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Using large ensembles of climate change mitigation scenarios for robust insights

Nature Climate Change volume 12, pages 428–435 (2022)Cite this article

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Abstract

As they gain new users, climate change mitigation scenarios are playing an increasing role in transitions to net zero. One promising practice is the analysis of scenario ensembles. Here we argue that this practice has the potential to bring new and more robust insights compared with the use of single scenarios. However, several important aspects have to be addressed. We identify key methodological challenges and the existing methods and applications that have been or can be used to address these challenges within a three-step approach: (1) pre-processing the ensemble; (2) selecting a few scenarios or analysing the full ensemble; and (3) providing users with efficient access to the information.

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Fig. 1: Overview of climate change mitigation scenarios and the diversification of their uses and users.
Fig. 2: Steps to use ensembles of scenarios.
Fig. 3: Methodological components to compile and pre-process scenario ensembles.
Fig. 4: Methodological components to single out scenarios.

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References

  1. Bauer, N. et al. Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison. Climatic Change 163, 1553–1568 (2020).

    Article  CAS  Google Scholar 

  2. 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. Climatic Change 123, 353–367 (2014).

    Article  Google Scholar 

  3. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).

    Article  Google Scholar 

  4. Roelfsema, M. et al. Taking stock of national climate policies to evaluate implementation of the Paris Agreement. Nat. Commun. 11, 2096 (2020).

    Article  CAS  Google Scholar 

  5. Schaeffer, R. et al. Comparing transformation pathways across major economies. Climatic Change 162, 1787–1803 (2020).

    Article  Google Scholar 

  6. Huppmann, D., Rogelj, J., Kriegler, E., Krey, V. & Riahi, K. A new scenario resource for integrated 1.5 °C research. Nat. Clim. Change 8, 1027–1030 (2018).

    Article  Google Scholar 

  7. Auer, C. et al. Climate change scenario services: from science to facilitating action. One Earth 4, 1074–1082 (2021).

    Article  Google Scholar 

  8. Weber, C. et al. Mitigation scenarios must cater to new users. Nat. Clim. Change 8, 845–848 (2018).

    Article  Google Scholar 

  9. Krabbe, O. et al. Aligning corporate greenhouse-gas emissions targets with climate goals. Nat. Clim. Change 5, 1057–1060 (2015).

    Article  Google Scholar 

  10. NGFS Climate Scenarios for Central Banks and Supervisors (Network for Greening the Financial System, 2021); https://www.ngfs.net/sites/default/files/media/2021/08/27/ngfs_climate_scenarios_phase2_june2021.pdf

  11. Final Report: Recommendations of the Task Force on Climate-Related Financial Disclosures (TCFD, 2017).

  12. Cointe, B., Cassen, C. & Nadaï, A. Organising policy-relevant knowledge for climate action. Sci. Technol. Stud. 32, 36–57 (2019).

    Article  Google Scholar 

  13. Hausfather, Z. & Peters, G. P. Emissions—the ‘business as usual’ story is misleading. Nature 577, 618–620 (2020).

    Article  CAS  Google Scholar 

  14. Skea, J., van Diemen, R., Portugal-Pereira, J. & Khourdajie, A. A. Outlooks, explorations and normative scenarios: approaches to global energy futures compared. Technol. Forecast. Soc. Change 168, 120736 (2021).

    Article  Google Scholar 

  15. IPCC Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change (eds Nakićenović, N. et al.) (IPCC, 2000).

  16. O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  17. Clarke, L. & Weyant, J. Introduction to the EMF 22 special issue on climate change control scenarios. Energy Econ. 31, S63 (2009).

    Article  Google Scholar 

  18. Rogelj, J. et al. A new scenario logic for the Paris Agreement long-term temperature goal. Nature 573, 357–363 (2019).

    Article  CAS  Google Scholar 

  19. Weyant, J. P. The cost of the Kyoto Protocol: a multi-model evaluation. Energy J. Special Issue 20, 1–398 (1999).

    Google Scholar 

  20. van Beek, L., Hajer, M., Pelzer, P., van Vuuren, D. & Cassen, C. Anticipating futures through models: the rise of integrated assessment modelling in the climate science–policy interface since 1970. Glob. Environ. Change 65, 102191 (2020).

    Article  Google Scholar 

  21. Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M. & Popper, S. W. Decision Making Under Deep Uncertainty: From Theory to Practice (Springer, 2019); https://doi.org/10.1007/978-3-030-05252-2

  22. Lempert, R. J., Popper, S. W. & Bankes, S. C. Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis (RAND Corporation, 2003); https://www.rand.org/pubs/monograph_reports/MR1626.html

  23. Guivarch, C., Rozenberg, J. & Schweizer, V. The diversity of socio-economic pathways and CO2 emissions scenarios: insights from the investigation of a scenarios database. Environ. Model. Softw. 80, 336–353 (2016).

