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

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

doi:https://doi.org/10.5194/esd-11-491-2020

Articles | Volume 11, issue 2

https://doi.org/10.5194/esd-11-491-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Large Ensemble Climate Model Simulations: Exploring Natural...

https://doi.org/10.5194/esd-11-491-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 2

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

Supplement

https://doi.org/10.5194/esd-11-491-2020-supplement

Model code and software

Multi-Model Large Ensemble Archive National Center for Atmospheric Research http://www.cesm.ucar.edu/projects/community-projects/MMLEA/

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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.