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This study explores our ability to simulate the atmospheric chemistry stemming from isoprene emissions in pristine and polluted regions of the Amazon basin. We confront two atmospheric chemistry models – a global, Eulerian chemistry-climate model (UM-UKCA) and a trajectory-based Lagrangian model (CiTTyCAT) – with recent airborne measurements of atmospheric composition above the Amazon made during the SAMBBA campaign of 2012. The simulations with the two models prove relatively insensitive to the chemical mechanism employed; we explore one based on the Mainz Isoprene Mechanism, and an updated one that includes changes to the chemistry of first generation isoprene nitrates (ISON) and the regeneration of hydroxyl radicals via the formation of hydroperoxy-aldehydes (HPALDS) from hydroperoxy radicals (ISO2). In the Lagrangian model, the impact of increasing the spatial resolution of trace gas emissions employed from 3.75° × 2.5° to 0.1° × 0.1° varies from one flight to another, and from one chemical species to another. What consistently proves highly influential on our simulations, however, is the model framework itself – how the treatment of transport, and consequently mixing, differs between the two models. The lack of explicit mixing in the Lagrangian model yields variability in atmospheric composition more reminiscent of that exhibited by the measurements. In contrast, the combination of explicit (and implicit) mixing in the Eulerian model removes much of this variability but yields better agreement with the measurements overall. We therefore explore a simple treatment of mixing in the Lagrangian model that, drawing on output from the Eulerian model, offers a compromise between the two models. We use this Lagrangian/Eulerian combination, in addition to the separate Eulerian and Lagrangian models, to simulate ozone at a site in the boundary layer downwind of Manaus, Brazil. The Lagrangian/Eulerian combination predicts a value for an AOT40-like accumulated exposure metric of around 1000 ppbv h, compared to just 20 ppbv h with the Eulerian model. The model framework therefore has considerable bearing on our understanding of the frequency at which, and the duration for which, the rainforest is exposed to damaging ground-level ozone concentrations.

Methane plays a central role in the atmosphere, affecting the atmospheric oxidising capacity through its reaction with OH, air quality through its role as an ozone precursor, and climate through its greenhouse gas properties. Methane emissions-driven models provide an opportunity to study the Earth system within a global model that features the most accurate representation of the methane cycle. Here we use the methane emissions-driven configuration of the UK Earth System Model, UKESM1-CH4. Previously we explored a zero anthropogenic methane scenario, which focused on attributing the composition, air quality and climate impacts of future anthropogenic methane emissions. Here, we study the potential co-benefits of methane mitigation: reducing NOx and CO emissions. We use SSP1-2.6 emissions pathways for CO and NO in these scenarios. The complex interactions between methane, CO, OH and NOx are represented more completely in the emissions-driven model. We calculate the sensitivity of ozone and OH to the CO and NO emissions changes, and their dependence on the methane burden. We also show the impacts on other near-term climate forcers such as aerosols and ozone. Global reductions in CO and NO emissions have disproportionate effects in different regions. We present analysis of the regional differences in ozone and OH response, based on the HTAP regions. We demonstrate much greater effects in South and East Asia than in the Europe and North America.

Dimethyl sulfide (DMS) is an important trace gas emitted from the ocean. The oxidation of DMS has long been recognised as being important for global climate through the role DMS plays in setting the sulfate aerosol background in the troposphere. However, the mechanisms in which DMS is oxidised are very complex and have proved elusive to accurately determine in spite of decades of research. As a result the representation of DMS oxidation in global chemistry-climate models is often greatly simplified. Recent field observations, laboratory and ab initio studies have prompted renewed efforts in understanding the DMS oxidation mechanism, with implications for constraining the uncertainty in the oxidation mechanism of DMS as incorporated in global chemistry-climate models. Here we build on recent evidence and develop a new DMS mechanism for inclusion in the UKCA chemistry-climate model. We compare our new mechanism (CS2-HPMTF) to a number of existing mechanisms used in UKCA (including the highly simplified 3 reactions, 2 species, ST mechanism used in CMIP6 studies) and to a range of recently developed mechanisms reported in the literature through a series of global and box model experiments. Global model runs with the new mechanism enable us to simulate the global distribution of hydroperoxyl methyl thioformate (HPMTF), which we calculate to have a burden of 2.6–26 Gg S (in good agreement with the literature range of 0.7–18 Gg S). We show that the sinks of HPMTF dominate uncertainty in the budget, not the rate of the isomerisation reaction forming it, and that based on the observed DMS/HPMTF ratio, rapid cloud uptake of HPMTF worsens the model-observation comparison. Our box model experiments highlight that there is significant variance in simulated secondary oxidation products from DMS across mechanisms used in the literature, with significant divergence in the sensitivity of these products to temperature exhibited; especially for methane sulfonic acid (MSA). Our global model studies show that our updated DMS scheme performs better than the current scheme used in UKCA when compared against a suite of surface and aircraft observations. However, sensitivity studies underscore the need for further laboratory and observational constraints.

The vertical distribution of ozone in the atmosphere, which features complex spatial and temporal variability set by a balance of production, loss, and advection, is relevant for both surface air pollution and climate via its role in radiative forcing. At present, the way in which regions of coherent ozone structure are defined relies on somewhat arbitrarily drawn boundaries. Here we consider a more general, data-driven method for defining coherent regimes of ozone structure. We apply an unsupervised classification technique called Gaussian mixture modeling (GMM), which represents the underlying distribution of ozone profiles as a linear combination of multi-dimensional Gaussian functions. In doing so, GMM identifies coherent groups or subpopulations of the ozone profile distribution. As a proof-of-concept study, we apply GMM to ozone profiles from three subsets of the UKESM1 coupled climate model runs carried out for CMIP6: specifically, the seasonal mean of a historical subset (2009–2014) and two subsets from two different future climate projections (i.e., SSP1-2.6 and SSP5-8.5). Despite not being given any spatiotemporal information, GMM identifies several spatially coherent regions of ozone structure. Using a combination of statistical guidance and post hoc judgment, we select a six-class representation of global ozone, consisting of two tropical classes and four mid-to-high-latitude classes. The tropical classes feature a relatively high-altitude tropopause, while the higher-latitude classes feature a lower-altitude tropopause and low values of tropospheric ozone, as expected based on broad patterns observed in the atmosphere. Both of the future projections feature lower ozone concentrations at 850 hPa than the historical benchmark, with signatures of ozone hole recovery. We find that the area occupied by the tropical classes is expanded in both future projections, which are most prominent during austral summer. Our results suggest that GMM may be a useful method for identifying coherent ozone regimes, particularly in the context of model analysis.

Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained, and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOx emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57 %–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67 %–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOx.

Emissions inventories are crucial inputs to air quality simulations and represent a major source of uncertainty. Various methods have been adopted to optimise emissions inventories, yet in most cases the methods were only applied to total anthropogenic emissions. We have developed a new approach that updates a priori emission estimates by source sector, which are particularly relevant for policy interventions. At its core is a perturbed emissions ensemble (PEE), constructed by perturbing parameters in an a priori emissions inventory within their respective uncertainty ranges. This PEE is then input to an air quality model to generate an ensemble of forward simulations. By comparing the simulation outputs with observations from a dense network, the initial uncertainty ranges are constrained and a posteriori emission estimates are derived. Using this approach, we were able to derive the transport sector NOX emissions for a study area centred around Beijing in 2016 based on a priori emission estimates for 2013. The absolute emissions were found to be 1.5–9 × 104 Mg, corresponding to a 57–93 % reduction from the 2013 levels, yet the night-time fraction of the emissions was 67–178 % higher. These results provide robust and independent evidence of the trends of traffic emission in the study area between 2013 and 2016 reported by previous studies. We also highlighted the impacts of the chemical mechanisms in the underlying model on the emission estimates derived, which is often neglected in emission optimisation studies. This work paves forward the route for rapid analysis and update of emissions inventories using air quality models and routine in situ observations, underscoring the utility of dense observational networks. It also highlights some gaps in the current distribution of monitoring sites in Beijing which result in an underrepresentation of large point sources of NOX.

Tropospheric ozone is an important component of the Earth System as it can affect both climate and air quality. In this work we use observed tropospheric column ozone derived from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) OMI-MLS, in addition to OMI ozone retrieved in discrete vertical layers, and compare it to tropospheric ozone from UM-UKCA simulations (which utilise the Unified Model, UM, coupled to UK Chemistry and Aerosol, UKCA). Our aim is to investigate recent changes (2005–2018) in tropospheric ozone in the North Atlantic region, and specifically its seasonal, interannual and decadal variability and to understand what factors are driving such changes. Through sensitivity experiments, timeseries correlation and comparison with the LIS-OTD lightning flash dataset, the model positive bias in the Tropics is attributed to shortcomings in the convection and lightning parameterisations. Use of OMI data, for which vertical averaging kernels and a priori information are available, suggests that the model negative bias at mid latitudes relative to OMI-MLS tropospheric column could be the result of vertical sampling. Ozone in the North Atlantic peaks in spring and early summer, with generally good agreement between the modelled and observed seasonal cycle. Recent trends in tropospheric ozone were investigated and the discrepancy between model and observations was linked to possible differences in lower stratospheric ozone trends and associated stratosphere to troposphere transport. Modelled tropospheric ozone interannual variability (IAV) is driven by IAV of tropical emissions of NOx from lightning and IAV of ozone transport from the stratosphere; however, the modelled and observed IAV differ. To understand the IAV discrepancy we investigated how modelled ozone and its drivers respond to large scale modes of variability. Using OMI height-resolved data and model idealised tracers, we were able to identify stratospheric transport of ozone into the troposphere as the main driver of the dynamical response of North Atlantic ozone to the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). Finally, the ozone response to El Nino Southern Oscillation (ENSO) and its impact on observed and modelled north Atlantic ozone variability was analysed.

The COVID-19 outbreak greatly limited human activities and reduced primary emissions particularly from urban on-road vehicles but coincided with Beijing experiencing “pandemic haze,” raising the public concerns about the effectiveness of imposed traffic policies to improve the air quality. This paper explores the relationship between local vehicle emissions and the winter haze in Beijing before and during the COVID-19 lockdown based on an integrated analysis framework, which combines a real-time on-road emission inventory, in situ air quality observations, and a localized numerical modeling system. We found that traffic emissions decreased substantially during the COVID-19 pandemic, but its imbalanced emission abatement of NOx (76%, 125.3 Mg/day) and volatile organic compounds (VOCs, 53%, 52.9 Mg/day) led to a significant rise of atmospheric oxidants in urban areas, resulting in a modest increase in secondary aerosols due to inadequate precursors, which still offset reduced primary emissions. Moreover, the enhanced oxidizing capacity in the surrounding regions greatly increased the secondary particles with relatively abundant precursors, which was transported into Beijing and mainly responsible for the aggravated haze pollution. We recommend that mitigation policies should focus on accelerating VOC emission reduction and synchronously controlling regional sources to release the benefits of local traffic emission control.

