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

Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6

Flavio Lehner1,2, Clara Deser2, Nicola Maher3, Jochem Marotzke3, Erich Fischer1, Lukas Brunner1, Reto Knutti1, and Ed Hawkins4
Flavio Lehner et al.
  • 1Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
  • 2Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, USA
  • 3Max Planck Institute for Meteorology, Hamburg, Germany
  • 4National Centre for Atmospheric Science, Dept. of Meteorology, University of Reading, Reading, UK

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, we revisit the framework from Hawkins and Sutton (2009) for uncertainty partitioning for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. We also investigate forced changes in variability itself, something that is newly possible with SMILEs. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections.

How to cite: Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple Large Ensembles and CMIP5/6, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5991,, 2020


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