Accurately simulating global surface ozone has long been one of the principal components of chemistry-climate modelling, but divergences in simulation outcomes have been reported as a result of the mechanistic complexity of tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy thus is a task of priority. Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6), we have transplanted a conventional ensemble learning approach, and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset, both of which have realised outstanding performances (R-square > 0.95, RMSE < 2.12 ppbV). The conventional ensemble learning approach is computationally cheaper and results in higher overall performance, but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable. The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability, but requires heavier computational burdens. Both of these multi-stage learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for use in future impact studies.

The influence of clouds on photochemistry remains a significant uncertainty in global chemistry models. Variability in cloud fraction, morphology, phase and optical properties provides significant challenges to models with horizontal resolutions that far exceed the scale of most clouds. Measured photolysis frequencies derived from the Charged-coupled device Actinic Flux Spectroradiometers (CAFS) on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission in 2016 provide an extensive set of statistics on how clouds alter the photolytic rates throughout remote ocean basins. Here we focus on north and tropical pacific transects during the first deployment (ATom-1) in August 2016 including regular profiles through cloudy, partly cloudy and clear conditions. Nine global chemistry–climate or –transport models provide similar statistics on J-values for regional domains encompassing the measured flight path. The statistical picture of the impact of clouds on J-values emerges through the distribution of the ratio of the cloud influenced models and measurement to corresponding cloud free model runs (J-cloudy/J-clear). The models all reproduce general patterns of enhancement above and shading below cloud, but diverge in distribution patterns and clear sky prevalence.

We present an assessment of the impacts on atmospheric composition and radiative forcing of short-lived pollutants following worldwide decrease in anthropogenic activity and emissions comparable to what has occurred in response to the COVID-19 pandemic, using the global composition-climate model UKCA. Changes in emissions reduce tropospheric hydroxyl radical and ozone burdens, increasing methane lifetime. Reduced SO emissions and oxidising capacity lead to a decrease in the sulphate aerosol burden and increase in aerosol particle size, with accompanying reductions to cloud droplet number concentration. However, large reductions in black carbon emissions increase the albedo of aerosols. Overall, the changes in ozone and aerosol direct effects (neglecting aerosol-cloud interactions) result in an instantaneous radiative forcing of -31 to -74 mWm. Upon cessation of emission reductions the short-lived climate forcers rapidly return to pre-COVID levels, meaning these changes are unlikely to have lasting impacts on climate assuming emissions return to pre-intervention levels.

We document the implementation of the Common Representative Intermediates Mechanism version 2, reduction 5 (CRIv2-R5) into the United Kingdom Chemistry and Aerosol model (UKCA) version 10.9. The mechanism is merged with the stratospheric chemistry already used by the StratTrop mechanism, as used in UKCA and the UK Earth System Model (UKESM1), to create a new CRI-Strat mechanism. CRI-Strat simulates a more comprehensive treatment of non-methane volatile organic compounds (NMVOCs) and provides traceability with the Master Chemical Mechanism (MCM). In total, CRI-Strat simulates the chemistry of 233 species competing in 613 reactions (compared to 87 species and 305 reactions in the existing StratTrop mechanism). However, while more than twice as complex than StratTrop, the new mechanism is only 75% more computationally expensive. CRI-Strat is evaluated against an array of in situ and remote sensing observations and simulations using the StratTrop mechanism in the UKCA model. It is found to increase production of ozone near the surface, leading to higher ozone concentrations compared to surface observations. However, ozone loss is also greater in CRI-Strat, leading to less ozone away from emission sources and a similar tropospheric ozone burden compared to StratTrop. CRI-Strat also produces more carbon monoxide than StratTrop, particularly downwind of biogenic VOC emission sources, but has lower burdens of nitrogen oxides as more is converted into reservoir species. The changes to tropospheric ozone and nitrogen budgets are sensitive to the treatment of NMVOC emissions, highlighting the need to reduce uncertainty in these emissions to improve representation of tropospheric chemical composition.

We quantify the impacts of halogenated ozone-depleting substances (ODSs), methane, N2O, CO2, and short-lived ozone precursors on total and partial ozone column changes between 1850 and 2014 using CMIP6 Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) simulations. We find that whilst substantial ODS-induced ozone loss dominates the stratospheric ozone changes since the 1970s, the increases in short-lived ozone precursors and methane lead to increases in tropospheric ozone since the 1950s that make increasingly important contributions to total column ozone (TCO) changes. Our results show that methane impacts stratospheric ozone changes through its reaction with atomic chlorine leading to ozone increases, but this impact will decrease with declining ODSs. The N2O increases mainly impact ozone through NOx-induced ozone destruction in the stratosphere, having an overall small negative impact on TCO. CO2 increases lead to increased global stratospheric ozone due to stratospheric cooling. However, importantly CO2 increases cause TCO to decrease in the tropics. Large interannual variability obscures the responses of stratospheric ozone to N2O and CO2 changes. Substantial inter-model differences originate in the models’ representations of ODS-induced ozone depletion. We find that, although the tropospheric ozone trend is driven by the increase in its precursors, the stratospheric changes significantly impact the upper tropospheric ozone trend through modified stratospheric circulation and stratospheric ozone depletion. The speed-up of stratospheric overturning (i.e. decreasing age of air) is driven mainly by ODS and CO2; increases. Changes in methane and ozone precursors also modulate the cross-tropopause ozone flux.