    Article  Google Scholar 

  24. Lamontagne, J. R. et al. Large ensemble analytic framework for consequence‐driven discovery of climate change scenarios. Earths Future 6, 488–504 (2018).

    Article  Google Scholar 

  25. Giannousakis, A. et al. How uncertainty in technology costs and carbon dioxide removal availability affect climate mitigation pathways. Energy 216, 119253 (2021).

    Article  CAS  Google Scholar 

  26. Moksnes, N. et al. Determinants of energy futures—a scenario discovery method applied to cost and carbon emission futures for South American electricity infrastructure. Environ. Res. Commun. 1, 025001 (2019).

    Article  Google Scholar 

  27. Cash, D. W. et al. Knowledge systems for sustainable development. Proc. Natl Acad. Sci. USA 100, 8086–8091 (2003).

    Article  CAS  Google Scholar 

  28. Bilogub, M. & Auer, C. Guidelines for Co-Production Workshops with Stakeholders from Policy, Business, and Finance with a Global Perspective (SENSES, 2019); https://climatescenarios.org/share/SENSES_CoproductionManual_Global.pdf

  29. Jaxa-Rozen, M. & Trutnevyte, E. Sources of uncertainty in long-term global scenarios of solar photovoltaic technology. Nat. Clim. Change 11, 266–273 (2021).

    Article  Google Scholar 

  30. Lamboll, R. D., Nicholls, Z. R. J., Kikstra, J. S., Meinshausen, M. & Rogelj, J. Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research. Geosci. Model Dev. 13, 5259–5275 (2020).

    Article  CAS  Google Scholar 

  31. Forster, P. et al. in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Ch. 2 (IPCC, WMO, 2018).

  32. Huppmann, D. et al. pyam: analysis and visualisation of integrated assessment and macro-energy scenarios. Open Res. Eur. 1, 74 (2021).

    Article  Google Scholar 

  33. Brutschin, E. et al. A multidimensional feasibility evaluation of low-carbon scenarios. Environ. Res. Lett. 16, 064069 (2021).

    Article  CAS  Google Scholar 

  34. Warszawski, L. et al. All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal. Environ. Res. Lett. 16, 064037 (2021).

    Article  CAS  Google Scholar 

  35. Tavoni, M. & Tol, R. S. J. Counting only the hits? The risk of underestimating the costs of stringent climate policy: a letter. Climatic Change 100, 769–778 (2010).

    Article  Google Scholar 

  36. van Sluisveld, M. A. E. et al. Comparing future patterns of energy system change in 2 °C scenarios to expert projections. Glob. Environ. Change 50, 201–211 (2018).

    Article  Google Scholar 

  37. Christensen, P., Gillingham, K. & Nordhaus, W. Uncertainty in forecasts of long-run economic growth. Proc. Natl Acad. Sci. USA 115, 5409–5414 (2018).

    Article  CAS  Google Scholar 

  38. Castles, I. & Henderson, D. The IPCC emission scenarios: an economic–statistical critique. Energy Environ. 14, 159–185 (2003).

    Article  Google Scholar 

  39. van Ruijven, B. J. Mind the gap—the case for medium level emission scenarios. Climatic Change 138, 361–367 (2016).

    Article  Google Scholar 

  40. Trutnevyte, E. et al. Societal transformations in models for energy and climate policy: the ambitious next step. One Earth 1, 423–433 (2019).

    Article  Google Scholar 

  41. Millner, A. & McDermott, T. K. J. Model confirmation in climate economics. Proc. Natl Acad. Sci. USA 113, 8675–8680 (2016).

    Article  CAS  Google Scholar 

  42. van der Wijst, K.-I., Hof, A. F. & van Vuuren, D. P. On the optimality of 2 °C targets and a decomposition of uncertainty. Nat. Commun. 12, 2575 (2021).

    Article  CAS  Google Scholar 

  43. Sanderson, B. M., Knutti, R. & Caldwell, P. A representative democracy to reduce interdependency in a multimodel ensemble. J. Clim. 28, 5171–5194 (2015).

    Article  Google Scholar 

  44. Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Phil. Trans. R. Soc. A 365, 2053–2075 (2007).

