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Using large ensembles of climate change mitigation scenarios for robust insights

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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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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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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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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