Climate nonlinearities: selection, uncertainty, projections, and damages

Cael, B B ORCID: https://orcid.org/0000-0003-1317-5718; Britten, G L; Mir Calafat, F ORCID: https://orcid.org/0000-0002-7474-135X; Bloch-Johnson, J; Stainforth, D; Goodwin, P. 2022 Climate nonlinearities: selection, uncertainty, projections, and damages. Environmental Research Letters, 17 (8), 084025. https://doi.org/10.1088/1748-9326/ac8238

Before downloading, please read NORA policies.

Preview

Text Cael_2022_Environ._Res._Lett._17_084025.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (698kB) | Preview

Abstract/Summary

Climate projections are uncertain; this uncertainty is costly and impedes progress on climate policy. This uncertainty is primarily parametric (what numbers do we plug into our equations?), structural (what equations do we use in the first place?), and due to internal variability (natural variability intrinsic to the climate system). The former and latter are straightforward to characterise in principle, though may be computationally intensive for complex climate models. The second is more challenging to characterise and is therefore often ignored. We developed a Bayesian approach to quantify structural uncertainty in climate projections, using the idealised energy-balance model representations of climate physics that underpin many economists' integrated assessment models (IAMs) (and therefore their policy recommendations). We define a model selection parameter, which switches on one of a suite of proposed climate nonlinearities and multidecadal climate feedbacks. We find that a model with a temperature-dependent climate feedback is most consistent with global mean surface temperature observations, but that the sign of the temperature-dependence is opposite of what Earth system models suggest. This difference of sign is likely due to the assumption tha the recent pattern effect can be represented as a temperature dependence. Moreover, models other than the most likely one contain a majority of the posterior probability, indicating that structural uncertainty is important for climate projections. Indeed, in projections using shared socioeconomic pathways similar to current emissions reductions targets, structural uncertainty dwarfs parametric uncertainty in temperature. Consequently, structural uncertainty dominates overall non-socioeconomic uncertainty in economic projections of climate change damages, as estimated from a simple temperature-to-damages calculation. These results indicate that considering structural uncertainty is crucial for IAMs in particular, and for climate projections in general.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1088/1748-9326/ac8238
ISSN:	1748-9326
Date made live:	23 Aug 2022 13:51 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533090

Actions (login required)

View Item

Document Downloads

More statistics for this item...