James Hansen’s latest paper “Global warming in the pipeline” (Hansen et al. (2023)) has already been heavily criticized in a lengthy comment by Michael Mann, author of the original IPCC ‘hockey stick’. However Mann does not deal with Hansen’s surprisingly high (4.8°C) new estimate of equilibrium climate sensitivity (ECS). This ECS estimate is 60% above Hansen’s longstanding previous estimate of 3°C. It is Hansen’s new, very high ECS estimate drives, in conjunction with various questionable subsidiary assumptions, his paper’s dire predictions of high global warming and its more extreme concluding policy recommendations, such as ‘solar radiation management’ geoengineering.
Hansen’s new 4.8°C ECS estimate is well above the best estimate of 3°C reached in the IPCC’s latest scientific assessment report (AR6), lies outside the AR6 likely (66%) range of 2.5–4°C and is almost at the top of the AR6 90% uncertainty range of 2–5°C.
Both Hansen’s new ECS estimate and his earlier estimate are based primarily on information about paleoclimate changes, particularly the extensively studied transition from the last glacial maximum (LGM) some 20,000 years ago to the preindustrial Holocene. But is his new estimate (or indeed his earlier estimate) justified by the evidence?
Hansen’s primary LGM to Holocene based ECS estimation.
For his LGM-based ECS estimate, Hansen assumes a 7.0°C rise in global mean surface temperature (GMST) between the LGM and preindustrial Holocene. This value is 56% above the 4.5°C rise that Hansen used previously2. Hansen cites three studies in support of his 7.0°C LGM cooling estimate (which comes from the second of these): Tierney et al. (2020), Osman et al. (2021) and Seltzer et al. (2021).
I published in 2021 an article that was heavily critical of the Osman et al. data-assimilation (reanalysis) based temperature reconstruction. Their reconstruction uses only ocean sea surface temperature (SST) proxies and is based on a single climate model that simulates an unusually cold LGM state and, strongly influenced by the model simulations, produced a 7°C estimate of LGM cooling. The proxy-only based reconstruction in the submitted version of Osman et al. appeared more reasonable, and as shown in my article implied LGM to preindustrial GMST change of about 4.5°C.
The Tierney et al. estimate of LGM cooling (6.1°C) is based on a much larger set of SST proxies, which includes all those employed by Osman et al. used, but similarly uses no land temperature proxies. Tierney et al. uses the same single-model data-assimilation temperature reconstruction method as does Osman et al. Therefore, my criticisms of Osman et al.’s reanalysis-derived LGM cooling estimate very largely apply also to Tierney et al.’s estimate.
The Seltzer et al. 5.8 ± 0.6 °C LGM cooling estimate is for land only and is limited to 45°S–35°N, so does not represent GMST cooling. The authors use a groundwater-based proxy type and a complex model to convert proxy values to surface temperatures. I have concerns about some aspects of their methods. Moreover, their 5.8°C land cooling estimate is based on the error-weighted average of only 15 groundwater records, which show widely varying cooling and may not be adequately representative of 45°S–35°N land areas. Seltzer et al. completely ignore the uncertainty associated with these issues. Their estimate is inconsistent with most proxy-derived estimates of mean ocean cooling over 45°S–35°N, which casts further doubt on its reliability.
I regard the recent Annan et al. (2022) LGM temperature reconstruction as producing a more reliable estimate of GMST cooling than the Tierney et al. (2020) and Osman et al. (2021) studies that Hansen et al. relies upon. The Annan et al. reconstruction uses a similar data-assimilation method to those studies, but with several key differences. Unlike them it uses a simulations from large set of acceptably dissimilar climate models, rather than a single model. Importantly, unlike those two studies, Annan et al. scale the model simulated temperature changes so that the initial guess model cooling estimates used are approximately centered on the data, to minimize model-generated bias. Moreover, Annan et al. use land temperature proxies as well as a larger set of ocean SST proxies to adjust the initial guess. They use the same SST proxy dataset as Tierney et al., but extend its coverage with data from a widely used earlier SST dataset, which in particular reduces Tierney et al.’s huge gaps in coverage of the Pacific ocean.
As a result of the much better proxy coverage, the use of multiple models and the debiasing step, the 4.5°C estimate of GMST cooling at the LGM that the Annan et al. reconstruction produces should be much more realistic than the Tierney et al. (2020) and Osman et al. (2021) estimates that Hansen uses. Indeed, Annan et al. criticize Tierney et al.’s approach, using very cold simulations from a single model, pointing out that it will produce a cool reconstruction regardless of whether the data points to a milder LGM climate.
By coincidence, Annan et al.’s 4.5°C GMST LGM cooling estimate is the same value as I adopted for the LGM derived climate sensitivity estimation in Lewis (2023). That study reworked Sherwood et al. (2020), a very influential World Climate Research Programme linked review of climate sensitivity evidence, which had assessed the LGM to be 5°C cooler than preindustrial.
Hansen’s ECS estimate is increased not only by adoption of an unrealistically large LGM cooling value, but also by very low estimates for the various changes in forcing between the LGM and the preindustrial Holocene. Hansen’s central estimate for the total Holocene to LGM forcing difference is −5.75 W/m2, much smaller than the −8.63 W/m2 assessed by Sherwood et al.(2020) and even further below the −8.93 W/m2 best estimate adopted in Lewis (2023). The LGM section of Table 1 in the Appendix gives details of all the data-variable estimates used to form the Sherwood et al. (2020), Lewis (2023) and Hansen et al. (2023) LGM–preindustrial Holocene based ECS estimates.