BACKGROUNDLong-term ozone (O3) exposure could lead to a series of non-communicable diseases and increase the mortality risks. However, cohort-based studies were still rather rare, and inconsistent exposure metrics might impair the credibility of epidemiological evidence synthetisation. To provide more accurate meta-estimation, this review updated the systematic review with inclusion of recent studies and summarised the quantitative associations between O3 exposure and cause-specific mortality risks based on unified exposure metrics.METHODSResearch articles reporting relative risks between incremental long-term O3 exposure and causes of mortality covering all-cause, cardiovascular diseases, respiratory diseases, chronic obstructive pulmonary disease, pneumonia, ischaemic heart diseases, ischaemic stroke, congestive heart failure, cerebrovascular diseases, and lung cancer, estimated from cohort studies were identified through systematic searches in MEDLINE, Embase and Web of Science. Cross-metric conversion factors were estimated linearly by decadal of observations during 1990-2019. The Hunter-Schmidt random effect estimator was applied to pool the relative risks.RESULTSA total of 25 studies involving 226,453,067 participants (14 unique cohorts covering 99,855,611 participants) were included in the systematic review. After linearly adjusting the inconsistent O3 exposure metrics into congruity, the pooled relative risks (RR) associated with every 10 nmol mol-1 (ppbV) incremental O3 exposure, by mean of warm-season daily maximum 8-hour average metric, was: 1.014 with 95% confidence interval (CI) ranging 1.009–1.019 for all-cause mortality; 1.025 (95% CI: 1.010–1.040) for respiratory mortality; 1.056 (95% CI: 1.029–1.084) for COPD mortality; 1.019 (95% CI: 1.004–1.035) for cardiovascular mortality; and 1.096 (95% CI: 1.065–1.129) for congestive heart failure mortality. Insignificant mortality risk associations were found for ischaemic heart disease, cerebrovascular diseases and lung cancer.DISCUSSIONThis review covered up-to-date studies, expanded the O3-exposure associated mortality causes into wider range of categories, and firstly highlighted the issue of inconsistency in O3 exposure metrics. Non-intercept linear regression-based cross-metric RR conversion was another innovation, but limitation lay in the observation reliance, indicating further calibration with more credible observations available. Large uncertainties in the multi-study pooled RRs would inspire more future studies to corroborate or contradict the results from this review.CONCLUSIONAdjustment for exposure metrics laid more solid foundation for multi-study meta-analysis, and wider coverage of surface O3 observations are anticipated to strengthen the cross-metric conversion in the future. Ever-growing numbers of epidemiological studies supported unneglectable cardiopulmonary hazards and all-cause mortality risks from long-term O3 exposure. However, evidences on long-term O3 exposure associated health effects were still scarce, and hence more relevant studies are encouraged to cover more population with regional diversity.REGISTRATIONThe review was registered in PROSPERO (CRD42021270637).FUNDINGThis study is mainly funded by UK Natural Environment Research Council, UK National Centre for Atmospheric Science, Australian Research Council and Australian National Health and Medical Research Council.HighlightsUpdated evidence for O3-mortality associations from 25 cohorts has been provided.Adjusting various O3exposure metrics can provide more accurate risk estimations.Long-term O3-exposure was associated with increased mortality from all-causes, respiratory disease, COPD, cardiovascular disease and congestive heart failure.

Ozone (O3) isopleths describe the nonlinear responses of O3 concentrations to changes in nitrogen oxides (NOX) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O3 control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O3 pollution of China in 2017 and derive O3 isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O3 pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O3 as 160 μg·m-3 (81.5 ppbV equally). The O3 responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NOX-saturated regimes, where a small amount of NOX reduction increases O3. Ambient O3 pollution in the eastern region generally is limited by VOCs, while in the west by NOX. The city-specific O3 isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O3 abatement in China.

Long-term exposure to ambient ozone (O3) can lead to a series of chronic diseases and associated premature deaths, and thus population-level environmental health studies hanker after the high-resolution surface O3 concentration database. In response to this demand, we innovatively construct a space-time Bayesian neural network parametric regressor to fuse TOAR historical observations, CMIP6 multimodel simulation ensemble, population distributions, land cover properties, and emission inventories altogether and downscale to 10 km × 10 km spatial resolution with high methodological reliability (R2 = 0.89-0.97, RMSE = 1.97-3.42 ppbV), fair prediction accuracy (R2 = 0.69-0.77, RMSE = 5.63-7.97 ppbV), and commendable spatiotemporal extrapolation capabilities (R2 = 0.62-0.76, RMSE = 5.38-11.7 ppbV). Based on our predictions in 8-h maximum daily average metric, the rural-site surface O3 are 15.1±7.4 ppbV higher than urban globally averaged across 30 historical years during 1990-2019, with developing countries being of the most evident differences. The globe-wide urban surface O3 are climbing by 1.9±2.3 ppbV per decade, except for the decreasing trends in eastern United States. On the other hand, the global rural surface O3 tend to be relatively stable, except for the rising tendencies in China and India. Using CMIP6 model simulations directly without urban-rural differentiation will lead to underestimations of population O3 exposure by 2.0±0.8 ppbV averaged over each historical year. Our original Bayesian neural network framework contributes to the deep-learning-driven environmental studies methodologically by providing a brand-new feasible way to realize data fusion and downscaling, which maintains high interpretability by conforming to the principles of spatial statistics without compromising the prediction accuracy. Moreover, the 30-year highly spatial resolved monthly surface O3 database with multiple metrics fills in the literature gap for long-term surface O3 exposure tracing.

Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling. However, the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority, and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required. 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 (R2 > 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 induces heavier computational burdens. Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.

Haze days induced by aerosol pollution in North and East China have posed a persistent and growing problem over the past few decades. These events are particularly threatening to densely populated cities such as Beijing. While the sources of this pollution are predominantly anthropogenic, natural climate variations may also play a role in allowing for atmospheric conditions conducive to formation of severe haze episodes over populated areas. Here, an investigation is conducted into the effects of changes in global dynamics and emissions on air quality in China’s polluted regions using 35 simulations developed from the Community Earth Systems Model Large Ensemble (CESM LENS) run over the period 1920–2100. It is shown that internal variability significantly modulates aerosol optical depth (AOD) over China; it takes roughly a decade for the forced response to balance the effects from internal variability even in China’s most polluted regions. Random forest regressions are used to accurately model (R2 > 0.9) wintertime AOD using just climate oscillations, the month of the year, and emissions. How different phases of each oscillation affect aerosol loading is projected using these regressions. AOD responses are identified for each oscillation, with particularly strong responses from El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO). As ENSO can be projected a few months in advance and improvements in linear inverse modeling (LIM) may yield a similar predictability for the PDO, results of this study offer opportunities to improve the predictability of China’s severe wintertime haze events and to inform policy options that could mitigate subsequent health impacts.

We document the implementation of the Common Representative Intermediates Mechanism version 2, reduction 5 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, 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. 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.

The distribution of ozone in the atmosphere is relevant for air pollution and radiative forcing. This distribution features complex spatial and temporal variability, set by balances between chemical production, loss processes, and advection. At present, the way in which ozone comparison regions are defined relies on somewhat arbitrarily drawn boundaries. In an effort to develop a more general, data-derived method for defining coherent regimes of ozone structure, we apply an unsupervised classification technique called Gaussian Mixture Modelling (GMM). We apply GMM to the output from the UKESM1 coupled climate model, including the historical run and two of the future climate projections. GMM identifies different ozone profile classes without using any latitude or longitude information, thereby highlighting coherent ozone structure regimes. We determine each of the model data set contains 9 groups of unique vertical classes. The classes depend on latitude, even though GMM was not given any latitude information. Polar and subpolar classes show low tropopause and low tropospheric ozone, and the tropical classes have high tropopause. Northern hemisphere high latitude classes have higher stratospheric ozone than southern hemisphere high latitude classes. We analyze how the spatial extent of the classes changes under different scenarios by comparing classes in SSP126 and SSP585 with a historical simulation. This work suggests that GMM may be a useful method for identifying coherent ozone regimes for comparing different model results and observational data.

A grand challenge in the field of chemistry-climate modelling is to understand the connection between anthropogenic emissions, atmospheric composition and the radiative forcing of trace gases and aerosols. The AerChemMIP model intercomparison project, part of CMIP6, focuses on calculating the radiative forcing of gases and aerosol particles over the period 1850 to 2100. We present an analysis of the trends in tropospheric ozone budget in the UKESM1 and other models from CMIP6 experiments. We discuss these trends in terms of chemical production and loss of ozone as well as physical processes such as transport and deposition. Where possible, AerChemMIP attribution experiments such as histSST-piCH4, will be used to quantify the effect of individual emissions and forcing changes on the historical ozone burden and budget. For future experiments, we focus on analogous experiments from the SSP3-70 scenario, a ‘regional rivalry’ shared socioeconomic pathway involving significant emissions changes.

Methane (CH4), the second most important greenhouse gas in terms of radiative forcing, is on the rise; but there are extensive opportunities for mitigation with existing technologies. Anthropogenic emissions account for around 60% of the global methane source, and the recent atmospheric methane growth rate puts us on a trajectory comparable to the most extreme future methane scenarios in the sixth Coupled Model Intercomparison Project (CMIP6). We use a new methane emissions-driven configuration of the UK Earth System Model (UKESM1) to explore the role of anthropogenic methane in the earth system. The full methane cycle is represented, including surface deposition, chemistry and interactive wetland emissions. As a baseline scenario we used Shared Socioeconomic Pathway 3-7.0 (SSP3-7.0) – the highest methane emissions scenario in CMIP6. In an idealised experiment, all anthropogenic methane emissions were instantaneously stopped from 2015 onwards in a coupled atmosphere-ocean simulation running from 2015-2050, to make a net-zero anthropogenic methane emissions scenario. Within a decade, significant changes can be seen in atmospheric composition and climate, compared to SSP3-7.0. The atmospheric methane burden declines to below pre-industrial levels within 12 years, and by the late 2030s reaches a constant level around 44% below that of the present day (2015). The tropospheric ozone burden and surface mean ozone concentrations decreased by 12% and 15% respectively by 2050 – key in terms of limiting global warming as well as improving air quality and human health. By 2050 the net-zero anthropogenic methane scenario results in a global mean surface temperature (GMST) 1˚C lower than the baseline, a significant value in the context of climate goals such as the Paris Agreement. Through decomposition of the radiation budget, the change in climate can be directly attributed to the reduction in methane and indirectly to the resulting changes in ozone, clouds and ozone precursors such as CO. In addition, the changes in climate result in impacts on the interactive wetland emissions via changes in temperature and wetland extent, highlighting the coupled nature of methane in the earth system. Cessation of anthropogenic methane emissions has profound impacts on near-term warming and on tropospheric ozone, but ultimately cannot single-handedly achieve the necessary reductions for meeting Paris goals.

We present the first incorporation and evaluation of the Common Representative Intermediates version 2.2 chemistry mechanism, CRI v2.2, for use in the United Kingdom Earth System Model (UKESM1). Tuned against the MCM v3.3.1, the CRI v2.2 mechanism builds on the previous CRI version, CRI v2.1, in UKESM1 (Archer-Nicholls et al., 2020) by updating isoprene chemistry and offers a more comprehensive description of tropospheric chemistry than the standard chemistry mechanism STRAT-TROP (ST).CRI v2.2 adds state-of-the-art isoprene chemistry with the introduction of HOx-recycling via the isoprene peroxy radical isomerisation pathway, making UKESM1 one of the first CMIP6 models to include this important chemistry. HOx-recycling has noticeable effects on oxidants in regions with large emissions of biogenic volatile organic compounds (BVOCs). Low altitude OH in tropical forested regions increases by 75-150% relative to ST, reducing the existing model low bias compared to observations. Consequently, isoprene surface mixing ratios decrease considerably (25-40%), significantly improving the model high bias relative to ST. Methane lifetime decreases by 2% and tropospheric ozone burden increases by 4%. Aerosol processes also differ between CRI v2.2 and ST, resulting in changes to the size and number distributions. Relative to ST, CRI v2.2 simulates an 8% decrease in the sulphate aerosol burden with 20% decreases in the nucleation and Aitken modes. By contrast, the secondary organic aerosol (SOA) nucleation mode burden increases by 11%. Globally, the average nucleation and Aitken mode aerosol number concentrations decrease by 20%.The differences in aerosol and gas phase chemistry between CRI v2.2 and ST are likely to have impacts on the radiation budget. We plan to use CRI v2.2 and ST to investigate the influence that the chemical mechanism has on the simulated chemistry-climate feedbacks from BVOCs. In addition, CRI v2.2 will serve as the basis for the addition of a scheme describing the formation of highly oxygenated organic molecules (HOMs) from BVOCs, facilitating a semi-explicit mechanism for new particle formation from organic species.

