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Ozone is a potent air pollutant in the lower troposphere and an important short-lived climate forcer (SLCF) in the upper troposphere. Studies investigating long-term trends in the tropospheric column ozone (TCO 3) have shown large-scale spatio-temporal inconsistencies. Here, we investigate the long-term trends in lower-tropospheric column ozone (LTCO 3 , surface–450 hPa sub-column) by exploiting a synergy of satellite and ozonesonde data sets and an Earth system model (UK's Earth System Model, UKESM) over North America, Europe, and East Asia for the decade 2008–2017. Overall, we typically find small LTCO 3 linear trends with large uncertainty ranges using the Ozone Monitoring Instrument (OMI) and the Infrared Atmospheric Sounding Interferometer (IASI), while model simulations indicate a stable LTCO 3 tendency. The satellite a priori data sets show negligible trends, indicating that any year-to-year changes in the spatio-temporal sampling of these satellite data sets over the period concerned have not artificially influenced their LTCO 3 temporal evolution. The application of the satellite averaging kernels (AKs) to the UKESM simulated ozone profiles, accounting for the satellite vertical sensitivity and allowing for like-for-like comparisons, has a limited impact on the modelled LTCO 3 tendency in most cases. While, in relative terms, this is more substantial (e.g. on the order of 100 %), the absolute magnitudes of the model trends show negligible change. However, as the model has a near-zero tendency, artificial trends were imposed on the model time series (i.e. LTCO 3 values rearranged from smallest to largest) to test the influence of the AKs, but simulated LTCO 3 trends remained small. Therefore, the LTCO 3 tendencies between 2008 and 2017 in northern-hemispheric regions are likely to be small, with large uncertainties, and it is difficult to detect any small underlying linear trends due to interannual variability or other factors which require further investigation (e.g. the radiative transfer scheme (RTS) used and/or the inputs (e.g. meteorological fields) used in the RTS). [ABSTRACT FROM AUTHOR]

Ozone is a potent air pollutant in the lower troposphere and an important short-lived climate forcer (SLCF) in the upper troposphere. Studies investigating long-term trends in tropospheric column ozone (TCO3) have shown large-scale spatiotemporal inconsistencies. Here, we investigate the long-term trends in lower tropospheric column ozone (LTCO3, surface-450 hPa sub-column) by exploiting a synergy of satellite and ozonesonde datasets and an Earth System Model (UKESM) over North America, Europe and East Asia for the decade 2008–2017. Overall, we typically find small LTCO3 linear trends with large uncertainty ranges from the Ozone Monitoring Instrument (OMI) and the Infrared Atmospheric Sounding Interferometer (IASI), while model simulations indicate a stable LTCO3 tendency. Trends in the satellite a priori datasets show negligible trends indicating year-to-year sampling is not an issue. The application of the satellite averaging kernels (AKs) to the UKESM ozone profiles, accounting for the satellite vertical sensitivity and allowing for like-for-like comparisons, has a limited impact on the modelled LTCO3 tendency in most cases. While, in relative terms, this is more substantial (e.g. in the order of 100 %), the absolute magnitudes of the model trends show negligible change. However, as the model has a near-zero tendency, artificial trends were imposed on the model time-series (i.e. LTCO3 values rearranged from smallest to largest) to test the influence of the AKs but simulated LTCO3 trends remained small. Therefore, the LTCO3 tendency between 2008 and 2017 in northern hemispheric regions are likely small, with large uncertainties, and it is difficult to detect any small underlying linear trends due to inter-annual variability or other factors which require further investigation. [ABSTRACT FROM AUTHOR]

Using state-of-the-art satellite ozone profile products, and chemical transport model, we provide an updated estimate of the tropospheric ozone radiative effect (TO3RE) and observational constraint on its variability over the decade 2008–2017. Previous studies have shown the short-term (i.e. a few years) globally weighted average TO3RE to be 1.17±0.03 W/m2, while our analysis suggests that the long-term (2008–2017) average TO3RE to be 1.21–1.28 W/m2. Over this decade, the modelled/observational TO3RE linear trends show negligible change (i.e. ±0.1 %/year), so the tropospheric ozone radiative contribution to climate has remained stable with time. Two model sensitivity experiments fixing emissions and meteorology to one year (i.e. start year – 2008) show that ozone precursor emissions (meteorological factors) have had limited (substantial) impacts on the long-term tendency of globally weighted average TO3RE. Here, the meteorological variability in the tropical/sub-tropical upper troposphere is dampening any tendency in TO3RE from other factors (e.g. emissions, atmospheric chemistry). [ABSTRACT FROM AUTHOR]

