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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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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’.

Fine particulate matter (PM2.5) and surface ozone (O3) are major air pollutants in megacities such as Delhi, but the design of suitable mitigation strategies is challenging. Some strategies for reducing PM2.5 may have the notable side effect of increasing O3. Here, we demonstrate a numerical framework for investigating the impacts of mitigation strategies on both PM2.5 and O3 in Delhi. We use Gaussian process emulation to generate a computationally efficient surrogate for a regional air quality model (WRF-Chem). This allows us to perform global sensitivity analysis to identify the major sources of air pollution and to generate emission-sector-based pollutant response surfaces to inform mitigation policy development. Based on more than 100 000 emulation runs during the pre-monsoon period (peak O3 season), our global sensitivity analysis shows that local traffic emissions from the Delhi city region and regional transport of pollution emitted from the National Capital Region (NCR) surrounding Delhi are dominant factors influencing PM2.5 and O3 in Delhi. They together govern the O3 peak and PM2.5 concentration during daytime. Regional transport contributes about 80% of the PM2.5 variation during the night. Reducing traffic emissions in Delhi alone (e.g. by 50 %) would reduce PM2.5 by 15 %–20 % but lead to a 20 %–25 % increase in O3. However, we show that reducing NCR regional emissions by 25 %–30 % at the same time would further reduce PM2.5 by 5 %–10 % in Delhi and avoid the O3 increase. This study provides scientific evidence to support the need for joint coordination of controls on local and regional scales to achieve effective reduction in PM2.5 whilst minimising the risk of O3 increase in Delhi.

Our understanding of the processes that control the burden and budget of tropospheric ozone has changed dramatically over the last 60 years. Models are the key tools used to understand these changes, and these underscore that there are many processes important in controlling the tropospheric ozone budget. In this critical review, we assess our evolving understanding of these processes, both physical and chemical. We review model simulations from the International Global Atmospheric Chemistry Atmospheric Chemistry and Climate Model Intercomparison Project and Chemistry Climate Modelling Initiative to assess the changes in the tropospheric ozone burden and its budget from 1850 to 2010. Analysis of these data indicates that there has been significant growth in the ozone burden from 1850 to 2000 (approximately 43 ± 9%) but smaller growth between 1960 and 2000 (approximately 16 ± 10%) and that the models simulate burdens of ozone well within recent satellite estimates. The Chemistry Climate Modelling Initiative model ozone budgets indicate that the net chemical production of ozone in the troposphere plateaued in the 1990s and has not changed since then inspite of increases in the burden. There has been a shift in net ozone production in the troposphere being greatest in the northern mid and high latitudes to the northern tropics, driven by the regional evolution of precursor emissions. An analysis of the evolution of tropospheric ozone through the 21st century, as simulated by Climate Model Intercomparison Project Phase 5 models, reveals a large source of uncertainty associated with models themselves (i.e., in the way that they simulate the chemical and physical processes that control tropospheric ozone). This structural uncertainty is greatest in the near term (two to three decades), but emissions scenarios dominate uncertainty in the longer term (2050–2100) evolution of tropospheric ozone. This intrinsic model uncertainty prevents robust predictions of near-term changes in the tropospheric ozone burden, and we review how progress can be made to reduce this limitation.

Major changes are occurring across the North Atlantic climate system, including in the atmosphere, ocean and cryosphere, and many observed changes are unprecedented in instrumental records. As the changes in the North Atlantic directly affect the climate and air quality of the surrounding continents, it is important to fully understand how and why the changes are taking place, not least to predict how the region will change in the future. To this end, this article characterizes the recent observed changes in the North Atlantic region, especially in the period 2005–2016, across many different aspects of the system including: atmospheric circulation; atmospheric composition; clouds and aerosols; ocean circulation and properties; and the cryosphere. Recent changes include: an increase in the speed of the North Atlantic jet stream in winter; a southward shift in the North Atlantic jet stream in summer, associated with a weakening summer North Atlantic Oscillation; increases in ozone and methane; increases in net absorbed radiation in the mid‐latitude western Atlantic, linked to an increase in the abundance of high level clouds and a reduction in low level clouds; cooling of sea surface temperatures in the North Atlantic subpolar gyre, concomitant with increases in the western subtropical gyre, and a decline in the Atlantic Ocean's overturning circulation; a decline in Atlantic sector Arctic sea ice and rapid melting of the Greenland Ice Sheet. There are many interactions between these changes, but these interactions are poorly understood. This article concludes by highlighting some of the key outstanding questions.

We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC.