Funder: Tsinghua University Initiative Scientific Research Program, Funder: National Centre for Atmospheric Science; doi: http://dx.doi.org/10.13039/501100000662, Funder: Met Office; doi: http://dx.doi.org/10.13039/501100000847, The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (

Surface ozone is a major pollutant threatening public health, agricultural production and natural ecosystems. While measures to improve air quality in megacities such as Delhi are typically aimed at reducing levels of particulate matter (PM), ozone could become a greater threat if these measures focus on PM alone, as some air pollution mitigation steps can actually lead to an increase in surface ozone. A better understanding of the factors controlling ozone production in Delhi and the impact that PM mitigation measures have on ozone is therefore critical for improving air quality. Here, we combine in situ observations and model analysis to investigate the impact of PM reduction on the non-linear relationship between volatile organic compounds (VOC), nitrogen oxides (NOx) and ozone. In situ measurements of NOx, VOC, and ozone were conducted in Delhi during the APHH-India programme in summer (June) and winter (November) 2018. We observed hourly averaged ozone concentrations in the city of up to 100 ppbv in both seasons. We performed sensitivity simulations with a chemical box model to explore the impacts of PM on the non-linear VOC-NOx-ozone relationship in each season through its effect on aerosol optical depth (AOD). We find that ozone production is limited by VOC in both seasons, and is particularly sensitive to solar radiation in winter. Reducing NOx alone increases ozone, such that a 50% reduction in NOx emissions leads to 10-50% increase in surface ozone. In contrast, reducing VOC emissions can reduce ozone efficiently, such that a 50% reduction in VOC emissions leads to ∼60% reduction in ozone. Reducing PM alone also increases ozone, especially in winter, by reducing its dimming effects on photolysis, such that a 50% reduction in AOD can increase ozone by 25% and it also enhances VOC-limitation. Our results highlight the importance of reducing VOC emissions alongside PM to limit ozone pollution, as well as benefitting control of PM pollution through reducing secondary organic aerosol. This will greatly benefit the health of citizens and the local ecosystem in Delhi, and could have broader application for other megacities characterized by severe PM pollution and VOC-limited ozone production. This journal is

As a result of the global COVID-19 pandemic, unprecedented lockdown measures have been imposed worldwide to reduce the spread of the disease, causing huge reductions in economic activity and corresponding reductions in transport, industrial and aircraft emissions. As well as lowering emissions of carbon dioxide, this has resulted in a dramatic reduction in the emissions of pollutants that also affect climate. We have used state-of-the-art computer simulations to quantify how changes in these components are likely to impact the chemical make-up of the atmosphere and the likely short-term impacts on climate.

We present an assessment of the impacts on atmospheric composition and radiative forcing of short‐lived pollutants following a 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 United Kingdom Chemistry and Aerosols Model (UKCA). Emission changes reduce tropospheric hydroxyl radical and ozone burdens, increasing methane lifetime. Reduced SO2 emissions and oxidizing capacity lead to a decrease in sulfate aerosol and increase in aerosol size, with accompanying reductions to cloud droplet concentration. However, large reductions in black carbon emissions increase aerosol albedo. Overall, the changes in ozone and aerosol direct effects (neglecting aerosol‐cloud interactions which were statistically insignificant but whose response warrants future investigation) yield a radiative forcing of −33 to −78 mWm−2. 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., Key Points Emission reductions are likely to have led to a global reduction in short‐lived climate forcers and tropospheric oxidizing capacityReductions in O3 and aerosol from both lower emissions and decreased sulfate oxidation resulted in a net negative radiative forcingThe radiative impacts are small and short‐lived. Longer term climate impacts must come through future sustained emission reductions

As one of the main drivers for climate change, it is important to understand changes in anthropogenic aerosol emissions and evaluate the climate impact. Anthropogenic aerosols have affected global climate while exerting a much larger influence on regional climate by their short lifetime and heterogeneous spatial distribution. In this study, the effective radiative forcing (ERF), which has been accepted as a useful index for quantifying the effect of climate forcing, was evaluated to understand the effects of aerosol on regional climate over a historical period (1850&ndash, 2014). Eastern United States (EUS), Western European Union (WEU), and Eastern Central China (ECC), are regions that predominantly emit anthropogenic aerosols and were analyzed using Coupled Model Intercomparison Project 6 (CMIP6) simulations implemented within the framework of the Aerosol Chemistry Model Intercomparison Project (AerChemMIP) in the UK&rsquo, s Earth System Model (UKESM1). In EUS and WEU, where industrialization occurred relatively earlier, the negative ERF seems to have been recovering in recent decades based on the decreasing trend of aerosol emissions. Conversely, the radiative cooling in ECC seems to be strengthened as aerosol emission continuously increases. These aerosol ERFs have been largely attributed to atmospheric rapid adjustments, driven mainly by aerosol-cloud interactions rather than direct effects of aerosol such as scattering and absorption.

