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Assumptions for emergent constraints

Nature volume 563, pages E1–E3 (2018)Cite this article

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arising from P. M. Cox, C. Huntingford & M. S. Williamson Nature 553, 319–322 (2018); https://doi.org/10.1038/nature25450

Uncertainty in equilibrium climate sensitivity (ECS) can potentially be narrowed by using an emergent constraint that relates a currently observable variable to climate-model-simulated ECS. A recent study1 reports an emergent constraint that reduces ECS uncertainty by about 60% and shifts the central estimate of ECS to a lower value than that reported by the Intergovernmental Panel on Climate Change (IPCC). However, we show here that the assumptions that underpin the theoretical basis of the emergent constraint from Cox et al.1 are not necessarily fulfilled. Alternative methods that more fully satisfy the theoretical assumptions indicate that the emergent constraint from Cox et al.1 reduces ECS uncertainty by at most about 11% relative to the raw model spread that informs the analysis, and it is ambiguous whether the emergent constraint from Cox et al.1 robustly lowers the central estimate of ECS. There is a Reply to this Comment by Cox, P. M. et al. Nature 563, https://doi.org/10.1038/s41586-018-0641-x (2018).

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Fig. 1: Analysis of simulated and observed time series using three methods to isolate unforced variability.
Fig. 2: Comparison of the central estimate and likely range of ECS for various methods and for four observational datasets.

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Authors and Affiliations

  1. Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA

    Patrick T. Brown & Ken Caldeira

  2. Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

    Martin B. Stolpe

Authors
  1. Patrick T. Brown
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  2. Martin B. Stolpe
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  3. Ken Caldeira
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Contributions

P.T.B. and M.B.S. performed the analysis. P.T.B. wrote an initial draft of the manuscript. All authors contributed to interpreting the results and refining the manuscript.

Corresponding author

Correspondence to Patrick T. Brown.

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Declared none.

Extended data figures and tables

Extended Data Fig. 1 Ψ metric of temperature variability versus time.

This figure is analogous to figure 2a in Cox et al.1, but without data from ACCESS1-0, inmcm4, IPSL-CM5B-LR, MPI-ESM-LR, GFDL-ESM2G, MIROC5 and bcc-csm1-1-m because not all required simulations were available for these models. Black lines correspond to Ψ calculated from the historical experiment, which includes all forcings. Blue lines correspond to Ψ calculated from the historical–natural experiment (‘historicalNat’), which includes only forcings from changes in incoming solar radiation and volcanic aerosols. Red lines correspond to Ψ calculated from the historical greenhouse gas experiment (‘historicalGHG’), which includes only forcing from well-mixed greenhouse gases (and ozone in some models). Thin lines are individual model-ensemble members (r1i1p1) and thick lines are multi-model means (MMMs). The data show that Ψ (as calculated by Cox et al.1) is non-stationary and inflated towards the end of the record, even in the case where models are forced by only greenhouse gases.

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Brown, P.T., Stolpe, M.B. & Caldeira, K. Assumptions for emergent constraints. Nature 563, E1–E3 (2018). https://doi.org/10.1038/s41586-018-0638-5

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