22.214.171.124 Inferences About Climate Sensitivity Based on the Last Glacial Maximum
The LGM is one of the key periods used to estimate ECS (Hansen et al., 1984; Lorius et al., 1990; Hoffert and Covey, 1992), since it represents a quasi-equilibrium climate response to substantially altered boundary conditions. When forced with changes in greenhouse gas concentrations and the extent and height of ice sheet boundary conditions, AOGCMs or EMICs identical or similar to those used for 20th- and 21st-century simulations produce a 3.3°C to 5.1°C cooling for this period in response to radiative perturbations of 4.6 to 7.2 W m–2 (Sections 126.96.36.199; see also Section 9.3.2; see also Masson-Delmotte et al., 2006). The simulated cooling in the tropics ranges from 1.7°C to 2.4°C. The ECS of the models used in PMIP2 ranges from 2.3°C to 3.7°C (Table 8.2), and there is some tendency for models with larger sensitivity to produce larger tropical cooling for the LGM, but this relationship is not very tight. Comparison between simulated climate change and reconstructed climate is affected by substantial uncertainties in forcing and data (Chapter 6 and Section 188.8.131.52). For example, the PMIP2 forcing does not account for changes in mineral dust, since the level of scientific understanding for this forcing is very low (Figure 6.5). The range of simulated temperature changes is also affected by differences in the radiative influence of the ice-covered regions in different models (Taylor et al., 2000). Nevertheless, the PMIP2 models simulate LGM climate changes that are approximately consistent with proxy information (Chapter 6).
Recent studies (Annan et al., 2005; Schneider von Deimling et al., 2006) attempt to estimate the PDF of ECS from ensemble simulations of the LGM by systematically exploring model uncertainty. Both studies investigate the relationship between climate sensitivity and LGM tropical SSTs, which are influenced strongly by CO2 changes. In a perturbed physics ensemble, Schneider von Deimling et al. (2006) vary 11 ocean and atmospheric parameters in a 1,000-member ensemble simulation of the LGM with the CLIMBER-2 EMIC (Table 8.3). They find a close relationship between ECS and tropical SST cooling in their model, implying a 5 to 95% range of ECS of 1.2°C to 4.3°C when attempting to account for model parameter, forcing and palaeoclimate data uncertainties. Similar constraints on climate sensitivity are found when proxy reconstructions of LGM antarctic temperatures are used instead of tropical SSTs (Schneider von Deimling et al., 2006). In contrast, Annan et al. (2005) use a perturbed physics ensemble based on a low-resolution version of the atmospheric component of the MIROC3.2 model, perturbing a range of model parameters over prior distributions determined from the ability of the model to reproduce seasonal mean climate in a range of climate variables. They find a best-fit sensitivity of about 4.5°C, and their results suggest that sensitivities in excess of 6°C are unlikely given observational estimates of LGM tropical cooling and the relationship between tropical SST and sensitivity in their model. Since the perturbed physics ensemble based on that atmospheric model does not produce sensitivities less than 4°C, this result cannot provide a lower limit or a PDF for ECS.
The discrepancy between the inferred upper limits in the two studies probably arises from both different radiative forcing and structural differences between the models used. Forcing from changes in vegetation cover and dust is not included in the simulations done by Annan et al. (2005), which according to Schneider von Deimling et al. (2006) would reduce the Annan et al. ECS estimates and yield better agreement between the results of the two studies. However, the effect of these forcings and their interaction with other LGM forcings is very uncertain, limiting confidence in such estimates of their effect (Figure 6.5). Structural differences in models are also likely to play a role. The Annan et al. (2005) estimate shows a weaker association between simulated tropical SST changes and ECS than the Schneider von Deimling et al. (2006) result. Since Annan et al. use a mixed-layer ocean model, and Schneider von Deimling a simplified ocean model, both models may not capture the full ocean response affecting tropical SSTs. The atmospheric model used in Schneider von Deimling is substantially simpler than that used in the Annan et al. (2005) study. Overall, estimates of climate sensitivity from the LGM are broadly consistent with other estimates of climate sensitivity derived, for example, from the instrumental period.