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. The stratospheric age of air (AoA) is a useful measure of the overallcapabilities of a general circulation model (GCM) to simulate stratospherictransport. Previous studies have reported a large spread in the simulation ofAoA by GCMs and coupled chemistry–climate models (CCMs). Compared toobservational estimates, simulated AoA is mostly too low. Here we attempt tountangle the processes that lead to the AoA differences between the modelsand between models and observations. AoA is influenced by both mean transportby the residual circulation and two-way mixing; we quantify the effects ofthese processes using data from the CCM inter-comparison projects CCMVal-2 (Chemistry–ClimateModel Validation Activity 2) and CCMI-1 (Chemistry–Climate Model Initiative, phase 1).Transport along the residual circulation is measured by theresidual circulation transit time (RCTT). We interpret the difference betweenAoA and RCTT as additional aging by mixing. Aging by mixing thus includesmixing on both the resolved and subgrid scale. We find that the spread in AoAbetween the models is primarily caused by differences in the effects ofmixing and only to some extent by differences in residual circulationstrength. These effects are quantified by the mixing efficiency, a measure ofthe relative increase in AoA by mixing. The mixing efficiency varies stronglybetween the models from 0.24 to 1.02. We show that the mixing efficiency isnot only controlled by horizontal mixing, but by vertical mixing and verticaldiffusion as well. Possible causes for the differences in the models' mixingefficiencies are discussed. Differences in subgrid-scale mixing (includingdifferences in advection schemes and model resolutions) likely contribute tothe differences in mixing efficiency. However, differences in the relativecontribution of resolved versus parameterized wave forcing do not appear to berelated to differences in mixing efficiency or AoA.

Quantifying the effect of mixing on the mean age of air in CCMVal-2 and CCMI-1 models