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  A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

Renoult, M., Annan, J. D., Hargreaves, J. C., Sagoo, N., Flynn, C., Kapsch, M.-L., et al. (2020). A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP. Climate of the Past, 16, 1715-1735. doi:10.5194/cp-16-1715-2020.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-80F9-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-FFE1-D
Genre: Journal Article

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 Creators:
Renoult, M., Author
Annan, J. D., Author
Hargreaves, J. C., Author
Sagoo, N., Author
Flynn, C., Author
Kapsch, Marie-Luise1, Author              
Mikolajewicz, Uwe1, Author              
Ohgaito, R., Author
Mauritsen, T., Author
Affiliations:
1Ocean Physics, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society, ou_913557              

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 Abstract: In this paper we introduce a Bayesian framework, which is flexible and explicit about the prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on Ordinary Least Squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (1.1–4.8, 5–95 percentiles) using the PMIP2, PMIP3 and PMIP4 data sets for the LGM, and 2.4 K (0.4–5.0) with the PlioMIP1 and PlioMIP2 data sets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (1.1–4.3) using the LGM and 2.4 K (0.4–5.1) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a slightly tighter constraint of 2.6 K (1.1–3.9). We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95 % probability of climate sensitivity mostly below 5 and never exceeding 6 K. The approach is compared with other approaches based on OLS, a Kalman filter method and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, suggesting a higher bound by construction in case of weaker correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation of their potential use in future probabilistic estimation of climate sensitivity

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Language(s): eng - English
 Dates: 2019-122020-07-272020-09-10
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.5194/cp-16-1715-2020
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Project name : CONSTRAIN
Grant ID : 820829
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)
Project name : HighECS
Grant ID : 770765
Funding program : -
Funding organization : European Research Council

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Title: Climate of the Past
Source Genre: Journal
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Publ. Info: Copernicus Publ.
Pages: - Volume / Issue: 16 Sequence Number: - Start / End Page: 1715 - 1735 Identifier: ISSN: 1814-9324
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000033790