Age of air as a diagnostic for transport timescales in global models

Abstract

Abstract. This paper presents the first results of an age-ofair (AoA) inter-comparison of six global transport models. Following a protocol, three global circulation models and three chemistry transport models simulated five tracers with boundary conditions that grow linearly in time. This allows for an evaluation of the AoA and transport times associated with inter-hemispheric transport, vertical mixing in the troposphere, transport to and in the stratosphere, and transport of air masses between land and ocean. Since AoA is not a directly measurable quantity in the atmosphere, simulations of 222 Rn and SF 6 were also performed. We focus this first analysis on averages over the period 2000-2010, taken from longer simulations covering the period 1988-2014. We find that two models, NIES and TOMCAT, show substantially slower vertical mixing in the troposphere compared to other models (LMDZ, TM5, EMAC, and ACTM). However, while the TOMCAT model, as used here, has slow transport between the hemispheres and between the atmosphere over land and ocean, the NIES model shows efficient horizontal mixing and a smaller latitudinal gradient in SF 6 compared to the other models and observations. We find consistent differences between models concerning vertical mixing of the troposphere, expressed as AoA differences and modelled 222 Rn gradients between 950 and 500 hPa. All models agree, however, on an interesting asymmetry in inter-hemispheric mixing, with faster transport from the Northern Hemisphere surface to the Southern Hemisphere than vice versa. This is attributed to a rectifier effect caused by a stronger seasonal cycle in boundary layer venting over Northern Hemispheric land masses, and possibly to a related asymmetric position of the intertropical convergence zone. The calculated AoA in the mid-upper stratosphere varies considerably among the models (4-7 years). Finally, we find that the inter-model differences are generally larger than differences in AoA that result from using the same model with a different resolution or convective parameterisation. Taken together, the AoA model inter-comparison provides a useful addition to traditional approaches to evaluate transport timescales. Results highlight that inter-model differences associated with resolved transport (advection, reanalysis data, nudging) and parameterised transport (convection, boundary layer mixing) are still large and require further analysis. For this purpose, all model output and analysis software are available.

Comparison of mean age of air in five reanalyses using the BASCOE transport model

“…The differences between the two calculations represent the transit times from the surface to the tropical tropopause, are nearly independent of the simulated year and range between 3 months (with Mean for 72° S-72° N and 16-28 km Pinatubo AMSU 199019921994199820002004200620102016 Year ERA-I or JRA-55) and 6 months (with MERRA). These values are close to the longest transit times reported in a recent intercomparison of global models (Krol et al, 2018) indicating slow transport from the surface to the tropical tropopause which we attribute to the omission of deep convective transport in our model. While the surface-based model AoA (solid lines in Fig.…”

“…The absolute value of AoA and its evolution over the past decades can be derived from the surface pressure and wind fields available in such reanalyses, using either an offline transport model (see, e.g., Chipperfield, 2006) or a chemistry-climate model nudged to the input reanalysis (Kunz et al, 2011;Kovács et al, 2017) to model the transport of inert tracers propagating from the troposphere to the stratosphere. This approach helped to identify shortcomings in the Brewer-Dobson circulation described by early reanalyses (Meijer et al, 2004;Pawson et al, 2007) and to assess the improvements in the next generation of reanalyses, e.g., from ERA-40 to ERA-Interim (Monge-Sanz et al, 2007;Dee et al, 2011;Monge-Sanz et al, 2012).…”

Influence of Atmospheric Transport on Estimates of Variability in the Global Methane Burden

“…The TM5-derived influence of atmospheric transport on the IHD is −0.35 ± 0.07 ppb/year using the NOAA method and −0.12 ± 0.08 ppb/year using hemispheric burdens. These results point to an acceleration of inter-hemispheric transport (Krol et al, 2018;Naus et al, 2019;Patra et al, 2011). After adjusting the measurements for transport influences, we obtain a positive trend of +0.37 ± 0.06 ppb/year (see Figure 4a), which indicates that the CH 4 emissions in the Northern Hemisphere are increasing faster than in the Southern Hemisphere.…”

ENSO-driven reverse coupling in interannual variability of pantropical water availability and global atmospheric CO2 growth rate

“…There is a time lag between regional land carbon fluxes and the global mean CO 2 concentration of wellmixed marine boundary layer (Conway et al 1994). The potential processes governing the lagged time of CO 2 growth to water availability were complicated, and were mainly related to water availability sensitivity of different biomes (Vicente-Serrano et al 2013), the CO 2 exchange processes through convection at the land surface, and transport processes of CO 2 within the atmosphere through atmospheric circulation (Krol et al 2018). The CO 2 growth rate is the global mean value of the observation network, and it is not a point observation, moreover the vertical mixing of air in the troposphere and transport time scales varies in different global transport models (Krol et al 2018).…”

“…Atmospheres in the tropics and subtropics have vigorous convection and are quickly mixed through a large volume of air associated with Walker and Hadley circulation (Krol et al 2018, Schuh et al 2019. Furthermore, except the deep inland areas, most of the pantropical lands are close to the marine atmosphere.…”