    Article  Google Scholar 

  45. Brunner, L. et al. Reduced global warming from CMIP6 projections when weighting models by performance and independence. Earth Syst. Dyn. 11, 995–1012 (2020).

    Article  Google Scholar 

  46. Harmsen, M. et al. Integrated assessment model diagnostics: key indicators and model evolution. Environ. Res. Lett. 16, 054046 (2021).

    Article  Google Scholar 

  47. Xexakis, G. & Trutnevyte, E. Are interactive web-tools for environmental scenario visualization worth the effort? An experimental study on the Swiss electricity supply scenarios 2035. Environ. Model. Softw. 119, 124–134 (2019).

    Article  Google Scholar 

  48. Rogelj, J. et al. in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Ch. 2 (IPCC, WMO, 2018).

  49. Clarke, L. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 6 (IPCC, Cambridge Univ. Press, 2014).

  50. Groves, D. G. & Lempert, R. J. A new analytic method for finding policy-relevant scenarios. Glob. Environ. Change 17, 73–85 (2007).

    Article  Google Scholar 

  51. Friedman, J. H. & Fisher, N. I. Bump hunting in high-dimensional data. Stat. Comput. 9, 123–143 (1999).

    Article  Google Scholar 

  52. Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees (Taylor & Francis, 1984).

  53. Elsawah, S. et al. Scenario processes for socio-environmental systems analysis of futures: a review of recent efforts and a salient research agenda for supporting decision making. Sci. Total Environ. 729, 138393 (2020).

    Article  CAS  Google Scholar 

  54. Schweizer, V. J. Reflections on cross-impact balances, a systematic method constructing global socio-technical scenarios for climate change research. Climatic Change 162, 1705–1722 (2020).

    Article  Google Scholar 

  55. Weimer-Jehle, W. Cross-impact balances: a system-theoretical approach to cross-impact analysis. Technol. Forecast. Soc. Change 73, 334–361 (2006).

    Article  Google Scholar 

  56. Schweizer, V. J. & Kriegler, E. Improving environmental change research with systematic techniques for qualitative scenarios. Environ. Res. Lett. 7, 044011 (2012).

    Article  Google Scholar 

  57. Alcamo, J. & Ribeiro, T. Scenarios as Tools for International Environmental Assessments (European Environment Agency, 2001).

  58. Auping, W. L., Pruyt, E., de Jong, S. & Kwakkel, J. H. The geopolitical impact of the shale revolution: exploring consequences on energy prices and rentier states. Energy Policy 98, 390–399 (2016).

    Article  Google Scholar 

  59. Tietje, O. Identification of a small reliable and efficient set of consistent scenarios. Eur. J. Oper. Res. 162, 418–432 (2005).

    Article  Google Scholar 

  60. Berntsen, P. B. & Trutnevyte, E. Ensuring diversity of national energy scenarios: bottom-up energy system model with modeling to generate alternatives. Energy 126, 886–898 (2017).

    Article  Google Scholar 

  61. Steinmann, P., Auping, W. L. & Kwakkel, J. H. Behavior-based scenario discovery using time series clustering. Technol. Forecast. Soc. Change 156, 120052 (2020).

    Article  Google Scholar 

  62. Gerst, M. D., Wang, P. & Borsuk, M. E. Discovering plausible energy and economic futures under global change using multidimensional scenario discovery. Environ. Model. Softw. 44, 76–86 (2013).

    Article  Google Scholar 

  63. Li, P.-H., Pye, S. & Keppo, I. Using clustering algorithms to characterise uncertain long-term decarbonisation pathways. Appl. Energy 268, 114947 (2020).

    Article  Google Scholar 

  64. Alcamo, J. Environmental Futures: The Practice of Environmental Scenario Analysis (Elsevier, 2008).

  65. Kemp-Benedict, E. Telling better stories: strengthening the story in story and simulation. Environ. Res. Lett. 7, 041004 (2012).

    Article  Google Scholar 

  66. Carlsen, H., Eriksson, E. A., Dreborg, K. H., Johansson, B. & Bodin, Ö. Systematic exploration of scenario spaces. Foresight 18, 59–75 (2016).

    Article  Google Scholar 

  67. Morgan, M. G. & Keith, D. W. Improving the way we think about projecting future energy use and emissions of carbon dioxide. Climatic Change 90, 189–215 (2008).

    Article  CAS  Google Scholar 

  68. Saltelli, A. et al. Global Sensitivity Analysis. The Primer (Wiley, 2007).

  69. Guivarch, C. & Monjon, S. Identifying the main uncertainty drivers of energy security in a low-carbon world: the case of Europe. Energy Econ. 64, 530–541 (2017).

    Article  Google Scholar 

  70. Fisch-Romito, V. & Guivarch, C. Transportation infrastructures in a low carbon world: an evaluation of investment needs and their determinants. Transp. Res. D 72, 203–219 (2019).

    Article  Google Scholar 

  71. Pye, S. et al. Assessing qualitative and quantitative dimensions of uncertainty in energy modelling for policy support in the United Kingdom. Energy Res. Soc. Sci. 46, 332–344 (2018).

    Article  Google Scholar 

  72. Kriegler, E. et al. Will economic growth and fossil fuel scarcity help or hinder climate stabilization? Overview of the RoSE multi-model study. Climatic Change 136, 7–22 (2016).

    Article  Google Scholar 

  73. 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 

  74. Alexander, P. et al. Assessing uncertainties in land cover projections. Glob. Change Biol. 23, 767–781 (2017).

    Article  Google Scholar 

  75. van Vuuren, D. et al. The costs of achieving climate targets and the sources of uncertainty. Nat. Clim. Change 10, 329–334 (2020).

    Article  Google Scholar 

  76. Meyer, M., Löschel, A. & Lutz, C. Carbon price dynamics in ambitious climate mitigation scenarios: an analysis based on the IAMC 1.5 °C scenario explorer. Environ. Res. Commun. 3, 081007 (2021).

    Article  Google Scholar 

  77. Diniz Oliveira, T. et al. A mixed‐effect model approach for assessing land‐based mitigation in integrated assessment models: a regional perspective. Glob. Change Biol. 27, 4671–4685 (2021).

    Article  Google Scholar 

  78. Zhou, T., Lu, J., Zhang, W. & Chen, Z. The sources of uncertainty in the projection of global land monsoon precipitation. Geophys. Res. Lett. 47, e88415 (2020).

  79. Ombadi, M., Nguyen, P., Sorooshian, S. & Hsu, K. Retrospective analysis and Bayesian model averaging of CMIP6 precipitation in the Nile River Basin. J. Hydrometeorol. 22, 217–229 (2021).

    Article  Google Scholar 

  80. Grübler, A. & Nakicenovic, N. Identifying dangers in an uncertain climate. Nature 412, 15 (2001).

    Article  Google Scholar 

  81. Schneider, S. H. What is ‘dangerous’ climate change? Nature 411, 17–19 (2001).

    Article  CAS  Google Scholar 

  82. Ho, E., Budescu, D. V., Bosetti, V., van Vuuren, D. P. & Keller, K. Not all carbon dioxide emission scenarios are equally likely: a subjective expert assessment. Climatic Change 155, 545–561 (2019).

    Article  CAS  Google Scholar 

  83. Kaack, L. H., Apt, J., Morgan, M. G. & McSharry, P. Empirical prediction intervals improve energy forecasting. Proc. Natl Acad. Sci. USA 114, 8752–8757 (2017).

    Article  CAS  Google Scholar 

  84. Manski, C. F., Sanstad, A. H. & DeCanio, S. J. Addressing partial identification in climate modeling and policy analysis. Proc. Natl Acad. Sci. USA 118, e2022886118 (2021).

    Article  CAS  Google Scholar 

  85. Trutnevyte, E., Guivarch, C., Lempert, R. & Strachan, N. Reinvigorating the scenario technique to expand uncertainty consideration. Climatic Change 135, 373–379 (2016).

    Article  Google Scholar 

  86. Moss, R. H. Assessing decision support systems and levels of confidence to narrow the climate information ‘usability gap’. Climatic Change 135, 143–155 (2016).

    Article  Google Scholar 

  87. Lemos, M. C., Kirchhoff, C. J. & Ramprasad, V. Narrowing the climate information usability gap. Nat. Clim. Change 2, 789–794 (2012).

    Article  Google Scholar 

  88. Spiegelhalter, D., Pearson, M. & Short, I. Visualizing uncertainty about the future. Science 333, 1393–1400 (2011).

    Article  CAS  Google Scholar 

  89. Strecher, V. J., Greenwood, T., Wang, C. & Dumont, D. Interactive multimedia and risk communication. JNCI Monogr. 1999, 134–139 (1999).

    Article  Google Scholar 

  90. Huppmann, D. et al. IAMC 1.5°C scenario explorer and data hosted by IIASA. Zenodo https://doi.org/10.5281/ZENODO.3363345 (2019).

