Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
AS1.31 | The role of the middle atmosphere in a changing climate: circulation, composition and radiative feedbacks
EDI
The role of the middle atmosphere in a changing climate: circulation, composition and radiative feedbacks
Convener: Peer Nowack | Co-conveners: Birgit Hassler, Gabriel Chiodo, Mohamadou Diallo, James Keeble
Anthropogenic emissions of greenhouse gases and ozone depleting substances have caused substantial changes in the chemical composition of the middle atmosphere that also impact the troposphere significantly. For example, increasing greenhouse gas levels are expected to dynamically and chemically modify stratospheric concentrations of key radiatively active species such as water vapor, ozone and aerosols, and can affect the strength of the Brewer-Dobson circulation (BDC). Long-term changes in the ozone layer can further influence the biosphere (e.g. by modulating UV exposure), feed back on surface climate, and have known impacts on the tropospheric circulation. These mechanisms may be coupled to a variety of Earth system feedbacks, and these interactions are still poorly understood.

We welcome abstracts which explore stratospheric composition changes and the resulting impacts on the tropospheric and stratospheric circulation as well as on surface climate. In particular, the session welcomes new studies on the influence of stratospheric composition on weather and climate, on the impacts of the recent Hunga-Tonga and other stratosphere-reaching volcanic eruptions, on long-term ozone trends (depletion and recovery), and on stratospheric water vapor. These studies may use a variety of datasets and methods, ranging from observations to model data, e.g. from the Chemistry Climate Model Initiative (CCMI-2) and the Coupled Model Intercomparison Project Phase 6 (CMIP6). We also welcome studies using novel analytical approaches such as machine learning or causal inference methods.