Quantifying the effect of mixing on the mean Age of Air in CCMVal-2 and CCMI-1 models

Abstract

Abstract. Stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry-climate models (CCMs). Compared to observational estimates simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both, mean transport along the residual circulation and two-way mixing; we quantify the effects of these 5 processes using data from the CCM inter-comparison projects CCMVal-2 and CCMI-1. Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread inAoA between the models is primarily caused by differences in the effects of mixing, and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase of AoA by only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parametrized wave forcing do not appear to be related to differences in mixing efficiency or AoA.

The influence of mixing on stratospheric circulation changes in the 21st century

“…the mean from ) the values range between 5.52 years in the MRI model and 3.18 years in the ACCESS model simulation. This topic had already been discussed for example in SPARC (2010) and in Dietmüller et al (2018). Analysing the hindcast simulations of the CCMVal-2 and the CCMI-1 projects, Dietmüller et al (2018) showed that it is mainly the mixing rather than the residual circulation that causes the large AoA spread and that this is likely linked to the different resolutions of the model simulations.…”

“…We therefore calculate the mixing efficiency, an independent measure for the relative strength of mixing for given residual mean transport changes (ratio of mixing mass flux to net mass flux) (Garny et al, 2014), across the 21st century by means of a one-dimensional transport model of the stratosphere in the CCMI-1 model simulations. In the companion paper, Dietmüller et al (2018) have already shown that the mixing efficiency can explain most of the climatological AoA model spread. In the present study, we quantify the impact of mixing efficiency (relative mixing strength) differences in individual model simulations.…”

“…The methodology of this study mostly follows the companion paper Dietmüller et al (2018). A short description of the most important concepts used here is given in the following.…”

“…In the climatologies, the tropical upwelling (as calculated via the vstar integral at the turn-around latitudes, see e.g. Okamoto et al, 2011) in the 70 hPa pressure level has been shown to be a good measure for the strength of the residual circulation throughout the stratosphere (see Dietmüller et al, 2018). To see if this relationship holds true for ∆AoA in our set of the CCMI model simulations, we present the inter-model correlations between ∆AoA and ∆RCTT as well as between the ∆AoA and the differences in tropical upwelling in 70 hPa in Fig.…”

“…Thompson et al, 2011). Yet the representation of the strength, the structure and also the predicted future changes of the stratospheric overturning circulation differ vastly among today's state-ofthe-art climate models (SPARC, 2010;Dietmüller et al, 2018), the same models that are applied to making predictions of the surface climate conditions across the 21st century.…”