The chemical environments of young planets are assumed to be largely influenced by impacts of bodies lingering on unstable trajectories after the dissolution of the protoplanetary disk. We explore the chemical consequences of impacts within the context of reducing planetary atmospheres dominated by carbon monoxide, methane and molecular nitrogen. A terawatt high-power laser was selected in order to simulate the airglow plasma and blast wave surrounding the impactor. The chemical results of these experiments are then applied to a theoretical atmospheric model. The impact simulation results in substantial volume mixing ratios within the reactor of 5% hydrogen cyanide (HCN), 8% acetylene (C$_2$H$_2$), 5% cyanoacetylene (HC$_3$N) and 1% ammonia (NH$_3$). These yields are combined with estimated impact rates for the Early Earth to predict surface boundary conditions for an atmospheric model. We show that impacts might have served as sources of energy that would have led to steady-state surface quantities of 0.4% C$_2$H$_2$, 400 ppm HCN and 40 ppm NH$_3$. We provide simulated transit spectra for an Earth-like exoplanet with this reducing atmosphere during and shortly after eras of intense impacts. We predict that acetylene is as observable as other molecular features on exoplanets with reducing atmospheres that have recently gone through their own `Heavy Bombardments', with prominent features at 3.05 $\mu$m and 10.5 $\mu$m., Comment: 26 pages, 12 figures, 2 tables, accepted for publication by The Astrophysical Journal

Atlantic Multidecadal Variability (AMV) is a key feature of Atlantic and global climate. The ACSIS program involves a unique grouping that will advance an integrated understanding of AMV. Atlantic Multi-decadal Variability (AMV) is the term used to describe the pattern of variability in North Atlantic sea surface temperatures (SSTs) that is characterised by decades of basin-wide warm or cool anomalies, relative to the global mean. AMV has been associated with numerous climate impacts in many regions of the world including decadal variations in temperature and rainfall patterns, hurricane activity, and sea-level changes. Given its importance, understanding the physical processes that drive AMV and the extent to which its evolution is predictable is a key challenge in climate science. A leading hypothesis is that natural variations in ocean circulation control changes in ocean heat content and, consequently, AMV phases. However, this view has been challenged recently by claims that changing natural and anthropogenic radiative forcings are critical drivers of AMV. Others have argued that changes in ocean circulation are not required. Here we review the leading hypotheses and mechanisms for AMV, and discuss the key debates. In particular, we highlight the need for a holistic understanding of AMV. This perspective is a key motivation for a major new UK research programme: the North Atlantic Climate System Integrated Study (ACSIS), which brings together seven of the UK’s leading environmental research institutes to enable a broad spectrum approach to the challenges of AMV. ACSIS will deliver the first fully integrated assessment of recent decadal changes in the North Atlantic, will investigate the attribution of these changes to their proximal and ultimate causes, and will assess the potential to predict future changes.

Fossil fuel and aerosol emissions have played important roles on climate over the Indian subcontinent over the last century. As the world transitions toward decarbonization in the next few decades, emissions pathways could have major impacts on India’s climate and people. Pathways for future emissions are highly uncertain, particularly at present as countries recover from COVID-19. This paper explores a multimodel ensemble of Earth system models leveraging potential global emissions pathways following COVID-19 and the consequences for India’s summertime (June–July–August–September) climate in the near- and long-term. We investigate specifically scenarios which envisage a fossil-based recovery, a strong renewable-based recovery and a moderate scenario in between the two. We find that near-term climate changes are dominated by natural climate variability, and thus likely independent of the emissions pathway. By 2050, pathway-induced spatial patterns in the seasonally-aggregated precipitation become clearer with a slight drying in the fossil-based scenario and wetting in the strong renewable scenario. Additionally, extreme temperature and precipitation events in India are expected to increase in magnitude and frequency regardless of the emissions scenario, though the spatial patterns of these changes as well as the extent of the change are pathway dependent. This study provides an important discussion on the impacts of emissions recover pathways following COVID-19 on India, a nation which is likely to be particularly susceptible to climate change over the coming decades.

Here we provide an overview of the REF-C1SD Specified-Dynamics experiment that was conducted as part of Phase 1 of the Chemistry-Climate Model Initiative (CCMI). The REF-C1SD experiment, which consisted of mainly online general circulation models (GCMs) constrained with (re)analysis fields, was designed to examine the influence of the large-scale circulation on past trends in atmospheric composition. The REF-C1SD simulations were produced across various model frameworks and we evaluate how well the simulations represent different measures of the dynamical and transport circulations. In the troposphere there are large (~ 40 %) differences in the climatological mean distributions and seasonal cycle amplitude of the meridional and vertical winds. In the stratosphere there are similarly large (~ 50 %) differences in the magnitude and seasonal cycle amplitude of the Transformed Eulerian Mean circulation and among various chemical and idealized tracers. For nearly all variables these differences are not related to the use of different reanalysis products; rather, we show they are associated with how the simulations were implemented, by which we refer both to how the large-scale flow was prescribed and to biases in the underlying free-running models. Furthermore, in most cases these differences are shown to be as large or even larger than the differences exhibited by free-running simulations produced using the exact same models. Overall, our results suggest that care must be taken when using specified-dynamics simulations to examine the influence of large-scale dynamics on composition.

