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We analyse historical (1850–2014) atmospheric hydroxyl (OH) and methane lifetime data from Coupled Model Intercomparison Project Phase 6 (CMIP6)/Aerosols and Chemistry Model Intercomparison Project (AerChemMIP) simulations. Tropospheric OH changed little from 1850 up to around 1980, then increased by around 9 % up to 2014, with an associated reduction in methane lifetime. The model-derived OH trends from 1980 to 2005 are broadly consistent with trends estimated by several studies that infer OH from inversions of methyl chloroform and associated measurements; most inversion studies indicate decreases in OH since 2005. However, the model results fall within observational uncertainty ranges. The upward trend in modelled OH since 1980 was mainly driven by changes in anthropogenic near-term climate forcer emissions (increases in anthropogenic nitrogen oxides and decreases in CO). Increases in halocarbon emissions since 1950 have made a small contribution to the increase in OH, whilst increases in aerosol-related emissions have slightly reduced OH. Halocarbon emissions have dramatically reduced the stratospheric methane lifetime by about 15 %–40 %; most previous studies assumed a fixed stratospheric lifetime. Whilst the main driver of atmospheric methane increases since 1850 is emissions of methane itself, increased ozone precursor emissions have significantly modulated (in general reduced) methane trends. Halocarbon and aerosol emissions are found to have relatively small contributions to methane trends. These experiments do not isolate the effects of climate change on OH and methane evolution; however, we calculate residual terms that are due to the combined effects of climate change and non-linear interactions between drivers. These residual terms indicate that non-linear interactions are important and differ between the two methodologies we use for quantifying OH and methane drivers. All these factors need to be considered in order to fully explain OH and methane trends since 1850; these factors will also be important for future trends.

The evolution of tropospheric ozone from 1850 to 2100 has been studied using data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We evaluate long-term changes using coupled atmosphere–ocean chemistry–climate models, focusing on the CMIP Historical and ScenarioMIP ssp370 experiments, for which detailed tropospheric-ozone diagnostics were archived. The model ensemble has been evaluated against a suite of surface, sonde and satellite observations of the past several decades and found to reproduce well the salient spatial, seasonal and decadal variability and trends. The multi-model mean tropospheric-ozone burden increases from 247 ± 36 Tg in 1850 to a mean value of 356 ± 31 Tg for the period 2005–2014, an increase of 44 %. Modelled present-day values agree well with previous determinations (ACCENT: 336 ± 27 Tg; Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP: 337 ± 23 Tg; Tropospheric Ozone Assessment Report, TOAR: 340 ± 34 Tg). In the ssp370 experiments, the ozone burden increases to 416 ± 35 Tg by 2100. The ozone budget has been examined over the same period using lumped ozone production (PO3) and loss (LO3) diagnostics. Both ozone production and chemical loss terms increase steadily over the period 1850 to 2100, with net chemical production (PO3-LO3) reaching a maximum around the year 2000. The residual term, which contains contributions from stratosphere–troposphere transport reaches a minimum around the same time before recovering in the 21st century, while dry deposition increases steadily over the period 1850–2100. Differences between the model residual terms are explained in terms of variation in tropopause height and stratospheric ozone burden.

The hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH4), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH4), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH4 differences among 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO1D), the mixing ratio of tropospheric ozone (O3), the abundance of nitrogen oxides (NOx≡NO+NO2), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO:NOx, and formaldehyde (HCHO) explain moderate differences in τCH4, while isoprene, methane, the photolysis frequency of NO2 by visible light (JNO2), overhead ozone column, and temperature account for little to no model variation in τCH4. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH4 during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O3, JO1D, NOx, and H2O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH4 are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapour on OH and τCH4, imparting an increasing and decreasing trend of about 0.5 % decade−1, respectively. The responses due to NOx, ozone column, and temperature are also in reasonably good agreement between the two studies.

Nitrogen oxides (NOx≡NO+NO2) in the upper troposphere (UT) have a large impact on global tropospheric ozone and OH (the main atmospheric oxidant). New cloud-sliced observations of UT NO2 at 450–280 hPa (∼6–9 km) from the Ozone Monitoring Instrument (OMI) produced by NASA and the Royal Netherlands Meteorological Institute (KNMI) provide global coverage to test our understanding of the factors controlling UT NOx. We find that these products offer useful information when averaged over coarse scales (20∘×32∘, seasonal), and that the NASA product is more consistent with aircraft observations of UT NO2. Correlation with Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) satellite observations of lightning flash frequencies suggests that lightning is the dominant source of NOx to the upper troposphere except for extratropical latitudes in winter. The NO2 background in the absence of lightning is 10–20 pptv. We infer a global mean NOx yield of 280±80 moles per lightning flash, with no significant difference between the tropics and midlatitudes, and a global lightning NOx source of 5.9±1.7 Tg N a−1. There is indication that the NOx yield per flash increases with lightning flash footprint and with flash energy.

Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry–climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J-cloudy (standard, sometimes cloudy conditions) to J-clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements.