Abstract
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 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 ECSinf 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 ECSinf 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 ECSinf by about 50 % from our best estimate.
Highlights
A key question in climate science is how the global mean surface temperature (GMST) responds to changes in greenhouse gases or other forcings
We carry out a number of sensitivity experiments to investigate causes of differences in observationally based ECSinf estimates due to differences in the input data (observations of surface temperature, ocean heat content (OHC) and radiative forcing (RF))
Our full model consists of a simple climate model (SCM) with an idealized representation of the Earth’s energy balance, a data model that describes how observations are related to the process states and a parameter model that expresses our prior knowledge of the parameters (Aldrin et al, 2012)
Summary
A key question in climate science is how the global mean surface temperature (GMST) responds to changes in greenhouse gases or other forcings. This is most likely due to the evolution of the pattern of sea surface temperature increase in the Pacific and Southern oceans and associated cloud feedbacks Whether this slow warming has manifested itself in the climate record used for the analysis is the difference between effective and equilibrium climate sensitivity (Armour, 2017; Knutti et al, 2017). According to Richardson et al (2016), there is a general bias in the surface temperature records since water heats more slowly than the air above and due to undersampling in fast-warming regions (e.g., the Arctic) Taking both effects into account, Armour (2017) shows that previous estimates of inferred effective climate sensitivity (ECSinf) of about 2.0 ◦C are consistent with estimates of ECS of 2.9 ◦C from climate models. We carry out a number of sensitivity experiments to investigate causes of differences in observationally based ECSinf estimates due to differences in the input data (observations of surface temperature, OHC and radiative forcing (RF))
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