Overview
Modelling of the ozone evolution has achieved substantial progress over the last decades, and now there are more than a dozen of global chemistry-climate models (CCMs) that can treat stratospheric processes in detail and generally agree well with observations and with each other. Some observed features, like close-to-negative trends in the lower-stratospheric northern mid-latitudes, are, however, not reproduced by the models. Representation of the tropospheric ozone is usually also much less satisfactory, since tropospheric chemistry is often simplified in global high-top models because of the computational constraints. Therefore, reliable estimates of changes in tropospheric composition are commonly assessed with high resolution chemistry transport models (CTMs), which include a more complete representation of chemical formation, loss, and deposition mechanisms as well as transport in the boundary layer and free troposphere. Among the most widely used models are WRF-Chem and CAMx and many studies have shown their ability to hind- and forecast tropospheric and surface ozone burdens. Although the skill of models in both domains (global chemistry-climate and regional air quality modelling) has advanced substantially in recent decades, the combination of the two modelling methodologies and respective expertise has not been applied yet to study column ozone trends and changes. Within STOA we aim at closing this gap through a series of targeted transient and sensitivity simulations comprising a joint CCM-CTM modelling framework. Such framework needs to consider the contributions of individual drivers of ozone evolution in the stratosphere and troposphere in the recent past, and thus requires a series of sensitivity (single forcing) experiments. Given all the highlighted uncertainties above, the ensemble strategy is required to obtain robust driver contributions. Once applied and validated for the recent past, this framework can then also be transferred to study the expected future changes under several socio-economic scenarios.