Understanding the past, present, and future evolution of methane remains a grand challenge. Here we have used a hierarchy of models, ranging from simple box models to a chemistry‐climate model (CCM), UM‐UKCA, to assess the contemporary and possible future atmospheric methane burden. We assess two emission data sets for the year 2000 deployed in UM‐UKCA against key observational constraints. We explore the impact of the treatment of model boundary conditions for methane and show that, depending on other factors, such as CO emissions, satisfactory agreement may be obtained with either of the CH4 emission data sets, highlighting the difficulty in unambiguous choice of model emissions in a coupled chemistry model with strong feedbacks. The feedbacks in the CH4‐CO‐OH system, and their uncertainties, play a critical role in the projection of possible futures. In a future driven by large increases in greenhouse gas forcing, increases in tropospheric temperature drive, an increase in water vapor, and, hence, [OH]. In the absence of methane emission changes this leads to a significant decrease in methane compared to the year 2000. However, adding a projected increase in methane emissions from the RCP8.5 scenario leads to a large increase in methane abundance. This is modified by changes to CO and NOx emissions. Clearly, future levels of methane are uncertain and depend critically on climate change and on the future emission pathways of methane and ozone precursors. We highlight that further work is needed to understand the coupled CH4‐CO‐OH system in order to understand better future methane evolution., Key Points We study methane in present and future climate in a model employing flux‐based treatment of methane emissionsWe examine the source and importance of feedbacks in the CH4‐CO‐OH system using a 0‐D box model and a 3‐D chemistry‐climate modelWe examine methane levels in future climate and the role played by climate and emissions changes

The 1257 CE eruption of Mount Samalas (Indonesia) is the source of the largest stratospheric injection of volcanic gases in the Common Era. Sulfur dioxide emissions produced sulfate aerosols that cooled Earth's climate with a range of impacts on society. The coemission of halogenated species has also been speculated to have led to wide-scale ozone depletion. Here we present simulations from HadGEM3-ES, a fully coupled Earth system model, with interactive atmospheric chemistry and a microphysical treatment of sulfate aerosol, used to assess the chemical and climate impacts from the injection of sulfur and halogen species into the stratosphere as a result of the Mt. Samalas eruption. While our model simulations support a surface air temperature response to the eruption of the order of -1°C, performing well against multiple reconstructions of surface temperature from tree-ring records, we find little evidence to support significant injections of halogens into the stratosphere. Including modest fractions of the halogen emissions reported from Mt. Samalas leads to significant impacts on the composition of the atmosphere and on surface temperature. As little as 20% of the halogen inventory from Mt. Samalas reaching the stratosphere would result in catastrophic ozone depletion, extending the surface cooling caused by the eruption. However, based on available proxy records of surface temperature changes, our model results support only very minor fractions (1%) of the halogen inventory reaching the stratosphere and suggest that further constraints are needed to fully resolve the issue.

Earth system models enable a broad range of climate interactions that physical climate models are unable to simulate. However, the extent to which adding Earth system components changes or improves the simulation of the physical climate is not well understood. Here we present a broad multivariate evaluation of the North Atlantic climate system in historical simulations of the UK Earth System Model (UKESM1) performed for CMIP6. In particular, we focus on the mean state and the decadal time scale evolution of important variables that span the North Atlantic climate system. In general, UKESM1 performs well and realistically simulates many aspects of the North Atlantic climate system. Like the physical version of the model, we find that changes in external forcing, and particularly aerosol forcing, are an important driver of multidecadal change in UKESM1, especially for Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation. However, many of the shortcomings identified are similar to common biases found in physical climate models, including the physical climate model that underpins UKESM1. For example, the summer jet is too weak and too far poleward; decadal variability in the winter jet is underestimated; intraseasonal stratospheric polar vortex variability is poorly represented; and Arctic sea ice is too thick. Forced shortwave changes may be also too strong in UKESM1, which, given the important role of historical aerosol forcing in shaping the evolution of the North Atlantic in UKESM1, motivates further investigation. Therefore, physical model development, alongside Earth system development, remains crucial in order to improve climate simulations.

We describe the scientific and technical implementation of two models for a core set of\ud experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6).\ud The models used are the physical atmosphere-land-ocean-sea ice model HadGEM3-GC3.1 and the\ud Earth system model UKESM1 which adds a carbon-nitrogen cycle and atmospheric chemistry to\ud HadGEM3-GC3.1. The model results are constrained by the external boundary conditions (forcing data)\ud and initial conditions.We outline the scientific rationale and assumptions made in specifying these.\ud Notable details of the implementation include an ozone redistribution scheme for prescribed ozone\ud simulations (HadGEM3-GC3.1) to avoid inconsistencies with the model's thermal tropopause, and land use\ud change in dynamic vegetation simulations (UKESM1) whose influence will be subject to potential biases in\ud the simulation of background natural vegetation.We discuss the implications of these decisions for\ud interpretation of the simulation results. These simulations are expensive in terms of human and CPU\ud resources and will underpin many further experiments; we describe some of the technical steps taken to\ud ensure their scientific robustness and reproducibility.

Surface concentrations of pollutants in the UK are generally well observed and column averaged data is increasingly available from satellites. However, there remains limited data on the vertical distribution of key pollutants in the UK boundary layer. As part of the Strategic Priorities Fund Clean Air programme, the Met Office Civil Contingencies Aircraft (MOCCA) has been instrumented to enable measurements of ozone, nitrogen dioxide, sulphur dioxide and particulate matter (PM2.5 and PM10) to be made in the UK boundary layer. These ongoing observations are being used to evaluate Air Quality in the Unified Model (AQUM), improve air quality forecasts and hence ultimately improve our confidence in the model data used to perform assessments of the health impacts of pollution in the UK. Here we present our methodology, initial investigation of model and aircraft data from flights during the first 6 months of the project and future plans for this work.

