Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

Goodwin, Philip; Cael, B. B.

doi:10.5194/esd-12-709-2021

Cited by 7 publications

(18 citation statements)

References 42 publications

(62 reference statements)

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“…for Shared Socio-economic Pathway (SSP) scenarios (O'Neill et al, 2016) given information from observational reconstructions of historic surface warming, (Morice et al, 2020), historic Δ ( ℎ ) ocean heat content, (Cheng et al, 2017), and other background information, . This ΔOHC( ℎ ) background information, , will include prior uncertainty distributions for radiative forcing from a range of sources including aerosols (Andrews and Forster, 2020;IPCC, 2013;Zelinka et al, 2014) prior distributions for a range of climate model parameters and the functional form of a range of parameterisations within an efficient climate model with a box-model representation of heat and Page 4 of 45 https://mc.manuscriptcentral.com/oxfclm Manuscripts submitted to Oxford Open Climate Change carbon fluxes in the atmosphere-ocean system (Goodwin, 2016;2018;Goodwin and Cael, 2021).…”

Section: Projecting Future Warming and Climate Feedback Trajectoriesmentioning

confidence: 99%

“…This study uses the Warming Acidification and Sea level Projector (WASP: Goodwin, 2016;2018): a box-model representation of heat and carbon fluxes across the atmosphere-ocean system and carbon fluxes between the atmosphere and terrestrial systems. Here, only the atmosphere-ocean components of the WASP model (Goodwin, 2016;2018;Goodwin and Cael, 2021) are used. This consists of a well-mixed atmosphere box connected to a well-mixed surface ocean box, itself connected to 4 sub-surface ocean boxes representing the upper ventilated region of the ocean, the intermediate water region, the deep ocean water region and the bottom water ocean region.…”

Section: δ ( ) ( )mentioning

confidence: 99%

“…WASP version 3 (Goodwin, 2018), with time-evolving climate feedbacks, is adopted and configured after Goodwin and Cael (2021) with climate feedback split into three components: the instantaneous (Planck) feedback, , fast feedbacks (operating on the timescale of water vapour residence in the atmosphere), , and multidecadal feedbacks (operating on timescales representing the sea-surface temperature warming pattern adjustment), (Appendix B).…”

Section: δ ( ) ( )mentioning

confidence: 99%

“…2b), and ocean ventilation timescales for different ocean regions. A full list of parameters varied is found in Goodwin and Cael (2021). The prior distributions for the magnitude of climate feedback terms are updated here, with prior distributions for all other terms as described in Goodwin and Cael (2021).…”

Section: δ ( ) ( )mentioning

confidence: 99%

“…considered in Sherwood et al, 2020) each break at least 3 of the 5 conditions imposed here. Goodwin and Cael (2021) defined three priors in terms of climate feedback terms acting on instantaneous (Planck), multiday (fast) and multidecadal timescales.…”

Section: New Prior Distributions Of Climate Feedback Parametersmentioning

confidence: 99%

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Probabilistic projections of future warming and climate sensitivity trajectories

Goodwin

2021

Oxford Open Climate Change

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Projections of future global mean surface warming for a given forcing scenario remain uncertain, largely due to uncertainty in the climate sensitivity. The ensemble of Earth system models from the Climate Model Intercomparison Project phase 6 (CMIP6) represent the dominant tools for projecting future global warming. However, the distribution of climate sensitivities within the CMIP6 ensemble is not representative of recent independent probabilistic estimates, and the ensemble contains significant variation in simulated historic surface warming outside agreement with observational datasets. Here, a Bayesian approach is used to infer joint probabilistic projections of future surface warming and climate sensitivity for SSP scenarios. The projections use an efficient climate model ensemble filtered and weighted to encapsulate observational uncertainty in historic warming and ocean heat content anomalies. The probabilistic projection of climate sensitivity produces a best estimate of 2.9 °C, and 5th to 95th percentile range of 1.5 to 4.6 °C, in line with previous estimates using multiple lines of evidence. The joint projection of surface warming over the period 2030 to 2040 has a 50% or greater probability of exceeding 1.5 °C above preindustrial for all SSPs considered: 119, 126, 245, 370 and 585. Average warming by the period 2050 to 2060 has a greater than 50% chance of exceeding 2 °C for SSPs 245, 370 and 585. These results imply that global warming is no longer likely to remain under 1.5 °C, even with drastic and immediate mitigation, and highlight the importance of urgent action to avoid exceeding 2 °C warming.

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Section: Projecting Future Warming and Climate Feedback Trajectoriesmentioning

confidence: 99%

Section: δ ( ) ( )mentioning

confidence: 99%

Section: δ ( ) ( )mentioning

confidence: 99%

Section: δ ( ) ( )mentioning

confidence: 99%

Section: New Prior Distributions Of Climate Feedback Parametersmentioning

confidence: 99%

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Probabilistic projections of future warming and climate sensitivity trajectories

Goodwin

2021

Oxford Open Climate Change

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Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model

et al. 2022

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Probabilistic projections of baseline (with no additional mitigation policies) future carbon emissions are important for sound climate risk assessments. Deep uncertainty surrounds many drivers of projected emissions. Here, we use a simple integrated assessment model, calibrated to century-scale data and expert assessments of baseline emissions, global economic growth, and population growth, to make probabilistic projections of carbon emissions through 2100. Under a variety of assumptions about fossil fuel resource levels and decarbonization rates, our projections largely agree with several emissions projections under current policy conditions. Our global sensitivity analysis identifies several key economic drivers of uncertainty in future emissions and shows important higher-level interactions between economic and technological parameters, while population uncertainties are less important. Our analysis also projects relatively low global economic growth rates over the remainder of the century. This illustrates the importance of additional research into economic growth dynamics for climate risk assessment, especially if pledged and future climate mitigation policies are weakened or have delayed implementations. These results showcase the power of using a simple, transparent, and calibrated model. While the simple model structure has several advantages, it also creates caveats for our results which are related to important areas for further research.

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Climate nonlinearities: selection, uncertainty, projections, and damages

Cael

Britten

Calafat

et al. 2022

Environ. Res. Lett.

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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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Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

Cited by 7 publications

References 42 publications

Probabilistic projections of future warming and climate sensitivity trajectories

Probabilistic projections of future warming and climate sensitivity trajectories

Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model

Climate nonlinearities: selection, uncertainty, projections, and damages

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