Climate nonlinearities: selection, uncertainty, projections, and damages
Climate nonlinearities: selection, uncertainty, projections, and damages
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.
Bayesian statistics, climate change, energy balance model, feedback temperature dependence, integrated assessment model, structural uncertainty
Cael, B.B.
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Britten, G.L.
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Mir Calafat, F.
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Bloch-Johnson, J.
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Stainforth, D.
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Goodwin, Philip
87dbb154-5c39-473a-8121-c794487ee1fd
1 August 2022
Cael, B.B.
458442c7-574e-42dd-b2aa-717277e14eba
Britten, G.L.
69d606e9-21d1-4658-8196-79fcb64fd781
Mir Calafat, F.
7cadda34-12f2-42fa-b56e-712b5a0068d6
Bloch-Johnson, J.
3f9fc956-7ec7-4dc3-bb88-cab358754471
Stainforth, D.
e9c5ab0e-e130-40f7-9d8f-18803cff00a6
Goodwin, Philip
87dbb154-5c39-473a-8121-c794487ee1fd
Cael, B.B., Britten, G.L., Mir Calafat, F., Bloch-Johnson, J., Stainforth, D. and Goodwin, Philip
(2022)
Climate nonlinearities: selection, uncertainty, projections, and damages.
Environmental Research Letters, 17 (8), [084025].
(doi:10.1088/1748-9326/ac8238).
Abstract
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.
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Cael_2022_Environ._Res._Lett._17_084025
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Accepted/In Press date: 19 July 2022
Published date: 1 August 2022
Additional Information:
Funding Information:
We thank the many scientists whose collective work has generated the time series, prior information, and statistical method on which our work relies. We also thank Chris Smith for providing the radiative forcing time series ensemble as well as insightful comments. Cael acknowledges support from the National Environmental Research Council through Enhancing Climate Observations, Models and Data. G L B acknowledges support from the Simons Foundation. D A S acknowledges support from the Grantham Research Institute on Climate Change and the Environment at the London School of Economics, the ESRC Centre for Climate Change Economics and Policy (CCCEP; Reference ES/R009708/1), and the Natural Environment Research Council through Optimising the Design of Ensembles to Syupport Science and Society (ODESSS; Reference NE/V011790/1).
Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.
Keywords:
Bayesian statistics, climate change, energy balance model, feedback temperature dependence, integrated assessment model, structural uncertainty
Identifiers
Local EPrints ID: 472863
URI: http://eprints.soton.ac.uk/id/eprint/472863
ISSN: 1748-9326
PURE UUID: e14e9228-e1f8-4fa4-9d72-ac22c9cd0ab7
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Date deposited: 20 Dec 2022 17:43
Last modified: 06 Jun 2024 01:52
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Contributors
Author:
B.B. Cael
Author:
G.L. Britten
Author:
F. Mir Calafat
Author:
J. Bloch-Johnson
Author:
D. Stainforth
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