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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/esd-2020-23
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2020-23
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  28 Apr 2020

28 Apr 2020

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This preprint is currently under review for the journal ESD.

Reduced global warming from CMIP6 projections when weighting models by performance and independence

Lukas Brunner1, Angeline G. Pendergrass2,1, Flavio Lehner1, Anna L. Merrifield1, Ruth Lorenz1, and Reto Knutti1 Lukas Brunner et al.
  • 1Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
  • 2National Center for Atmospheric Research, Boulder, Colorado, US

Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as mean and likely range. Here, we use a model weighting approach, which accounts for a model's historical performance based on several diagnostics as well as possible model inter-dependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we remove each CMIP6 model from the ensemble in turn, use it as pseudo-observation in the historical period, and evaluate the weighted CMIP6 ensemble against it in the future. This is complemented by a second perfect model test drawing on the previous-generation CMIP5 models as pseudo-observations. In addition, we show that our independence diagnostics correctly clusters models known to be similar based on a CMIP6 family tree, which enables applying a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (ERA5 and MERRA2), to constrain CMIP6 projections in weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios. Our results show a reduction in projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 °C, compared to 4.1 °C without weighting; the likely (66 %) uncertainty range is 3.1 °C to 4.6 °C, a decrease of 13 %. For SSP1-2.6, weighted end-of-century warming is 1 °C (0.7 °C to 1.4 °C). Applying the weighting to estimates of Transient Climate Response (TCR) yields 1.9 °C (1.6 °C to 2.1 °C – a reduction in the likely uncertainty range of 46 %), which is consistent with estimates from previous model generations and other lines of evidence.

Lukas Brunner et al.

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Lukas Brunner et al.

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Short summary
In this study we weight climate models by their performance in simulating aspects of historical climate and their degree of inter-dependence. Our method is found to increase projection skill and to correct for structurally similar models. The weighted end-of century mean warming (2081–2100 relative to 1995–2014) is 3.7 °C with a likely (66 %) range of 3.1 °C to 4.6 °C for the strong climate change scenario SSP5-8.5; this is a reduction of 0.4 °C compared to the unweighted mean.
In this study we weight climate models by their performance in simulating aspects of historical...
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