Production of ozone and secondary organic aerosols (SOA) in the troposphere is driven by the photo-oxidation of volatile organic compounds (VOCs). Crucial intermediates in these oxidation steps are peroxy radicals, which enable ozone generation when reacting with NO. Recent pioneering studies have shown peroxy radical chemistry to have much broader impacts on the atmosphere, with many of these species undergoing autoxidation and forming highly oxidised organic molecules (HOMs), including accretion products, which can form new particles, contribute to SOA growth and influence global climate. However, explicitly simulating the full complexity of this chemistry is impractical due to the many thousands of VOC species in the atmosphere; techniques for reducing complexity are therefore necessary. The Master Chemical Mechanism (MCM) is a near-explicit scheme, with ~6,000 species and ~19,000 reactions, but is almost exclusively used in box-model applications due to its high cost. The Common Representative Intermediates (CRIv2-R5) mechanism is an effective compromise, preserving the ozone forming potential of the MCM from the emission and atmospheric degradation of isoprene, α/β-pinene, and 19 other primary VOC species whilst reducing the number of species and reactions to be feasible in a 3D model (approximately 240 species and 650 reactions, including 47 non-transported peroxy radical species and their associated reactions).We have implemented CRIv2R5 into a global chemistry-climate model, the UK Earth System Model (UKESM1). We present results from a present-day emissions scenario for the Coupled Model Intercomparison Project (CMIP6) to enable a broad scope of model simulations with more basic chemistry and observations to evaluate the model changes against. We find significant differences to tropospheric ozone production and oxidative capacity of the atmosphere, with a strong sensitivity to magnitude and speciation of VOC emissions, highlighting the importance of accurately simulating VOC chemistry to understand trends in tropospheric ozone under changing emissions and climate.Moving forward, having the comprehensive CRIv2R5 mechanism within UKESM1 provides the framework for investigating the impacts of recently discovered peroxy radical chemical processes on global climate. We present further work that has focused on expanding the CRI mechanism in box-model studies with a semi-explicit treatment key peroxy radical processes including (i) the autoxidation of peroxy radicals from the hydroxyl radical and ozone initiated reactions of α-pinene, (ii) the formation of multiple generations of peroxy radicals, (iii) formation of accretion products (dimers) and (iv) isoprene-driven suppression of accretion product formation as observed in experiments. This new CRI-HOM mechanism is now being implemented into the global UKESM1 model and coupled with its aerosol mechanism. This work will enable pioneering investigations linking best process-level understanding of gas-phase peroxy radical chemistry to SOA formation and thus improving our understanding of the relationship between biogenic VOC emissions and global climate.

The time lag between the occurrence of emissions and the compilation of an inventory is inevitable. When an emissions inventory is used to simulate air quality, uncertainties in the emissions are propagated into uncertainties in the modelled pollutant concentrations. Such uncertainties can be particularly high in regions undergoing rapid emission changes. Beijing, for instance, has implemented a series of pollution control measures over the past several years and various studies have confirmed significant decreases in the emissions of pollutants such as CO and NOX. Hence, it is crucial to quantify and constrain the uncertainties in existing emission estimates for this region.We sample the uncertainties in an emissions inventory for Beijing using a high-resolution advanced Gaussian dispersion model with perturbed emissions ensembles (PEEs), and constrain these uncertainties using a comprehensive set of in situ observations, including vertically resolved measurements made from a tower in central Beijing using low-cost sensors. We first construct a PEE by varying key emission parameters including source sectors, vertical and diurnal profiles within their uncertainty ranges estimated through expert elicitation. By removing the baseline contribution to the concentrations, we are able to evaluate the performance of the PEE in simulating the local signal. Based on knowledge gained from the initial PEE, we design a second PEE with optimised uncertainty ranges with which we constrain the uncertainties in the base emission estimates.Our study shows the applicability of perturbed emissions ensembles and high-resolution, three-dimensional observations in systematically sampling and constraining emission uncertainties. This method has wide implications for air quality modelling, particularly in regions with rapid emission changes or for studies in which emissions inventories are out-dated.

Tropospheric ozone (O3) can adversely affect human health and environmental ecosystems and it is therefore vitally important to understand its formation pathways from both natural and anthropogenic precursors. Background O3 levels in the Northern Hemisphere have increased by more than a factor of two over the last century and it is believed that this increase is strongly tied to the increase in and distribution of anthropogenic nitrogen oxide (N0x) emissions. This is important as the changing level of O3 in the background troposphere impacts the ability of countries downwind to achieve their air quality standards.As part of the NERC funded North Atlantic Climate System Integrated Study (ACSiS) and Methane Observations and Yearly Assessments (MOYA) projects, multiple research flights have taken place over the North Atlantic Ocean, spanning an area from 55oN to 12oN and 8oW to 25oW using the UK’s large research aircraft (The Facility for Airborne Atmospheric Measurements – FAAM). Flights took place in all seasons from 2017 – 2020. A variety of gas and aerosol measurements were made, including NOx, O3, CO and a range of VOCs and an overview of the data is presented here. Measurements were taken in a range of air masses, including biomass burning outflow from West Africa, urban outflow from Europe and emissions from the busy shipping lanes to the West of Portugal.Data was analysed to assess O3 formation from the different emission sources, in particular examining the difference between anthropogenic and natural emissions. In addition, the output of regional chemistry models is compared to the data in order to assess the performance of the models in predicting O3 and its precursors.

Marine ecosystems are an important component of the climate feedback system. One of the main pathways for ocean-climate interaction is through the oxidation of DMS (dimethyl sulphide), a gas released from phytoplankton in the sea surface. DMS derived products are known to be important in marine cloud formation and the Earth’s radiation budget. Aerosol-Cloud interactions currently represent the largest uncertainty in climate modelling (Boucher et al., 2013). Our research focuses on airborne measurements using real-time high resolution instruments to identify and quantify trace oceanic biogenic gases on board the FAAM research aircraft. Here we present aircraft measurements made over the North Atlantic ocean using a HR-ToF-CIMS, across three seasons during the most recent ACSIS/ARNA campaigns. Here we report some of the first observations of an alternative DMS oxidation product, hydperoxymethyl thioformate (HPMFT) using chemical ionisation mass spectrometry with iodide reagent ion. Observations of this novel species have never been reported in the atmosphere but laboratory studies suggest that the main oxidation route of DMS occurs through this species, in certain environments (Berndt et al., 2019). This has potentially significant climate implications, none of which are currently represented in global climate models. The fate of this newly measured species once in the atmosphere is uncertain but is likely to alter our understanding of the marine sulphur cycle. These observations along with laboratory and modelling studies will aid in being able to understand the role of HPMFT in the ocean-climate feedback system.References T. Berndt, W. Scholz, B. Mentler, L. Fischer, E. H. Hoffmann, A. Tilgner, N. Hyttinen, N. L. Prisle, A. Hansel, and H. Herrmann, The Journal of Physical Chemistry Letters 2019 10 (21), 6478-6483,DOI: 10.1021/acs.jpclett.9b02567Boucher O, Randall D, Artaxo P, Bretherton C, Feingold G, Forster P, Kerminen VM, Kondo Y, Liao H, Lohmann U, Rasch P, Satheesh S, Sherwood S, Stevens B, Zhang X. In: Clouds and aerosols. Cambridge: Cambridge University Press; 2013. United Kingdom and new york, NY, USA, book section Chapter 7, pp 571–658. https://doi.org/10.1017/CBO9781107415324.016.

