Preprints
https://doi.org/10.5194/egusphere-2024-391
https://doi.org/10.5194/egusphere-2024-391
22 Feb 2024
 | 22 Feb 2024
Status: this preprint is open for discussion.

Towards robust community assessments of the Earth's climate sensitivity

Kate Marvel and Mark Webb

Abstract. The eventual planetary warming in response to elevated atmospheric carbon dioxide concentrations is not precisely known. This climate sensitivity S depends primarily on the net physical climate feedbacks, usually denoted as λ. Multiple lines of evidence can constrain this feedback parameter: proxy-based and model evidence from past equilibrium climates, process-based understanding of the physics underlying changes, and recent observations of temperature change, top-of-atmosphere energy imbalance, and ocean heat content. However, despite recent advances in combining these lines of evidence, the estimated range of S remains large. Here, using a Bayesian framework, we discuss three sources of uncertainty: uncertainty in the evidence, structural uncertainty in the model used to interpret that evidence, and differing prior beliefs, and show how these affect the conclusions we may draw from a single line of evidence. We then propose a method to combine multiple estimates of the evidence, multiple multiple explanatory models, and the subjective assessments of different experts in order to arrive at an assessment of λ (and hence, climate sensitivity S) that may be rapidly updated as new information arrives and truly reflects the existing community of experts.

Kate Marvel and Mark Webb

Status: open (until 02 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Kate Marvel and Mark Webb
Kate Marvel and Mark Webb

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Short summary
The climate sensitivity S to doubled atmospheric carbon dioxide has remained stubbornly uncertain for decades. Multiple lines of evidence can be used to constrain S, but any analysis relies on unavoidable subjective decisions. Here, we present a framework for combining the subjective judgments of multiple experts in a fair and robust way.