The amount of dioxygen (O2) in the atmosphere may have varied from as little as 10 % to as high as 35 % during the Phanerozoic eon (541 Ma–Present). These changes in the amount of O2 are large enough to have lead to changes in atmospheric mass, which may alter the radiative budget of the atmosphere, leading to this mechanism being invoked to explain discrepancies between climate model simulations and proxy reconstructions of past climates. Here we present the first fully 3D numerical model simulations to investigate the climate impacts of changes in O2 during different climate states using the HadGEM3-AO and HadCM3-BL models. We show that simulations with an increase in O2 content result in increased global mean surface air temperature under conditions of a pre-industrial Holocene climate state, in agreement with idealised 1D and 2D modelling studies. We demonstrate the mechanism behind the warming is complex and involves trade-off between a number of factors. Increasing atmospheric O2 leads to a reduction in incident shortwave radiation at Earth's surface due to Rayleigh scattering, a cooling effect. However, there is a competing warming effect due to an increase in the pressure broadening of greenhouse gas absorption lines and dynamical feedbacks, which alter the meridional heat transport of the ocean, warming polar regions and cooling tropical regions. Case studies from past climates are investigated using HadCM3-BL which show that in the warmest climate states, increasing oxygen may lead to a temperature decrease, as the equilibrium climate sensitivity is lower. For the Maastrichtian (72.1–66.0 Ma), increasing oxygen content leads to a better agreement with proxy reconstructions of surface temperature at that time irrespective of the carbon dioxide content. For the Asselian (298.9–295.0 Ma), increasing oxygen content leads to a warmer global mean surface temperature and reduced carbon storage on land, suggesting that high oxygen content may have been a contributing factor in preventing a Snowball Earth during this period of the early Permian. These climate model simulations reconcile the surface temperature response to oxygen content changes across the hierarchy of model complexity and highlight the broad range of Earth system feedbacks that need to be accounted for when considering the climate response to changes in atmospheric oxygen content.

Here we present results from an evaluation of model simulations from the International Hemispheric Transport of Air Pollution Phase II (HTAPII) and Chemistry Climate Model Initiative (CCMI) model inter-comparison projects against a comprehensive series of ground based, aircraft and satellite observations of ozone mixing ratios made at various locations across India. The study focuses on the recent past (observations from 2008–2013, models from 2008–2010) as this is most pertinent to understanding the health impacts of ozone. To our understanding this is the most comprehensive evaluation of these models' simulations of ozone across the Indian sub-continent to date. This study highlights some significant successes and challenges that the models face in representing the oxidative chemistry of the region. The multi-model range in area weighted surface ozone over the Indian subcontinent is 37.26–56.11 ppb, whilst the population weighted range is 41.38–57.5 ppb. When compared against surface observations from the Modelling Atmospheric Pollution and Networking (MAPAN) network of eight semi-urban monitoring sites spread across India, we find that the models tend to simulate higher ozone than that which is observed. However, observations of NOx and CO tend to be much higher than modelled mixing-ratios, suggesting that the underlying emissions used in the models do not characterise these regions accurately and/or that the resolution of the models is not adequate to simulate the photo-chemical environment about these surface observations. Empirical Orthogonal Function (EOF) analysis is used in order to identify the extent to which the models agree with regards to the spatio-temporal distribution of the tropospheric ozone column, derived using OMI-MLS observations. We show that whilst the models agree with the spatial pattern of the first EOF of observed tropospheric ozone column, most of the models simulate a peak in the first EOF seasonal cycle represented by principle component 1, which is later than the observed peak . This suggest a widespread systematic bias in the timing of emissions or some other unknown seasonal process. In addition to evaluating modelled ozone mixing ratios, we explore modelled emissions of NOx, CO, VOCs, and the ozone response to the emissions. We find a high degree of variation in emissions from non-anthropogenic sources (e.g. lightning NOx and biomass burning CO) between models. Total emissions of NOx and CO over India vary more between different models in the same MIP than the same model used in different MIPs, making it impossible to diagnose whether differences in modelled ozone are due to emissions or model processes. We therefore recommend targeted experiments to pinpoint the exact causes of discrepancies between modelled and observed ozone and ozone precursors for this region. To this end, a higher density of long term monitoring sites measuring not only ozone but also ozone precursors including speciated VOCs, located in more rural regions of the Indian sub-continent, would enable improvements in assessing the biases in models run at the resolution found in HTAPII and CCMI.

The Met Office Unified Model (UM) is a state-of-the-art weather and climate model that is used operationally worldwide. UKCA is the chemistry and aerosol sub model of the UM that enables interactive composition and physical atmosphere interactions, but which adds an additional 120,000 lines of code to the model. Ensuring that the UM code and UM-UKCA (the UM running with interactive chemistry and aerosols) is well tested is therefore essential. While a comprehensive test harness is in place at the Met Office and partner sites to aid in development, this is not available to many UM users. Recently the Met Office have made available a Virtual Machine environment that can be used to run the UM on a desktop or laptop PC. Here we describe the development of a UM-UKCA configuration that is able to run within this Virtual Machine while only needing 6 GB of memory, before discussing the applications of this system for model development, testing, and training.

An intercomparison has been set up to study the representation of the atmospheric chemistry of the pre-industrial troposphere in earth system and other global tropospheric chemistry-transport models. The intercomparison employed a constrained box model and utilised tropospheric trace gas composition data for the pre-industrial times at ninety mid-latitude surface locations. Incremental additions of four organic compounds: methane, ethane, acetone and propane, were used to perturb the constrained box model and generate responses in hydroxyl radicals and tropospheric ozone at each location and with each chemical mechanism. Although the responses agreed well across the chemical mechanisms from the selected earth system and other global tropospheric chemistry-transport models, there were differences in the detailed responses between the chemical mechanisms that could be tracked down by sensitivity analysis to differences in the representation of C1–C3 chemistry. Inter-mechanism ranges in NOx compensation points were about 0.17 ± 0.12 when expressed relative to the inter-mechanism average. Monte Carlo uncertainty analysis carried out with a single chemical mechanism put the intra-mechanism range a factor of three higher at 0.50 ± 0.12. Similar differences between inter-mechanism and intra-mechanism ranges were found for hydroxyl radical depletion but were up to a factor of six wider for ozone formation from incremental additions of organic compounds. The cause of the discrepancies between the inter- and intra-mechanism ranges was found to be the large uncertainties that are present in the laboratory determinations of the rate coefficients and product channel branching ratios of some key chemical reactions involving organic peroxy radicals and hydroperoxides. Whilst these large uncertainties are present in the laboratory determinations, there will be irreducible uncertainties in the predictions from the earth system and other chemistry-transport models of methane and tropospheric ozone trends since pre-industrial times and hence their contributions to the radiative forcing of climate change. Further definitive laboratory studies of the reaction rates and product yields of the reactions of the simple organic peroxy radicals and hydroperoxides are required to resolve and reduce current uncertainties in earth system and chemistry-transport model predictions.

The atmospheric global budget and distribution of C 1 –C 3 alkyl nitrates have been investigated using a global three-dimensional chemistry transport model, STOCHEM-CRI. Alkyl nitrates (RONO 2 ) are significant NO x reservoir species and the more detailed VOC oxidation mechanism (CRI v2-R5) leads to greater photochemical production. RONO 2 are significant sources of NO x in regions remote from NO x sources. The study shows that the global burden and the atmospheric life-time of C 1 –C 3 alkyl nitrates are 113 Gg and 9–10 days, respectively, which are in excellent agreement with estimates established by previous studies. The abundance of alkyl nitrates have been found to be higher in the continental atmosphere, with CH 3 ONO 2 mixing ratios up to 20 ppt over the Amazon rainforest. Up to 15, 10, 2, and 5 ppt of modelled CH 3 ONO 2 , C 2 H 5 ONO 2 , n-C 3 H 7 ONO 2 and i-C 3 H 7 ONO 2 have been found in the northern hemisphere over regions with large anthropogenic emissions of NO x and VOCs. The combination of atmospheric production and long-range transport led to high alkyl nitrate levels at high latitudes. The model performance for C 1 –C 3 alkyl nitrates was established using observations from nine flights and nine field campaigns. The comparison shows a tendency towards model under-prediction of the observations, particularly in the southern hemispheric marine boundary layer, possibly due to the absence of oceanic production mechanisms and air-sea exchange processes in the model. The discrepancies between model and observed seasonal cycles, especially of CH 3 ONO 2 , in both hemispheres are discussed.