The combination of the large GMST change and small forcing change that Hansen adopts for the LGM results in a central ECS estimate of 4.8°C. By comparison, the Lewis (2023) LGM-based ECS estimate was 2.2°C, somewhat less than the corresponding ECS estimate from Sherwood et al. of 2.8°C. Both these estimates are well below the lower bound of the 3.6 – 6.0 °C ECS 95% uncertainty range for ECS that Hansen estimates.
Hansen supports his LGM-based ECS estimate with one based on the difference in GMST between the LGM and the previous, Eemian, interglacial period. His estimate is therefore inflated by his assumption that the LGM was very cool. Moreover, compared to the LGM there is even greater uncertainty in Eemian GMST, ice-sheet forcing, and other non-CO2 forcing. IPCC AR6 mentions all these drawbacks and additionally points outs that accounting for varying orbital forcing is challenging for this period. Sherwood et al. (2020) did not attempt to estimate climate sensitivity from the Eemian to LGM or any other pre-LGM climate transition because the data available are far more limited than for the much more extensively studied LGM. I likewise did not do so in Lewis (2023), nor do I attempt such an estimate here.
Hansen’s analysis of the PETM event and its implications.
Hansen also considers the Paleocene-Eocene Thermal Maximum (PETM) warming event some 56 million years ago, adopting a best estimate for the warming involved of 5.6°C and assuming, as did Sherwood et al. (2020) and Lewis (2023), that greenhouse gases alone produced the increase in forcing that caused the warming.
Rather than estimating ECS using an estimate of the PETM forcing change, Hansen calculates what the PETM CO2 level would have had to be to cause warming of 5.6°C, based on his ECS estimate of 4.8°C, assuming a pre-PETM CO2 level of 910 ppm and that non-CO2 greenhouse gases contributed 25% as much forcing as CO2 did. Hansen thereby calculates a PETM CO2 level of 1630 ppm. In fact, the correct figure seems to be approximately1565 ppm; Hansen appears to have gone wrong somewhere in his calculations.
Although Hansen’s 910 ppm pre-PETM CO2 concentration value is closely in line with other estimates, the circa 1600 ppm value for the PETM CO2 concentration implied by his 4.8°C ECS and his other assumptions is much lower than the estimate of 2400 ppm, with a ±1 standard deviation range of 1700–3100 ppm, that Sherwood et al. (2020) reached after assessing the available evidence. It is also near the bottom of the 1400–3150 ppm uncertainty range given in AR6.
Moreover, Sherwood et al. assessed the best estimate GMST rise at the PETM to be slightly lower than did Hansen, at 5.0°C, and the non-CO2 greenhouse gas forcing to be higher, at 40% of CO2 forcing. All these differences contribute to Sherwood et al.’s PETM-based ECS estimate being, at 2.5°C, barely half Hansen’s assumed 4.8°C. Moreover, Lewis (2023), using identical data-variable assumptions as Sherwood et al. (2020) but the AR6 formula for CO2 forcing, which is more accurate at high concentrations than the simple formula Sherwood et al. used, obtains an even lower PETM-based ECS estimate of 2.2°C.
The PETM section of Table 1 in the Appendix gives details of all the data-variable estimates used to form the Sherwood et al. (2020)and Lewis (2023) PETM based ECS estimates, and the Hansen et al. (2023) estimate of CO2 concentration at the PETM. Figure 1 shows what the central estimate of the total change in forcing involved was, as a multiple of the forcing from a doubling of preindustrial CO2 concentration, for all three studies’ LGM and PETM estimates.
Although there are much greater uncertainties involved when estimating ECS from the PETM event than for the LGM – preindustrial Holocene transition, the available PETM evidence is closely consistent with the lower, 2.2°C and 2.8°C, ECS estimates derived from the LGM data-values used respectively by Lewis (2023) and Sherwood et al. (2020), but not with Hansen’s very high 4.8°C estimate of ECS.
It is also clear that Hansen’s claim that today’s human-made greenhouse gas forcing is, at 4.6 W/m2, at least comparable to the PETM forcing (which his assumptions imply was 4.67 W/m2) is strongly at variance with the evidence as assessed by Sherwood et al. That evidence implies a best estimate of PETM forcing of 1.98× that for a doubling of preindustrial CO2 concentration (versus 1.17× on Hansen’s assumptions), double or more the latest (2022) AR6-basis estimate, in Forster et al. (2023), of 0.88× for greenhouse gas forcing (1.00× when including that from ozone, a short lived greenhouse gas).
Figure 1.The forcing change between the LGM and preindustrial Holocene, and between before and during the PETM, implicit in each study’s assumptions. The forcing changes are expressed as a multiple of that from a doubling of preindustrial CO2 concentration. The associated ECS estimate equals in each case the corresponding assumed GMST change divided by the forcing change shown, so a higher forcing change implies a lower estimated ECS value.
I do not consider that Hansen’s climate sensitivity estimation properly assesses and fairly reflects all the available relevant evidence. Unfortunately, unlike Sherwood et al.(2020), IPCC AR6 and Lewis (2023), Hansen et. al. (2023) estimates ECS using only paleoclimate proxy-derived evidence, which generally varies considerably according to the proxies involved and to the methods used to interpret them. This opens the door for biased (cherry picked) assessments. For instance, Hansen et. al. do not even mention any studies (e.g. Annan and Hargreaves (2013) and (2022)) that find a much lower LGM – preindustrial warming than their chosen value.
Although I respect Hansen’s ability and considerable scientific contributions, in my view papers he leads are increasingly strongly biased towards overheated projections and dire conclusions. The “political recommendations” with great impact on the society in Hansen et al. (2023) cannot be justified because their foundation is very shaky, as shown here for climate sensitivity and, in relation to warming-in-the-pipeline and ocean heating, in Michael Mann’s critique.
Nicholas Lewis 6 November 2023
Table 1. Paleoclimate evidence data-variable best-estimate valuesa used to estimate S and ECS