We use a tracer version of the TOMCAT global 3-D chemical transport model to investigate the physical and chemical processes driving the abundance of hydrogen cyanide (HCN) in the troposphere and stratosphere over the period 2004–2016. The modelled HCN distribution is compared with version 4.1 of the Atmospheric Chemistry Experiment - Fourier Transform Spectrometer (ACE-FTS) HCN satellite data, which provides profiles up to around 42 km, and with ground-based column measurements from the Network for the Detection of Atmospheric Composition Change (NDACC). ACE-FTS has so far provided over 17 years of data, from 2004, which allow us to monitor both the seasonal and interannual variations of HCN and its transport through the atmosphere. In particular, by analysing the long time series, we are able to detect the effects on atmospheric composition of large wildfire events like those observed in 2006 and 2015 in Indonesia. Our 3-D model simulations confirm previous lower altitude balloon comparisons that the currently recommended NASA Jet Propulsion Laboratory (JPL) reaction rate coefficient of HCN with OH greatly overestimates the HCN loss. The use of the rate coefficient proposed by Kleinb ö hl et al. (2006) in combination with the HCN oxidation by O(1D) gives good agreement between ACE-FTS observations and the model. Furthermore, investigation of the individual photochemical loss terms shows that the reduction of the HCN mixing ratio with height in the middle stratosphere is mainly driven by the O(1D) sink with only a small contribution from reaction with OH. From comparisons of the model tracers with ground-based HCN observations we test the magnitude of the ocean sink in two different published schemes (Li et al. 2000, 2003). We find that in our 3-D model the two schemes produce HCN abundances which are very different to the NDACC observations but in different directions. A model HCN tracer using the Li et al. (2000) scheme overestimates the HCN concentration by almost a factor two, while a HCN tracer using the Li et al. (2003) scheme underestimates the observations by about one-third. To obtain good agreement between the model and observations we need to scale the magnitudes of the global ocean sinks by the factors of 0.25 and 2 for the schemes of Li et al. (2000) and Li et al. (2003), respectively. This work shows that the atmospheric photochemical sinks of HCN now appear well constrained but improvements are needed in parameterising the major ocean uptake sink. [ABSTRACT FROM AUTHOR]

Volcanic eruptions can cause significant disruption to society, and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office's Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3 ∘ N, 153.2 ∘ E). The eruption emitted around 1.5±0.2 Tg of SO2 , which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption but is unable, in its standard configuration, to capture the extent and precise location of the highest magnitude vertical column density (VCD) regions within the observed volcanic cloud. Using the structure–amplitude–location (SAL) score and the fractional skill score (FSS) as metrics for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 d after the eruption where VCDs of SO2 (in Dobson units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 d only. The lower skill for these high- SO2 -VCD regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard horizontal diffusion parameters used in NAME by a factor of 4 results in a slightly increased model skill during the first 5 d of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. The skill of NAME to simulate high SO2 VCDs and the temporal evolution of the NH-mean SO2 mass burden is dominated by the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), the NAME simulations show a similar peak in SO2 mass burden to that derived from TROPOMI (1.4–1.6 Tg of SO2) with an average SO2 e -folding time of 14–15 d in the NH. Our work illustrates how the synergy between high-resolution satellite retrievals and dispersion models can identify potential limitations of dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds. [ABSTRACT FROM AUTHOR]

Here we present a description of the UKCA StratTrop chemical mechanism, which is used in the UKESM1 Earth system model for CMIP6. The StratTrop chemical mechanism is a merger of previously well-evaluated tropospheric and stratospheric mechanisms, and we provide results from a series of bespoke integrations to assess the overall performance of the model. We find that the StratTrop scheme performs well when compared to a wide array of observations. The analysis we present here focuses on key components of atmospheric composition, namely the performance of the model to simulate ozone in the stratosphere and troposphere and constituents that are important for ozone in these regions. We find that the results obtained for tropospheric ozone and its budget terms from the use of the StratTrop mechanism are sensitive to the host model; simulations with the same chemical mechanism run in an earlier version of the MetUM host model show a range of sensitivity to emissions that the current model does not fall within. Whilst the general model performance is suitable for use in the UKESM1 CMIP6 integrations, we note some shortcomings in the scheme that future targeted studies will address. [ABSTRACT FROM AUTHOR]

