Articles | Volume 11, issue 2
https://doi.org/10.5194/esd-11-491-2020
https://doi.org/10.5194/esd-11-491-2020
Research article
 | 
29 May 2020
Research article |  | 29 May 2020

Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6

Flavio Lehner, Clara Deser, Nicola Maher, Jochem Marotzke, Erich M. Fischer, Lukas Brunner, Reto Knutti, and Ed Hawkins

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Latest update: 13 Apr 2024
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Short summary
Projections of climate change are uncertain because climate models are imperfect, future greenhouse gases emissions are unknown and climate is to some extent chaotic. To partition and understand these sources of uncertainty and make the best use of climate projections, large ensembles with multiple climate models are needed. Such ensembles now exist in a public data archive. We provide several novel applications focused on global and regional temperature and precipitation projections.
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