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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Jul 2020

17 Jul 2020

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A revised version of this preprint is currently under review for the journal ESD.

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Manuel Schlund1, Axel Lauer1, Pierre Gentine2,3, Steven C. Sherwood4, and Veronika Eyring1,5 Manuel Schlund et al.
  • 1Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 2Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027
  • 3Earth Institute and Data Science Institute, Columbia University, New York, NY 10027
  • 4Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney 2052, Australia
  • 5University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany

Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS) which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which stem the major source of uncertainty in ECS. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here has been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a large decrease of the correlation coefficients for most emergent relationships, indicating that nearly all of the constraints are less skillful in predicting ECS than they were in CMIP5. Many do not appear sufficiently skillful to be useful in constraining ECS, and several of them do not pass a significance test. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble the emergent constraint was originally derived from. The emergent-constrained best estimate of ECS increased from CMIP5 to CMIP6, with a best estimate range of 2.97–3.88 K for CMIP5 and 3.41–4.36 K for CMIP6. This can be at least partly explained by the increased number of high-ECS models in CMIP6 with an ECS above 4.5 K without a corresponding change in the constraint predictors. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that emergent constraints focusing on specific processes contributing to ECS are more promising than emergent constraints based on statistical model analysis.

Manuel Schlund et al.

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Manuel Schlund et al.

Manuel Schlund et al.


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Latest update: 20 Oct 2020
Publications Copernicus
Short summary
As an important measure of climate change, the Equilibrium Climate Sensitivity (ECS) describes the change in surface temperature after a doubling of the atmospheric CO2 concentration. Climate models from the Coupled Model Intercomparison Project (CMIP) show a wide range in ECS. Emergent constraints are a technique to reduce uncertainties in ECS with observational data. Emergent constraints developed with data from CMIP phase 5 show reduced skill and higher ECS ranges when applied to CMIP6 data.
As an important measure of climate change, the Equilibrium Climate Sensitivity (ECS) describes...