Previous studies have used surface observations of pollutants (e.g., nitrogen dioxide [NO2] and aerosols) to evaluate improvements in United Kingdom (UK) air quality over recent decades. However, surface monitoring networks provide limited spatial coverage and are not always representative of air quality over a wider region. Satellite observations, such as tropospheric column NO2 (TCNO2), aerosol optical depth (AOD) and sub-column (0-6 km) ozone (SCO3), provide widespread monitoring of air quality on a national scale. In this study, we use such observations to analyse trends in UK air quality, between 2005 and 2015, finding significant decreases in TCNO2 and AOD over UK pollution hotspots. The largest changes in short-lived NO2 are located over populated (i.e., source) regions, associated with large emissions of nitrogen oxides (NOx). AOD changes are more spread out, and less strongly associated with source regions, due to a longer aerosol lifetime, secondary aerosol formation and non-urban aerosol sources. SCO3 shows no significant trends over England/Wales, but significant positive trends over Scotland, which is consistent with previous studies of changes in background tropospheric ozone in other remote regions of north-western Europe. [ABSTRACT FROM AUTHOR]

This paper documents the tropospheric chemical mechanism scheme used in the TOMCAT 3-D chemical transport model. The current scheme includes a more detailed representation of hydrocarbon chemistry than previously included in the model, with the inclusion of the emission and oxidation of ethene, propene, butane, toluene and monoterpenes. The model is evaluated against a range of surface, balloon, aircraft and satellite measurements. The model is generally able to capture the main spatial and seasonal features of high and low concentrations of carbon monoxide (CO), ozone (O3), volatile organic compounds (VOCs) and reactive nitrogen. However, model biases are found in some species, some of which are common to chemistry models and some that are specific to TOMCAT and warrant further investigation. The most notable of these biases are (1) a negative bias in Northern Hemisphere (NH) winter and spring CO and a positive bias in Southern Hemisphere (SH) CO throughout the year, (2) a positive bias in NH O3 in summer and a negative bias at high latitudes during SH winter and (3) a negative bias in NH winter C2 and C3 alkanes and alkenes. TOMCAT global mean tropospheric hydroxyl radical (OH) concentrations are higher than estimates inferred from observations of methyl chloroform but similar to, or lower than, multi-model mean concentrations reported in recent model intercomparison studies. TOMCAT shows peak OH concentrations in the tropical lower troposphere, unlike other models which show peak concentrations in the tropical upper troposphere. This is likely to affect the lifetime and transport of important trace gases and warrants further investigation. [ABSTRACT FROM AUTHOR]

Peroxyacetyl nitrate (PAN) is an important chemical species in the troposphere as it aids the long-range transport of NOx and subsequent formation of O3 in relatively clean remote regions. Over the past few decades observations from aircraft campaigns and surface sites have been used to better understand the regional distribution of PAN. However, recent measurements made by satellites allow for a global assessment of PAN in the upper troposphere-lower stratosphere (UTLS). In this study, we investigate global PAN distributions from two independent retrieval methodologies, based on measurements from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument, on board Envisat from the Institute of Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology, and the Department of Physics and Astronomy, University of Leicester (UoL). Retrieving PAN from MIPAS is challenging due to the weak signal in the measurements and contamination from other species. Therefore, we compare the two MIPAS datasets with observations from the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS), in situ aircraft data and the 3-D chemical transport model TOMCAT. MIPAS shows peak UTLS PAN concentrations over the biomass burning regions (e.g. ranging from 150 to > 200 pptv at 150 hPa) and during the summertime Asian monsoon as enhanced convection aids the vertical transport of PAN from the lower atmosphere. At 150 hPa, we find significant differences between the two MIPAS datasets in the tropics, where IMK PAN concentrations are larger by 50-100 pptv. Comparisons between MIPAS and ACE-FTS show better agreement with the UoL MIPAS PAN concentrations at 200 hPa, but with mixed results above this altitude. TOMCAT generally captures the magnitude and structure of climatological aircraft PAN profiles within the observational variability allowing it to be used to investigate the MIPAS PAN differences. TOMCAT-MIPAS comparisons show that the model is both positively (UoL) and negatively (IMK) biased against the satellite products. These results indicate that satellite PAN observations are able to detect realistic spatial variations in PAN in the UTLS, but further work is needed to resolve differences in existing retrievals to allow quantitative use of the products. [ABSTRACT FROM AUTHOR]