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

Viewed

Total article views: 15,008 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
11,147 3,701 160 15,008 731 207 165
  • HTML: 11,147
  • PDF: 3,701
  • XML: 160
  • Total: 15,008
  • Supplement: 731
  • BibTeX: 207
  • EndNote: 165
Views and downloads (calculated since 05 Feb 2020)
Cumulative views and downloads (calculated since 05 Feb 2020)

Viewed (geographical distribution)

Total article views: 15,008 (including HTML, PDF, and XML) Thereof 13,805 with geography defined and 1,203 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 05 Oct 2024
Download
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.
Altmetrics
Final-revised paper
Preprint