Bayesian estimation of climate sensitivity using observationally constrained simple climate models

Bodman, Roger; Jones, Roger

doi:10.1002/wcc.397

Cited by 21 publications

(25 citation statements)

References 83 publications

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“…Bayesian methods have recently been reviewed by Annan (2015) and Bodman and Jones (2016) and limitation by assuming constant sensitivity over time, the role of the ECSinf prior distribution and equal efficacy for different forcings have been discussed. Recently, studies have suggested that assuming equal efficacy for all forcings bias the ECS estimate low (Marvel et al, 2015;Shindell et al, 2015) even when ERFs are used (see also Appendix D).…”

Section: Discussionmentioning

confidence: 99%

Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

Skeie¹,

Berntsen²,

Aldrin³

et al. 2017

Preprint

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Abstract. Inferred Effective Climate Sensitivity (ECSinf) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian-15 framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. The mean value of the estimated ECSinf is 2.0°C, with a 90% credible interval of 1.2-3.1°C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. We show a strong sensitivity of the estimated ECSinf to the choice of a priori RF time series, excluding pre-1950 20 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0-700m) and the deep ocean OHC or using only one OHC data set (instead of four in the main analysis), both give an enhancement the mean ECSinf by about 50% from our best estimate.

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Section: Discussionmentioning

confidence: 99%

Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

Skeie¹,

Berntsen²,

Aldrin³

et al. 2017

Preprint

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show abstract

“…Bayesian methods have recently been reviewed by Annan (2015) and Bodman and Jones (2016), and limitation by assuming constant sensitivity over time, the role of the ECS inf prior distribution and equal efficacy for different forcings have been discussed. Implementing an alternative prior for ECS inf as in Skeie14, where 1 / ECS inf is uniformly distributed, shifted the mean ECS inf to lower values from 2.0 • C (median 1.9 • C, 90 % CI 1.2-3.1 • C) to 1.6 • C (median 1.6 • C, 90 % CI 0.97-2.5 • C).…”

Section: Discussionmentioning

confidence: 99%

Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

et al. 2018

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Abstract. Inferred effective climate sensitivity (ECS inf ) is estimated using a method combining radiative forcing (RF) time series and several series of observed ocean heat content (OHC) and near-surface temperature change in a Bayesian framework using a simple energy balance model and a stochastic model. The model is updated compared to our previous analysis by using recent forcing estimates from IPCC, including OHC data for the deep ocean, and extending the time series to 2014. In our main analysis, the mean value of the estimated ECS inf is 2.0 • C, with a median value of 1.9 • C and a 90 % credible interval (CI) of 1.2-3.1 • C. The mean estimate has recently been shown to be consistent with the higher values for the equilibrium climate sensitivity estimated by climate models. The transient climate response (TCR) is estimated to have a mean value of 1.4 • C (90 % CI 0.9-2.0 • C), and in our main analysis the posterior aerosol effective radiative forcing is similar to the range provided by the IPCC. We show a strong sensitivity of the estimated ECS inf to the choice of a priori RF time series, excluding pre-1950 data and the treatment of OHC data. Sensitivity analysis performed by merging the upper (0-700 m) and the deep-ocean OHC or using only one OHC dataset (instead of four in the main analysis) both give an enhancement of the mean ECS inf by about 50 % from our best estimate.

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“…An additional problem arising from this is the resulting difficulty in synthesising estimates of climate system properties generated by different statistical methods with different, and often not explicitly introduced, assumptions. These methods include OLS but also alternative Bayesian approaches such as estimates of the climate sensitivity using energy-balance models Aldrin et al, 2012;Bodman and Jones, 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Renoult¹,

Hargreaves²,

Sagoo³

et al. 2020

Preprint

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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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Bayesian estimation of climate sensitivity using observationally constrained simple climate models

Cited by 21 publications

References 83 publications

Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

Climate sensitivity estimates – sensitivity to radiative forcing time series and observational data

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

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