Between 1985 and 2014 observations show an expansion of Southern Ocean sea ice. This phenomena is not simulated in CMIP6 Atmosphere-Ocean General Circulation Model (AOGCM) simulations, nor in a large ensemble of HadGEM3-GC3.1-LL simulations. Here we quantify the impact of this discrepancy on radiative feedback and global temperature trends. We find that both satellite reconstructions of the Earth’s energy budget and atmosphere-only GCM simulations forced with observed SST and sea-ice trends support the idea that the expansion of sea-ice led to a negative surface albedo feedback over the Southern Ocean. In contrast, the decline of sea-ice in AOGCMs gives rise to a positive feedback. We estimate that, had the AOGCMs warmed with the observed Southern Ocean sea-ice evolution, their global feedback parameter would be less positive, by 0.15 to 0.36 Wm−2K−1, and their global temperature trends reduced by 0.072 to 0.02 K per decade over this period.

Climate feedbacks over the historical period (1850–2014) have been investigated in large ensembles of historical, hist-ghg, hist-aer, and hist-nat experiments, with 47 members for each experiment. Across the historical ensemble with all forcings, a range in estimated Effective Climate Sensitivity (EffCS) between approximately 3–6 K is found, a considerable spread stemming solely from initial condition uncertainty. The spread in EffCS is associated with varying Sea Surface Temperature (SST) patterns seen across the ensemble due to their influence on different feedback processes. For example, the level of polar amplification is shown to strongly control the amount of sea ice melt per degree of global warming. This mechanism is responsible for the large spread in shortwave clear-sky feedbacks and is the main contributor to the different forcing efficacies seen across the different forcing agents, although in HadGEM3-GC3.1-LL these differences in forcing efficacy are shown to be small. The spread in other feedbacks is also investigated, with the level of tropical SST warming shown to strongly control the longwave clear-sky feedbacks, and the local surface-air-temperatures and large scale tropospheric temperatures shown to influence cloud feedbacks. The metrics used to understand the spread in feedbacks can also help to explain the disparity between feedbacks seen in the historical experiment simulations and a more accurate modeled estimate of the feedbacks seen in the real world derived from an atmosphere-only experiment prescribed with observed SSTs (termed amip-piForcing).