Articles | Volume 10, issue 1
https://doi.org/10.5194/esd-10-91-2019
© Author(s) 2019. 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-10-91-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing
Climate Change Research Centre, UNSW Sydney, Australia
ARC Centre of Excellence for Climate Extremes, Australia
Nadja Herger
Climate Change Research Centre, UNSW Sydney, Australia
ARC Centre of Excellence for Climate System Science, Australia
Ethan Gutmann
National Center for Atmospheric Research, Boulder, Colorado, USA
Dorit Hammerling
National Center for Atmospheric Research, Boulder, Colorado, USA
Reto Knutti
Institute for Atmospheric and Climate Science, ETH Zurich,
Switzerland
Martin Leduc
Ouranos, Montréal, Québec, Canada
Université du Québec à Montréal, Montréal,
Québec, Canada
Ruth Lorenz
Institute for Atmospheric and Climate Science, ETH Zurich,
Switzerland
Robert Pincus
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, Colorado, USA
NOAA Earth System Research Lab, Physical Sciences Division, Boulder, Colorado, USA
Gavin A. Schmidt
NASA Goddard Institute for Space Studies, New York, NY, USA
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Latest update: 31 May 2023
Short summary
Best estimates of future climate projections typically rely on a range of climate models from different international research institutions. However, it is unclear how independent these different estimates are, and, for example, the degree to which their agreement implies robustness. This work presents a review of the varied and disparate attempts to quantify and address model dependence within multi-model climate projection ensembles.
Best estimates of future climate projections typically rely on a range of climate models from...
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