A new estimate of climate sensitivity using Last Glacial Maximum model-data constraints that includes parametric, feedback, and proxy uncertainties

The Last Glacial Maximum (LGM) provides potentially useful constraints on equilibrium climate sensitivity (ECS) because it is the most recent period of large greenhouse gas and temperature change. In addition, the wealth of proxy data from ice cores, ocean cores, and terrestrial records during this time period helps to test the relationship between greenhouse gas concentrations and temperature. A previous study (Schmittner et al., 2011) has estimated probability distributions of ECS using a small ensemble of model simulations that varies model sensitivity to atmospheric CO2 concentrations by changing only one model parameter. However, that estimate neglected cloud feedbacks, although they are the largest source of uncertainty in comprehensive climate models. Here, we provide a new estimate of ECS using a much larger ensemble of simulations (>1000) including cloud feedbacks and other uncertainties. We apply a new method to diagnose separately shortwave and longwave cloud feedbacks from comprehensive models and include them in the University of Victoria Earth System Climate Model (UVic-ESCM). We also explore parametric uncertainties in dust forcing, snow albedo, and atmospheric diffusivities, which all influence important feedbacks in UVic-ECSM. Finally, we use Bayesian statistics to compare LGM proxy data with this new model ensemble and to provide a new probabilistic estimate of ECS that better includes dominant sources of model and data uncertainty.