A grand challenge in the field of chemistry-climate modelling is understanding the connection between anthropogenic emissions, atmospheric composition and the radiative forcing of trace gases and aerosols. The 6th phase of the Coupled Model Intercomparison Project (CMIP6) includes a number of climate model experiments that can be used for this purpose. AerChemMIP [Collins et al.2017] focuses on calculating the radiative forcing of gases and aerosol particles over the period 1850 to 2100, and comprises several tiers of experiments designed to attribute the effect of changes in emissions. The UK Earth System Model, UKESM-1, is a novel climate model developed for CMIP6 [Sellar et al., 2019] and is a community research tool for studying past and future climate. It includes a detailed treatment of tropospheric chemistry, interactive BVOC emissions and extensive stratospheric chemistry. The North Atlantic Climate System is an area of current interest [Robson et al., 2020] and is the focus of the UKRI 'ACSIS' project. ACSIS brings together scientists from a range of different specialisms to understand complex changes in the North Atlantic climate system. By understanding how these changes relate to external drivers of climate, such as human activity, or natural variability, ACSIS aims to improve our capability to detect, explain and predict changes in the North Atlantic climate system. We present an analysis of the evolution of atmospheric composition over the period 1950-2015. The work is based on a recent global multi-model evaluation of tropospheric ozone for CMIP6 [Griffiths et al., 2020] , but focuses on changes over the North Atlantic region in UKESM-1. We draw on CMIP and AerChemMIP simulations to provide an initial survey of the response of this region to changing emissions , focusing on atmospheric composition and attempting attribution from a series of targeted experiments involving perturbed emissions .

Formaldehyde is a key intermediate in photochemical oxidation of volatile organic compounds in the troposphere and is also directly emitted by a range of sources, including biomass burning and fuel combustion. Airborne measurements of formaldehyde have therefore been used to investigate oxidation in biomass burning (BB) plumes intercepted during the Methane Observations and Yearly Assessments (MOYA) campaign. The MOYA campaign took place January/February 2019 in Uganda and Zambia and mixing ratios of formaldehyde were obtained using the University of Leeds formaldehyde Laser Induced Fluorescence (LIF) instrument. A range of air masses were intercepted including multiple near-field biomass burning (BB) plumes, with up to 140 ppb of formaldehyde observed, and urban emission plumes from the capital city of Kampala in Uganda, where up to 7 ppb of formaldehyde was measured. Formaldehyde emission factors have been calculated for Ugandan BB (1.20 ± 0.23 g kg-1) which agree well with literature (1.23 ± 0.65 g kg-1) for Savannah combustion. Production of formaldehyde as a function of plume age has also been investigated in order to discriminate direct emission from photochemical formation in BB plumes. BB plumes were also intercepted during other aircraft campaigns several days downwind of emission such as a plume transported from Canadian wildfires observed in the North Atlantic during ACSIS-5/ARNA-1 where levels of up to 18.30 ppb were detected, indicative of sustained photochemical oxidation within the plume. Comparison of urban, near-field BB and far-field BB plumes provides a variety of environments and photochemical ages to test our understanding of combustion oxidation processes.

We study the evolution of tropospheric ozone over the period 1979-2010 using a chemistry-climate model employing a stratosphere-troposphere chemistry scheme. By running with specified dynamics, the key feedback of composition on meteorology is constrained, isolating the chemical response. By using historical forcings and emissions representative, interactions between processes are realistically represented. We use the model to ask how the ozone responds over time and to investigate model responses, sensitivities, feedbacks and trends. We find that the CFC-driven decrease in stratospheric ozone plays a significant role in the tropospheric ozone burden. Over the period 1979-1994, the decline in transport of ozone from the stratosphere partially offsets an emissions-driven increase in tropospheric ozone production. From 1994-2010, stratospheric ozone recovery, and a levelling off in emissions, effect a small increase in the tropospheric ozone burden. These results have implications for the impact of future stratospheric ozone recovery on air quality and radiative forcing.

In this study, we show using a state-of-the-art Earth system model, UKESM1, that emissions and climate scenario depending, there could be large changes in surface ozone by the end of the twenty-first century, with unprecedentedly large increases over South and East Asia. We also show that statistical modelling of the trends in future ozone works well in reproducing the model output between 1900 and 2050. However, beyond 2050, and especially under large climate change scenarios, the statistical model results are in poorer agreement with the fully interactive Earth system model output. This suggests that additional processes occurring in the Earth system model such as changes in the production of ozone at higher temperatures or changes in the influx of ozone from the stratosphere, which are not captured by the statistical model, have a first order impact on the evolution of surface ozone over the twenty-first century. We show in a series of idealized box model simulations, with two different chemical schemes, that changes in temperature lead to diverging responses between the schemes. This points at the chemical mechanisms as being a source of uncertainty in the response of ozone to changes in temperature, and so climate, in the future. This underscores the need for more work to be performed to better understand the response of ozone to changes in temperature and constrain how well this relationship is simulated in models. This article is part of a discussion meeting issue ‘Air quality, past present and future’.