  91. McCollum, D. L. et al. Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals. Nat. Energy 3, 589–599 (2018).

    Article  Google Scholar 

  92. Parker, A. M., Srinivasan, S. V., Lempert, R. J. & Berry, S. H. Evaluating simulation-derived scenarios for effective decision support. Technol. Forecast. Soc. Change 91, 64–77 (2015).

    Article  Google Scholar 

  93. Xexakis, G. & Trutnevyte, E. Empirical testing of the visualizations of climate change mitigation scenarios with citizens: a comparison among Germany, Poland, and France. Glob. Environ. Change 70, 102324 (2021).

    Article  Google Scholar 

  94. Gong, M. et al. Testing the scenario hypothesis: an experimental comparison of scenarios and forecasts for decision support in a complex decision environment. Environ. Model. Softw. 91, 135–155 (2017).

  95. McMahon, R., Stauffacher, M. & Knutti, R. The unseen uncertainties in climate change: reviewing comprehension of an IPCC scenario graph. Climatic Change 133, 141–154 (2015).

    Article  Google Scholar 

  96. Bryant, B. P. Scenario Discovery Tools to Support Robust Decision Making, Documentation of the ‘sdtoolkit’ Package for R (RAND Corporation, 2015); https://cran.r-project.org/web/packages/sdtoolkit/sdtoolkit.pdf

  97. Hadka, D., Herman, J., Reed, P. & Keller, K. An open source framework for many-objective robust decision making. Environ. Model. Softw. 74, 114–129 (2015).

    Article  Google Scholar 

  98. Kwakkel, J. H. The exploratory modeling workbench: an open source toolkit for exploratory modeling, scenario discovery, and (multi-objective) robust decision making. Environ. Model. Softw. 96, 239–250 (2017).

    Article  Google Scholar 

  99. Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).

    Article  Google Scholar 

  100. Branger, F., Giraudet, L.-G., Guivarch, C. & Quirion, P. Global sensitivity analysis of an energy–economy model of the residential building sector. Environ. Model. Softw. 70, 45–54 (2015).

    Article  Google Scholar 

  101. New, M. & Hulme, M. Representing uncertainty in climate change scenarios: a Monte-Carlo approach. Integr. Assess. 1, 203–213 (2000).

    Article  Google Scholar 

  102. McJeon, H. C. et al. Technology interactions among low-carbon energy technologies: what can we learn from a large number of scenarios? Energy Econ. 33, 619–631 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

This work received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 821124 (NAVIGATE). R.S. also acknowledges funding received from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil, grant no. 310992/2020-6, and from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 821471 (ENGAGE). P.F. acknowledges funding from the CAMPAIGNers H2020 research project (grant agreement 101003815).

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Authors and Affiliations

  1. CIRED, Ecole des Ponts, Nogent-sur-Marne, France

    Céline Guivarch

  2. CIRED, CNRS, Nogent-sur-Marne, France

    Thomas Le Gallic

  3. Potsdam Institute for Climate Impact Research, Potsdam, Germany

    Nico Bauer & Elmar Kriegler

  4. E3-Modelling, Athens, Greece

    Panagiotis Fragkos

  5. International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

    Daniel Huppmann, Tamás Krisztin, Keywan Riahi & Fabian Wagner

  6. Renewable Energy Systems group, University of Geneva, Geneva, Switzerland

    Marc Jaxa-Rozen & Evelina Trutnevyte

  7. Department of Mechanical Engineering, Aalto University, Espoo, Finland

    Ilkka Keppo

  8. RFF-CMCC European Institute on Economics and the Environment (EIEE), Centro Euro-Mediterraneo sui Cambiamenti Climatici, Milan, Italy

    Giacomo Marangoni & Massimo Tavoni

  9. UCL Energy Institute, University College London, London, UK

    Steve Pye

  10. COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Roberto Schaeffer

  11. Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy

    Massimo Tavoni

  12. PBL Netherlands Environmental Assessment Agency, The Hague, the Netherlands

    Detlef van Vuuren

  13. Utrecht University, Utrecht, the Netherlands

    Detlef van Vuuren

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  1. Céline Guivarch
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Contributions

C.G. led the work. C.G. and T.L.G. jointly prepared the manuscript and T.L.G. designed the figures. All authors contributed to the text.

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Correspondence to Céline Guivarch.

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Nature Climate Change thanks Ben Sanderson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Illustrative cases of uses of climate mitigation ensembles (extended version of Table 1) and associated references.

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Guivarch, C., Le Gallic, T., Bauer, N. et al. Using large ensembles of climate change mitigation scenarios for robust insights. Nat. Clim. Chang. 12, 428–435 (2022). https://doi.org/10.1038/s41558-022-01349-x

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