the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Middle atmosphere chemical and dynamical effects in the CCMI-2022 stratospheric aerosol injection scenario
Abstract. Stratospheric aerosol injection (SAI) could slow surface warming and help prevent some irreversible tipping points in the climate system. However, potential side effects include changes in stratospheric ozone and warming due to infrared ab sorption by the aerosol, which can alter surface ultraviolet radiation, hydrology, and weather through stratosphere-troposphere coupling. Previous multi-model studies have reported large model discrepancies regarding these effects. Here we present results from an experiment within the Chemistry-Climate Model Initiative Phase 2 (CCMI-2022), designed to constrain inter-model uncertainties by applying a common stratospheric aerosol forcing to five chemistry-climate models using an SAI scenario offsetting all surface warming after 2025 in a moderate greenhouse gas emission scenario. All models show a global total column ozone decrease in the first three decades of no more than ∼10 DU relative to a no-SAI case. Despite sizable differences in stratospheric heating, the models yield a qualitatively similar pattern of ozone redistribution. Changes in key processes, such as the ClOx activation and NOx passivation, and the strengthening of the deep branch of the Brewer-Dobson circulation, are widely robust across all models, though their relative importance and contribution to ozone changes varies considerably. In three of the models, we separate chemical and dynamical contributions, and find significant nonlinearities from feedbacks between chemistry and dynamics, highlighting where model development and sensitivity experiments are most needed to advance the understanding of the middle atmosphere in future mitigation scenarios involving SAI.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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Data sets
CCMI-2022 post-processed model data Andrin Jörimann https://zenodo.org/records/18331211
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