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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/esd-2019-93
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2019-93
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Feb 2020

Submitted as: research article | 05 Feb 2020

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This preprint is currently under review for the journal ESD.

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

Abstract. 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, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method error (up to 50 %) and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. 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.

Flavio Lehner et al.

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Status: open (until 18 Mar 2020)
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Latest update: 24 Feb 2020
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Short summary
Future projections of climate change are uncertain, because we cannot predict weather perfectly for any given day or year, we are unable to build perfect computer models of climate, and we do not perfectly know how much greenhouse gases humans will emit in the future. Here, we show that 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 the public domain.
Future projections of climate change are uncertain, because we cannot predict weather perfectly...
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