Inferred effective climate sensitivity from a selection of CMIP6 models

Abstract. Climate sensitivity is a key metric in climate science, defined as the equilibrium global mean surface temperature response following a doubling of the atmospheric CO₂ concentration. Climate sensitivity, which is an idealized concept, can be quantified using different approaches, all with their own limitations. In this study, we employ a method previously used to infer climate sensitivity based on Earth system observations and radiative forcing estimates over the industrialized period, on historical climate model simulations. We estimate climate sensitivity using the transient response in historical simulations from four models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with a total of 40 ensemble members. The surface temperature and ocean heat content evolution from each ensemble member is known, effective radiative forcing time series for each model are diagnosed from targeted simulations within CMIP6 and the effective climate sensitivity (ECS_eff) for each model is calculated from the abrupt four times CO₂ experiment. The inferred effective climate sensitivity (ECS_inf) from the transient model response is generally lower than the models’ ECS_eff, as expected due to time and state dependencies of climate sensitivity. For different ensemble members, the ECS_inf can be quite different. For CNRM-CM6-1, the posterior mean ECS_inf varies by 2.2 K among its 30 ensemble members, but the 90 % confidence intervals all contain the model’s ECS_eff. The posterior mean ECS_inf for HadGEM3 GC3.1-LL varies by as much as 4 K. For NorESM2-LM and NorESM2-MM where the ECS_eff is 2.5 K for both models, the posterior mean ECS_inf varies by 0.62 K and 1.2 K respectively, and only one of the in total six ensemble members has posterior mean ECS_inf larger than ECS_eff. Because the observed historical climate represents only one realization of the Earth’s climate, these results highlight the importance of natural variability when estimating climate sensitivity using observations over the historical period and not only the uncertainty in the historical forcing due to aerosols.