What’s the worst case? Climate sensitivity
by Judith Curry
Are values of equilibrium climate sensitivity > 4.5 C plausible?
For background, see these previous posts on climate sensitivity [link]
Here are some possibilistic arguments related to climate sensitivity. I don’t think the ECS example is the best one to illustrate these ideas [see previous post], and I probably won’t include this example in anything I try to publish on this topic (my draft paper is getting too long anyways). But possibilistic thinking does point you in some different directions when pondering the upper bound of plausible ECS values.
5. Climate sensitivity
Equilibrium climate sensitivity (ECS) is defined as the amount of temperature change in response to a doubling of atmospheric CO2 concentrations, after the climate system has reached equilibrium. The issue with regards to ECS is not scenario discovery; rather, the challenge is to clarify the upper bounds of possible and plausible worst cases.
The IPCC assessments of ECS have focused on a ‘likely’ (> 66% probability) range, which has mostly been unchanged since Charney et al. (1979), to be between 1.5 and 4.5 oC. The IPCC AR4 (2007) did not provide any insight into a worst-case value of ECS, stating that values substantially higher than 4.5 oC cannot be excluded, with tail values in Figure 9.20 exceeding 10 oC. The IPCC AR5 (2013) more clearly defined the upper range, with a 10% probability of exceeding 6 oC.
Since the IPCC AR5, there has been considerable debate as to whether ECS is on the lower end of the likely range (e.g., < 3 oC) or the higher end of the likely range (for a summary, see Lewis and Curry, 2018). The analysis here bypasses that particular debate and focuses on the upper extreme values of ECS.
High-end values of ECS are of considerable interest to economists. Weitzman (2009) argued that probability density function (PDF) tails of the equilibrium climate sensitivity, fattened by structural uncertainty using a Bayesian framework, can have a large effect on the cost-benefit analysis. Proceeding in the Bayesian paradigm, Weitzman fitted a Pareto distribution to the AR4 ECS values, resulting in a fat tail that produced a probability of 0.05 of ECS exceeding 11 oC, and a 0.01% probability of exceeding 20 oC.
The range of ECS values derived from global climate models (CMIP5) that were cited by the IPCC AR5 is between 2.1 and 4.7 oC. To better constrain the values of ECS based on observational information available at the time of the AR5, Lewis and Grunwald (2018) combined instrumental period evidence with paleoclimate proxy evidence using objective Bayesian and frequentist likelihood-ratio methods. They identified a 5–95% range for ECS of 1.1–4.05 oC. Using the same analysis methods, Lewis and Curry (2018) updated the analysis for the instrumental period by extending the period and using revised estimates of forcing to determine a 5-95% range of 1.05 – 2.7 oC. The observationally-based values should be regarded as estimates of effective climate sensitivity, as they reflect feedbacks over too short a period for equilibrium to be reached.
Values of climate sensitivity exceeding 4.5 oC derived from observational analyses are arguably associated with deficiencies in the diagnostics or analysis approach (e.g. Annan and Hargreaves, 2006; Lewis and Curry, 2015). In particular, use of a non-informative prior (e.g. Jeffreys prior), or a frequentist likelihood-ratio method, narrows the upper tail considerably. However, as summarized by Frame et al. (2006), there is no observational constraint on the upper bound of ECS.
The challenges of identifying an upper bound for ECS are summarized by Stevens et al. (2016) and Knutti et al. (2017). Stevens et al. (2016) describes a systematic approach for refuting physical storylines for extreme values. Stevens et al.’s physical storyline for a very high ECS (>4.5 oC) is comprised of three conditions: (i) the aerosol cooling influence in recent decades would have to have been strong enough to offset most of the effect of rising greenhouse gases; (ii) tropical sea-surface temperatures at the time of the last glacial maximum would have to have been much cooler than at present; and (iii) cloud feedbacks from warming would have to be strong and positive.
An interesting challenge to identifying the plausible upper bound for ECS has been presented by a newly developed climate model, the DOE E3SM (Golaz et al. 2019), which includes numerous technical and scientific advances. The model’s value of ECS has been determined to be 5.3 oC, higher than any of the CMIP5 model values and outside the IPCC AR5 likely range. This high value of ECS is attributable to very strong shortwave cloud feedback. The DOE E3SM model’s value of shortwave cloud feedback is larger than all CMIP5 models; however, shortwave cloud feedback is weakly constrained by observations and physical understanding. A stronger argument for placing the DOE E3SM value of climate sensitivity in the ‘borderline impossible’ category is Figure 23 in Golaz et al. (2019), which shows that the global mean surface temperature simulated by the model during the period 1960-2000 is as much as 0.5 oC lower than observed, and that since the mid-1990s the simulated temperature rises far faster than the observed temperature. This case illustrates the challenge of refuting scenarios associated with a complex storyline or model, which was noted by Stevens et al. (2016).
An additional issue regarding climate model derived values of ECS was raised by recent paper by Mauritsen et al. (2019). An intermediate version of the MPI-ESM1.2 global climate model produced an ECS value of ~ 7 oC, caused by the parameterization of low-level clouds in the tropics. Since this model version produced substantially more warming than observed in the historical period, this model version was rejected…
