Future trends in stratosphere-to-troposphere transport in CCMI models

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One of the key questions in the air quality and climate sciences is how tropospheric ozone concentrations will change in the future. This will depend on two factors: changes in stratosphere-to-troposphere transport (STT) and changes in tropospheric chemistry. Here we aim to identify robust changes in STT using simulations from the Chemistry Climate Model Initiative (CCMI) under a common climate change scenario (RCP6.0). We use two idealized stratospheric tracers to isolate changes in transport: stratospheric ozone (O_(3)S), which is exactly like ozone but has no chemical sources in the troposphere, and st80, a passive tracer with fixed volume mixing ratio in the stratosphere. We find a robust increase in the tropospheric columns of these two tracers across the models. In particular, stratospheric ozone in the troposphere is projected to increase 10 %–16 % by the end of the 21st century in the RCP6.0 scenario. Future STT is enhanced in the subtropics due to the strengthening of the shallow branch of the Brewer–Dobson circulation (BDC) in the lower stratosphere and of the upper part of the Hadley cell in the upper troposphere. The acceleration of the deep branch of the BDC in the Northern Hemisphere (NH) and changes in eddy transport contribute to increased STT at high latitudes. These STT trends are caused by greenhouse gas (GHG) increases, while phasing out of ozone-depleting substances (ODS) does not lead to robust transport changes. Nevertheless, the decline of ODS increases the reservoir of ozone in the lower stratosphere, which results in enhanced STT of O_(3)S at middle and high latitudes. A higher emission scenario (RCP8.5) produces stronger STT trends, with increases in tropospheric column O_(3)S more than 3 times larger than those in the RCP6.0 scenario by the end of the 21st century.
© Author(s) 2020. This study has been partly carried out using the high-performance computing and storage facilities provided by CISL/NCAR. The EMAC simulations have been performed at the German Climate Computing Center (DKRZ) through support from the Bundesministerium für Bildung und Forschung (BMBF). DKRZ and its scientific steering committee are gratefully acknowledged for providing the HPC and data-archiving resources for the consortial project ESCiMo (Earth System Chemistry integrated Modelling). We acknowledge the UK Met Office for use of the MetUM. This research was supported by the New Zealand Government’s Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. Olaf Morgenstern acknowledges funding by the New Zealand Royal Society Marsden Fund (grant 12-NIW006). The authors wish to acknowledge the contribution of NeSI high-performance computing facilities to the results of this research. New Zealand’s national facilities are provided by the New Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation, and Employment’s Research Infrastructure programme (, last access: April 2020). The GEOSCCM is supported by the NASA MAP program, and the high-performance computing resources were provided by the NASA Center for Climate Simulation (NCCS). The authors are grateful to the editor and the referees for the insightful reviews that contributed to notably improve the paper. Marta Abalos acknowledges funding from the Program Atracción de Talento de la Comunidad de Madrid (fund no. 2016-T2/AMB-1405) and the Spanish National Project STEADY (project no. CGL2017-83198-R).