Preprints
https://doi.org/10.5194/esd-2020-85
https://doi.org/10.5194/esd-2020-85

  10 Feb 2021

10 Feb 2021

Review status: this preprint is currently under review for the journal ESD.

On structural errors in emergent constraints

Benjamin M. Sanderson1,3, Angeline Pendergrass2,3, Charles D. Koven4, Florent Brient5, Ben B. B. Booth6, Rosie A. Fisher1,3, and Reto Knutti7 Benjamin M. Sanderson et al.
  • 1CERFACS, Toulouse, France
  • 2Cornell University, Ithaca, NY, USA
  • 3National Center for Atmospheric Research, Boulder, CO, USA
  • 4Lawrence Berkeley National Lab, CA, USA
  • 5LMD/IPSL, Sorbonne Université, Paris, France
  • 6UK Met Office, Exeter, UK
  • 7ETH Zurich, Switzerland

Abstract. Studies of emergent constraints have frequently proposed that a single metric alone can constrain future responses of the Earth system to anthropogenic emissions. The prevalence of this thinking has led to literature and messaging which is sometimes confusing to policymakers, with a series of studies over the last decade making confident, yet contradictory, claims on the probability bounds of key climate variables. Here, we illustrate that emergent constraints are more likely to occur where the variance across an ensemble of climate models of both observable and future climate arises from common structural assumptions and few degrees of freedom. Such cases are likely to occur when processes are represented in a common, oversimplified fashion throughout the ensemble, about which we have the least confidence in performance out of sample. We consider these issues in the context of a number of published constraints, and argue that the application of emergent constraints alone to estimate uncertainties in unknown climate responses can potentially lead to bias and overconfidence in constrained projections. Together with statistical robustness and plausibility of mechanism, assessments of climate responses must include multiple lines of evidence to identify biases that arise from common oversimplified modeling assumptions which impact both present and future climate simulations in order to mitigate against the influence of common structural biases.

Benjamin M. Sanderson et al.

Status: open (until 24 Mar 2021)

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Benjamin M. Sanderson et al.

Benjamin M. Sanderson et al.

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Short summary
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases an common simplifications which may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints, and a number of practical case studies.
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