. B. B. et al. High sensitivity of future global warming to land carbon cycle processes. Environ. Res. Lett. 7, 024002 (2012).
Visser, H., Dangendorf, S., Van Vuuren, D. P., Bregman, B. & Petersen, A. C. Signal detection in global mean temperatures after ‘Paris’: an uncertainty and sensitivity analysis. Clim. Past 14, 139–155 (2018).
IPCC. Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).
van Vuuren, D. P. et al. Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials. Energy Policy 37, 5125–5139 (2009).
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).
Kriegler, E. et al. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Clim. Change 123, 353–367 (2014).
Luderer, G. et al. Residual fossil CO2 emissions in 1.5–2 °C pathways. Nat. Clim. Change 8, 626–633 (2018).
Millar, R. J. & Friedlingstein, P. The utility of the historical record for assessing the transient climate response to cumulative emissions. Phil. Trans. R. Soc. A 376, 20160449 (2018).
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. Modell. Softw. 59, 10–29 (2014).
Lemoine, D. & McJeon, H. C. Trapped between two tails: trading off scientific uncertainties via climate targets. Environ. Res. Lett. 8, 034019 (2013).
Lamontagne, J. R., Reed, P. M., Marangoni, G., Keller, K. & Garner, G. G. Robust abatement pathways to tolerable climate futures require immediate global action. Nat. Clim. Change 9, 290–294 (2019).
Gillingham, K. et al. Modeling uncertainty in integrated assessment of climate change: a multimodel comparison. J. Assoc. Environ. Resour. Econ. 5, 791–826 (2018).
Sutton, R. T. ESD ideas: a simple proposal to improve the contribution of IPCC WGI to the assessment and communication of climate change risks. Earth Syst. Dynam. 9, 1155–1158 (2018).
IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Rogelj, J. et al. Differences between carbon budget estimates unravelled. Nat. Clim. Change 6, 245–252 (2016).
Vose, D. Risk Analysis: A Quantitative Guide (John Wiley & Sons, 2008).
Meinshausen, M., Raper, S. C. B. & Wigley, T. M. L. Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).
The common Integrated Assessment Model (IAM) documentation. IAMC https://www.iamcdocumentation.eu/index.php/IAMC_wiki (2018).
Wijst, K.-I. v. d. Optimal Policy for Carbon Pricing: Challenging the Hotelling Rule and Dissecting Mitigation Cost Uncertainties. Master of Mathematical Sciences thesis, Utrecht Univ. (2018).
Sobol, I. M. On sensitivity estimation for nonlinear mathematical models. Matem. Mod. 2, 112–118 (1990).
Saltelli, A. Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145, 280–297 (2002).
The research presented in this paper benefitted from funding under the European Union’s Horizon 2020 Framework Programme for Research and Innovation under grant agreement nos. 82112 (NAVIGATE) and 641816 (CRESCENDO). In addition, C.J. was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (grant no. GA01101).
The authors declare no competing interests.
Peer review information Nature Climate Change thanks James Glynn and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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van Vuuren, D.P., van der Wijst, KI., Marsman, S. et al. The costs of achieving climate targets and the sources of uncertainty. Nat. Clim. Chang. 10, 329–334 (2020). https://doi.org/10.1038/s41558-020-0732-1
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