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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-11-491-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6
Institute for Atmospheric and Climate Science, ETH Zürich,
Zurich, Switzerland
Climate and Global Dynamics Laboratory, National Center for
Atmospheric Research, Boulder, USA
Clara Deser
Climate and Global Dynamics Laboratory, National Center for
Atmospheric Research, Boulder, USA
Nicola Maher
Max Planck Institute for Meteorology, Hamburg, Germany
Jochem Marotzke
Max Planck Institute for Meteorology, Hamburg, Germany
Erich M. Fischer
Institute for Atmospheric and Climate Science, ETH Zürich,
Zurich, Switzerland
Lukas Brunner
Institute for Atmospheric and Climate Science, ETH Zürich,
Zurich, Switzerland
Reto Knutti
Institute for Atmospheric and Climate Science, ETH Zürich,
Zurich, Switzerland
Ed Hawkins
National Centre for Atmospheric Science, Department of Meteorology,
University of Reading, Reading, UK
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Latest update: 20 Mar 2023
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.
Projections of climate change are uncertain because climate models are imperfect, future...
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